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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

computers, Materials & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang Witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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Current and Voltage Calculations of the 400 kV Transmission Line Using Laboratory Measurements and computer Simulation 33rd

Current and Voltage Calculations of the 400 kV Transmission...

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Torma, Matej Fekete, Krešimir Kljajić, Ružica Čvek, Kristijan Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Kneza Trpimira 2b Osijek31000 Croatia

ISBN: (纸本)9783031805967

In this paper, the current, voltages, as well as active and reactive power of the 400 kV transmission line are calculated using three different methods. The objective of the paper is to demonstrate and compare three different approaches that are based on laboratory measurement, analytical approach, and computer simulation. Measurements are carried out on the laboratory model of a real-life 400 kV transmission line while analytical approach is conducted using a short line model with lumped parameters. As a last approach, the transmission line is modeled in a computer program for power flow analysis, and power flow simulations with the same parameters as for the previous two methods are performed. All three methods are used to calculate voltages, currents, and powers at the beginning and at the end of the transmission line for a few scenarios. Results are compared and discussed in order to demonstrate the possibilities of the applied approaches for the education of power system engineers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Power transmission lines

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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： Syeda Shamaila Zareen Guangmin Sun Mahwish Kundi Syed Furqan Qadri Salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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computer Network Design for Office Building Environment 32nd

Computer Network Design for Office Building Environment

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Blažević, Damir Keser, Tomislav Mance, Matko Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Kneza Trpimira 2B Osijek Croatia

ISBN: (纸本)9783031514937

This paper presents an example of designing a computer network for a business building. When creating the project documentation, care was taken to ensure that it complies with current standards and the Rulebook on technical conditions for the electronic communication network of commercial and residential buildings. The technical description explains what structured cabling is, describes the importance of following its rules and standards, and defines the ways of marking all the equipment used in the creation of this computer network. Furthermore, an overview of all equipment with its characteristics is presented in detail. The logical scheme defines the so-called vertical network, i.e., the backbone, that is, it shows how each individual part of the active equipment inside the building is connected, how the communication cabinets are connected between the floors, and how the network switches and routers inside the communication cabinets are connected to each other. For each socket and cable, a unique label is defined, this creates a horizontal office network. This is the main part of structured cabling, where the precise definition of each individual part of the computer network provides the basis for safe and stable network operation, the possibility of easy upgrades in the future, and the elimination of possible difficulties that may arise. Application of the quality of service (QoS) on the network will be considered is the and recommendations given to avoid undesirable congestion during the flow of data by properly planning the QoS rules, such as packet marking and traffic shaping. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quality of service

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Comparative Study of Single-Input and Dual-Input PSS in Multi-machine System 32nd

Comparative Study of Single-Input and Dual-Input PSS in Mult...

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Košorog, Tomislav Mehmedović, Muharem Marić, Predrag Kljajić, Ružica Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Kneza Trpimira 2B Osijek Croatia

ISBN: (纸本)9783031514937

To meet growing demand for electricity, power systems are operating near their operating limits. Due to technical limitations, the transmission lines are close to their transmission capacity and with different dynamic characteristics interaction can occur between the parts of a single power system or between interconnected power systems. Such interactions can result in low-frequency oscillations that further limit the transmission capacity of the system. Low-frequency oscillations can be reduced by increasing the damping torque of the synchronous generators. The most efficient way to increase damping torque is to implement power system stabilizers in the generator excitation circuit. In this paper, single-input and dual-input PSS are implemented in IEEE 14-Bus test system and their influence on the small-signal stability is compared. PSS location is determined by participation analysis, while tuning of the PSS is performed using the pole placement method. For the different tuning parameters, the system performance was analyzed in both time-domain and S-domain for single-input and dual-input PSSs implementations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electric power system interconnection

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Predicting the Specific Gravity of Must During Fermentation Using Machine Learning Models 32nd

Predicting the Specific Gravity of Must During Fermentation ...

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Kovačević, Ivana Orić, Mihaela Tolić, Ivana Hartmann Nyarko, Emmanuel Karlo Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031514937

In this article, the process of fermentation in winemaking is investigated. The authors analyze fermentation data and propose several machine learning models to assess the effectiveness of ongoing fermentation processes based on measurements of specific gravity and temperature. To evaluate the performance of each model, various metrics such as R-squared, Mean Squared Error, Root Mean Squared Error, and Maximum Absolute Error are employed. The results suggest that the models have the potential to predict fermentation outcomes in winemaking and provide winemakers with a valuable tool to optimize the process and ensure high-quality wine production. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 999078 China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City 999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science 999078 China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering 639815 Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation 27th

Task Consistent Prototype Learning for Incremental Few-Shot...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

ISBN: (纸本)9783031783463

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Deep learning-based vehicle event identification

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Multimedia Tools and Applications 2024年第41期83卷 89439-89457页

作者： Chen, Yen-Yu Chen, Jui-Chi Lian, Zhen-You Chiang, Hsin-You Huang, Chung-Lin Chuang, Cheng-Hung Department of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology No.57 Sec. 2 Zhongshan Rd. Taichung411030 Taiwan Department of Computer Science and Information Engineering Asia University Taichung Japan Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan

In recent years, there has been a rapid development of intelligent driving assistance systems. Although most vehicles nowadays are equipped with driving assistance systems, the number of car accidents continues to rise. The main cause of car accidents is still largely attributed to human factors. Therefore, there has been an increasing focus on research related to vehicle event recognition and traffic video analysis. This study used deep learning methods to automatically recognize vehicle events from recorded driving videos. We expanded Inception-V3 to a 3D structure and replaced the bottom architecture of SlowFastNet with 3D-Inception-V3 to form a 3D-Inception-V3 + SlowFastNet model, making the network more suitable for video data recognition. In the experiment, we cut all videos into one-second clips and label each clip with vehicle event categories, including vehicle stopping, straight driving, turning, and collision. The experiment utilized training and testing procedures with eight distinct models, i.e., R3D, I3D, I3D + Kinetics-400, SlowFastNet(50), SlowFastNet(101), SlowFastNet(152), SlowFastNet(200), and the proposed 3D-Inception-V3 + SlowFastNet models. Comparative analysis through experiments revealed that our proposed network achieved the highest recognition accuracy of 93.3%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Intelligent edge CDN with smart contract-aided local IoT sharing

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High-Confidence Computing 2024年第4期4卷 56-68页

作者： Jiamin Fan Daming Liu Guoming Tang Kui Wu Xun Shao Department of Computer Science University of VictoriaVictoria BC V8W 2Y2Canada College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China Peng Cheng Laboratory Shenzhen 518000China Department of Electrical and Electronic Information Engineering Toyohashi University of TechnologyToyohashi Aichi 441-8580Japan

A content delivery network(CDN)aims to reduce the content delivery latency to end-users by using distributed cache ***,deploying and maintaining cache servers on a large scale is very *** solve this problem,CDN providers have developed a new content delivery strategy:allowing end-users’s IoT edge devices to share their storage/bandwidth *** new edge CDN platform must address two core questions:(1)how can we incentivize end users to share IoT devices?(2)how can we facilitate a safe and transparent content transaction environment for end users?This paper introduces SmartSharing,a new content delivery network solution to address these *** smartSharing,the over-the-top(OTT)IoT devices belonging to end-users are used as mini-cache *** motivate end users to share the idle devices and storage/bandwidth resources,SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms(specifically,a tailored expectation-maximization(EM)algorithm).To facilitate content trading among end users,SmartSharing creates a secure and transparent transaction platform based on smart contracts in *** addition,SmartSharing’s performance evaluation is through trace-driven simulations in the real world and a prototype using content metadata and the achieved pricing *** evaluation results show that CDN providers,end users and content providers can all benefit from our SmartSharing framework.

关键词： CDN over edge IoT Game theory EM algorithm Smart contracts

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