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Chinese Automation Congress (CAC)

作者： Qing Zhang Tingyang Jiao Yinxing Ma Youmin Zhang Xiaohui Wu Pengyue Wu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China The Faculty of Computing Science The University of Alberta Edmonton Alberta Canada Concordia University Montreal Canada Huaneng Weihai Power Generation Co. Ltd Wei’hai China Xi’an Thermal Power Research Institute Co. Ltd Xi’an China

ISBN: (纸本)9781665426480

In order to improve the prediction accuracy of the carbon brush temperature trend of the generator, analyzing the operating state of the generator more accurately, and reduce other accidents like the unplanned outage caused by carbon brush failure of the generator in the power plant. A multi-step temperature prediction model based on the LSTM (Long Short-Term Memory) neural network is proposed. The data comes from real operating carbon brush temperature of Weihai Power Plant in ten days. Then the temperature prediction model is established to achieve an accurate prediction of the carbon brush temperature in the future day. The prediction error is stable within 0.4 °C. By comparing the predicted results of the BP model and Elman model, the error between the predicted results of each model and the actual temperature data is analyzed. By comparing the indicators and analyzing the actual curves, the results show that LSTM neural network has higher accuracy in predicting the temperature of generator carbon brush. This method is of reference significance for the accurate analysis of the operation state of the generator and the load analysis of the excitation system.

关键词： Analytical models Temperature distribution Brushes Neural networks Predictive models Market research Generators

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Molecular model for information addressing and transporting based on DNA nanotechnology

Communications in Computer and Information Science

引用

Communications in Computer and information Science 2014年 472卷 29-34页

作者： Chen, Zhihua Qiang, Xiaoli Zhang, Kai Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology WuhanHubei430074 China College of Computer Science South-Central University for Nationalities WuhanHubei430074 China School of Computer Science Wuhan University of Science and Technology WuhanHubei430081 China

ISBN: (纸本)9783662450482

DNA-based information processing includes storage, addressing, transporting and error-correction, etc. Here we describe a feasible model that can extract and transport the information from specified sources to targeted destinations. This model is established based on the DNA nanotechnologies of hybridization, DNA strand displacement and nanoparticle aggregation reactions. By adding the extracting strands, the data strands of specified sources can be extracted, and then transported to the targets by the transporting strands. The result could be detected by observing the change of color through the naked eyes. Theoretical analysis indicates that thismodel could achieve the parallel information extracting and transporting from different specified sources to different targeted destination, which offers a realistic technology for the flexibility of managing information. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Error correction

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Use Knowledge to Augment Data from Scratch: An Explainable Deep Immunohistochemical Diagnosis Model

SSRN

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SSRN 2024年

作者： Zhao, Jing Liu, Yuhan Zhang, Xinzhu Sun, Shiliang Yin, Minzhi School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Pathology Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai200127 China

Pathological diagnosis has an important role as the last step in cancer diagnosis. When the naked eye is not enough to identify cancer, we usually use further immunohistochemical diagnosis, which is the detection of specific proteins to obtain protein sequences and their negative/positive expression, and make the final diagnosis based on the sequences. This process is a classification task for protein detection sequences. However, when deep learning goes into this field, it often has the problem of lack of data and poor explainability. Based on the above description, to solve the problem of small sample classification, we propose to extract knowledge from scratch, build protein knowledge graphs, and reuse protein embeddings from the knowledge graph to improve classification performance. In addition, we give explanation for the diagnostic model by visualizing importance and coupling degree of proteins. In our experiments, we find that our proposed classification model has good classification performance and explainability. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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Chinese control Conference

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai China The Center for Bioinformatics and Computational Biology East China Normal University Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and high-dimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment decision. However, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model (SAE-SVM), for breast cancer diagnosis. The stacked auto-encoders with the greedy layer-wise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Breast cancer Feature extraction Machine learning Support vector machines Electronic mail Principal component analysis

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FBG-Based Triaxial Force Sensor Integrated with an Eccentrically Configured Imaging Probe for Endoluminal Optical Biopsy

arXiv

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arXiv 2020年

作者： Wu, Zicong Gao, Anzhu Liu, Ning Jin, Zhu Yang, Guang-Zhong Hamlyn Centre for Robotic Surgery Imperial College London LondonSW7 2AZ United Kingdom Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Accurate force sensing is important for endoluminal intervention in terms of both safety and lesion targeting. This paper develops an FBG-based force sensor for robotic bronchoscopy by configuring three FBG sensors at the lateral side of a conical substrate. It allows a large and eccentric inner lumen for the interventional instrument, enabling a flexible imaging probe inside to perform optical biopsy. The force sensor is embodied with a laser-profiled continuum robot and thermo drift is fully compensated by three temperature sensors integrated on the circumference surface of the sensor substrate. Different decoupling approaches are investigated, and nonlinear decoupling is adopted based on the cross-validation SVM and a Gaussian kernel function, achieving an accuracy of 10.58 mN, 14.57 mN and 26.32 mN along X, Y and Z axis, respectively. The tissue test is also investigated to further demonstrate the feasibility of the developed triaxial force sensor. Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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Dynamic Sparse Coding-Based Value Estimation Network for Deep Reinforcement Learning

SSRN

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SSRN 2023年

作者： Zhao, Haoli Li, Zhenni Su, Wensheng Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology GDUT Guangzhou510006 China Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing GDUT Ministry of Education Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT GDUT Ministry of Education Guangzhou510006 China

Deep Reinforcement Learning (DRL) is one powerful tool for varied control automation problems. Performances of DRL highly depend on the accuracy of value estimation for states from environments. However, the Value Estimation Network (VEN) in DRL can be easily influenced by the phenomenon of catastrophic interference from environments and training, and moreover, the efficiency of DRL is restricted by the training with redundant parameters. In this paper, we propose a Dynamic Sparse Coding-based (DSC) DRL model to obtain precise sparse representations and sparse parameters in VEN through training, i.e., DSC-VEN, to improve the control performance, which is not only applicable in Q-learning but also in actor-critic structured DRL. In detail, to alleviate interference in VEN, we propose to employ dynamic sparse coding to learn sparse representations for accurate value estimation with a dynamic sparsity regularizer. To avoid influences from redundant parameters, we employ dynamic sparse coding to prune weights with dynamic thresholds. Therefore, we develop a dynamic sparse coding-based VEN algorithm, which can improve control performances not only for Q-learning structured discrete-action DRL but also actor-critic structured continuous-action DRL. We have validated the proposed DSC-VEN in both discrete and continuous action benchmark environments. Experiments demonstrate that the proposed algorithms with dynamic sparse coding can obtain higher control performances than existing benchmark DRL algorithms in both discrete-action and continuous-action environments, e.g., over 25% increase in Puddle World and about 10% increase in Hopper. Moreover, the proposed algorithm can reach convergence efficiently with fewer episodes in different environments. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models

arXiv

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arXiv 2023年

作者： Qi, Xiyu Wu, Yifan Mao, Yongqiang Zhang, Wenhui Zhang, Yidan Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China

The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B). Recognizing SAM's dependency on manual guidance given its category-agnostic nature, we identified unexplored potential within few-shot semantic segmentation tasks for remote sensing imagery. This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided mask to produce coarse pixel-wise prompts for SAM. Extensive experiments on the DLRSD datasets underlines the superiority of our approach, outperforming other available few-shot methodologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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SA-UCBSS: Sparsity-Based Adaptive Underdetermined Convolutive Blind Source Separation

SSRN

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SSRN 2023年

作者： Xie, Yuan Zou, Tao Yang, Junjie Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangzhou Key Laboratory of Integrated Automation of Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation is the process of separating unknown source signals only based on received mixing signals when the source signal cannot be accurately known. Traditional methods are mainly based on linear approximate transformation model, which often have large systematic errors in high reverberation environment, resulting in poor separation performance. To avoid such limitation, a sparsity-based adaptive underdetermined convolutive blind source sepparation (SA-UCBSS) algorithm is proposed by unifying a convolutive approximate transformation model and an Lp-norm regularization penalty. A crosstalk canceller is designed for room impulse response pretreatment with p-norm optimization to eliminate audible echoes, and a sparse optimization scheme is designed for the parameter initialization to make the iterative recovery process converge faster. Then, a convolutive approximate transformation model is built to achieve better accurate estimation of mixing system. SA-UCBSS algorithm is proposed to achieve better source separation by adaptively tuning the value of parameter p. Experimental results including BSS of synthesized and live-recorded speech convolutive mixtures show that SA-UCBSS algorithm has superior separating property and better robustness compared with several popular algorithms. © 2023, The Authors. All rights reserved.

关键词： Blind source separation

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Robust fault tolerant controller design using indirect adaptive sliding mode control strategy

Robust fault tolerant controller design using indirect adapt...

引用

IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Hui Li Li-Ying Hao Xiang-Gui Guo Dalian Maritime University Dalian Liaoning CN Information Science and Technology College Dalian Maritime University Dalian Liaoning China The College of Information Engineering Dalian Ocean University Dalian Liaoning China Tianjin Key Laboratory for Control Theory & Applications in Complicated System Tianjin Univnrsity of Technology Tianjin China

ISBN: (纸本)9781479987313

In this paper, indirect adaptive sliding mode control schemes are developed to solve the robust fault tolerant control (FTC) design problem of actuator fault and perturbation compensations for linear time-invariant systems. While both eventual faults on actuators and perturbations are unknown, the adaptive schemes are addressed to estimate the upper bounds of perturbations online, as well as to estimate control effectiveness on actuators. Thus, on the basis of the information from adaptive schemes, an adaptive sliding mode controller is designed to compensate the effects of faults and perturbations automatically. According to Lyapunov stability theory, it is shown that both the reachability of sliding surfaces and the stability of sliding mode dynamics can be ensured even in the presence of actuator faults and disturbances. Moreover, the adaptive sliding mode control can reduce the phenomenon of chattering. An example is provided to further illustrate the fault compensation effectiveness.

关键词： Actuators Fault tolerance Fault tolerant systems Sliding mode control Upper bound Adaptive systems Uncertainty

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The 2022 report of synergetic roadmap on carbon neutrality and clean air for China:Accelerating transition in key sectors

引用

Environmental Science and Ecotechnology 2024年第3期19卷 4-18页

作者： Yu Lei Zhicong Yin Xi Lu Qiang Zhang Jicheng Gong Bofeng Cai Cilan Cai Qimin Chai Huopo Chen Renjie Chen Shi Chen Wenhui Chen Jing Cheng Xiyuan Chi Hancheng Dai Xiangzhao Feng Guannan Geng Jianlin Hu Shan Hu Cunrui Huang Tiantian Li Wei Li Xiaomei Li Jun Liu Xin Liu Zhu Liu Jinghui Ma Yue Qin Dan Tong Xuhui Wang Xuying Wang Rui Wu Qingyang Xiao Yang Xie Xiaolong Xu Tao Xue Haipeng Yu Da Zhang Ning Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Xin Zhang Zengkai Zhang Bo Zheng Yixuan Zheng Jian Zhou Tong Zhu Jinnan Wang Kebin He Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100041China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100041China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China National Center for Climate Change Strategy and International CooperationBeijing100035China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of HealthFudan UniversityShanghai200032China School of Economics and Management Beijing University of Chemical TechnologyBeijing100029China Department of Earth System Science University of CaliforniaIrvineCA92697USA National Meteorological Center China Meteorological AdministrationBeijing100081China College of Environmental Sciences and Engineering Peking UniversityBeijing100871China Policy Research Center for Environment and Economy Ministry of Ecology and Environment of the People's Republic of ChinaBeijing100029China Jiangsu Key Laboratory of Atmospheric Environment Monitoring

China is now confronting the intertwined challenges of air pollution and climate *** the high synergies between air pollution abatement and climate change mitigation,the Chinese government is actively promoting synergetic control of these two *** Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key *** Synergetic Roadmap 2022 report is the first annual update,featuring 20 indicators across five aspects:synergetic governance system and practices,progress in structural transition,air pollution and associated weather-climate interactions,sources,sinks,and mitigation pathway of atmospheric composition,and health impacts and benefits of coordinated *** to the comprehensive review presented in the 2021 report,the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant *** milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time,a decline in the production of crude steel and cement after years of growth,and the surging penetration of electric ***,in 2022,China issued the first national policy that synergizes abatements of pollution and carbon emissions,marking a new era for China's pollution-carbon *** changes highlight China's efforts to reshape its energy,economic,and transportation structures to meet the demand for synergetic control and sustainable ***,the country has witnessed a slowdown in carbon emission growth,improved air quality,and increased health benefits in recent years.

关键词： Synergetic roadmap Carbon neutrality Clean air

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