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检索条件"主题词=software algorithms"

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Object Detection for Assisting Blind Individuals Using ESP32-CAM and GPS

Object Detection for Assisting Blind Individuals Using ESP32...

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Anupam Kumar Shashi Ranjan Mehta Jasneet Chawla Satyam Bharti BE CSE Chandigarh University Mohali Chandigarh BE CSE Chandigarh University Mohali Punjab

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The purpose of this study is to develop a conceptual framework of assistive technologies that will help meet the objective of increasing the independence and safety of visually impaired persons. This framework is based on recent innovations in object detection, depth sensing, and communication for real-time spatial awareness and response. To increase efficiency in gathering the environment data the system combines several hardware components such as a high frame rate web camera, infrared depth sensor as well as ESP32-CAM microcontroller board that plays a crucial role in capturing visions and spatial data. The object detection feature, which forms the basis of the system, uses the YOLO algorithm a prominent algorithm in real-time object recognition and localization that allows the framework to quickly identify and locate objects in the user’s environment. From the software side, the sensor inclusion and data treatment are maintained with ordinary C++ of the Arduino-Based hardware. The companion application is built with React Native and is best suited to Android platforms with additional nice features of its user-friendly interface. These are major features of this application which include notification or warning to the user of the obstacles, landmarks, or areas of interest around the environment. Also, the system under development has incorporated real-time GPS tracking so that the attendant or family members can have an idea of where the user is at any given time, giving more security. Featuring an innovative compact and wheelable design, this mobility solution aims to decrease the level of reliance of the visually impaired individual on mechanized aids or human guidance assistance with activities such as walking and getting around during daily activities in unknown territories. Thus, this work brings the advanced sensory and notification technologies in one small pocket device which is an actual and practical invention solely serving to enhance the lives of visu

关键词： YOLO Technological innovation software algorithms Visual impairment Assistive technologies Real-time systems Hardware Safety Object recognition Global Positioning System

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Revisiting Reliability in Large-Scale Machine Learning Research Clusters

Revisiting Reliability in Large-Scale Machine Learning Resea...

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IEEE Symposium on High-Performance Computer Architecture

作者： Apostolos Kokolis Michael Kuchnik John Hoffman Adithya Kumar Parth Malani Faye Ma Zachary DeVito Shubho Sengupta Kalyan Saladi Carole-Jean Wu FAIR at Meta

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Reliability is a fundamental challenge in operating large-scale machine learning (ML) infrastructures, particularly as the scale of ML models and training clusters continues to grow. Despite decades of research on infrastructure failures, the impact of job failures across different scales remains unclear. This paper presents a view of managing two large, multi-tenant ML clusters, providing quantitative analysis, operational experience, and our own perspective in understanding and addressing reliability concerns at scale. Our analysis reveals that while large jobs are most vulnerable to failures, smaller jobs make up the majority of jobs in the clusters and should be incorporated into optimization objectives. We identify key workload properties, compare them across clusters, and demonstrate essential reliability requirements for pushing the boundaries of ML training at *** hereby introduce a taxonomy of failures and key reliability metrics, analyze 11 months of data from two state-of-the-art ML environments with 4 million jobs and over 150 million A100 GPU hours. Building on our data, we fit a failure model to project Mean Time to Failure for various GPU scales. We further propose a method to estimate a related metric, Effective Training Time Ratio, as a function of job parameters, and we use this model to gauge the efficacy of potential software mitigations at scale. Our work provides valuable insights and future research directions for improving the reliability of AI supercomputer clusters, emphasizing the need for flexible, workload-agnostic, and reliability-aware infrastructure, system software, and algorithms.

关键词： Training Statistical analysis Taxonomy software algorithms Graphics processing units Machine learning Supercomputers software reliability System software Resilience

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Improving the Efficiency of Adaptive Operation of the Decameter Communication Line

Improving the Efficiency of Adaptive Operation of the Decame...

引用

Systems of Signals Generating and Processing in the Field of on Board Communications

作者： Aleksey Rybakov Grigoriy Fokin Software Defined Radio Laboratory The Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

Increasing the noise immunity of a digital radio communication system (decameter communication line) through the use of software equalizers is a modern and relevant topic for the development of adaptation algorithms and mechanisms for using signal-code sequences for such a radio link. It is necessary to provide fundamentally new solutions for the task of creating ionospheric radio communication lines. A critical condition for each of these options is the effect of the Doppler shift. The study highlights the importance of adaptive technologies to ensure stable and reliable communications in a volatile and heterogeneous distribution environment. The proposed method of reducing the effect of Doppler shift by using a channel equalizer contains new ideas about using the variability of the signal preamble to adjust the receiver. Contribution. The simulation results showed that the use of equalizers significantly increases noise immunity, reducing the required signal-to-noise ratio (SNR) at a given level of bit errors (BER) by 10 −3 .

关键词： Doppler shift Equalizers Simulation Bit error rate software algorithms Receivers Radio links software Reliability Signal to noise ratio

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Enactment of Deep Reinforcement Learning Agent for Duty Ratio Control of Dual Active Bridge Converter

Enactment of Deep Reinforcement Learning Agent for Duty Rati...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Stutee Patra Aditi Chatterjee Department of Electrical Engineering C.V. Raman Global University Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

This paper presents a deep reinforcement learning (DRL) agent for duty ratio control of the dual active bridge (DAB) converter using triple phase shift (TPS) modulation. The operation of a dual active bridge under the TPS modulation technique was briefly discussed, stating its advantage over the other phase shift techniques, thus creating the requirement of a model-free control approach for the converter. The reinforcement learning method has evolved as the deep reinforcement learning method that can solve complex problems with rising computational accuracy. This paper focuses on a deep deterministic policy gradient (DDPG) agent founded on actor-critic policy, i.e. implemented as the DRL controller. A detailed discussion about the control strategy using the DRL controller was presented. The model was simulated using MATLAB/Simulink software version 2024b to verify the voltage regulation of the dual active bridge using the DDPG agent. Finally, the conclusion presents the advantages of using a DRL controller for the TPS modulation of the DAB converter.

关键词： Training Accuracy Phase modulation Computational modeling software algorithms Bridge circuits Deep reinforcement learning software Mathematical models MATLAB

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The Thermal Cutting Process of Flat Material on Figured Parts: Digital Twin Based on the Voxel Representation

The Thermal Cutting Process of Flat Material on Figured Part...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Mikhail Verkhoturov Galina Verkhoturova Nail Khannanov Department of Computer Science Ufa University of Science and Technology Ufa Russian Federation Department of Computational Mathematics and Cybernetics Ufa University of Science and Technology Ufa Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This paper presents a novel approach to constructing a digital twin (DT) for the thermal cutting process of flat materials, such as those used in laser or gas cutting. The primary goal is to optimize the cutting tool path while accounting for thermal effects on the material, which can lead to overheating and deformation. The proposed method utilizes a discrete-logical (voxel-based) representation to model temperature changes in the material during cutting. A computational experiment demonstrates the effectiveness of the developed algorithm, which minimizes both idle travel and local overheating zones. The results show a 14% improvement in path optimization compared to traditional methods, highlighting the potential of integrating real-time temperature data into the cutting process. This approach enhances the quality of cut blanks and reduces production costs, making it a valuable tool for industrial applications.

关键词： Temperature distribution software algorithms Production Traveling salesman problems Routing Minimization Real-time systems software Digital twins Optimization

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Research on Brake Drum Surface Defect Detection System Based on Machine Vision Technology

Research on Brake Drum Surface Defect Detection System Based...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Xuefeng Han Bin Zheng School of Intelligent Manufacturing Panzhihua University Panzhihua

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

This paper focuses on the development of an efficient and accurate brake drum surface defect detection system to meet the growing demands for brake safety performance in the manufacturing industry. The system analyzes common defect types on brake drum surfaces, such as chips and cracks, and designs a comprehensive detection scheme. In terms of hardware architecture, it integrates high-resolution image acquisition equipment and a customized lighting system to ensure clear and comprehensive images of the brake drum surface under different lighting conditions. At the software level, the system implements image preprocessing steps, including noise filtering and contrast enhancement, to optimize image quality. Subsequently, advanced feature extraction algorithms are used to precisely locate and analyze the characteristic information of potential defects. Ultimately, the system achieves the identification of brake drum surface defects. Experimental results show that the system not only effectively distinguishes between different types of surface defects but also exhibits significant advantages in detection speed and accuracy, significantly enhancing the efficiency of quality control on brake drum production lines. Compared with traditional manual detection methods, this system significantly reduces the rates of missed and false detections while alleviating the human workload, providing strong support for the intelligent upgrade of the brake manufacturer industry.

关键词： Accuracy Machine vision software algorithms Lighting Hardware Safety Brakes Surface cracks Surface treatment Defect detection

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Resource and Performance Improvements of Optimized Convolutional Neural Networks for FPGA Implementations of Automatic Modulation Recognition

Resource and Performance Improvements of Optimized Convoluti...

引用

Annual Conference on Information Sciences and Systems (CISS)

作者： Joshua A Rothe Haya Shajaiah Whiting School of Engineering Johns Hopkins University Baltimore MD United States

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Automatic Modulation Recognition (AMR), commonly found in software defined radios, relies on speed and accuracy to effectively interpret the modulation type of incoming signals. Convolutional Neural Networks (CNNs) are growing in popularity over traditional algorithms due to their excellent performance with classification-type problems – but these are typically resource intensive, and Radio Frequency (RF) receivers are typically part of a larger system that can benefit from less resources being tied to this classification task. Field Programmable Gate Array (FPGA) implementations provide better performance than CPU and GPU implementations in most cases, and the added benefit of these functions being off the processor allows the entire system to perform better. Since resource utilization is such a critical component of effective CNN implementation, and it is often inversely tied to performance, the effectiveness of various hardware optimization techniques as well as their effects on performance should be considered by the designer. This work evaluates the tradeoffs of various model optimizations and how they affect both implementation and performance, synthesizing the models using Xilinx’s quantization-aware FINN library. In this work, a methodology is presented for the generation of I and Q signals for CNN model training and evaluation, and the performance and hardware utilization benchmarks are evaluated to determine both design considerations and effective approaches for optimizing these models for real-world implementation.

关键词： Training software algorithms Modulation Logic gates Hardware Convolutional neural networks Resource management Field programmable gate arrays Optimization software radio

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Development of robotic arm for fruits and vegetables sorting

Development of robotic arm for fruits and vegetables sorting

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： C. Likithkumar Thrisha K Anbarasi S Navarasu S Electronics and Instrumentation Engineering Faculty SRM Institute Of Science and Technology Chennai India Electronics and Instrumentation Engineering Student SRM Institute Of Science and Technology Chennai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Among various automation approaches, the pick and-place robotic arm stands out due to its versatility and potential for wide application in sorting, picking, and placing tasks. The robotic arm is equipped with advanced computer vision capabilities for automated agricultural tasks. The system incorporates object detection algorithms using THONY software to accurately identify and localize fruits or vegetables within the arm's workspace. The integration of object detection and classification enhances the system's versatility and adaptability, making it suitable for agricultural applications which includes sorting. By automating repetitive tasks and utilizing real-time object detection, the robotic arm enhances operational efficiency, reduces labor requirements, and improves precision in handling and sorting.

关键词： Deep learning Computer science Computer vision software algorithms Object detection Manipulators software Real-time systems Object recognition Sorting

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Addressing Practical Limitations in DC Motor Speed Control

Addressing Practical Limitations in DC Motor Speed Control

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Cybernetics & Informatics (K&I)

作者： Stefan Chamraz Richard Balogh Slovak University of Technology Bratislava Slovakia

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

This paper focuses on the precise control of DC motor speed in its whole range. Complexity of such task is demonstrated on the first order system control. We find that simplified models from textbooks often fail to explain the system’s real-world behaviour. Our approach involves a detailed analysis of all components of the control loop, from speed measurement to motor control. This approach allows us to design a more robust controller that better accounts for practical limitations. A key aspect of our work is a thorough analysis of the dimensions of physical quantities and their impact on controller design.

关键词： Microcontrollers Velocity control software algorithms Decentralized control Process control Pulse width modulation DC motors Hardware Delays Velocity measurement

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Optimal BESS Scheduling with Real-Time Assessment for Time-of-Use Customers Using Particle Swarm Optimization

Optimal BESS Scheduling with Real-Time Assessment for Time-o...

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Power, Energy and Electrical Engineering (CPEEE), International Conference on

作者： Alioune Diouf Yasuhiro Noro Electrical Engineering and Electronics Program Kogakuin University Tokyo Japan Department of Electrical and Electronics Engineering Kogakuin University Tokyo Japan

ISBN: (数字)9798331520090

ISBN: (纸本)9798331520106

This study presents an optimal day-ahead operational scheduling strategy for a battery energy storage system(BESS) to achieve savings for a time-of-use(TOU) customer. This optimization problem is solved using a Particle Swarm Optimization(PSO) algorithm with an evaluation of its sensitivity to photovoltaic(PV) and load forecast errors. A new initialization process based on two non-optimized algorithms is proposed to enhance PSO convergence during the optimal scheduling process. By charging the BESS with surplus PV generation or during off-peak periods and discharging it during peak times or when contracted demand limits are exceeded, an initial particle population is provided as an input to the PSO. The state of charge(SOC) is maintained within the normal range by stopping the BESS output selected by the PSO as needed. The PSO's performance is assessed in real-time operation using historical PV and load data. The proposed method is simulated using MATLAB software to verify its effectiveness showing 17% and 10% cost saving over one day and one month, respectively. Additionally, the PSO demonstrates robust performance for forecast error under 10%.

关键词： Schedules Costs Sensitivity Electricity software algorithms Optimal scheduling Real-time systems software Scheduling Particle swarm optimization

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