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3d-Swim: 3d Vision Based Seam Width Measurement for Industrial Composite Fiber Layup In-Situ Inspection

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SSRN

SSRN 2022年

作者： Zhang, Yinlong Yuan, Libiao Liang, Wei Xia, Xiaofang Pang, Zhibo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Shenyang Pharmaceutical University China School of Computer Science and Technology Xidian University Xian710071 China Department of Automation Technology ABB Corporate Research Sweden Vasteras72178 Sweden Stockholm11428 Sweden

Composite fiber layup in-situ inspection plays an important role in the advanced manufacturing. An accurate seam width measurement is an indispensable procedure during the layup inspection. However, the traditional seam measurements may fail in presence of surface curvature variance, curved edges between sheets, to name a few. This paper intentionally designs a novel 3D vision based Seam Width Measurement for Industrial Composite Fiber Layup In-situ inspection (called 3D-SWiM). It aims to address three major challenges in measuring the seam width between the composite fiber sheets, namely, seam region over-segmentation, failures on region of interests (ROI) seam detection, seam width measurement inconsistency. Firstly, the region growing with semantic refinement is designed for 3D point cloud clustering, to overcome the over-segmentation on seam regions. Afterwards, the gridding model with edge discrimination is applied to extract the 3D potential candidates in the edge areas. Eventually, we use the cubic B-spline fitting model to describe the seam region curves and the improved particle swarm optimization model to estimate the minimum and maximum distance between the seam edges. Our method has been evaluated extensively on the developed composite fiber layup platform. The experimental results prove the competitive accuracy against the state-of-the-art methods in seam width measurement. © 2022, The Authors. All rights reserved.

关键词： Inspection

Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1045-1063页

作者： Chaoyue Zu Chao Yang Jian Wang Wenbin Gao Dongpu Cao Fei-Yue Wang the College of Computer Science and Technology Jilin UniversityChangchun 130012China Jiangsu XCMG Construction Machinery Research Institute Ltd. Xuzhou 221004China the College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012 Chongqing Xibu Automobile Proving Ground Management Co.Ltd. Chongqing 404100China the State Grid JIBEI Electric Power Company Limited Management Training Center China the Advanced Vehicle Engineering Center Cranfield UniversityCranfield MK430ALUK the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance(MVCA)algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently,without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore,MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay(< 100100 ms) and low packet loss(< 5%) can bring little influence to those trajectory planning algorithms that only depend on V2 V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.

关键词： Collision avoidance intelligent vehicles intervehicle communication simulation testing trajectory planning

A Learning Convolutional Neural Network Approach for Network Robustness Prediction

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

Driving on Highway by Using Reinforcement Learning with CNN and LSTM Networks

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Driving on Highway by Using Reinforcement Learning with CNN ...

IEEE International Conference on Intelligent Engineering systems (INES)

作者： László Szőke Szilárd Aradi Tamás Bécsi Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics H-1111 Budapest Hungary Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences H-1111 Budapest Hungary

ISBN: (数字)9781728110592

ISBN: (纸本)9781728110608

This work presents a powerful and intelligent driver agent, designed to operate in a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. Our goal is to create an agent that is capable of navigating safely in changing highway traffic and successfully accomplish to get through the defined section keeping the reference speed. Meanwhile, creating a state representation that is capable of extracting information from images based on the actual highway situation. The algorithm uses Convolutional Neural Network (CNN) with Long-Short Term Memory (LSTM) layers as a function approximator for the agent with discrete action space on the control level, e.g., acceleration and lane change. Simulation of Urban MObility (SUMO), an open-source microscopic traffic simulator is chosen as our simulation environment. It is integrated with an open interface to interact with the agent in real-time. The agent can learn from numerous driving and highway situations that are created and fed to it. The representation becomes more general by randomizing and customizing the behavior of the other road users in the simulation, thus the experience of the agent can be much more diverse. The article briefly describes the modeling environment, the details on the learning agent, and the rewarding scheme. After evaluating the experiences gained from the training, some further plans and optimization ideas are briefed.

关键词： Road transportation Learning (artificial intelligence) Vehicles Training Neural networks Aerospace electronics Acceleration

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

Fully linear graph convolutional networks for semi-supervised learning and clustering

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arXiv 2021年

作者： Cai, Yaoming Zhang, Zijia Cai, Zhihua Liu, Xiaobo Ding, Yao Ghamisi, Pedram School of Computer Science China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China Xi'an Research Institute of High Technology Xi'an710000 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria

This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closedform solution with a decoupled procedure, resulting in a generalized linear framework and making it easier to implement, train, and apply. We show that (1) FLGC is powerful to deal with both graph-structured data and regular data, (2) training graph convolutional models with closed-form solutions improve computational efficiency without degrading performance, and (3) FLGC acts as a natural generalization of classic linear models in the non-Euclidean domain, e.g., ridge regression and subspace clustering. Furthermore, we implement a semisupervised FLGC and an unsupervised FLGC by introducing an initial residual strategy, enabling FLGC to aggregate long-range neighborhoods and alleviate over-smoothing. We compare our semi-supervised and unsupervised FLGCs against many state-of-the-art methods on a variety of classification and clustering benchmarks, demonstrating that the proposed FLGC models consistently outperform previous methods in terms of accuracy, robustness, and learning efficiency. The core code of our FLGC is released at https://***/AngryCai/FLGC. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology

What are the differences in yield formation among two cucumber (Cucumis sativus L.) cultivars and their F1 hybrid?

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Journal of Integrative Agriculture 2020年第7期19卷 1789-1801页

作者： WANG Xiu-juan KANG Meng-zhen FAN Xing-rong YANG Li-li ZHANG Bao-gui HUANG San-wen Philippe DE REFFYE WANG Fei-yue The State Key Laboratory of Management and Control for Cormplex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China Bejjing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBejing 100190P.R.China Innovation Center for Parallel Agriculture Qingdao Academy of Intelligent IndustriesQingdao 266109P.R.China School of Computer Science and Information Engineering Chongqing Technology and Business UniversityChongqing 400067P.R.China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083P.R.China College of Land Science and Technology China Agricultural UniversityBeijing 100193P.R.China Agricultural Genomes Institute at Shenzhen Chinese Academy of Agricultural SciencesShenzhen 518124P.R.China AMAP University MontpellierCIRADCNRSINRAIRDMontpellier 34000France The School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100049P.R.China

To elucidate the mechanisms underlying the differences in yield formation among two parents(P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model(FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm(PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organlevel model may provide a computational approach to define the target of breeding by combination with a genetic model.

关键词： cucumber biomass production functional-structural plant model source-sink ratio fruit-setting PSO heterosis

A hybrid correction framework using disentangled seasonal-trend representations and MoE for NWP solar irradiance forecast

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Applied Energy 2025年 397卷

作者： Weijing Dou Kai Wang Shuo Shan Kanjian Zhang Haikun Wei Victor Sreeram State Grid Electric Power Research Institute (NARI Group Corporation) National Key Laboratory of Risk Defense Technology and Equipment for Power Grid Operation Nanjing 211106 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Southeast University Shenzhen Research Institute Shenzhen 518063 China Department of Electrical Electronics and Computer Engineering The University of Western Australia Perth Australia

Day-ahead solar irradiance forecast holds important value for optimizing energy utilization within the power system and ensuring stable grid scheduling. The forecast outputs of numerical weather prediction (NWP) are widely acknowledged as one of the indispensable data sources for day-ahead solar irradiance forecast tasks. In previous studies, post-processing methods have generally been employed as correction models to enhance the accuracy of NWP solar irradiance forecasts. However, irradiance sequences contain complex mixed patterns and exhibit various seasonal periodic differences. Based on the analysis of NWP global horizontal irradiance (GHI) error characteristics in this study, errors in NWP GHI forecasts also show obvious seasonal variations. Given these issues, it is challenging for a single correction model to achieve good correction performance and strong seasonal robustness. Therefore, this paper proposes a hybrid model comprising representation learning module, feature sparse activation module, and encoder-decoder-based correction module to address the aforementioned problems. A contrastive-learning-based representation learning module named CoST is introduced to learn disentangled seasonal features and trend features of irradiance sequences. A learnable mixture-of-experts (MoE) layer is adopted to sparsely activate the seasonal-trend features that contribute more to improving correction accuracy. The encoder-decoder-based correction module takes the sparsely activated seasonal-trend features as inputs, achieving the final corrected NWP GHI forecasts. The correction performance of the proposed method was validated on both publicly available datasets and actual field dataset. The results for various datasets show that our proposed CoST-MoELSTM model achieves the highest improvement for NWP forecasts, with increases of 29.82 %, 36.54 %, and 26.58 %. Additionally, we conducted a detailed analysis of the correction performance of CoST-MoELSTM across different

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