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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Tian, Zhenda Liu, Keping Liu, Yongbai Wang, Gang Shen, Yao Sun, Zhongbo Changchun University of Technology Department of Control Science And Engineering Changchun China Changchun University of Technology Mechanical Engineering Changchun China Changchun University of Technology School of Electrical And Information Engineering Jilin Engineering Normal University Department of Control Science And Engineering Changchun China

ISBN: (纸本)9798350359145

Radical basis function (RBF) neural network is proposed for the upper limb rehabilitation robot driven by series elastic actuator (SEA). The human-in-charge (HIC) mode and the robot-in-charge (RIC) mode are performed via the devised interaction control algorithm, where in the different control modes can be switched smoothly to avoid human-robot confrontation and improve the rehabilitation effect. On the basis of the RBF neural network with nonlinear mapping property, the human-robot interaction control modes can be dynamically adjusted via utilizing human-robot interaction force, which realizes the human-robot collaboration and adaptive control. The simulation results prove that the advised control method has a excellent performance different control modes. © 2023 IEEE.

关键词： Human robot interaction

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Improving Date Fruit Classification Using Cycle GAN-Generated Dataset

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Computer Modeling in engineering & Sciences 2022年第4期131卷 331-348页

作者： Dina M.Ibrahim Nada M.Elshennawy Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computers and Control Engineering Department Faculty of EngineeringTanta UniversityTanta37133Egypt

Dates are an important part of human *** are high in essential nutrients and provide a number of health *** fruits are also known to protect against a number of diseases,including cancer and heart *** fruits have several sizes,colors,tastes,and *** are a lot of challenges facing the date *** of the most significant challenges is the classification and sorting of *** there is no public dataset for date fruits,which is a major limitation in order to improve the performance of convolutional neural networks(CNN)models and avoid the overfitting *** this paper,an augmented date fruits dataset was developed using Deep Convolutional Generative Adversarial Networks(DCGAN)and CycleGAN approach to augment our collected date fruit *** augmentation is required to address the issue of a restricted number of images in our datasets,as well as to establish a balanced *** are three types of dates in our proposed dataset:Sukkari,Ajwa,and *** dataset augmentation,we train our created dataset using ResNet152V2 and CNN models to assess the classification process for our three categories in the *** train these two models,we start with the original ***,the models were trained using the DCGAN-generated dataset,followed by the CycleGAN-generated *** resulting results demonstrated that when using the ResNet152V2model,the CycleGAN-generated dataset had the highest classification performance with 96.8%accuracy,followed by the CNN model with 94.3%accuracy.

关键词： Dates fruits data augmentation DCGAN CycleGAN,deep learning convolution neural networks

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Vibration Reduction by a Partitioned Dynamic Vibration Absorber with Acoustic Black Hole Features

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Chinese Journal of Mechanical engineering 2024年第4期37卷 120-134页

作者： Xiaoning Zhao Chaoyan Wang Hongli Ji Jinhao Qiu Li Cheng State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and AstronauticsNanjing 210016China Nanjing Institute of Information Technology Nanjing 210016China Department of mechanical Engineering Hong Kong Polytechnic UniversityKowloon 999077Hong KongChina

Vibration quality is a vital indicator for assessing the progress of modern *** dynamic vibration absorber(DVA)based on the acoustic black hole(ABH)feature is a new passive control method that manipulates *** offers efficient energy focalization and broad-spectrum vibration suppression,making it highly promising for applications in large equipment such as aircraft,trains,and *** previous advancements in ABH-DVA development,certain challenges remain,particularly in ensuring effective coupling with host structures during *** address these issues,this study proposes a partitioned ABH-featured dynamic vibration absorber(PABH-DVA)with partitions in the radial direction of the *** employing a plate as the host structure,simulations and experiments were conducted,demonstrating that the PABH-DVA outperforms the original symmetric ABH-DVA in terms of damping *** study also calculated and compared the coupling coefficients of the two ABH-DVAs to uncover the mechanism behind the enhanced *** results revealed that the PABH-DVA exhibits more coupled modes,occasionally with lower coupling coefficients than the symmetric *** influence of frequency ratio and modal mass was further analyzed to explain the reasons behind the PABH-DVA's superior damping ***,the study discussed the impact of the number of slits and their *** research further explains the coupling mechanism between the ABH-DVA and the controlled structure,and provides new ideas for the further application of ABH in engineering.

关键词： Acoustic black hole Vibration control Dynamic vibration absorber Coupling analysis

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Dispatch of a Coal Mine-Integrated Energy System:Optimization Model with Interval Variables and Lower Carbon Emission

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Tsinghua Science and technology 2024年第5期29卷 1441-1462页

作者： Hejuan Hu Xiaoyan Sun Bo Zeng Dunwei Gong Yong Zhang Patrick Nyonganyi Henerica Tazvinga School of Information and Control Engineering ChinaUniversity of Mining and TechnologyXuzhou 221116China Laboratory of Alternate Electrical PowerSystem with Renewable Energy Sources North ChinaElectric Power UniversityBeijing 102206China School of Information Science andTechnology Qingdao University of Science and TechnologyQingdao 266061China Department of Automation Tsinghua UniversityBeijing 100084China South Africa WeatherService Pertoria 0001South Africa

In the coal mining process,a large amount of Coal Mine-Associated energy(CMAE),such as coal mine methane and underground wastewater,is *** on the modeling and optimization dispatching of a Coal Mine-Integrated Energy System(CMIES)with CMAE effectively saves energy and reduces carbon *** has great uncertainties owing to the affections of the hydrogeology conditions and mining *** addition,thermal loads have high comfort requirements in mines,which brings great challenges to the optimization dispatching of ***,this paper studies the architecture and solution of CMIESs with a flexible thermal load and source-load ***,to effectively improve the electric and thermal conversion efficiency,the architecture of CMIES,including a concentrating solar power station,is ***,for the scheduling model with bilateral uncertainty,the interval representation method with interval variables is proposed,and a multi-objective scheduling model based on the interval variables and flexible thermal load is ***,we propose a solution method for the model with interval variables.A case study is conducted to demonstrate the performance of our model and method for lowering carbon emissions and cost.

关键词： Integrated Energy Systems(IESs) Coal Mine-Integrated Energy Systems(CMIES) bilateral uncertainties concentrating solar power station interval optimization

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Statistical Learning Theory for control: A Finite-Sample Perspective

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IEEE control Systems 2023年第6期43卷 67-97页

作者： Tsiamis, Anastasios Ziemann, Ingvar Matni, Nikolai Pappas, George J. ETH Zürich Department of Information Technology and Electrical Engineering Zürich8092 Switzerland KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States

Learning algorithms have become an integral component to modern engineering solutions. Examples range from self-driving cars and recommender systems to finance and even critical infrastructure, many of which are typically under the purview of control theory. While these algorithms have already shown tremendous promise in certain applications [1], there are considerable challenges, in particular, with respect to guaranteeing safety and gauging fundamental limits of operation. Thus, as we integrate tools from machine learning into our systems, we also require an integrated theoretical understanding of how they operate in the presence of dynamic and system-theoretic phenomena. Over the past few years, intense efforts toward this goal - an integrated theoretical understanding of learning, dynamics, and control - have been made. While much work remains to be done, a relatively clear and complete picture has begun to emerge for (fully observed) linear dynamical systems. These systems already allow for reasoning about concrete failure modes, thus helping to indicate a path forward. Moreover, while simple at a glance, these systems can be challenging to analyze. Recently, a host of methods from learning theory and high-dimensional statistics, not typically in the control-theoretic toolbox, have been introduced to our community. This tutorial survey serves as an introduction to these results for learning in the context of unknown linear dynamical systems (see 'Summary'). We review the current state of the art and emphasize which tools are needed to arrive at these results. Our focus is on characterizing the sample efficiency and fundamental limits of learning algorithms. Along the way, we also delineate a number of open problems. More concretely, this article is structured as follows. We begin by revisiting recent advances in the finite-sample analysis of system identification. Next, we discuss how these finite-sample bounds can be used downstream to give guaranteed performa

关键词： Learning algorithms

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A Three-Dimensional Force Estimation Method for the Cable-Driven Soft Robot Based on Monocular Images

A Three-Dimensional Force Estimation Method for the Cable-Dr...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhu, Xiaohan Bu, Ran Li, Zhen Xu, Fan Wang, Hesheng School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Automation China Artificial Intelligence Research Institute School of Information and Control Engineering China University of Mining and Technology China

ISBN: (纸本)9781665481090

Soft manipulators are known for their superiority in coping with high-safety-demanding interaction tasks, e.g., robot-assisted surgeries, elderly caring, etc. Yet the challenges residing in real-time contact feedback have hindered further applications in precise manipulation. This paper proposes an end-to-end network to estimate the 3D contact force of the soft robot, with the aim of enhancing its capabilities in interactive tasks. The presented method features directly utilizing monocular images fused with multidimensional actuation information as the network inputs. This approach simplifies the preprocessing of raw data compared to related studies that utilize 3D shape information for network inputs, consequently reducing configuration reconstruction errors. The unified feature representation module is devised to elevate low-dimensional features from the system's actuation signals to the same level as image features, facilitating smoother integration of multimodal information. The proposed method has been experimentally validated in the soft robot testbed, achieving satisfying accuracy in 3D force estimation (with a mean relative error of 0.84% compared to the best-reported result of 2.2% in the related works). © 2024 IEEE.

关键词： Robotic surgery

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Deep Reinforcement Learning Based Approach for Dynamic Optimal Power Flow in Active Distribution Network 41

Deep Reinforcement Learning Based Approach for Dynamic Optim...

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第41届中国控制会议

作者： Xinghua Liu Bangji Fan Jiaqiang Tian Department of Power Grid Information and Control Engineering School of Electrical EngineeringXi'an University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The dynamics and randomness of the active distribution network(ADN) bring many difficulties to optimal power flow(OPF).In this paper,we propose a deep reinforcement learning method to solve the dynamic optimal power flow(DOPF)of ADN including photovoltaic(PV) power and energy storage systems(ESS).In the distributed generations(DG) modeling of ADN,PV and ESS are treated as PQ nodes,constant power factor control is adopted in the reactive power management of PV to enhance the universality of the *** from the traditional methods,the OPF covering multiple periods is described as a Markov process,which takes the reactive output of PV and the active and reactive output of ESS as the *** the continuity of actions,the deep deterministic policy gradient(DDPG) algorithm is introduced to train the agent to obtain the actions at each time of the ***,the method in this paper is verified in an improved IEEE 33-bus distribution network with real data of PV power and *** experimental results show that the proposed method can adjust the decision-making of the agent according to the real-time active output of DG and loads,to minimize the total active power network loss in a day.

关键词： Dynamic optimal power flow active distribution network distributed generations deep reinforcement learning

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Mn-based MXene with high lithium-ion storage capacity

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Nano Research 2024年第5期17卷 4181-4191页

作者： Yanyan Wu Dongqing Liu Xiaonan Wang Usman Ghani Muhammad Asim Mushtaq Jinfeng Yang Huarui Sun Panagiotis Tsiakaras Xingke Cai School of Science and Ministry of Industry and Information Technology Key Laboratory of Micro-Nano Optoelectronic Information System Harbin Institute of Technology ShenzhenShenzhen 5180552 China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China Institute for Advanced Study Shenzhen UniversityShenzhen 518060China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China Laboratory of Alternative Energy Conversion Systems Department of Mechanical EngineeringSchool of EngineeringUniversity of ThessalyPedion Areos 38834Greece

3d-transition metal(Fe,Co,Ni,and Mn)-based MXene materials have been predicted to demonstrate exceptional electrochemical performance because of their good electrical conductivity and the presence of metallic atoms with multiple charge ***,until now,there have been no reports on MXenes based on Fe,Co,Ni,and Mn,due to the lack of 3d-metal-layered ***,we successfully synthesized the first 3d-transition metal-based MXenes,Mn_(2)CT_(x) by exfoliating a layered precursor derived from the anti-perovskite bulk *** as-prepared Mn_(2)CT_(x) MXene nanosheets were employed as anode materials in lithium-ion batteries,which exhibited stable storage capacity of 764.7 mAh·g^(-1) at 0.5 C,placing its storage capacities at an upper-middle level compared with other reported MXene materials as well as other Mn-based anode ***,this study opens a new avenue for MXene research by synthesizing 3d-transition metal-based MXenes for electrochemical applications.

关键词： Mn_(3)GaC Mn_(2)CT_(x) MXenes lithium-ion battery anode materials negative fading capacity

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A Weighted Average Consensus Approach for Decentralized Federated Learning

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Machine Intelligence Research 2022年第4期19卷 319-330页

作者： Alessandro Giuseppi Sabato Manfredi Antonio Pietrabissa Department of Computer Controland Management EngineeringUniversity of Rome La SapienzaRome 00185Italy Department of Electrical Engineering and Information Technology University of Naples Federico ⅡNaples 80125Italy

Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training *** was proposed for edge computing and Internet of things(IoT)tasks in which a centralized server was responsible for coordinating and governing the training *** remove the design limitation implied by the centralized entity,this work proposes two different solutions to decentralize existing FedL algorithms,enabling the application of FedL on networks with arbitrary communication topologies,and thus extending the domain of application of FedL to more complex scenarios and new *** the two proposed algorithms,one,called FedLCon,is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solutions,as also shown by the reported validation tests.

关键词： Federated learning(FedL) deep learning federated averaging(FedAvg) machine learning(ML) artificial intelligence discrete-time consensus distributed systems.

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Photovoltaic VSG control with Power Reference Regulation Using Fuzzy PI

Photovoltaic VSG Control with Power Reference Regulation Usi...

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2023 China Automation Congress, CAC 2023

作者： Li, Jie Li, Jia-Hua Ren, Hai-Peng School of Electrical Engineering Xi' An University of Technology Xi'an China Xi' An University of Technology Department of Information and Control Xi'an China

ISBN: (纸本)9798350303759

Virtual synchronous generator (VSG) control strategy is widely used for photovoltaic grid-tied inverter to provide better stability for low inertia green energy generation. However, due to the fluctuation of photovoltaic output, the traditional virtual synchronous machine control strategies with constant power reference cause significant fluctuations in the DC side voltage, which does harm to the stable operation of the system. Energy storage equipment is generally used to stabilize the DC bus voltage, which imposes high cost to the customer, especially for the distributed roof photovoltaic generation. To deal with this problem, the active power reference of the VSG is regulated using the proportional integral (PI) control framework according to the error between the DC bus voltage reference and the measured DC bus voltage. To accelerate the regulation speed, a set of fuzzy rules are proposed to adjust the proportional integral parameters, which also avoid the difficulty in the traditional PI parameter adjustment. Simulation results show the effectiveness of the DC side voltage stabilization method, and the improved performance using the fuzzy interference parameter adjustment for PI controller. © 2023 IEEE.

关键词： Synchronous generators

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