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4th International Symposium on computer Technology and Information Science, ISCTIS 2024

作者： Mao, Kaili Zhao, Qingjuan Beijing Institute of Control Engineering Beijing China University Beijing Computer School Beijing Information Science and Technology Beijing China

ISBN: (纸本)9798350354560

Paraphrase identification, with the objective to determine whether two sentences are paraphrases of each other, has fostered many applications including natural language inference and document retrieval. Traditional machine learning models for paraphrase identification are generally based on hand-crafted features. Recent deep neural network models generally take semantic vectors as inputs and generate classification results directly. However, existing solutions still have much room for improvement. First, existing models do not fully exploit sequence information (i.e., the order of words) and topic information, which are important for text under-standing. Second, although deep models achieve promising results, they require a large amount of labelled data and have low interpretability. To cope with these problems, we propose a new model called PIM-ST (Paraphrase Identification Model Incorporating Sequence and Topic Information). Specifically, besides semantic information, PIM-ST extracts sequence information and topic information, and adopts a simple machine learning model LightGBM as the classifier. We conduct comparative experiments and ablation studies on two public datasets Microsoft Research Paraphrase Corpus (MSRP) and Quora Question Pairs. Experimental results show that PIM-ST achieves better results than existing state-of-the-art models. © 2024 IEEE.

关键词： Semantics

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Perception of premium services based on the evolution of hyper-service network in mobile edge computing

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Journal of Cloud Computing 2025年第1期14卷 1-17页

作者： Zhang, Xiaojun Liu, Zhizhong Chu, Dianhui Zhao, Meiyue School of Computer and control Engineering Yantai University Yantai264005 China School of Computer and control Engineering Harbin Institute of Technology Weihai264209 China

Recently, massive services from multiple domains are available on the network with the rapid development of Mobile Edge Computing (MEC) technology. These various services construct a service network through services’ collaboration, which can provide users with rich resources at anytime and anywhere. However, in MEC, due to the proliferation of services, as well as the dynamics of Quality of Service (QoS), it becomes more and more difficult for users to discover the premium services effectively, which seriously impacts users’ satisfaction and the utilization of resources. Therefore, how to proactively perceive the emergence of premium services in the service networks has become an challenge. To tackle this issue, we propose a method for perception of the premium services based on the evolution of hyper-service network (named as PPSHSN). First, based on the hypergraph theory, we construct a hyper-service network with the optimal connection method. Then, we develop the evolution mechanism of the hyper-service network and optimize the hyper-service network with the hyperdegree premium value evolution algorithm, thus to conduct the evolution of the service networks. Next, we devise the evaluation indexes based on the topological characteristics of hypergraphs, which are updated according to the evolution of the hyper-service network. Finally, we complete perception of premium services based on the evolution results of the hyper-service network. The effectiveness and feasibility of our proposed method are verified through extensive simulation experiments. © The Author(s) 2025.

关键词： Hypermedia systems

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A Novel Visual Transformer for Long-Distance Pipeline Pattern Recognition in Complex Environment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2933-2945页

作者： Zhu, Chengyuan Pu, Yanyun Yang, Kaixiang Yang, Qinmin Chen, C.L. Philip Zhejiang University College of Control Science and Engineering Zhejiang Hangzhou310007 China Huzhou Institute of Zhejiang University Zhejiang Huzhou313000 China South China University of Technology College of Computer Science and Engineering Guangdong Guangzhou510006 China

The effective warning of dangerous events along long-distance pipelines is critical to ensure the safety of oil and gas transportation. Distributed optical fiber sensing (DOFS) technology can assist operators to identify threat vibration signals. However, due to the complex and changeable environmental background noise along long-distance pipeline, most of the existing methods only extract one-dimensional features of the signal, making it difficult to distinguish various types of environmental noise, strong interference, and dangerous events. Besides, the samples of different classes in the actual scene are unbalanced. The sample size of dangerous events is often smaller than others. To address these problems, we use image encoding to transform the time series signals collected by the DOFS system into image data, and fully extract the time dependence and the correlation between different elements in the signal. Moreover, a visual transformer model PipelineADWinT is proposed. The self-attention mechanism of diagonal-axial window designed in this model can perfectly combine image encoding features, and obtain local to global multiscale features through hierarchical structure. By optimizing the loss function, the model's ability to handle the class imbalance problem is enhanced. The experimental results show that PipelineADWinT has more comprehensive classification performance and fewer false alarms than all the baseline models, which proves the effectiveness and superiority of the model. © 2020 IEEE.

关键词： Pattern recognition

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Biomass Fuel Classification from Agricultural Residues Using Machine Learning 12

Biomass Fuel Classification from Agricultural Residues Using...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Pachauri, Nikhil Gupta, Himanshu Verma, Om Prakash Kumar Singh, Sushil Verma, Deepak Rn, Ravikumar Manipal Academy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Instrumentation and Control Engineering Karnataka Manipal India Dr B R Ambedkar National Institute of Technology Jalandhar Department of Instrumentation and Control Engineering Jalandhar India Marwadi University Department of Computer Engineering of Computer Engineering Gujarat Rajkot India Marwadi University Dept. of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9798350340204

This work incorporates an adaptive learning-based boosting (ADboost) ML classifier to classify four types of fuel: agricultural residue, coals, wood, and produced biomass. Further, the ADboost's hyperparameters, such as learning rate, maximum number of splits, and minimum leaf size are adjusted using teaching learning-based optimization (TLBO), resulting in TADboost. The performance of TADboost is compared against various popular ML (NN, BAG, NB, and SVM) models. Simulated result reveals that the suggested classifiers outperform other compared ML classifiers for fuel classification with the classification accuracy, precision, recall, F1-score, and kappa as 0.9659, 0.9671, 0.9449, 0.9558, and 0.9482, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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ViT lightweight training at IoT edge based on transfer learning

ViT lightweight training at IoT edge based on transfer learn...

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2024 International Conference on Optics, Electronics, and Communication engineering, OECE 2024

作者： Li, Zhixin Long, Yang Miao, Jiahao School of Computer Technology and Engineering Changchun Institute of Technology Jilin Changchun130012 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin132022 China

ISBN: (纸本)9781510685437

Recently, the vision transformer (ViT) model of deep learning has achieved surprising performance in the field of computer vision and has been widely used in IoT edge devices. However, the training of ViT models requires a large amount of data and computing resources, which is a challenge for resource-constrained edge IoT devices. To solve the above problems, this paper proposed a lightweight ViT method based on transfer learning. The primary concept of this method is to train large-scale ViT models in the cloud (CloudViT) and deploying small-scale ViT models at the edge (EdgeViT). Firstly, through the method of transfer learning, some underlying parameters of CloudViT were utilized to construct EdgeViT. The purpose is to enable EdgeViT to learn from CloudViT, acquiring knowledge and improving its performance. Secondly, adding a randomly initialized LayerNorm layer before the MLPHead during the training process of EdgeViT, it can improve further model performance. Finally, Experiment results demonstrated that EdgeViT could achieve 91.3% of CloudViT’s performance with only half the parameters and floating-point operations (FLOPs). Moreover, finetuning EdgeViT with a 60% reduction in training time still allows it to achieve 81.3% of CloudViT’s performance. Relevant conclusions can provide technical support for the proposed method. © 2024 SPIE.

关键词： Data modeling Clouds Internet of things Performance modeling Instrument modeling Neural networks Head Visual process modeling Image classification

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Changes in physiology, antioxidant system, and gene expression in Microcystis aeruginosa under fenoxaprop-p-ethyl stress

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Environmental Science and Pollution Research 2024年第19期31卷 28754-28763页

作者： Liu, Sijia Ni, Jiawei Guan, Ying Tao, Jianwei Wu, Liang Hou, Meifang Wu, Shichao Xu, Wenwu Zhang, Chu Ye, Jing School of Chemical and Environmental Engineering Shanghai Institute of Technology Shanghai201418 China Los Angeles Regional Water Quality Control Board Los AngelesCA90013 United States School of Ecological Technology and Engineering Shanghai Institute of Technology Shanghai201418 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Fenoxaprop-p-ethyl (FE) is one of the typical aryloxyphenoxypropionate herbicides. FE has been widely applied in agriculture in recent years. Human health and aquatic ecosystems are threatened by the cyanobacteria blooms caused by Microcystis aeruginosa, which is one of the most common cyanobacteria responsible for freshwater blooming. Few studies have been reported on the physiological effects of FE on M. aeruginosa. This study analyzed the growth curves, the contents of chlorophyll a and protein, the oxidative stress, and the microcystin-LR (MC-LR) levels of M. aeruginosa exposed to various FE concentrations (i.e., 0, 0.5, 1, 2, and 5 mg/L). FE was observed to stimulate the cell density, chlorophyll a content, and protein content of M. aeruginosa at 0.5- and 1-mg/L FE concentrations but inhibit them at 2 and 5 mg/L FE concentrations. The superoxide dismutase and catalase activities were enhanced and the malondialdehyde concentration was increased by FE. The intracellular (intra-) and extracellular (extra-) MC-LR contents were also affected by FE. The expression levels of photosynthesis-related genes psbD1, psaB, and rbcL varied in response to FE exposure. Moreover, the expressions of microcystin synthase-related genes mcyA and mcyD and microcystin transportation-related gene mcyH were significantly inhibited by the treatment with 2 and 5 mg/L FE concentrations. These results might be helpful in evaluating the ecotoxicity of FE and guiding the rational application of herbicides in modern agriculture. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Gene expression

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A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 462-473页

作者： Bingyun Li Qinmin Yang Innocent Kamwa IEEE the College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Huzhou Institute of Zhejiang University China the Department of Electrical and Computer Engineering Laval UniversityQuebec CityQCG1V 0A6Canada

Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this *** this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg *** holds a large-scale ESS that is shared among users in the form of energy *** sells energy to users and sets the selling price *** maximizes its profit through optimal pricing and ESS *** purchase some energy from operator for the reduction of their demand charges after operator's selling price is *** game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization *** upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'*** bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing *** studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.

关键词： Bi-level optimization demand charge energy storage system(ESS)sharing energy transaction mathematical program with equilibrium constraints(MPEC) stackelberg game

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GK-Means SOM Algorithm Used to Plan the Paths for Multiple Agents Exploring Multiple Target Points 1st

GK-Means SOM Algorithm Used to Plan the Paths for Multiple A...

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1st International Conference on Cognitive Computation and Systems, ICCCS 2022

作者： Yang, Hexing Zhao, Qingjie Wang, Lei Liu, Wangwang Chong, Ling School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9789819927883

Multi-agent technology is widely used in many fields such as intelligent manufacturing, logistics and environment exploration. In this paper, we propose a greedy K-means self-organizing map algorithm to balance the tasks and plan the paths for multiple agents exploring multiple target points, where greedy k-means adopts greedy strategy to ensure the tasks allocated for agents tending equally, and the self-organizing map networks are used for parallel path planning to speed up the problem solved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Self organizing maps

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Efficient Tool Condition Monitoring Using SOM-Hebb Classifier on FPGA 6

Efficient Tool Condition Monitoring Using SOM-Hebb Classifie...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Akanksha, A. Somani Kokate, R.D. Abhilasha, Mishra Maharashtra Institute of Technology Department of Electronics & Computer Engineering Aurangabad India College of Engineering Department of Instrumentation and Control Engineering Pune India

ISBN: (纸本)9798350304480

Tool condition monitoring is vital for enhancing productivity, reducing costs, and improving product quality in manufacturing industries. Existing approaches, such as threshold-based methods, sensor-based methods, and machine learning-based techniques, have limitations in terms of accuracy, adaptability, interpretability, and computational costs. This research proposes a tool condition monitoring system based on the Self-Organizing Map (SOM) algorithm and the Hebbian learning algorithm. The system is implemented on a Field-Programmable Gate Array (FPGA) platform for real-time processing in manufacturing environments. A dataset is acquired using sensors, pre-processed, and divided into training and testing sets. The SOM-Hebb classifier is designed with appropriate parameters, and the tool condition monitoring algorithm involves projection histograms, discrete Fourier transform (DFT), and the SOM-Hebb classifier. The FPGA implementation achieves high-speed processing and meets timing constraints. Simulation results demonstrate the system's functionality and accuracy, with a classification accuracy of 99.22%. The system exhibits a processing time of 35 clock cycles. This research provides a promising approach for efficient and accurate tool condition monitoring in manufacturing industries. © 2023 IEEE.

关键词： Field-Programmable Gate Array (FPGA) Hebbian learning algorithm Real-time processing Self-Organizing Map (SOM) Tool condition monitoring

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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