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International Conference on Intelligent Transportation

作者： Peng Chen Zemao Zhu Guangquan Lu Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Hai Dian District Beijing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Hai Dian District Beijing

ISBN: (纸本)9781538670255

Traffic signal control is a complex problem and it is difficult to determine an optimal strategy to control multi-directional traffic at multiple intersections. Recent years have witnessed numerous successes of deep learning neural networks in the fields of artificial intelligence. Motivated by the dominant performance of neural networks, this study attempts to develop a novel adaptive signal control approach by fusing deep learning (DL) and reinforcement learning (RL), i.e., deep reinforcement learning (DRL), for arterial signal coordination. DRL can considerably improve the ability to deal with large amounts of data processing, systematic perception and expression, which is key to coordinated control of arterial intersections. The proposed algorithm is implemented by utilizing real-time traffic detection data and aims to optimize the hybrid global and local reward functions. The experimental results obtained by traffic simulation software SUMO demonstrate the advantage of the proposed approach, as well as its efficiency and effectiveness compared with fixed-time and actual signal control methods.

关键词： Aerospace electronics Machine learning Adaptive control Traffic control Roads Optimization

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On the selection of solutions for mutation in differential evolution

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Frontiers of Computer Science 2018年第2期12卷 297-315页

作者： Yong WANG Zhi-Zhong LIU Jianbin LI Han-Xiong LI Jiahai WANG School of Information Science and Engineering Central South University Changsha 410083 China School of Computer Science and Informatics De Montfort University Leicester LE1 9BH UK Institute of Information Security and Big Data Central South University Changsha 410083 China Department of Systems Engineering and Engineering Management City University of Hong Kong Hong Kong China State Key Laboratory of High Performance Complex Manufacturing Central South University Changsha 410083 China School of Data and Computer Science Sun Yat-sen University Guangzhou 510006 China

Differential evolution （DE） is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms.

关键词： differential evolution mutation the selection of solutions for mutation evolutionary algorithms

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Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

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International Conference on Computer Vision (ICCV)

作者： Cheng Xu Zhaoqun Li Qiang Qiu Biao Leng Jingfei Jiang School of Computer Science & Engineering Beihang University Duke University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Shape Three-dimensional displays Correlation Two dimensional displays Feature extraction Solid modeling Computational modeling

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New Discoveries of Domination Between Traffic Matrices

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 561-566页

作者： Peng-fei LIU Wen-guo YANG Tian-de GUO School of Mathematics Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100049China

In this paper the definition of domination is generalized to the case that the elements of the traffic matrices may have negative values. It is proved that D3 dominates D3 ＋ λ（D2 - D1） for any λ ≥0 if D1 dominates D2. Let u（D） be the set of all the traffic matrices that are dominated by the traffic matrix D. It is shown that u （ D∞） and u （D ∈） are isomorphic. Besides, similar results are obtained on multi-commodity flow problems. Fhrthermore, the results are the generalized to integral flows.

关键词： robust network design multi-commodity flows domination

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Local edge direction based non-local means for image denoising

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Journal of Measurement Science and Instrumentation 2019年第3期10卷 236-240页

作者： JIA Li-na JIAO Feng-yuan LIU Rui-qiang GUI Zhi-guo Department of Electronic Information Engineering Shanxi University Taiyuan 030013 China Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data North University of China Taiyuan 030051 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin 150001 China

Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.

关键词： image denoising neighborhood filter non-local means (NLM) steering kernel regression (SKR)

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy

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Nano Research 2018年第12期11卷 6316-6324页

作者： Xiaoyang Lin Zhizhong Si Wenzhi Fu Jianlei Yang Side Guo Yuan Cao Jin Zhang Xinhe Wang Peng Liu Kaili Jiang Weisheng Zhao Fert Beijing Research Institute School ofMicroelectronics & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China Beihang-GoertekJoint Microelectronics Institute Qingdao Research InstituteBeihang UniversityQingdao 266000China Fert Beijing Research Institute School of Computer Science and Engineering & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics & Tsinghua-Foxconn Nanotechnology Research CenterCollaborative Innovation Center of Quantum MatterTsinghua UniversityBeijing 100084China

Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in corresponding device applications. However, facile methods to realize accurate, intelligent, and large-area characterizations of these 2D nanostructures are still highly desired. Herein, we report the successful application of machine-learning strategy in the optical identification of 2D nanostructures. The machine-learning optical identification (MOI) method endows optical microscopy with intelligent insight into the characteristic color information of 2D nanostructures in the optical photograph. The experimental results indicate that the MOI method enables accurate, intelligent, and large-area characterizations of graphene, molybdenum disulfide, and their heterostructures, including identifications of the thickness, existence of impurities, and even stacking order. With the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote fundamental research and wafer-scale device applications of 2D nanostructures.

关键词： machine-learning optical identification two-dimensional (2D) material heterostructure artificial intelligence

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Reassessing Immediate Coal Phase-Out: Dual Imperatives of Capacity Control and Renewables Expansion in China’s Net-zero Strategy

Nexus

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Nexus 2025年

作者： Jiaxing Wang Xi Lu Hao Wang Yang Ou Jianxiao Wang Dan Tong Yan Li Ziwen Ruan Zihua Yin Wenlong Zhou Benjamin McLellan Emily Xing Anna M. MacLennan Jingli Fan Xian Zhang Michael B. McElroy Kebin He State Key Laboratory of Regional Environment and Sustainability School of Environment Tsinghua University Beijing 100084 China Institute for Carbon Neutrality Tsinghua University Beijing 100084 China Institute of Carbon Neutrality Peking University100091 Beijing China China Oil & Gas Pipeline Network Corporation Beijing 100013 China College of Environmental Sciences and Engineering Peking University Beijing 100091 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing 100091 China Department of Earth System Science Tsinghua University Beijing 100084 China College of environmental science and Engineering Tongji University Shanghai 200092 China Centre for Sustainable Development and Energy Policy Research School of Energy and Mining Engineering China University of Mining and Technology Beijing 100083 China Graduate School of Energy Science Kyoto University Kyoto 606-8501 Japan Harvard-China Project on Energy Economy and Environment Harvard University Cambridge MA 02138 USA The Administrative Centre for China’s Agenda 21 Ministry of Science and Technology Beijing 100038 China School of Engineering and Applied Science Harvard University Cambridge MA 02138 USA Department of Earth and Planetary Sciences Harvard University Cambridge MA 02138 USA

Immediate cessation of investments in new coal-fired power plants is widely regarded as a crucial measure for China to achieve net-zero emission. However, there exists a lack of systematic evaluations regarding the stringent removal of coal power from China's energy system, raising potential concerns around balancing emission reductions and sustainable transformation. In this study, a comprehensive analysis coupling an integrated assessment model with an hourly power system optimization model reveals that implementing stringent policies to limit coal power expansion yields immediate emission reduction benefit. However, premature and singular capacity control of coal power may hinder the electrification of Chinese society, adversely impacting the adequacy of the power system beyond 2050. These findings underscore the importance of implementing supporting measures when implementing coal power control policies, including the crucial deployment of renewable energy at a pace significantly exceeding the current rate to achieve complementarity with coal power control policies.

关键词： net-zero emission renewable energy expansion coal power control energy system transition

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Adaptive Teacher-and-Student Model for Heterogeneous Domain Adaptation

Adaptive Teacher-and-Student Model for Heterogeneous Domain ...

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IEEE International Conference on data Mining (ICDM)

作者： Yunyun Wang Xuzhang Chen Songcan Chen Hui Xue College of Computer Science Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts & Telecommunications Nanjing China College of Computer Science & Technology/AI MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics & Astronautics Nanjing China College of Computer Science and Engineering Southeast University Nanjing China

In heterogeneous domain adaptation (HDA), since the feature spaces of the source and target domains are different, knowledge transfer from the source to the target domain is really challenging. How to align the different feature spaces and then adaptively transfer the related knowledge is critical for HDA. In this paper, we develop an adaptive teacher-and-student model for heterogeneous domain adaptation (AtsHDA). In AtsHDA, the source domain as a teacher and the target domain as a student are aligned or co-adapted to each other first, so that their correlation can be maximized. Then the target domain adaptively learns from the source domain. Specifically, there is a balance between the learning by the target domain itself and the instruction from the source domain. That is, when the guidance from the source domain is helpful for learning, the learning of target classifier emphasizes the instruction of source knowledge, and considers its own knowledge more, otherwise. Further, an ensemble method is designed to decide such a balance. Finally, empirical results show that AtsHDA can achieve competitive results compared with the state-of-arts.

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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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