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A Unified Formulation of Geometry-aware Discrete Dynamic Movement Primitives

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arXiv 2022年

作者： Abu-Dakka, Fares J. Saveriano, Matteo Kyrki, Ville Electronic and Computer Science Department Faculty of Engineering Mondragon Unibertsitatea Arrasate20500 Spain Automatic Control Lab Department of Industrial Engineering University of Trento Trento Italy Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Aalto Finland

Learning from demonstration (LfD) is considered as an efficient way to transfer skills from humans to robots. Traditionally, LfD has been used to transfer Cartesian and joint positions and forces from human demonstrations. The traditional approach works well for some robotic tasks, but for many tasks of interest, it is necessary to learn skills such as orientation, impedance, and/or manipulability that have specific geometric characteristics. An effective encoding of such skills can be only achieved if the underlying geometric structure of the skill manifold is considered and the constrains arising from this structure are fulfilled during both learning and execution. However, typical learned skill models such as dynamic movement primitives (DMPs) are limited to Euclidean data and fail in correctly embedding quantities with geometric constraints. In this paper, we propose a novel and mathematically principled framework that uses concepts from Riemannian geometry to allow DMPs to properly embed geometric constrains. The resulting DMP formulation can deal with data sampled from any Riemannian manifold including, but not limited to, unit quaternions and symmetric and positive definite matrices. The proposed approach has been extensively evaluated both on simulated data and real robot experiments. The performed evaluation demonstrates that beneficial properties of DMPs, such as convergence to a given goal and the possibility to change the goal during operation, apply also to the proposed formulation. © 2022, CC BY-NC-ND.

关键词： Demonstrations

Heliostat Relative Efficiency Metric Inspiration for Central Receiver System

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SSRN

SSRN 2023年

作者： He, Caitou Chen, Huiling Zhang, Weizhong Zhao, Yuhong Feng, Jieqing College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325006 China State Key Laboratory of CAD&CG Zhejiang University Hangzhou310058 China Institute of Industrial Intelligence and Systems Engineering College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Data Science Fudan University Shanghai200433 China

The optimization of heliostat field layout is a topic of considerable interest to both industry and academia, as variations in solar radiation contribution and flux peak at different positions on the site can have significant impacts. Prior research has predominantly focused on optimizing from the perspective of collected solar radiation. Building on the analytical radiative flux distribution model, this study presents a novel relative efficiency metric for heliostat layout optimization, with a focus on peak expression. In this expression, mirror reflectivity, atmospheric attenuation, and receiver cosine effect can all be readily derived from the relative position, whereas the radiation diffusion factor, an important element whose relationship with slant range and slope error is unknown, requires further exploration. To this end, this study undertakes theoretical derivation, experimental simulation, and numerical approximation to determine the relationship between the diffusion factor and slant range for a specific heliostat. Notably, the results of this study demonstrate that the relative efficiency metricis highly consistent with the heliostat layout of PS10, highlighting its potential for practical application. Additionally, the study confirms several heliostat field design logics and principles for central receiver system. © 2023, The Authors. All rights reserved.

关键词： Solar radiation

Verifying Quantum Advantage Experiments with Multiple Amplitude Tensor Network Contraction

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Physical Review Letters 2024年第3期132卷 030601-030601页

作者： Yong Liu Yaojian Chen Chu Guo Jiawei Song Xinmin Shi Lin Gan Wenzhao Wu Wei Wu Haohuan Fu Xin Liu Dexun Chen Zhifeng Zhao Guangwen Yang Jiangang Gao Zhejiang Lab Hangzhou 311121 China Tsinghua University Beijing 100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China National Supercomputing Center in Wuxi Wuxi 214000 China Information Engineering University Zhengzhou 450001 China National Research Center of Parallel Computer Engineering and Technology Beijing 100190 China

The quantum supremacy experiment, such as Google Sycamore [F. Arute et al., Nature (London) 574, 505 (2019).], poses a great challenge for classical verification due to the exponentially increasing compute cost. Using a new-generation Sunway supercomputer within 8.5 d, we provide a direct verification by computing 3×106 exact amplitudes for the experimentally generated bitstrings, obtaining a cross-entropy benchmarking fidelity of 0.191% (the estimated value is 0.224%). The leap of simulation capability is built on a multiple-amplitude tensor network contraction algorithm which systematically exploits the “classical advantage” (the inherent “store-and-compute” operation mode of von Neumann machines) of current supercomputers, and a fused tensor network contraction algorithm which drastically increases the compute efficiency on heterogeneous architectures. Our method has a far-reaching impact in solving quantum many-body problems, statistical problems, as well as combinatorial optimization problems.

关键词： Quantum benchmarking Quantum circuits Quantum computation Tensor network methods

A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised industrial Image Anomaly Detection

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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

FedSiam-DA: Dual-aggregated Federated Learning via Siamese Networks under Non-IID Data

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arXiv 2022年

作者： Yang, Ming Wang, Yanhan Wang, Xin Zhang, Zhenyong Wu, Xiaoming Cheng, Peng Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou310027 China

Federated learning is a distributed learning that allows each client to keep the original data locally and only upload the parameters of the local model to the server. Despite federated learning can address data island, it remains challenging to train with data heterogeneous in a real application. In this paper, we propose FedSiam-DA, a novel dual-aggregated contrastive federated learning approach, to personalize both local and global models, under various settings of data heterogeneity. Firstly, based on the idea of contrastive learning in the Siamese Network, FedSiam-DA regards the local and global model as different branches of the Siamese Network during the local training and controls the update direction of the model by constantly changing model similarity to personalize the local model. Secondly, FedSiam-DA introduces dynamic weights based on model similarity for each local model and exercises the dual-aggregated mechanism to further improve the generalization of the global model. Moreover, we provide extensive experiments on benchmark datasets, the results demonstrate that FedSiam-DA achieves outperforming several previous FL approaches on heterogeneous datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

Research Status of 6LoWPAN in the Field of Internet of Things 5

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Research Status of 6LoWPAN in the Field of Internet of Thing...

5th International Conference on Automation, control and Robotics engineering, CACRE 2020

作者： Zhao, Liang Wang, Guangwen School of Control Science and Engineering Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China

ISBN: (纸本)9781728198880

The increasing number of wireless sensor nodes brings more application markets and challenges to the Internet of things (IoT). The traditional IPv4 address resources cannot meet the needs of the development of the IoT, so we need a communication mechanism with better compatibility and scalability. With the proposal of the 6LoWPAN protocol, the application of 6LoWPAN to the communication network of the IoT has become the focus of research. This paper studies the research status of 6LoWPAN in the field of Internet of things in the past four years, and analyzes the system architecture, system composition and monitoring parameters of 6LoWPAN through different application scenarios. Experiments in related literatures show that 6LoWPAN has stable network transmission performance and can be used for precise monitoring and management in combination with web applications. In addition, RPL routing protocol security algorithm can be one of the future research directions. © 2020 IEEE.

关键词： Internet of things

Model Predictive control with Reach-avoid Analysis

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arXiv 2023年

作者： Ren, Dejin Lu, Wanli Lv, Jidong Zhang, Lijun Xue, Bai State Key Lab. of Computer Science Institute of Software CAS Beijing China University of Chinese Academy of Sciences Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

In this paper we investigate the optimal controller synthesis problem, so that the system under the controller can reach a specified target set while satisfying given constraints. Existing model predictive control (MPC) methods learn from a set of discrete states visited by previous (sub-)optimized trajectories and thus result in computationally expensive mixed-integer nonlinear optimization. In this paper a novel MPC method is proposed based on reach-avoid analysis to solve the controller synthesis problem iteratively. The reach-avoid analysis is concerned with computing a reach-avoid set which is a set of initial states such that the system can reach the target set successfully. It not only provides terminal constraints, which ensure feasibility of MPC, but also expands discrete states in existing methods into a continuous set (i.e., reach-avoid sets) and thus leads to nonlinear optimization which is more computationally tractable online due to the absence of integer variables. Finally, we evaluate the proposed method and make comparisons with state-of-the-art ones based on several examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Model predictive control

Competition and Cooperation Mechanism Between Agency Selling and Wholesale: An Application of the Emerging E-commerce Model

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Competition and Cooperation Mechanism Between Agency Selling...

IEEE International Conference on industrial engineering and engineering Management

作者： Haonan Wang C. K. M. Lee Ping Ji Gang Li Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong China Laboratory for Artificial Intelligence in Design Hong Kong Science Park New Territories Hong Kong SAR China School of Intelligent Finance and Business XJTLU Entrepreneur College (Taicang) Xi'an Jiaotong - Liverpool University China School of Management Xi'an Jiaotong University The State Key Lab for Manufacturing Systems Engineering The Key Lab of the Ministry of Education for Process Control and Efficiency Engineering Xi'an China

Recently, the platform economy allows enterprises to collaborate with business partners and provide additional channels for various parties. E-commerce platforms have become integral channels for connecting suppliers with consumers. In the platform economy, agency selling and reselling are two predominant sales models that often coexist for the same product types, resulting in a competitive relationship. This study analyzes transaction data for 30,000 Stock Keeping Units (SKUs) provided by the Chinese e-commerce platforms over one month. We employ three models to compare the sales quantity and profits of manufacturers and online platforms and to identify the most efficient mode for stakeholders based on the model establishment and empirical evidence. Our findings suggest that apart from the existing channel to provide products to customers, manufacturers enter the platform which will increase the profit of platform operators. On the other hand, platform operators may also affect the profit of the manufacturers.

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Weighted Fusion of Multiple Classifiers for Human Activity Recognition

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Weighted Fusion of Multiple Classifiers for Human Activity R...

International Conference on Information Fusion

作者： Kezhu Zuo Xinde Li Jean Dezert Yilin Dong School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of CSE School of Automation Southeast University Nanjing China The French Aerospace Lab ONERA DTIS/MIDL Palaiseau France College of Computer Science and Engineering Shanghai Maritime University Shanghai China

Human Activity Recognition (HAR) based on wear-able device has become a hot topic of research due to its wide range of applications in health-care, fitness and smart homes. However, the classification of some activities with similar sensor readings, such as standing and sitting, is usually more challenging for the design of efficient activity recognition algorithms. Considering the inconsistent performance of different classifiers, which can provide information complementary for individual classifier, we propose a novel multi-classifier fusion method based on belief functions (BFs) theory for HAR. Specifically, at first, four classifiers are trained using time-domain and frequency-domain features to obtain basic belief assignments (BBA) of activity, respectively. Then, three assessment criteria are utilized to evaluate the reliability of the classifiers and a scoring matrix is constructed. Next, the algorithm of Belief Function based the Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) is employed to calculate the weighting coefficients for each classifier. Finally, the discounting and Dempster’s rules are adopted to combine the multiple classifiers and further decision making. Several experiments were conducted to illustrate the performance of the proposed method using the UCI smartphone dataset, and the results show that the proposed method is more accurate than the state-of-art methods.

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