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Sarkheyli-Hägele, ArezooORCID iD iconorcid.org/0000-0001-6925-0444
Publications (8 of 8) Show all publications
Shokrollahi, A., Persson, J. A., Malekian, R., Sarkheyli-Hägele, A. & Karlsson, F. (2024). Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches. Sensors, 24(5), Article ID 1533.
Open this publication in new window or tab >>Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches
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2024 (English)In: Sensors, E-ISSN 1424-8220, Vol. 24, no 5, article id 1533Article, review/survey (Refereed) Published
Abstract [en]

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

Keywords
passive infrared sensors (PIR), smart buildings, IoT (internet of things), occupancy information, people counting, activity detection, machine learning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mau:diva-66548 (URN)10.3390/s24051533 (DOI)001183072000001 ()38475069 (PubMedID)2-s2.0-85187481668 (Scopus ID)
Available from: 2024-03-28 Created: 2024-03-28 Last updated: 2024-04-11Bibliographically approved
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
Open this publication in new window or tab >>The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning
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2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 11, article id 6516Article in journal (Refereed) Published
Abstract [en]

This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed.

Place, publisher, year, edition, pages
MDPI, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-60144 (URN)10.3390/app13116516 (DOI)001004726600001 ()2-s2.0-85163091186 (Scopus ID)
Available from: 2023-06-07 Created: 2023-06-07 Last updated: 2023-09-05Bibliographically approved
Zhang, X.-Y., Zhou, K.-Q., Li, P.-C., Xiang, Y.-H., Zain, A. M. & Sarkheyli-Hägele, A. (2022). An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies. IEEE Access, 10, 96159-96179
Open this publication in new window or tab >>An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies
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2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 96159-96179Article in journal (Refereed) Published
Abstract [en]

Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optima easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, the values for both accuracy and convergence speed are higher than other algorithms. The results also indicate that the ICSSOA has an outstanding ability to jump out of the local optimum.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Statistics, Sociology, Optimization, Chaos, Standards, Search problems, Convergence, Adaptive weighting modification, cubic chaos mapping, levy flight, reverse learning, sparrow search algorithm
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-55407 (URN)10.1109/ACCESS.2022.3204798 (DOI)000857703700001 ()2-s2.0-85137937933 (Scopus ID)
Available from: 2022-10-17 Created: 2022-10-17 Last updated: 2024-02-05Bibliographically approved
Jiang, W., Zhou, K.-Q., Sarkheyli-Hägele, A. & Zain, A. M. (2022). Modeling, reasoning, and application of fuzzy Petri net model: a survey. Artificial Intelligence Review, 55, 6567-6605
Open this publication in new window or tab >>Modeling, reasoning, and application of fuzzy Petri net model: a survey
2022 (English)In: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 55, p. 6567-6605Article in journal (Refereed) Published
Abstract [en]

A fuzzy Petri net (FPN) is a powerful tool to model and analyze knowledge-based systems containing vague information. This paper systematically reviews recent developments of the FPN model from the following three perspectives: knowledge representation using FPN, reasoning mechanisms using an FPN framework, and the latest industrial applications using FPN. In addition, some specific modeling and reasoning approaches to FPN to solve the 'state-explosion problem' are illustrated. Furthermore, detailed analysis of the discussed aspects are shown to reveal some interesting findings, as well as their developmental history. Finally, we present conclusions and suggestions for future research directions.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Fuzzy Petri net, Knowledge representation, Modeling, Reasoning, Industrial application
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-50915 (URN)10.1007/s10462-022-10161-0 (DOI)000767725000001 ()2-s2.0-85126103876 (Scopus ID)
Available from: 2022-04-04 Created: 2022-04-04 Last updated: 2024-02-05Bibliographically approved
Kadish, D., Sarkheyli-Hägele, A., Font, J., Hägele, G., Niehorster, D. C. & Pederson, T. (2022). Towards Situation Awareness and Attention Guidance in a Multiplayer Environment using Augmented Reality and Carcassonne. In: CHI PLAY '22: Extended Abstracts of the 2022 Annual Symposium on Computer-Human Interaction in Play: . Paper presented at CHI PLAY '22: The Annual Symposium on Computer-Human Interaction in Play, Bremen Germany, November 2 - 5, 2022. ACM Digital Library
Open this publication in new window or tab >>Towards Situation Awareness and Attention Guidance in a Multiplayer Environment using Augmented Reality and Carcassonne
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2022 (English)In: CHI PLAY '22: Extended Abstracts of the 2022 Annual Symposium on Computer-Human Interaction in Play, ACM Digital Library, 2022, p. -9Conference paper, Published paper (Refereed)
Abstract [en]

Augmented reality (AR) games are a rich environment for researching and testing computational systems that provide subtle user guidance and training. In particular computer systems that aim to augment a user’s situation awareness benefit from the range of sensors and computing power available in AR headsets. The main focus of this work-in-progress paper is the introduction of the concept of the individualized Situation Awareness-based Attention Guidance (SAAG) system used to increase humans’ situating awareness and the augmented reality version of the board game Carcassonne for validation and evaluation of SAAG. Furthermore, we present our initial work in developing the SAAG pipeline, the generation of game state encodings, the development and training of a game AI, and the design of situation modeling and eye-tracking processes.  

 

Place, publisher, year, edition, pages
ACM Digital Library, 2022
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:mau:diva-56501 (URN)10.1145/3505270.3558322 (DOI)2-s2.0-85143123116 (Scopus ID)978-1-4503-9211-2 (ISBN)
Conference
CHI PLAY '22: The Annual Symposium on Computer-Human Interaction in Play, Bremen Germany, November 2 - 5, 2022
Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2024-02-05Bibliographically approved
Hägele, G. & Sarkheyli-Hägele, A. (2020). Situational Hazard Recognition and Risk Assessment Within Safety-Driven Behavior Management in the Context of Automated Driving. In: Rogova, G McGeorge, N Ruvinsky, A Fouse, S Freiman, M (Ed.), Proceedings 2020 IEEE International Conference on Cognitive andComputational Aspects of Situation Management (CogSIMA), Virtual Conference24-28 August 2020: . Paper presented at IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) (pp. 188-194). IEEE
Open this publication in new window or tab >>Situational Hazard Recognition and Risk Assessment Within Safety-Driven Behavior Management in the Context of Automated Driving
2020 (English)In: Proceedings 2020 IEEE International Conference on Cognitive andComputational Aspects of Situation Management (CogSIMA), Virtual Conference24-28 August 2020 / [ed] Rogova, G McGeorge, N Ruvinsky, A Fouse, S Freiman, M, IEEE , 2020, p. 188-194Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of hazard recognition and risk assessment in open and non-predictive environments to support decision making and action selection. Decision making and action selection incorporate decreasing situational risks and maintain safety as operational constraints. Commonly, neither existing application-related safety standards nor the situation modeling or knowledge representation is considered in that context. This contribution introduces a novel approach denoted as a Safety-Driven Behavior Management focusing on situation modeling and the problem of knowledge representation in its sub-functions in the context of situational risks. It combines the safety standards-oriented hazard analysis and the risk assessment approach with the machine learning-based situation recognition. An example illustrating the approach is presented in this paper.

Place, publisher, year, edition, pages
IEEE, 2020
Series
IEEE Conference on Cognitive and Computational Aspects of Situation Management, ISSN 2379-1667
Keywords
Situation model, safety critical systems, situational risk assessment
National Category
Computer Systems
Identifiers
urn:nbn:se:mau:diva-42111 (URN)10.1109/CogSIMA49017.2020.9216183 (DOI)000628978500028 ()2-s2.0-85094824832 (Scopus ID)978-1-7281-6001-6 (ISBN)
Conference
IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)
Available from: 2021-05-05 Created: 2021-05-05 Last updated: 2024-02-05Bibliographically approved
Hägele, G. & Sarkheyli-Hägele, A. (2020). Situational risk assessment within safety-driven behavior management in the context of UAS. In: 2020 International Conference on Unmanned Aircraft Systems (ICUAS): . Paper presented at 2020 International Conference on Unmanned Aircraft Systems (ICUAS), 1-4 Sept. 2020, Athens, Greece, Greece (pp. 1407-1415). IEEE
Open this publication in new window or tab >>Situational risk assessment within safety-driven behavior management in the context of UAS
2020 (English)In: 2020 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, 2020, p. 1407-1415Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of hazard recognition and risk assessment in open and non-predictive environments to support decision making and action selection for UAS. Decision making and action selection incorporate decreasing situational risks and maintain safety as operational constraints. Commonly, neither existing safety standards nor the situation modeling or knowledge representation is considered in that context. This contribution applies a novel approach denoted as a Safety-Driven Behavior Management for UAS focusing on situation modeling, and the problem of knowledge representation in the context of situational risks. It combines the safety standards-oriented hazards analysis and the risk assessment approach with the machine learning-based situation recognition. The illustrative scenario and first experimental results underline the feasibility of the novel approach.

Place, publisher, year, edition, pages
IEEE, 2020
Series
Conference proceedings (International Conference on Unmanned Aircraft Systems), ISSN 2373-6720, E-ISSN 2575-7296
Keywords
aerospace computing, aerospace safety, autonomous aerial vehicles, decision making, emergency services, health hazards, knowledge representation, learning (artificial intelligence), public administration, risk management, situational risk assessment, safety-driven behavior management, UAS, hazard recognition, action selection, safety standards, situation modeling, situation recognition, machine learning, oriented hazards analysis, Hazards, Task analysis, Planning, Standards
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mau:diva-40294 (URN)10.1109/ICUAS48674.2020.9214072 (DOI)000612041300181 ()2-s2.0-85094969654 (Scopus ID)978-1-7281-4278-4 (ISBN)978-1-7281-4279-1 (ISBN)
Conference
2020 International Conference on Unmanned Aircraft Systems (ICUAS), 1-4 Sept. 2020, Athens, Greece, Greece
Available from: 2021-02-01 Created: 2021-02-01 Last updated: 2024-02-05Bibliographically approved
Sarkheyli, A., Song, W. W. & Sarkheyli-Hägele, A. (2018). Development of Dynamic Intelligent Risk Management Approach (ed.). In: (Ed.), 2018 3rd International Conference On Computational Intelligence and applications (Iccia): . Paper presented at 3rd International Conference on Computational Intelligence and Applications (ICCIA), Hong Kong, Hong Kong (28-30 July 2018) (pp. 128-132). IEEE
Open this publication in new window or tab >>Development of Dynamic Intelligent Risk Management Approach
2018 (English)In: 2018 3rd International Conference On Computational Intelligence and applications (Iccia), IEEE, 2018, p. 128-132Conference paper, Published paper (Refereed)
Abstract [en]

A dynamic Risk Management (RM) provides monitoring, recognition, assessment, and follow-up action to reduce the risk whenever it rises. The main problem with dynamic RM (when applied to plan for, how the unknown risk in unexpected conditions should be addressed in information systems) is to design an especial control to recover/avoid of risks/attacks that is proposed in this research. The methodology, called Dynamic Intelligent RM (DIRM) is comprised of four phases which are interactively linked; (1) Aggregation of data and information (2) Risk identification (3) RM using an optional control and (4) RM using an especial control. This study, therefore, investigated the use of artificial neural networks to improve risk identification via adaptive neural fuzzy interface systems and control specification using learning vector quantization. Further experimental investigations are needed to estimate the results of DIRM toward unexpected conditions in the real environment.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Risk Management, Dynamic Risk Management, Artificial Neural Networks
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-12736 (URN)10.1109/ICCIA.2018.00031 (DOI)000470235800024 ()2-s2.0-85066311649 (Scopus ID)29544 (Local ID)978-1-5386-9571-5 (ISBN)29544 (Archive number)29544 (OAI)
Conference
3rd International Conference on Computational Intelligence and Applications (ICCIA), Hong Kong, Hong Kong (28-30 July 2018)
Available from: 2020-02-29 Created: 2020-02-29 Last updated: 2024-02-05Bibliographically approved
Projects
Dynamic Intelligent Sensor Intensive Systems; Malmö University; Publications
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
Internet of Things Master's Program; Malmö UniversitySituation Awareness-based Attention Guidance; Malmö University, Internet of Things and People (IOTAP)
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6925-0444

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