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Exploring single atom catalysts of transition-metal doped phosphorus carbide monolayer for HER:A first-principles study

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Journal of Energy Chemistry 2021年第1期30卷 155-162,I0006页

作者： Dachang Chen Zhiwen Chen Xiaoxing Zhang Zhuole Lu Song Xiao Beibei Xiao Chandra Veer Singh School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072HubeiChina Department of Materials Science and Engineering University of TorontoToronto M5S 3E4OntarioCanada Department of Mechanical and Industrial Engineering University of TorontoToronto M5S 3G8OntarioCanada Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan 430068HubeiChina School of Energy and Power Engineering Jiangsu University of Science and TechnologyZhenjiang 212003JiangsuChina

Hydrogen has been identified as one of the most promising sustainable and clean energy. Developing hydrogen evolution reaction(HER) catalyst with high activity is essential for satisfying the future requirements. Considering novel advantages of two-dimensional materials and high catalytic activity of atomic transition metal, in this study, using density functional theory calculation, the HER on single transitionmetal(23 different TM atoms) doped phosphorus carbide monolayer(α-PC) has been investigated. The Volmer–Tafel and Volmer–Heyrovsky reaction mechanisms, and the stability of the most promising HER catalyst are also included. The results show that Ir-αPC with high physical and thermal stability has the most optimal value of Gibbs free adsorption energy for H atom. The relationship of d band center and the HER activity shows a volcano-like curve. The calculation of reaction energy barrier indicates that the Volmer-Heyrovsky step is more favorable than the Volmer-Tafel step.

关键词： Hydrogen evolution reaction Phosphorus carbide monolayer Transition-metal doping Density functional theory

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Learning the continuous-time optimal decision law from discrete-time rewards

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National Science Open 2024年第5期3卷 130-147页

作者： Ci Chen Lihua Xie Kan Xie Frank Leroy Lewis Yilu Liu Shengli Xie School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou 510006China UTA Research Institute the University of Texas at ArlingtonFort Worth 76118USA Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA Oak Ridge National Laboratory Oak Ridge 37830USA Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement *** this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical *** find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral *** apply this finding to solve output-feedback design problems in power *** results reveal that our approach removes an intermediate stage of identifying dynamical *** work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

关键词： continuous-time state and action decision law learning discrete-time reward dynamical systems reinforcement learning

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A Unified Formulation of Geometry-aware Discrete Dynamic Movement Primitives

arXiv

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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

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Discrete Continuous Nonlinear control system for Non-Affine Periodic Action Plant

Discrete Continuous Nonlinear Control System for Non-Affine ...

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2020 International Russian automation Conference, RusAutoCon 2020

作者： Eremin, E.L. Nikiforova, L.V. Shelenok, E.A. Amur State University Information and Control Systems Department Blagoveshchensk Russia Pacific National University Automation and System Engineering Department Khabarovsk Russia

ISBN: (纸本)9781728161303

In the present paper authors discusses the problem of construct the discrete algorithms of nonlinear-periodic control system for one class of non-affine continuous plants which working in the priory uncertainty conditions. As methods to solve the posed in paper problem hyperstability criterion, Ldissipativity conditions and continuous models method are used. With the help of numerical experiments it is depicted the operating quality of the proposed hybrid nonlinear-periodic control system. The obtained results may be useful for construction the automated control system for dynamic plants which mathematical model contains non-linearity with respect to input signal. The paper results may be useful for construct the control systems for the class of non-affine dynamic plants with mathematical models are equivalent discussed plant's model. © 2020 IEEE.

关键词： Quality control

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A Study for UAV Autonomous Safe Landing-Site Selection on Rough Terrain 2021

A Study for UAV Autonomous Safe Landing-Site Selection on Ro...

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2nd International Conference on Computing and Data Science, CONF-CDS 2021

作者： Zheng, Wenlong Yi, Jianjun Xiang, Haojie Zhou, Bo Wang, Danwei Zhao, Changchun Department of Mechanical Engineering East China University of Science and Technology Shanghai200237 China Intelligent Unmanned System Control Technology Joint Lab Shanghai201109 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Shanghai Aerospace Control Technology Institute Shanghai201109 China

ISBN: (纸本)9781450389570

Autonomous safe landing of UAV is an important function in many scenarios such as force landing and delivery. This paper proposes a method to autonomously select a safe landing site for vertical take-off and landing (VTOL) UAV based on point cloud, which can minimize combined risks posed during touch down at the chosen landing site. The most suitable landing site of a landing zone is selected according to the terrain complexity. In this paper, (1) fine-grained grid elevation map converted from the terrain point cloud is used to calculate the potential risk such as slope, roughness and maximum height difference. (2) A comprehensive risk model is designed to consider all above risks to recognize obstacles and risk areas, and combine the flight distance factors to obtain the final cost map. (3) We process cost map as image by OpenCV to accelerate the processing and reduce reaction time. Terrain point clouds of simulation scene and real world are used for experiments and experimental results show that the selected landing sites can meet the safety requirements, which demonstrate the effectiveness and feasibility of our proposed method. © 2021 ACM.

关键词： Unmanned aerial vehicles (UAV)

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Multi-objective Optimization of A CCHP system Model Integrated with P2G for Decarbonization

Multi-objective Optimization of A CCHP System Model Integrat...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Pengyu Wei Olusola Bamisile Dongsheng Cai Jian Li Humphrey Adun Qi Huang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu China Energy Systems Engineering Department Cyprus International University KKTC Mersin Turkey

To realize the dual carbon policy, the goal of energy saving, and emission reduction, it is important to develop ways to better promote the low carbon economic operation of energy systems. Therefore, this research proposes an optimal operation strategy considering the electric-gas-cooling-heat demand response. Considering the demand response of each participant, an integrated energy system model with photovoltaic, wind energy, and traditional grid as energy supply. Also, electric energy, natural gas, cooling, and heating subsystems with co-production are established. With minimizing the overall system operation cost and optimizing environmental protection, the improved particle swarm optimization algorithm is used to solve the multi-constrained objective optimization problem. Finally, the optimized model is tested. The results from this study show that the energy system under cogeneration can find a balance between economy and environmental friendliness, and provide a reference for the subsequent energy supply configuration.

关键词： Photovoltaic systems Renewable energy sources Wind energy systems operation system integration Demand response Particle swarm optimization

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MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models

arXiv

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arXiv 2024年

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

Fruit harvesting poses a significant labor and financial burden for the industry, highlighting the critical need for advancements in robotic harvesting solutions. Machine vision-based fruit detection has been recognized as a crucial component for robust identification of fruits to guide robotic manipulation. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated using several metrics, which outperforms the existing state-of-the-art algorithms in both our MetaFruit dataset and other open-sourced fruit datasets, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024, CC BY-NC-SA.

关键词： Fruits

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Metafruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models

SSRN

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SSRN 2024年

Fruit harvesting poses a significant labor and financial burden for the industry, highlighting the critical need for advancements in robotic harvesting solutions. Machine vision-based fruit detection has been recognized as a crucial component for robust identification of fruits to guide robotic manipulation. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated using several metrics, which outperforms the existing state-of-the-art algorithms in both our MetaFruit dataset and other open-sourced fruit datasets, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset and detection framework are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024, The Authors. All rights reserved.

关键词： Fruits

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Development and prospect of direct‐current circuit breaker in China

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High Voltage 2021年第1期6卷 1-15页

作者： Weijiang Chen Rong Zeng Junjia He Yi Wu Xiaoguang Wei Taixun Fang Zhanqing Yu Zhao Yuan Yifei Wu Wandi Zhou Bing Yang Lu Qu STATE GRID Corporation of China BeijingChina Department of Electrical Engineering Key Laboratory of Control and Simulation of Power System and Generation EquipmentTsinghua UniversityBeijingChina School of Electric and Electronic Engineering Huazhong University of Science&TechnologyWuhanChina School of Electrical Engineering Xi'an Jiaotong UniversityXi'anChina Global Energy Interconnection Research Institute Co. LtdBeijingChina NR Electric Co. LtdNanjingChina

The direct‐current circuit breaker(DCCB)is the most ideal choice for DC fault isolation in DC *** a late start,China's research and development on the DCCB have made outstanding *** article provides a brief glance of current China's DCCB development *** begins by sorting out the technical route according to the topology of *** it systematically summarises both mechanical and hybrid DCCBs with focussing on the aspects of topology structure and principle,key technology and characteristics,prototype development and *** is apparent that Chinese scientists and engineers confronted the worldwide problem of large capacity DC breaking,and put forward a comprehensive solution which consists of an innovative topology structure based on coupled negative voltage circuit,breaking throughs on the key technologies such as highly controllable and reliable fault current commutation,millisecond‐level ultra‐fast and efficient electromagnetic repulsion mechanism,high tolerance and high stability power electronic switch,low residual voltage and fast response energy consumption device,*** article states that the world's first set of hybrid high‐voltage(HV)DCCB,and the first set of mechanical HV DCCB have been *** DCCBs will soon be deployed to the DC grids which have the highest voltage levels therefore require the strongest breaking *** achievements are leading the world in the development and application of *** article also discusses the overall development trends of DCCB in the areas of new topologies,key techno-logical breakthroughs and application scenarios,*** discussions serve as references for DCCB's future technological advancement and its ever‐expanding applications.

关键词： breaking breakthrough discussions

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Two-Level Optimization Strategy for Vehicle Speed and Battery Thermal in Connected and Automated Evs

SSRN

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SSRN 2023年

作者： Ma, Yan Ma, Qian Liu, Yongqin Gao, Jinwu Chen, Hong Department of Control Science and Engineering Jilin University Renmin Street 5988 Changchun130012 China National Key Laboratory of Automotive Chassis Integration and Bionics Jilin University Changchun China Department of School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Electronic and Information Engineering Tongji University Shanghai201804 China

Connected and automated vehicle (CAV) technology can realize the possibility of further optimizing thermal and energy management. This article proposes a two-level control framework for vehicle speed and battery thermal management (BTM) optimizations simultaneously of electric vehicle (EV). In the upper layer, iterative dynamic programming (IDP) is presented for speed planning using intelligent traffic information to minimize powertrain energy consumption. Then, the optimal battery temperature trajectory with minimal energy consumption is computed based on the planned speed. The lower layer controllers follow reference speed and temperature trajectories in real time to accurately adjust vehicle speed, battery temperature and system energy consumption. Meanwhile, to improve the estimation accuracy of the tracking model, we integrate the Gaussian process (GP) model in the model predictive control (MPC) and build the learning-based MPC strategy in the lower layer. Simulation results verify the performance of the method which is able to reduce the powertrain energy consumption by 20.95%. And in high and low temperature environments, it reduces the BTM energy consumption by up to 15.69% and 5.03% compared to normal MPC while maintaining the battery temperature. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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