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Journal of Network Intelligence 2024年第2期9卷 790-803页

作者： Liu, Shu-Tang Fan, Ji-Jie Wang, Rui-Dong Han, Han Zhang, De-Yang Faculty of Humanities and Arts Macau University of Science and Technology 999078 China Academic Affairs Office Minjiang University Fuzhou350108 China Kyrgyz National University Named after Jusup Balasagyn Bishkek720033 Kyrgyzstan School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China Henan Provincial Institute of Scientific and Technical Information Zhengzhou450003 China

Anomaly detection in time series is challenging for machine learning due to the uncertainty and complex pattern in time series. In this paper, we propose a Kalman filter-based cycle-consistent adversarial learning framework (KFCGAN) for time series anomaly detection, assuming that the abnormal pattern prevents the model from reconstructing the original and filtered signals. Specifically, KFCGAN comprises a Kalman filter module and a cycle-consistent adversarial learning module that utilizes an autoencoder as its generator. Firstly, the Kalman filter module generates the filtered signals from the original signals as the target domain. Then, the forward direction of the cycleconsistent adversarial learning module learns the transformation of the original time series domain to the filtered time series domain. At the same time, the backward direction optimizes it by transforming the target domain to the original domain. Finally, anomaly detection can be modeled as the signal reconstruction problem between the original and target domains. The experimental results on five real-world datasets show that the proposed method outperforms the start-of-the-art, demonstrating that KFCGAN can effectively capture abnormal time series patterns. © 2024, J. Network Intell. All rights reserved.

关键词： Time series

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Research on the Product Recommendation Algorithm Based on Pyspark and Jupyter Notebook 2

Research on the Product Recommendation Algorithm Based on Py...

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2nd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2022

作者： Lu, Yixian Wu, Haoming Che, Hao Faculty of Science Computer Science University of Alberta EdmontonAB Canada School of Physical Science and Technology Electronic Information Science and Technology Southwest Jiaotong University Sichuan Chengdu China International Education College Computer Science and Technology Henan University Henan Kaifeng475000 China

ISBN: (纸本)9781510662940

Due to the problems caused by the development of the Internet such as information redundancy and junk information overflow, it is crucial that e-commercial companies utilize recommendation algorithms to personalize their online shopping system for every user in order to promote sales. After discussing the pros and cons of demographic filtering, content-based filtering and collaborative filtering, the authors mainly focused on collaborative filtering. This article elaborated on how to design the collaborative filtering algorithm and improve its efficiency. Compared to other recommendation algorithms, collaborative filtering can help customers discover potential interests. Moreover, the system only needs feedback matrixes to train the matrix decomposition model and requires no additional relevant features. One major defect of collaborative filtering is called cold start, which means if a new item is added during training, the system cannot create embedding and make a prediction for it. The technology called WALS projection can solve this problem to some degree. © 2023 SPIE.

关键词： Collaborative filtering

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Energy-Efficient Brain-Inspired Self-Attention-Spiking Neural Network Framework for Mix-Type Wafer Defect Recognition

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IEEE Sensors Journal 2025年第12期25卷 21888-21899页

作者： Peng, Dandan Wang, Yitian Zhou, Xinhe Liu, Jiale Liu, Chenyu Han, Te The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon999077 Hong Kong University of California San Diego School of Computer Science CA United States University of Electronic Science and Technology of China Glasgow College Chengdu611731 China Northwestern Polytechnical University School of Mechanical Engineering Xi'an China Beijing Institute of Technology School of Management Beijing100081 China

In semiconductor manufacturing, wafer defect recognition plays a critical role. As the technology advances and wafer feature sizes decrease, defect detection has become more challenging, particularly for mix-type defects. Although artificial neural network (ANN) models are currently used for this task, the high precision floating-point operations required by ANN models put significant pressure on edge computing devices in the industry. Additionally, ANN models have poor cognitive abilities and high data dependencies. Spiking neural networks (SNNs), as an excellent brain-inspired computational model, have the potential to address these problems. In this paper, we present Spiky-SANet, a novel Spiking-Self-Attention Network, which emulates the transmission of membrane potential signals in the brain using SNNs. This approach significantly reduces energy consumption during the running process, enabling efficient deployment on edge computing devices. Moreover, we introduce a multi-time-step self-attention (MTS-SA) module that emulates the interactive behavior between visual neurons and brain neurons, thereby enhancing the model's cognitive ability. Additionally, we tackle the issue of imbalanced focus on positive and negative samples by employing an asymmetric loss (ASL) function, effectively mitigating the influence of powerful negative samples. Our framework represents the pioneering attempt of SNNs in the field of wafer defect detection, achieving state-of-the-art performance and paving the way for more efficient and energy-saving semiconductor manufacturing processes. © 2025 IEEE.

关键词： Semiconductor device manufacture

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Indoor Positioning for the Elderly Based on Fuzzy C-Means Clustering and KNN Algorithm

Indoor Positioning for the Elderly Based on Fuzzy C-Means Cl...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Qian Zhang Xiaonan Bo Jiaqi Yang Chen Cui School of Computer Science and Technology Henan Polytechnic University Jiaozuo China School of Physics and Electronic Information Henan Polytechnic University Jiaozuo China School of Software Henan Polytechnic University Jiaozuo China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Location-based services (LBS) for elderly care is a trending topic in smart homes. The main concern is the precise positioning for elderly individuals. To address the issue of poor positioning accuracy and lengthy processing time associated with traditional UHF radio frequency identification (RFID) indoor positioning algorithms that use geometric methods, a new positioning method has been proposed. This method combines fuzzy clustering and k-nearest neighbor (KNN) ratio algorithms and has been applied to indoor positioning of the elderly. RFID is used to detect the location of elderly individuals and collect their location information. Fuzzy C-means clustering replaces traditional hard clustering algorithms. The algorithm estimates feature points in the cluster center reasonably based on the membership degree of reference points. This increases the difference between reference points and reduces the complexity of feature matching. Additionally, the KNN algorithm is combined with the clustering algorithm to perform fine matching. The experimental results show that the proposed algorithm can enhance positioning efficiency by 12.3% compared to the traditional algorithm.

关键词： Seminars Location awareness Clustering algorithms Smart homes Classification algorithms Sensors Complexity theory

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Graph-based Group Division Network for Referring Expression Comprehension

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6170-6183页

作者： Ke, Jingcheng Wang, Jia Wong, Waikeung Toomey, Anne Wen, Jie Guangdong University of Technology School of Computer Science and Technology GuangZhou China Guangdong Pharmaceutical University College of Medical Information Engineering GDPU Guangdong Guangzhou China The Hong Kong Polytechnic University School of Fashion and Textiles Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong Hong Kong The Royal College of Art Kensington Gore School of Design London South KensingtonSW7 2EU United Kingdom Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

Referring expression comprehension (REC) aims at locating the target object described by an expression. We observe that most of the graph-based REC methods only focus on establishing relations between all objects in an image and the given expression during the graph construction while ignoring the relationships between objects in the same category. As a result, these methods are sub-optimal in locating the target object described by the expression, particularly when the target object is surrounded by objects of similar categories. Meanwhile, during reasoning, numerous irrelevant objects are considered for expression, which will introduce significant harmful noise. To address these issues, this paper proposes a new graph-based group division network (GBGDN). Different from the existing works, our work partitions the constructed graphs into several sub-graphs based on the categories of objects and expressions. In each sub-graph, the common visual features of objects will be strengthened through a feature enhancement strategy. Subsequently, the enhanced sub-graphs and expressions undergo joint processing via a filtering-based reasoning module designed to reduce the influence of unrelated nodes in each subgraph, facilitating more accurate reasoning and matching. Experimental results across various datasets, including RefCOCO /+/g, Flickr30K Entities, RefClef, and Ref-reasoning, showcase the superiority of our proposed method over existing approaches. Most importantly, our method does not need pre-training. © 1991-2012 IEEE.

关键词： Graph theory

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UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

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China Communications 2023年第10期20卷 70-84页

作者： Zhongjie Li Weijie Yuan Qinghua Guo Nan Wu Ji Zhang Department of Electronic and Electrical Engineering Southern University of Science and TechonologyShenzhen 518055China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular *** this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional *** first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed *** refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)***,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

关键词： orthogonal time frequency space(OTFS) channel estimation hidden Markov model(HMM) unitary approximate message passing(UAMP)

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Rockburst mechanism and the law of energy accumulation and release in mining roadway: a case study

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International Journal of Coal science & technology 2022年第5期9卷 96-112页

作者： Feng Du Ji Ma Xiaofei Guo Tianfeng Wang Xiaohang Dong Jiashuo Li Shulei He Dilinaer Nuerjuma Beijing Key Laboratory for Precise Mining of Intergrown Energy and Resources China University of Mining and Technology(Beijing)100083 BeijingChina State and Local Joint Engineering Laboratory for Gas Drainage&Ground Control of Deep Mines Henan Polytechnic University454003 JiaozuoHenanChina School of Safety Science and Engineering Henan Polytechnic University454003 JiaozuoHenanChina

The rockburst dynamic disasters in the process of deep coal mining become more and more *** the rockburst occurred in the 23130 working face of Yuejin Coal Mine as the engineering background,we study the characteristics of mining stress feld around roadway,the plastic failure morphological characteristics of surrounding rock and the accumulation/release law of elastic energy before and after *** analysis model quantitatively describing the physical process of rockburst in the mining roadway is established,and the calculation method of dynamic release of elastic energy in the physical process of rockburst is *** mechanism of rockburst in mining roadway is *** results show that an“L-shaped”stress concentration zone is formed within 100 m of the 23130 working face,and the principal stress ratio of the surrounding rock of the transportation roadway is 2.59–*** change of the direction of the maximum principal stress has a signifcant efect on the burst appearance *** failure strength of diferent sections of the mining roadway is characterized by the elastic energy release *** the increase of the working face distance,the elastic energy released by burst failure and the expansion variation of failure boundary radius show a nonlinear variation law that tends to decrease steadily after sharp *** closer to the working face,the higher the burst *** a distance of 10 m from the working surface,the maximum principal stress reaches its maximum *** butterfy-shaped failure system generated by the surrounding rock of the roadway has energy self-sustainability,and the elastic energy released by the sudden expansion of the butterfy leaf is enough to cause a burst damage of 1.9 *** work could provide theoretical support for the prediction and prevention of rockburst.

关键词： Coal Rockburst Mining roadway Plastic failure Dynamic and static load Elastic energy

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Learning Fine-Grained Information with Capsule-Wise Attention for Salient Object Detection

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IEEE Transactions on Multimedia 2023年 1-14页

作者： Zhao, Sanyuan Wen, Zongzheng Qi, Qi Lam, Kin-Man Shen, Jianbing School of Computer Science & Technology Beijing Institute of Technology China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Macau China

With the popularity of convolutional neural networks being used for salient object detection (SOD), the performance has been significantly improved. However, how to integrate crucial features for modeling salient objects needs further exploration. In this work, we propose an effective feature selection scheme to solve this task. Firstly, we provide a Simplified Atrous Spatial Pyramid Pooling (SASPP) module to lightweight the multi-scale features. Dealing with the SASSP features, we design a pixel-level local feature selection scheme named Multi-Scale Capsule-wise Attention (MSCA). It aggregates features from multi-scales by dynamic routing and helps the network to generate fine-grained prediction maps. In addition, we exploit holistic features by the Spatial-wise Attention and Channel-wise Attention (SA/CA) mechanisms, which adaptively extracts spatial or channel information. We also propose a Multi-crossed Layer Connections (MLC) structure in the upsampling stage, to fuse features from not only different levels but also different scales. The salient object prediction is performed in a coarse-to-fine manner. By conducting comprehensive experiments on five benchmark datasets, our method achieves the best performance when compared to existing state-of-the-art approaches. IEEE

关键词： Object detection

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DSTNet: Distinguishing Source and Target Areas for Image Copy-Move Forgery Detection 27th

DSTNet: Distinguishing Source and Target Areas for Image Cop...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Kaiqi Yuan, Xiaochen Huang, Guoheng Liu, Kun School of Cyberspace Security Shandong University of Political Science and Law Jinan China Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9783031783111

In copy-move forgery detection, most relevant studies concern locating the copy-move areas without the distinction of source and target regions. This paper proposes an end-to-end network, DSTNet, to identify the source and target based on consistency detection between the copy-move region and the non-copy-move region. The DSTNet is composed of two stages, the Pre-processing stage and the Discrimination stage. Pre-processing Stage extracts internal information of copy-move and non-copy-move areas and conducts a series of operations to meet the requirements of network input. Discrimination stage allows multiple patches for input and classifies the input patches. Specifically, the Pre-processing stage, contains the Copy-move Patches Selection (CM Patches Selection) and Genuine Patches Selection, can select pairs of copy-move and none copy-move patches. We train the proposed DSTNet on two large synthetic datasets and use the public datasets CASIA and Comofod for evaluation. The experiment shows that our method achieves excellent results. Particularly, we achieve a 5.4% higher F1 based on ground-truth of copy-move mask (GT-CM) on CASIA dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Large datasets

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Anti-Interference Low-Power Double-Edge Triggered Flip-Flop Based on C-Elements

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Tsinghua science and technology 2022年第1期27卷 1-12页

作者： Zhengfeng Huang Xiao Yang Tai Song Haochen Qi Yiming Ouyang Tianming Ni Qi Xu School of Electronic Science and Applied Physics Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China School of Electrical Engineering Anhui Polytechnic UniversityWuhu 241000China

When the input signal has been interfered and glitches occur,the power consumption of Double-Edge Triggered Flip-Flops(DETFFs)will significantly *** effectively reduce the power consumption,this paper presents an anti-interference low-power DETFF based on *** improved C-element is used in this DETFF,which effectively blocks the glitches in the input signal,prevents redundant transitions inside the DETFF,and reduces the charge and discharge frequencies of the *** C-element has also added pull-up and pull-down paths,reducing its *** with other existing DETFFs,the DETFF proposed in this paper only flips once on the clock edge,which greatly reduces the redundant transitions caused by glitches and effectively reduces power *** paper uses HSPICE to simulate the proposed DETFF and other 10 *** findings show that compared with the other 10 types of DETFFs,the proposed DETFF has achieved large performance indexes in the total power consumption,total power consumption with glitches,delays,and power delay product.A detailed analysis of variance indicates that the proposed DETFF features less sensitivity to process,voltage,temperature,and Negative Bias Temperature Instability(NBTI)-induced aging variations.

关键词： Double-Edge Triggered Flip-Flop(DETFF) glitch low-power C-element

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