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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Jusung Park Seungkyun Park Kyuho Kim Sunkyu Yu Namkyoo Park Department of Electrical and Computer Engineering Photonic Systems Laboratory Seoul National University Seoul Korea Department of Electrical and Computer Engineering Intelligent Wave Systems Laboratory Seoul National University Seoul Korea

An improved retrieval method for effective permittivity and permeability of hyperbolic metamaterials at oblique incidence is presented. We revisit a branching problem at oblique incidence. © 2024 The Authors OCIS... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Codes Metamaterials Permeability Permittivity Electo-optic effects

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On tuning of ADRC with competing design indices:a quantitative study

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Control Theory and Technology 2023年第1期21卷 16-33页

作者： Yutao Zhao Yi Huang Zhiqiang Gao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Department of Electrical Engineering and Computer Science Center for Advanced Control TechnologiesCleveland State UniversityClevelandOH 44115USA

Controller optimization has mostly been done by minimizing a certain single cost *** practice,however,engineers must contend with multiple and conflicting considerations,denoted as design indices(DIs)in this *** to account for such complexity and nuances is detrimental to the applications of any advanced control *** paper addresses this challenge heads on,in the context of active disturbance rejection controller(ADRC)and with four competing DIs:stability margins,tracking,disturbance rejection,and noise *** this end,the lower bound for the bandwidth of the extended state observer is first established for guaranteed closed-loop ***,one by one,the mathematical formula is meticulously derived,connecting each DI to the set of controller *** our best knowledge,this has not been done in the context of *** formulas allow engineers to see quantitatively how the change of each tuning parameter would impact all of the DIs,thus making the guesswork *** example is given to show how such analytical methods can help engineers quickly determine controller parameters in a practical scenario.

关键词： Uncertain systems Active disturbance rejection control(ADRC) Design indices(DIs) Extended state observer(ESO)

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A General Method for Power Equipment State Classification Using Time Series Data Based on Attention Mechanism

A General Method for Power Equipment State Classification Us...

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2023 IEEE International Conference on Power Science and Technology, ICPST 2023

作者： Zhao, Wenqing Wang, Jifa Chen, Rui Liu, Liang Xu, Lijiao North China Electric Power University School of Control and Computer Engineering Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding China North China Electric Power University School of Control and Computer Engineering Baoding China

ISBN: (纸本)9798350311358

With refinement of power equipment, the identification and classification of its operation states is quite important for the stable operation of the system. As the power equipment states are rather complicated, it is difficult to realize general classification due to the limitation of data types and model characteristics when using deep learning. In the light of this, based on the time series data of various power equipment, an equipment state classification method is proposed by combining Recurrent Neural Network (RNN), Long short-term memory (LSTM) and attention mechanism. Firstly, RNN is used as the bottom frame network, and the LSTM core network integrating attention mechanism is added to construct the complex Feature Extraction Module (FEM). Secondly, main feature extraction is implemented in the LSTM network integrated with attention mechanism. Then, the extracted features are input into the complex FEM model and trained to match the evaluation state and obtain the evaluation results. In this paper, confusion matrix, F1micro and accuracy are used as evaluation indexes. Three cases were selected to verify the method and five groups of comparison models were set for each case. Results show that the proposed method has good accuracy in power equipment state classification and also has strong generalization ability in other data classification. © 2023 IEEE.

关键词： Long short-term memory

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Impact of PHEV operating conditions and demand side management on the economic operation of a community microgrid system 33

Impact of PHEV operating conditions and demand side manageme...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Dey, Bishwajit Fose, Nande Krishnamurthy, Senthil Ratshitanga, Mukovhe Moodley, Prathaban Cape Peninsula University of Technology Centre for Intelligent Systems and Emerging Technologies Department of Electrical Electronics and Computer Engineering P.O. BOX 1906 Bellville7535 South Africa Upper Grayston Office Park Sandton2146 South Africa

ISBN: (纸本)9798331535162

By classifying loads into shiftable and non-shiftable groups and rearranging a distribution system's load demand model, demand-side management (DSM) reduces operating expenses. This is possible by shifting variable loads when utility costs per unit are lower. This work adopts a bi-level optimization technique to minimize the operational costs of a low-voltage microgrid (MG) system that incorporates plug-in hybrid electric vehicles (PHEVs), renewable energy sources, and fossil fuel generators while operating in grid-connected mode. According to the DSM participation level, the load model is reconfigured at the first optimization stage. Then, the restructured load demand models are considered, and recommendations for distributed generator scheduling are developed to reduce the production costs of the second-level microgrid system. The study's optimization technique used the popular differential evolution (DE) method applied to several power system optimization issues. When DSM, various grid participation and pricing schemes, and other factors were considered, the cost of producing active power was reduced. To ascertain their effect on the MG system's generation cost, scenarios involving different PHEV state of charge (SoC) levels and a taxable rate during V2G were also investigated. © 2025 IEEE.

关键词： Demand side management

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Nonlinear topology network routing shortest path based on improved particle swarm optimization algorithm 5

Nonlinear topology network routing shortest path based on im...

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5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Zheng, Hui Jin, Jin Jin, Dongshuai Wang, Yifan Zhang, Guoquan Zhang, Shenguo Northwest Minzu University Gansu Engineering Research Center for Eco-Environmental Intelligent Networking College of Electrical Engineering Lanzhou China Northwest Minzu University College of Mathematics and Computer Science Lanzhou China

ISBN: (纸本)9798350310603

In the nonlinear topology network routing, through the research on the routing algorithm that guarantees QoS in the network, the different performance characteristics of the network are fully considered, and the network bandwidth, network delay, packet loss rate, demonstration jitter and other indicators are comprehensively considered constraints, citing the respective advantages of particle swarm optimization and simulated annealing algorithms in modern meta-heuristic algorithms, an improved particle swarm optimization algorithm SAPSO is applied to a multi-constrained nonlinear topology network routing model. The algorithm can be successfully applied to this problem through simulation verification. The introduction of simulated annealing algorithm can well prevent falling into local optimal solution. Using the SAPSO algorithm can reduce the comprehensive cost of the network in a multi-constrained nonlinear topology network. © 2023 IEEE.

关键词： Topology

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Integral Reinforcement Reciprocal Zhang Neural Net for Time-Varying Linear Equations

Integral Reinforcement Reciprocal Zhang Neural Net for Time-...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Wang, Changyuan Chen, Long Zhang, Yunong Li, Shuai University of Macau Faculty of Science and Technology China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland Technical Research Center of Finland Oulu Finland

ISBN: (纸本)9798331517021

Solving time-varying linear equations is a com-mon challenge encountered in engineering and science. The development of neural dynamic systems has led to the creation of various feedback neural nets that effectively solve continuous time-varying linear equations. In this context, reciprocal Zhang neural net (RZNN) was developed, which is focused by this work, providing an explicit inverse-free continuous model for solving time-varying linear equations. However, the continuous model often struggle with noise interference, e.g., Gaussian noise, during the solving process. This paper enhances RZNN model with an integral reinforcement term to improve robustness of the explicit continuous models with noisy conditions. Furthermore, we optimize the RZNN solving process, making it easier for implementation in circuit systems and numerical simulations. © 2024 IEEE.

关键词： Continuous time systems

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Impact of an electric vehicle, solar PV, and battery energy storage system in a distribution system 33

Impact of an electric vehicle, solar PV, and battery energy ...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Gbadamosi, Saheed Lekan Emmanuel Okafor, Chukwuemeka Krishnamurthy, Senthil Ratshitanga, Mukovhe Moodley, Prathaban Cape Peninsula University of Technology Centre for Intelligent Systems and Emerging Technologies Department of Electrical Electronics and Computer Engineering P.O. BOX 1906 Bellville7535 South Africa Upper Grayston Office Park Sandton2146 South Africa

ISBN: (纸本)9798331535162

The modern distribution power system has witnessed a tremendous increase in integrating renewable energy sources (wind and solar photovoltaic), electric vehicles, and battery energy storage systems. This is consequent upon the commitment of many countries (around the world) to reduce global carbon emissions and ensure an acceptable global temperature increase. This research focuses on the impacts of the integration of solar photovoltaic (PV) systems, electric vehicles (EVs), and battery energy storage systems (BESS) on the distribution system while minimizing the voltage deviations and power losses of the considered distribution network. Through a time-based Power System Simulation tool, the study evaluates four configurations to assess the technical impacts on the distribution network. The Simulation results showed that electric vehicle integration leads to increased power losses and a reduction in voltage deviations. Integrating battery energy storage systems into the distribution system increases the power losses (from 0.9222 MW to 1.602 MW) depending on the location of the battery energy storage system in the distribution network. Therefore, the study reveals the need for optimal sizing and placement of battery energy storage systems in the distribution network for optimal system performance. © 2025 IEEE.

关键词： battery energy storage Electric vehicle power loss solar PV voltage deviation

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Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments 43

Enhancing Drone Surveillance with NeRF: Real-World Applicati...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Lindén, Joakim Burresi, Giovanni Forsberg, Håkan Daneshtalab, Masoud Söderquis, Ingemar School of Innovation Design and Engineering Mälardalen University Division of Intelligent Future Technologies Västerås Sweden Saab Aeronautics Stockholm Sweden School of Electrical Engineering and Computer Science Royal Institute of Technology Division of Electronics and Embedded Systems Stockholm Sweden

ISBN: (纸本)9798350349610

Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone process. We present a method for generating synthetic data tailored to the use-case achieving excellent performance in a real-world usecase. We provide a method for producing automatically annotated synthetic visual data of multi rotor unmanned aerial vehicles (UAV) and other airborne objects in a simulated environment with a high degree of scene diversity, from collection of 3D models to generation of annotated synthetic datasets (synthsets). In our data generation framework SynRender we introduce a novel method of using Neural Radiance Field (NeRF) methods to capture photo-realistic high-fidelity 3D-models of multirotor UAVs in order to automate data generation for an object detection task in diverse environments. By producing data tailored to the real-world setting, our NeRF-derived results show an advantage over generic 3D asset collection-based methods where the domain gap between the simulated and real-world is unacceptably large. In the spirit of keeping research open and accessible to the research community we release our dataset VISER DroneDiversity used in this project, where visual images, annotated boxes, instance segmentation and depth maps are all generated for each image sample. © 2024 IEEE.

关键词： Drones

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Graph-based robot optimal path planning with bio-inspired algorithms

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Biomimetic Intelligence & Robotics 2023年第3期3卷 75-90页

作者： Tingjun Lei Timothy Sellers Chaomin Luo Daniel W.Carruth Zhuming Bi Department of Electrical and Computer Engineering Mississippi State UniversityMississippi State 39762USA Center for Advanced Vehicular Systems Mississippi State UniversityMississippi State 39762USA Department of Civil and Mechanical Engineering Purdue University Fort WayneFort Wayne 46805USA

Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based ***,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal *** limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy *** address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough *** resulting mDPderived graph is then modeled using a Maklink graph *** applying the iSOA approach,the trajectory of an autonomous robot in the workspace is ***,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature *** proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art *** experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.

关键词： Autonomous robot Path planning Bio-inspired algorithm Graph-based model Improved seagull optimization algorithm(iSOA)

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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