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检索条件"任意字段=International Conference on Electrical Engineering Computing Science and Automatic Control"

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Distributed Population-Based Simultaneous Perturbation Stochastic Approximation for Fine-Tuning Large Language Models 21st

Distributed Population-Based Simultaneous Perturbation Stoch...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Tan, Yajing Huang, Yuwei Duan, Qiqi Yang, Yijun Shi, Yuhui Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9789819601219

Very recently, a memory-efficient version (called MeZO) of simultaneous perturbation stochastic approximation (SPSA), one well-established zeroth-order optimizer from the automatic control community, has shown competitive fine-tuning performance on large language models (LLMs) with billions of weights under the limited memory of a single GPU, which often hinders the direct usage of the standard backpropagation (BP) update. Furthermore, population-based evolutionary methods (such as evolution strategies from OpenAI and population-based training from DeepMind) have shown a promising direction to improving and scaling up various large-scale AI systems via efficiently utilizing increasingly available distributed computing resources, owing to their relatively simple yet efficient data and control flows. In this paper, we propose a population-based SPSA for fine-tuning LLMs with non-differentiable objective functions. Specifically, our proposed method combines the desirable exploration ability of distributed population-based evolutionary methods with the powerful exploitation ability of SPSA in the modern distributed computing environment wherein the efficient implementation of BP is still challenging because of its complex dependencies in forward-pass and backward-pass information flows. Simulation experiments on a set of challenging (non-differentiable) natural language process tasks empirically validate the potential of our method on fine-tuning even in a relatively small-scale distributed computing scenario. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stochastic systems

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High Switched Reluctance Generator for PSO Optimized WECS

High Switched Reluctance Generator for PSO Optimized WECS

引用

2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Fabbina, C. Rahiman, Riyaz A Prabha, M. Janaki, N. Shadhik, M. Penthala, Thanuja Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India School of Electronics and Communication Reva University Bangalore India Annapoorana Engineering College Department of Electrical and Electronics Engineering India Vels Institute of Science Technology and Advanced Studies Department of Electrical and Electronics Engineering Tamilnadu Chennai India Dhaanish Ahmed College of Engineering Department of Electrical and Electronics Engineering Padappai India SRMIST Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350333244

In order to implement switching reluctance generators (SRG) for wind energy conversion into the power distribution system (PDS), fundamental particle swarm optimization (PSO) is presented in this research. SRGs are an excellent alternative even if they are not frequently utilized in wind energy applications due to their high levels of resilience, efficacy and speed range. controlling the electricity sent to the load is a never ending task. In order to keep the produced power as consistent as feasible given that the wind speed is changing, the voltage of excitation needs to be adjusted. In this section we offer a PSO method for enhancing a SRG efficiency in a distributed wind power system. The output power profile may be calculated by continuous simulations of generator model using a variety of control parameters due to Switched Reluctance Generator's extremely nonlinear characteristics. It is a multidimensional search space, therefore there are a lot of cycles. In order to shorten modelling duration and widen the search space, we have employed evolutionary methods. An elementary PSO approach is being developed to find the optimal firing angles and DC bus voltage levels under different operating conditions. With outstanding energy efficiency and minimal torque ripple, performance is guaranteed. Using this method, SRGs will operate at a slower speed than base speed in the currently under control area. The simulation results show that it is possible to achieve a level of performance that balances less torque ripple with improved economy. © 2023 IEEE.

关键词： Distributed Wind

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Aluminium-Metal Oxide (Al2O3) Performance Evaluation;Utilising Nanofluid Cooling Technology and Artificial Intelligence for Maximum Power Point Tracking Of Perovskite Solar Cells

Aluminium-Metal Oxide (Al2O3) Performance Evaluation;Utilisi...

引用

2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kotiyal, Arnav Hussein, Layth Dixit, Krishna Kant Upadhyay, Shrikant Nimmalaharathi Francis Raj, J. Relin Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of EIE School of Engineering Sree Vidyanikethan Engineering College AP India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350355338

Improving the efficiency of PV systems linked to the grid is the primary goal of this research. One factor that might affect productivity is the maximum power point tracker's accuracy. Incremental conductance (INC) and perturb-and-observe are two of the few dependable methods for maximum power point tracking (MPPT). Make the MPPT control system more efficient with the aid of AI. This study enhances the design of an ANN for an MPPT system, which in turn increases the power output of a grid-connected PV system. Prior research on ANN-based MPPT systems mostly used temperature and solar irradiance as their only parameters;however, this study offers new parameters that significantly enhance the performance of the ANN. Secondly, neuron activation function type, learning technique, and the number of hidden layer neurons can all be optimized using a genetic algorithm (GA). Simulation and comparison results demonstrate that the revised ANN architecture enhances MPPT functionality. © 2024 IEEE.

关键词： Aluminum oxide

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Personalization and Smartphone Implementation of ADRO Hearing Aid Amplification by Bayesian Machine Learning

Personalization and Smartphone Implementation of ADRO Hearin...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： Nafisa Zarrin Tasnim Aoxin Ni Edward Lobarinas Nasser Kehtarnavaz Department of Electrical and Computer Engineering University of Texas at Dallas Richardson USA Callier Center for Communication Disorders University of Texas at Dallas Richardson USA

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

The amplification function of hearing aids is normally prescribed based on population-based prescriptions, often overlooking individual hearing needs. Previous studies have shown that personalization or individualization of prescriptive amplification can lead to improved hearing. This paper introduces a personalized amplification strategy that applies a Bayesian learning personalization algorithm to the Adaptive Dynamic Range Optimization (ADRO) prescription. The personalized ADRO amplification strategy is then implemented on a smartphone platform as an app that can be deployed in the field or in real-world audio environments. The results of hearing preference experiments from six participants with hearing loss are reported. The results, on average, indicate higher preference for the personalized ADRO relative to the standard ADRO prescription.

关键词： Training Machine learning algorithms Pipelines Auditory system Hearing aids Real-time systems Bayes methods Tuning Standards Optimization

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Research on the Construction of Knowledge QA System Driven by Large Language Model: One Case Study of Power Transformer Domain 7

Research on the Construction of Knowledge QA System Driven b...

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7th international conference on Data science and Information Technology, DSIT 2024

作者： Xu, Hao Kang, Zhenyuan Zhang, Yan Jin, Zhenqiang Wang, Mulan Ge, Linlin Lu, Haihua Management Nanjing University School of Management Engineering Nanjing Institute of Technology School of Information Nanjing China Nanjing Institute of Technology School of Electrical Power Engineering Shen Guorong College Nanjing China Nanjing Institute of Technology School of Management Engineering Nanjing China Jiangsu Power Transmission & Transformation Co. Ltd Nanjing China Nanjing Institute of Technology School of Industrial Center Nanjing China

ISBN: (纸本)9798350384093

The QA system can provide various professionals with fast and accurate knowledge support, significantly improving work efficiency. The introduction of LLMs has further enhanced the accuracy and efficiency of QA systems. A QA system based on LLMs for power transformers can effectively improve the efficiency of fault diagnosis and the resolution of technical issues in the power transformer domain. This study first proposes an automatic dataset construction method for QA based on LLMs, through which a QA dataset of basic knowledge about power transformers is obtained. This dataset then performs low-rank adaptive fine-tuning on the LLM. Subsequently, an external knowledge base covering fundamental knowledge and fault cases of power transformers is built. Finally, with the aid of prompt texts, knowledge responses were generated by RAG in combination with the fine-tuned LLM. Experimental results show that compared with the QA systems driven by general LLMs, the method proposed in this paper generates more concise and professional responses, thus promoting, to some extent, the application of LLMs in the power domain. © 2024 IEEE.

关键词： Distribution transformers

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Field Deployment of Personalized DSLv5 Hearing Aid Amplification by Bayesian Machine Learning

Field Deployment of Personalized DSLv5 Hearing Aid Amplifica...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： Aoxin Ni Edward Lobarinas Nasser Kehtarnavaz Department of Electrical and Computer Engineering University of Texas at Dallas Richardson TX USA Callier Center for Communication Disorders University of Texas at Dallas Richardson TX USA

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

This paper presents a real-time smartphone app that enables field deployment of a personalized DSLv5 amplification strategy based on a multi-band Bayesian machine learning algorithm. This implementation allows for the personalization of DSLv5 in real-world audio environments. The app includes a training and a testing session module. The training session allows reaching an optimum set of personalized gain values across a number of frequency bands. This is achieved by conducting paired audio comparisons by the user in a time-efficient manner. The testing session assesses comparisons between the personalized gain setting versus the standard DSLv5 prescription gain setting. The details of the steps taken to achieve this real-time implementation on smartphone platforms are presented. The results of a clinical experiment conducted on six participants with hearing loss show that the personalized settings on average are preferred over the standard settings by a factor of six times.

关键词： Training Machine learning algorithms Machine learning Auditory system electrical engineering computing Hearing aids Real-time systems Bayes methods Standards Testing

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Integration of Virtual Vibration Absorbers in UAV Motion control Design for Monitoring Civil Structures in Dam engineering

Integration of Virtual Vibration Absorbers in UAV Motion Con...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： Hugo Yañez-Badillo Daniel Galvan-Perez Francisco Beltran-Carbajal Ivan Rivas-Cambero Departamento de Investigación TecNM: Tecnológico de Estudios Superiores de Tianguistenco Santiago Tianguistenco Estado de México Mexico Departamento de Posgrado Universidad Politécnica de Tulancingo Tulancingo de Bravo Hidalgo Mexico Departamento de Energia Universidad Autónoma Metropolitana Unidad Azcapotzalco Ciudad de México Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

This paper explores the innovative use of virtual vibration absorbers in the motion control systems of unmanned aerial vehicles (UAVs) specifically for civil structure monitoring in dam engineering. By integrating virtual vibration absorbers, UAVs can achieve greater stability and accuracy in data col-lection, leading to more precise monitoring of infrastructure health in disturbed scenarios. The research explores the design, implementation, and performance evaluation of these systems, highlighting their potential for enhancing UAV efficiency and reliability in civil engineering applications. Numerical simulations are included to illustrate the effectiveness of the proposed methodology. The principal findings and conclusions of the research are also presented.

关键词： Vibrations Dams Power system stability Autonomous aerial vehicles Numerical simulation Stability analysis Motion control Vehicle dynamics Monitoring Numerical stability

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Secrecy Rate Threshold VS. An Optimum Power Splitting Factor Using Beamforming Technique in PHY Layer Security for MU-MIMO Systems 3

Secrecy Rate Threshold VS. An Optimum Power Splitting Factor...

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3rd IEEE international Maghreb Meeting of the conference on sciences and Techniques of automatic control and Computer engineering, MI-STA 2023

作者： Elhoula, Abeer A S Abousetta, Marai M. Ghmati, Tarek Saleh M. Libyan Academy School of Applied Science and Engineering Department of Electrical and Computer Engineering Libya Elemrgib University Al-Graboulli Faculty of Engineering Department of Electrical and Computer Engineering Libya

ISBN: (纸本)9798350319897

This paper describes a new approach to the problem of interception of wireless communication channels between the legitimate users. Physical PHY Layer Security (PLS) is new topic enhancing the secrecy performance of a Multi-User Multiple-Input-Multiple-Output (MU-MIMO) system for wireless communication from a single base-station to many users. Beamforming techniques such as "Pre-Maximum Ratio Combining (P-MRC) and Pre-Equal Gain Combining (P-EGC)"with assumption of perfect Channel State Information CSI is considered in order to achieve the perfect secure transmission between legitimate users. Along with beamforming, Artificial Noise (AN) is a technology that is being studied. The plotted achievable worst-case of secrecy rate down to the best secrecy rate value (threshold of secrecy rate) versus the power allocation fractions (The power splitting factor). The achieved threshold of secrecy rates for the implemented MU-MIMO were used by Matched Filter (MF) beamforming schemes (P-MRC) and (P-EGC) and suggested that the (P-MRC) is considered as an optimal beamforming technique in order to achieve a best secrecy rate or best threshold of secrecy rate for a nearest Information User (IU) to the Eavesdropper Eve. © 2023 IEEE.

关键词： Beamforming

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Bearing Failure Classification with Metaheuristic Adjust of the Extreme Learning Machine Model

Bearing Failure Classification with Metaheuristic Adjust of ...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： Luis Elias Salgado Solano Oliverio Arellano Cárdenas Luis Martín Flores Nava Felipe Gómez Castaneda Electrical Engineering Department CINVESTAV-IPN Mexico City Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

In this study, we propose the use of an Extreme Learning Machine model with a metaheuristic adjust of parameters, to recognize mechanical vibration data from bearings, in order to determine whether the device is in good condition or experiencing some type of malfunction. Our approach involved designing a supervised Extreme Learning Machine model whose parameters are adjusted by a Grey Wolf approach. We employed a dataset from Case Western Reserve University, available on their website. In this database the signals were gathered by sampling an accelerometer at 12 kS/s, placed radially on a bearing. The data was collected for four scenarios: one with vibrational signals from a healthy bearing, and three with bearings having induced faults in the inner race, outer race, and ball, respectively. This intelligent system was developed using Python in the Spyder environment, integrated with Pytorch framework, obtaining promising results. Keywords-Extreme Learning Machine, metaheuristics, bearing failures, artificial intelligence.

关键词： Vibrations Extreme learning machines Databases Metaheuristics Neural networks Learning (artificial intelligence) electrical engineering computing Data models Classification algorithms Intelligent systems

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Enhancement of Security in Cloud computing Using Optimal Risk Access control Model

Enhancement of Security in Cloud Computing Using Optimal Ris...

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2023 international conference on Innovative computing, Intelligent Communication and Smart electrical Systems, ICSES 2023

作者： Arunarani, A.R. Sherin Shibi, C. Kanimozhi, N. Sumathy, G. Maheshwari, A. SRM Institute of Science and Technolgy Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur603203 India

ISBN: (纸本)9798350319200

This paper develops an Optimal Risk Access control Model (ORACM) for enhancing security in cloud computing. In this proposed model, three important risk aspects are considered such as environment attribute, resource attribute and subject attribute. Based on these factors, the suggested risk validation indexes were created, and by fusing Adaptive Snake Optimization (ASO) and Policy Approach Access control (PAAC), the risk validation computation method would be quantitatively validated. In the PAAC, the optimization method is taken into account while choosing the decision. Based on the validation, optimization and choosing the best risk decision is reducing waiting time. conventional access control continues to receive access needs as extended as they are genuine once the architecture is in an emergency state (like when the CPU and memory remain saturated), raising the danger of a system crash. Consider a case where the system is presently in an emergency, on the other hand. The authentication token may be misused, leading to the unauthorized access of system resources and services, if the security of the primary environment depreciates. The suggested strategy is put into practice, and its effectiveness is assessed by taking into account performance indicators including waiting time, convergence analysis, and CPU usage. It is also contrasted with traditional approaches. © 2023 IEEE.

关键词： adaptive snake optimization and convergence analysis policy approach access control risk access control method

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