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• 1.
The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China.
The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China. The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China. Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 37611-37619Artikel i tidskrift (Refereegranskat)

The texture feature tensor established from a subband time–frequency image (TFI) was extracted and used to identify the fault states of a rolling bearing. The TFI of adaptive optimal-kernel distribution was optimally partitioned into TFI blocks based on the minimum frequency band entropy. The texture features were extracted from the co-occurrence matrix of each TFI block. Based on the order of the segmented frequency bands, the texture feature tensor was constructed using the multidimensional feature vectors from all the blocks; this preserved the inherent characteristic of the TFI structure and avoided the information loss caused by vectorizing multidimensional features. The linear support higher order tensor machine based on the feature tensor was applied to identify the fault states of the rolling bearing.

• 2.
Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India. Department of Engineering-Renewable Energy and Thermodynamics, Aarhus University, Denmark..
Departamento de Enxeñería Eléctrica-Universidade de Vigo, Campus de Lagoas-Marcosende, Vigo, Spain.. Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geoteknologi. Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam..
A comparison between reconstruction methods for generation of synthetic time series applied to wind speed simulation2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 1353096-135398Artikel i tidskrift (Refereegranskat)

Wind energy is an attractive renewable sources and its prediction is highly essential for multiple applications. Over the literature, there are several studies have been focused on the related researches of synthetic wind speed data generation. In this research, two reconstruction methods are developed for synthetic wind speed time series generation. The modeling is constructed based on different processes including independent values generation from the known probability distribution function, rearrangement of random values and segmentation. They have been named as Rank-wise and Step-wise reconstruction methods. The proposed methods are explained with the help of a standard time series and the examination on wind speed time series collected from Galicia, the autonomous region in the northwest of Spain. Results evidenced the potential of the developed models over the state-of-the-art synthetic time series generation methods and demonstrated a successful validation using the means of mean and median wind speed values, autocorrelations, probability distribution parameters with their corresponding histograms and confusion matrix. Pros and cons of both methods are discussed comprehensively.

• 3.
Beihang University, Beijing, China.
Beihang University, Beijing, China. Beihang University, Beijing, China. Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Beihang University, Beijing, China. Beihang University, Beijing, China.
An Active Learning Method Based on Uncertainty and Complexity for Gearbox Fault Diagnosis2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 9022-9031Artikel i tidskrift (Refereegranskat)

It is crucial to implement an effective and accurate fault diagnosis of a gearbox for mechanical systems. However, being composed of many mechanical parts, a gearbox has a variety of failure modes resulting in the difficulty of accurate fault diagnosis. Moreover, it is easy to obtain raw vibration signals from real gearbox applications, but it requires significant costs to label them, especially for multi-fault modes. These issues challenge the traditional supervised learning methods of fault diagnosis. To solve these problems, we develop an active learning strategy based on uncertainty and complexity. Therefore, a new diagnostic method for a gearbox is proposed based on the present active learning, empirical mode decomposition-singular value decomposition (EMD-SVD) and random forests (RF). First, the EMD-SVD is used to obtain feature vectors from raw signals. Second, the proposed active learning scheme selects the most valuable unlabeled samples, which are then labeled and added to the training data set. Finally, the RF, trained by the new training data, is employed to recognize the fault modes of a gearbox. Two cases are studied based on experimental gearbox fault diagnostic data, and a supervised learning method, as well as other active learning methods, are compared. The results show that the proposed method outperforms the two common types of methods, thus validating its effectiveness and superiority.

• 4.
Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China. Department of Automation, Shanghai Jiao Tong University, Shanghai, China. Department of Automation, Shanghai Jiao Tong University, Shanghai, China. Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China. Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland. Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
Semantic Integration of Plug-and-Play Software Components for Industrial Edges Based on Microservices2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 125882-125892Artikel i tidskrift (Refereegranskat)

The industrial cyber-physical system enables collaboration between distributed nodes across industrial clouds and edge devices. Flexibility and interoperability could be enhanced significantly by introducing the service-oriented architecture to industrial edge devices. From the industrial edge computing perspective, software components shall be dynamically composed across heterogeneous edge devices to perform various functionalities. In this paper, a knowledge-driven Microservice-based architecture to enable plug-and-play software components is proposed for industrial edges. These software components can be dynamically configured based on the orchestration of microservices with the support of the knowledge base and the reasoning process. These semantically enhanced plug-and-play microservices could provide rapid online reconfiguration without any programming efforts. The use of the plug-and-play software components is demonstrated by an assembly line example.

• 5.
Hunnan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China.
Hunnan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China. Hunnan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China. Hunnan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
Performance Evaluation of Multipath TCP Scheduling Algorithms2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 29818-29825Artikel i tidskrift (Refereegranskat)

One of the goals of 5G is to provide enhanced mobile broadband and enable low latency in some use cases. To achieve this aim, the Internet Engineering Task Force (IETF) has proposed the Multipath TCP (MPTCP) by utilizing the feature of dual connectivity in 5G, where a 5G device can be served by two different base stations. However, the path heterogeneity between the 5G device to the server may cause packet out-of-order problem. The researchers proposed a number of scheduling algorithms to tackle this issue. This paper introduces the existing algorithms, and with the aim to make a thorough comparison between the existing scheduling algorithms and provide guidelines for designing new scheduling algorithms in 5G, we have conducted an extensive set of emulation studies based on the real Linux experimental platform. The evaluation covers a wide range of network scenarios to investigate the impact of different network metrics, namely, RTT, buffer size and file size on the performance of existing widely-deployed scheduling algorithms.

• 6.
Department of Information and Communication Engineering, Xi’an Jiaotong University, Xi’an, China.
Department of Information and Communication Engineering, Xi’an Jiaotong University, Xi’an, China. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
Physical Layer Security Based on Interference Alignment in K-User MIMO Y Wiretap Channels2017Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 5, s. 5747-5759Artikel i tidskrift (Refereegranskat)

This paper studies the secure degree of freedom (SDOF) of the multiway relay wiretap system K -user MIMO Y wiretap channel, where each legitimate user equipped with M antennas intends to convey independent signals via an intermediate relay with N antennas. There exists an eavesdropper which is equipped with Neantennas close to the legitimate users. In order to eliminate the multiuser interference and keep the system security, interference alignment is mainly utilized in the proposed broadcast wiretap channel (BWC) and multi-access BWC (MBWC), and cooperative jamming is adopted as a supplementary approach in the MBWC model. The general feasibility conditions of the interference alignment are deduced asM≥K−1,2M>N and N≥((K(K−1))/2) . In the BWC model, we have deduced the secure degrees of freedom (SDOF) asKmin{M,N}−min{Ne,K(K−1)/2} , which is always a positive value. While in the MBWC model, the SDOF is given by Kmin{M,N} . Finally, since the relay transmits the synthesized signals of the legal signal and the jamming signal in the MBWC model, we propose a power allocation scheme to maximize the secrecy rate. Simulation results demonstrate that our proposed power allocation scheme can improve secrecy rate under various antenna configurations.

• 7.
Department of the Real Estate Development and Management, Ankara University, Ankara, Turkey.
Department of Physical Planning Development, Maitama Sule University Kano, Nigeria. Department of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate, Iraq. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam. Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geoteknologi. Sustainable Developments in Civil Engineering Research Group, Faculty of civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 141533-141548Artikel i tidskrift (Refereegranskat)

Streamflow modeling is considered as an essential component for water resources planning and management. There are numerous challenges related to streamflow prediction that are facing water resources engineers. These challenges due to the complex processes associated with several natural variables such as non-stationarity, non-linearity, and randomness. In this study, a new model is proposed to predict long-term streamflow. Several lags that cover several years are abstracted using the potential of Extreme Gradient Boosting (XGB) then after the selected inputs variables are imposed into the predictive model (i.e., Extreme Learning Machine (ELM)). The proposed model is compared with the stand-alone schema in which the optimum lags of the variables are supplied into the XGB and ELM models. Hydrological variables including rainfall, temperature and evapotranspiration are used to build the model and predict the streamflow at Goksu-Himmeti basin in Turkey. The results showed that XGB model performed an excellent result in which can be used for predicting the streamflow pattern. Also, it is clear from the attained results that the accuracy of the streamflow prediction using XGB technique could be improved when the high number of lags was used. However, the implementation of the XGB is tree-based technique in which several issues could be raised such as overfitting problem. The proposed schema XGBELM in which XGB approach is selected the correlated inputs and ranking them according to their importance; then after, the selected inputs are supplied into the ELM model for the prediction process. The XGBELM model outperformed the stand-alone schema of both XGB and ELM models and the high-lagged schema of the XGB. It is important to indicate that the XGBELM model found to improve the prediction ability with minimum variables number.

• 8.
Computer Science Department, Baoji University of Arts and Sciences, Baoji, China.
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. Faculty of Nature and Life Sciences, Laboratory of Water and Environment, University Hassiba Benbouali Chlef, Hay Es-Salem Chlef, Algeria. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam. School of Agricultural, Computational and Environmental Sciences, Centre for Applied Climate Sciences, Institute of Life Sciences and Environment, University of Southern Queensland, Springfield, QLD, Australia. Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geoteknologi. Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model2020Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 8, s. 12026-12042Artikel i tidskrift (Refereegranskat)

Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information’s are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model’s estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm −2 compared to 4.24 and 3.24 Wm −2 (MLR) and 8.33 and 5.37 Wm −2 (ARIMA).

• 9.
Department of Computer Science, Faculty of Computers and Information, Luxor University, Luxor, Egypt.
Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt. Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
An Adaptive Image Steganography Method Based on Histogram of Oriented Gradient and PVD-LSB Techniques2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 185189-18204Artikel i tidskrift (Refereegranskat)

Pixel value differencing (PVD) and least significant bit substitution (LSB) are two widely used schemes in image steganography. These two methods do not consider different content in a cover image for hiding the secret data. The content of most digital images has different edge directions in each pixel, and the local object shape or appearance is mostly characterized by the distribution of its intensity gradients or edge directions. Exploiting these characteristics for embedding various secret information in different edge directions will eliminate sequential embedding and improve robustness. Thus, a histogram of oriented gradient (HOG) algorithm is proposed to find the dominant edge direction for each $2\times 2$ block of cover images. Blocks of interest (BOIs) are determined adaptively based on the gradient magnitude and angle of the cover image. Then, the PVD algorithm is used to hide secret data in the dominant edge direction, while the LSB substitution is utilized in the other two remaining pixels. Extensive experiments using various standard images reveal that the proposed scheme provides high embedding capacity and better visual quality compared with several other PVD- and LSB-based methods. Moreover, it resists various steganalysis techniques, such as pixel difference histogram and RS analysis.

• 10.
Department of Architecture, Faculty of Engineering, Al Azhar University .
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Electrical Engineering Department, Faculty of Engineering, Al Azhar University.
Urban Transition in the Era of the Internet of Things: Social Implications and Privacy Challenges2018Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 6, s. 36428-36440Artikel i tidskrift (Refereegranskat)

The Internet of Things (IoT) could become an important aspect of urban life in the next decade. In the IoT paradigm, various information and communication technologies (ICTs) are used in concert to substantially reduce urban problems. Smart cities and ubiquitous cities will adopt ICTs in the urban development process; however, IoT-based cities will experience considerably stronger effects than those that adopt conventional ICTs. IoT cities allow urban residents and “things”to be connected to the Internet by virtue of the extension of the Internet Protocol from IPv4 to IPv6 and of cutting-edge device and sensor technology. Therefore, the urban transition resulting from the influence of IoT may be a critical issue. The privacy-related vulnerabilities of IoT technologies may negatively affect city residents. Furthermore, disparities in the spread of IoT systems across different countries may allow some countries to subvert the privacy of other countries' citizens. The aim of this paper is to identify the potential prospects and privacy challenges that will emerge from IoT deployment in urban environments. This paper reviews the prospects of and barriers to IoT implementation at the regional, city, and residential scales from the perspectives of security and privacy. The IoT technology will be a continual presence in life in general and in urban life in particular. However, the adoption of the IoT paradigm in cities will be complicated due to the inherent presence of unsecured connections. Moreover, the IoT systems may rob people of some of their humanity, infringing on their privacy, because people are also regarded as “things”in the IoT paradigm. Given the social trepidation surrounding IoT implementation, local and international associations related to IoT privacy, and legislation and international laws, are needed to maintain the personal right to privacy and to satisfy the demands of institutional privacy in urban contexts.

• 11.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. VTT Technical Research Center of Finland. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Department of Electrical Engineering and Automation, Aalto University, Finland.
Hyperdimensional computing in industrial systems: the use-case of distributed fault isolation in a power plant2018Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 6, s. 30766-30777Artikel i tidskrift (Refereegranskat)

This paper presents an approach for distributed fault isolation in a generic system of systems. The proposed approach is based on the principles of hyperdimensional computing. In particular, the recently proposed method called Holographic Graph Neuron is used. We present a distributed version of Holographic Graph Neuron and evaluate its performance on the problem of fault isolation in a complex power plant model. Compared to conventional machine learning methods applied in the context of the same scenario the proposed approach shows comparable performance while being distributed and requiring simple binary operations, which allow for a fast and efficient implementation in hardware.

• 12.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Department of Radiation Sciences, Biomedical Engineering, Umeå University, Umeå, Sweden.
A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization during Paced Deep Breathing2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 34403-34415Artikel i tidskrift (Refereegranskat)

Objective: Autonomic function during deep breathing (DB) is normally scored based on the assumption that the heart rate is synchronized with the breathing. We have observed individuals with subtle arrhythmias during DB where autonomic function cannot be evaluated. This study presents a novel method for analyzing cardiorespiratory synchronization: feature-based analysis of the similarity between heart rate and respiration using principles of hyperdimensional computing. Methods: Heart rate and respiration signals were modeled using Fourier series analysis. Three feature variables were derived and mapped to binary vectors in a high-dimensional space. Using both synthesized data and recordings from patients/healthy subjects, the similarity between the feature vectors was assessed using Hamming distance (high-dimensional space), Euclidean distance (original space), and with a coherence-based index. Methods were evaluated via classification of the similarity indices into three groups. Results: The distance-based methods achieved good separation of signals into classes with different degree of cardiorespiratory synchronization, also providing identification of patients with low cardiorespiratory synchronization but high values of conventional DB scores. Moreover, binary high-dimensional vectors allowed an additional analysis of the obtained Hamming distance. Conclusions: Feature-based similarity analysis using hyperdimensional computing is capable of identifying signals with low cardiorespiratory synchronization during DB due to arrhythmias. Vector-based similarity analysis could be applied to other types of feature variables than based on spectral analysis. Significance: The proposed methods for robustly assessing cardiorespiratory synchronization during DB facilitate the identification of individuals where the evaluation of autonomic function is problematic or even impossible, thus, increasing the correctness of the conventional DB scores.

• 13.
School of Computer and Information Technology, Nanyang Normal University, Nanyang, China.
School of Computer and Information Technology, Nanyang Normal University, Nanyang, China.. Laboratory for Machine Vision and Security Research, College of Computing and Software Engineering, Kennesaw State University, Marietta, USA. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
A Novel Definition of Equivalent Uniform Dose Based on Volume Dose Curve2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 45850-45857Artikel i tidskrift (Refereegranskat)

With the improvement of mobile device performance, the requirement of equivalent dose description in intensity-modulated radiation therapy is increasing in mobile multimedia for health-care. The emergence of mobile cloud computing will provide cloud servers and storage for IMRT mobile applications, thus realizing visualized radiotherapy in a real sense.Equivalent uniform dose (EUD) is a biomedical indicator based on the dose measure. In this study, the dose volume histogram is used to describe the dose distribution of different tissues in target and nontarget regions. The traditional definition of equivalent uniform dose such as the exponential form and the linear form has only a few parameters in the model for fast calculation. However, there is no close relationship between this traditional definition and the dose volume histogram.In order to establish the consistency between the equivalent uniform dose and the dose volume histogram, this paper proposes a novel definition of equivalent uniform dose based on the volume dose curve, called VD-EUD. By using a unique organic volume weight curve, it is easy to calculate VD-EUD for different dose distributions. In the definition, different weight curves are used to represent the biological effects of different organs. For the target area, we should be more careful about those voxels with low dose (cold point); thus, the weight curve is monotonically decreasing. While for the nontarget area, the curve is monotonically increasing. Furthermore, we present the curves for parallel, serial and mixed organs of nontarget areas separately, and we define the weight curve form with only two parameters. Medical doctors can adjust the curve interactively according to different patients and organs. We also propose a fluence map optimization model with the VD-EUD constraint, which means the proposed EUD constraint will lead to a large feasible solution space.We compare the generalized equivalent uniform dose (gEUD) and the proposed VD-EUD by experiments, which show that the VD-EUD has a closer relationship with the dose volume histogram. If the biological survival probability is equivalent to the VD-EUD, the feasible solution space would be large, and the target areas can be covered.By establishing a personalized organic weight curve, medical doctors can have a unique VD-EUD for each patient. By using the flexible and adjustable equivalent uniform dose definition, we can establish VD-EUD-based fluence map optimization model, which will lead to a larger solution space than the traditional dose volume constraint-based model. The VD-EUD is a new definition; thus, we need more clinical testing and verification.

• 14.
Department of Management Science, University of Strathclyde, Glasgow, United Kingdom.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China. Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
A Dynamic Prescriptive Maintenance Model Considering System Aging and Degradation2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 94941-94943, artikel-id 8762155Artikel i tidskrift (Refereegranskat)

This paper develops a dynamic maintenance strategy for a system subject to aging and degradation. The influence of degradation level and aging on system failure rate is modeled in an additive way. Based on the observed degradation level at the inspection, repair or replacement is carried out upon the system. Previous researches assume that repair will always lead to an improvement in the health condition of the system. However, in our study, repair reduces the system age but on the other hand, increases the degradation level. Considering the two-fold influence of maintenance actions, we perform reliability analysis on system reliability as a first step. The evolution of system reliability serves as a foundation for establishing the maintenance model. The optimal maintenance strategy is achieved by minimizing the long-run cost rate in terms of the repair cycle. At each inspection, the parameters of the degradation processes are updated with maximum a posteriori estimation when a new observation arrives. The effectiveness of the proposed model is illustrated through a case study of locomotive wheel-sets. The maintenance model considers the influence of degradation and aging on system failure and dynamically determines the optimal inspection time, which is more flexible than traditional stationary maintenance strategies and can provide better performance in the field.

• 15. Ma, Shuai
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
Deep Learning for Track Quality Evaluation of High-speed Railway based on Vehicle-body Vibration PredictionIngår i: IEEE Access, E-ISSN 2169-3536Artikel i tidskrift (Refereegranskat)
• 16.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
Survey of Blockchain from the Perspectives of Applications, Challenges and Opportunities2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 117134-117154Artikel i tidskrift (Refereegranskat)

Blockchain is the underlying technology of a number of digital cryptocurrencies. Blockchain is a chain of blocks that store information with digital signatures in a decentralized and distributed network. The features of blockchain, including decentralization, immutability, transparency and auditability, make transactions more secure and tamper proof. Apart from cryptocurrency, blockchain technology can be used in financial and social services, risk management, healthcare facilities, and so on. A number of research studies focus on the opportunity that blockchain provides in various application domains. This paper presents a comparative study of the tradeoffs of blockchain and also explains the taxonomy and architecture of blockchain, provides a comparison among different consensus mechanisms and discusses challenges, including scalability, privacy, interoperability, energy consumption and regulatory issues. In addition, this paper also notes the future scope of blockchain technology.

• 17.
Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
A New Secure Authentication Protocol for Telecare Medicine Information Systemand Smart Campus2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 23514-23526Artikel i tidskrift (Refereegranskat)

Telecare Medicine Information System (TMIS)'s security importance attracts a lot of attention these days. Whatever the security of TMIS improves, its application becomes wider. To address this requirement, recently, Li et al. proposed a new privacy-preserving RFID authentication protocol for TMIS. After that, Zhou et al. and also Benssalah et al. presented their scheme, which is not secure, and they presented their new authentication protocol and claim that their proposal can provide higher security for TMIS applications. In this stream, Zheng et al. proposed a novel authentication protocol with application in smart campus, including TMIS. In this paper, we present an efficient impersonation and replay attacks against Zheng et al. with the success probability of 1 and a desynchronization attack which is applicable against all of the rest three mentioned protocols with the success probability of 1-2^{-n} , where n is the protocols parameters length. After that, we proposed a new protocol despite these protocols can resist the attacks presented in this paper and also other active and passive attacks. Our proposed protocol's security is also done both informally and formally through the Scyther tool.

• 18.
Department of Electrical Engineering and Automation, Aalto University, Helsinki .
Department of Electrical Engineering and Automation, Aalto University, Helsinki . Department of Electrical Engineering and Automation, Aalto University, Helsinki . VTT Technical Research Center of Finland Ltd, Espoo. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Department of Electrical Engineering and Automation, Aalto University, Helsinki .
Automatic Generation of a High-Fidelity Dynamic Thermal-hydraulic Process Simulation Model from a 3D Plant Model2018Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 6, s. 45217-45232, artikel-id 434288Artikel i tidskrift (Refereegranskat)

Dynamic thermal-hydraulic simulation models have been extensively used by process industry for decision support in sectors such as power generation, mineral processing, pulp and paper, and oil and gas. Ever-growing competitiveness in the process industry forces experts to rely even more on dynamic simulation results to take decisions across the process plant lifecycle. However, time-consuming development of simulation models increases model generation costs, limiting their use in a wider number of applications. Detailed 3D plant models, developed during early plant engineering for process design, could potentially be used as a source of information to enable rapid development of high-fidelity simulation models. This paper presents a method for automatic generation of a thermal-hydraulic process simulation model from a 3D plant model. Process structure, dimensioning and component connection information included in the 3D plant model is extracted from the machine-readable export of the 3D design tool and used to automatically generate and configure a dynamic thermal-hydraulic simulation model. In particular, information about the piping dimensions and elevations is retrieved from the 3D plant model and used to calculate head loss coefficients of the pipelines and to configure the piping network model. This step, not considered in previous studies, is crucial for obtaining high-fidelity industrial process models. The proposed method is tested using a laboratory process and the results of the automatically generated model are compared with experimental data from the physical system as well as with a simulation model developed using design data utilized by existing methods on the state-of-the-art. Results show that the proposed method is able to generate high-fidelity models which are able to accurately predict the targeted system, even during operational transients.

• 19.
Department of Electrical Engineering and Automation, Aalto University.
VTT Technical Research Centre of Finland . VTT Technical Research Centre of Finland Ltd, Espoo. Department of Electrical Engineering and Automation, Aalto University. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Department of Electrical Engineering and Automation, Aalto University.
An Integrated Implementation Methodology of a Lifecycle-Wide Tracking Simulation Architecture2018Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 6, s. 15391-15407Artikel i tidskrift (Refereegranskat)

A tracking simulator is a decision support application in which dynamic estimation is used to continuously align the results of an online first principle simulation model with the measurements of the targeted plant. They are a holistic application where current and future plant information is available for operation support of process plants. Existing tracking simulators have focused on the application of online and offline methods for estimation of their underlying first principle models (FPMs). However, these systems have been less attractive than similar alternatives based on empirical modeling, due to the lack of systematic approaches that address challenges across the tracking simulation lifecycle, such as laborious development of FPMs and high integration costs with the process or with other systems and simulation methods. In contrast, the approach presented in this paper integrates a tracking simulation architecture and various simulation methods to address the described challenges as follows. In order to tackle time-consuming development of FPMs, a method for generating tracking simulation models from models created during design phase is proposed. The process of connecting the tracking simulator to the physical plant and initializing the tracking simulator is automated. An optimization method for tracking simulation applications is developed to overcome drawbacks of available methods. The simulation architecture developed applies the proposed methodology during the various phases of tracking simulation. Furthermore, it exploits industrial communication standards to avoid the need for point-to-point integration of various simulators and other systems used over the course of the tracking simulator lifecycle. The work is demonstrated with laboratory process equipment.

• 20.
Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, School of Computer and Communication Engineering, University of Science and Technology Beijing.
Department of Information and Communication Engineering, Harbin Engineering University, Harbin. Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, School of Computer and Communication Engineering, University of Science and Technology Beijing. School of Information and Electronics, Beijing Institute of Technology, Beijing. Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, University of Science and Technology.
Optimal mode selection with uplink data rate maximization for D2D-aided underlaying cellular networks2016Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 4, s. 8844-8856, artikel-id 7762100Artikel i tidskrift (Refereegranskat)

The device-to-device (D2D) communication has been regarded as an effective technique for complementing and enhancing the conventional cellular systems owing to its capability of substantially improving both the spectral and power efficiencies of wireless networks. However, the severe interference imposed on the conventional cellular users (CUs) by the geographically close-by D2D pairs may cause a significant performance erosion in the D2D-aided underlaying cellular networks (CNs). In this paper, performance analysis for the D2D-aided underlaying CNs in terms of throughput is provided. We first derive the closed-form expressions of the coverage probability for both the conventional cellular links and the D2D links, followed by giving out the approximated expressions of the ergodic data rate for both an individual cellular/D2D link and the whole underlaying network. Furthermore, the key parameters (e.g., the density of D2D users (DUs) or CUs, and the average geographical distance between a pair of D2D peers) significantly impacting the channel capacity are adaptively adjusted for maximizing the sum data rate of the proposed underlaying networks. In addition, both theoretical analysis and simulation results reveal the attainability of the maximal throughput by optimizing the critical parameters, such as the density of DUs, provided that the scale factor between the DUs and sum users (i.e., comprising both conventional CUs and DUs) can be effectively balanced subject to the constraints specified in the proposed scheme

• 21.
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou.
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
Cloud Robotics: Current Status and Open Issues2016Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 4, s. 2797-2807Artikel i tidskrift (Refereegranskat)

With the development of cloud computing, big data and other emerging technologies, the integration of cloud technology and multi-robot systems makes it possible to design multi-robot systems with improved energy efficiency, high real-time performance and low cost. In order to address the potential of clouds in enhancing robotics for industrial systems, this paper describes the basic concepts and development process of cloud robotics and the overall architecture of these systems. Then, the major driving forces behind the development of cloud robotics are carefully analyzed from the point of view of cloud computing, big data, open source resources, robot cooperative learning, and network connectivity. Subsequently, the key issues and challenges in the current cloud robotic systems are proposed, and some possible solutions are also given. Finally, the potential value of cloud robotic systems in different practical applications is discussed.

• 22.
College of Communications Engineering PLA University of Science and Technology, Nanjing.
College of Communications Engineering PLA University of Science and Technology, Nanjing. PLA Academy of National Defense Information, Wuhan. National Digital Switching System Engineering & Technological Research Center, Zhengzhou. Department of Computer and Information Science, Korea University. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
A Cloud-Based Architecture for the Internet of Spectrum Devices (IoSD) over Future Wireless Networks2016Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 4, s. 2854-2862Artikel i tidskrift (Refereegranskat)

The dramatic increase in data rates in wireless networks has caused radio spectrum usage to be an essential and critical issue. Spectrum sharing is widely recognized as an affordable, near-term method to address this issue. This article first characterizes the new features of spectrum sharing in future wireless networks, including heterogeneity in sharing bands, diversity in sharing patterns, crowd intelligence in sharing devices, and hyper-densification in sharing networks. Then, to harness the benefits of these unique features and promote a vision of spectrum without bounds and networks without borders, this article introduces a new concept of the Internet of Spectrum Devices (IoSD) and develops a cloud-based architecture for IoSD over future wireless networks, with the prime aim of building a bridging network among various spectrum monitoring devices (SMDs) and massive spectrum utilization devices (SUDs), and enabling a highly-efficient spectrum sharing and management paradigm for future wireless networks. Furthermore, this article presents a systematic tutorial on the key enabling techniques of the IoSD, including big spectrum data analytics, hierarchal spectrum resource optimization, and quality of experience (QoE)- oriented spectrum service evaluation. In addition, the unresolved research issues are also presented.

• 23.
School of Electronic and Information Engineering, Beijing Jiaotong University.
School of Electronic and Information Engineering, Beijing Jiaotong University. School of Electronic and Information Engineering, Beijing Jiaotong University. 5G-IC, Department of Electrical and Electronic Engineering, Institute for Communication Systems, University of Surrey. School of Electronic and Information Engineering, Beijing Jiaotong University. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
Caching Strategy Based on Hierarchical Cluster for Named Data Networking2017Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 5, s. 8433-8443, artikel-id 7898837Artikel i tidskrift (Refereegranskat)

The in-network caching strategy in named data networking can not only reduce the unnecessaryfetching of content from the original content server deep in the core network and improve the user responsetime, but also ease the trafc in the core network. However, challenges exist in in-network caching, suchas the distributed locations of storage and relatively small cache space which limit the hit rate, and thecache management introduces further overhead. In this paper, we propose a two-layer hierarchical clusterbasedcaching solution to improve in-network caching efciency. A network is grouped into several clusters,then, a clusterhead is nominated for each cluster to make caching decision. The clustering approach offersscalability and permits multiple aspects of inputs to be used for decision making. Our solution jointlyconsiders the location and content popularity for caching.We implement our strategy in ndnSIM and test it onGEANT-based network and AS3967 network. Our simulation results show signicant improvement over itspeers.INDEX

• 24.
School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru, Malaysia.
Sustainable and Smart Township Research Centre (SUTRA), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia. Department of Water Resources, University of Baghdad, Baghdad, Iraq. Department of Civil Engineering, Razi University, Kermanshah, Iran. Sustainable and Smart Township Research Centre (SUTRA), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia. Department of Civil Engineering, Razi University, Kermanshah, Iran. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam. Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geoteknologi. School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru, Malaysia.
Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model: Case Study in Tropical Region2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 74471-74481Artikel i tidskrift (Refereegranskat)

The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models are proposed for forecasting highly non-linear streamflow of Pahang River, located in a tropical climatic region of Peninsular Malaysia. Three different bio-inspired optimization algorithms namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE) were individually used to tune the membership function of ANFIS model in order to improve the capability of streamflow forecasting. Different combination of antecedent streamflow was used to develop the forecasting models. The performance of the models was evaluated using a number of metrics including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination ( R2 ), and Willmott’s Index (WI) statistics. The results revealed that increasing number of inputs has a positive impact on the forecasting ability of both ANFIS and hybrid ANFIS models. The comparison of the performance of three optimization methods indicated PSO improved the capability of ANFIS model (RMSE = 7.96; MAE = 2.34; R2=0.998 and WI = 0.994) more compared to GA and DE in forecasting streamflow. The uncertainty band of ANFIS-PSO forecast was also found the lowest (±0.217), which indicates that ANFIS-PSO model can be used for reliable forecasting of highly stochastic river flow in tropical environment.

• 25.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
RISE SICS North, 973 47, Luleå. National Institute of Applied Sciences, Strasbourg, France. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Department of Electrical Engineering and Automation, Aalto University, Helsinki . National Institute of Applied Sciences, Strasbourg, France.
A Comprehensive Model of Data Center: from CPU to Cooling Tower2018Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 6, s. 2169-3536Artikel i tidskrift (Refereegranskat)

Aiming at addressing environmental challenges, large data centers such as Facebook, Google, Yahoo, etc., are increasing share of green power in their daily energy consumption. Such trends drive research into new directions, e.g. sustainable data centers. The research often relies on expressive models that provides sufficient details however practical to re-use and expand. There is a lack of available data center models that capture internal operating states of the facility from the CPU to the cooling tower. It is a challenge to develop a model that allows to describe complete data center of any scale including its connection to the grid. This paper proposes such a model building on existing work. The challenge was to put the pieces of data center together and model behavior of each element so that interdependencies between components and parameters and operating states are captured correctly and in sufficient details. The proposed model was used in the project “Data center microgrid integration” and proven to be adequate and important to support such study.

• 26.
Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Department of Management Science, University of Strathclyde, Glasgow, U.K.. Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China. Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China. Oceanic Intelligent Technology Innovation Center, CSSC Systems Engineering Research Institute, Beijing, China.
A Review on Deep Learning Applications in Prognostics and Health Management2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 162415-162438Artikel, forskningsöversikt (Refereegranskat)

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

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