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共 14707 条记录，以下是21-30 订阅

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Optimizing Wind Speed Estimation with Least Squares Generative Adversarial Networks: A Case Study of Pakistan 6

Optimizing Wind Speed Estimation with Least Squares Generati...

引用

6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Ashfaq, Iqra Zahid, Hasan Naveed, Fajr Abid, Aaisha Uddin, Zaki NUST CEME Dept. of Electrical Engineering Islamabad Pakistan GIKI Dept. of Electrical Engineering Swabi Pakistan Fast NUCES Dept. of Computer Science Islamabad Pakistan NUST CEME Dept. of Mechatronics Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Wind energy has emerged as a key player in sustainable power generation. Accurate prediction of wind speed is critical for optimizing wind farm operations, energy output forecasting, and reducing dependency on traditional power sources. In this study, we propose a Least-Squares Generative Adversarial Network (LSGAN) for wind speed prediction in a specific region of Pakistan, utilizing data from Tandoghulamali, which includes features like wind speeds at various altitudes and wind direction. The model is designed to predict wind speed at 80 meters, a crucial target variable for wind turbine operations. The LSGAN model effectively leverages adversarial training, achieving high accuracy with a Mean Absolute Error (MAE) of 1.54 and a Root Mean Squared Error (RMSE) of 1.77. Through a detailed comparison of actual versus predicted values, the model demonstrates its ability to generalize well across unseen data, with stable performance during the adversarial learning process. This approach opens up new possibilities for applying advanced machine learning techniques in renewable energy forecasting, particularly in regions with sparse historical data. © 2024 IEEE.

关键词： Wind forecasting

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A Photovoltaic Interleaved Boost Integrated CL Resonant Converter for Magnetron Application

A Photovoltaic Interleaved Boost Integrated CL Resonant Conv...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Molaei, Aref Kanathipan, Kajanan Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350376067

A new single-stage high-gain interleaved boost modular CL resonant converter is proposed in this paper for photovoltaic-powered magnetron application. The proposed converter is able to provide high voltage gain for the magnetron which is a device for microwave applications. Perturb and Observe (P&O) method was used to achieve Maximum Power Point Tracking (MPPT) in the integrated interleaved boost stage by controlling the duty-cycle of the two primary leg switches. The operational principles of the converter are briefly discussed in this paper. Results on a 1.7kW, 230V/3.8kV design are presented to verify the proposed converter performance. © 2024 IEEE.

关键词： Magnetrons

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Smart Meter-based Outage Detection and Fault Location in a Microgrid in Gaidouromantra, Greece 14

Smart Meter-based Outage Detection and Fault Location in a M...

引用

14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Bangali, Sanjeev Athanasios, Vasilakis Dimitrios, Lagos Arya, Prasoon Singhai, Jigyasa Das, Sarasij Dept. of Electrical Engineering Indian Institute of Science Bengaluru India Dept. of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9781837242689

In the existing literature, Digital relay-based protection schemes are proposed for microgrids (MGs). Digital relays are expensive and uneconomical for practical microgrids. However, in most MGs, smart meters (SMs) with communication facilities are available at the consumer end for energy measurements. These SMs can measure the electrical quantities and communicate the measurements to the local server. This paper demonstrates that SMs can also be used for outage detection and fault location in practical MGs. Gaidouromantra MG is one such MG in Kythnos island, Greece, with SMs and communication facilities. This paper proposes two algorithms to detect outages and fault locations. The first algorithm is a voltage-based algorithm which assumes that SM communication is intact. The second algorithm extends the first. It uses current measurements along with voltage measurements to tackle the problem of SM communication disconnection after the fault. The algorithms use measurements from the SMs to detect the outage in the feeder and locate the fault with the guidance of the MG's topology. Additionally, the extended algorithm can be used to indicate SM communication failure and no-load/disconnection status of houses on the island, if any occur. The algorithms are validated using the Gaidouromantra MG model on Simulink and hardware-in-loop simulations of the Gaidouromantra MG. The proposed algorithms can generally be applied to MGs with radial structures consisting of a single grid-forming inverter and multiple grid-following inverters, as in Gaidouromantra. © The Institution of engineering & Technology 2024.

关键词： Microgrids

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Implementation of human-following robot system applying virtual delay to posture of a leader 3

Implementation of human-following robot system applying virt...

引用

3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Takemori, Fumiaki Tottori University Faculty of Engineering Dept. of Electrical Engineering and Computer Science Tottori Japan

ISBN: (纸本)9798350374766

This paper develops an implementation of a measurement and control system in which a vehicle follows its predecessor while maintaining a certain distance. First, we construct a model that virtually delays the reference coordinates of the leading target backwards, and propose a control law that follows the steering angle of the following vehicle to that virtual delay point. Next, a range sensor is used to detect the position and orientation of the leader, and the position and orientation is estimated using a template matching method based on the person's body contour point group measured by the sensor. Finally, the effectiveness (including robustness) of the proposed system is demonstrated in some simulations and human tracking experiments. © 2024 IEEE.

关键词： Template matching

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Performance Analysis of Single Ended Primary Inductor Converter with Adaptive and Static Proportional Integral Control Variants 11

Performance Analysis of Single Ended Primary Inductor Conver...

引用

11th International Conference on electrical engineering, computer science and Informatics, EECSI 2024

作者： Panawan, A. Nur Annisa Alif Samman, Faizal Arya Sahali, Ida Rachmaniar Smedley, Kayue M. Hasanuddin University Dept. of Electrical Engineering Gowa Indonesia University of California Dept. of Electrical Engineering and Computer Science IrvineCA United States

ISBN: (纸本)9798350355314

The Single-Ended Primary-Inductor Converter (SEPIC) is one type of DC-DC converter that has been widely utilized. In certain conditions, this DC-DC converter may exhibit inappropriate behavior regarding stability and reliability, thus requiring a controller to ensure that the voltage, current, or power values they produce align with the desired one. In this research, we examine and simulate the performance of the SEPIC converter using a Proportional Integral (PI) controller and compare the use of tuning methods: Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fuzzy Logic in regulating the PI control parameters. Simulations will be performed through MATLAB. From the simulation results, it is observed that the PI tuning methods using GA, PSO, and Fuzzy Logic are superior to the ZN method, and with the advantage of adaptive process for PI tuning, the fuzzy logic method outperforms in terms of convenience to the other three methods. © 2024 IEEE.

关键词： MATLAB

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A New Half-Bridge/Dual-Stacked-Switches Structured Electrolytic Capacitor-less AC/DC Bi-Directional On-Boad Charger for High-Voltage EV Battery

A New Half-Bridge/Dual-Stacked-Switches Structured Electroly...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Derakhshan, Siamak Lam, John York University Dept. Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350376067

This paper introduces a novel half-bridge (HB)/dual-stacked-switches based electrolytic capacitor-less bidirectional AC/DC converter for high voltage (HV) electric vehicle (EV) systems. The proposed two-stage AC/DC converter incorporates a closed-loop Power Factor Correction (PFC) mechanism based on the dq model of the input LCL filter, ensuring high-quality input current with minimized Total Harmonic Distortion (THD) during both conversion and inversion operations of the converter. Moreover, the front-end HB PFC circuit operates in Continuous Conduction Mode (CCM) that results in the elimination of the high peak current in the switches. Additionally, a dedicated closed-loop control system is deployed in the dual-stacked-switches resonant converter to significantly reduce the output voltage ripple, thus eliminating the use of bulky electrolytic-type storage capacitors. Output voltage regulation is realized by using Variable Frequency Modulation (VFM). Soft-switching operation is guaranteed for all the switches and diodes using CLLC resonant circuit for both conversion and inversion modes of operation. The proposed circuit's performance is verified through a 1kW, 120Vrms/800Vdc proof-of-concept prototype. © 2024 IEEE.

关键词： Electrolytic capacitors

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Prompt-Based Domain Incremental Learning with Modular Classification Layer 27

Prompt-Based Domain Incremental Learning with Modular Classi...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Wang, Boyu Ma, Yue Qiu, Qinru Dept. of Electrical Engineering and Computer Science Syracuse University United States

ISBN: (纸本)9781643685489

Continual learning is referred to as machine learning model's ability to learn a sequence of tasks or data over time without forgetting previously learned knowledge. In particular, we focus on domain incremental learning where the model trained on one domain, e.g., real-life photos, has to be augmented to accommodate data from new domains, e.g., cartoon images. Typical incremental learning relies on rehearsal-based methods, which store trained samples in a buffer and replay them during training alongside new data. This results in significant memory overhead and raise concerns about data privacy. Recently, prompt-based methods address these challenges and outperform them by utilizing the pre-trained Vision Transformer (ViT) and replacing the replay buffer with a prompt pool. However, existing prompt-based models fail to capture domain-specific knowledge and perform poorly in domain incremental learning. In this paper, we propose "domain prompt incremental learning via dynamic neural network", which combines the advantages of architecture-based and prompt-based methods. Specifically, our framework maintains two types of prompt: a instance-level prompt that improves the model's generalization ability is shared across all input samples;and a domain prompt that encodes domain-specific knowledge is assigned for each task. Furthermore, a separated classification head is trained for each domain so that the model has a pre-trained ViT and an ensemble of classification layers, one for each domain. The experimental results shows that our approach outperforms state-of-the-art methods by 2.3% in average on two domain incremental learning (DIL) benchmarks. © 2024 The Authors.

关键词： Contrastive Learning

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AI-Enabled Cyber Physical System And Battery Life Estimation For Smart Grid Applications 49

AI-Enabled Cyber Physical System And Battery Life Estimation...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： N.k, Anantha Padmanabhan Kumar, Aman Chitransh, Varun Singh, Rajeev Kumar Lal, Vivek Nandan Singh, Sanjay Kumar Dept. of Computer Science and Engineering Varanasi India Dept. of Electrical Engineering Varanasi India

ISBN: (纸本)9798350331820

In this paper, the development of an efficient three- layer Cyber Physical System (CPS) using a multi-output power electronic interface integrated with IoT-enabled modules and AI- based algorithms is presented. The power electronic interface layer of the proposed CPS eliminates the need of bulky electrolytic capacitors and reduces the switching loss. This layer facilitates DC to DC and DC to three-phase AC power conversion for battery charging and smart-grid applications respectively. AI-based algorithms are utilized for accurately predicting the Remaining Useful Life (RUL) of batteries. The performance of the proposed CPS is validated through simulation and experimental measurement results using a 700 W prototype system. The measurement results show that the proposed CPS enables real-time monitoring, accurate prediction of RUL, predictive maintenance decisions, data analytics, and optimized power flow. © 2023 IEEE.

关键词： Cyber Physical System

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Preliminary Design of Hybrid Simulation Framework for Workload Analysis in Automotive Edge Computing 15

Preliminary Design of Hybrid Simulation Framework for Worklo...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kwon, JiHyun Park, SangEun Chwa, Hoon Sung Kwak, Jeongho Choi, Ji-Woong Kim, BaekGyu Dgist Dept. of Electrical Engineering and Computer Science Daegu Korea Republic of

ISBN: (纸本)9798350364637

Many vehicular tasks shall be completed in a timely manner as they move. In automotive edge computing, a vehicle can offload such tasks to an edge server in proximity to process them faster than using the cloud computing model. However, such an edge server has limited communication coverage and relatively smaller computing power compared to the general cloud server, it is important to distribute the offloaded workloads appropriately. One needs to understand such workload patterns and quantitatively analyze them for such workload distribution. We propose a preliminary hybrid simulation framework that generates compute workloads via a combination of the virtual vehicular traffic and the physical edge-server platforms. © 2024 IEEE.

关键词： Computation offloading

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Project or Factorize? A Case Study of Multiview CCA and PARAFAC2 Tensor Factorization 58

Project or Factorize? A Case Study of Multiview CCA and PARA...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Chen, Jia Papalexakis, Evangelos E. University of California Dept. of Electrical and Computer Engineering Riverside United States Uuniversity of Californiav Dept. of Computer Science and Engineering Riverside United States

ISBN: (纸本)9798350354058

Consider learning the shared representations from multiple unlabeled views. Previous work either projects different views to the same space while enforcing the agreement among the projected views such as multiview canonical correlation analysis (MCCA), or factorizes different views while ensuring the common latent components across the views such as PARAFAC2, a tensor decomposition method. In this paper we first investigate a fundamental question: 'Do these two approaches learn different representations?' Preliminary numerical results suggest that in practice they do, which, in turn, begs the question of how we can leverage this observation in order to compute a superior representation. In this paper, we present a simple proof-of-concept scheme which augments MCCA with PARAFAC2 representations and vice-versa, and we demonstrate on multiple real datasets that such scheme can improve upon the baseline representation, paving the way for future research on optimally combining the strengths of projection and factorization methods for multiview representation learning. © 2024 IEEE.

关键词： Tensors

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