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Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network

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Chinese Journal of Electrical engineering 2023年第1期9卷 120-128页

作者： Yunhao Yang Heng Zhang Shurong Peng Sheng Su Bin Li College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China State Grid Jining Electric Power Corporation Jining 272100China School of Electrical&Information Engineering Changsha University of Science&TechnologyChangsha 410114China

Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is *** the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 *** presented method can obtain more useful information than conventional point and interval ***,a prediction of the future complete probability distribution of wind power can be *** to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind *** with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.

关键词： Dilated causal neural network nuclear density estimation wind power probability prediction quantile regression probability density distribution

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6643-6657页

作者： Guo, Jingjing Li, Jiaxing Liu, Zhiquan Xiong, Yupeng Ma, Yong Vasilakos, Athanasios V. Li, Xinghua Ma, Jianfeng Xidian University School of Cyber Engineering Xi’an710071 China Jinan University College of Cyber Security Guangzhou510632 China Jiangxi Normal University Department of Computer Science and Technology Nanchang330022 China Department of Networks and Communications College of Computer Science and Information Technology Dammam31441 Saudi Arabia Grimstad4879 Norway

The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%. © 2002-2012 IEEE.

关键词： Federated Learning Computational Modeling Accuracy Servers Training Data Models Machine Learning Mobile Computing Particle Measurements Convergence Federated Learning Contribution Evaluation Trust Evaluation Model Projection Theory Secure Aggregation Federated Learning Computational Complexity Test Dataset Global Model Selection Of Participants Model Projections Aggregation Method Shapley Value Speed Of The Model Contributions Of Participants Number Of Participants Accuracy Of Model Computational Cost Local Data Learning Task Layer Model Participation Outcomes Updated Model Value Of Participation Model Aggregation Server Aggregates Global Convergence Iterative Rounds Trust Value Local Updates MNIST Dataset Total Amount Of Data Local Training Aggregation Algorithm Complex Model Structure

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ISAC-Fi: Enabling Full-Fledged Monostatic Sensing over Wi-Fi Communication

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 139-153页

作者： Chen, Zhe Hu, Chao Zheng, Tianyue Cao, Hangcheng Yang, Yanbing Chu, Yen Jiang, Hongbo Luo, Jun Fudan University Institute of Space Internet School of Computer Science Shanghai200437 China Sichuan University College of Computer Science Chengdu610017 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Hunan University College of Computer Science and Electronics Engineering Changsha410012 China University of Electronic Science and Technology of China Chengdu610054 China

Although Wi-Fi communications have been exploited for sensing purpose for over a decade, the bistatic or multistatic nature of Wi-Fi still poses multiple challenges, hampering real-life deployment of integrated sensing and communication (ISAC) within the Wi-Fi framework. In this article, we aim to redesign Wi-Fi so that monostatic sensing (mimicking radar) can be achieved over the multistatic communication infrastructure. Specifically, we propose, design, and implement ISAC-Fi as an ISAC-ready Wi-Fi prototype. We first present a novel self-interference cancellation scheme, in order to extract reflected (radio frequency) signals for sensing purpose in the face of transmissions. We then subtly revise the existing Wi-Fi framework so as to seamlessly operate monostatic sensing under the Wi-Fi communication standard. Finally, we offer two ISAC-Fi designs: 1) a Universal Software Radio Peripheral (USRP)-based design emulates a totally redesigned ISAC-Fi device and 2) another plug-and-play design allows for backward compatibility by attaching an extra module to an arbitrary Wi-Fi device. We perform extensive experiments to validate the efficacy of ISAC-Fi and also to demonstrate its superiority over existing Wi-Fi sensing proposals. © 2024 IEEE.

关键词： Wi-Fi

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Graph Neural Networks for Drug Response Prediction 9

Graph Neural Networks for Drug Response Prediction

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Zhu, Jiehuang Liu, Xianfeng Zhang, Jin Wu, Sheng Hunan Normal University College of Information and Computer Engineering Changsha China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350383706

Drug response prediction plays a crucial role in precision medicine, such as cancer analysis and treatment. Due to the uncertainty of drug efficacy and the heterogeneity of cancer, predicting drug response in vitro is expected to assist in guiding anticancer drug design and understanding cancer biology. Based on the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE), we propose a deep learning method for predicting drug sensitivity. Specifically, we first construct graphs of drugs and cell lines in the benchmark dataset. And we design a drug feature extraction module based on Graph Transformer and a cell line feature extraction module based on Graph Attention Networks (GAT) to capture the embeddings of drugs and cell lines. Finally, we feed the drug embeddings and cell line embeddings into the module for prediction. Experiment results show that our method outperforms several advanced models in drug response prediction, demonstrating the good performance and enormous application potential in precision medicine. In addition, the results of ablation experiments demonstrate the effectiveness of the modules in our model. © 2023 IEEE.

关键词： Embeddings

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Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D

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Chinese Journal of Electrical engineering 2022年第1期8卷 53-62页

作者： Shurong Peng Heng Zhang Yunhao Yang Bin Li Sheng Su Shijun Huang Guodong Zheng School of Electrical&Information Engineering Changsha University of Science&TechnologyChangsha 410114China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China

The charging load of electric vehicles(EVs)has a strong spatiotemporal *** the dynamic spatiotemporal distribution of the charging load of EVs is of great significance for the grid to cope with the access of large-scale *** studies lack a prediction model that can accurately describe the dual dynamic changes of EVs charging the load time and ***,a spatial-temporal dynamic load forecasting model,dilated causal convolution-2D neural network(DCC-2D),is ***,a hole factor is added to the time dimension of the three-dimensional convolutional convolution kernel to form a two-dimensional hole convolution layer so that the model can learn the spatial dimension *** entire network is then formed by stacking the layers,ensuring that the network can accept long-term historical input,enabling the model to learn time dimension *** model is simulated with the actual data of the charging pile load in a certain area and compared with the ConvLSTM *** results prove the validity of the proposed prediction model.

关键词： Time and space dynamic prediction dilated convolution charging load convolutional neural network

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Image Fuzzy Enhancement Based on Membrane Computing Particle Swarm Optimization Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 658-672页

作者： Hou, Xiao-Mao Cai, Liu-Ping Wang, Yan-Hui Automation and Information Engineering College Hunan Chernical Vocational Technology College Zhuzhou412000 China Graduate School Department Saint Paul University Tuguegarao3500 Philippines School of Computer Science and Engineering Hunan University of Information Technology Changsha410151 China

Image fuzzy enhancement is a research hotspot in the field of image processing, which aims to recover enhanced beginning clear images from degraded images. Based on the research of traditional particle swarm optimization algorithm and fuzzy enhancement algorithm, an image fuzzy enhancement method based on membrane computing particle swarm algorithm is proposed. Firstly, in order to make full use of the sparse characteristics of the clear image, the coefficient decomposition under wavelet domain and tightly supported wavelet domain is performed on the image respectively. Then, a joint optimisation model is constructed using L1 parametric constraints to achieve pretzel noise cancellation. Next, the MMH-PSO algorithm is designed by improving the particle swarm algorithm using membrane computing and Metropolis-Hastings sampling. Based on the simulated annealing algorithm temperature drop process, Metropolis-Hastings sampling is used to add randomness to the particle swarm algorithm so that it has the ability to jump out of the local optimum. The use of membrane computing enhances the parallelism of the particle swarm algorithm and can reduce the time complexity in solving complex problems. Finally, MMH-PSO is used to simultaneously search out the magnitude of the two fuzzy parameters in the traditional fuzzy enhancement algorithm in order to improve the accuracy of the algorithm. The experimental results show that the proposed algorithm has better SSIM values than the traditional fuzzy enhancement algorithm, which effectively improves the image quality and makes the image edge information more abundant. © 2024, J. Network Intell. All rights reserved.

关键词： Simulated annealing

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An intelligent hybrid MAC protocol for a sensor-based personalized healthcare system

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Digital communications and Networks 2022年第2期8卷 174-185页

作者： Linbo Deng Jinsong Gui Tian Wang Jiawei Tan Xiong Li School of Computer Science and Engineering Central South UniversityChangSha410083China College of Computer Science and Technology Huaqiao UniversityXiamen361021China Institute for Cyber Security School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China

Sensors based on personalized healthcare systems have been widely used in the medical ***,energy limitations have greatly hindered the further development of medical *** the traditional Medium Access Control(MAC)protocol,the duration of low-power listening is fixed because it ignores that the available energy of sensors is different in some situations,which leads to a high delay and low energy *** this paper,a Maximum Listening Length MAC(MLL-MAC)protocol is proposed to fully utilize the energy in the sensor-based *** MLL-MAC protocol is an improvement of the Receiver-Initiated(RI)MAC *** main advance is that the sensor node performs the following additional operations:(1)The sender sends a beacon when it wakes up and sends data,thus establishing a communication link with the receiver in the listening state;(2)The receiver keeps listening as long as possible to reduce the delay when it wakes up and listens to the channel,which is different from the previous strategy in which the node turns into a sleep state immediately without receiving ***,the sensor node can dynamically determine whether to send beacons and prolong listening duration according to its available energy *** MLL-MAC protocol is evaluated through theoretical analysis and experimental *** results show that,compared with the RI-MAC protocol,the MLL-MAC protocol can reduce the average end-to-end delay by 41.4%and improve the energy utilization by 15.1%.

关键词： Sensor based system Delay Energy consumption Network lifetime Adaptive listening length

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Flexible and Effective Cellular Traffic Data Synthesis with Large Language Model

Flexible and Effective Cellular Traffic Data Synthesis with ...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Duan, Sijing Lyu, Feng Cen, Jinfeng Ren, Ju Yang, Peng Zhang, Yaoxue Tsinghua University Department of Computer Science and Technology Beijing China Central South University School of Computer Science and Engineering Changsha China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China

ISBN: (纸本)9798350351255

Cellular traffic data hold significant potential for applications such as network planning, traffic prediction, mobility modeling, and personalized recommendations. However, limited data accessibility hinders more open data-driven research. Previous studies have explored data synthesis, while exhibiting flexible limitations in supporting conditional traffic synthesis, and are vulnerable to multidimensional data modeling. In this paper, we present LLMCell, a flexible and effective framework that leverages the arbitrary conditioning and contextual understanding capabilities of the large language model (LLM) to generate high-quality synthetic cellular traffic data. The LLMCell comprises three key components: i) a textual encoder for converting raw cellular traffic data into textual representations, ii) a generative model learner to fine-tune pre-trained LLM based on encoded textual representation for cellular traffic generation, and iii) a synthetic data sampling module for final synthetic data sampling and textual-to-data transformation. Experiments conducted on a large-scale dataset demonstrate the superior fidelity and utility of LLMCell over state-of-the-art baselines, and the synthetic data can effectively preserve user privacy. We release our synthetic dataset to the public to benefit future research in the wireless network community1 © 2024 IEEE.

关键词： Cellular Traffic Flexible Data Synthesis Large Language Model

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