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共 58303 条记录，以下是141-150 订阅

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Multi-Agent hierarchical workflow for autonomous code generation with Large Language Models

Multi-Agent hierarchical workflow for autonomous code genera...

引用

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

作者： Akilesh, S. Sekar, Rajeev Om Kumar, C.U. Prakalya, D. Suguna, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

This paper presents a multi-agent hierarchical workflow tailored for automating data analysis, code generation, and visualization, focusing specifically on user-provided CSV datasets. The workflow integrates AlphaCodium with LangChain, LangGraph, and GPT, enabling autonomous code generation, unit test development, and debugging. The system operates by analyzing the uploaded dataset, assigning agents that work sequentially: a programmer agent generates code using the Skeleton-Of-Code approach, a unit test agent verifies the code, and an executor agent runs the code. A debugging agent is also included to identify and resolve any issues. The AI agents involved are capable of dynamically accessing online resources, including documentation and references to enhance their decision-making processes. This work attempts to exemplify the application of AI in automating not only code execution but also the planning and validation stages by writing unit tests in the software development process to reflect the increasing role of AI in advancing automation for rapid code generation within the software industry. © 2025 IEEE.

关键词： Software design

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Reinforcement Learning for Quadrupedal Locomotion: Current Advancements and Future Perspectives 9

Reinforcement Learning for Quadrupedal Locomotion: Current A...

引用

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

作者： Gurram, Maurya Uttam, Prakash Kumar Ohol, Shantipal S. Computer Science and Engineering Vellore Institute of Technology AP Amaravati India Electrical Electronics and Computer Science Indian Institute of Science Bangalore India COEP University Pune India

ISBN: (纸本)9798331509262

In recent years, reinforcement learning (RL) based quadrupedal locomotion control has emerged as an extensively researched field, driven by the potential advantages of autonomous learning and adaptation compared to traditional control methods. This paper provides a comprehensive study of the latest research in applying RL techniques to develop locomotion controllers for quadrupedal robots. We present a detailed overview of the core concepts, methodologies, and key advancements in RL-based locomotion controllers, including learning algorithms, training curricula, reward formulations, and simulation-to-real transfer techniques. The study covers both gait-bound and gait-free approaches, highlighting their respective strengths and limitations. Additionally, we discuss the integration of these controllers with robotic hardware and the role of sensor feedback in enabling adaptive behavior. The paper also outlines future research directions, such as incorporating exteroceptive sensing, combining model-based and model-free techniques, and developing online learning capabilities. Our study aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art in RL-based locomotion controllers, enabling them to build upon existing work and explore novel solutions for enhancing the mobility and adaptability of quadrupedal robots in real-world environments. © 2025 IEEE.

关键词： Controllability

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Fault diagnosis of train control vehicle equipment based on dual-channel feature fusion 5

Fault diagnosis of train control vehicle equipment based on ...

引用

5th international conference on Signal Processing and computer Science, SPCS 2024

作者： Yan, Xiaorong Zhang, Zhenhai School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

It is of great significance to extract the features of a large number of fault text data generated during the operation of train control vehicle equipment in the subsequent fault diagnosis task. In this paper, a fault diagnosis model based on BiGRU and CNN based on dual-channel feature fusion is constructed. Firstly, the dynamic word vectors were generated by the BERT pre-trained model from the data cleaning fault vocabulary database. Then, in order to fully extract the global and local semantic information of the data, a fault diagnosis model based on BiGRU-CNN dual-channel feature fusion was constructed, and an improved cross-entropy loss function was introduced to focus on the difficult to classify samples. Finally, the fused high-dimensional feature vector matrix is reduced by the PCA method, the redundant information is removed, and the SVM classifier is used to complete the fault data classification and realize fault diagnosis. Experimental results show that the model can obtain word vectors with strong representation ability, and the extracted features with this word vector as the input of the dual-channel model have global and local semantic information, and can effectively solve the problem of inaccurate classification of small sample data. The model in this paper has significantly improved the accuracy, recall rate, and F1 value, which can provide some support for the daily diagnosis of train control vehicle equipment. © 2025 SPIE.

关键词： Automobile parts and equipment

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Comparing Advanced Programming Languages Based on Machine Learning (ML) 15th

Comparing Advanced Programming Languages Based on Machine Le...

引用

15th international conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Quraishi, Rayaan Salian, Aditya Khasgiwala, Deep Khandare, Anand Thakur College of Engineering and Technology Department of Computer Engineering Maharashtra Mumbai India

ISBN: (纸本)9789819786305

Artificial intelligence, data science, and automation are just a few of the fields where machine learning has emerged as an influential force. The choice of programming language is crucial for the creation and implementation of ML models. The goal of this research study is to give practitioners and researchers valuable insights into language selection criteria and their influence on ML projects by conducting a comprehensive evaluation of the advanced programming languages used for ML applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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MU-PTO: Multi-Unmanned Aerial Vehicle Supported Passive Tracking Optimization for Dynamic Maritime Lost Items 24

MU-PTO: Multi-Unmanned Aerial Vehicle Supported Passive Trac...

引用

2024 international conference on Industrial automation and Robotics, IAR 2024

作者： Chen, Ziwei Tang, Kezong Jiang, Yuanhao Duan, Baomin Zhao, Cong Meng, Chengjian School of Information Engineering Jingdezhen Ceramic University Jiangxi Jingdezhen China School of Computer Science and Technology East China Normal University Shanghai China Zaozhuang Xuecheng District Meteorological Bureau Shandong Zaozhuang China

ISBN: (纸本)9798400711268

For the problem of passive localization of lost objects on the dynamic sea surface, a Multi-Unmanned Aerial Vehicle (UAV) supported Passive Tracking Optimization (MU-PTO) has been proposed, which mainly includes: firstly, a theory-driven passive localization method is designed to achieve information fusion among multi-UAVs based on the geometrical dilution of precision, and the theoretical derivation and analytical equations are given;secondly, a multi-UAV tracking optimization model based on the Improved Multi-Operator Differential Evolution (IMODE) algorithm is designed to achieve precise positioning during dynamic tracking. The results show that the positioning error in the initial state does not exceed 1.824 km, and the error can be reduced to within 1e-6 km with only 100 iterations of optimization. © 2024 Copyright held by the owner/author(s).

关键词： Information fusion

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Visual inertial SLAM system based on ELSED point and line features 5

Visual inertial SLAM system based on ELSED point and line fe...

引用

5th international conference on Signal Processing and computer Science, SPCS 2024

作者： Guo, Hongchang Li, Shaobo Gong, Mao School of Automation and Electrical Engineering Inner Mongolia University of Science and Technology Baotou014010 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Currently, the line segment extraction algorithms used in point-line fusion SLAM tend to detect a long line segment as a number of short segments, which reduces the efficiency and accuracy of line segment matching. Additionally, the use of the computationally expensive LBD descriptor for line segment matching leads to a lower system efficiency. To address these issues, this paper proposes a point-line feature fusion visual-inertial SLAM system based on the ELSED line segment detection algorithm. The proposed system suppersses the extraction of shorter line segments and solves the problem of long line segments being divided into multiple short line segments, thereby improving the accuracy of the SLAM system. The line matching part uses a matching strategy based on the improved LK optical flow, replacing the computationally expensive LBD descriptor matching. Finally, experiments on the EuRoC dataset verify that the SLAM system designed in this paper reduces the average absolute trajectory error by 34.45% compared to the VINS-Mono system, and by 17.38% compared to the PL-VINS algorithm in some complex indoor scenarios. The results show that the proposed algorithm effectively improves the positioning accuracy and robustness of the point-line SLAM system. © 2025 SPIE.

关键词： Optical flows

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Research on Reliability Improvement and Index Structure Optimization Based on B+ Trees 24

Research on Reliability Improvement and Index Structure Opti...

引用

3rd international conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Yu, Yan Tang, Junmiao Zhang, Hesong Zhang, Hesheng School of Mechatronics Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798400710834

In aerospace systems, B+ trees are typically used for data management and indexing. However, traditional B+ trees have issues with data reliability and slower indexing speeds when handling massive data volumes. To address these issues, an improved B+ tree structure is proposed. This improvement involves a custom memory pool to manage the creation and deletion of tree nodes and employs a dynamic optimization algorithm to reduce the height of the B+ tree. Additionally, an automatic snapshot and logging system is introduced to ensure the recoverability of the B+ tree in case of system failures, enhancing its performance and reliability in complex environments. Comparative experiments indicate that the improved B+ tree achieves a 14.02% increase in efficiency for node insertion operations, a 27.39% speed improvement in searching for specific keys, and reduced memory consumption due to the dynamic optimization algorithm, demonstrating significant advantages in memory-constrained aerospace systems. © 2024 Copyright held by the owner/author(s).

关键词： Memory management

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Design and simulation of harmonic radar baseband system 24

Design and simulation of harmonic radar baseband system

引用

3rd international conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Zhu, Haitian Qin, Mimi Guangxi University of Science and Technology School of Automation Guangxi Liuzhou China Qingdao University of Technology School of Mechanical and Automotive Engineering Shandong Qingdao China

ISBN: (纸本)9798400710834

The baseband system serves as the core component responsible for signal processing in harmonic radar systems. However, designing a baseband system that meets requirements poses several challenges. In this study, we addressed the design and simulation of a baseband system for a 3.6GHz harmonic radar. A fundamental signal at a frequency of 3.3MHz was selected, with 90° phase-shift binary phase shift keying (BPSK) modulation employed as the modulation scheme for the baseband system. MATLAB was utilized for program development to simulate the reception of nonlinear target-reflected harmonic signals. Results of signal processing for the second and third harmonics were provided. Furthermore, an FPGA-based hardware circuit for the harmonic radar was designed, followed by hardware testing and target data collection. The practical feasibility of the design approach was validated. Comparative analysis between measured and simulated data revealed the system’s proficiency in identifying harmonic signals during signal processing. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Optimizing Cost Efficiency in Cloud-Based automation with DevOps Techniques 3

Optimizing Cost Efficiency in Cloud-Based Automation with De...

引用

3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Garg, Avni Singh, Tribhuvan Rajiv, Asha Dhamodaran, S. Sahoo, Girija Shankar Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University 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

ISBN: (纸本)9798331542375

Cloud automation has created a trend offering several advantages to businesses, including scalability and cost-effectiveness. Although deploying such automation in a cloud setting is fundamental, optimizing its cost efficiency proves challenging, particularly with DEVOPS techniques. DEVOPS integrates development and operations to promote collaboration and automate different parts of the software delivery process. It is also possible to elaborate the DEVOPS techniques by implementing them through cloud-based automation, which can greatly benefit both efficiency and cost. A key area for cost optimization is infrastructure management. By using DEVOPS techniques for the provisioning and deployment of resources, organizations can automate what have previously been tedious and manual processes, which can lead to lower infrastructure costs and more efficiency. Utilization of resources can be enhanced through DEVOPS techniques. Auto-scaling automatically adapts resources based on demand, while load balancing distributes workloads across resources to improve performance and costs. However, one area in which DEVOPS can dramatically enhance companies' cost efficiency is continuous monitoring and optimizing. Automated monitoring tools fire alerts notifying businesses about the most precise area where work is being done inefficiently in real-time, helping companies to save money. © 2025 IEEE.

关键词： automation Businesses Collaboration Dramatically Efficiency

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Object Detection Based on Improved CenterNet in UAV Perspectives 5

Object Detection Based on Improved CenterNet in UAV Perspect...

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

作者： Wang, Xingdi Wang, Kejie Chen, Fuyang College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798331533694

Most current object detection algorithms are based on natural images collected in ideal environments, featuring close-range data acquisition, minimal target scale variation, and predominantly medium-to-large objects with clear contours. However, UAV aerial images present unique challenges for pedestrian detection: Firstly, rapid changes in UAV flight altitude and shooting angles result in large target scale variations, leading to blurred contours and indistinct features;Secondly, the diversity of observation angles causes changes in target shape and features, making it difficult to extract consistent features and increasing the rate of false detections. To address these issues, we propose an improved CenterNet-based method for small object detection from UAV perspectives. We integrate a large-kernel attention mechanism and enhance the Deep Layer Aggregation (DLA) network to improve small object feature extraction. Additionally, we modify the convolutional kernel sampling region to enhance the network's adaptability to shape variations. Experimental results demonstrate that our proposed method effectively improves the accuracy of small object detection. © 2025 IEEE.

关键词： Aerial photography

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