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arXiv 2020年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

In order to further improve the search efficiency of Neural Architecture Search (NAS), we propose B-DARTS, a novel pipeline combining broad scalable architecture with Confident Learning Rate (CLR). In B-DARTS, Broad Convolutional Neural Network (BCNN) is employed as the scalable architecture for DARTS, a popular differentiable NAS approach. On one hand, BCNN is a broad scalable architecture whose topology achieves two advantages compared with the deep one, mainly including faster single-step training speed and higher memory efficiency (i.e. larger batch size for architecture search), which are all contributed to the search efficiency improvement of NAS. On the other hand, DARTS discovers the optimal architecture by gradient-based optimization algorithm, which benefits from two superiorities of BCNN simultaneously. Similar to vanilla DARTS, B-DARTS also suffers from the performance collapse issue, where those weight-free operations are prone to be selected by the search strategy. Therefore, we propose CLR, that considers the confidence of gradient for architecture weights update increasing with the training time of over-parameterized model, to mitigate the above issue. Experimental results on CIFAR-10 and ImageNet show that 1) B-DARTS delivers state-of-the-art efficiency of 0.09 GPU day using first order approximation on CIFAR-10;2) the learned architecture by B-DARTS achieves competitive performance using state-of-the-art composite multiply-accumulate operations and parameters on ImageNet;and 3) the proposed CLR is effective for performance collapse issue alleviation of both B-DARTS and DARTS. Copyright © 2020, The Authors. All rights reserved.

关键词： Pipelines

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The Architectural Unit Setting up and Architectural Characteristics of Néré, <i>Parkia biglobosa</i>, Jack, R. Br. (Fabaceae)

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American Journal of Plant Sciences 2022年第1期13卷 109-136页

作者： Beda Innocent Adji Doffou Sélastique Akaffou Véronique Letort Mengzhen Kang Xiujuan Wang Marc Jaeger Philippe De Reffye Kouadio Henri Kouassi Yao Patrice Houphouet Jerôme Duminil Yves Caraglio Sylvie Sabatier Université Jean Lorougnon Guédé UFR Agroforesterie Daloa C&#244 te d’Ivoire CIRAD UMR-botAnique et Modélisation de l’Architecture des Plantes et des végétations (AMAP) Montpellier France MICS CentraleSupélec-Université Paris-Saclay Gif-sur-Yvette France Key Laboratory of Management and Control for Complex Systems (CASIA-LMCCS) Institute of Automation Chinese Academy of Sciences Beijing China IRD UMR-DIADE Montpellier France

Parkia biglobosa is a much-loved and over-exploited African savannah species for its socio-economic importance. Knowing and taking into account its architectural unit, which is the basis for diagnosing phenology, prod... 详细信息

Parkia biglobosa is a much-loved and over-exploited African savannah species for its socio-economic importance. Knowing and taking into account its architectural unit, which is the basis for diagnosing phenology, productivity and tree health, could provide a new perspective on its sustainable management. The aim of this study is to establish the architectural development in Parkia biglobosa by retrospective analysis. To achieve this objective, 390 individuals of all sizes ranging from seedlings to senescent trees were observed and analysed under various soil and climatic conditions in Côte d’Ivoire. The results showed that Parkia biglobosa is a light plant but shading tolerant. It is a mixed vegetative axis plant, the stem is orthotropic* in its proximal part and plagiotropic* (collapsing) in its distal part in young stage. The tree then transitions to an adult and old stage into a tree with a plagiotropic* axis in the proximal and distal parts, the trunk is built up by superimposing collapsed relay axes that gradually straighten, branching is sympodial*, growth is defined and sexuality is terminal and lateral. The ontogeny takes place in three phases: initiation of development and establishment of the crown (young), then flowering and establishment of the architectural unity (adult) and finally the death of secondary axes in the crown, duplication of the architecture by a series of partial and total reiterations (old). The level of organisation is 5: the phytomere, the module or growth unit, the axis, the architectural unit and the reiterated complex. Retrospective analysis of the modules showed that the dimensions of the growth units are indicators of morphological variation and species adaptation to a changing climate (P < 0.05). However, the equations generated by the morphological and habitat dimension linkage models are not significant (R² and

关键词： Parkia biglobosa Architectural Development Architectural Unit Côte d’Ivoire

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Inferring users' usage patterns for drug abuse surveillance 17

Inferring users' usage patterns for drug abuse surveillance

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17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019

作者： Liu, Ruoran Li, Qiudan Dajun Zeng, Daniel State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Institute of Automation Beijing 100190 China University of Chinese Academy of Sciences Beijing China China

ISBN: (纸本)9781728125046

Inferring drug usage patterns includes age of drug abuse and intention of rehabilitation, which is of much importance for drug abuse surveillance. The challenges are how to mine patterns from posts and interaction relationships between users. In this paper, we propose a novel drug usage pattern inference method, which improves the inference accuracy by integrating the semantic features and interaction relationships effectively. Experimental results on a real-world dataset demonstrate the efficacy of the proposed method. © 2019 IEEE.

关键词： Semantics

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A Self-identifying Checkerboard-like Pattern for Camera Calibration

A Self-identifying Checkerboard-like Pattern for Camera Cali...

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第三十九届中国控制会议

作者： Zhang Gao Mingzhu Zhu Junzhi Yu School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking University

This paper proposes a new type of planar pattern for camera calibration featuring its checkerboard-like structure and self-identity. White and black blocks are arranged by a novel method to ensure the uniqueness of each local pattern, and meanwhile maintain all checkerboard corners. The pattern is generated by combining identification units based on a random search strategy. Positions of local patterns can be acquired by searching the global pattern, achieving the benefit of self-identity. Because of the checkerboard-like structure, no burden is introduced in sampling compared to traditional calibrations. Experimental results show the effectiveness of the proposed calibration method. The conceived pattern is especially suitable for the calibration of multi-camera system with limited overlapping scope.

关键词： Camera calibration pattern self-identity checkerboard-like

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Semi-autonomous greenhouse environment control by combining expert knowledge and machine learning

Semi-autonomous greenhouse environment control by combining ...

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Chinese automation Congress (CAC)

作者： Mengzhen Kang Yuchen Weng Haitong Pang Liangde Li Xing-Rong Fan Jing Hua Fangle Chang Xiujuan Wang Longhua Ma The 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 Beijing China Ningbo Institute of Technology Zhejiang University Ningbo China The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao AgriTech Co. Ltd Qingdao Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The concept of "autonomous greenhouse" hints the automatic setting of control target, and giving orders on facilities to reach these targets (set point). Information and communication technologies are widely used, but how to integrate common sense or knowledge in decision-making is still challenging. Semi-autonomous greenhouse control is to set rules with joint advantages of experienced grower and powerful machinery, with the support of knowledge graph and semantic analysis. The contribution of each side is dependent on the intelligence level of computers, the availability of data, etc. In this work, the hybrid system between human and machinery is shown with the temperature control in greenhouse. The target is set mainly by expert knowledge, while the temperature controlled by vent opening is decided by an algorithm. Result shows that the target is better reached compared to that of the experience-based approach. This example shows the feasibility of human-machine hybrid intelligence system for greenhouse environment control. Although this approach is tested for temperature control, it can be extended to control multiple factors simultaneously (temperature, light intensity, humidity, etc.) by driving multiple facilities (heating, lightening, venting, etc.).

关键词： Green products Temperature measurement Monitoring Temperature sensors Vents Agriculture Temperature distribution

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Three-Dimensional Path Following control of an Underactuated Robotic Dolphin Using Deep Reinforcement Learning

Three-Dimensional Path Following Control of an Underactuated...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Jincun Liu Zhenna Liu Zhengxing Wu Junzhi Yu State Key Laboratory for Turbulence and Complex Systems BIC-ESAT College of Engineering Peking University Beijing China Shandong Labor Vocational and Technical College Jinan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

In this paper, a novel improved deep reinforcement learning (DRL) strategy is utilized to solve the three-dimensional path-following control problem for an underactuated robotic dolphin in order to meet the complex and intractable hydrodynamic model. Firstly, a brief overview of the developed robotic dolphin consisting of two-degree-of-freedom paired pectoral fins and the slider-crank-based flapping two-joint tail is introduced. After that, path-following controller includes a novel lookahead-based guidance law, decoupling motion strategy, and a deep deterministic policy gradient algorithm based on the the prior knowledge is proposed. The novel lookahead based 3-D LOS guidance law is employed to transform the 3-D waypoints to the desired heading and pitch angles with its simplicity, intuitiveness, and small computational footprint. Finally, the effectiveness of the proposed controller is demonstrated after numerous simulations. It will offer a rich vein of insight for the real-time task execution in bioinspired underwater robots.

关键词： Robots Dolphins Sports Oscillators Biological system modeling Turning Aerospace electronics

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization laboratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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Decision-Making in Driver-automation Shared control:A Review and Perspectives

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1289-1307页

作者： Wenshuo Wang Xiaoxiang Na Dongpu Cao Jianwei Gong Junqiang Xi Yang Xing Fei-Yue Wang IEEE California PATH University of CaliforniaBerkeleyCA 94720 USA Department of Engineering University of CambridgeCambridge CB21PZU.K. Mechanical and Mechatronics Engineering University of WaterlooWaterloo N2L3G1Canada Department of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to *** human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making *** interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of *** open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared ***,we give a further discussion on the key future challenges and *** are likely to shape new potential research directions.

关键词： Automated vehicle decision-making human driver human-vehicle interaction shared control

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An effective deep reinforcement learning approach for adaptive traffic signal control

An effective deep reinforcement learning approach for adapti...

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Chinese automation Congress (CAC)

作者： Mingrui Yu Jaijun Chai Yisheng Lv Gang Xiong School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Intelligent traffic signal timing is critical to reduce traffic congestion and vehicle delay. Recent studies have shown promising results of deep reinforcement learning for traffic signal control. However, existing studies have only focused on selecting which direction (phase) to let vehicles go, not on phase duration. In this paper, we propose a deep reinforcement learning algorithm that automatically learns an optimal policy to adaptively determine phase duration. To improve algorithm performance and stability, we propose a phase sensitive neural network structure based on the deep deterministic policy gradient (DDPG) model, i.e. we design a deep neural network controller for each specific traffic signal phase with DDPG; we develop some interesting training techniques to improve training efficiency, i.e. dividing the training process into three stages and introducing the episode-break mechanism. We test the proposed methods on an isolated intersection under diverse traffic demands. Experiments show that our method is more effective.

关键词： Training Mathematical model Neural networks Reinforcement learning Vehicle dynamics Stability analysis Numerical stability

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An IoT Edge Computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

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IEEE International Conference on Networking, Sensing and control

作者： Shichao Chen Qijie Li Hua Zhang Fenghua Zhu Gang Xiong Ying Tang Macau University of Science and Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Macau China Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Engineering Research Center of Intelligent Systems and Technology Institute of Automation and Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Glassboro NJ USA Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT.

关键词： Energy consumption Processor scheduling Computer architecture Internet of Things Optimization Monitoring Edge computing

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