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检索条件"任意字段=International Conference on Measurement and Modeling of Computer Systems"

共 90984 条记录，以下是371-380 订阅

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Leveraging Neuromorphic Computing for Efficient and Scalable Data Analytics

Leveraging Neuromorphic Computing for Efficient and Scalable...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Shubham Gupta Ishwar Bansal Geeta Geeta Model Institute of Engineering and Technology Jammu J&K India Software Developer Amazon Development Center Herndon USA School of Sciences Geeta University Panipat India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Neuromorphic computing is a new data analytical paradigm that mimics the behavior of biological neural systems to offer better computational power. State-of-the-art performance in conventional deep learning models (CNNs and transformers) comes at the expense of exorbitant energy and computation time. The real-time processing, low latency, and better energy efficiency make neuromorphic architectures to be a more appealing solution to kiosks where inferences on a large scale of data are being performed. In this paper, we discuss neuromorphic computing, how it can minimize data processing, its superiority compared to traditional AI models, and its future selection for diverse applications. Neuromorphic systems demonstrate a scalable way to the next-generation artificial intelligence by taking advantage of event-driven processing and dedicated hardware

关键词： Deep learning Data analysis Neuromorphic engineering Computational modeling Biological system modeling computer architecture Real-time systems Energy efficiency Data models Artificial intelligence

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Predictive modeling of Battery Second-Life Performance Using Support Vector Regression and Voltage Features 2

Predictive Modeling of Battery Second-Life Performance Using...

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2nd international conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Hossian, Md. Ismail Hannan, Nasif Das, Hridoy Rahman Akash, Md. Ashiqur Ashraf, Md Sazid Shufian, Abu American International University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Texas Tech University Dept. of Electrical and Computer Engineering LubbockTX United States Lamar University Dept. of Electrical and Computer Engineering BeaumontTX United States

ISBN: (纸本)9798331506995

Extensively used in electric vehicles (EVs), lithium-ion (Li-ion) batteries, undergo significant degradation after several charge-discharge cycles, leading to their retirement from high-demand applications. However, these batteries can still serve fewer demanding applications in their second life. Estimating the State of Health (SoH) in second-life applications is crucial for ensuring their reliable performance. This paper presents a method for SoH estimation using a Support Vector Regression (SVR) model trained in first-life battery data and applied to second-life usage. Two health indicators, TIECVD (Time Interval of Equal Charging Voltage Difference) and TIEDVD (Time Interval of Equal Discharging Voltage Difference) are extracted from voltage measurements and used as predictors in the SVR model. The method is validated using NASA's battery ageing dataset, consisting of three batteries (B0005, B0006, and B0007) with a rated capacity of 2 Ah. The proposed model achieves RMSE values of 0.0061, 0.0158, and 0.0047 for the respective batteries during second life SoH estimation. This approach provides a practical, low-cost solution for estimating battery health in second-life applications without extensive monitoring systems. © 2025 IEEE.

关键词： Lithium-ion batteries

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Enhancing Pavement Condition Assessment: A Comprehensive Review of Affordable Sensing Technologies for Cycle Tracks 11

Enhancing Pavement Condition Assessment: A Comprehensive Rev...

引用

11th international conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2025

作者： Garcia, Jeziel Antonio Ayala Shah, Syed Baig, Muhammad Hassam Qureshi, Waqar Shahid Ullah, Ihsan School of Computer Science University of Galway Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9789897587450

This paper explores the potential of low-cost sensing technologies for assessing the condition of cycling track pavement. As cycling gains popularity, the demand for efficient pavement maintenance solutions increases. Machine survey-based pavement condition assessment often relies on expensive, specialized equipment, which is not always suitable for cycle tracks due to limited budgets and accessibility, hence the need for low-cost solutions. The integration of low-cost sensing technologies and data collection, such as inertial measurement units (IMUs), low-cost imaging sensors, and crowdsourced data collection, presents a promising alternative for cycle track pavement surface assessment. This paper highlights the advantages of employing out-of-the-shelf devices such as smartphones with GPS, cameras, and IMU, or action cameras with GPS and IMU to collect images, their location, and vehicle vibration in a specific orientation. This data is then used to estimate pavement roughness, detect surface distress, and compute pavement surface condition. Literature reviews reveal a significant gap in the utilization of these technologies for cycle tracks, suggesting a promising area for further research and application. Furthermore, it proposes a software framework for data collection and visualization to combine these technologies to enhance the efficiency and reliability of pavement assessment for cycling infrastructure at a lower cost than machine-based pavement assessment and faster and more quantitative than manual surveys. This paper emphasizes the need for more practical and scalable solutions that support the maintenance of sustainable transportation systems. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Budget control

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Reference geometries and indicators for the proce b window development of wire Directed Energy Deposition 1

Reference geometries and indicators for the proce b window d...

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1st international conference on Digital Sovereignty, ICDS 2024

作者： Tzimanis, Konstantinos Porevopoulos, Nikolas Bourlesas, Nikolas Stavropoulos, Panagiotis Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Rio Patra26504 Greece

The industrial interest for Directed Energy Deposition (DED) proce bes increases;however, intensive experimental work is needed for the determination of proce b inputs each time a new material and machine are investigated, leading to waste of time and resources. Therefore, this work presents a structured way of determining the values of key proce b parameters based on reference geometries such as single tracks, thin walls, overlapping tracks and complex geometries as well as metrological responses from where acceptance thresholds are developed. In the present study, the methodology is implemented in a wire laser-based DED proce b;the working material is stainle b steel AISI316L while monitoring devices that capture the melt pool features, the over-built and finally the proce b performance are retrofitted to accelerate the transformation of proce b data to meaningful information and proce b knowledge. © 2025 The Author(s).

关键词： Velocity measurement

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A High-Gain DC-DC Boost Converter for Solar Microgrid systems Using a Switching Inductor and a Capacitor 1

A High-Gain DC-DC Boost Converter for Solar Microgrid System...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prasanth, S. Priyadarsini, S. Lakshmi, R. Sneha, G. Ajitha, S.S. Saravanakumar, R. Narendra, Konnaiah Gari Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India PSR Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Siddharth Institute of Engineering and Technology Department of Elecrical and Electroncis Engineering Puttur India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electonics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518578

This paper outlines the design and implementation of a solar microgrid-specific high-gain DC-DC booster converter that makes use of a variable inductor and capacitors. To improve the suitability of photovoltaic (PV) panels for incorporation into microgrids, the proposed converter efficiently increases their low-voltage outputs. Conventional boost converters can't offer that kind of voltage increase because they lose too much performance and put too much strain on their internal components. The converter increases the voltage gain dramatically while maintaining a high degree of efficiency thanks to a switching inductor-capacitor network. The main breakthrough is an inventive multi-stage energy transfer and storage device that cleverly employs a switching inductor and capacitor. This configuration increases the output voltage while decreasing the voltage and current ripples, which in turn reduces the amount of electromagnetic interference (EMI) and increases the life of the components. Thorough assessments of the converter's working via theoretical modeling and simulation studies confirm its performance across several load and input situations common to solar microgrids. The prototype's testing findings show a significant voltage increase and a peak efficiency of over 95% when compared to traditional boost converters. Moreover, the converter's reliable performance over a wide range of voltage sources and workload scenarios proves its suitability for practical solar applications. As a result, the proposed high-gain boost converter will greatly improve the efficiency and reliability of solar microgrids, leading to a rise in the use of renewable energy sources in DGS. © 2025 IEEE.

关键词： Microgrids

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Wearable IMUs: Advancing Human Motion Analysis with Deep Learning 3rd

Wearable IMUs: Advancing Human Motion Analysis with Deep Le...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Das, Satyesh Das, Divyesh Parashar, Ashana Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais India Electronics and Computer Engineering Thapar Institute of Engineering and Technology Patiala India Mobile Automation Vodafone Intelligent Solutions Nashik India

ISBN: (纸本)9789819726707

In recent times, there has been a notable surge in the exploration of studying human body movements through the utilization of inertial measurement units that can be worn. This trend stems from its substantial impact on academic and industrial circles, encompassing applications ranging from portable healthcare solutions to sports and interfacing with computers through human interaction. Developing human activity recognition (HAR) systems that achieve remarkable accuracy in identification while relying on just one sensor remains an ongoing technological hurdle. In this paper, we explore both supervised and partially supervised approaches using convolutional neural networks and denoising autoencoders approaches. The goal of our study revolves around increasing the classification precision pertaining to previous related works and decreasing reliance on human-engineered features. Since the raw IMU data is composed of time series, it is split using a running window approach and segments of roughly one second of data are fed to the neural networks. The above approaches are tested with two different variations of the original dataset, obtained with data augmentation approaches, to balance uneven class distribution. The dataset contains measurements of one single IMU sensor positioned in the belt of different users performing seven actions: running, jumping, walking, falling, sitting, standing, and lying. The score of best 8-layer CNN-based system was 0.972. The worst represented class, falling, with just 2 min of recorded data, has an F1-score of 0.919. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Edge Node Load Balancing Using Game Theoretic Approaches

Edge Node Load Balancing Using Game Theoretic Approaches

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Emerging systems and Intelligent Computing (ESIC), international conference on

作者： Rajdeep Haldar Saptadip Rooj Neeraj Sharma Ritambhar Das Arnab Mandal Anjan Bandyopadhyay Dept. of Information Technology Kalinga Institute of Industrial Technology (KIIT) Bhubaneswar India School of Computer Science and Engineering Lovely Professional University Jalandhar India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Bhubaneswar India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

This paper explores the application of game theoretic approaches to load balancing across edge nodes within distributed computing systems. Focusing on a non-cooperative game model, we aim to enhance system efficiency by strategically optimizing resource allocation and load distribution among autonomous edge nodes. Initial simulations demonstrate that our approach not only addresses the inherent complexities of edge environments but also outperforms traditional load balancing mechanisms in terms of both operational efficiency and system responsiveness.

关键词： Computational modeling Focusing Games Load management Complexity theory Resource management Game theory Distributed computing Load modeling Edge computing

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EloMetrics: Advanced Outcome Prediction for Chess Matches with Elo Ratings and Logistic Regression

EloMetrics: Advanced Outcome Prediction for Chess Matches wi...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Reyes, Ma Julianna Re-An D.G. Dicreto, Eirnan Santos, Emmanuel Gabriel D. Limbag, Daniella Franxene P. Sampedro, Gabriel Avelino College of Computer Studies De La Salle University Manila Philippines Networks and Distributed Systems Laboratory University of the Philippines Diliman Quezon City Philippines

ISBN: (纸本)9798331510756

Chessis a complex game characterized by diverse strategies and time constraints, making quick decision-making essential for success. While Elo ratings are widely recognized as indicators of player skill, the predictability of match outcomes based solely on these ratings remains a challenge. The study aims to develop a model for accurately predicting the outcome of chess games using logistic regression, focusing on Elo rating differences and the number of moves in each game. The dataset includes over 20,000 games from ***, and only games with decisive outcomes (excluding draws) were used. The research categorizes Elo ratings into various classes and evaluates model performance across these ranges. The model achieves a predictive accuracy of 68.18%, demonstrating the significance of Elo ratings and move counts in determining game results. Performance metrics, including precision, recall, and Fl-score, further validate the model's effectiveness. The study concludes that while Elo ratings and move count are strong predictors of chess outcomes, further refinement is needed to improve performance at high-ranking skill levels. The model performs well for experts but loses accuracy in master-level games, reflecting higher skill-level complexities. The insights gained from this research contribute to a deeper understanding of predictive modeling in chess, sug-gesting potential avenues for further investigation into additional Influencing factors. © 2025 IEEE.

关键词： Logistic regression

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Effect of Weather Conditions and Obstructions on Vehicle Model, Logo, and License Plate Recognition systems Using Deep Learning

Effect of Weather Conditions and Obstructions on Vehicle Mod...

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international conference on computer and Automation Engineering, ICCAE

作者： Yitzak Wheimdal E. Foz Jacob Dylan D. Soliman Marloun P. Sejera School of Electrical Electronics and Computer Engineering Mapùa University Manila Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Vehicle recognition systems are used in various sectors of our modern society. Environmental factors greatly affect the efficiency of these systems. This study aims to determine the effect of weather conditions such as direct sunlight, cloudy weather, and rain on a YOLO based system's ability to detect a vehicle's model, logo, and license plate.

关键词： YOLO Deep learning Rain Automation Computational modeling Clouds Licenses Environmental factors License plate recognition Meteorology

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Performance Optimization and Practical Exploration of Transformer Architecture in Speech Synthesis

Performance Optimization and Practical Exploration of Transf...

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Gengmei Yao Xiaoying Ye Computer Science Department Guangdong Neusoft Institute Foshan Guangdong China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

The Transformer architecture has been widely used in the field of speech synthesis due to its powerful modeling capabilities and flexibility. However, the existing Transformer architecture still encounters many performance limitations in practical operation, such as insufficient naturalness of sound quality, slow inference speed, poor cross language adaptability, and high computational resource consumption. In response to these issues, this article proposes a series of targeted performance optimization strategies, including improving the model architecture to enhance the naturalness of sound quality, accelerating the inference process to meet real-time requirements, enhancing the model's adaptability to multiple languages and cross domains, and adopting efficient algorithms and hardware optimization to reduce computational resource consumption. Through experimental verification, these optimization strategies effectively promote the performance improvement of speech synthesis systems, greatly enhancing their feasibility and efficiency in practical applications.

关键词： Adaptation models Computational modeling Process control computer architecture Transformer cores Transformers Real-time systems Multilingual Speech synthesis Optimization

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