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检索条件"任意字段=IEEE Conference on Automation Science and Engineering"

共 60728 条记录，以下是131-140 订阅

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Enhanced Onychomycosis Diagnosis Using a Dynamic Weighted Ensemble Classifier: Integrating LightGBM and Transfer Learning with a Game Theory Approach 15

Enhanced Onychomycosis Diagnosis Using a Dynamic Weighted En...

引用

15th ieee Annual Computing and Communication Workshop and conference, CCWC 2025

作者： Idress, Wail M. Zhao, Yuqian Abouda, Khalid A. Ahmed, Aala A Hassan, Wifag Abdalla, Ola Elhindi, Tahani School of Automation Central South University Changsha China Omdurman Islamic University Department of Electrical and Electronic Engineering Omdurman14415 Sudan Alzaiem Alazhari University Electrical Engineering Department Khartoum North13311 Sudan School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan Khartoum Teaching Hospital of Dermatology and Venereology Khartoum Sudan

ISBN: (纸本)9798331507695

Onychomycosis is one of the most common fungal nail infections for which diagnosis remains difficult, particularly in developing countries like Sudan, as access to specialist dermatologists and diagnostic resources is limited. The traditional diagnostic methods are based on visual inspection and subjective observation that results in misdiagnosis. This paper aims to use a LightGBM classifier for the classification of nail images. In the transfer learning (TL) model, Inception-ResNet-v2, DenseNet121, and EfficientNetB0 are used. A new dynamic weighted ensemble classifier according to the game theory strategy is proposed for the fusion of the TL model and LightGBM classifier model outputs. To overcome the shortage of data availability, a dataset was collected specifically for training the deep learning models with Sudanese patients. The proposed ensemble method outperforms individual deep learning and feature-based models, delivering optimal performance and significantly improving accuracy. This study achieved a diagnostic accuracy of 96.8%, with an AUC of 0.955, sensitivity of 95.45%, and specificity of 91.59%, which surpassed the diagnostic performance of many dermatologists who participated in the study. These results demonstrate the potential of this approach for improving onychomycosis diagnosis in resource-limited settings. © 2025 ieee.

关键词： Dermatology

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LIBMixer: An all-MLP Architecture for Cell Library Characterization towards Design Space Optimization 25

LIBMixer: An all-MLP Architecture for Cell Library Character...

引用

30th Asia and South Pacific Design automation conference, ASP-DAC 2025

作者： Lee, Jaeseung Jang, Sunggyu Lee, Jakang Kang, Seokhyeong Pohang University of Science and Technology Pohang Korea Republic of

ISBN: (纸本)9798400706356

Cell library characterization is a fundamental stage of electronic design automation (EDA), as it provides essential electrical models for circuit simulation and design quality assessment. However, the development of advanced nodes demands increasing computational resources and engineering efforts for characterization. We introduce LIBMixer, a machine learning-based framework for fast and accurate library characterization designed to enhance design space optimization. Leveraging multi-layer perceptron architectures, LIBMixer efficiently manages complex relationships between technology and electrical characteristics. It achieves a 31.5× faster runtime than conventional EDA tools while enhancing alignment. Compared to state-of-the-art methods, LIBMixer targets 6.4× more standard cells for both power and timing information. This scalability improvement enables the practical synthesis of IP cores, demonstrating high correlation across of power, performance, and area results. Additionally, Pareto fronts of synthesis design results with LIBMixer-inferred libraries closely match those from foundry files. Experimental results highlight the effectiveness of LIBMixer as a fast and reliable alternative for PVT analysis. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Electronic design automation

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PULSE: Progressive Utilization of Log-Structured Techniques to Ease SSD Write Amplification in B-epsilon-tree 25

PULSE: Progressive Utilization of Log-Structured Techniques ...

引用

30th Asia and South Pacific Design automation conference, ASP-DAC 2025

作者： Peng, Huai-De Chen, Yi-Shen Chen, Tseng-Yi Chang, Yuan-Hao Department of Computer Science and Information Engineering National Central University Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798400706356

During the unprecedented expansion of global data, efficient storage solutions are essential for processing massive datasets stored on modern storage devices. B-epsilon-tree (Bϵ-tree) is one of the most well-known techniques that provides a write-optimized structure for database file systems. With the excellent access performance and high energy efficiency of solid-state drives (SSDs), they are expected to yield promising outcomes for large-scale data computation. However, their integration into storage systems has the challenges of write amplification, which impacts SSD endurance and reliability. This work identifies significant write amplification issues with Bϵ-tree implementations on SSDs due to the complicated management of key-value pairs. To mitigate the impact of write amplification, we propose PULSE, a novel scheme that rethinks Bϵ-tree designs by leveraging log-structured techniques optimized for SSDs. Moreover, PULSE integrates auxiliary indexing and a dual flush selector to minimize write amplification. Experimental results demonstrate that PULSE significantly mitigates write amplification by more than 62.6% on SSDs for the representative benchmarks compared to the Bϵ-tree. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Flash-based SSDs

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Detection of Known Signals Under Non-Gaussian Noise Based on the Laplacian Kernel

Detection of Known Signals Under Non-Gaussian Noise Based on...

引用

2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Yang, Yibang Chen, Yuyang Huang, Guanna Xu, Weichao School of Automation Guangdong University of Technology Guangdong Guangzhou510006 China School of Intelligent Manufacturing and Electrical Engineering Guangzhou Institute of Science and Technology Guangdong Guangzhou510006 China

ISBN: (纸本)9798331506797

This paper presents a new robust detector employing the Laplacian kernel function to detect known signals in environments with impulsive noise. The effectiveness of the detector is assessed by comparing it to several widely used detectors. Monte Carlo simulations were also conducted to determine the optimal kernel width for specific noise conditions and to evaluate its asymptotic relative efficiency compared to the local optimum detector. The detector's excellent performance and its ability to function without prior knowledge of the noise enables it to be an alternative to current detection methods. © 2025 ieee.

关键词： asymptotic relative efficiency (ARE) Laplacian kernel function non-Gaussian noise Signal detection

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Design and Electromagnetic Simulation of Axial-Flux Hybrid-Excitation Motor for Electric Ship Propulsion

Design and Electromagnetic Simulation of Axial-Flux Hybrid-E...

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2024 International conference Optoelectronic Information and Optical engineering, OIOE 2024

作者： Wang, Chu Geng, Weiwei China Ship Scientific Research Center Jiangsu Province Wuxi China School of Automation Nanjing University of Science and Technology Jiangsu Province Nanjing China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

In this paper, a novel axial flux hybrid excitation motor (AFHEM) for electric ship propulsion system is proposed. On the basis of traditional axial flux permanent magnet motor (AFPMM), a new type of AFHEM was composed of field winding and magnetic flux ring, and adopting a dual rotor single stator topology structure. The unique outer magnetic ring and magnetic flux regulation principle of the proposed AFHEM were discussed, and finite element analysis was used to simulate and analyze the motor, including torque output capability, air-gap magnetic flux density, and risk analysis of permanent magnet demagnetization under different excitation currents. By adjusting the excitation current, the weak magnetic and overload capacity of the motor can be improved, and it has high torque output and magnetic field regulation ability. © 2025 SPIE.

关键词： Permanent magnets

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Design of a 1-5GHz Inverter-Based Phase Interpolator for Spin-Wave Detection 25

Design of a 1-5GHz Inverter-Based Phase Interpolator for Spi...

引用

30th Asia and South Pacific Design automation conference, ASP-DAC 2025

作者： Zhu, Yuyang Yang, Zunsong Cheng, Zhenyu Sarker, Md Shamim Yamahara, Hiroyasu Seki, Munetoshi Tabata, Hitoshi Iizuka, Tetsuya Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo113-0032 Japan Systems Design Lab. School of Engineering The University of Tokyo Tokyo113-0032 Japan Department of Bioengineering The University of Tokyo Tokyo113-8656 Japan

ISBN: (纸本)9798400706356

This paper details the design and implementation of a wide-band, digital-controlledinverter-based phase interpolator (PI) for a spin-wave detection circuit. The PI contains input phase generation part for coarse phase tunning, and PI core part for fine phase tunning. Fabricated using 65nm CMOS technology, the circuit operates across a frequency range of 1GHz to 5GHz with a 1V supply. The chip is implemented in 65nm CMOS technology and operates from 1GHz to 5GHz, with 1V supply. The power consumption of the proposed PI core is 1.89mW at 5GHz. The measured differential non-linearity and integral non-linearity are 0.6 LSB and 5.17 LSB respectively at the worst case. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： CMOS integrated circuits

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Leveraging Machine Learning for Dynamic Resource Allocation in Cloud automation 3

Leveraging Machine Learning for Dynamic Resource Allocation ...

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3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Ahluwalia, Gunveen Suresh, Vaibhav Goutham Praveena, M. D. Anto Acharjya, Kalyan Paridaname, Trushna Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

Cloud automation enables the dynamic management and allocation of resources in a computing environment and is one of the most common uses available. Yet despite their power when cloud systems were simple and small, traditional approaches to allocating resources have proven far less feasible lately. This scenario has also created the need for new evolution methods that will adapt to dynamic cloud environments and improve the system's overall performance. A solution to this challenge comes from machine learning, which has been increasingly used recently. ML algorithms can process vast amounts of data and identify patterns and trends to predict resource use and user needs. Deep learning helps cloud-enabled automation systems adapt in real-time to changing conditions, distribute resources efficiently and improve user experience. For example, reinforcement learning and deep learning are ML techniques that can be applied to resource allocation algorithms or demand prediction models to increase accuracy. Integrating cloud automation and machine learning brings effectiveness in dynamic resource allocation, boosting the overall performance of a system, which leads to savings, better experience, and greater flexibility in using cloud resources. Reinforcement and deep learning are machine learning techniques to optimize order allocation algorithms and enhance demand estimation models. © 2025 ieee.

关键词： Learning algorithms

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Machine Learning-Based Real-Time Detection of Power Analysis Attacks Using Supply Voltage Comparisons 25

Machine Learning-Based Real-Time Detection of Power Analysis...

引用

30th Asia and South Pacific Design automation conference, ASP-DAC 2025

作者： Wang, Nan Liu, Ruichao Shan, Yufeng Zhu, Yu Chen, Song School of Information Science and Engineering East China Univ. of Science and Tech. Shanghai China School of Microelectronics University of Science and Technology of China Hefei China

ISBN: (纸本)9798400706356

Modern power analysis attacks (PAAs) pose significant threats to hardware security, and reliably securing integrated systems against advanced PAAs has become a significant design target in integrated circuits. However, the detection accuracy of most countermeasures to PAAs significantly decreases when power side-channel information is mixed with voltage noise. In this paper, a real-time PAA detection technique is proposed to achieve high detection accuracy even with large voltage noise. The voltage drops of certain power grid (PG) nodes caused by PAA are evaluated by a number of voltage comparisons between PG nodes, which compensate for the effects of voltage noise. These voltage comparison results are analyzed by machine learning algorithms, and a linear support vector machine (SVM) model is selected as the PAA detection model. The PAAs on an IBM benchmarked microprocessor are applied to evaluate the detection accuracy, and our proposed PAA detection method achieves 93.11% accuracy in detecting a resistance of 1 ω with noise equal to 20% of Vdd. Furthermore, the power and area overheads (evaluated using a 65-nm CMOS process) of this method are reduced by 68% and 75%, respectively, compared to those of the existing machine learning-based PAA detection techniques. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Support vector machines

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Enhancing GPS/IMU Localization Accuracy in Autonomous Vehicles through Deep Learning-Based Error Correction Models 9

Enhancing GPS/IMU Localization Accuracy in Autonomous Vehicl...

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9th International conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Onyema, Uchenna Charles Mahmoud, Shafik College of Science and Engineering University of Derby Derby United Kingdom

ISBN: (纸本)9798331509262

Improving the precision of GPS/IMU localisation in autonomous cars is crucial for ensuring safe and efficient navigation. Several research studies have concentrated on enhancing the precision of localisation systems by employing sensor fusion and sophisticated algorithms. This study proposes a novel approach to enhance GPS/IMU localization accuracy in autonomous vehicles using deep learning-based error correction models. Leveraging Long Short-Term Memory (LSTM) networks, the method captures and corrects inherent errors in GPS/IMU data, leading to significantly improved positional accuracy. The LSTM model was meticulously designed to process sequential data, incorporating an LSTM layer with 50 units to capture temporal relationships and a Dense output layer to predict corrected longitude and latitude values. The model was trained using the Adam optimizer and mean squared error (MSE) loss function, achieving notable reductions in prediction error across 50 epochs. Comparative analyses between actual and predicted coordinates demonstrated the model's high precision. The model was further validated through deployment in a Flask application for continuous testing and a web application for real-time tracking of autonomous vehicles. Results underscore the potential of deep learning models to substantially improve localization accuracy, thereby enhancing the reliability and performance of autonomous navigation systems. © 2025 ieee.

关键词： Deep learning

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Single Cell Energy Router Justification for Three Phase Near Zero Energy Buildings 14

Single Cell Energy Router Justification for Three Phase Near...

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14th Annual ieee Applied Power Electronics conference and Exposition, APEC 2025

作者： Hokmabad, Hossein Nourollahi Shahsavar, Tala Hemmati Matiushkin, Oleksandr Jalakas, Tanel Husev, Oleksandr Belikov, Juri Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia Gdansk University of Technology Faculty of Electrical and Automation Engineering Gdansk Poland Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (纸本)9798331516116

The rise of building-integrated photovoltaics and distributed electric vehicle charging has led to significant phase imbalances in utility grids, challenging service providers due to limited behind-the-meter visibility. This paper introduces a novel Single-Cell Three-Phase (SC-TP) Energy Router (ER) that accesses all phases and balances them without the complexities and costs associated with conventional three-phase systems. Our comparative analysis shows that the SC-TP ER reduces phase unbalancing by 16% and achieves cost savings, offering a viable alternative to three-phase solutions with 25% reduction in cost. © 2025 ieee.

关键词： Power grids

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