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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Joint Model Assignment and Resource Allocation for Cost-Effective Mobile Generative Services

Joint Model Assignment and Resource Allocation for Cost-Effe...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gao, Shuangwei Yang, Peng Kong, Yuxin Lyu, Feng Zhang, Ning Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Central South University School of Computer Science and Engineering Changsha China University of Windsor Department of Electrical and Computer Engineering WindsorON Canada

ISBN: (纸本)9798350351255

Artificial Intelligence Generated Content (AIGC) services can efficiently satisfy user-specified content creation demands, but the high computational requirements pose various challenges to supporting mobile users at scale. In this paper, we present our design of an edge-enabled AIGC service provisioning system to properly assign computing tasks of generative models to edge servers, thereby improving overall user experience and reducing content generation latency. Specifically, once the edge server receives user requested task prompts, it dynamically assigns appropriate models and allocates computing resources based on features of each category of prompts. The generated contents are then delivered to users. The key to this system is a proposed probabilistic model assignment approach, which estimates the quality score of generated contents for each prompt based on category labels. Next, we introduce a heuristic algorithm that enables adaptive configuration of both generation steps and resource allocation, according to the various task requests received by each generative model on the edge. Simulation results demonstrate that the designed system can effectively enhance the quality of generated content by up to 4.7% while reducing response delay by up to 39.1% compared to benchmarks. © 2024 IEEE.

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A Fine Grained Stochastic Geometry-Based Analysis on LEO Satellite Communication Systems

IEEE Networking Letters

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IEEE Networking Letters 2023年第4期5卷 237-240页

作者： Sun, Yanshi Ding, Zhiguo Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates The University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom

Recently, stochastic geometry has been applied to provide tractable performance analysis for low earth orbit (LEO) satellite networks. However, existing works mainly focus on analyzing the 'coverage probability', which provides limited information. To provide more insights, this letter provides a more fine grained analysis on LEO satellite networks modeled by a homogeneous Poisson point process (HPPP). Specifically, the distribution and moments of the conditional coverage probability given the point process are studied. The developed analytical results can provide characterizations on LEO satellite networks, which are not available in existing literature, such as 'user fairness' and 'what fraction of users can achieve a given transmission reliability'. Simulation results are provided to verify the developed analysis. Numerical results show that, in a dense satellite network, it is beneficial to deploy satellites at low altitude, for the sake of both coverage probability and user fairness. © 2019 IEEE.

关键词： Stochastic systems

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Self-Adaptive Physics Informed Neural Network for Paper Insulation Degree of Polymerization Prediction 50

Self-Adaptive Physics Informed Neural Network for Paper Insu...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Alquennah, Alamera Nouran AlShaikh Saleh, Mohammad Ghrayeb, Ali Abu-Rub, Haitham Refaat, Shady S. Al-Hajri, Mohammed Khatri, Sunil Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Electrical and Computer Engineering Texas A&M University at Qatar Qatar Foundation Doha Qatar Department of Electrical and Computer Engineering College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire United Kingdom Doha Qatar

ISBN: (纸本)9781665464543

This paper proposes a self adaptive physics informed neural network (SAPINN) model to predict the degree of polymerization (DP) of oil-impregnated paper insulation to quantify the level of degradation and the remaining useful lifetime. The prediction is performed based on historical DP values and the corresponding prediction time step, which are used as input data points to the proposed model. The DP mathematical model is used to constrain the training phase of the AI-model through a weighted sum loss function. The weights of this loss function are adjusted for each epoch through a self-adaptive weighting method to determine the relative importance of the data component and the mathematical model throughout the training by defining these weights as trainable parameters. The trained model is then tested using different datasets which are not part of the training phase. The training and testing datasets are generated synthetically through an algorithm that considers the deviation from the ideal DP degradation curve and incorporates actual measurement noise. The performance of the proposed SAPINN is compared to the baseline PINN and NN (in the absence of physics) to highlight the importance of embedding the mathematical model and the self adaptation algorithm, and theses experiments demonstrate that SAPINN significantly enhances the DP prediction. © 2024 IEEE.

关键词： Forecasting

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A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

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Journal of Energy Chemistry 2025年第4期103卷 759-772页

作者： Lei Cai Jing Yan Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu School of Computer Science and Engineering Xi’an University of TechnologyXi’an 710048ShaanxiChina School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjing 211167JiangsuChina Department of Energy Aalborg UniversityAalborg 9220Denmark

To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles'degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM)network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.

关键词： Battery degradation trajectory Early prediction Autoencoder Synthetic data generation

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Machine-Learning Based Optimal Design of a Wireless Power Transfer Coil for Battery-Powered Tram 11

Machine-Learning Based Optimal Design of a Wireless Power Tr...

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Noh, Eunchong So, Junhyuk Lee, Seung-Hwan University of Seoul School of Electrical and Computer Engineering Seoul Korea Republic of Postech Department of Computer Science and Engineering Pohang Korea Republic of

ISBN: (纸本)9788957083505

This paper proposes an optimal design method for a wireless power transfer (WPT) coil using a machine learning regression model. The proposed method was applied to design a WPT coil for a tram wireless power charging system. Designing a wireless power transmission coil with ferrite is challenging since it is difficult to calculate self-inductance, coupling coefficient, and winding resistance with an analytical model. This paper proposes a method for optimal design by inferring coil parameters using a machine learning regression model based on finite element analysis (FEA) simulation data. The proposed design methodology achieves optimal efficiency for coupling coefficients and shapes under given design conditions by using parameter estimation with a regression model and NSGA-II. This methodology searches not only for optimal shapes but also for optimal coupling coefficients, resulting in high coil-coil efficiency. The proposed WPT coil design methodology was used to design a coil for a 200kW 60kHz WPT system for a battery-powered tram, achieving a coil-coil efficiency of up to 97%. © 2023 The Korean Institute of Power Electronics.

关键词： Efficiency

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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Data Augmentation via Face Morphing for Recognizing Intensities of Facial Emotions

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IEEE Transactions on Affective Computing 2023年第2期14卷 1228-1235页

作者： Huang, Tsung-Ren Hsu, Shin-Min Fu, Li-Chen National Taiwan University Department of Psychology Center for Artificial Intelligence & Advanced Robotics Taipei106319 Taiwan National Taiwan University Center for Artificial Intelligence & Advanced Robotics Department of Electrical Engineering Department of Computer Science and Information Engineering Taipei106319 Taiwan

Being able to recognize emotional intensity is a desirable feature for a facial emotional recognition (FER) system. However, the development of such a feature is hindered by the paucity of intensity-labeled data for model training. To ameliorate the situation, the present study proposes using face morphing as a novel way of data augmentation to synthesize faces that express different degrees of a designated emotion. Such an approach has been successfully validated on humans and machines. Specifically, humans indeed perceived different levels of intensified emotions in these parametrically synthesized faces, and FER systems based on neural networks indeed showed improved sensitivities to intensities of different emotions when additionally trained on the synthesized faces. Overall, the proposed data augmentation method is not only simple and effective but also useful for building FER systems that recognize facial expressions of mixed emotions. © 2010-2012 IEEE.

关键词： Face recognition

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Examining the Performance of Melanoma Classification using Superpixel Segmentation: A Comparative Analysis

Examining the Performance of Melanoma Classification using S...

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2023 International Conference on Microelectronics, ICM 2023

作者： Aydoghmishi, Faezeh Mohammadi Modak, Sudipta Abdel-Raheem, Esam Rueda, Luis University of Windsor Department of Electrical and Computer Engineering WindsorON Canada University of Windsor School of Computer Science WindsorON Canada

ISBN: (纸本)9798350380828

Skin cancer, characterized by the abnormal growth of skin cells, is a severe and prevalent condition. Despite the advancement in digital diagnosis techniques, existing skin cancer detection methods often fail to achieve satisfactory performance in melanoma identification in dermoscopy imaging. This study presents a new melanoma classification algorithm that utilizes a superpixel-based segmentation technique, simple linear iterative clustering, and transfer learning-based convolutional neural networks to achieve the objective. A key innovation of our methodology is the introduction of a novel approach for efficiently merging superpixels, that enhances the quality of segmentation resulting in better classification performance. Following the segmentation phase, several convolutional neural networks have been utilized for feature extraction from segmented images and classification. The proposed method shows an enhanced performance in melanoma classification which is 91.67%, 95.33%, 91.23%, and 90.48% in terms of accuracy, precision, recall and F1-score, respectively. Our results indicate that the superpixel segmentation technique considerably enhances the classification models' accuracy compared to k-means segmentation methods. A comparative analysis between the proposed method and several state-of-the-art methods in the field has also been presented in the context. © 2023 IEEE.

关键词： Convolutional neural networks

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Privacy-preserving Linear Computations in Spiking Neural P Systems 7

Privacy-preserving Linear Computations in Spiking Neural P S...

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7th Symposium on Working Formal Methods, FROM 2023

作者： Plesa, Mihail-Iulian Gheorghe, Marian Ipate, Florentin University of Bucharest Bucharest Romania Department of Computer Science University of Bradford Bradford United Kingdom School of Electrical Engineering and Computer Science

Spiking Neural P systems are a class of membrane computing models inspired directly by biological neurons. Besides the theoretical progress made in this new computational model, there are also numerous applications of P systems in fields like formal verification, artificial intelligence, or cryptography. Motivated by all the use cases of SN P systems, in this paper, we present a new privacy-preserving protocol that enables a client to compute a linear function using an SN P system hosted on a remote server. Our protocol allows the client to use the server to evaluate functions of the form t1k+t2 without revealing t1,t2 or k and without the server knowing the result. We also present an SN P system to implement any linear function over natural numbers and some security considerations of our protocol in the honest-but-curious security model. © M.I. Plesa, M. Gheorghe & F. Ipate.

关键词： Privacy-preserving techniques

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