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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yuhang, He Yangyang, Sun Zhijun, Xue Shijiao, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9798331519254

Autonomous driving detection technology in real-world road scenarios faces numerous challenges, including variable weather conditions and complex road environments. Therefore, developing an object detection model with robust domain adaptation is crucial. In this paper, we investigate the use of image style transfer techniques to leverage target domain images for enhancing model performance. Experimental results show that our proposed approach significantly improves detection efficacy. Notably, our method outperforms the Oracle results in tasks such as transitioning from Cityscapes to Foggy Cityscapes, highlighting its effectiveness in addressing domain adaptation challenges. © 2024 IEEE.

关键词： Image enhancement

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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IEARACDA: Predicting circRNA-disease associations based on information enhancement and arctangent rank approximation

IEARACDA: Predicting circRNA-disease associations based on i...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Song, Zheqi Liu, Tiyao Wu, Wenhao Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Circular RNAs (circRNAs) are non-coding RNA molecules that play a significant role in cell regulation and disease occurrence. In recent years, the use of computational methods to predict circRNAs associated with diseases has become a research focus. However, these methods are often constrained by the noise interference and an insufficient exploration of the underlying information in known associations. Therefore, this paper proposes an information enhancement and arctangent rank approximation based method to predict potential circRNA-disease associations (IEARACDA). Firstly, information enhancement methods are used to make circRNA and disease similarity information more complete and to reduce the sparsity of known association. Subsequently, the arctangent rank approximation method is employed to mitigate the potential bias issues that may arise from the nuclear norm, and optimization is carried out using the inexact augmented Lagrange multiplier (IALM) method. Finally, potential associations are predicted through heterogeneous graph inference method. The experimental results demonstrate that IEARACDA exhibits superior performance compared to existing methods. Furthermore, case studies for hepatocellular carcinoma and lung cancer provide additional confirmation of the accuracy and practical significance of the model predictions. © 2024 IEEE.

关键词： Lung cancer

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STC: Student-Teacher Collaborative Model for Multi-Target Domain Adaptation 21

STC: Student-Teacher Collaborative Model for Multi-Target Do...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Jiayi, Xu Jiaqi, Yu Yixin, Wang Shijia, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9798331519254

Cross-domain object detection poses significant challenges due to the susceptibility of object detection models to data variance, particularly the domain shifts that can occur between different domains. To address the limitation, we draw inspiration from knowledge distillation, proposing a collaborative learning framework. Our method employs CycleGAN to generate target-style images, and during pretraining, an unsupervised domain adaptation teacher model is trained for each source-target pair. In the distillation process, our proposed algorithm implements an out-of-distribution estimation strategy to select samples that best align with the current model, thereby enhancing the cross-domain distillation process. Furthermore, each expert model is encouraged to collaborate by designating the student model as a bridge between different target domains, facilitated by the Exponential Moving Average (EMA) algorithm. Experiments show that the proposed method leverages structured information, not only does it perform well across various target domains, but it also yields favorable results compared to state-of-the-art unsupervised methods that are specifically trained on single source-target pair. © 2024 IEEE.

关键词： Contrastive Learning

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Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

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computer Modeling in engineering & Sciences 2023年第2期134卷 767-782页

作者： Tingshun Li Jiaohui Xu Zesan Liu Dadi Wang Wen Tan School of Control and Computer Engineering North China Electric Power UniversityBeijing102206China R&D State Grid Information&Telecommunication Group Co.Beijing102211China

The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter,where this affects their capacity for power generation as well as their *** identifying the icing of the blades of wind turbines in remote areas is thus important,and a general model is needed to this *** paper proposes a universal model based on a Deep Neural Network(DNN)that uses data from the Supervisory control and Data Acquisition(SCADA)*** datasets from SCADA are first preprocessed through undersampling,that is,they are labeled,normalized,and *** features of icing of the blades of a turbine identified in previous studies are then used to extract training data from the training dataset.A middle feature is proposed to show how a given feature is correlated with icing on the *** indicators for the model,including a reward function,are also designed to assess its predictive ***,the most suitable model is used to predict the testing data,and values of the reward function and the predictive accuracy of the model are *** proposed method can be used to relate continuously transferred features with a binary status of icing of the blades of the turbine by using variables of the middle *** results here show that an integrated indicator systemis superior to a single indicator of accuracy when evaluating the prediction model.

关键词： DNN predicting blade icing SCADA data wind power reward function

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Finite-time synchronization control for a class of delayed neural networks:an improved two-step control method

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Science China(Information Sciences) 2025年

作者： Yue CHEN Song ZHU Mouquan SHEN Xiaoyang LIU Shiping WEN School of Mathematics China University of Mining and Technology College of Electrical Engineering and Control Science Nanjing Technology University School of Computer Science and Technology Jiangsu Normal University Faculty of Engineering and Information Technology University of Technology Sydney

In this paper,a two-step control method is proposed,leveraging the generalized Halanay inequality and existing finite-time stability theorems,to achieve finite-time synchronization for a class of neural networks with bounded time-varying *** the first step,the system state is attenuated from V （t0） to γV （t0） using the generalized Halanay inequality,where0<γ 1 is a free *** the second step,by applying existing finite-time stability theorems,the system state further decays from γV （t0） to *** on the above ideas,two novel finite-time stability lemmas for the error system are presented,and the convergence rate as well as the settling time is ***,the value of γ that results in the shortest settling time for the error system is also *** the help of the derived lemmas,several sufficient algebraic criteria are established to achieve finite-time synchronization between the considered delayed neural *** results of this paper not only improve the existing two-step control method but also overcome the limitations of certain one-step finite-time control ***,the validity and practical applicability of the obtained theoretical results are demonstrated through two numerical examples and an image protection experiment.

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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computer Modeling in engineering & Sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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A Novel Random Pulse Width Modulation for PMSM Drive System Based on Unified Voltage Modulation 6

A Novel Random Pulse Width Modulation for PMSM Drive System ...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Kan, Jinpeng Meng, Yongqing Shi, Yixin Li, Fengjiao Chen, Wenjie Li, Changyi Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

The electromagnetic interference (EMI) problem caused by power electronic switching devices and pulse width modulation (PWM) affects the normal operation of the motor drive system. In this paper, a random PWM based on unified modulation is proposed for permanent magnet synchronous motor (PMSM) drive system. The theoretical basis of unified modulation and the implementation scheme of random modulation are analyzed. Finally, the effectiveness of the proposed random PWM is verified by simulation. © 2024 IEEE.

关键词： Pulse width modulation

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Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

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Intelligent Automation & Soft Computing 2023年第8期37卷 1753-1771页

作者： Qi Liu Jing Li Xianmin Wang Wenpeng Zhao School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510002China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang UniversityFuzhou350121China

Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core *** methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic *** tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense ***,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding ***,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor ***,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target *** carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.

关键词： Semi-supervised learning attention mechanism feature augmentation consistency regularization

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