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IEEE Symposium Series on Computational Intelligence

作者： Qinglai Wei Benkai Li Ruizhuo Song The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

This paper concerns with a novel generalized policy iteration (GPI) algorithm with approximation errors. Approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The convergence of the developed algorithm is established to show that the iterative value function is convergent to a finite neighborhood of the optimal performance index function. Finally, numerical examples and comparisons are presented.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Neuro-dynamic programming Generalized policy iteration Nonlinear systems Optimal control Neural networks Reinforcement learning

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Estimation of Reduced Greenhouse Gas Emission from Municipal Solid Waste Incineration with Electricity Recovery in Prefecture- and County-Level Cities of China

SSRN

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SSRN 2022年

作者： Zhao, Qing Tang, Weihao Han, Mengjie Cui, Wenjing Zhu, Lei Xie, Huaijun Li, Wei Wu, Fengchang National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management Institute of Eco-environmental and Soil Sciences Guangdong Academy of Sciences Guangzhou510650 China Key Laboratory of Pollution Ecology and Environmental Engineering Institute of Applied Ecology Chinese Academy of Sciences Shenyang110016 China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology School of Environmental Science and Technology Dalian University of Technology Linggong Road 2 Dalian116024 China State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China

Municipal solid waste (MSW) without proper managements could be a significant source of greenhouse gas (GHG) emissions. MSW incineration with electricity recovery (MSW-IER) is recognized as a sustainable way to utilize waste, but its effectiveness on reducing GHG emissions at the city scale in China remain unclear due to limited data of MSW compositions. In this study, MSW compositions in 297 cities of China from 2002 to 2017 were predicted using a random forest model trained by a combination of socio-economic, climate and spatiotemporal factors. Spatiotemporal and climatic factors (such as economic development level, precipitation) accounted for 6.5%-20.7% and 20.1%-37.6% to total contributions on MSW composition, respectively. The GHG emissions from MSW-IER in each Chinese city were further calculated based on the predicted MSW compositions. The plastic is the main GHG emission source, accounting for over 91% of the total emission during 2002-2017. Compared to baseline (landfill) emission, the GHG emission reduction from MSW-IER was 12.5 × 107 kg CO2-eq in 2002 and 415 × 107 kg CO2-eq in 2017, with an average annual growth rate of 26.3%. The results provide basic data for estimating GHG emission in MSW management in China. © 2022, The Authors. All rights reserved.

关键词： Municipal solid waste

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Cluster-based Relevance Analysis Between Construction Mode and BCR for Optimizing the DAS

Cluster-based Relevance Analysis Between Construction Mode a...

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Data Driven control and Learning systems (DDCLS)

作者： Jiran Zhu Hua Leng Haiguo Tang Min Fan Yaling Liu Qing Yang College of Electronic and Information Engineering Tongji University Jiading Shanghai China State Grid Corporation of Hunan Electric Power corporation research institute Changsha Hunan China College of Electrical and Information Engineering Hunan University Changsha Hunan China School of Automation Chongqing University Chongqing Key Laboratory of Complex System Safety and Control (Ministry of Education) Chongqing University Chongqing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Distribution automation system (DAS) is the key of intelligent power distribution. With the construction and operation of DAS, studying on the optimization of DAS has received much attention from both academia and industry. This study proposed cluster-based relevance analysis between construction mode and benefit-cost ratio (BCR) for optimizing the DAS. This method clusters the DASs of different cities, and analyzes the clustering results. Then, the method analyzes BCR of DAS in different categories and uses comparative analysis, grey relational analysis (GRA) and sensitivity analysis to analyze the relevance between the construction mode and BCR of typical city's DAS and determine the major factors affecting the BCR comprehensively. Finally, it integrates all analysis results to make optimization suggestions for DAS. The case study shows that the method is effective and feasible, which can provide direction and reference basis for the optimization of DAS in cities of different scale.

关键词： Urban areas Principal component analysis Investment Optimization Sensitivity analysis Power distribution Automation

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A joint call for actions to advance taxonomy in China

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Zoological systematics 2022年第3期47卷 188-197页

作者： Chaodong Zhu Arong Luo Ming Bai Michael COrr Zhonge Hou Siqin Ge Jun Chen Yibo Hu Xuming Zhou Gexia Qiao Hongzhi Kong Limin Lu Xiaohua Jin Lei Cai Xinli Wei Ruilin Zhao Wei Miao Qingfeng Wang Zhongli Sha Qiang Lin Meng Qu Jianping Jiang Jiatang Li Jing Che Xuelong Jiang Xiaoyong Chen Lianming Gao Zongxin Ren Chunlei Xiang Shixiao Luo Donghui Wu Dong Liu Yanqiong Peng Tao Su Chenyang Cai Tianqi Zhu Wanzhi Cai Xingyue Liu Hu Li Huaijun Xue Zhen Ye Xuexin Chen Pu Tang Shujun Wei Hong Pang Qiang Xie Feng Zhang Feng Zhang Xianjin Peng Aibing Zhang Taiping Gao Changfa Zhou Chen Shao Libin Ma Zhaoming Wei Yunxia Luan Ziwei Yin Wu Dai Cong Wei Xiaolei Huang Jingxian Liu Xiangsheng Chen Tianci Yi Zhisheng Zhang Zhulidezi Aishan Qin Li Hongying Hu CAS Key Laboratory of Zoological Systematics and Evolution Institute of ZoologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Animal Ecology and Conservation Biology Institute of ZoologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Systematic and Evolutionary Botany Institute of BotanyChinese Academy of SciencesBeijing 100093China State Key Laboratory of Mycology(SKLM) Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Aquatic Biodiversity and Conservation Institute of HydrobiologyChinese Academy of SciencesWuhan 430072China Wuhan Botanical Garden Chinese Academy of SciencesWuhan 430074China Department of Marine Organism Taxonomy&Phylogeny Institute of OceanologyChinese Academy of SciencesQingdao 266071China CAS Key Laboratory of Tropical Marine Bio-resources and Ecology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization&Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province Chengdu Institute of BiologyChinese Academy of SciencesChengdu 610041China State Key Laboratory of Genetic Resources and Evolution&Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain Kunming Institute of ZoologyChinese Academy of SciencesKunming 650221China CAS Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China CAS Key Laboratory of Plant Resource Conservation and Sustainable Utilization South China Botanical GardenChinese Academy of SciencesGuangzhou 510650China CAS Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China CAS Key Laboratory of Tropical Forest Ecology Xishuangbanna Tropical Botanical GardenChinese Academy of SciencesMenglunMenglaYunnan 66630

Taxonomy plays an important role in understanding the origin, evolution, and ecological functionality of biodiversity. There are large number of unknown species yet to be described by taxonomists, which together with their ecosystem services cannot be effectively protected prior to description. Despite this, taxonomy has been increasingly underrated insufficient funds and permanent positions to retain young talents. Further, the impact factordriven evaluation systems in China exacerbate this downward trend, so alternative evaluation metrics are urgently necessary. When the current generation of outstanding taxonomists retires,there will be too few remaining taxonomists left to train the next generation. In light of these challenges, all co-authors worked together on this paper to analyze the current situation of taxonomy and put out a joint call for immediate actions to advance taxonomy in China.

关键词： Endangered taxonomists morphology-based taxonomy integrative taxonomy capacity building systematics

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A Survey on Big Data for Human Body Shape

A Survey on Big Data for Human Body Shape

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Hongli Peng Xiuqin Shang Chao Guo Gang Xiong Timo R.Nyberg Dong Fan Yiming Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences Cloud Computing Center Chinese Academy of Sciences Department of Industrial Engineering and Management School of ScienceAalto University

In recent years, the rapid development of network and E-commerce technologies, promotes the development of human body big data. New technologies, such as 3D scanning and 3D printing will improve our life quality. The new socialized manufacturing network composed of Io T, advanced logistics network and 3D printers. Everyone is allowed to have chance to participate in any process of the whole production manufacturing lifecycle by some ways, such as crowdsourcing. Boosts the creation, production and consumption patterns of individuation, real-time transformation and E-commerce, possibly will lead to a new industrial revolution. In this revolutionary change wave, public concerns focus on the human body big data used for social manufacturing of wearable products or personalized medical treatment. Although many scholars have done a lot of research work, the collection of the human body shape big data still faces many challenges. There is no complete, separated theoretical research works in progress, it should be summarized and improved systematically. In this paper, we review that big data for human body shape is gaining momentum. The 3D scanning and 3D modeling of human body shape are summarized and concluded. The main applications of these big data are analyzed.

关键词： big data human body shape 3D scanning 3D modeling clothing footwear mass customization

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Fitting a Functional Structural Plant Model based on Global Sensitivity Analysis

Fitting a Functional Structural Plant Model based on Global ...

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IEEE International Conference on Automation Science and Engineering

作者： Yubin LIN Mengzhen KANG Jing HUA National Key Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467304290

Calibration of a functional structural plant model is a challenging task because of the complexity of model structure. Parameter estimation through gradient-based optimization technique was highly dependent on initial parameter values. This motivated the use of global sensitivity analysis technique to choose parameter subset in fitting the data sequence. Global sensitivity indices were computed using the source sink ratio as the output of interest, which regulates all organ growth. By fitting on chrysanthemum data from nine sampling dates, it is shown that sensitivity analysts method helps to identify the influential parameters for a given sampling date. As a result, fitting process is less dependent on the initial parameter values. Current work provides a new method of calibrating a plant growth model with multiple outputs.

关键词： Sensitivity analysis plant models organ growth MACHINE SHOP PRACTICE

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The Application of the Depth Camera in the Social Manufacturing: A review

The Application of the Depth Camera in the Social Manufactur...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Zhen Liang Ni Zhen Shen Chao Guo Gang Xiong Timo R.Nyberg Xiuqin Shang Shuangshuang Li Yiming Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Cloud Computing Center Chinese Academy of Sciences Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences Department of Industrial Engineering and Management School of ScienceAalto University

In recent years, the Information Technology develops rapidly. With the development of the Information Technology, the social manufacturing, a new mode of manufacturing, has been proposed. The social manufacturing combines the Internet technology, the three-dimensional scanning technology and the virtual reality technology, and is used in the apparel industry and other fashion industries for the mass customization. The three-dimensional scanning technology and the virtual fitting mirror technology are inseparable from the depth camera. Depth cameras play an important role in the social manufacturing. Manufacturers use depth cameras to obtain three-dimensional models of the body of customers to design suitable clothes. Customers can visually see the effect of clothes by using a virtual fitting mirror which is based on the depth camera. The social manufacturing makes mass customization possible, and the depth camera help develop the social manufacturing. This paper introduces the principles of depth cameras, and the application of depth cameras in the social manufacturing.

关键词： social manufacturing depth camera PMD Kinect time of flight structured light

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Airport Aviation Noise Prediction Based on an Optimized Neural Network

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Transactions of Nanjing University of Aeronautics and Astronautics 2021年第S01期38卷 32-39页

作者： MA Lina TIAN Yong WU Xiaoyong College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China State Key Laboratory of Air Traffic Management System and Technology Nanjing210007P.R.China Department of Operation and Control Hunan Air Travel Co.Ltd.Changsha 410100P.R.China

In order to alleviate noise pollution and improve the sustainability of airport operation,it is of great significance to develop an effective method to predict airport aviation noise. A three-layer neural network is constructed to gain computational simplicity and execution economy. With the preferred node number and transfer functions obtained in comparative tests,the constructed network is further optimized through the genetic algorithm for performance improvements in prediction. Results show that the proposed model in this paper is superior in accuracy and stability for airport aviation noise prediction,contributing to the assessment of future environmental impact and further improvement of operational sustainability for civil airports.

关键词： noise prediction neural network genetic algorithm sustainable air transport

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Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective

arXiv

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arXiv 2021年

作者： Cao, Siqi Fu, Di Yang, Xu Wermter, Stefan Liu, Xun Wu, Haiyan CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing China Department of Psychology University of Chinese Academy of Sciences Beijing China Department of Experimental Psychology University of Oxford Oxford United Kingdom State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Informatics University of Hamburg Hamburg Germany Centre for Cognitive and Brain Sciences Department of Psychology University of Macau Taipa China

Sensory and emotional experiences such as pain and empathy are essential for mental and physical health. Cognitive neuroscience has been working on revealing mechanisms underlying pain and empathy. Furthermore, as trending research areas, computational pain recognition and empathic artificial intelligence (AI) show progress and promise for healthcare or human-computer interaction. Although AI research has recently made it increasingly possible to create artificial systems with affective processing, most cognitive neuroscience and AI research do not jointly address the issues of empathy in AI and cognitive neuroscience. The main aim of this paper is to introduce key advances, cognitive challenges and technical barriers in computational pain recognition and the implementation of artificial empathy. Our discussion covers the following topics: How can AI recognize pain from unimodal and multimodal information? Is it crucial for AI to be empathic? What are the benefits and challenges of empathic AI? Despite some consensus on the importance of AI, including empathic recognition and responses, we also highlight future challenges for artificial empathy and possible paths from interdisciplinary perspectives. Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy. © 2021, CC BY-NC-ND.

关键词： Cognitive systems

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Characteristics of Labile Organic Carbon Fractions Under Different Types of Subsidence Waterlogging Areas in the Coal Mining Area with High Ground Water Table: A Case Study in Xinglongzhuang Coal Mine, China

SSRN

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SSRN 2023年

作者： Fu, Yanhua Hu, Zhenqi Zhu, Qi Rong, Ying School of Economics and Management Tianjin Chengjian University Tianjin300384 China School of Environment Science and Spatial Informatics China University of Mining and Technology Xuzhou221116 China Chinese Research Academy of Environmental Sciences National Engineering Laboratory for Lake Pollution Control and Ecological Restoration State Environmental Protection Key Laboratory for Lake Pollution Control Beijing100012 China The Research Center of System and Industrial Technology Zhongyuan University of Technology Zhengzhou451191 China

The restoration of subsidence waterlogging areas has the potential to facilitate carbon accumulation in the coal mining areas with high ground water table. Nevertheless, the impact of restoration and management strategy on the soil organic carbon (SOC) and the labile organic carbon(LOC) have not been sufficiently demonstrated. In this study, SOC and LOC fractions, including light fraction organic carbon(LFOC), particulate organic carbon(POC), and dissolved organic carbon(DOC) within the 0-50cm profiles, were determined in the reference cropland(RC) and three types of subsidence waterlogging areas including aquaculture waterlogging area(AWA), deep waterlogging area(DWA) and shallow waterlogging area(SWA) in Xinglongzhuang Coal Mine, Shandong Province, China. Our results showed that compared with the reference cropland SOC and LOC decreased by 4.13%, 25.03%, and 77.91% in SWA, DWA, and AWA, respectively. The vital depletion was in the 0-10cm soil layer, which indicates that the recovery of total functional pools of SOC after subsidence and the succession of ecological systems takes a longer time. The liability of POC was highest among the three types of labile organic fractions which could indicate the mining subsidence triggered the soil redistribution and impact the carbon accumulation. With comparatively less interference on the soil profile, the liability of LFOC and POC decreased with soil depth, while DOC increased in DWA and SWA. And there was a large discrepancy in AWA, with the liability of LOC fractions fluctuating because of intensive engineering soil disturbance. Labile organic C fractions were closely associated and correlated with soil properties, especially TN and TP. Overall, the restoration and management have a vital impact on the soil organic carbon pool, it is suggested that shallow water, natural geomorphology-based topography, less soil disturbance, well development of vegetation communities, and continuous monitoring of soil and water properties

关键词： Subsidence

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