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检索条件"任意字段=IEEE International Conference on Engineering of Complex Computer Systems"

共 202637 条记录，以下是171-180 订阅

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Control of Grid-Connected Inverters Using PLL for Synchronization and Harmonic Mitigation

Control of Grid-Connected Inverters Using PLL for Synchroniz...

引用

2025 ieee Texas Power and Energy conference, TPEC 2025

作者： Yousefpour, Kamran Yousefpour, Mohammad Molla, Seyyed Javad Hosseini Andani, Mansour Taheri Varnousdaderani, Sepideh Alaei Andani, Hamid Taheri Mississippi State University Department of Electrical and Computer Engineering United States Azad University Iran Babol Noshirvani University of Technology Department of Electrical and Computer Engineering Iran School of Manufacturing Systems and Networks Arizona State University United States Azad University Department of Computer Engineeering Iran Isfahan University of Technology Department of Electronic Engineering Iran

ISBN: (纸本)9798331541125

This paper presents the design and simulation of a single-phase grid-connected inverter control system, focusing on enhancing power quality and dynamic performance. The control system comprises a resonant controller for real and reactive power regulation, ensuring sinusoidal current injection into the grid. Additionally, a Phase-Locked Loop (PLL) is incorporated to improve synchronization with the grid voltage, generating accurate reference signals for current control and mitigating harmonic disturbances. The PLL design leverages specific gain parameters, μ1,μ2, and μ3, optimized for a critically damped response. A comprehensive simulation study evaluates the system's performance under different conditions, including soft-start operation, transient power commands, and grid voltage distortion with low-order harmonics (Sth, 7th, LIth, and 13th). Results highlight the system's robust dynamic response, low total harmonic distortion (THD), and effective harmonic attenuation, demonstrating its suitability for grid-connected distributed energy resource (DER) applications. This work provides a scalable and reliable control framework for next-generation inverter systems operating in modern, complex grid environments. © 2025 ieee.

关键词： Harmonic distortion

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CNN-DBO: Dung Beetle Optimization Based Urban Area Change Detection Using Satellite Images

CNN-DBO: Dung Beetle Optimization Based Urban Area Change De...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pujari, Jambukeshwar Wasim, Javed Tripathi, Aprna Mangalayatan University Department of Computer Engineering and Applications Aligarh20221385 India Mangalayatan University Department of Computer Engineering and Applications Aligarh India Manipal University Department of Data Science Engineering Jaipur India

ISBN: (纸本)9798331512965

In recent years, Geographic Information systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective. © 2025 ieee.

关键词： Convolutional neural networks

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Nanostructure-Based Highly Sensitive and Reliable Piezo-Transmittance Strain Sensor and Integrated System 38

Nanostructure-Based Highly Sensitive and Reliable Piezo-Tran...

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Chung, Myung-Kun Jeon, Su-Min Yang, Jae-Soon Yoo, Jae-Young Kim, Min-Uk Kim, Beom-Jun Lee, Tae-Yeon Jo, Min-Seung Yoon, Jun-Bo School of Electrical Engineering Korea Republic of Department of Semiconductor Convergence Engineering Korea Republic of Northwestern University Center for Bio-Integrated Electronics EvanstonIL United States

ISBN: (纸本)9798331508890

Advancements in human-machine interaction, health monitoring, and robotics have led to increasing demand for reliable strain sensors. Of the various flexible sensors, piezo-transmittance strain sensors adjust their optical transmittance in response to strain and can overcome limitations seen in other sensor types. However, current piezo-transmittance strain sensors face challenges related to controllability and applicability, as well as constraints from bulky designs and complex wired connections. This paper reports a highly sensitive, reliable, and mechanically durable piezo-transmittance strain sensor that addresses these challenges. In addition, we propose a simple, wireless integrated sensor system that eliminates the need for bulky and complex wiring, offering a practical solution for human motion detection applications. © 2025 ieee.

关键词： Flexible electronics

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SMT-based Layout Synthesis for Silicon-based Quantum Computing with Crossbar Architecture 24

SMT-based Layout Synthesis for Silicon-based Quantum Computi...

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43rd international conference on computer-Aided Design, ICCAD 2024

作者： Huang, Sheng-Tan Jiang, Ying-Jie Fang, Shao-Yun Cheng, Chung-Kuan Department of Electrical Engineering National Taiwan University of Science and Technology Taiwan Department of Computer Science and Engineering University of California San Diego United States

ISBN: (纸本)9798400710773

With the announcement of the most advanced silicon spin quantum-bit (qubit) chip by Intel, silicon-based quantum circuit manufacturing technology has shown the superior potential to realize quantum computing to other technologies because of the mature semiconductor manufacturing technologies and the compatibility with electronics. Due to the bottleneck in scalability caused by interconnections in silicon-based quantum circuits, crossbar architectures serve as one of the most promising solutions for circuitry implementation. This paper proposes the first satisfiability modulo theories (SMT) formulation to optimally solve the layout synthesis problem by simultaneously performing scheduling, mapping, and routing for silicon-based quantum systems with a specific crossbar architecture. The experiments demonstrate that the proposed method can effectively generate layout synthesis results with minimal usage of swap and shuttle gates within the shortest circuit depths, and the solutions greatly outperform those derived from a state-of-the-art work. © 2024 Copyright is held by the owner/author(s).

关键词： Qubits

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Enhancing Cloud Infrastructure Security with GraphSAGE-Based Intrusion Detection systems 1

Enhancing Cloud Infrastructure Security with GraphSAGE-Based...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Kumar, G. Nanda Kishor Syed, Shakir Mogal, Amit Karbhari Abid, Tahera Perada, Anvesh Suneetha, Sampathirao University of South Florida United States Malla Reddy University Department of Computer Science and Engineering Hyderabad India Bargersville IN United States Mvp Samaj's Cmcs College Department of Computer Science and Application Nashik India Nawab Shah Alam Khan College of Engineering and Technology Department of Information Technology Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798331529246

Cloud computing provides scalable and adaptable infrastructure however is significantly susceptible to cyber-attacks. This work presents a hybrid methodology that integrates the GOA and GA for feature selection, alongside the Residual GraphSAGE model for intrusion detection. The hybrid GOA-GA method enhances feature selection by optimizing the balance between exploration and exploitation, mitigating overfitting, and augmenting classification performance. The Residual GraphSAGE model alleviates over smoothing while effectively capturing node and edge interactions for precise intrusion classification. Assessment on a benchmark dataset reveals exceptional performance, attaining an accuracy of 91.74%, surpassing established models such as GCN and ResNet. This study emphasizes the need of hybrid optimization and sophisticated graph-based learning in improving cloud infrastructure security. © 2025 ieee.

关键词： Network intrusion

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A Review of Various Optimization Techniques for the Selection of Insulating Fluids in Power and Distribution Transformer Applications

A Review of Various Optimization Techniques for the Selectio...

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2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Sarathkumar, D. Raj, Raymon Antony Geethalakshmi, M. Jayakumar, T. Manivel, Murugesan Ponnusamy, Sabarish Kongu Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638 060 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu 621215 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Department of Electrical and Electronics Engineering Tamil Nadu Samayapuram621112 India

ISBN: (纸本)9798331529833

The selection of insulating fluids is critical for the reliable and efficient operation of power and distribution transformers. This paper provides a comprehensive review of various optimization techniques employed to select the most suitable insulating fluids for these applications. The focus is on evaluating the performance, environmental impact, and economic feasibility of different fluids, including mineral oils, synthetic esters, and natural esters. We analyze optimization methods such as genetic algorithms, particle swarm optimization, and multi-criteria decision-making approaches, highlighting their advantages and limitations. Additionally, this review addresses recent advancements and future directions in the field, aiming to guide researchers and engineers in making informed decisions for transformer insulation. By synthesizing existing studies, this paper contributes to the development of more efficient, sustainable, and cost-effective transformer systems. © 2025 ieee.

关键词： Distribution transformers

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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 ieee.

关键词： Contrastive Learning

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Mask-guided Multi-scale Spatial-Spectral Transformer for Snapshot Compressive Imaging

Mask-guided Multi-scale Spatial-Spectral Transformer for Sna...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yin, Heyuan Yang, Jingxiang Liu, Jia Xiao, Liang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Analysis of Mathematical Theory and Modeling of Complex Systems Ministry of Industry and Information Technology China

ISBN: (纸本)9798350368741

Effectively reconstructing 3D hyperspectral images (HSIs) from 2D measurements presents a significant challenge in Coded Aperture Snapshot Spectral Imaging (CASSI) systems. While recent transformers exhibit potential in HSI reconstruction, they often suffer from inadequate exploration of multi-scale spatial-spectral self-similarity, leading to mean effects and information loss. Additionally, these methods struggle with insufficient modeling of the degradation inherent in the compressive imaging process. To address these issues, we propose a novel Mask-guided Multi-scale Spatial-Spectral Transformer (MMSST). Specifically, we introduce a Degradation Aware Mask Attention (DAMA) module to incorporate degradation information of the compressive imaging process. Furthermore, MMSST leverages Local-Regional SpAtial attention (LRSA) and Global-Regional SpEctral attention (GRSE) to effectively exploit multi-scale self-similarity across spatial and spectral dimensions. Extensive experimental results demonstrate the effectiveness of our MMSST. © 2025 ieee.

关键词： hyperspectral reconstruction Snapshot Compressive Imaging spatial-spectral transformer

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Carrier Phase Shifting Control Method for Ultra-High Power Amplifier 5

Carrier Phase Shifting Control Method for Ultra-High Power A...

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5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Li, Dapeng Na, Rui Zheng, Dezhi School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331533694

Digital control technology significantly enhances the performance of amplifiers, directly impacting the operational effectiveness of the system. However, for ultra-high power amplifiers, the interference from ultra-high voltage and current in complex environments presents challenges for precise digital control. To improve the stability and reliability of the system, this paper proposes a carrier phase shift control method for ultra-high power amplifiers. This method effectively mitigates the influence of internal circulating currents and enhances the quality of the system's output signal. Both simulation and experimental results demonstrate the advantages of the proposed control algorithm in improving key performance indicators of the system. Specifically, signal distortion across all spectral segments is kept below 1 %, and the dynamic range reaches 50 dB. © 2025 ieee.

关键词： Power amplifiers

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Automated Feature engineering for Single-Trial EEG and Eye-Tracking Classification in Predictive Text Interfaces 13

Automated Feature Engineering for Single-Trial EEG and Eye-T...

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Kastrati, Ard Michael Winters, R. Djuric, Nemanja Tashev, Ivan J. Wang, Yu-Te ETH Zurich Switzerland Pinnacle Group Inc Microsoft Research United States Academia Sinica China

ISBN: (纸本)9798331521929

Brain-computer Interfaces (BCIs) offer a direct connection between the human brain and digital systems, enabling innovative applications. However, realizing the full potential of BCIs remains challenging due to issues like noise, artifacts, and limited data availability. In this study, we develop a multimodal classifier that integrates electroencephalogram (EEG) and eye-tracking (ET) data to decode user responses to predictive text suggestions. Utilizing an automated feature engineering approach, our pipeline efficiently generates and selects relevant features without extensive manual intervention or deep theoretical insights. Applied to a recent BCI case study involving predictive text input, our method achieved higher classification accuracies compared to traditional approaches. Additionally, it revealed novel insights, such as behavioral patterns where participants did not fully read incorrect predictions and the enhanced performance of multimodal classifiers when combining ICA-preprocessed EEG data with ET data. While automated feature engineering is standard in other domains, it is seldom applied in BCI research. Our findings demonstrate that this approach is a valuable tool for data-driven exploration and the development of competitive single-trial classifiers in novel multimodal BCI paradigms, particularly during the initial stages of research with limited data. © 2025 ieee.

关键词： Behavioral research

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