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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Wang, Tao Yang, Haiyang Tan, Zhiyong Yu, Yao University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes School of Automation and Electrical Engineering Ministry of Education University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781665465366

In deep reinforcement learning, reinforcement learning is responsible for interacting with the environment to produce data, and artificial neural networks are responsible for value function fitting. It is observed that artificial neural networks converged differently to different inputs, which, in our analysis, is due to imbalanced data. Therefore, we propose selective data collection to boost the quality of the data by then discarding the excess data. It has been proved experimentally that our method can significantly contribute to the convergence rate of the reinforcement learning algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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Industrial Internet Cloud Platform System Based on EMQX 17th

Industrial Internet Cloud Platform System Based on EMQX

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Gao, Shanzhao Shi, Zhongsuo School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789811663277

With the rapid development of industry and information technology, the construction of Industry 4.0 in China is also gradually promoted. In the process of advancing Industry 4.0, the combination of industry, Internet and information technology will be a general trend. This paper designs the industrial Internet cloud platform system based on EMQX. The EMQX messaging server uses the MQTT communication protocol to connect devices on the industrial site to the Internet. By accessing the cloud platform, users can control the on-site devices remotely. The cloud platform system designed in this paper realizes the combination of industry and the Internet. The cloud platform system runs stably and meets the needs of users. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Industry 4.0

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Sample-Efficient Deep Reinforcement Learning via Balance Sample 37

Sample-Efficient Deep Reinforcement Learning via Balance Sam...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Yang, Haiyang Wang, Tao Tan, Zhiyong Yu, Yao University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781665465366

In this paper, we propose two algorithms to improve sample efficiency by focusing on late stage samples in episodes. The first algorithm is Balanced Sample Experience Replay (BSER). Unlike the traditional random sampling approach, this algorithm improves the final score and stability in environment by learning more late stage experience in the corresponding episode. The second algorithm is weight-corrected DQN (WCDQN). This algorithm differs from the traditional undifferentiated update approach by differentially updating the samples used for training to improve the final score and stability in environment. We tested both algorithms on a classic Atari game environment and demonstrated the effectiveness of the algorithms. © 2022 IEEE.

关键词： Efficiency

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A Multi-scale Contextual Attention Mechanism for Convolutional Neural Networks 37

A Multi-scale Contextual Attention Mechanism for Convolution...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Xie, Yun Cao, Chanting Liao, Mingchao Yu, Yao University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781665465366

In recent years, attention mechanism has been widely studied in the field of computer vision, which can effectively improve the performance of visual tasks. In the past, many classical attention models have studied the modeling of nonlinear relationships in the spatial or channel dimensions of feature maps, ignoring the use of contextual relationships to capture the information interaction of the three dimensions to obtain a global attention feature map. In this paper, we investigate an effective multi-scale contextual attention mechanism, which can obtain feature information of different receptive fields through the combination of multi-branch conventional convolution and dilated convolution, which can increase the image receptive field, and combine global features and detailed features to effectively use contextual information. In addition, since the input tensors interact with each other on the three dimensions of the feature map and was adjusted by an adaptive parameter, this also makes the three-dimensional attention weights we obtain more differentiated. Our MCA model is simple and can be flexibly embedded in a variety of classical backbone networks, and experimental evaluation of the proposed attention mechanism on common datasets for image classification and object detection also proves the effectiveness of our attention meachine. © 2022 IEEE.

关键词： Convolution

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Research on Multi-AGVs dynamic scheduling based on deep reinforcement learning 19

Research on Multi-AGVs dynamic scheduling based on deep rein...

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19th IEEE International Conference on e-Business engineering, ICEBE 2023

作者： Qin, Zhuorui Wu, Xiaoqian Zheng, Yimin Wu, Qingyao South China University of Technology School of Software Engineering Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Shenzhen Bangqi Technology Intelligent Development Co. Ltd Shenzhen China Pazhou Lab Guangzhou China

ISBN: (纸本)9798350325553

Driven by the latest advances in industrial artificial intelligence, automatic guided vehicles (AGVs) have been widely used for material handling in flexible workshops. In the pursuit of agile and flexible production transportation planning, the ability to handle unpredictable and dynamic events is becoming increasingly important. In order to achieve this real-time dynamic control, this study proposes a distributed multi-agent architecture to solve the dynamic Multi-AGVs scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agent, capture the relationship between production transportation operation information and scheduling goals, and make real-time scheduling decisions for flexible jobs with constant arrival intervals. Specialized state, action, and reward representations are proposed to handle variable specifications of dynamic scheduling problems. The performance of this method under different scene scales was verified through simulation studies. Numerical results show that compared with existing traditional heuristic scheduling strategies, this method not only has superior performance, but also its advantages persist even if the manufacturing system job scale changes. © 2023 IEEE.

关键词： Reinforcement learning

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Adaptive Video Watermarking with Perceptual Guarantee and Efficiency Optimization

Adaptive Video Watermarking with Perceptual Guarantee and Ef...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fei Zhang Hongxia Wang Mingze He Ling Yang Jinhe Li School of Cyber Science and Engineering Sichuan University Chengdu China Key Laboratory of Data Protection and Intelligent Management (Sichuan University) Ministry of Education China

Existing video watermarking embeds robust watermarks in each frame of the video for copyright protection and tracking. However, just as any content written on a blank paper is easily perceived, embedding watermarks in the texture-poor frames impairs imperceptibility. Common geometric attacks such as scaling and rotation pose a significant challenge to the existing video watermarking. Image watermarking based on moments is robust against geometric attacks. However, moment-based watermarking is difficult to migrate to the video due to its lack of perceptual guarantee and high computational cost. In this paper, we propose an adaptive video watermarking scheme by exploring the relationship between moments and video textures, which can adaptively select texture-rich frames to embed watermarks for perceptual guarantee. Furthermore, we utilize the properties of moment calculation in videos to optimize efficiency. Extensive experiments show that the proposed method can achieve better imperceptibility than existing methods while maintaining strong robustness.

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RPA-SCD: Rhythm and Pitch Aware Dual-Branch Network for Songs Conversion Detection

RPA-SCD: Rhythm and Pitch Aware Dual-Branch Network for Song...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mingshan Du Hongxia Wang Rui Zhang Zihan Yan School of Cyber Science and Engineering Sichuan University Chengdu China Key Laboratory of Data Protection and Intelligent Management (Sichuan University) Ministry of Education China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Song voice conversion tools have gained more and more popularity in the recent past. People have been uploading their self-made forgery songs on video websites, and these songs have been converted in timbre. However, singing voice conversion technology may cause copyright infringement of the songs. In order to protect the copyright of songs, the method of singing voice conversion detection needs to be investigated. We propose Rhythm and Pitch Aware Songs Conversion Detection (RPA-SCD), a dual-branch network for song voice conversion detection. RPA-SCD can predict forged song fragments through rhythm and pitch which are the global and local information of music. To evaluate the proposed method, we contribute a multilingual song conversion detection(MSCD) dataset. Our proposed model achieves the EER of 2.30% in the original domain of MSCD, which is lower than other benchmarks for speech forgery detection. The experiments show that our approach achieves state-of-the-art performance on the song conversion detection task. The MSCD dataset can be found at https://***/file/d/1rFsvMYihVtk81uFbL7UpyUEs-qBgsX6H/view?usp=drive_link. The code can be found at https://***/Samantha-Du/RPA-SDD.

关键词： Codes Neural networks Speech enhancement Copyright protection Benchmark testing Rhythm Forgery

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Enhanced Screen Shooting Resilient Document Watermarking

Enhanced Screen Shooting Resilient Document Watermarking

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Heng Wang Hongxia Wang Xinyi Huang Zhenhao Shi School of Cyber Science and Engineering Sichuan University Chengdu China Ministry of Education Key Laboratory of Data Protection and Intelligent Management (Sichuan University) China

The widespread adoption of smartphones has introduced new challenges to document copyright protection, prompting the emergence of Screen-Shooting Resilient Document Watermarking (SSRDW) technology. In recent years, underpainting-based SSRDW techniques have proven to be highly effective. However, after careful study, we find that existing methods fail to simultaneously meet four essential criteria for SSRDW: high imperceptibility, strong robustness, adaptability to text processing, and high efficiency. In this paper, we introduce an enhanced underpainting-based SSRDW approach capable of satisfying all four requirements. Our approach enhances imperceptibility by employing underpainting embedding methods independent of text content. Additionally, we introduce a fast resynchronization mechanism to improve time efficiency. Furthermore, we propose an enhanced watermark extraction method that enhances robustness and enables watermark retrieval even in scenarios involving text processing. Extensive experimental validation underscores the superior performance of our enhanced SSRDW method.

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Improving multi-view document clustering: leveraging multi-structure processor and hybrid ensemble clustering module 30

Improving multi-view document clustering: leveraging multi-s...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bai, Ruina Bai, Qi Huang, Ruizhang Chen, Yanping Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center National Education Ministry College of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Mathematical Sciences Zhejiang University Hangzhou310058 China

ISBN: (纸本)9782493814104

We introduce a multi-view document clustering model called DMsECN (Deep Multi-structure Ensemble Clustering Network), comprising a multi-structure processor and a hybrid ensemble clustering module. Unlike existing models, DMsECN distinguishes itself by creating a consensus structure from multiple clustering structures. The multi-structure processor comprises two stages, each contributing to the extraction of clustering structures that preserve both consistency and complementarity across multiple views. Representation learning extracts both view and view-fused representations from multi-views through the use of contrastive learning. Subsequently, multi-structure learning employs distinct view clustering guidance to generate the corresponding clustering structures. The hybrid ensemble clustering module merges two ensemble methods to amalgamate multiple structures, producing a consensus structure that guarantees both the separability and compactness of clusters within the clustering results. The attention-based ensemble primarily concentrates on learning the contribution weights of diverse clustering structures, while the similarity-based ensemble employs cluster assignment similarity and cluster classification dissimilarity to guide the refinement of the consensus structure. Experimental results demonstrate that DMsECN outperforms other models, achieving new state-of-the-art results on four multi-view document clustering datasets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Structure (composition)

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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

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Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

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