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Energy Analysis of a CPG-controlled Miniature Robotic Fish

Journal of Bionic Engineering 2018年第2期15卷 260-269页

作者： Junzhi Yu Shifeng Chen Zhengxing Wu Xingyu Chen Ming Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China School of lnformation and Electrical Engineering Shandong Jianzhu University Jinan 250101 China

Bionic robotic fish has a significant impact on design and control of innovative underwater robots capable of both rapid swimming and high maneuverability. This paper explores the relationship between Central Pattern Generator （CPG） based locomotion control and energy consumption of a miniature self-propelled robotic fish. To this end, a real-time energy measurement system compatible with the CPG-based locomotion control is firstly built on an embedded system. Then, tests are conducted on the untethered actual robot. The results indicate that different CPG feature parameters involving amplitude, frequency, and phase lag play distinct roles in energy consumption under different swimming gaits. Specifically, energy consumption is positively correlated with the changes in the amplitude and frequency of CPGs, whereas the phase lag of CPGs has little influence on the energy consumption. It may offer important inspiration for improving energy efficiency and locomotion performance of versatile swimming gaits.

关键词： bionic robotic fish energy analysis Central Pattern Generator （CPG） swimming motion control

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Rücktitelbild: PD-L1 Aptamer-Functionalized Metal–Organic Framework Nanoparticles for Robust Photo-Immunotherapy against Cancer with Enhanced Safety (Angew. Chem. 5/2023)

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Angewandte Chemie 2023年第5期135卷

作者： Dr. Jingfang Zhang Dr. Wenzhe Li Dr. Yafei Qi Dr. Guorong Wang Prof. Lele Li Prof. Zhengyu Jin Prof. Jie Tian Prof. Yang Du Beijing Key Laboratory for Bioengineering and Sensing Technology School of Chemistry and Biological Engineering University of Science and Technology Beijing Beijing 100083 China CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing 100190 China State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical Sciences Peking University Beijing 100191 China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Radiology Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical Sciences Beijing 100730 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing 100191 China

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A pigeon-inspired optimization algorithm for many-objective optimization problems

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Science China(Information Sciences) 2019年第7期62卷 131-138页

作者： Zhihua CUI Jiangjiang ZHANG Yechuang WANG Yang CAO Xingjuan CAI Wensheng ZHANG Jinjun CHEN Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Beijing Institute of Petrochemical Technology Faculty of Information Technology Beijing University of Technology State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences School of Software University of Technology Sydney

Dear editor,Swarm intelligence optimization algorithms are inspired by the behaviour of biological groups in nature. Such algorithms have the advantages of a clear structure, simple operation, comprehensible principles, strong parallelism, effective search abilities, and strong robustness. They can effectively solve difficult problems that traditional methods cannot. Pigeon-inspired optimization (PIO), a novel biomimetic swarm intelligence optimization algorithm, was proposed by Duan and Qiao in

关键词： A pigeon-inspired optimization algorithm for many-objective optimization problems Pareto

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A unified CPM framework using the least-squares generalized finite difference method for surface PDEs

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Engineering with Computers 2025年

作者： Tang, Zhuochao Fu, Zhuojia Chen, Meng Ling, Leevan School of Management Science and Engineering Anhui University of Technology Maanshan243002 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Maanshan243002 China Center for Numerical Simulation Software in Engineering and Sciences College of Mechanics and Materials Hohai University Nanjing211100 China Department of Mathematics Nanchang University Nanchang China Institute of Mathematics and Interdisciplinary Sciences Nanchang University Nanchang China Xinjiang Vocational and Technical University of Tianshan Kizilsu Kirgiz Autonomous Prefecture Nanchang China Department of Mathematics Hong Kong Baptist University Kowloon Tong Hong Kong

This paper introduces a novel framework, the unified closest point method (CPM) using the least-squares generalized finite difference method (LS-GFDM), to solve the surface partial differential equations (PDEs). Our approach addresses key limitations of existing embedding methods by unifying the computation of finite difference weights and interpolation into a single step, enabling efficient and reasonably accurate solutions on scattered data points. Unlike traditional CPM frameworks that rely on the finite difference method (FDM) and require interpolation to restrict solutions on the surface, potentially introducing additional errors, our LS-GFDM eliminates the need for interpolation. Furthermore, compared to the orthogonal gradient method (OrG) based on radial basis function (RBF) methods, which requires specialized data point arrangements, our method offers greater flexibility by accommodating any arbitrary point set. Higher-order schemes for LS-GFDM are easily achieved by increasing the order of the Taylor series expansion, and the method can be readily extended to other embedded methods. Numerical experiments demonstrate that the proposed framework is less sensitive to the narrow band domain size and showcases good effectiveness and robustness for solving surface PDEs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Finite difference method

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A Novel Blockchain Oracle Implementation Scheme Based on Application Specific Knowledge Engines

A Novel Blockchain Oracle Implementation Scheme Based on App...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Wang, Shuai Lu, Hao Sun, Xingkai Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781728128535

In blockchain ecosystems, an Oracle is a service tool which provides real-world data for smart contracts and other blockchain applications. At present, there are several Oracle implementation schemes, e.g. centralized Oracles, decentralized Oracles, and hardware Oracles. However, these schemes typically suffer from single source of data and low scalability. Application Specific Knowledge Engine (ASKE) is an integrated topic/application-centered knowledge portal that supports effective information retrieval and analysis. Inspired by ASKE, in this paper, we propose a novel Oracle implementation scheme. The proposed scheme can realize multi-source data extraction and analysis, then working prototypes are demonstrated to show the validity of the scheme. © 2019 IEEE.

关键词： Blockchain

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Ordinal Optimization for Optimal Orientation Problems in 3D Printing

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IFAC-PapersOnLine 2020年第5期53卷 97-102页

作者： Can Luo Gang Xiong Zhishuai Li Zhen Shen Li Wan MengChu Zhou Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems 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 Donggguan China Ten Dimensions (Guangdong) Technology Co. Ltd. Foshan China Macau University of Science and Technology Taipa Macau

Compared with a traditional manufacturing process, 3D printing has advantages of performance and cost in personalized customization and has been applied in many fields. The problem of 3D model orientation optimization is a crucial one in practice. In this paper, based on the mathematical relationship between model orientation and printing time, surface quality, and supporting area, the model orientation problem is transformed into a multi-objective optimization problem with goal of minimizing printing time, surface quality, and supporting area. Ordinal Optimization (OO) is not only applicable to problems with random factors, but also to solve complex deterministic problems. The model orientation is a complex deterministic problem. We solve it with OO in this paper and use linear weighting to convert the multi-objective optimization problem into single-objective one. Finally, we compare the experimental results of solving 3D model orientation problems solved by OO and Genetic Algorithm (GA). The results show that OO requires less calculation time than GA while achieving comparable performance.

关键词： Digital Manufacturing Genetic Algorithm Intelligent Optimization Machine Learning Ordinal Optimization Orientation Optimization 3D Printing

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Faster R-CNN Based Indoor Flame Detection for Firefighting Robot

Faster R-CNN Based Indoor Flame Detection for Firefighting R...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Jia Guo Zengguang Hou Xiaoliang Xie Shuncai Yao Qiaoli Wang Xuechen Jin School of Electrical and Control Engineering North University of China Taiyuan China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Firefighters are primarily tasked to handle fire incidents, but they are often exposed to high risks when extinguishing fire. Firefighting robots are actively being researched to reduce fire fighters injuries and deaths as well as increase their effectiveness on performing tasks. However, the major concern is how to make the flame detection methods to satisfy the high precision requirement of firefighting robot. Therefore, the flame detection of firefighting robot has become a hot topic in this area. In this paper, a Faster R-CNN model is proposed to detect flame in noisy images of fire ground. Firstly, the region generation network is used to extract the candidate flame regions. Secondly, the candidate flame regions are convoluted and pooled to extract the flame characteristics. Thirdly, the output features of Region Proposal Network (RPN) are fed into two fully connected layers: a box-regression layer which recognizes the locations of objects and a box-classification layer which classifies the objects. The dataset used in the experiment was obtained by video capture. The network is pre-trained based on Google platform Tensorflow, and the obtained precision and frame rate of the proposed method are up to 99.8% and 1.4 FPS, respectively. The experimental results demonstrate that the method equipped merits such as automatically extract the flame characteristics, effectively improve the precision of flame detection, and has excellent generalization ability and robustness.

关键词： Service robots Feature extraction Convolutional neural networks Image classification Regression analysis Object detection Support vector machines

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A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Das, Polash Kumar Hu, Bin Liu, Chang Cui, Kaixin Ranjan, Prabhat Xiong, Gang School of Electronic and Information Engineering South China University of Technology Guangzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Cloud Computing Center Chinese Academy of Science Dongguan China

ISBN: (纸本)9781728128535

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature. © 2019 IEEE.

关键词： Local binary pattern

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Blockchain-based Social Manufacturing for Customization Production

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IFAC-PapersOnLine 2020年第5期53卷 53-58页

作者： Xiuqin Shang Xi Chen Lulu Niu Gang Xiong Zhen Shen Xisong Dong Zhengchao Shen Chao Liu Bangwen Xi the Research Center of Precision Sensing and Control the state key laboratory for management and control of complex systems & Beijing engineering research center of intelligent systems and technology institute of automation Chinese Academy of Sciences Beijing 100190 China Beijing University of Posts and Telecommunications Beijing 100876 China Alstom Beizhong Power (Beijing) Co. Ltd. Beijing 100040 China the System Integration Electronics (Hangzhou)Co. LTD No. 58 No. 11 Road Foxconn Qiantang Tech-Industrial Area HETZ Hangzhou 310018 China

This paper proposes the social manufacturing based on Blockchain, which realizes precise and efficient customization through smart contract and distributed accounting. It is a new manufacturing mode of service-oriented, distributed and collaborative customization. This paper proposes the definition of SM based on Blockchain. In the process of using to Blockchain, it can form a strong trust relationship, so that multiple nodes / roles in SM can save the complete copy of the whole database, rather than a centralized permission / role. Secondly, the Blockchain-based SM system is developed for customized manufacturing. Finally, its application case and the optimization results are given. In SM, it is the key that how to transform customized manufacturing actions into socialized actions of manufacturing resources through internet communities and interconnect Socialized Manufacturing Resources (SMRs) in upstream and downstream. In this paper, the smart contract relationship is established based on Blockchain between SMRs to build credit mechanism in SM, Which can ensure the effective circulation and association of SMRs.

关键词： Social Manufacturing Blockchain Optimization SM Resources

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Robust Trajectory Tracking of Delta Parallel Robot Using Sliding Mode control

Robust Trajectory Tracking of Delta Parallel Robot Using Sli...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Tingting Su Xu Liang Guangping He Quanliang Zhao Lei Zhao North China University of Technology Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

For purpose of realizing robust trajectory tracking of parallel Delta robot with uncertain model parameters, a sliding mode control approach based on Delta robot dynamics is proposed. Once the reference trajectory is given, we can obtain the sliding mode controller, so that Delta parallel robot can move along the reference trajectory. Meanwhile, the disturbance and uncertainty of robot system are considered in the proposed method, which is beneficial to improve the reliability of controller and practicability of robot. The system stability is proved by Lyapunov method. Simulation experiment results demonstrate that the robust trajectory tracking of parallel Delta robot can be realized by the proposed method, and the designed controller is simple, robust and reliable.

关键词： Parallel robots Trajectory Trajectory tracking Robot kinematics Mathematical model

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