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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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A Multichannel CNN-GRU Hybrid Architecture for sEMG Gesture Recognition

A Multichannel CNN-GRU Hybrid Architecture for sEMG Gesture ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shouliang Song Anming Dong Jiguo Yu Yubing Han You Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Haikan New Media Research Institute Co. Ltd.

Surface electromyography (sEMG) signal is a physiological electrical signal produced by muscle contraction. Different gestures can be effectively recognized from the characteristics of the sEMG signal. Currently, convolutional neural networks (CNNs) have been widely used in sEMG gesture recognition systems due to their capabilities in acquiring spatial features of sEMG signals. However, these classical CNNs are inefficient in extracting temporal correlation that resides in the time serials of sEMG signals, which is definitely important for gesture recognition. To overcome such a drawback of traditional CNN-based gesture recognition methods, we propose a multichannel hybrid deep learning model for gesture recognition by combining the multichannel CNNs with a gated recurrent unit (GRU). Specifically, we use multiple CNNs to preprocess the original multichannel EMG signals in a one-by-one manner to obtain the spatial features in the current observing window. The outputs of the multiple CNNs are concatenated and fed to a temporal-feature extracting module, which is designed by cascading a GRU with an attention mechanism. Through the GRU, the temporal features of successive signal frames can be established, while the attention mechanism is introduced to further focus on the key information in recognizing the gestures, which is beneficial to improve the robustness and accuracy of the model. Experiments show that the recognition accuracy of the proposed method reaches 97.6% and 96.7% on the Ninapro DB2 and Ninapro DB5 datasets, respectively. Compared with the classical CNN method, the performance improvement is 2.9% and xx% higher than that of the traditona CNN model, respectively.

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A Hybrid Fault Diagnosis Method for Rolling Bearing Based on Attention and CNN

A Hybrid Fault Diagnosis Method for Rolling Bearing Based on...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Songtao Wang Qianlong Liu Jun Li Kun Liang Hui Cai Hangwei Tian Shandong Hi-Speed Construction Management Group Co. Ltd. Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. Jinan China Shandong Hi-Speed Group Linteng Highway Co. Ltd. Jinan China School of Qilu Transportation Shandong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

In the field of mechanical manufacturing, rolling bearings are important core components. To achieve stable rotational operation, improving diagnostic accuracy has become an urgent issue. In this paper, a hybrid fault diagnosis method is proposed, which combines attention and convolutional neural network and introduces Savitzky-Golay filter, called Savitzky-Golay Convolutional Attention network (SGCA-Net). The method adopts a dual-branch structure for rolling bearing fault diagnosis, which can effectively eliminate noise, and combines attention mechanism and convolutional neural network to perform feature learning on data. The experimental results show that the proposed method can accurately identify various faults, and has high accuracy and robustness.

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A Lithium-Ion Battery Health State Assessment and Remaining Useful Life Prediction Method Based on Multi Slope Features

SSRN

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SSRN 2024年

作者： Chen, Gaige Zhong, Siqi Yang, Yahong Zhang, Xiaoshen Deng, Wei Liu, Jinsong School of Communications and Information Engineering School of Artificial Intelligence Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Union Research Center of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Shaanxi Xi’an710121 China State Key Laboratory of Space Power Sources Shanghai Institute of Space Power-sources Shanghai200245 China Xi'an Aviation Brake Technology Co. Ltd Shaanxi Xi’an710065 China Contemporary Amperex Technology Co. Ltd. Fujian Ningde352107 China

In the field of battery management, accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to guarding battery and device safety. However, current prediction methods are mainly based on battery voltage, current, temperature and other data, and there is a challenge problem with neglecting the intrinsic connection between these data. To address this problem, a lithium-ion battery health state assessment and remaining useful life prediction method based on multi slope features of discharge curves is proposed. First, new slope features are proposed based on the lithium-ion battery discharge mechanism;second, slope features are utilized to construct health indicator (HI) and estimate state of health (SOH);then, unsupervised clustering of unlabeled state of health data is performed to construct a health state assessment model;and finally, the remaining useful life prediction model is constructed based on grey wolf optimizer and gated recurrent unit (GWO-GRU). The proposed method is validated using the aging data of lithium-ion batteries released by NASA Ames Research Center, and the dominant is emphasized by comparison with other important methods. The results show that the method can effectively assess the health state of lithium-ion batteries and accurately predict their remaining useful life, which can provide support for the realization of lithium-ion battery health management and intelligent operation and maintenance. © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Segment Anything Model is a Good Teacher for Local Feature Learning

arXiv

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arXiv 2023年

作者： Wu, Jingqian Xu, Rongtao Wood-Doughty, Zach Wang, Changwei Xu, Shibiao Lam, Edmund Y. The University of Hong Kong Pokfulam Hong Kong The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China Northwestern University EvanstonIL60201 United States The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in any scene and any downstream task. Data-driven local feature learning methods need to rely on pixel-level correspondence for training. However, a vast number of existing approaches ignored the semantic information on which humans rely to describe image pixels. In addition, it is not feasible to enhance generic scene keypoints detection and description simply by using traditional common semantic segmentation models because they can only recognize a limited number of coarse-grained object classes. In this paper, we propose SAMFeat to introduce SAM (segment anything model), a foundation model trained on 11 million images, as a teacher to guide local feature learning. SAMFeat learns additional semantic information brought by SAM and thus is inspired by higher performance even with limited training samples. To do so, first, we construct an auxiliary task of Attention-weighted Semantic Relation Distillation (ASRD), which adaptively distillates feature relations with category-agnostic semantic information learned by the SAM encoder into a local feature learning network, to improve local feature description using semantic discrimination. Second, we develop a technique called Weakly Supervised Contrastive Learning Based on Semantic Grouping (WSC), which utilizes semantic groupings derived from SAM as weakly supervised signals, to optimize the metric space of local descriptors. Third, we design an Edge Attention Guidance (EAG) to further improve the accuracy of local feature detection and description by prompting the network to pay more attention to the edge region guided by SAM. SAMFeat’s performance on various tasks such as image matching on HPatches, and long-term visual localization on Aachen Day-Night showcases its superiority over previous local features. The release code is available at https://***/vignywang/SAMFeat

关键词： Computer vision

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Fusion and Localization Technology of 3D Model Attitude and Real Physical Environment Visualization information of power Inspection Site 1

引用

Annual International Conference on 3D Imaging Technology, IC3DIT 2019

作者： Min, Xu Hai, Yu Zhansheng, Hou Nige, Li Xingchuan, Bao Lin, Peng Gang, Wang Liang, Zhu Hao, Lu Zhifeng, Wang Peng, Zhao Global Energy Interconnection Research Institute Nanjing Jiangsu210003 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China State Grid Shanxi Electric Power Information & Communication Company Shanxi Xian710048 China

ISBN: (数字)9789811538674

ISBN: (纸本)9789811538667

In this paper, the closed loop detection technology of depth vision scene recognition is adopted to overcome the problem that the visual odometer cannot initially locate, and the disadvantage that the location and azimuth can only be estimated publicly, which is easy to accumulate positioning errors. Through closed-loop detection, the scene map collected at present is matched with the scene in the atlas in real time, and the binocular vision attitude adjustment algorithm is assisted to obtain the topological determination with high accuracy. In this way, the navigation and positioning accuracy based on depth vision sensor in the indoor environment of the substation is greatly improved, and the accurate positioning of substation personnel is realized. Thus, the dangerous area is delimited, and the safety control of personnel on-site operation station is realized, which greatly improves personal safety. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Personnel

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Research on network Intrusion Detection Technology Based on DCGAN

Research on Network Intrusion Detection Technology Based on ...

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IEEE Advanced information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Wang Chao Wang Wenhui Dong Jiahan Guo Guangxin State Grid Beijing Electric Power Company Beijing China Global Energy Interconnection Research Institute Beijing China State Grid Key Laboratory of Information & Network Security Beijing China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Traditional network intrusion detection algorithms tend to lack learning in a small number of classes due to data imbalance. In reality, intrusion detection systems pay more attention to the detection accuracy of a small number of classes, that is, attack samples. In order to improve the detection accuracy of intrusion detection system, a network intrusion detection method based on deep convolution generative adversarial networks (DCGAN) was proposed. Firstly, the sample data of network intrusion is preprocessed, and the character data set is replaced by image data. Then, DCGAN is used to train and test the sample data. Both the generator and the discriminator are constructed by CNN. The generator is used to construct attack samples, balance the number of training samples, and solve the over fitting problem caused by insufficient training samples. Finally, the trained discriminator is used to test the classification accuracy of samples. Experimental results show that, compared with the traditional algorithm, the proposed algorithm can not only balance the detection accuracy of various types of samples, but also has higher detection accuracy for attack samples.

关键词： Training Fitting network intrusion detection Generative adversarial networks Generators Classification algorithms information technology

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Motion compensation and object detection for neuromorphic camera

Motion compensation and object detection for neuromorphic ca...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wan, Yuxin Fei, Rong Tang, Yu Bai, Xueru Xie, Guo Li, Aimin Xi'An University of Technology School of Computer Science and Engineering Xi'an China Xi'An University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an China Xidian University Guangzhou Institute Guangzhou China Xi'An University of Technology School of Computer Science and Engineering Xi'an China Xi'An University of Technology School of Automation and Information Engineering Xi'an China

ISBN: (纸本)9781665401265

Compared to conventional cameras, the new type of vision camera-neuromorphic cameras, which can avoid motion blur and have the advantages of high spatiotemporal resolution, high dynamic range, low latency, etc. In this paper, two motion compensation methods are introduced for operating event flow based on differential neuromorphic camera, one is motion compensation based on event counting image and time image, another is motion compensation based on image of warped events. Comprehensive experimental study is carried on two datasets, the motion compensation method based on event counting image and time image is better at generating clear motion compensation images, highlighting moving objects inconsistent with the background motion, while the motion compensation method based on image of warped events tends to generate motion compensated images of moving objects with more prominent or clearer edges. Simultaneously, two motion compensation methods are further applied to realize the object detection method based on threshold or maximum contrast. The former method can be adapted in many cases, while the latter method can well detect the translational moving target. © 2021 IEEE.

关键词： Object detection

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network Topology Overlapping Group Detection Based on h-Core Pruning

Network Topology Overlapping Group Detection Based on h-Core...

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Qi Zhang Yan Liu Yuheng Cai Zhong Yuan Network Security Research Institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781665447300

The network group detection has a large number of application researches and has achieved certain results in network security, IP entity location and load balancing. However, due to the large scale of the network topology graph and the large number of terminal nodes, most of the existing overlapping group detection algorithms are relatively complex, and the data size that can be processed is limited, and cannot be directly applied to the network topology. This paper proposes a network topology overlapping group detection algorithm based on h-core pruning. This algorithm decomposes the network into smaller strong connected components, reduces the interference of noisy nodes to overlapping group discovery, and more efficiently detect the largest k-plex group on the target network. A large number of experiments show that the method in this paper is effective. Compared with the full-enum algorithm, our method increases the network size that can be handled by the largest k-plex group by two orders of magnitude, and can detect overlapping groups in the network topology more efficiently.

关键词： network topology Signal processing algorithms Interference Signal processing Load management Topology Noise measurement

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Research on data security sharing mechanism of power internet of things based on blockchain 9

Research on data security sharing mechanism of power interne...

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9th IEEE Joint International information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Yang, Huiting Bai, Yunxiao Zou, Zhenwan Zhang, Qiang Wang, Bin Yang, Ruxia State Grid Xinjiang Electric Power Research Institute Urumqi China Global Energy Interconnection Research Institute Co. Ltd Nanjing China State Grid Key Laboratory of Information and Network Security Nanjing China

The rapid growth of power Internet of Things devices has led to traditional data security sharing mechanisms that are no longer suitable for attribute and permission management of massive devices. In response to this problem, this article proposes a blockchain-based data security sharing mechanism for the power Internet of Things, which reduces the risk of data leakage through decentralization in the architecture and promotes the integration of multiple information and methods. © 2020 IEEE.

关键词： Internet of things

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