检索结果-内蒙古大学图书馆

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 ieee.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Hydro-Environmental Autonomous Cleaning System (HEACS)

Hydro-Environmental Autonomous Cleaning System (HEACS)

引用

2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Ingale, Kiran Junghare, Rugved Jadhav, Atharva Jain, Rishi Jadhav, Digvijayy Jadhav, Tanaya Jasuja, Ishita Vishwakarma Institute of Technology Department of Multidisciplinary Engineering Pune India

ISBN: (纸本)9798331529833

The Hydro-Environmental Autonomous Cleaning System (HEACS) addresses the critical issue of water pollution by autonomously collecting waste from water bodies and monitoring water quality. This system integrates a conveyor belt mechanism powered by DC motors for efficient waste collection, including oil spills. Water quality is maintained using Total Dissolved Solids (TDS) sensors, which trigger the injection of purifying chemicals like 777 Bioconcentrate, chlorine dioxide, Copper Sulphate, or Aluminium Sulphate when necessary. Collected garbage weight is measured using load sensors, with real-time data displayed on an LCD connected to an Arduino Uno. Autonomous navigation and operation are managed via Rx/Tx modules, ensuring effective performance in various environmental conditions. HEACS offers a comprehensive solution for water pollution control, combining automation, effective waste management, and environmental monitoring to protect aquatic ecosystems. © 2025 ieee.

关键词： Oil spills

来源：评论

学校读者我要写书评

暂无评论

Integration of Process Optimization and automation: A Way to AI Powered Digital Transformation 1

Integration of Process Optimization and Automation: A Way to...

引用

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

作者： Kotte, Kulasekhara Reddy Thammareddi, Latha Kodi, Divya Anumolu, Venkateswara Rao Arun Kumar, K. Joshi, Shubham INC SLRI Solutions India India Cyber Security Senior Data Analyst Truist Financial India Technical Architect ERP Solutions- Information Technology India Big Data Architect India Bipin Tripathi Kumaon Institute of Technology Department of Computer Science & Engineering Almora India

ISBN: (纸本)9798331518578

One of the most important factors facilitating AI-powered digital transformation is the combination of process automation and optimisation. Organisations may improve operational efficacy, save costs, and streamline procedures by utilising cutting-edge technology like robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI). While automation seeks to reduce human participation in repetitive and rule-based operations, process optimisation concentrates on improving current business processes to attain improved productivity. The development of intelligent, adaptable, and self-improving systems is made possible by the combination of these two methodologies. Predictive findings, real-time decision-making, and customised customer experiences are further made possible by AI-driven analytics. This connection promotes innovation, scalability, and agility across sectors, giving businesses a competitive edge in the quickly changing digital market. The article examines the main principles, advantages, and difficulties of this shift and provides a road map for businesses looking to automate and optimise processes using AI in order to attain operational excellence and sustainable growth. © 2025 ieee.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Machine Learning and Deep Learning Models for Food Waste Detection Using Image Processing

Comparative Study of Machine Learning and Deep Learning Mode...

引用

2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Rajagopal, R. Chaithanya, K. Sonth, Ankita Kallappagoudar, Prasanna Basanagouda Mohammadishaq Nadaf, Mohammadzaid Ananda Theertha, H. Dept of Computer Science Alliance College of Engineering and Design Alliance University Bangalore India Bangalore India Alliance College of Engineering and Design Dept of Computer Science Bangalore India Alliance College of Engineering and Design Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529833

For the purpose of reducing financial losses and environmental impacts, food waste detection is essential. Inorder to detect food waste using image processing, this study investigates the efficiency of several machine learning algorithms, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), Random Forest, and Inception Model (IV3). A set of images of food are then processed. Before each algorithm is used for classification, pre-processing methods like segmentation and feature extraction are used. © 2025 ieee.

关键词： k-nearest neighbors

来源：评论

学校读者我要写书评

暂无评论

Real-Time Drone System for Detecting, Tracking, and Following of a Mobile Robot in ROS/Gazebo

Real-Time Drone System for Detecting, Tracking, and Followin...

引用

2025 international conference on Control, automation, and Instrumentation, IC2AI 2025

作者： Salman, Ali Dika, Mohammad Diab, Hassan Owayjan, Michel Achkar, Roger American University of Science & Technology Faculty of Engineering and Computer Science Beriut Lebanon Nantes University Saint-Nazaire France

ISBN: (纸本)9798331529604

Surveillance drones equipped with video transmission capabilities play a crucial role in modern security systems, with the integration of OpenCV for object detection marking a significant advancement. This study evaluates the detecting accuracy by varying the distance between the drone and the target object, addressing gaps in current research through a detailed practical insight. This research explores key aspects of drone-based video surveillance, including object detecting, tracking and real-time following, aiming to enhance understanding and methodology in computer Vision (CV) applications with Unmanned Aerial Vehicles (UAVs). The Simulation of a surveillance drone system in Gazebo simulator within the ROS2 framework. The drone is programmed to dynamically follow and track a mobile robot, enabling systematic analysis of the object detection algorithm's performance. The findings contribute to advancing the reliability and effectiveness of drone-based video surveillance systems for future innovations in security and monitoring applications. © 2025 ieee.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

A Recurrent Neural Network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 ieee.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

From Efficiency to Innovation: LLM 5.0 and the Future of Industry

From Efficiency to Innovation: LLM 5.0 and the Future of Ind...

引用

2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Gantayat, Pradosh Kumar Kaur, Tejinder Majhi, Madhusmita Jena, Uttam Kumar Das, Swatismita Soni Department of Artificial Intelligence and Data Science Telangana Hyderabad501203 India Department of Mmictbm Mullana Haryana Ambala133207 India Department of Computer Science and Engineering Telangana Hyderabad501203 India Raajdhani Engineering College Biju Patnaik University of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Raajdhani Engineering College Bhubaneswar India

ISBN: (纸本)9798331509828

Industry 5.0 is a new way of thinking that is consistent with the ideas of Industry 4.0 but places greater emphasis on sustainability, sustainability, and human-centricity. Unlike Industry 4.0, which emphasizes the efficient integration of advanced technologies such as the Internet, AI, robots, and automation, Industry 5.0 seeks to balance human-machine collaboration to create more inclusive, meaningful, and sustainable systems. In Industry 5.0, LLM has a bigger role than automating similar activities. LLMs make sectors more resilient, adaptable and aligned with social values by fostering innovation, improving human-centred processes and promoting sustainable practices. Industry 5.0 will create a revolutionary force in the global industrial landscape thanks to a combination of emerging technologies that will further enhance the synergies between human creativity and machine intelligence. © 2025 ieee.

关键词： Human-AI Collaboration Industrial Efficiency Industry 5.0 Innovation LLM 5.0

来源：评论

学校读者我要写书评

暂无评论

Bibliometric and Content Analysis of Large Language Models Research in Software engineering: The Potential and Limitation in Software engineering

引用

international Journal of Advanced computer science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings international conference on Software engineering and ACM international conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (science and Information Organization). All Rights Res

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

Observe Then Act: Asynchronous Active Vision-Action Model for Robotic Manipulation

引用

ieee Robotics and automation Letters 2025年第4期10卷 3422-3429页

作者： Wang, Guokang Li, Hang Zhang, Shuyuan Guo, Di Liu, Yanhong Liu, Huaping Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

In real-world scenarios, many robotic manipulation tasks are hindered by occlusions and limited fields of view, posing significant challenges for passive observation-based models that rely on fixed or wrist-mounted cameras. In this letter, we investigate the problem of robotic manipulation under limited visual observation and propose a task-driven asynchronous active vision-action model. Our model serially connects a camera Next-Best-View (NBV) policy with a gripper Next-Best-Pose (NBP) policy, and trains them in a sensor-motor coordination framework using few-shot reinforcement learning. This approach enables the agent to reposition a third-person camera to actively observe the environment based on the task goal, and subsequently determine the appropriate manipulation actions. We trained and evaluated our model on 8 viewpoint-constrained tasks in RLBench. The results demonstrate that our model consistently outperforms baseline algorithms, showcasing its effectiveness in handling visual constraints in manipulation tasks. © 2025 ieee.

关键词： Robot programming

来源：评论

学校读者我要写书评

暂无评论

Smart Vision Bot 5th

Smart Vision Bot

引用

5th international conference on Data science, Machine Learning and Applications

作者： Nandeshwar, Vikas J. Borse, Om Buthale, Prateek Borse, Chirayu Borhade, Bharati Borse, Sayali Vishwakarma Inst Technol Dept Engn Sci & Humanities Pune 411037 Maharashtra India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Smart vision robots have become increasingly essential in various industries and applications due to their ability to perform complex visual tasks with high precision and efficiency. This project focuses on the development of an autonomous surveillance and tracking system. The system aims to utilize computer vision algorithms for object detection and tracking. It has potential applications in security, automation, and remote monitoring. The project aims to enhance safety and efficiency in various environments through autonomous surveillance capabilities.

关键词： automation Autonomous Surveillance Bot Smart Vision Real-time

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：