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检索条件"任意字段=International Conference on Advances in Intelligent Control and Computer Engineering"

共 47494 条记录，以下是161-170 订阅

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An innovative study on an intelligent logistics handling robots

An innovative study on an intelligent logistics handling rob...

引用

2024 international conference on computer Application and Information Security, ICCAIS 2024

作者： Zhong, Yuling Yao, Rui Guangdong Polytechnic of Industuy and Commerce Guangzhou510000 China South China Normal University Guangzhou510000 China

ISBN: (纸本)9781510689312

With the advancement of Industry 4.0 and the logistics industry, traditional manual handling is gradually phasing out, giving rise to the emergence of intelligent logistics handling robots. The robot in this study integrates laser radar, cameras, and a robotic arm, utilizing STM32 as the main control unit and leveraging IoT technology. It demonstrates precise navigation and obstacle avoidance, efficient object recognition and handling, and supports remote control and monitoring. Additionally, it exhibits excellent adaptability across various environments. From an economic perspective, this robot effectively reduces labor costs while enhancing operational efficiency. © The Authors.

关键词： Robotic arms

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HeterSim: Enable Flexible Simulation for Heterogeneous Distributed Deep Learning Platform

HeterSim: Enable Flexible Simulation for Heterogeneous Distr...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Tang, Yinan Zhang, Hongwei Wang, Li Guo, Zhenhua Zhao, Yaqian Li, Rengang IEIT SYSTEM Co. Ltd. Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Distributed deep learning has become an essential technique for accelerating deep learning, but its performance is often influenced by the heterogeneous computing nodes and heterogeneous communication networks within the distributed deep learning platform. Due to the high costs of practical deployment and running of distributed deep learning, it is almost impossible for researchers to optimize the training strategy of distributed deep learning tasks in real-world environments. In this paper, we propose HeterSim, a simulator specifically designed for heterogeneous distributed deep learning platforms. HeterSim enables flexible configuration of computing node performance and network connections, and supports to define and simulate distributed deep learning workloads using graph-based representations. HeterSim also allows for the modification of communication strategies in the distributed deep learning process, thereby assisting researchers in validating their designs for distributed deep learning. We verify the feasibility and flexibility of HeterSim, by generating a simulation platform at the scale of millions of nodes, and successfully simulate the distributed deep learning process of Resnet50. We aim to provide HeterSim as a flexible and user-friendly simulator for researchers, targeting heterogeneous distributed deep learning platforms, and helping researchers evaluate and optimize the strategy of distributed deep learning tasks at a lower cost. © 2025 SPIE.

关键词： computer simulations Switches Deep learning Education and training Data modeling Modeling Computation time computer architecture Manufacturing Mathematical modeling

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TrackNCount - intelligent Vehicle Tracking, Counting, and Speed Estimation using YOLOv8 and DeepSORT Algorithms 6

TrackNCount - Intelligent Vehicle Tracking, Counting, and Sp...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Paluru Ambareesh Ram Venkatesh, Digumalla Manisri Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

For effective city planning and traffic control, it is now crucial to develop some effective monitoring systems for vehicle traffic in order to address this quickly growing tendency within a city. As a result, the TrackNCount project combines DeepSORT with YOLOv8 for object identification to create a smart system for tracking, counting, and speed estimation of vehicles. The idea behind this design is to use sophisticated computer vision techniques in conjunction with efficient algorithms to handle real-world scenarios. Setting up a reliable procedure that recognizes cars, gives them distinct IDs, and records their movements through video frames is our first priority. The system computes vehicle speeds using the Euclidean distance formula, manages item trajectories using double-ended queues, and detects entering and outgoing vehicles based on directional thresholds. Motion trails and bounding boxes are two further visual aids that improve the results' readability. With the added benefit of simple speed estimation feature integration, the model provides exceptional accuracy in real-time vehicle monitoring and counting. Experiments conducted on a variety of traffic scenarios consistently showed that this system can deliver precise vehicle counts in designated directions and speed calculations with a small margin of error. The findings show that TrackNCount is a viable and scalable solution for data-driven city planning and intelligent traffic management. © 2025 IEEE.

关键词： Street traffic control

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Adaptive Electric Vehicle Charge controllers Using Deep Reinforcement Learning for Real-Time Optimization 1

Adaptive Electric Vehicle Charge Controllers Using Deep Rein...

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

作者： Kavitha, N. Bdair, Mohammad Chandrasekar, P. Veerakumar, S. Kumar, M. Hema Kumar, Vinay Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of EEE Tamil Nadu Chennai India School of Architecture Computing & Engineering University of East London London United Kingdom Bannari Amman Institute of Technology Department of EEE Tamil Nadu Erode India Sona College of Technology Department of ECE Tamil Nadu Salem India B N M Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331518578

The increasing adoption of electric vehicles necessitates intelligent charging systems to address challenges such as grid overload, renewable energy integration, and user prioritization. This paper presents a new framework in optimizing the efficiency of electric car charging: Hierarchical Multi-Agent Deep Reinforcement Learning. In this architecture, a global coordinator relies on Proximal Policy Optimization to coordinate across stations, and the local agents follow Deep Q-Networks to manage their resources at the station level. Test results: A comparison of the test results with regular methods shows an average waiting time reduced by 30%, a 15% increase in the usage of renewable energy, and a reduction of 22% in the system latency. For grid loads, consistent results from the operation of this architecture ensure that minimal unserved energy demand leads to the balanced serving of loads. This method, therefore, aligns well with sustainable energy goals through simultaneous improvement in effectiveness of charging infrastructure and on the flexible scale of solution. © 2025 IEEE.

关键词： Deep reinforcement learning

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Building AI-Powered Recommendation Engines Within Salesforce Ecosystems 1

Building AI-Powered Recommendation Engines Within Salesforce...

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

作者： Janakiraman, Arulselvan Shah, Bhumika Thummala, Venkata Reddy Kumar, Naresh Balasubramaniam, Vijayanand Goel, Om Anna University Tamil Nadu Chennai600025 India University of the Cumberlands WilliamsburgKY40769 United States Visvesvaraya Technological University Karnataka Belgaum India Greater Noida201306 India Bharathiar University Tamil Nadu Coimbatore641046 India ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9798331518578

AI tool has become so useful and important for companies that it can either drive customers to a company or a company to a sale. This Recommendations Engine have become a crucial part of how to present the right recommendations to customers, to achieve better experience in Salesforce journey. So, many AI-powered recommendation engines extended within the Salesforce universe. These engines are usually very intelligent, utilizing advanced machine learning algorithms to apply customer data to make more personalized recommendations. Engines that integrate with Salesforce CRM can access a trove of customer data, enabling progressively more accurate and nuanced recommendations. Furthermore, AI-driven recommendation engines within the Salesforce ecosystem can be tailored to specific industry and business needs. For instance, in e-commerce, recommend engines suggest products interacts with customers browsing history and purchasing trends, as well as recommending tailored financial products and services in finance. While on the other hand AI-based recommendation engines present in the Salesforce ecosystem can further be said to implement improved internal processes for sales and marketing strategies as well as you can gain real-time access to customer preferences and behavior. This makes companies highly flexible and able to adapt to changing customer needs and tastes. © 2025 IEEE.

关键词： Sales

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AURA: Advancing Human-Robot Interaction Through Unified Voice and Gesture control 8

AURA: Advancing Human-Robot Interaction Through Unified Voic...

引用

8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kumar, Lakshya Anand, Pooja Ganpatye, Ameya Amity University Greater Dept. of Electronics & Communication Engineering Noida India MIT World Peace University Dept. of Electrical & Electronics Engineering Pune India

ISBN: (纸本)9798331520762

The development of artificial intelligence (AI) assistants has revolutionized human-machine interaction, but traditional systems often rely on singular input modalities such as voice commands. This project, AURA: AI Robot with Unified Voice and Gesture control, integrates voice and air gesture recognition to create a seamless and intuitive user interface. Utilizing a Raspberry Pi, camera, speaker, and microphone, the system combines computer vision and natural language processing to interpret user commands dynamically. The AI assistant is designed to respond accurately to both vocal instructions and gestures, enabling hands-free and voice-free operation in various scenarios. AURA's innovative approach enhances accessibility, convenience, and user experience, with potential applications in smart homes, healthcare, and personal robotics. This unified control mechanism demonstrates the future of multimodal interaction, emphasizing efficiency and adaptability in AI-driven systems. © 2025 IEEE.

关键词： intelligent robots

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Multi-agent Reinforcement Learning for Cooperative On-Ramp Merging of Connected Automated Vehicles 9th

Multi-agent Reinforcement Learning for Cooperative On-Ramp M...

引用

9th international conference on intelligent IoT as a Service

作者： Zhao, Boyuan Cui, Jianxun Harbin Inst Technol Sch Transportat Sci & Engn Harbin 150000 Peoples R China HIT Chongqing Res Inst 618 Liangjiang Ave Chongqing Peoples R China

ISBN: (纸本)9783031705069;9783031705076

Ramp merging areas on highways often serve as bottleneck areas, leading to frequent interactions and accidents between vehicles on the ramp and the arterial road. This results in severe congestion and reduced traffic performance. The emergence of Connected Autonomous Vehicles (CAVs) offers advanced solutions to address these issues and improve traffic operations at ramp merging areas. While previous studies have explored CAV decision-making approaches such as optimization control, model predictive control, and reinforcement learning, they face difficulties in accurately modeling the complex and dynamic scenarios of ramp merging. To overcome these challenges, this paper proposes a collaborative decision-making and control model based on Multi-agent Reinforcement Learning (MARL) for mixed vehicles (CAV-HDV) in multi-lane ramp merging scenarios on arterial roads. The paper introduces three novel MARL algorithms and conducts simulations in six different scenarios to evaluate traffic performance under various lane numbers and traffic densities. The results demonstrate the effectiveness of the proposed collaborative model for ramp merging vehicles. The proposed algorithms significantly reduce collision rates and improve traffic efficiency.

关键词： Highway Ramp Merging Multi Agent Reinforcement Learning Connected Automatic Vehicle Decision-making and control

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Hyperspectral Image Analysis Using Maximum Abundance Classification 13

Hyperspectral Image Analysis Using Maximum Abundance Classif...

引用

13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Zhang, Nian Rouamba, Sougre-Nonma Mahmoud, Wagdy H. Thompson, Lara A. Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC United States

ISBN: (纸本)9798331516147

This paper presents a satellite hyperspectral image processing method that utilizes a maximum abundance classifier to categorize different regions of hyperspectral images into ground truth classes. First, the class names for each endmember and their corresponding columns in the signature matrix are identified, followed by the visualization of their spectral profiles. Abundance maps for the endmembers are then generated using the fully constrained least squares (FCLS) method. Afterward, the maximum abundance classifier is applied, and the resulting classified image is displayed with color-coded pixels. The abundance maps illustrate the spatial distribution of endmembers across the hyperspectral image, where the abundance values of each pixel represent the proportion of each endmember present. By determining the highest abundance value for each pixel and assigning it to the corresponding endmember class, the pixels within the hyperspectral images are classified. Experimental results demonstrate that the proposed MAC method effectively handles mixed pixels. In addition, it can effectively deal with the mixed pixel problem in hyperspectral images because it identifies components by calculating the abundance values for each pixel rather than relying solely on single spectral features. © 2025 IEEE.

关键词： Image segmentation

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Revolutionizing Face Recognition with IIoT: Smarter, Faster, Connected

Revolutionizing Face Recognition with IIoT: Smarter, Faster,...

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2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Grover, Yash Sharma, Pulkit Kansal, Nishtha Vivekananda Institute of Professional Studies School of Engineering & Technology Technical Campus New Delhi India

ISBN: (纸本)9798331513894

The integration of facial recognition technology with Industrial Internet of Things (IIoT) systems offers significant potential to enhance operational efficiency, security, and safety within industrial environments. Automating processes like access control and attendance tracking minimizes human intervention, reduces errors, and streamlines workflows. From a security perspective, facial recognition strengthens access management, reduces unauthorized entries, and enhances real-time surveillance capabilities. In safety-critical industries, this technology can enforce compliance with protective protocols and monitor workers' well-being by detecting signs of fatigue or stress. Additionally, the use of artificial intelligence (AI) and machine learning facilitates real-time data analysis, providing insights to optimize operations and prevent system failures. This paper further examines the broader applications of IoT-driven facial recognition, highlighting promising research areas such as intelligent security systems, biometric authentication, automated surveillance, and smart access control. Practical implementations span smart homes, healthcare monitoring, financial transactions, and public safety, where real-time facial identification enhances both security and operational performance. Despite these advantages, challenges like data privacy concerns and the need for robust technological infrastructure remain critical. Strategies such as data encryption, edge computing, and adherence to privacy regulations like GDPR are essential to protect personal information. As advancements in AI, machine learning, and 5G networks continue, facial recognition systems integrated with IIoT will become increasingly accurate and efficient, paving the way for smarter, more secure, and highly efficient industrial and public environments. © 2025 IEEE.

关键词： Access control

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Classifying Land Cover Using Hyperspectral Image and LiDAR Data 13

Classifying Land Cover Using Hyperspectral Image and LiDAR D...

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13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Zhang, Nian Thompson, Lara A. Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC20008 United States

ISBN: (纸本)9798331516147

This paper proposes an advanced approach to land cover classification by integrating hyperspectral and LiDAR data to leverage their complementary strengths. Hyperspectral sensors capture detailed reflectance across numerous spectral bands, providing unique spectral signatures that differentiate various land cover types. On the other hand, LiDAR technology offers high-resolution elevation data, enabling accurate characterization of surface structures. This paper details the process of converting unorganized LiDAR point clouds into organized formats for effective analysis, followed by a robust data fusion technique that combines spectral and spatial information. Utilizing a land cover classification methodology on the MUUFL Gulfport dataset, collected in November 2010, we evaluate the effectiveness of our approach by comparing classification accuracies of multiple machine learning algorithms, including kernel classification model, support vector machines, and others. Our results demonstrate significant improvements in classification accuracy when hyperspectral and LiDAR data are fused, highlighting the efficacy of this integrated methodology for complex land cover analysis. © 2025 IEEE.

关键词： Support vector machines

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