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Boeva, Veselka, ProfessorORCID iD iconorcid.org/0000-0003-3128-191x
Alternative names
Publications (10 of 46) Show all publications
Al-Saedi, A. A. & Boeva, V. (2023). Group-Personalized Federated Learning for Human Activity Recognition Through Cluster Eccentricity Analysis. In: Iliadis L., Maglogiannis I., Alonso S., Jayne C., Pimenidis E. (Ed.), Engineering Applications of Neural Networks: 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings. Paper presented at 24th International Conference on Engineering Applications of Neural Networks, EANN 2023, León, 14 June through 17 June 2023 (pp. 505-519). Springer Science+Business Media B.V.
Open this publication in new window or tab >>Group-Personalized Federated Learning for Human Activity Recognition Through Cluster Eccentricity Analysis
2023 (English)In: Engineering Applications of Neural Networks: 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings / [ed] Iliadis L., Maglogiannis I., Alonso S., Jayne C., Pimenidis E., Springer Science+Business Media B.V., 2023, p. 505-519Conference paper, Published paper (Refereed)
Abstract [en]

Human Activity Recognition (HAR) plays a significant role in recent years due to its applications in various fields including health care and well-being. Traditional centralized methods reach very high recognition rates, but they incur privacy and scalability issues. Federated learning (FL) is a leading distributed machine learning (ML) paradigm, to train a global model collaboratively on distributed data in a privacy-preserving manner. However, for HAR scenarios, the existing action recognition system mainly focuses on a unified model, i.e. it does not provide users with personalized recognition of activities. Furthermore, the heterogeneity of data across user devices can lead to degraded performance of traditional FL models in the smart applications such as personalized health care. To this end, we propose a novel federated learning model that tries to cope with a statistically heterogeneous federated learning environment by introducing a group-personalized FL (GP-FL) solution. The proposed GP-FL algorithm builds several global ML models, each one trained iteratively on a dynamic group of clients with homogeneous class probability estimations. The performance of the proposed FL scheme is studied and evaluated on real-world HAR data. The evaluation results demonstrate that our approach has advantages in terms of model performance and convergence speed with respect to two baseline FL algorithms used for comparison. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2023
Series
Communications in Computer and Information Science (CCIS), ISSN 1865-0929, E-ISSN 1865-0937 ; 1826
Keywords
Clustering, Eccentricity Analysis, Federated Learning, HAR, Non-IID data, Computer aided instruction, Iterative methods, Learning systems, Pattern recognition, Privacy-preserving techniques, Centralised, Clusterings, Eccentricity analyse, Human activity recognition, IID data, ITS applications, Learning models, Well being, Health care
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25227 (URN)10.1007/978-3-031-34204-2_41 (DOI)2-s2.0-85164039066 (Scopus ID)9783031342035 (ISBN)
Conference
24th International Conference on Engineering Applications of Neural Networks, EANN 2023, León, 14 June through 17 June 2023
Funder
Knowledge Foundation, 20220068
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-07Bibliographically approved
Sundstedt, V., Boeva, V., Zepernick, H.-J., Goswami, P., Cheddad, A., Tutschku, K., . . . Arlos, P. (2023). HINTS: Human-Centered Intelligent Realities. In: Håkan Grahn, Anton Borg and Martin Boldt (Ed.), 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023: . Paper presented at 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023, Karlskrona, June 12-13, 2023 (pp. 9-17). Linköping University Electronic Press
Open this publication in new window or tab >>HINTS: Human-Centered Intelligent Realities
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2023 (English)In: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023 / [ed] Håkan Grahn, Anton Borg and Martin Boldt, Linköping University Electronic Press, 2023, p. 9-17Conference paper, Published paper (Refereed)
Abstract [en]

During the last decade, we have witnessed a rapiddevelopment of extended reality (XR) technologies such asaugmented reality (AR) and virtual reality (VR). Further, therehave been tremendous advancements in artificial intelligence(AI) and machine learning (ML). These two trends will havea significant impact on future digital societies. The vision ofan immersive, ubiquitous, and intelligent virtual space opensup new opportunities for creating an enhanced digital world inwhich the users are at the center of the development process,so-calledintelligent realities(IRs).The “Human-Centered Intelligent Realities” (HINTS) profileproject will develop concepts, principles, methods, algorithms,and tools for human-centered IRs, thus leading the wayfor future immersive, user-aware, and intelligent interactivedigital environments. The HINTS project is centered aroundan ecosystem combining XR and communication paradigms toform novel intelligent digital systems.HINTS will provide users with new ways to understand,collaborate with, and control digital systems. These novelways will be based on visual and data-driven platforms whichenable tangible, immersive cognitive interactions within realand virtual realities. Thus, exploiting digital systems in a moreefficient, effective, engaging, and resource-aware condition.Moreover, the systems will be equipped with cognitive featuresbased on AI and ML, which allow users to engage with digitalrealities and data in novel forms. This paper describes theHINTS profile project and its initial results. ©2023, Copyright held by the authors   

Place, publisher, year, edition, pages
Linköping University Electronic Press, 2023
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 199
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:bth-25413 (URN)10.3384/ecp199001 (DOI)9789180752749 (ISBN)
Conference
35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023, Karlskrona, June 12-13, 2023
Funder
Knowledge Foundation, 20220068
Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-12-28Bibliographically approved
van Dreven, J., Boeva, V., Abghari, S., Grahn, H., Al Koussa, J. & Motoasca, E. (2023). Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities. Electronics, 12(6), Article ID 1448.
Open this publication in new window or tab >>Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities
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2023 (English)In: Electronics, E-ISSN 2079-9292, Vol. 12, no 6, article id 1448Article in journal (Refereed) Published
Abstract [en]

This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis can help to identify and diagnose faulty behavior automatically by utilizing artificial intelligence or machine learning. In our survey, we review and discuss 57 papers published in the last 12 years, highlight the recent trends, identify current research gaps, discuss the limitations of current techniques, and provide recommendations for future studies in this area. While there is an increasing interest in the topic, and the past five years have shown much advancement, the absence of open-source high-quality labeled data severely hinders progress. Future research should aim to explore transfer learning, domain adaptation, and semi-supervised learning to improve current performance. Additionally, a researcher should increase knowledge of district heating data using data-centric approaches to establish a solid foundation for future fault detection and diagnosis in district heating.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
artificial intelligence, data mining, machine learning, review
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-24457 (URN)10.3390/electronics12061448 (DOI)000958374200001 ()2-s2.0-85152400101 (Scopus ID)
Funder
Knowledge Foundation, 20220068
Available from: 2023-04-20 Created: 2023-04-20 Last updated: 2023-04-28Bibliographically approved
Nordahl, C., Boeva, V. & Grahn, H. (2023). MultiStream EvolveCluster. In: The 36th Canadian Conference on Artificial Intelligence: . Paper presented at The 36th Canadian Conference on Artificial Intelligence, Montreal, 5-9 June 2023.
Open this publication in new window or tab >>MultiStream EvolveCluster
2023 (English)In: The 36th Canadian Conference on Artificial Intelligence, 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a novel multi-stream clustering algorithm, MultiStream EvolveCluster (MS-EC), that can be used for continuous and distributed monitoring and analysis ofevolving time series phenomena. It can maintain evolving clustering solutions separatelyfor each stream/view and consensus clustering solutions reflecting evolving interrelationsamong the streams. Each stream behavior can be analyzed by different clustering techniques using a distance measure and data granularity that is specially selected for it. Theproperties of the MultiStream EvolveCluster algorithm are studied and evaluated withrespect to different consensus clustering techniques, distance measures, and cluster evaluation measures in synthetic and real-world smart building datasets. Our evaluation resultsshow a stable algorithm performance in synthetic data scenarios. In the case of real-worlddata, the algorithm behavior demonstrates sensitivity to the individual streams’ data quality and the used consensus clustering technique.

Keywords
evolve clustering, data stream mining, consensus clustering
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25534 (URN)
Conference
The 36th Canadian Conference on Artificial Intelligence, Montreal, 5-9 June 2023
Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2023-11-03Bibliographically approved
Åleskog, C., Devagiri, V. M. & Boeva, V. (2022). A Graph-based Multi-view Clustering Approach for Continuous Pattern Mining. In: Witold Pedrycz and Shyi-Ming Chen (Ed.), Recent Advancements in Multi-View Data Analytics: (pp. 201-237). Springer Science+Business Media B.V.
Open this publication in new window or tab >>A Graph-based Multi-view Clustering Approach for Continuous Pattern Mining
2022 (English)In: Recent Advancements in Multi-View Data Analytics / [ed] Witold Pedrycz and Shyi-Ming Chen, Springer Science+Business Media B.V., 2022, p. 201-237Chapter in book (Refereed)
Abstract [en]

Today’s smart monitoring applications need machine learning models and data mining algorithms that are capable of analysing and mining the temporal component of data streams. These models and algorithms also ought to take into account the multi-source nature of the sensor data by being able to conduct multi-view analysis. In this study, we address these challenges by introducing a novel multi-view data stream clustering approach, entitled MST-MVS clustering, that can be applied in different smart monitoring applications for continuous pattern mining and data labelling. This proposed approach is based on the Minimum Spanning Tree (MST) clustering algorithm. This algorithm is applied for parallel building of local clustering models on different views in each chunk of data. The MST-MVS clustering transfers knowledge learnt in the current data chunk to the next chunk in the form of artificial nodes used by the MST clustering algorithm. These artificial nodes are identified by analyzing multi-view patterns extracted at each data chunk in the form of an integrated (global) clustering model. We further show how the extracted patterns can be used for post-labelling of the chunk’s data by introducing a dedicated labelling technique, entitled Pattern-labelling. We study and evaluate the MST-MVS clustering algorithm under different experimental scenarios on synthetic and real-world data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2022
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 106
Keywords
data stream, clustering analysis, pattern mining, minimum spanning tree
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-22261 (URN)10.1007/978-3-030-95239-6_8 (DOI)2-s2.0-85130970889 (Scopus ID)978-3-030-95239-6 (ISBN)
Available from: 2021-11-01 Created: 2021-11-01 Last updated: 2022-06-10Bibliographically approved
Abghari, S., Boeva, V., Casalicchio, E. & Exner, P. (2022). An Inductive System Monitoring Approach for GNSS Activation. In: Maglogiannis, I, Iliadis, L, Macintyre, J, Cortez, P (Ed.), IFIP Advances in Information and Communication Technology: . Paper presented at 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, Hersonissos, 17 June 2022 - 20 June 2022 (pp. 437-449). Springer Science+Business Media B.V., 647
Open this publication in new window or tab >>An Inductive System Monitoring Approach for GNSS Activation
2022 (English)In: IFIP Advances in Information and Communication Technology / [ed] Maglogiannis, I, Iliadis, L, Macintyre, J, Cortez, P, Springer Science+Business Media B.V., 2022, Vol. 647, p. 437-449Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a Global Navigation Satellite System (GNSS) component activation model for mobile tracking devices that automatically detects indoor/outdoor environments using the radio signals received from Long-Term Evolution (LTE) base stations. We use an Inductive System Monitoring (ISM) technique to model environmental scenarios captured by a smart tracker via extracting clusters of corresponding value ranges from LTE base stations’ signal strength. The ISM-based model is built by using the tracker’s historical data labeled with GPS coordinates. The built model is further refined by applying it to additional data without GPS location collected by the same device. This procedure allows us to identify the clusters that describe semi-outdoor scenarios. In that way, the model discriminates between two outdoor environmental categories: open outdoor and semi-outdoor. The proposed ISM-based GNSS activation approach is studied and evaluated on a real-world dataset contains radio signal measurements collected by five smart trackers and their geographical location in various environmental scenarios.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2022
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868422X ; 647
Keywords
Activation analysis, Chemical activation, Global positioning system, Long Term Evolution (LTE), Monitoring, Radio navigation, Clustering analysis, Context detection, Environmental context detection, Environmental contexts, Global navigation satellite system signal, Global Navigation Satellite Systems, Inductive system, Inductive system monitoring, System monitoring, System signals, Base stations, GNSS signal
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-23550 (URN)10.1007/978-3-031-08337-2_36 (DOI)000927893200036 ()2-s2.0-85133294290 (Scopus ID)9783031083365 (ISBN)
Conference
18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, Hersonissos, 17 June 2022 - 20 June 2022
Available from: 2022-08-15 Created: 2022-08-15 Last updated: 2023-03-09Bibliographically approved
Al-Saedi, A. A., Boeva, V., Casalicchio, E. & Exner, P. (2022). Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview. Sensors, 22(15), Article ID 5544.
Open this publication in new window or tab >>Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview
2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 15, article id 5544Article, review/survey (Refereed) Published
Abstract [en]

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
artificial intelligence, context-awareness, edge computing, wireless sensor network, computer network, human, wireless communication, Computer Communication Networks, Humans, Wireless Technology
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-23537 (URN)10.3390/s22155544 (DOI)000839768900001 ()2-s2.0-85135202158 (Scopus ID)
Note

open access

Available from: 2022-08-12 Created: 2022-08-12 Last updated: 2022-08-26Bibliographically approved
Devagiri, V. M., Boeva, V. & Abghari, S. (2022). Domain Adaptation Through Cluster Integration and Correlation. In: Candan K.S., Dinh T.N., Thai My.T., Washio T. (Ed.), IEEE International Conference on Data Mining Workshops, ICDMW: . Paper presented at 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022, Orlando, 28 November through 1 December 2022 (pp. 119-126). IEEE Computer Society
Open this publication in new window or tab >>Domain Adaptation Through Cluster Integration and Correlation
2022 (English)In: IEEE International Conference on Data Mining Workshops, ICDMW / [ed] Candan K.S., Dinh T.N., Thai My.T., Washio T., IEEE Computer Society, 2022, p. 119-126Conference paper, Published paper (Refereed)
Abstract [en]

Domain shift is a common problem in many real-world applications using machine learning models. Most of the existing solutions are based on supervised and deep-learning models. This paper proposes a novel clustering algorithm capable of producing an adapted and/or integrated clustering model for the considered domains. Source and target domains are represented by clustering models such that each cluster of a domain models a specific scenario of the studied phenomenon by defining a range of allowable values for each attribute in a given data vector. The proposed domain integration algorithm works in two steps: (i) cross-labeling and (ii) integration. Initially, each clustering model is crossly applied to label the cluster representatives of the other model. These labels are used to determine the correlations between the two models to identify the common clusters for both domains, which must be integrated within the second step. Different features of the proposed algorithm are studied and evaluated on a publicly available human activity recognition (HAR) data set and real-world data from a smart logistics use case provided by an industrial partner. The experiment's goal on the HAR data set is to showcase the algorithm's potential in automatic data labeling. While the conducted experiments on the smart logistics use case evaluate and compare the performance of the integrated and two adapted models in different domains. © 2022 IEEE.

Place, publisher, year, edition, pages
IEEE Computer Society, 2022
Series
IEEE International Conference on Data Mining Workshops, ICDMW, ISSN 2375-9232, E-ISSN 2375-9259 ; 2022
Keywords
Cluster analysis, Clustering algorithms, Deep learning, Learning systems, Clustering model, Clustering techniques, Data set, Domain adaptation, Human activity recognition, Learning models, Machine learning models, Novel clustering, Real-world, Target domain, Data integration
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-24336 (URN)10.1109/ICDMW58026.2022.00025 (DOI)000971492200017 ()2-s2.0-85148440164 (Scopus ID)9798350346091 (ISBN)
Conference
22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022, Orlando, 28 November through 1 December 2022
Available from: 2023-03-03 Created: 2023-03-03 Last updated: 2023-05-26Bibliographically approved
Nordahl, C., Boeva, V., Grahn, H. & Netz Persson, M. (2022). EvolveCluster: an evolutionary clustering algorithm for streaming data. Evolving Systems (4), 603-623
Open this publication in new window or tab >>EvolveCluster: an evolutionary clustering algorithm for streaming data
2022 (English)In: Evolving Systems, ISSN 1868-6478, E-ISSN 1868-6486, no 4, p. 603-623Article in journal (Refereed) Published
Abstract [en]

Data has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.

Place, publisher, year, edition, pages
SPRINGER HEIDELBERG, 2022
Keywords
Evolving data stream; Clustering; Data stream clustering
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-22395 (URN)10.1007/s12530-021-09408-y (DOI)000717906700001 ()2-s2.0-85119001929 (Scopus ID)
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2021-11-26 Created: 2021-11-26 Last updated: 2023-11-03Bibliographically approved
Al-Saedi, A. A., Boeva, V. & Casalicchio, E. (2022). FedCO: Communication-Efficient Federated Learning via Clustering Optimization †. Future Internet, 14(12), Article ID 377.
Open this publication in new window or tab >>FedCO: Communication-Efficient Federated Learning via Clustering Optimization †
2022 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 14, no 12, article id 377Article in journal (Refereed) Published
Abstract [en]

Federated Learning (FL) provides a promising solution for preserving privacy in learning shared models on distributed devices without sharing local data on a central server. However, most existing work shows that FL incurs high communication costs. To address this challenge, we propose a clustering-based federated solution, entitled Federated Learning via Clustering Optimization (FedCO), which optimizes model aggregation and reduces communication costs. In order to reduce the communication costs, we first divide the participating workers into groups based on the similarity of their model parameters and then select only one representative, the best performing worker, from each group to communicate with the central server. Then, in each successive round, we apply the Silhouette validation technique to check whether each representative is still made tight with its current cluster. If not, the representative is either moved into a more appropriate cluster or forms a cluster singleton. Finally, we use split optimization to update and improve the whole clustering solution. The updated clustering is used to select new cluster representatives. In that way, the proposed FedCO approach updates clusters by repeatedly evaluating and splitting clusters if doing so is necessary to improve the workers’ partitioning. The potential of the proposed method is demonstrated on publicly available datasets and LEAF datasets under the IID and Non-IID data distribution settings. The experimental results indicate that our proposed FedCO approach is superior to the state-of-the-art FL approaches, i.e., FedAvg, FedProx, and CMFL, in reducing communication costs and achieving a better accuracy in both the IID and Non-IID cases. © 2022 by the authors.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
clustering, communication efficiency, convolutional neural network, federated learning, Internet of Things, Convolutional neural networks, Cost reduction, Learning systems, Privacy-preserving techniques, Central servers, Clustering optimizations, Clusterings, Communication cost, Optimization approach, Shared model, Workers'
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-24176 (URN)10.3390/fi14120377 (DOI)000901037100001 ()2-s2.0-85144590253 (Scopus ID)
Note

open access

Available from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-08-03Bibliographically approved
Projects
Product Sustainability Information: supporting communication between customers and product developers (PROSIT) [20180130]; Blekinge Institute of Technology, Faculty of Engineering, Department of Strategic Sustainable Development; Publications
Kwok, S. Y., Hallstedt, S. & Boeva, V. (2021). Understanding Customer Preference: Outline of a New Approach to Prioritise Sustainability Product Information. In: Scholz, Steffen G., Howlett, Robert J., Setchi, Rossi (Ed.), Sustainable Design and Manufacturing 2020 Proceedings of the 7th International Conference on Sustainable Design and Manufacturing (KES-SDM 2020): . Paper presented at Sustainable Design and Manufacturing 2020, online, 9-11 September. SpringerFaludi, J., Hoffenson, S., Kwok, S. Y., Saidani, M., Hallstedt, S., Telenko, C. & Martinez, V. G. (2020). A research roadmap for sustainable design methods and tools. Sustainability, 12(19), Article ID 8174. Kwok, S. Y., Schulte, J. & Hallstedt, S. (2020). Approach for Sustainability Criteria and Product Life: Cycle Data Simulation in Concept Selection. In: Proceedings of the Design Society: DESIGN Conference: . Paper presented at Design 2020 Conference, online, OCTOBER 26-29, 2020 (pp. 1979-1988). Cambridge University Press
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3128-191x

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