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Chinese control and Decision Conference, CCDC

作者： Jiexi Liu Chenyu Wang Lei Xu Beijing Institute of Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Beijing China

ISBN: (纸本)9781665478977

Target Detection is one of the most important tasks in Computer Vision, which has broad application prospects in many scenes. For the past few years, great progress has been made in this field with the rapid development of deep learning technology. However, it’s still a big challenge at complex scenarios such as dim environment for traditional single-modal visible images. To address this problem, researchers introduce thermal images as additional modal in consideration of that thermal cameras are less susceptible to interference and explore how to fuse the two modalities information effectively. Nevertheless, the lack of large labeled and high-quality visible-thermal datasets hampers the usage of convolutional neural networks for detection. Therefore, we propose to use image-to-image translation model combined with a differentiable data augmentation method to generate fake thermal images from labeled visible images and use multi-modal target detection model to prove the validity of the method. Our experiment results show that our method can provide us a large labeled dataset of synthetic visible-thermal image pairs with better generalization and the introduction of thermal modality can obtain a better performance than single modality.

关键词： Deep learning Computer vision Fuses Object detection Interference Detectors Data models

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Time series generator adversarial network with stochastic process for the degradation generation and prediction

Time series generator adversarial network with stochastic pr...

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Data Driven control and Learning systems (DDCLS)

作者： Anqi Shangguan Nan Feng Lingxia Mu Rong Fei Xinhong Hei Guo Xie Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China School of computer science and Engineering Xi'an University of Technology Xi'an China

Since the cumbersome collection process and high cost, the collected degradation of the product is basically small samples, which will affect the accuracy of reliability evaluation. It is necessary to expand the degradation to improve the accuracy of later reliability assessment. Therefore, a degradation generation and prediction method is proposed combining the time series generator adversarial network (TimeGAN) and stochastic process. Firstly, the input degradation is expanded by the sliding window to improve the later training accuracy; Then, the construction of the generator in TimeGAN is linked with the stochastic process to make the generation data more realistic. Finally, the results of degradation prediction by the Gated Recurrent Unit (GRU) can be obtained. Two datasets and different generation methods are adopted to evaluate the effectiveness of the proposed method. The results shows that the Kullback-Leibler(KL) divergence is the smallest, and the prediction error is the smallest compared with the other methods. So, the proposed method is proved that it is valid in the degradation generation and prediction, and can be used for the further reliability assessment of the product in the industrial system.

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Construction and verification of a power simulation system middle platform based on domain driven design and microservices

Construction and verification of a power simulation system m...

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2023 International Conference on Artificial Intelligence, systems and Network Security, AISNS 2023

作者： Guo, Qi Zhang, Jie Lu, Yuanhong Tu, Liang Guo, Haiping Luo, Hui Huang, Libin CSG Electric Power Research Institute Co. Ltd State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou510663 China National Energy Power Grid Technology RandD Centre Guangzhou510663 China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou510663 China

ISBN: (纸本)9798400716966

Aiming at the problems of complex design, low scalability and low reusability of resources in the power simulation system, a middle-platform design scheme based on domain-driven design and microservice data is proposed. Through an in-depth study of domain-driven design theory and the characteristics of microservices, this paper proposes a middle-platform design scheme for the power simulation system based on exploring the structure of the power simulation system and the system domain-driven model, and combining the functional requirements and structural characteristics of the simulation software. Finally, this paper verifies that the middle-platform construction of the power simulation system based on domain-driven design and microservices has the characteristics of high scalability, high maintainability and high reusability of resources through the application and analysis of typical business scenarios of power simulation. © 2023 ACM.

关键词： Reusability

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Dynamic Event-Triggered Consensus on Matrix-weighted Networks:Leadless Case

Dynamic Event-Triggered Consensus on Matrix-weighted Network...

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第42届中国控制会议

作者： Lintao Dou Haoyu Wei Jian Huang Lulu Pan Haibin Shao Dewei Li the College of Intelligent Science National University of Defense Technology the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of Chinaand Shanghai Engineering Research Center of Intelligent Control and Management

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks,where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the ***,a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks,in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold,which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno *** event-triggered control protocols are proposed to guarantee leaderless consensus for multi-agent systems on matrix-weighted networks,***,the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks,generalizing those results only applicable for scalar-weighted *** proposed approach allows each agent to broadcast and receive information only at its triggering ***,simulation examples are provided to demonstrate the theoretical results.

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Multi-Resolution Planar Region Extraction for Uneven Terrains

arXiv

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arXiv 2023年

作者： Sun, Yinghan Zheng, Linfang Chen, Hua Zhang, Wei Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Computer Science University of Birmingham United Kingdom

This paper studies the problem of extracting planar regions in uneven terrains from unordered point cloud measurements. Such a problem is critical in various robotic applications such as robotic perceptive locomotion. While existing approaches have shown promising results in effectively extracting planar regions from the environment, they often suffer from issues such as low computational efficiency or loss of resolution. To address these issues, we propose a multi-resolution planar region extraction strategy in this paper that balances the accuracy in boundaries and computational efficiency. Our method begins with a pointwise classification preprocessing module, which categorizes all sampled points according to their local geometric properties to facilitate multi-resolution segmentation. Subsequently, we arrange the categorized points using an octree, followed by an in-depth analysis of nodes to finish multi-resolution plane segmentation. The efficiency and robustness of the proposed approach are verified via synthetic and real-world experiments, demonstrating our method’s ability to generalize effectively across various uneven terrains while maintaining real-time performance, achieving frame rates exceeding 35 FPS. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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The Algorithm of Sequential Music Recommendation Based on BERT

The Algorithm of Sequential Music Recommendation Based on BE...

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Culture-Oriented Science and technology (CoST), International Conference on

作者： Zhen Li Kaiyue Li Qingqing Yang State Key Laboratory of Media Convergence of Communication Key Laboratory of Acoustic Visual Technology and Intelligent Control System (Communication University of China) Ministry of Culture and Tourism Beijing Key Laboratory of Modern Entertainment Technology Communication University of China Communication University of China Beijing China

Music recommendation algorithms aim to predict the songs that users might be interested in based on various factors, such as their listening history. The best existing method is Bert_Music, a sequential music recommendation model based on Bidirectional Encoder Representation from Transformer (BERT). This model leverages user sequence context information to improve recommendation performance. However, Bert_Music only considers the sequence information of songs and ignores their content characteristics. To address this limitation, we propose an improved version of Bert_Music, which is a sequential music recommendation model based on Bidirectional Encoder Representation from Transformer with tag information (BERT_MUSIC_T), uses song ID (Identity) and tag information to make more accurate recommendations. We compared the performance of Bert_Music_T with that of the Bert_Music model on the *** dataset and found that the Precision has increased by up to 2.85%. These results prove the validity of our method.

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RHNet: A Real-Time High-Resolution Network for Surface Defect Detection

RHNet: A Real-Time High-Resolution Network for Surface Defec...

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第43届中国控制会议

作者： Yijie Wang Zhigang Ren Jianpu Cai Zongze Wu Shengli Xie School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network(RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small ***, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module(SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements.

关键词： Surface defect inspection Anomaly detection Semantic segmentation Deep learning Pattern recognition

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Study on the excitation inrush suppression strategy of phase-selective switching converter with closing resis-tor switch considering dispersion 10

Study on the excitation inrush suppression strategy of phase...

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10th China International Conference on Electricity Distribution, CICED 2022

作者： Liu, Zhijiang Lin, Xuehua Huang, Libin Li, ShuYong State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid. Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9781665452687

ISBN: (纸本)9781665452687

A detailed analysis of the excitation inrush current bias phenomenon in the event is carried out in the context of a certain time when charging of a converter is carried out in an ultra-high voltage DC project of the Southern China Power Grid to obtain the cause of the event, which is confirmed by RTDS tests. By analysing the relationship between the moment of closing of different auxiliary contacts and the intermittent angle of the excitation inrush current, the shortest input time at which the closing resistance can play a role in weakening the excitation inrush current is proposed at different moments of closing. Finally, considering the discrete nature of circuit breaker closing, the proposed closing resistor best access time and the best closing angle, the effectiveness of the proposed strategy is verified by simulation tests. © 2022 IEEE.

关键词： Electric circuit breakers

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Multipath planning based on multimodal optimization slime mold algorithm

Multipath planning based on multimodal optimization slime mo...

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Culture-Oriented Science and technology (CoST), International Conference on

作者： Luli Gao Hui Ren School of Information and Communication Engineering Communication University of China Bejing China Key laboratory of Acoustic Visual Technology and Intelligent Control System(Communication University of China)Ministry of Culture and Tourism Bejing China Beijing Key Laboratory of Modern Entertainment Technology (Communication University of China) Bejing China

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

Multipath planning aims to find multiple optimal paths simultaneously, and its essence is a multimodal optimization problem. In recent years, swarm intelligence (SI) optimization algorithms have provided effective solutions for multimodal optimization. In this paper, the multimodal optimization slime mold algorithm (MOSMA) based on master-slave structure and local search strategy (LS) is proposed to solve multipath planning problems in small cultural complex. Firstly, a local search strategy is introduced to eliminate the influence of the “centerbias” operator and improve the quality of solutions. Secondly, the master-salves structure is introduced to preserve the diversity of the population and enable it to simultaneously locate multiple solutions. Finally, the performance of MOSMA is analyzed on several multimodal functions, and an example of path planning in a small cultural complex is given.

关键词： Location awareness Costs Search problems Main-secondary Path planning Planning Cultural differences

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

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

ISBN: (纸本)9781665468190

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. © 2022 IEEE.

关键词： Embeddings

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