https://www.mdu.se/

mdu.sePublications
Change search
Link to record
Permanent link

Direct link
Publications (10 of 100) Show all publications
Seceleanu, T., Xiong, N., Enoiu, E. P. & Seceleanu, C. (2024). Building a Digital Twin Framework for Dynamic and Robust Distributed Systems. In: Lect. Notes Comput. Sci.: . Paper presented at 8th International Conference on Engineering of Computer-Based Systems, ECBS 2023, Västerås, 16 October 2023 through 18 October 2023 (pp. 254-258). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Building a Digital Twin Framework for Dynamic and Robust Distributed Systems
2024 (English)In: Lect. Notes Comput. Sci., Springer Science and Business Media Deutschland GmbH , 2024, p. 254-258Conference paper, Published paper (Refereed)
Abstract [en]

Digital Twins (DTs) serve as the backbone of Industry 4.0, offering virtual representations of actual systems, enabling accurate simulations, analysis, and control. These representations help in predicting system behaviour, facilitating multiple real-time tests, and reducing risks and costs while identifying optimization areas. DTs meld cyber and physical realms, accelerating the design and modelling of sustainable innovations. Despite their potential, the complexity of DTs presents challenges in their industrial application. We sketch here an approach to build an adaptable and trustable framework for building and operating DT systems, which is the basis for the academia-industry project A Digital Twin Framework for Dynamic and Robust Distributed Systems (D-RODS). D-RODS aims to address the challenges above, aiming to advance industrial digitalization and targeting areas like system efficiency, incorporating AI and verification techniques with formal support. 

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 14390 LNCS
Keywords
AI, digital twins, industrial automation, resource utilization, verification and validation, Actual system, Analysis and controls, Distributed systems, Resources utilizations, Simulation analysis, Simulation control, System behaviors, Verification-and-validation, Virtual representations, Artificial intelligence
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-65247 (URN)10.1007/978-3-031-49252-5_22 (DOI)2-s2.0-85180149728 (Scopus ID)9783031492518 (ISBN)
Conference
8th International Conference on Engineering of Computer-Based Systems, ECBS 2023, Västerås, 16 October 2023 through 18 October 2023
Available from: 2024-01-03 Created: 2024-01-03 Last updated: 2024-01-03Bibliographically approved
Zhang, L., Xiong, N., Pan, X., Yue, X., Wu, P. & Guo, C. (2023). Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery. Algorithms, 16(11), Article ID 520.
Open this publication in new window or tab >>Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery
Show others...
2023 (English)In: Algorithms, E-ISSN 1999-4893, Vol. 16, no 11, article id 520Article in journal (Refereed) Published
Abstract [en]

In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model’s detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model’s effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection. 

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2023
Keywords
decoupled head, small-object detection, WIoU, YOLOv7-tiny model, Aerial photography, Antennas, Image enhancement, Object recognition, Unmanned aerial vehicles (UAV), Aerial vehicle, Loss functions, Object detection method, Objects detection, Photographic imagery, Small object detection, Small objects, Wise intersection over union, Object detection
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:mdh:diva-65015 (URN)10.3390/a16110520 (DOI)001115260900001 ()2-s2.0-85178308460 (Scopus ID)
Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-20Bibliographically approved
Wu, P., Xiong, N., Li, G. & lv, J. (2023). Incremental Bayesian Classifier for Streaming Data with Concept Drift. In: Lecture Notes on Data Engineering and Communications Technologies: (pp. 509-518). Springer Science and Business Media Deutschland GmbH, 153
Open this publication in new window or tab >>Incremental Bayesian Classifier for Streaming Data with Concept Drift
2023 (English)In: Lecture Notes on Data Engineering and Communications Technologies, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 153, p. 509-518Chapter in book (Other academic)
Abstract [en]

Classification is an important task in the field of machine learning. Most classifiers based on offline learning are invalid for open data streams. In contrast, incremental learning is feasible for continuous data. This paper presents the Incremental Bayesian Classifier “Incremental_BC”, which continuously updates the probabilistic information according to each new training sample via recursive calculation. Further, the Incremental_BC is improved to deal with the flowing data whose distribution and property evolve with time, i.e., the concept drift. The effectiveness of the proposed methods has been verified by the results of simulation tests on benchmark data sets.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Keywords
Classification (of information), Data streams, E-learning, Bayesian classifier, Concept drifts, Continuous data, Data stream, Incremental learning, Machine-learning, Off-line learning, Online learning, Open datum, Streaming data, Open Data, Concept drift
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-61963 (URN)10.1007/978-3-031-20738-9_58 (DOI)000964184200058 ()2-s2.0-85147842399 (Scopus ID)
Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-05-03Bibliographically approved
Antonic, N., Khalid, A. H., Hamila, M. E. & Xiong, N. (2023). Online Tuning of PID Controllers Based on Membrane Neural Computing. In: Lecture Notes on Data Engineering and Communications Technologies: (pp. 455-464). Springer Science and Business Media Deutschland GmbH, 153
Open this publication in new window or tab >>Online Tuning of PID Controllers Based on Membrane Neural Computing
2023 (English)In: Lecture Notes on Data Engineering and Communications Technologies, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 153, p. 455-464Chapter in book (Other academic)
Abstract [en]

PID controllers are still popular in a wide range of engineering practices due to their simplicity and robustness. Traditional design of a PID controller needs manual setting of its parameters in advance. This paper proposes a new method for online tuning of PID controllers based on hybridized neural membrane computing. A neural network is employed to adaptively determine the proper values of the PID parameters in terms of evolving situations/stages in the control process. Further the learning of the neural network is performed based on a membrane algorithm, which is used to locate the weights of the network to optimize the control performance. The effectiveness of the proposed method has been demonstrated by the preliminary results from simulation tests.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Keywords
Bioinformatics, Controllers, Electric control equipment, Membranes, Proportional control systems, Tuning, Engineering practices, Gain tuning, Membrane algorithm, Membrane computing, Neural computing, Neural membranes, Neural-networks, Online gain tuning, Online tuning, PID controllers, Three term control systems, Neural network, PID controller
National Category
Control Engineering
Identifiers
urn:nbn:se:mdh:diva-61961 (URN)10.1007/978-3-031-20738-9_52 (DOI)000964184200052 ()2-s2.0-85147852132 (Scopus ID)
Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2024-01-18Bibliographically approved
Xiong, N., Li, M., Li, K., Xiao, Z., Liao, L. & Wang, L. (2023). Preface. Lecture Notes on Data Engineering and Communications Technologies, 153, xiii-xiv
Open this publication in new window or tab >>Preface
Show others...
2023 (English)In: Lecture Notes on Data Engineering and Communications Technologies, ISSN 2367-4512, Vol. 153, p. xiii-xivArticle in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
National Category
Computer Engineering
Identifiers
urn:nbn:se:mdh:diva-61959 (URN)2-s2.0-85147879121 (Scopus ID)
Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-02-22Bibliographically approved
Xiong, N. & Punnekkat, S. (2023). Tiny Federated Learning with Bayesian Classifiers. In: IEEE Int Symp Ind Electron: . Paper presented at IEEE International Symposium on Industrial Electronics. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Tiny Federated Learning with Bayesian Classifiers
2023 (English)In: IEEE Int Symp Ind Electron, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Tiny machine learning (TinyML) represents an emerging research direction that aims to realize machine learning on Internet of Things (IoT) devices. The current TinyML research seems to focus on supporting the deployment of deep learning models on microprocessors, while the models themselves are trained on high performance computers or clouds. However, in the resource/time constrained IoT contexts, it is more desirable to perform data analytics and learning tasks directly on edge devices for crucial benefits such as increased energy efficiency, reduced latency as well as lower communication cost.To address the above challenge, this paper proposes a tiny federated learning algorithm for enabling learning of Bayesian classifiers based on distributed tiny data storage, referred to as TFL-BC. In TFL-BC, Bayesian learning is executed in parallel across multiple edge devices using local (tiny) training data and subsequently the learning results from local devices are aggregated via a central node to obtain the final classification model. The results of experiments conducted on a set of benchmark datasets demonstrate that our algorithm can produce final aggregated models that outperform single tiny Bayesian classifiers and that the result of tiny federated learning (of Bayesian classifier) is independent of the number of data partitions used for generating the distributed local training data.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Bayesian classifier, edge computing, federated learning, tiny machine learning, Classification (of information), Data Analytics, Deep learning, Digital storage, Energy efficiency, Learning algorithms, Learning systems, 'current, High performance computers, Learning models, Machine learning research, Machine-learning, Training data, Internet of things
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64430 (URN)10.1109/ISIE51358.2023.10228115 (DOI)2-s2.0-85172110028 (Scopus ID)9798350399714 (ISBN)
Conference
IEEE International Symposium on Industrial Electronics
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2023-10-09Bibliographically approved
López, D., Ramírez-Gallego, S., García, S., Xiong, N. & Herrera, F. (2021). BELIEF: A distance-based redundancy-proof feature selection method for Big Data. Information Sciences, 558, 124-139
Open this publication in new window or tab >>BELIEF: A distance-based redundancy-proof feature selection method for Big Data
Show others...
2021 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 558, p. 124-139Article in journal (Refereed) Published
Abstract [en]

With the advent of Big Data era, data reduction methods are in highly demand given their ability to simplify huge data, and ease complex learning processes. Concretely, algorithms able to select relevant dimensions from a set of millions are of huge importance. Although effective, these techniques also suffer from the “scalability” curse when they are brought into tackle large-scale problems. In this paper, we propose a distributed feature weighting algorithm which precisely estimates feature importance in large datasets using the well-know algorithm RELIEF in small problems. Our solution, called BELIEF, incorporates a novel redundancy elimination measure that generates similar schemes to those based on entropy, but at a much lower time cost. Furthermore, BELIEF provides a smooth scale-up when more instances are required to increase precision in estimations. Empirical tests performed on our method illustrate the estimation ability of BELIEF in manifold huge sets – both in number of features and instances, as well as its reduced runtime cost as compared to other state-of-the-art methods. 

Place, publisher, year, edition, pages
Elsevier Inc., 2021
Keywords
Apache spark, Big Data, Feature selection (FS), High-dimensional, Redundancy elimination
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-53524 (URN)10.1016/j.ins.2020.12.082 (DOI)000634824100008 ()2-s2.0-85100519874 (Scopus ID)
Available from: 2021-04-01 Created: 2021-04-01 Last updated: 2022-08-29Bibliographically approved
Seceleanu, T., Xiong, N. & Seceleanu, C. (2021). Control as a service - Intelligent networking. In: Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021: . Paper presented at 45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021, 12 July 2021 through 16 July 2021 (pp. 1887-1892). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Control as a service - Intelligent networking
2021 (English)In: Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021, Institute of Electrical and Electronics Engineers Inc. , 2021, p. 1887-1892Conference paper, Published paper (Refereed)
Abstract [en]

The paper introduces elements of a service based perspective of a scalable and dynamic automation system architecture. The approach is based on potentially multi-role devices (implementing node management, processing and networking functionalities) hosting a set of services requested by input nodes. In addition, artificial intelligence support is described to provide means of reaching deployment optimality and reliability. Formal approaches are deemed necessary for both verification of the artificial intelligence approach and of the resulting solutions. A model-based design path is complementary considered in order to lead to an increased efficiency in resource utilization, to lowering design efforts, and ensure a formally correct allocation of services, according to system requirements and constraints.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2021
Keywords
Dynamic systems, Networked automation systems, Service oriented architectures, Application programs, Automation, Intelligent networks, Automation systems, Design effort, Formal approach, Intelligent networking, Model- based designs, Node management, Resource utilizations, System requirements, Artificial intelligence
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-56129 (URN)10.1109/COMPSAC51774.2021.00285 (DOI)000706529000274 ()2-s2.0-85115884209 (Scopus ID)9781665424639 (ISBN)
Conference
45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021, 12 July 2021 through 16 July 2021
Available from: 2021-10-07 Created: 2021-10-07 Last updated: 2021-11-11Bibliographically approved
Dust, L., Murcia, M. L., Mäkilä, A., Nordin, P., Xiong, N. & Herrera, F. (2021). Federated Fuzzy Learning with Imbalanced Data. In: Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021: . Paper presented at 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 (pp. 1130-1137). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Federated Fuzzy Learning with Imbalanced Data
Show others...
2021 (English)In: Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Institute of Electrical and Electronics Engineers Inc. , 2021, p. 1130-1137Conference paper, Published paper (Refereed)
Abstract [en]

Federated learning (FL) is an emerging and privacy-preserving machine learning technique that is shown to be increasingly important in the digital age. The two challenging issues for FL are: (1) communication overhead between clients and the server, and (2) volatile distribution of training data such as class imbalance. The paper aims to tackle these two challenges with the proposal of a federated fuzzy learning algorithm (FFLA) that can be used for data-based construction of fuzzy classification models in a distributed setting. The proposed learning algorithm is fast and highly cheap in communication by requiring only two rounds of interplay between the server and clients. Moreover, FFLA is empowered with an an imbalance adaptation mechanism so that it remains robust against heterogeneous distributions of data and class imbalance. The efficacy of the proposed learning method has been verified by the simulation tests made on a set of balanced and imbalanced benchmark data sets.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2021
Keywords
Class imbalance, Communication cost, Distributed learning, Federated learning, Fuzzy rule-based model, Imbalanced data, Incremental learning
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-57626 (URN)10.1109/ICMLA52953.2021.00185 (DOI)000779208200177 ()2-s2.0-85125878050 (Scopus ID)9781665443371 (ISBN)
Conference
20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Available from: 2022-03-16 Created: 2022-03-16 Last updated: 2022-11-08Bibliographically approved
Dokic, N., Tomic, M., Stevic, J., Dokic, D. & Xiong, N. (2021). Intelligent traffic signal control based on reinforcement learning. In: International Conference on Intelligent Systems Design and Applications, ISDA2020: . Paper presented at International Conference on Intelligent Systems Design and Applications ISDA2020, 12 Dec 2020, Växjö, Sweden. Växjö, Sweden: Springer
Open this publication in new window or tab >>Intelligent traffic signal control based on reinforcement learning
Show others...
2021 (English)In: International Conference on Intelligent Systems Design and Applications, ISDA2020, Växjö, Sweden: Springer , 2021Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Växjö, Sweden: Springer, 2021
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-53943 (URN)
Conference
International Conference on Intelligent Systems Design and Applications ISDA2020, 12 Dec 2020, Växjö, Sweden
Projects
ADAPTER: Adaptive Learning and Information Fusion for Online Classification Based on Evolving Big Data Streams
Available from: 2021-05-24 Created: 2021-05-24 Last updated: 2021-05-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9857-4317

Search in DiVA

Show all publications