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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yuanhao Liu Wei Liang Chu Wang Xin Zhang Huanbing Gao Yinlong Zhang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences School of Information and Electrical Engineering Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Safe & smart move (SSM) aims to help industrial vehicular robot (IVR), such as warehousing robots, running safely and smartly by detecting the obstacles in front and maintaining a safe distance & attitude. However, the traditional SSM methods suffer the drawbacks of limited ranges, inaccurate regression of safety distance & orientation between the robot and the forward unexpected obstacles, and the inappropriate IVR control strategies. To solve these issues, this paper designs an innovative real-time safety-aware framework using the complementary Wheel-Encoder and Inertial-RGB-Depth measurements to ensure the IVR running safely, reasonably and reliably. To estimate the robot running states, we take advantages of the complementary wheel encoder, monocular RGB images and inertial measurements to obtain the robot positions, orientations, angular and linear velocities in a tightly-coupled manner. To improve the safety relative distance & attitude estimation accuracy, we exploit the RGB and depth measurements in a task-oriented fashion. The forward obstacles with the specific contours are detected using the Mask-RCNN model, followed by the Gaussian Mixture Model to obtain the forward obstacle depth. Eventually, an IVR control strategy is designed to maintain the robot safety distance and orientation, and to keep the minimum damage to the robot mechanical system. The presented method has been evaluated on the developed industrial vehicular robot safety perception platform. The experimental results prove the competitiveness against the state-of-the-art methods.

关键词： Service robots Warehousing Robot vision systems Wheels Reliability engineering Safety Mobile robots Mechanical systems Velocity measurement Gaussian mixture model

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Tunable and Functional Ionic Liquids Embedded Hyper-Crosslinked Polymers as H2o-Tolerant Adsorbents for Highly Efficient Gaseous Toluene Capture

SSRN

引用

SSRN 2024年

作者： Nie, Xiongfei Qiu, Xinjie Zhang, Ruina Ke, Quanli Liu, Huayan Zhang, Xiangping Lu, Hanfeng Cui, Guokai Zhang, Suojiang Innovation Team of Air Pollution Control Institute of Catalytic Reaction Engineering State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology College of Chemical Engineering Zhejiang University of Technology Hangzhou310014 China Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China

Developing advanced sorbents for efficient aromatic VOCs sorption from humid conditions are highly desired. In this work, a series of tunable and functional ionic liquids embedded hyper-crosslinked polymers (ILHCPs) containing different structures of anion-functional ionic liquids (ILs) with 1,4-dichloroxylene (DCX) as the crosslinker were designed and prepared for highly efficient gaseous toluene adsorption under humidity. These ILHCPs could achieve high surface area (600~900 m2 g−1), high micropore volume (0.1~0.3 cm3 g−1), small average pore size (4~5 nm), and large water contact angle (up to 110°) through tuning the anion structures and the initial IL:DCX molar ratio. Gaseous toluene adsorption isotherms on typical ILHCPs were tested under 298, 308, and 318 K, and a dual-site Langmuir-Freundlich (DSLF) model was used for well describing adsorption isotherms, which can be divided into two contributions for site I and site II. The isosteric heats of gaseous toluene adsorption were calculated. Furthermore, toluene adsorption under different humidity conditions were also tested. Compared with DCX-based self-condensed HCP, toluene adsorption capacity of ILHCPs remained steadily (270 mg g−1) even under 80% humidity. To our best knowledge, this is the first example of tuning functional ILHCPs for efficient H2O-tolerant gaseous toluene adsorption. The method may also open a door to achieving high adsorption efficiency of other volatile organic compounds by functional IL-based porous materials. © 2024, The Authors. All rights reserved.

关键词： Gas adsorption

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Management-Oriented Operating systems: Harnessing the Power of DAOs and Foundation Models

Management-Oriented Operating Systems: Harnessing the Power ...

引用

IEEE International Conference on systems, Man and cybernetics

作者： Rui Qin Juanjuan Li Xiaolong Liang Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This paper presents Management-Oriented Operating systems (M2OS) that leverages the power of parallel intelligence theory, Decentralized Autonomous Organizations (DAOs) and foundation models to revolutionize the manner of management in cyber-Physical-Social systems (CPSS). The parallel architecture on M2OS is proposed, including the parallel interactive actual M2OS and artificial M2OS. Among them, the artificial M2OSs provide digital infrastructures for organizations to operate, collaborate, and make decisions in the virtual space, and conduct computational experiments to evaluate management decisions and predict future states of the actual M2OS. Through parallel execution and closed-loop feedback between the artificial and actual M2OSs, the management and control, experimentation and evaluation, as well as learning and training of the actual M2OS can be realized. Moreover, the functional layers of M2OS, including the infrastructure layer, data layer, scenario layer, modeling layer, decision layer, and application layer, are discussed. These layers work together to support the intelligent, autonomous, collaborative, and adaptive nature of the M2OS, and facilitate data-driven decision-making, optimize business operations, and empower managers with real-time actionable insights. The proposed M2OS paradigm has great potential to transform the management paradigm and opens up new possibilities for intelligent and collaborative decision-making.

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Research on Short-term Wind Speed Prediction Based on Adaptive Hybrid Neural Network with Error Correction

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer Science 2023年第4期50卷 1290页

作者： Long, Hongyu He, Yunlong Xiang, Wei Guan, Zhenqi Tan, Hao Yu, Jianbo Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing401120 China

As a clean and efficient renewable energy, wind power is increasingly valued by countries all over the world. However, due to the inherent volatility, intermittency, and uncontrollability of wind power, the secure, stable, and economical operation of the power grid will face unprecedented pressure with wind power integration on a vast scale. Wind power predictions and management are significantly impacted by wind speed. Therefore, it is crucial to propose an accurate, stable, and efficient wind speed prediction method. In this paper, the original wind speed data is denoised using singular spectrum analysis to solve the problem of strong randomness and volatility of wind speed data. Then, the denoised data is processed using wavelet analysis, and the high-frequency and low-frequency sequences are respectively predicted using CNN-BiLSTM and CNN-BP, while the weights are optimized using the PSO algorithm. Finally, the prediction accuracy is further improved by incorporating the error correction module. In the two datasets used for testing, the outcomes of the trial indicate that the wind speed prediction model proposed in this paper performs extremely well in short-term wind speed prediction, superior to other models. The five evaluation indices (MAE, MSE, MAPE, RMSE, and R2) for the first dataset are respectively 0.166, 0.055, 0.05, 0.234, and 0.953. Similar results were obtained for the second dataset. Furthermore, The ablation tests also show that each module suggested in this research is successful in enhancing prediction accuracy. © 2023, International Association of Engineers. All rights reserved.

关键词： Error correction

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SWDPM: A Social Welfare-Optimized Data Pricing Mechanism

arXiv

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arXiv 2023年

作者： Yu, Yi Yao, Shengyue Wang, Fei-Yue Li, Juanjuan Lin, Yilun Urban Computing Lab Shanghai AI Laboratory Shanghai China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

Data trading has been hindered by privacy concerns associated with user-owned data and the infinite reproducibility of data, making it challenging for data owners to retain exclusive rights over their data once it has been disclosed. Traditional data pricing models relied on uniform pricing or subscription-based models. However, with the development of Privacy-Preserving Computing techniques, the market can now protect the privacy and complete transactions using progressively disclosed information, which creates a technical foundation for generating greater social welfare through data usage. In this study, we propose a novel approach to modeling multi-round data trading with progressively disclosed information using a matchmaking-based Markov Decision Process (MDP) and introduce a Social Welfare-optimized Data Pricing Mechanism (SWDPM) to find optimal pricing strategies. To the best of our knowledge, this is the first study to model multi-round data trading with progressively disclosed information. Numerical experiments demonstrate that the SWDPM can increase social welfare 3 times by up to 54% in trading feasibility, 43% in trading efficiency, and 25% in trading fairness by encouraging better matching of demand and price negotiation among traders. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Capacity Estimation of Lithium-ion Battery based on Electrochemical Model with Electrolyte Dynamics ⁎

引用

IFAC-PapersOnLine 2020年第2期53卷 5909-5914页

作者： Guangwei Chen Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

The remaining capacity of a battery is a crucial indicator, which has significant impact on state of Charge (SoC) estimation and the safe operations of electric vehicles. In this paper, an electrochemical model considering the electrolyte dynamics is proposed to estimate the real capacity of a lithium-ion battery. The electrochemical model with electrolyte dynamics governed by several partial differential equations has the potential to accurately describe varieties of phenomenons inside the battery. Furthermore, a Pade’ one-order approximation is adopted to obtain the transfer function between the boundary lithium-ion concentration and input current, then a boundary state estimator is proposed to estimate the the boundary lithium-ion concentration. After that, the least square method is used to obtain the adaptive update law for maximum concentration estimation in anode. Finally, the correctness of the aforementioned estimation methods is verified through simulation.

关键词： lithium-ion battery electrochemical model capacity electrolyte dynamics Pade’ approximation least square method

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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Domain generalization for vision-based driving trajectory generation

arXiv

引用

arXiv 2021年

作者： Wang, Yunkai Zhang, Dongkun Cui, Yuxiang Chen, Zexi Jing, Wei Chen, Junbo Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Autonomous Driving Lab Alibaba DAMO Academy Hangzhou China

One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems. We leverage an adversarial learning approach to train a trajectory generator as the decoder. Based on the pre-trained decoder, we infer the latent variables corresponding to the trajectories, and pre-train the encoder by regressing the inferred latent variable. Finally, we fix the decoder but fine-tune the encoder with the final trajectory loss. We compare our proposed method with the state-of-the-art trajectory generation method and some recent domain generalization methods on both datasets and simulation, demonstrating that our method has better generalization ability. Our project is available at https://***/view/dg-traj-gen. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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A Spatial-Temporal Attention Model for Human Trajectory Prediction

引用

IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction

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Real-Time Trajectory Planning for Aerial Perching

arXiv

引用

arXiv 2022年

作者： Ji, Jialin Yang, Tiankai Xu, Chao Gao, Fei The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China

This paper presents a novel trajectory planning method for aerial perching. Compared with the existing work, the terminal states and the trajectory durations can be adjusted adaptively, instead of being determined in advance. Furthermore, our planner is able to minimize the tangential relative speed on the premise of safety and dynamic feasibility. This feature is especially notable on micro aerial robots with low maneuverability or scenarios where the space is not enough. Moreover, we design a flexible transformation strategy to eliminate terminal constraints along with reducing optimization variables. Besides, we take precise SE(3) motion planning into account to ensure that the drone would not touch the landing platform until the last moment. The proposed method is validated onboard by a palm-sized micro aerial robot with quite limited thrust and moment (thrust-to-weight ratio 1.7) perching on a mobile inclined surface. Sufficient experimental results show that our planner generates an optimal trajectory within 20ms, and replans with warm start in 2ms. © 2022, CC BY-NC-ND.

关键词： Antennas

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