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Schmidt, Mischa
Publications (10 of 27) Show all publications
Schmidt, M., Schülke, A., Venturi, A., Kurpatov, R. & Henríquez, E. B. (2018). Cyber-Physical System For Energy Efficient Stadium Operation: Methodology And Experimental Validation. ACM Transactions on Cyber-Physical Systems, 2(4), Article ID 25.
Open this publication in new window or tab >>Cyber-Physical System For Energy Efficient Stadium Operation: Methodology And Experimental Validation
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2018 (English)In: ACM Transactions on Cyber-Physical Systems, ISSN 2378-962X, Vol. 2, no 4, article id 25Article in journal (Refereed) Published
Abstract [en]

The environmental impacts of medium to large scale buildings receive substantial attention in research,industry, and media. This paper studies the energy savings potential of a commercial soccer stadium duringday-to-day operation. Buildings of this kind are characterized by special purpose system installations likegrass heating systems and by event-driven usage patterns. This work presents a methodology to holisticallyanalyze the stadium’s characteristics and integrate its existing instrumentation into a Cyber-PhysicalSystem, enabling to deploy different control strategies flexibly. In total, seven different strategies for controllingthe studied stadium’s grass heating system are developed and tested in operation. Experiments inwinter season 2014/2015 validated the strategies’ impacts within the real operational setup of the CommerzbankArena, Frankfurt, Germany. With 95% confidence, these experiments saved up to 66% of mediandaily weather-normalized energy consumption. Extrapolated to an average heating season, this correspondsto savings of 775 MWh and 148 t of CO2 emissions. In winter 2015/2016 an additional predictive nighttimeheating experiment targeted lower temperatures, which increased the savings to up to 85%, equivalent to1 GWh (197 t CO2) in an average winter. Beyond achieving significant energy savings, the different controlstrategies also met the target temperature levels to the satisfaction of the stadium’s operational staff. Whilethe case study constitutes a significant part, the discussions dedicated to the transferability of this workto other stadiums and other building types show that the concepts and the approach are of general nature.Furthermore, this work demonstrates the first successful application of Deep Belief Networks to regress andpredict the thermal evolution of building systems.

Place, publisher, year, edition, pages
ACM Digital Library, 2018
Keywords
Stadium Operation, Under-soil Heating, Statistical Inference, Predictive Control, Deep Belief Network, Energy Efficiency, System Modeling
National Category
Computer Systems Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-67705 (URN)10.1145/3140235 (DOI)000446005700003 ()
Note

Validerad;2018;Nivå 2;2018-12-07 (johcin)

Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-12-11Bibliographically approved
Schmidt, M. & Åhlund, C. (2018). Smart Buildings as Cyber-Physical Systems:Data-Driven Predictive Control Strategies for Energy Efficiency. Renewable & sustainable energy reviews, 90, 742-756
Open this publication in new window or tab >>Smart Buildings as Cyber-Physical Systems:Data-Driven Predictive Control Strategies for Energy Efficiency
2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 90, p. 742-756Article in journal (Refereed) Published
Abstract [en]

Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing buildingstock’s energy efficiency must improve. Predictive building control promises to contribute to that by increasing theefficiency of building operations. Predictive control complements other means to increase performance such as refurbishmentsas well as modernizations of systems. This survey reviews recent works and contextualizes these with thecurrent state of the art of interrelated topics in data handling, building automation, distributed control, and semantics.The comprehensive overview leads to seven research questions guiding future research directions.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Energy Efficiency, Predictive Control, Cyber-Physical System, Existing Buildings
National Category
Computer Systems Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-67706 (URN)10.1016/j.rser.2018.04.013 (DOI)000434917700050 ()2-s2.0-85045248317 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-04-26 (andbra)

Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-07-19Bibliographically approved
Bansal, S. & Schmidt, M. (2017). Energy Disaggregation Methods for Commercial Buildingsusing Smart Meter and Operational data. In: The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence: Artificial Intelligence for Smart Grids and Smart Buildings. Paper presented at 31st AAAI Conference on Artificial Intelligence (AAAI-17, San Francisco, 4–9 February 2017 (pp. 325-329). AI Access Foundation
Open this publication in new window or tab >>Energy Disaggregation Methods for Commercial Buildingsusing Smart Meter and Operational data
2017 (English)In: The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence: Artificial Intelligence for Smart Grids and Smart Buildings, AI Access Foundation , 2017, p. 325-329Conference paper, Published paper (Refereed)
Abstract [en]

One of the key information pieces in improving energy efficiency of buildings is the appliance level breakdown of energy consumption. Energy disaggregation is the process of obtaining this breakdown from a building level aggregate data using computational techniques. Most of the current research focuses on residential buildings, obtaining this information from a single smart meter and often relying on high frequency data. This work is directed at commercial buildings equipped with building management and automation systems providing low frequency operational and contextual data. This paper presents a machine learning method to disaggregate energy consumption of the building using this operational data as input features. Experimental results on two publicly available datasets demonstrate the effectiveness of the approach, which surpasses existing methods. For all but one appliance of House 2 of the publicly available REDD dataset, improvements in normalized error in assigned power range between 20% (Lighting) and 220% (Stove). For another dataset from an educational facility in Singapore, disaggregation accuracy of 92% is reported for the facility’s cooling system.

Place, publisher, year, edition, pages
AI Access Foundation, 2017
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-61569 (URN)2-s2.0-85046106576 (Scopus ID)9781577357865 (ISBN)
Conference
31st AAAI Conference on Artificial Intelligence (AAAI-17, San Francisco, 4–9 February 2017
Available from: 2017-01-20 Created: 2017-01-20 Last updated: 2018-05-09Bibliographically approved
Schmidt, M., Moreno, M. V., Schülke, A., Macek, K., Mařik, K. & Pastor, A. G. (2017). Optimizing legacy building operation: the evolution into data-driven predictive cyber-physical systems. Energy and Buildings, 148, 257-279
Open this publication in new window or tab >>Optimizing legacy building operation: the evolution into data-driven predictive cyber-physical systems
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2017 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 148, p. 257-279Article in journal (Refereed) Published
Abstract [en]

Fossil fuels serve a substantial fraction of global energy demand, and one major energy consumer is the global building stock. In this work, we propose a framework to guide practitioners intending to develop advanced predictive building control strategies. The framework provides the means to enhance legacy and modernized buildings regarding energy efficiency by integrating their available instrumentation into a data-driven predictive cyber-physical system. For this, the framework fuses two highly relevant approaches and embeds these into the building context: the generic model-based design methodology for cyber-physical systems and the cross-industry standard process for data mining. A Spanish school's heating system serves to validate the approach. Two different data-driven approaches to prediction and optimization are used to demonstrate the methodological flexibility: (i) a combination of Bayesian regularized neural networks with genetic algorithm based optimization, and (ii) a reinforcement learning based control logic using fitted Q-iteration are both successfully applied. Experiments lasting a total of 43 school days in winter 2015/2016 achieved positive effects on weather-normalized energy consumption and thermal comfort in day-to-day operation. A first experiment targeting comfort levels comparable to the reference period lowered consumption by one-third. Two additional experiments raised average indoor temperatures by 2 K. The better of these two experiments only consumed 5% more energy than the reference period. The prolonged experimentation period demonstrates the cyber-physical system-based approach's suitability for improving building stock energy efficiency by developing and deploying predictive control strategies within routine operation of typical legacy buildings.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Energy Efficiency, Predictive Control, Cyber-Physical System, Reinforcement learning, Optimization, Neural Network, Evolutionary Algorithms
National Category
Computer Sciences Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-63299 (URN)10.1016/j.enbuild.2017.05.002 (DOI)000404705000021 ()2-s2.0-85019391553 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 288409
Note

Validerad;2017;Nivå 2;2017-05-29 (rokbeg)

Available from: 2017-05-10 Created: 2017-05-10 Last updated: 2018-07-10Bibliographically approved
Cano, M. V., Gomez, A. F., Venturi, A., Schmidt, M. & Schülke, A. (2015). Context Sensitive Indoor Temperature Forecast for Energy Efficient Operation of Smart Buildings (ed.). In: (Ed.), (Ed.), 2015 IEEE World Forum on Internet of Things (WF-IoT) (WF-IoT 2015): (pp. 705-710). Paper presented at . Piscataway, NJ: IEEE Communications Society
Open this publication in new window or tab >>Context Sensitive Indoor Temperature Forecast for Energy Efficient Operation of Smart Buildings
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2015 (English)In: 2015 IEEE World Forum on Internet of Things (WF-IoT) (WF-IoT 2015), Piscataway, NJ: IEEE Communications Society, 2015, p. 705-710Chapter in book (Refereed)
Abstract [en]

This paper analyzes the potential of knowledge discovery from sensed data, which enables real-time systems monitoring, management, prediction and optimization in smart buildings. State of the art data driven techniques generate predictive short-term indoor temperature models based on real building data collected during daily operation. The most accurate results are achieved by the Bayesian Regularized Neural Network technique. Our results show that we are able to achieve a low relative predictive error for each room temperature in the range of 1.5% - 2.8% with low standard deviation of the residuals.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Keywords
Smart Buildings, Thermal Comfort, Data Modeling
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-21198 (URN)10.1109/WF-IoT.2015.7389140 (DOI)c3642e20-4744-41c5-87e2-4a1a2a6e81b9 (Local ID)c3642e20-4744-41c5-87e2-4a1a2a6e81b9 (Archive number)c3642e20-4744-41c5-87e2-4a1a2a6e81b9 (OAI)
Note
Upprättat; 2015; 20160118 (missch)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-02-26Bibliographically approved
Kurpatov, R., Schmidt, M. & Schulke, A. (2015). Enabling complex building energy visualization by integrative event-driven data provisioning (ed.). In: (Ed.), (Ed.), 2015 International Conference and Workshops on Networked Systems (NetSys): Cottbus, 9-12 March 2015. Paper presented at International Conference and Workshops on Networked Systems (NetSys) : 09/03/2015 - 12/03/2015. Piscataway, NJ: IEEE Communications Society
Open this publication in new window or tab >>Enabling complex building energy visualization by integrative event-driven data provisioning
2015 (English)In: 2015 International Conference and Workshops on Networked Systems (NetSys): Cottbus, 9-12 March 2015, Piscataway, NJ: IEEE Communications Society, 2015Conference paper, Published paper (Refereed)
Abstract [en]

Constantly increasing energy efficiency requirements for buildings call for top-quality systems and services for optimizing the building energy life cycle. The deep integration of ICT systems in heterogeneous building environment demands new approaches, advanced technical algorithms and sophisticated tools. In this paper we introduce our Intelligent Energy Management Platform (INTELLEM), an integrative solution for monitoring, analyzing and visualizing energy performance across multiple buildings. We verified our architectural approach by implementing a prototype capturing real data streams of considerable volume over a prolonged period of time, proving that our event-driven approach ensures the responsiveness of the whole system. We implemented a rich Web-interface for visualization, comprising a set of components for comprehensive analysis of building energy performance.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Keywords
Internet, building management systems, data visualisation, energy conservation, energy management systems, power engineering computing, INTELLEM, Web-interface, building energy performance, complex building energy visualization, integrative event-driven data provisioning, intelligent energy management platform, Authentication, Buildings, Data visualization, Europe, Service-oriented architecture, Standards
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-34350 (URN)10.1109/NetSys.2015.7089085 (DOI)886a544f-16df-4836-81ca-be345ba7018d (Local ID)978-1-4799-5804-7 (ISBN)886a544f-16df-4836-81ca-be345ba7018d (Archive number)886a544f-16df-4836-81ca-be345ba7018d (OAI)
Conference
International Conference and Workshops on Networked Systems (NetSys) : 09/03/2015 - 12/03/2015
Note
Upprättat; 2015; 20150921 (missch)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-01-14Bibliographically approved
Schmidt, M., Schulke, A., Venturi, A. & Kurpatov, R. (2015). Energy Efficiency Gains in Daily Grass Heating Operation of Sports Facilities through Supervisory Holistic Control. In: Buildsys'15: 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built. Paper presented at 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built, Seoul, South Korea, Nov 4-5 2015 (pp. 85-94). New York: ACM Digital Library
Open this publication in new window or tab >>Energy Efficiency Gains in Daily Grass Heating Operation of Sports Facilities through Supervisory Holistic Control
2015 (English)In: Buildsys'15: 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built, New York: ACM Digital Library, 2015, p. 85-94Conference paper, Published paper (Refereed)
Abstract [en]

In recent reflections on environmental impacts of buildings, medium to large scale sports stadiums have gained substantial attention. These stadiums of e.g. professional soccer teams are characterized by special system installations like grass heating systems serving the crucial commercial asset(s) and by event-driven usage patterns. Public buildings of this size imply situation-specific operational modes combined with high levels of safety and comfort requirements. In this paper we provide experimental verification of the energy savings potential of a professional soccer stadium's grass heating system during day-to-day operation. Our supervisory holistic control based on state of the art information and communication technology (ICT) is verified by seven experiments which we executed within the real operational setup of the Commerzbank Arena in Frankfurt, Germany. Our experiments operated different control strategies of increasing complexity. In winter 2014/2015 we achieved weather normalized energy savings of more than 56% compared to the last heating season. In an average heating season this would amount to savings of approximately 780 MWh and 150 t CO2$. At the same time we violated minimum temperature targets less than 6% of the time. These results stress the feasibility and benefits of applying holistic context-aware control strategies to large scale legacy consumption systems using supervisory ICT platforms. We demonstrate significant efficiency improvements and establish a new energy baseline that future control strategy evolutions will have to benchmark against.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2015
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-20556 (URN)10.1145/2821650.2821661 (DOI)000380608700010 ()2-s2.0-84959052599 (Scopus ID)5ea67e1e-445e-47b5-a9c2-2e1866eaea16 (Local ID)978-1-4503-3981-0 (ISBN)978-1-4503-3981-0 (ISBN)5ea67e1e-445e-47b5-a9c2-2e1866eaea16 (Archive number)5ea67e1e-445e-47b5-a9c2-2e1866eaea16 (OAI)
Conference
2nd ACM International Conference on Embedded Systems for Energy-Efficient Built, Seoul, South Korea, Nov 4-5 2015
Note

Validerad; 2016; Nivå 1; 2016-10-06 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Schmidt, M., Schulke, A., Venturi, A. & Kurpatov, R. (2015). Predictability of energy characteristics for cooling, ventilation and heating systems in sports facilities (ed.). In: (Ed.), (Ed.), I2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT): Washington, DC , 18-20 Feb 2015. Paper presented at IEEE Power & Energy Society Innovative Smart Grid Technologies Conference : 18/02/2015 - 20/02/2015. Piscataway, NJ: IEEE Communications Society
Open this publication in new window or tab >>Predictability of energy characteristics for cooling, ventilation and heating systems in sports facilities
2015 (English)In: I2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT): Washington, DC , 18-20 Feb 2015, Piscataway, NJ: IEEE Communications Society, 2015Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we analyze operational energy data of the cooling, ventilation and heating systems of the professional soccer stadium Commerzbank Arena in Frankfurt, Germany. We analyze data collected over a six month period in 2014 statistically and show that depending on the stadium's operational context consumption patterns vary largely among the different systems resulting in very different behaviors. The results provide insights into what drives the energy consumption for different systems of a large commercial sports facility: the static heating system is purely dependent on outside air temperature, ventilation exhibits a pronounced daily consumption pattern irrespective of the temperature and cooling is driven by a combination of event operation and air temperature. These insights will allow us to predict, plan and balance the energy demands of different subsystems more accurately, resulting in energetic improvements of the stadium operation in the form of load shedding while maintaining the systems' service levels.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Keywords
building services, cooling, energy consumption, facilities, heating, load shedding, sport, ventilation, Commerzbank arena, Frankfurt, Germany, air temperature, energy characteristic predictability, energy demand, soccer stadium, sports facility, static heating system, Buildings, Business, Context, Energy consumption, Heating, Ventilation, Context Awareness, Control Systems, Energy Management, Green Buildings, HVAC, Sports Stadium
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-30814 (URN)10.1109/ISGT.2015.7131848 (DOI)84939137908 (Scopus ID)4c4bad1b-74a4-4cec-a1e7-bc03aa73fce2 (Local ID)978-1-4799-1785-3 (ISBN)4c4bad1b-74a4-4cec-a1e7-bc03aa73fce2 (Archive number)4c4bad1b-74a4-4cec-a1e7-bc03aa73fce2 (OAI)
Conference
IEEE Power & Energy Society Innovative Smart Grid Technologies Conference : 18/02/2015 - 20/02/2015
Note
Upprättat; 2015; 20150921 (missch)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-02-26Bibliographically approved
Bohli, J.-M., Kurpatov, R. & Schmidt, M. (2015). Selective Decryption of Outsourced IoT Data. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT): Milan, Italy, 14-16 Dec, 2015. Paper presented at 2nd World Forum on Internet of Things (WF-IoT, Milan, Italy, 14-16 Dec, 2015 (pp. 739-744). Piscataway, NJ: IEEE Communications Society
Open this publication in new window or tab >>Selective Decryption of Outsourced IoT Data
2015 (English)In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT): Milan, Italy, 14-16 Dec, 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 739-744Conference paper, Published paper (Refereed)
Abstract [en]

Outsourcing of IoT-data is a common concept when using third-party services, e.g. an external building management service is given access to the building's sensor measurements. However, the measurements captured from building systems often need to be protected from unauthorized access, because they are linked to persons, processes or business secrets, and therefore data protection requirements apply. Nevertheless, due to the large data volumes, external storage is desirable from a business perspective. We propose the concept of a Security Broker based on symmetric encryption schemes. It offers a key management scheme to flexibly create short decryption keys for time intervals or specific ranges of measurement values. We further show how the scheme can be generalized or a combination of the schemes can be applied. We implemented a prototype in Java and analyzed its performance. The evaluation proved the mechanism's applicability for mainstream applications running on off-the-shelf computing equipment.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Keywords
Smart Buildings, IoT, Cloud, Security
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-20723 (URN)10.1109/WF-IoT.2015.7389146 (DOI)000380567200130 ()2-s2.0-84964556218 (Scopus ID)764913d8-50f1-49f7-a1ab-6e97fe6a09fa (Local ID)764913d8-50f1-49f7-a1ab-6e97fe6a09fa (Archive number)764913d8-50f1-49f7-a1ab-6e97fe6a09fa (OAI)
Conference
2nd World Forum on Internet of Things (WF-IoT, Milan, Italy, 14-16 Dec, 2015
Note

Validerad; 2016; Nivå 1; 2016-10-11 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Schmidt, M., Venturi, A., Schülke, A. & Kurpatov, R. (2015). The Energy Efficiency Problematics in Sports Facilities: Identifying Savings in Daily Grass Heating Operation (ed.). In: (Ed.), (Ed.), Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems: . Paper presented at ACM/IEEE Sixth International Conference on Cyber-Physical Systems : 14/04/2014 - 16/04/2015 (pp. 189-197). New York: ACM Digital Library
Open this publication in new window or tab >>The Energy Efficiency Problematics in Sports Facilities: Identifying Savings in Daily Grass Heating Operation
2015 (English)In: Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, New York: ACM Digital Library, 2015, p. 189-197Conference paper, Published paper (Refereed)
Abstract [en]

Recently, reflections on modern sports stadiums' environmental impacts have gained substantial attention. Large-scale stadiums of e.g. professional soccer teams are characterized by having installations of grass heating systems serving the crucial commercial asset and at the same being the sub-system with the highest yearly thermal energy consumption. Public buildings of this size imply situation-specific operational modes combined with high levels of safety and comfort requirements. In this paper we provide a first study on the energy savings potential of a professional soccer stadium's grass heating system during day-to-day operation. In practice, limited heating capacities of the arena have to be adhered to, which causes the current operation to often result in under-performance of other, less critical facility units. Our analysis of dynamic operational and contextual data serves as foundation for long-term energy efficiency measures. We study relevant parameters related to the current control schemes and the stadium's context. Concretely, the grass root temperature as critical observable is studied with respect to weather conditions and the resulting thermal behavior. We provide an improved control strategy and quantify the anticipated savings of this strategy to be as high as 34% compared to the last heating season. For the future, the documented thermal characteristics will enable the formulation of more advanced control strategies to positively influence the grass heating operation. This will lead to further improvements in balancing the heating demand across all thermal facility sub-systems by integrating operational context with forecasts of the thermal behavior in the future.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2015
Keywords
context awareness, control systems, cyber-physical systems, energy management, grass heating, smart buildings, sports stadium
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-28073 (URN)10.1145/2735960.2735978 (DOI)1b94dc51-429a-475c-aa01-fc581b894ebf (Local ID)978-1-4503-3455-6 (ISBN)1b94dc51-429a-475c-aa01-fc581b894ebf (Archive number)1b94dc51-429a-475c-aa01-fc581b894ebf (OAI)
Conference
ACM/IEEE Sixth International Conference on Cyber-Physical Systems : 14/04/2014 - 16/04/2015
Note
Upprättat; 2015; 20150921 (missch)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-02-26Bibliographically approved
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