Change search
Refine search result
1 - 49 of 49
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Oldest first
  • Newest first
Select all
  • 1.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Online feature learning for condition monitoring of rotating machinery2017In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 64, 187-196 p.Article in journal (Refereed)
    Abstract [en]

    Condition-based maintenance of rotating machinery requires efficient condition monitoring methods that enable early detection of abnormal operational conditions and faults. This is a challenging problem because machines are different and change characteristics over time due to wear and maintenance. The efficiency and scalability of conventional condition monitoring methods are limited by the need for manual analysis and re-configuration. The problem to extract relevant features from condition monitoring signals and thereby detect and analyze changes in such signals is a central issue, which in principle can be addressed using machine learning methods. Former work demonstrates that dictionary learning can be used to automatically derive signal features that characterize different operational conditions and faults of a rotating machine, but the use of such methods for online condition monitoring purposes is an open problem. Here we investigate online learning of features using dictionary learning. We describe dictionary distance and signal fidelity based heuristics for anomaly detection, and we study the time--propagated features and sparse approximation of vibration and acoustic emission signals in three different case studies. We present results of numerical experiments with different hyperparameters affecting the approximation accuracy, computational cost, and the adaptation rate of the learned features. We find that the learned features change rapidly when a fault appears in the machine or changes characteristics, and that the dictionary is different in normal and faulty conditions. We find that the learned features change slowly under normal variations of the operational conditions in comparison to the rapid adaptation observed when a fault appears (bearing defects, magnetite particles in the lubricant, or plastic deformation of steel). Furthermore, a sparse signal approximation with 2.5\% preserved coefficients based on a propagated dictionary is sufficient for anomaly detection in the cases considered here. Furthermore, we find that a sparse signal approximation with 2.5\% preserved coefficients based on a propagated dictionary is sufficient for bearing defect detection.

  • 2.
    Sandin, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Emruli, Blerim
    SICS Swedish ICT, SE-722 13 Västerås .
    Sahlgren, Magnus
    SICS Swedish ICT, SE-164 29 Kista .
    Random indexing of multi-dimensional data2017In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 52, no 1, 267-290 p.Article in journal (Refereed)
    Abstract [en]

    Random indexing (RI) is a lightweight dimension reduction method, which is used for example to approximate vector-semantic relationships in online natural language processing systems. Here we generalise RI to multi-dimensional arrays and thereby enable approximation of higher-order statistical relationships in data. The generalised method is a sparse implementation of random projections,which is the theoretical basis also for ordinary RI and other randomisation approaches to dimensionality reduction and data representation. We present numerical experiments which demonstrate that a multi-dimensional generalisation of RI is feasible, including comparisons with ordinary RI and principal component analysis (PCA). The RI method is well suited for online processing of data streams because relationship weights can be updated incrementally in a fixed-size distributed representation,and inner products can be approximated on the fly at low computational cost. An open source implementation of generalised RI is provided.

  • 3. Emruli, Blerim
    et al.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Vector space architecture for emergent interoperability of systems by learning from demonstration2016In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509Article in journal (Refereed)
    Abstract [en]

    The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems interoperable using handcrafted "semantic" descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration.

  • 4.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Schnabel, Stephan
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    Marklund, Pär
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Exploratory Analysis of Acoustic Emissions in Steel using Dictionary Learning2016In: IEEE Ultrasonics Symposium 2016, Tours France, September 18-21, 2016, Piscataway, NJ: IEEE conference proceedings, 2016, 7728825Conference paper (Refereed)
    Abstract [en]

    Analysis of acoustic emissions (AE) from steel deformation is a challenging condition monitoring problem due to the high frequencies and data rates involved, and the difficulty to separate signals from noise. The problem to characterize and identify different AE sources calls for methods that goes beyond conventional time and frequency domain analysis. Feature learning is common in the field of machine learning and is successfully used to approximate and classify other kinds of complex signals. Former studies show that AE classification results depend on the choice of predefined features that are extracted from the raw AE signal, but little is known about feature learning in this context. Here we use dictionary learning and sparse coding to optimize a set of shift-invariant features to the AE signal measured in a steel tensile strength test. The specimen undergoes elastic and plastic deformation and eventually cracks. We investigate the learned features and their repetition rates and use principal component analysis (PCA) to illustrate that the resulting sparse AE code is useful for classification of the three strain stages, without reference to the signal amplitude. Therefore, feature learning is a potentially useful approach to the AE analysis problem, which also opens up for further studies of automated methods for anomaly detection in AE.

  • 5.
    Delsing, Jerker
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    van Deventer, Jan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Eliasson, Jens
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Johansson, Jonny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Löfqvist, Torbjörn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Concepts and Architecture for a Thumb-Sized Smart IoT Ultrasound Measurement System2016In: IEEE Ultrasonic Symposium 2016, Piscataway, NJ: IEEE conference proceedings, 2016Conference paper (Refereed)
    Abstract [en]

    This paper presents the technology concepts for a “thumb”-sized self-contained ultrasonic IoT measurement sys- tem. An overall architecture is proposed, and key elements are discussed with solutions using existing technology, thus arguing that realization is possible with the current technology.

    Such an ultrasonic IoT measurement system is constrained by its size and available energy, although it requires at least decent computational and communication resources. Because streaming data from such a device is not advisable from an energy viewpoint, there is a need for resource efficient (energy, memory and computational power) data analysis.

    An architecture with the following parts as well as some implementation details and performance data are proposed here:

    • Energy supply, battery and super capacitor

    • Transducer excitation achieving almost zero electrical losses

    • Event detection sensor interface

    • Data aggregation using sparse approximation and learned

      feature dictionaries, adapted to resource constrained em-

      bedded systems

    • IoT communication protocols and implementations enabling

      event -based communication and System of Systems integra- tion capabilities

      The optimization of system level performance requires each subsystem to be optimized for the specific measurement situation taking into account the subsystem interdependencies. This can be performed using a combined electrical and acoustical model of the system. Here, the model allowing electronic and acoustic co-simulation using SPICE is an important tool bridging the electronic and acoustic domains. 

  • 6.
    Dadhich, Siddharth
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Bodin, Ulf
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Andersson, Ulf
    Machine Learning approach to Automatic Bucket Loading2016In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, 1260-1265 p., 7535925Conference paper (Refereed)
    Abstract [en]

    The automation of bucket loading for repetitive tasks of earth-moving operations is desired in several applications at mining sites, quarries and construction sites where larger amounts of gravel and fragmented rock are to be moved. In load and carry cycles the average bucket weight is the dominating performance parameter, while fuel efficiency and loading time also come into play with short loading cycles. This paper presents the analysis of data recorded during loading of different types of gravel piles with a Volvo L110G wheel loader. Regression models of lift and tilt actions are fitted to the behavior of an expert driver for a gravel pile. We present linear regression models for lift and tilt action that explain most of the variance in the recorded data and outline a learning approach for solving the automatic bucket loading problem. A general solution should provide good performance in terms of average bucket weight, cycle time of loading and fuel efficiency for different types of material and pile geometries. We propose that a reinforcement learning approach can be used to further refine models fitted to the behavior of expert drivers, and we briefly discuss the scooping problem in terms of a Markov decision process and possible value functions and policy iteration schemes.

  • 7.
    Emruli, Blerim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Vector space architecture for emergent interoperability of systems by learning from demonstration2015In: Biologically Inspired Cognitive Architectures, ISSN 2212-683X, E-ISSN 2212-6848, Vol. 11, 53-64 p.Article in journal (Refereed)
    Abstract [en]

    The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems interoperable using handcrafted "semantic" descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration.

  • 8.
    Barraza, Sergio Martin Del Campo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Towards zero-configuration condition monitoring based on dictionary learning2015In: Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015): Aug. 31 2015-Sept. 4 2015, Nice, Piscatataway, NJ: IEEE Communications Society, 2015, 1306-1310 p., 7362595Conference paper (Refereed)
    Abstract [en]

    Condition-based predictive maintenance can significantly improve overall equipment effectiveness provided that appropriate monitoring methods are used. Online condition monitoring systems are customized to each type of machine and need to be reconfigured when conditions change, which is costly and requires expert knowledge. Basic feature extraction methods limited to signal distribution functions and spectra are commonly used, making it difficult to automatically analyze and compare machine conditions. In this paper, we investigate the possibility to automate the condition monitoring process by continuously learning a dictionary of optimized shift-invariant feature vectors using a well-known sparse approximation method. We study how the feature vectors learned from a vibration signal evolve over time when a fault develops within a ball bearing of a rotating machine. We quantify the adaptation rate of learned features and find that this quantity changes significantly in the transitions between normal and faulty states of operation of the ball bearing.

  • 9.
    Sandin, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Gustafsson, Lennart
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Intelligent Industrial Processes & Enabling ICT – A Machine Learning and Intelligence Perspective2014Report (Other academic)
    Abstract [en]

    Intelligent Industrial Processes (IIP) and Enabling Information and Communication Technology (Enabling ICT) are two out of the nine areas of excellence in research and innovation at the Luleå University of Technology (LTU), which are formed to foster interdisciplinary research and innovation in strategically important areas. This report presents a perspective on the role of machine learning and intelligence in these two areas, focusing in particular on future ICT for industrial process automation (ProcessIT) up to the year 2030. The study that is presented here complements similar studies made in other fields, with the common goal to create the first inputs for a broader discussion and formulation of strategic objectives in the form of a roadmap.This report presents my interpretation of the concept of Intelligent Industrial Processes and the role of ICT in that context, including novel information processing methods and devices that are inspired by biological circuits and systems. This report also includes brief introductions and definitions of important concepts; a summary of seven documents presenting international strategic agendas and objectives; a summary of identified strengths, weaknesses, opportunities and threats; a description of selected research trends with references to interesting results; a tentative outline of interesting research problems and first steps towards 2030; and a list of research groups with complementary competences that may merit future partnership. It is concluded that the Open Research and Innovation Platform that is outlined in a parallel study would be a valuable resource for machine-learning research, development and education because transparent access to data is a key enabling factor. In terms of machine-learning research it is concluded that we need to take the step from studies of isolated learning algorithms and applications to closed-loop learning architectures for large-scale sensor-actuator systems, possibly including human- machine interaction, decision support systems and models of complex systems such as maintenance systems and markets.The aim to develop intelligent industrial processes using a new generation of ICT is an ambitious interdisciplinary initiative, which is likely to force us thinking beyond conventional methods and to educate a new generation of engineers that understand the necessary concepts.

  • 10.
    Sandin, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Khan, Asad I.
    Clayton School of Information Technology, CSIT, Monash University.
    Dyer, Adrian G.
    Department of Physiology, Monash University.
    Amin, Anang Hudaya M.
    Faculty of Information Science & Technology (FIST), Multimedia University, Melaka.
    Indiveri, Giacomo
    Institute of Neuroinformatics, University of Zurich and ETH Zurich.
    Chicca, Elisabetta
    Cognitive Interaction Technology, Center of Excellence, Bielefeld University.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Concept Learning in Neuromorphic Vision Systems: What Can We Learn from Insects?2014In: Journal of Software Engineering and Applications, ISSN 1945-3116, E-ISSN 1945-3124, Vol. 7, no 5, 387-395 p.Article in journal (Refereed)
    Abstract [en]

    Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in particular when size and power limitations apply. The development of neuromorphic electronic systems implementing models of biological sensory-motor systems in silicon is one promising approach to addressing these challenges. Concept learning is a central part of animal cognition that enables appropriate motor response in novel situations by generalization of former experience, possibly from a few examples. These aspects make concept learning a challenging and important problem. Learning methods in computer vision are typically inspired by mammals, but recent studies of insects motivate an interesting complementary research direction. There are several remarkable results showing that honeybees can learn to master abstract concepts, providing a road map for future work to allow direct comparisons between bio-inspired computing architectures and information processing in miniaturized “real” brains. Considering that the brain of a bee has less than 0.01% as many neurons as a human brain, the task to infer a minimal architecture and mechanism of concept learning from studies of bees appears well motivated. The relatively low complexity of insect sensory-motor systems makes them an interesting model for the further development of bio-inspired computing architectures, in particular for resource-constrained applications such as miniature robots, wireless sensors and handheld or wearable devices. Work in that direction is a natural step towards understanding and making use of prototype circuits for concept learning, which eventually may also help us to understand the more complex learning circuits of the human brain. By adapting concept learning mechanisms to a polymorphic computing framework we could possibly create large-scale decentralized computer vision systems, for example in the form of wireless sensor networks.

  • 11.
    Emruli, Blerim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Analogical mapping with sparse distributed memory: a simple model that learns to generalize from examples2014In: Cognitive Computation, ISSN 1866-9956, E-ISSN 1866-9964, Vol. 6, no 1, 74-88 p.Article in journal (Refereed)
    Abstract [en]

    We present a computational model for the analogical mapping of compositional structures that com- bines two existing ideas known as holistic mapping vec- tors and sparse distributed memory. The model enables integration of structural and semantic constraints when learning mappings of the type x_i → y_i and computing analogies x_j → y_j for novel inputs x_j. The model has a one-shot learning process, is randomly initialized and has three exogenous parameters: the dimensionality D of representations, the memory size S and the prob- ability χ for activation of the memory. After learning three examples the model generalizes correctly to novel examples. We find minima in the probability of generalization error for certain values of χ, S and the number of different mapping examples learned. These results indicate that the optimal size of the memory scales with the number of different mapping examples learned and that the sparseness of the memory is important. The optimal dimensionality of binary representations is of the order 10^4, which is consistent with a known analytical estimate and the synapse count for most cortical neurons. We demonstrate that the model can learn analogical mappings of generic two-place relationships and we calculate the error probabilities for recall and generalization.

  • 12.
    Emruli, Blerim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Vector space architecture for emergent interoperability of systems by learning from demonstration2014In: Biologically Inspired Cognitive Architectures, ISSN 2212-683X, E-ISSN 2212-6848, Vol. 9, 33-45 p.Article in journal (Refereed)
    Abstract [en]

    The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems interoperable using handcrafted "semantic" descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration.

  • 13.
    Barraza, Sergio Martin Del Campo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sparse feature learning for condition monitoring2014Conference paper (Other academic)
  • 14.
    Sandin, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Gustafsson, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Fault detection with hourly district energy data: Probabilistic methods and heuristics for automated detection and ranking of anomalies2013Report (Other academic)
    Abstract [en]

    This project is motivated by the difficulties experienced by district energy utilities to detect faults in large-scale district energy systems. Faults that remain undetected can be costly and the industry loose credibility when customers detect faults and receive incorrect bills. Faults are common in district energy systems due to the high number of substations and instrumentation components. Also, the standard energy-metering instrumentation is designed for low cost and billing, not for automated fault detection. Large variations in building dynamics, building subsystems, human behaviour and the environment make the system complex to model and analyse. Therefore, conventional methods for fault detection are not applicable and the use of ad hoc methods for fault detection often result in numerous false alarms that are costly to analyse and manage. There is a growing interest among the utilities to develop services and functions that are based on data with high temporal resolution. Energy metering regulations are also expected to become more demanding in the future, which drives technology standards towards high-resolution data. This trend results in high rates of streaming data at the management level, which is more challenging to validate. Therefore, more efficient methods for fault detection are needed.This project deals with probabilistic methods for automated anomaly detection that are useful for the identification of faults in large-scale district energy systems. These methods are compatible with the information that is available in modern energy meter data management systems. We focus on methods and heuristics that can be applied automatically with a minimum of human assistance to enable cost-efficient analysis of data. With these methods, operators do not have to rely on ad hoc tests or manual inspection of graphs to detect anomalies in the data. Instead, operators can focus on the analysis of a subset of substations that are identified as abnormal. Intraday and intraweek variations in the thermal load are accounted for by automatically grouping hours of the week with similar thermal load characteristics. Alternatively, intraweek cycles can be accounted for by grouping days of the week with similar characteristics. Robust regression is used to model variable relationships with historical data. A robust outlier detection method is used to determine if variables deviate from the expectation defined by a regression model. Robust statistical methods are used to score outliers, so that outstanding substations can be identified automatically with a ranking procedure. The regression models can also be used for imputation of missing energy metering data, which is a common problem that is not always solved in an accurate way. We also present methods for the detection of long-term drift, which can be costly and otherwise difficult to detect, and the detection of poor precision in measurement data, which for example can result from oversized flow valves, misconfiguration and noise. In addition to fault detection, the proposed methods can be useful also for maintenance scheduling because substations that behave in a way that is consistent with the historical record can be given a lower maintenance priority compared to substations with abnormal behaviour.The proposed methods are studied using hourly data from a population of about one thousand district heating substations. Sample code of key functions is provided. We find that substations with documented faults, unknown faults and abnormal characteristics can be identified in about 5% of the substations. The lack of a well-defined dataset makes the development and evaluation of methods for fault detection challenging, and the fact that historical energy metering data includes abnormal data is often ignored in the literature. The proposed methods need to be implemented in a full-scale district energy management system under the supervision of experienced operators before the effects on the fault detection rate and cost efficiency can be properly evaluated. However, we are convinced that the proposed algorithms can be implemented in present data management systems and that they offer significant advantages over the methods that are commonly used today.

  • 15.
    Emruli, Blerim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Gayler, Ross W.
    La Trobe University.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Analogical mapping and inference with binary spatter codes and sparse distributed memory2013In: The 2013 International Joint Conference on Neural Networks (IJCNN): Dallas, Texas 4-9 Aug 2013, Piscataway, NJ: IEEE Communications Society, 2013, 1-8 p.Conference paper (Refereed)
    Abstract [en]

    Analogy-making is a key function of human cognition. Therefore, the development of computational models of analogy that automatically learn from examples can lead to significant advances in cognitive systems. Analogies require complex, relational representations of learned structures, which is challenging for both symbolic and neurally inspired models. Vector symbolic architectures (VSAs) are a class of connectionist models for the representation and manipulation of compositional structures, which can be used to model analogy. We study a novel VSA network for the analogical mapping of compositional structures, which integrates an associative memory known as sparse distributed memory (SDM). The SDM enables non-commutative binding of compositional structures, which makes it possible to predict novel patterns in sequences. To demonstrate this property we apply the network to a commonly used intelligence test called Raven’s Progressive Matrices. We present results of simulation experiments for the Raven’s task and calculate the probability of prediction error at 95% confidence level. We find that non-commutative binding requires sparse activation of the SDM and that 10–20% concept-specific activation of neurons is optimal. The optimal dimensionality of the binary distributed representations of the VSA is of the order 10^4, which is comparable with former results and the average synapse count of neurons in the cerebral cortex.

  • 16.
    Barraza, Sergio Martin Del Campo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Albertsson, Kim
    Luleå University of Technology, Professional Support, IT-Service.
    Nilsson, Joakim
    Engineering Physics student at the Luleå University of Technology.
    Eliasson, Jens
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    FPGA prototype of machine learning analog-to-feature converter for event-based succinct representation of signals2013In: IEEE International Workshop on Machine Learning for Signal Processing, Piscataway, NJ: IEEE Signal Processing Society, 2013, 6661996Conference paper (Refereed)
    Abstract [en]

    Sparse signal models with learned dictionaries of morphological features provide efficient codes in a variety of applications. Such models can be useful to reduce sensor data rates and simplify the communication, processing and analysis of information, provided that the algorithm can be realized in an efficient way and that the signal allows for sparse coding. In this paper we outline an FPGA prototype of a general purpose "analog-to-feature converter", which learns an overcomplete dictionary of features from the input signal using matching pursuit and a form of Hebbian learning. The resulting code is sparse, event-based and suitable for analysis with parallel and neuromorphic processors. We present results of two case studies. The first case is a blind source separation problem where features are learned from an artificial signal with known features. We demonstrate that the learned features are qualitatively consistent with the true features. In the second case, features are learned from ball-bearing vibration data. We find that vibration signals from bearings with faults have characteristic features and codes, and that the event-based code enable a reduction of the data rate by at least one order of magnitude.

  • 17.
    Fischer, Tobias
    et al.
    GSI, Darmstadt.
    Blaschke, David
    Institute for Theoretical Physics, University of Wroclaw.
    Hempel, Matthias
    Department of Physics, University of Basel.
    Klähn, Thomas
    Institute for Theoretical Physics, University of Wroclaw.
    Lastowiecki, Rafal
    Institute for Theoretical Physics, University of Wroclaw.
    Liebendörfer, Matthias
    Department of Physics, University of Basel.
    Martinez-Pinedo, Gabriel
    GSI, Darmstadt.
    Pagliara, Giuseppe
    Institut für Theoretische Physik, Ruprecht-Karls-Universität, Heidelberg.
    Sagert, Irina
    Institut für Theoretische Physik, Ruprecht-Karls-Universität, Heidelberg.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Schaffner-Bielich, Jörgen
    Institut für Theoretische Physik, Ruprecht-Karls-Universität, Heidelberg.
    Typel, Stefan
    GSI, Darmstadt and Excellence Cluster Universe, München.
    Core collapse supernovae in the QCD phase diagram2012In: Physics of Atomic Nuclei, ISSN 1063-7788, E-ISSN 1562-692X, Vol. 75, no 5, 613-620 p.Article in journal (Refereed)
    Abstract [en]

    We compare two classes of hybrid equations of state with a hadron-to-quark matter phase transition in their application to core collapse supernova simulations. The first one uses the quark bag model and describes the transition to three-flavor quark matter at low critical densities. The second one employs a Polyakov-loop extended Nambu-Jona-Lasinio (PNJL) model with parameters describing a phase transition to two-flavor quark matter at higher critical densities. These models possess a distinctly different temperature dependence of their transition densities which turns out to be crucial for the possible appearance of quark matter in supernova cores. During the early post-bounce accretion phase quark matter is found only if the phase transition takes place at sufficiently low densities as in the study based on the bag model. The increase critical density with increasing temperature, as obtained for our PNJL parametrization, prevents the formation of quark matter. The further evolution of the core collapse supernova as obtained applying the quark bag model leads to a structural reconfiguration of the central protoneutron star where, in addition to a massive pure quark matter core, a strong hydrodynamic shock wave forms and a second neutrino burst is released during the shock propagation across the neutrinospheres. We discuss the severe constraints in the freedom of choice of quark matter models and their parametrization due to the recently observed 2M ⊙ pulsar and their implications for further studies of core collapse supernovae in the QCD phase diagram.

  • 18.
    Sandin, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Gustafsson, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Eklund, Robert
    Södertörns Fjärrvärme AB, Box 3073, 145 03 Norsborg, Sweden.
    Basic methods for automated fault detection and energy data validation in existing district heating systems2012In: 13th international symposium on district heating and cooling: 3rd of September - 4th of September Copenhagen, Denmark, District Energy Development Center , 2012Conference paper (Refereed)
    Abstract [en]

    Fault detection and diagnostics (FDD) of district heating substations (DHS) are important activities because malfunctioning components can lead to incorrect billing and waste of energy. Although FDD has been an activate research area for nearly two decades, only a few simple tools are commonly deployed in the district energy industry. Some of the methods proposed in the literature are promising, but their complexity may prevent broader application. Other methods require sensor data that are not commonly available, or cannot be expected to function well in practice due to oversimplification. Here we present two basic methods for improved FDD and data validation that are compatible with the data acquisition systems that are commonly used today. We propose that correlation analysis can be used to identify substations with similar supply temperatures and that the corresponding temperature difference is a useful quantity for FDD. The second method is a limit- checking approach for the validation of thermal power usage, which is sensitive to faults affecting both the primary flow and temperature sensors in a DHS. These methods are suitable for automated FDD and are demonstrated with hourly data provided by a Swedish district energy company.

  • 19.
    Sandin, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Emruli, Blerim
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sahlgren, Magnus
    Swedish Institute of Computer Science, Stockholm.
    Incremental dimension reduction of tensors with random index2011Report (Other academic)
    Abstract [en]

    We present an incremental, scalable and efficient dimension reduction technique for tensors that is based on sparse random linear coding. Data is stored in a compactified representation with fixed size, which makes memory requirements low and predictable. Component encoding and decoding are performed on-line without computationally expensive re-analysis of the data set. The range of tensor indices can be extended dynamically without modifying the component representation. This idea originates from a mathematical model of semantic memory and a method known as random indexing in natural language processing. We generalize the random-indexing algorithm to tensors and present signal-to-noise-ratio simulations for representations of vectors and matrices. We present also a mathematical analysis of the approximate orthogonality of high-dimensional ternary vectors, which is a property that underpins this and other similar random-coding approaches to dimension reduction. To further demonstrate the properties of random indexing we present results of a synonym identification task. The method presented here has some similarities with random projection and Tucker decomposition, but it performs well at high dimensionality only (n>10^3). Random indexing is useful for a range of complex practical problems, e.g., in natural language processing, data mining, pattern recognition, event detection, graph searching and search engines. Prototype software is provided. It supports encoding and decoding of tensors of order >= 1 in a unified framework, i.e., vectors, matrices and higher order tensors.

  • 20. Ciarcelluti, Paolo
    et al.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Have neutron stars a dark matter core?2011In: Physics Letters B, ISSN 0370-2693, E-ISSN 1873-2445, Vol. 695, no 1-4, 19-21 p.Article in journal (Refereed)
    Abstract [en]

    Recent observational results for the masses and radii of some neutron stars are in contrast with typical observations and theoretical predictions for "normal" neutron stars. We propose that their unusual properties can be interpreted as the signature of a dark matter core inside them. This interpretation requires that the dark matter is made of some form of stable, long-living or in general non-annihilating particles, that can accumulate in the star (Sandin and Ciarcelluti (2009). In the proposed scenario all mass-radius measurements can be explained with one nuclear matter equation of state and a dark core of varying relative size. This hypothesis will be challenged by forthcoming observations and could eventually be a useful tool for the determination of dark matter.

  • 21.
    Sanda, Mohammed-Aminu
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences.
    Abrahamsson, Lena
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Human Work Science.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Lean instrumentation framework for sensor pruning and optimization in condition monitoring2011In: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies: St. David's Hotel, Cardiff, Wales, 20 - 22 June 2011 ; CM2011/MFPT2011, Longborough, Glos: Coxmoor Publishing Co. , 2011, Vol. 1, 202-215 p.Conference paper (Refereed)
    Abstract [en]

    This paper discusses a lean instrumentation framework for guiding the introduction of the lean concept in condition monitoring in order to enhance the organizational capability (i.e. human, technical and management trichotomy) and reduce the complexity in the maintenance management systems of industrial companies. Additionally, decision-making, based on severity diagnosis and prognosis in condition monitoring, is a complex maintenance function which is based on large data-set of sensors measurements. Yet, the entirety of such decision-making is not dependent on only the sensors measurements, but also on other important indices, such as the human factors, organizational aspects and knowledge management. This is because, the ability to identify significant features from large amount of measured data is a major challenge for automated defect diagnosis, a situation that necessitate the need to identify signal transformations and features in new domains. The need for the lean instrumentation framework is justified by the desire to have a modern condition monitoring system with the capability of pruning to the optimal level the number of sensors required for efficient and effective serviceability of the maintenance process. It is concluded that there are methodologies that can be developed to enable more efficient condition monitoring systems, with benefits for many processes along the value chain.

  • 22. Ciarcelluti, Paolo
    et al.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Does neutron stars have a dark matter core?2010Report (Other academic)
  • 23.
    Blaschke, David
    et al.
    University of Wroclaw.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Skokov, Vladimir
    Joint Institute for Nuclear Physics.
    Accessibility of dense QCD phases in heavy-ion collisions2010In: "White book" of the future nuclotron-based ion collider facility (NICA) in Dubna, Russia, 2010Chapter in book (Other academic)
  • 24.
    Blaschke, D.
    et al.
    University of Wroclaw.
    Klähn, T.
    University of Wroclaw.
    Łastowiecki, R.
    University of Wroclaw.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    How strange are compact star interiors?2010In: Journal of Physics G: Nuclear and Particle Physics, ISSN 0954-3899, E-ISSN 1361-6471, Vol. 37, no 9Article in journal (Refereed)
    Abstract [en]

    We discuss a Nambu-Jona-Lasinio (NJL)-type quantum field theoretical approach to the quark matter equation of state with color superconductivity and construct hybrid star models on this basis. It has recently been demonstrated that with increasing baryon density, the different quark flavors may occur sequentially, starting with down-quarks only, before the second light quark flavor and at highest densities the strange quark flavor also appears. We find that color superconducting phases are favorable over non-superconducting ones, which entails consequences for thermodynamic and transport properties of hybrid star matter. In particular, for NJL-type models no strange quark matter phases can occur in compact star interiors due to mechanical instability against gravitational collapse, unless a sufficiently strong flavor mixing as provided by the Kobayashi-Maskawa-'t Hooft determinant interaction is present in the model. We discuss observational data on mass-radius relationships of compact stars which can put constraints on the properties of the dense matter equation of state.

  • 25. Blaschke, David
    et al.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Skokov, Vladimir
    Typel, Stefan
    Accessibility of color superconducting quark matter phases in heavy-ion collisions2010In: Vol. 3, no 3, 741-745 p.Article in journal (Refereed)
    Abstract [en]

    We discuss a hybrid equation of state (EoS) that fulfills constraints for mass-radius relationships and cooling of compact stars. The quark matter EoS is obtained from a Polyakov-loop Nambu-Jona-Lasinio (PNJL) model with color superconductivity, and the hadronic one from a relativistic mean-field (RMF) model with density-dependent couplings (DD-RMF). For the construction of the phase transition regions we employ here for simplicity a Maxwell construction. We present the phase diagram for symmetric matter which exhibits two remarkable features: (1) a "nose"-like structure of the hadronic-to-quark matter phase border with an increase of the critical density at temperatures below T \sim 150 MeV and (2) a high critical temperature for the border of the two-flavor color superconducting (2SC) phase, Tc >160 MeV. We show the trajectories of heavy-ion collisions in the plane of excitation energy versus baryon density calculated using the UrQMD code and conjecture that for incident energies of 4 . . . 8 A GeV as provided,  e.g., by the Nuclotron-M at JINR Dubna or by lowest energies at the future heavy-ion collision experiments CBM at FAIR and NICA at JINR, the color superconducting quark matter phase becomes accessible.

  • 26.
    Sandin, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Ciarcelluti, Paolo
    University of Liège.
    Effects of mirror dark matter on neutron stars2009In: Astroparticle physics, ISSN 0927-6505, E-ISSN 1873-2852, Vol. 32, no 5, 278-284 p.Article in journal (Refereed)
    Abstract [en]

    If dark matter is made of mirror baryons, they presumably are present in all gravitationally bound structures. Here, we investigate some effects of mirror dark matter on neutron stars and discuss possible observational consequences. The general-relativistic hydrostatic equations are generalized to spherical objects with multiple fluids that interact by gravity. We use the minimal parity-symmetric extension of the standard model, which implies that the microphysics is the same in the two sectors. We find that the mass-radius relation is significantly modified in the presence of a few percent mirror baryons. This effect mimics that of other exotica, e.g., quark matter. In contrast to the common view that the neutron-star equilibrium sequence is unique, we show that it depends on the relative number of mirror baryons to ordinary baryons. It is therefore, history dependent. The critical mass for core collapse, i.e., the process by which neutron stars are created, is modified in the presence of mirror baryons. We calculate the modified Chandrasekhar mass and fit it with a polynomial. A few percent mirror baryons is sufficient to lower the critical mass for core collapse by ... This could allow for the formation of extraordinary compact neutron stars with low mass.

  • 27.
    Blaschke, David
    et al.
    University of Wroclaw.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Klähn, Thomas
    University of Wroclaw.
    Berdermann, Jens
    Deutsches Elektronen-Synchrotron.
    Sequential deconfinement of quark flavors in neutron stars2009In: Physical Review C. Nuclear Physics, ISSN 0556-2813, E-ISSN 1089-490X, Vol. 80, no 6, 65807- p.Article in journal (Refereed)
    Abstract [en]

    A scenario is suggested in which the three light quark flavors are sequentially deconfined under increasing pressure in cold asymmetric nuclear matter as found, for example, in neutron stars. The basis for this analysis is a chiral quark matter model of Nambu–Jona-Lasinio (NJL) type with diquark pairing in the spin-1 single-flavor, spin-0 two-flavor, and three-flavor channels. Nucleon dissociation sets in at about the saturation density, n0, when the down-quark Fermi sea is populated (d-quark drip line) because of the flavor asymmetry induced by β equilibrium and charge neutrality. At about 3n0, u-quarks appear and a two-flavor color superconducting (2SC) phase is formed. The s-quark Fermi sea is populated only at still higher baryon density, when the quark chemical potential is of the order of the dynamically generated strange quark mass. Two different hybrid equations of state (EOSs) are constructed using the Dirac-Brueckner Hartree-Fock (DBHF) approach and the EOS of Shen et al. [H. Shen, H. Toki, K. Oyamatsu, and K. Sumiyoshi, Nucl. Phys. A637, 435 (1998)] in the nuclear matter sector. The corresponding hybrid star sequences have maximum masses of 2.1 and 2.0 M respectively. Two- and three-flavor quark-matter phases exist only in gravitationally unstable hybrid star solutions in the DBHF case, whereas the Shen-based EOSs produce stable configurations with a 2SC phase component in the core of massive stars. Nucleon dissociation via d-quark drip could act as a deep crustal heating process, which apparently is required to explain superbursts and cooling of x-ray transients.

  • 28.
    Klähn, Thomas
    et al.
    Argonne National Laboratory, Argonne, IL.
    Roberts, Craig
    Argonne National Laboratory, Argonne, IL.
    Blaschke, David
    University of Wroclaw.
    Sandin, Fredrik
    Neutron stars and the high density equation of state2009In: AIP Conference Proceedings: BULK NUCLEAR PROPERTIES: 5th ANL/MSU/JINA/INT FRIB Workshop, American Institute of Physics (AIP), 2009, Vol. 1128, 175-185 p.Conference paper (Other academic)
    Abstract [en]

    One of the key ingredients to understand the properties of neutrons stars1 (NS) is the equation of state at finite densities far beyond nuclear saturation. Investigating the phase structure of quark matter that might be realized in the core of NS inspires theory and observation. We discuss recent results of our work to point out our view on challenges and possibilities in this evolving field by means of a few examples.

  • 29.
    Blaschke, David
    et al.
    University of Wroclaw.
    Klahn, Thomas
    Argonne National Laboratory, Argonne, IL.
    Sandin, Fredrik
    Equation of state at high densities and modern compact star observations2008In: Journal of Physics G: Nuclear and Particle Physics, ISSN 0954-3899, E-ISSN 1361-6471, Vol. 35, no 1, 014051-6 p.Article in journal (Refereed)
    Abstract [en]

    Recently, observations of compact stars have provided new data of high accuracy which put strong constraints on the high-density behaviour of the equation of state of strongly interacting matter otherwise not accessible in terrestrial laboratories. The evidence for neutron stars with high mass (M = 2.1 +/- 0.2 M-circle dot for PSR J0751 + 1807) and large radii (R > 12 km for RX J1856-3754) rules out soft equations of state and has provoked a debate whether the occurrence of quark matter in compact stars can be excluded as well. In this contribution, it is shown that modern quantum field theoretical approaches to quark matter including colour superconductivity, and a vector meanfield allow a microscopic description of hybrid stars which fulfil the new, strong constraints. The deconfinement transition in the resulting stiff hybrid equation of state is weakly first order so that its signals have to be expected due to specific changes in transport properties governing the rotational and cooling evolution caused by the colour superconductivity of quark matter. A similar conclusion holds for the investigation of quark deconfinement in future generations of nucleus-nucleus collision experiments at low temperatures and high baryon densities such as CBM @ FAIR.

  • 30.
    Blaschke, David
    et al.
    University of Wroclaw.
    Sandin, Fredrik
    Klähn, Thomas
    Argonne National Laboratory, Argonne, IL.
    1-2-3-flavor color superconductivity in compact stars2008In: Journal of Physics G: Nuclear and Particle Physics, ISSN 0954-3899, E-ISSN 1361-6471, Vol. 35, no 10Article in journal (Refereed)
    Abstract [en]

    We suggest a scenario where the three light quark flavors are sequentially deconfined under increasing pressure in cold asymmetric nuclear matter as, e.g., in neutron stars. The basis for our analysis is a chiral quark matter model of Nambu–Jona-Lasinio (NJL) type with diquark pairing in the single flavor color-spin-locking (CSL), 2-flavor (2SC) and 3-flavor color-flavor locking (CFL) channels, and a Dirac–Brueckner–Hartree–Fock (DBHF) approach in the nuclear matter sector. We find that nucleon dissociation sets in at about the saturation density, n0, when the down-quark Fermi sea is populated (d-quark dripline) due to the flavor asymmetry imposed by β-equilibrium and charge neutrality. At about 3n0 u-quarks appear forming a 2-flavor color superconducting (2SC) phase, while the s-quark Fermi sea is populated only at still higher baryon density. The hybrid star sequence has a maximum mass of 2.1 M. Two- and 3-flavor quark matter phases are found only in gravitationally unstable hybrid star solutions.

  • 31.
    Blaschke, David
    et al.
    University of Wroclaw.
    Klähn, Thomas
    Argonne National Laboratory, Argonne, IL.
    Sandin, Fredrik
    Color superconducting quark matter in compact stars2008In: Exotic States of Nuclear Matter: Proceedings of the International Symposium EXOCT07, Catania University, Italy, 11 - 15 June 2007 / [ed] Umberto Lombardo; Marcello Baldo; Fiorella Burgio; Hans-Josef Schulze, Hackensack, NJ: World Scientific and Engineering Academy and Society, 2008, 256-263 p.Conference paper (Refereed)
  • 32.
    Blaschke, David
    et al.
    Instytut Fizyki Teoretycznej, Uniwersytet Wrocławski.
    Sandin, Fredrik
    Klähn, Thomas
    Argonne National Laboratory, Argonne, IL.
    Berdermann, Jens
    DESY Zeuthen, Berlin.
    Single-flavor CSL phase in compact stars2008In: Hadronic physics: Joint Meeting Heidelberg-Liège-Paris-Wroclaw, HLPW 2008, Spa, Belgium, 6 - 8 March 2008 / [ed] Joseph Cugnon; Jean-Philippe Lansberg, Melville, NY: American Institute of Physics (AIP), 2008, 183-192 p.Conference paper (Refereed)
    Abstract [en]

    We suggest a scenario where the three light quark flavors are sequentially deconfined under increasing pressure in cold asymmetric nuclear matter as, e.g., in neutron stars. The basis for our analysis is a chiral quark matter model of Nambu-Jona-Lasinio (NJL) type with diquark pairing in the spin-1 single flavor (CSL), spin-0 two flavor (2SC) and three flavor (CFL) channels. We find that nucleon dissociation sets in at about the saturation density, n0, when the down-quark Fermi sea is populated (d-quark dripline) due to the flavor asymmetry induced by β-equilibrium and charge neutrality. At about 3n0 u-quarks appear and a two-flavor color superconducting (2SC) phase is formed. The s-quark Fermi sea is populated only at still higher baryon density, when the quark chemical potential is of the order of the dynamically generated strange quark mass. We construct two different hybrid equations of state (EoS) using the Dirac-Brueckner Hartree-Fock (DBHF) approach and the EoS by Shen et al. in the nuclear matter sector. The corresponding hybrid star sequences have maximum masses of, respectively, 2.1 and 2.0 M⊙. Two- and three-flavor quark-matter phases exist only in gravitationally unstable hybrid star solutions in the DBHF case, while the Shen-based EoS produce stable configurations with a 2SC phase component in the core of massive stars. Nucleon dissociation due to d-quark drip at the crust-core boundary fulfills basic criteria for a deep crustal heating process which is required to explain superbusts as well as cooling of X-ray transients.

  • 33. Sandin, Fredrik
    et al.
    Hansson, Johan
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    The observational legacy of preon stars: probing new physics beyond the LHC2007Report (Other academic)
    Abstract [en]

    We discuss possible ways to observationally detect the superdense cosmic objects composed of hypothetical sub-constituent fermions beneath the quark/lepton level, recently proposed by us. The characteristic mass and size of such objects depend on the compositeness scale, and their huge density cannot arise within a context of quarks and leptons alone. Their eventual observation would therefore be a direct vindication of physics beyond the standard model of particle physics, possibly far beyond the reach of the Large Hadron Collider (LHC), in a relatively simple and inexpensive manner. If relic objects of this type exist, they can possibly be detected by present and future x-ray observatories, high-frequency gravitational wave detectors, and seismological detectors. To have a realistic detection rate, i.e., to be observable, they must necessarily constitute a significant fraction of cold dark matter.

  • 34. Sandin, Fredrik
    Exotic phases of matter in compact stars2007Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This doctoral thesis in physics at Luleå University of Technology is devoted to the phenomenology of compact stars, and theoretical models of their interior. Particle physics has provided fundamental concepts and details needed to develop a description of matter and the evolution of the universe. It is, however, difficult to obtain information about the properties of matter at low temperature and high density from such experiments. In this context, astrophysical observations constitute an important source of information, thanks to the high resolution of present and near-future terrestrial and space-based observatories. The density of matter in neutron stars exceeds that in atomic nuclei, and little is known about the nature of their interior. It is clear that the interaction between the smallest observed building blocks of atomic nuclei, the quarks, becomes weaker with increasing density. Matter should therefore dissolve into a state of nearly free quarks at high densities, and models of classical superconductivity advocate that this state is a superconductor. The argument for this is simple: a low-temperature Fermi system with a weak attractive interaction is unstable with respect to formation of Cooper pairs. It is not known if this state of matter exists in neutron stars, but models suggest that it is possible. The major part of the work summarised in this thesis is the development of a model of superconducting quark matter, and its consequences for the phenomenology of neutron stars and their formation in the collapse of massive stars. It is a Nambu - Jona-Lasinio model with self-consistently calculated quark masses and pairing gaps, which properly accounts for the beta-equilibrium and/or charge neutrality constraints in compact stars and heavy-ion collisions. Phase diagrams and equations of state of superconducting quark matter are presented, and the influence of different assumptions about the effective quark interaction is investigated. The effect of neutrino untrapping in hypothetical quark cores of newborn neutron stars is investigated, and phase diagrams for quark matter with trapped neutrinos are presented. While no evidence for the presence of quark matter in neutron stars exists, it is explicitly shown that observations do not contradict this possibility. On the contrary, the presence of a quark matter core in neutron stars can overcome problems with hadronic equations of state. In contrast to the expectation from more simple model calculations, the results presented here suggest that strange quarks do not play a significant role in the physics of compact stars. While there is some room for bare strange stars, such models suffer from low maximum masses, and the presence of a hadronic shell tends to render stars with strange matter cores unstable. Regardless of the nature of matter in neutron stars, general relativity and the standard model of particle physics limit their density to < 10^16 g/cm^3. In the light of established theory, any object with a density exceeding this limit should be a black hole. It is suggested in this thesis that if quarks and leptons are composite objects, as suggested, e.g., by the three particle generations in the standard model, a yet unobserved class of compact objects with extremely high densities could exist. As the hypothetical pre-quark particles are called preons, these objects are named 'preon stars'. The properties of preon stars are estimated, and it is shown that their maximum mass depends on the quark compositeness energy scale. In general, the mass of these objects should not exceed that of the Earth, and their maximum size is of the order of metres. Several methods to observationally detect preon stars are discussed, notably, by gravitational lensing of gamma-ray bursts and by measuring high-frequency gravitational waves from binary systems. To have a realistic detection rate, i.e., to be observable, they must constitute a significant fraction of cold dark matter. This condition could be met if they formed in a primordial phase transition, at a temperature of the order of the quark compositeness energy scale. Some unexplained features observed in spectra of gamma-ray bursts are discussed, as they are similar to the signature expected from a preon-star gravitational lensing event. An observation of objects with these characteristics would be a direct vindication of physics beyond the standard model.

  • 35. Sandin, Fredrik
    et al.
    Blaschke, D.
    Universität Rostock.
    Quark core of protoneutron stars in the phase diagram of quark matter2007In: Physical Review D. Particles and fields, ISSN 0556-2821, E-ISSN 1089-4918, Vol. 75, no 12Article in journal (Refereed)
    Abstract [en]

    We study the effect of neutrino trapping in new-born quark stars within a three-flavor Nambu-Jona-Lasinio (NJL) model with self-consistently calculated quark masses. The phase diagrams and equations of state for charge neutral quark matter in beta-equilibrium are presented, with and without trapped neutrinos. The compact star sequences for different neutrino untrapping scenarios are investigated and the energy release due to neutrino untrapping is found to be of the order of 1053 erg. We find that hot quark stars characterized, e.g., by an entropy per baryon of 1-2 and a lepton fraction of 0.4, as models for the cores of newborn protoneutron stars are in the two-flavor color superconducting (2SC) state. High temperatures and/or neutrino chemical potentials disfavor configurations with a color-flavor-locked (CFL) phase. Stable quark star solutions with CFL cores exist only at low temperatures and neutrino chemical potentials.

  • 36. Sandin, Fredrik
    et al.
    Hansson, Johan
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Observational legacy of preon stars: probing new physics beyond the CERN LHC2007In: Physical Review D. Particles and fields, ISSN 0556-2821, E-ISSN 1089-4918, Vol. 76, no 12Article in journal (Refereed)
    Abstract [en]

    We discuss possible ways to observationally detect the superdense cosmic objects composed of hypothetical subconstituent fermions beneath the quark/lepton level, recently proposed by us. The characteristic mass and size of such objects depend on the compositeness scale, and their huge density cannot arise within a context of quarks and leptons alone. Their eventual observation would therefore be a direct vindication of physics beyond the standard model of particle physics, possibly far beyond the reach of the Large Hadron Collider (LHC), in a relatively simple and inexpensive manner. If relic objects of this type exist, they can possibly be detected by present and future x-ray observatories, high-frequency gravitational wave detectors, and seismological detectors. To have a realistic detection rate, i.e., to be observable, they must necessarily constitute a significant fraction of cold dark matter.

  • 37.
    Klähn, T.
    et al.
    Physics Division, Argonne National Laboratory.
    Blaschke, D.
    Institut für Physik, Universität Rostock.
    Sandin, Fredrik
    Fuchs, C.
    Institut für Theoretische Physik, Universität Tübingen.
    Faessler, A.
    Institut für Theoretische Physik, Universität Tübingen.
    Grigorian, H.
    Joint Institute for Nuclear Research, Dubna.
    Röpke, G.
    Institut für Physik, Universität Rostock.
    Trumper, J.
    Max-Planck-Institut fur Extraterrestrische Physik, Garching.
    Modern compact star observations and the quark matter equation of state2007In: Physics Letters B, ISSN 0370-2693, E-ISSN 1873-2445, Vol. 654, no 5-6, 170-176 p.Article in journal (Refereed)
    Abstract [en]

    We present a hybrid equation of state (EoS) for dense matter that satisfies phenomenological constraints from modern compact star (CS) observations which indicate high maximum masses (M = 2 Msun) and large radii (R> 12 km). The corresponding isospin symmetric EoS is consistent with flow data analyses of heavy-ion collisions and a deconfinement transition at approx. 0.55 fm-3. The quark matter phase is described by a 3-flavor Nambu--Jona-Lasinio model that accounts for scalar diquark condensation and vector meson interactions while the nuclear matter phase is obtained within the Dirac-Brueckner-Hartree-Fock (DBHF) approach using the Bonn-A potential. We demonstrate that both pure neutron stars and neutron stars with quark matter cores (QCSs) are consistent with modern CS observations. Hybrid star configurations with a CFL quark core are unstable.

  • 38. Sandin, Fredrik
    et al.
    Öztas, A. M.
    Hacettepe University, Ankara.
    Condition for gapless color-antitriplet excitations in Nambu-Jona-Lasinio models2006In: Physical Review C. Nuclear Physics, ISSN 0556-2813, E-ISSN 1089-490X, Vol. 73, no 35203Article in journal (Refereed)
    Abstract [en]

    We present an exact condition for the existence of gapless quasiparticle excitations in Nambu-Jona-Lasinio models of color superconducting quark matter with a quark-quark interaction in the scalar color-antitriplet channel. The condition can be represented by a rotated ellipse in the plane of mass and chemical potential differences for the paired quark fields.

  • 39. Sandin, Fredrik
    Exotic phases of matter in compact stars2005Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Astrophysical observations constitute an increasingly important source of information, thanks to the high resolution of present and near-future terrestrial and orbiting observatories. Especially, the properties of matter at low temperatures and extremely high densities can only be studied by observations of compact stellar objects. Traditionally, astrophysicists distinguish between three different types of compact objects. These are white dwarfs, neutron stars, and black holes. Neutron stars are presumably unique astrophysical laboratories for a broad range of physical phenomena. Examples are exotic phases of matter at super-nuclear densities and hypothetical new states of matter, e.g., superconducting quark matter. This Licentiate thesis treats a subset of the exotic phases of matter that could exist in compact stellar objects. In particular, a model of three-flavour colour superconducting quark matter is derived and a signature for a new state of matter is suggested. The former is an effective so-called Nambu-Jona-Lasinio model of quark matter at high densities, which incorporates local colour and electric charge neutrality, as well as self consistently determined quark masses and superconducting condensates. The phase diagram of three-flavour colour superconducting quark matter is presented and the effect of superconducting condensates on the possible configurations of compact stars is discussed. In the second part of the thesis, it is shown that a hypothetical class of particles, so-called preons, which might be the building blocks of quarks and leptons, could allow for a new class of compact objects, preon stars. The properties of preon stars are estimated and potential methods to observe them are discussed.

  • 40.
    Hansson, Johan
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Sandin, Fredrik
    Preonstjärnor - en ny sorts himlakropp?2005In: Populär astronomi, ISSN 1650-7177, Vol. 4, no 8, 8-13 p.Article in journal (Other (popular science, discussion, etc.))
  • 41.
    Blaschke, D.
    et al.
    Gesellschaft für Schwerionenforschung mbH (GSI).
    Fredriksson, Sverker
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Grigorian, H.
    Institut für Physik, Universität Rostock.
    Öztas, A.M.
    Department of Physics, Hacettepe University.
    Sandin, Fredrik
    The Phase diagram of three-flavor quark matter under compact star constraints2005In: Physical Review D. Particles and fields, ISSN 0556-2821, E-ISSN 1089-4918, Vol. 72, no 6Article in journal (Refereed)
    Abstract [en]

    The phase diagram of three-flavor quark matter under compact star constraints is investigated within a Nambu-Jona-Lasinio model. Global color and electric charge neutrality is imposed for beta-equilibrated superconducting quark matter. The constituent quark masses and the diquark condensates are determined self-consistently in the plane of temperature and quark chemical potential. Both strong and intermediate diquark coupling strengths are considered. We show that in both cases, gapless superconducting phases do not occur at temperatures relevant for compact star evolution, i.e., below T~50 MeV. The stability and structure of isothermal quark star configurations are evaluated. For intermediate coupling, quark stars are composed of a mixed phase of normal (NQ) and two-flavor superconducting (2SC) quark matter up to a maximum mass of 1.21 M[sun]. At higher central densities, a phase transition to the three-flavor color flavor locked (CFL) phase occurs and the configurations become unstable. For the strong diquark coupling we find stable stars in the 2SC phase, with masses up to 1.33 M[sun]. A second family of more compact configurations (twins) with a CFL quark matter core and a 2SC shell is also found to be stable. The twins have masses in the range 1.30...1.33 M[sun]. We consider also hot isothermal configurations at temperature T=40 MeV. When the hot maximum mass configuration cools down, due to emission of photons and neutrinos, a mass defect of 0.1 M[sun] occurs and two final state configurations are possible

  • 42.
    Hansson, Johan
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Sandin, Fredrik
    Preon stars: a new class of cosmic compact objects2005In: Physics Letters B, ISSN 0370-2693, E-ISSN 1873-2445, Vol. 616, no 1-2, 1-7 p.Article in journal (Refereed)
    Abstract [en]

    In the context of the standard model of particle physics, there is a definite upper limit to the density of stable compact stars. However, if a more fundamental level of elementary particles exists, in the form of preons, stability may be re-established beyond this limiting density. We show that a degenerate gas of interacting fermionic preons does allow for stable compact stars, with densities far beyond that in neutron stars and quark stars. In keeping with tradition, we call these objects "preon stars", even though they are small and light compared to white dwarfs and neutron stars. We briefly note the potential importance of preon stars in astrophysics, e.g., as a candidate for cold dark matter and sources of ultra-high energy cosmic rays, and a means for observing them.

  • 43. Sandin, Fredrik
    Compact stars in the standard model - and beyond2005In: European Physical Journal C, ISSN 1434-6044, E-ISSN 1434-6052, Vol. 40, no Supplement 2, 15-22 p.Article in journal (Refereed)
    Abstract [en]

    In the context of the standard model of particle physics, there is a definite upper limit to the density of stable compact stars. However, if there is a deeper layer of constituents, below that of quarks and leptons, stability may be re-established far beyond this limiting density and a new class of compact stars could exist. These objects would cause gravitational lensing of gamma-ray bursts and white dwarfs, which might be observable as line features in the spectrum. Such observations could provide means for obtaining new clues about the fundamental particles and the nature of cold dark matter.

  • 44.
    Hansson, Johan
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Sandin, Fredrik
    Stor som en kula men tyngre än jorden: Svenska forskare kan ha upptäckt de första kompakta objekten i universum på sjuttio år2005In: Forskning & Framsteg, ISSN 0015-7937, no 7, 40-43 p.Article in journal (Other (popular science, discussion, etc.))
  • 45. Sandin, Fredrik
    et al.
    Barczyk, Monika
    CERN.
    Bogaerts, Andre
    CERN.
    Caprini, Mihai
    CERN.
    Jones, Robert
    CERN.
    Kolos, Sergei
    CERN.
    Smizanska, Maria
    CERN.
    Tremblet, Louis
    CERN.
    Werner, Per
    CERN.
    Online Histogramming Requirements2002In: CERN Technical Note, 2002Chapter in book (Other academic)
  • 46.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Unsupervised feature learning for rotating machineryIn: International Journal of COMADEM, ISSN 1363-7681Article in journal (Other academic)
    Abstract [en]

    A smart sensorized bearing can be described as a bearing with built-in sensors for condition monitoring. These bearings require new methods for the processing of the information coming from the sensors. Smart bearings are expected to become the central components in the condition monitoring sensor system of future rotating machines. Condition monitoring typically requires expert knowledge about the machine that is monitored, making it costly to adapt the methods to the different machines, environment and operational conditions. This approach deals with an unsupervised learning method that allows for automatic characterization of signals with repeating structure in the time domain. The method is sparse coding with dictionary learning. We present the information obtained from time domain and frequency domain techniques and describe the conditions required to make the fault diagnosis possible. In contrast, we describe how our approach can autonomously depict deviations from the normal state of operation of machine by monitoring a dictionary of atomic waveforms learned from a signal. We study the propagation over time of a learned dictionary when the vibration of a rotating machine is monitored in normal and faulty states of operation, and we find that the adaptation rates of some atomic waveforms change significantly when a fault occurs.

  • 47.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Schnabel, Stephan
    Luleå University of Technology, Department of Engineering Sciences and Mathematics.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Marklund, Pär
    Luleå University of Technology, Department of Engineering Sciences and Mathematics.
    Detection of particle contaminants in rolling element bearings with unsupervised acoustic emission feature learningIn: Tribology International, ISSN 0301-679X, E-ISSN 1879-2464Article in journal (Other academic)
  • 48.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Strömbergsson, Daniel
    Luleå University of Technology, Department of Engineering Sciences and Mathematics.
    A dictionary learning approach to monitoring of wind turbine drivetrain bearingsIn: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216Article in journal (Other academic)
    Abstract [en]

    Condition monitoring and predictive maintenance are central for efficient operation of wind farms due to the challenging operating conditions, rapid technology development and high number of aging wind turbines. In particular, preventive maintenance planning requires early detection of faults with few false positives. This is a challenging problem due to the complex and weak signatures of some faults, in particular of faults occurring in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded from three wind turbines over about four years of operation, thereby contributing novel test results based on real world data. Results of former studies addressing condition--monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on data from test rigs operating under controlled conditions. Furthermore, most former studies focus on classification tasks using relatively small sets of labeled data, which are useful for quantitative method comparisons but gives little information about how useful these approaches are in practice. In this study dictionaries are learned from gearbox vibrations in three different turbines known to be in healthy conditions, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We calculate the dictionary distance between the initial and propagated dictionaries and find time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement. When repeating that experiment with a dictionary that initially is learned from the vibration of another type of rotating machine, the corresponding difference of dictionary distances is three times lower and do not appear abnormal. We also investigate the distance between dictionaries learned from geographically nearby turbines of the same type in healthy conditions and find that the features learned are similar, and that a dictionary learned from one turbine can be useful for monitoring of another similar turbine.

  • 49.
    Sandin, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Martin del Campo Barraza, Sergio
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Dictionary Learning with Equiprobable Matching PursuitManuscript (preprint) (Other academic)
    Abstract [en]

    Sparse signal representations based on linear combinations of learned atomshave been used to obtain state-of-the-art results in several practical signalprocessing applications. Approximation methods are needed to processhigh-dimensional signals in this way because the problem to calculate optimalatoms for sparse coding is NP-hard. Here we study greedy algorithms forunsupervised learning of dictionaries of shift-invariant atoms and propose anew method where each atom is selected with the same probability on average,which corresponds to the homeostatic regulation of a recurrent convolutionalneural network. Equiprobable selection can be used with several greedyalgorithms for dictionary learning to ensure that all atoms adapt duringtraining and that no particular atom is more likely to take part in the linearcombination on average. We demonstrate via simulation experiments thatdictionary learning with equiprobable selection results in higher entropy ofthe sparse representation and lower reconstruction and denoising errors, bothin the case of ordinary matching pursuit and orthogonal matching pursuit withshift-invariant dictionaries. Furthermore, we show that the computational costsof the matching pursuits are lower with equiprobable selection, leading tofaster and more accurate dictionary learning algorithms.

1 - 49 of 49
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf