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Information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Jing Yang Xu Yang Zhang-Bing Zhou Zhi-Yong Liu School of Information Engineering China University of Geosciences(Beijing) State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Information Engineering China University of Geosciences(Beijing) TELECOM SudParis Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9781665430302

Point correspondence is an important problem in remote sensing images matching task which lays the foundation for the image registration task. Due to the existence of the rotation transformation and the large scale, and the change of scenery between the two obtained images, the point correspondence problem is still challenging. Tradition algorithms such as the SIFT and SURF aim at using the point appearance to find the point correspondence. However, when the above situations happens, the appearance based algorithms will fail to solve the problem. In this paper, the structural information between points is introduced to the correspondence problem and a spectral graph matching algorithm is proposed based on the probabilistic scheme and the multiplicative updating scheme. The proposed algorithm has three advantages compared with the traditional algorithms. First, incorporating the structural cues to the matching problem makes it more robust to the large scale and the geometric transformation. Second, the best assignment between points which is interpreted by a probabilistic manner is figured out by ranking the probabilities. Third, the optimization problem is solved by an approximate spectral method efficiently and a multiplicative updating algorithm is introduced to ensure the final discrete solution. Experiments on the real remote sensing images validate the effectiveness of the proposed algorithm.

关键词： Image registration Software algorithms Approximation algorithms Probabilistic logic Sensors Internet Task analysis

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Spiking Adaptive Dynamic Programming with Poisson Process 12th

Spiking Adaptive Dynamic Programming with Poisson Process

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12th International Conference on Advances in Swarm Intelligence, ICSI 2021

作者： Wei, Qinglai Han, Liyuan Zhang, Tielin The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Research Center for Brain-Inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783030788100

A new iterative spiking adaptive dynamic programming (SADP) algorithm based on the Poisson process for optimal impulsive control problems is investigated with convergence discussion of the iterative process. For a fixed time interval, a 3-tuple can be computed, and then the iterative value functions and control laws can be obtained. Finally, a simulation example verifies the effectiveness of the developed algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Dynamic programming

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MULTI-VIEWPOINT AND MULTI-EVALUATION WITH FELICITOUS INDUCTIVE BIAS BOOST MACHINE ABSTRACT REASONING ABILITY

arXiv

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arXiv 2022年

作者： Wei, Qinglai Chen, Diancheng Yuan, Beiming State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Great endeavors have been made to study AI's ability in abstract reasoning, along with which different versions of RAVEN's progressive matrices (RPM) are proposed as benchmarks. Previous works give inkling that without sophisticated design or extra meta-data containing semantic information, neural networks may still be indecisive in making decisions regarding the RPM problems, after relentless training. Evidenced by thorough experiments, we show that, neural networks embodied with felicitous inductive bias, intentionally design or serendipitously match, can solve the RPM problems efficiently, without the augment of any extra meta-data. Our work also reveals that multiviewpoint with multi-evaluation is a key learning strategy for successful reasoning. Nevertheless, we also point out the unique role of meta-data by showing that a pre-training model supervised by the meta-data leads to a RPM solver with better performance. Source code can be found in https://***/QinglaiWeiCASIA/RavenSolver. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 617-626页

作者： Teng Liu Bin Tian Yunfeng Ai Fei-Yue Wang Department of Automotive Engineering Chongqing UniversityChongqing 400044 the Vehicle Intelligence Pioneers Inc. Qingdao 266109China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 101408China

As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel *** management strategy(EMS)is playing a key role to improve the energy efficiency of this *** paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two *** high-level establishes a parallel system first,which includes a real powertrain system and an artificial ***,the synthesized data from this parallel system is trained by a bidirectional LSTM *** lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)*** is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined ***,real vehicle testing is implemented and relevant experiment data is collected and *** results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.

关键词： Bidirectional long short-term memory(LSTM)network cyber-physical system(CPS) energy management parallel system reinforcement learning(RL)

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Artificial intelligence in gastric cancer:applications and challenges

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Gastroenterology Report 2022年第1期10卷 227-242页

作者： Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian School of Artificial Intelligence University of Chinese Academy of SciencesBeijingP.R.China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingP.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Radiology DepartmentPeking University Cancer Hospital&InstituteBeijingP.R.China College of Medicine and Biological Information Engineering School Northeastern UniversityShenyangLiaoningP.R.China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingP.R.China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxiP.R.China

Gastric cancer(GC)is one of the most common malignant tumors with high *** diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical ***,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer ***,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data *** are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including ***,we aim to provide a broad framework to summarize current research on AI in *** the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological *** the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype *** treatment decisions,AI can help with surgical margin determination and prognosis ***,current approaches are challenged by data scarcity and poor *** tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

关键词： gastric cancer artificial intelligence radiomics endoscopy computed tomography pathology

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Parallel cognition:hybrid intelligence for human-machine interaction and management

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Frontiers of Information Technology & Electronic Engineering 2022年第12期23卷 1765-1779页

作者： Peijun YE Xiao WANG Wenbo ZHENG Qinglai WEI Fei-Yue WANG State Key Laboratory for Management and Control of Complea Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China QingdaoAcademy of Intelligent Industries Qingdao 266109China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Macao Institute of System Engineering Macao University of Science and TechnologyMacao 999078China

As an interdisciplinary research approach,traditional cognitive science adopts mainly the experiment,induction,modeling,and validation *** models are sometimes not applicable in cyber-physical-socialsystems (CPSSs),where the large number of human users involves severe heterogeneity and *** reduce the decision-making conflicts between people and machines in human-centered systems,we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages:descriptive cognition based on artificial cognitive systems (ACSs),predictive cognition with computational deliberation experiments,and prescriptive cognition via parallel behavioral *** make iteration of these stages constantly on-line,a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual’s cognitive *** experiments on two representative scenarios,urban travel behavioral prescription and cognitive visual reasoning,indicate that our parallel cognition learning is effective and feasible for human behavioral prescription,and can thus facilitate human-machine cooperation in both complex engineering and social systems.

关键词： Cognitive learning Artificial intelligence Behavioral prescription

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Traffic Signal control Using Offline Reinforcement Learning

Traffic Signal Control Using Offline Reinforcement Learning

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Chinese automation Congress (CAC)

作者： Xingyuan Dai Chen Zhao Xiaoshuang Li Xiao Wang Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781665426480

The problem of traffic signal control is essential but remains unsolved. Some researchers use online reinforcement learning, including the off-policy one, to derive an optimal control policy through interaction between agents and environments in simulation. However, it is difficult to deploy the policy in real transportation systems due to the gap between simulated and real traffic data. In this paper, we consider an offline reinforcement learning method to tackle the problem. First, we construct a realistic traffic environment and obtain offline data based on a classic actuated traffic signal controller. Then, we use an offline reinforcement learning algorithm, namely conservative Q-learning, to learn an efficient control policy via offline datasets. We conduct experiments on a typical road intersection and compare the conservative Q-learning policy with the actuated policy and two data-driven policies based on off-policy reinforcement learning and imitation learning. Empirical results indicate that in the offline-learning setting the conservative Q-learning policy performs significantly better than other baselines, including the actuated policy, but the other two data-driven policies perform poorly in test scenarios.

关键词： Q-learning automation Roads Transportation Optimal control Cloning Data models

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Greenhouse Environment control based on Computational Experiments 20

Greenhouse Environment Control based on Computational Experi...

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4th High Performance Computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on Big Data and Artificial Intelligence, BDAI 2020

作者： Weng, Yuchen Wang, Xiujuan Hua, Jing Wang, Haoyu Kang, Mengzhen State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Haidian Beijing China

ISBN: (纸本)9781450375603

Greenhouse environment control is challenging since it is a partially open system being complex, nonlinear and highly uncertain. For such a complex system, empirical knowledge is often not sufficient to control the environment well. This paper proposes an approach for environment control based on computational experiments. The data source is from the second Autonomous Greenhouses International Challenge. Descriptive model is built with lightGBM algorithm for air temperature and CO2 concentration inside greenhouse. Computational experiments were carried out to optimize the ventilation opening, which has double effect of cooling and CO2 concentration decreasing. The aim of optimization is to keep the temperature as close as possible to the set line, while keeping a necessary level of CO2 concentration. The results indicate that the temperature can be better controlled than the original empirical methods for different conditions, while the CO2 concentration is maintained. The proposed approach overcomes the shortage of controlling ventilation with static knowledge, and can be applied to the real greenhouse. © 2020 ACM.

关键词： Greenhouses

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In-Situ 3D Printing Monitoring in Dynamic Environments via Self-Supervised Deep Neural Network Adaptation

In-Situ 3D Printing Monitoring in Dynamic Environments via S...

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Fang Wang Gang Xiong Qihang Fang Zhen Shen Di Wang Li Wan Xisong Dong Fei-Yue Wang 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 Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China Beijing Ten Dimensions Technology Co. Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting and monitoring camera status, can severely impact the effectiveness and robustness of monitoring systems. To tackle this problem in monitoring system, this paper introduces a novel approach for deep neural network (DNN) adaptation to the dynamic environments through self-supervised learning and applies it to during in-situ monitoring. Specifically, we introduce a self-supervised learning strategy that leverages the auxiliary reconstruction task during in-situ monitoring, subsequently applying self-supervised fine-tuning to classification tasks with a new imbalanced-aware classification loss. Our methodology was rigorously evaluated using a real-world dataset for 3D printing defect detection. The experimental outcomes affirm the robustness of our approach, showcasing a higher defect detection accuracy rate than baselines. This substantially mitigates the adverse effects associated with printing defects, thereby increasing the reliability and quality of 3D printing processes.

关键词： Adaptation models Lighting Artificial neural networks Self-supervised learning Three-dimensional printing Feature extraction Robustness Monitoring Image reconstruction Defect detection

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EiHi Net: Out-of-Distribution Generalization Paradigm

arXiv

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arXiv 2022年

作者： Wei, Qinglai Yuan, Beiming Chen, Diancheng State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Systems Engineering Macau University of Science and Technology 999078 China

This paper develops EiHi to solve the out of distribution (O.o.D.) problem in recognition, especially the distribution shift of background. EiHi net is a model learning paradigm that can be blessed on any visual backbone. Different from the traditional deep model, namely find out correlated relationship between sample features and corresponding categories, EiHi net suffers less from spurious correlations. Fusing SimCLR and VIC-Reg via explicit and dynamical establishment of original - positive - negative sample pair as a minimal learning element, EiHi net iteratively establishes a relationship close to the causal one between features and labels, while suppresses the spurious correlations. To further reinforce the ability of the proposed model on causal reasoning, we develop a human-in-the-loop strategy, with few guidance samples, to prune the representation space directly. Finally, it is shown that the developed EiHi net makes significant improvements in the most difficult and typical O.o.D. dataset NICO, compared with the current SOTA results, without any domain (e.g. background) information. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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