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检索条件"任意字段=3rd International Conference on Automation, Robotics and Computer Engineering, ICARCE 2024"

共 803 条记录，以下是111-120 订阅

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IoT-based Smart Municipal Garbage Management System for Fertilizer Processing System 3

IoT-based Smart Municipal Garbage Management System for Fert...

引用

3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Islam, Riadul Roy, Anup Kumar Ahsan, Ali Rahman Enam, Md Mahfuzur Tanvir Islam Mim, Md. Shufian, Abu Lamar University Dept. of Electrical Engineering TX United States American International University-Bangladesh Dept. of Electrical & Electronic Engg Dhaka Bangladesh

ISBN: (纸本)9798331534400

Food disposal is an important issue that has emerged in the modern world and has become more of a concern in our society in recent years, affecting both rich and poor countries. The Collection of waste is a much-needed municipal service for city people for environmental balance. This operation requires high expenses because of various factors such as manpower, navigation of vehicles, fuel, maintenance, and environmental costs. This study highlights a prototype that has been designed considering the above factors of essential implementation of the smart garbage bin to develop a proper municipal waste management system that is useful for the further fertilizer processing system. The prototype discussed here has been developed by a GSM module and ultrasonic distance measurement technology integrated into microcontrollers. This system has been simulated using EDA tools before hardware-based implementation according to the requirements. The proposed system offers a proper web server and SMS notification alert system to stop municipal garbage bins from overflowing. Using data server proper resources can be utilized such as creating location-based shortest paths and tracking worker's performance, etc. A food waste management awareness system also can be displayed to raise public awareness for food scraps to be recycled and utilized to create fertilizer for new plantings. The suggested approach promises to contribute to a more sustainable and cleaner urban environment by encouraging citizen participation in recycling and trash reduction initiatives and optimizing waste management strategies for the respected organizations. © 2024 IEEE.

关键词： Global positioning system

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Enhancing Unmanned Aerial Vechicle Surveillance: Real Time Drone Detection with Yolov7 Deep Learning 3

Enhancing Unmanned Aerial Vechicle Surveillance: Real Time D...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Priyadharshini, M. Srinidhi, K. Murugesh, V. Department of Computer Science and Engineering Hyderabad501 203 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9798350369083

This is an advanced object detection system with YOLOv7 model, implemented for UAV surveillance as outlined in the article. By integrating the system with Roboflow serving as its database manager and data augmentation software, it benefits from a broad array of reputable datasets which are improving training and inferences. In the blog, we cover all steps from model cloning (on TensorFlow) with one/two script line conversion to preprocessing of datasets and work automation for training or evaluation. The optimizations in the implementation of YOLOv7 dog led to scalability, allow more object categories and deploy security for real time surveillance with autonomous navigation. Extensive testing demonstrates that our system exceeds state-of-the-art methods in terms of detection performance as well as efficiency, demonstrating the significant benefits for UAV hotspots gained from a deployment of advanced deep learning techniques. © 2024 IEEE.

关键词： Deep learning

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Sign to Text: Automated Sign Language Interpretation using LSTM and computer Vision 3

Sign to Text: Automated Sign Language Interpretation using L...

引用

3rd international conference on automation, Computing and Renewable Systems, ICACRS 2024

作者： Sankara Mahalingam, M. Suresh Kumar, N. Harika, Chintapudi Harika, Chennamsetty Reddy, Chintala Sankar Kalyan, Dasari Pavan Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Virudhnagar India

ISBN: (纸本)9798331532420

Sign language serves as a crucial communication tool for individuals with speech or hearing impairments. Recent advancements in computer vision and deep learning offer opportunities to develop efficient, real-time sign language recognition systems that bridge communication gaps. This study proposes a sign language recognition model utilizing the Mediapipe model and Long Short-Term Memory (LSTM) networks to detect and interpret American Sign Language (ASL) gestures in real time. The proposed system extracts and analyzes key landmarks across the body, hands, and face, leveraging CNN-LSTM architecture to capture both spatial and temporal features essential for accurate gesture recognition. Evaluated on a diverse ASL dataset, the proposed model achieved an accuracy of 92%, demonstrating robustness and adaptability in varied environments. The proposed approach holds promise for applications in assistive technologies and educational tools, providing an accessible solution for sign language interpretation. © 2024 IEEE.

关键词： Long short-term memory

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A Corrosion Detection Method for Hydraulic Metal Structures Based on an Improved YOLOv10 Model

A Corrosion Detection Method for Hydraulic Metal Structures ...

引用

automation, robotics and computer engineering (icarce), international conference on

作者： Haodong Cheng Fei Kang Sisi Zhu School of Infrastructure Engineering Dalian University of Technology Dalian China China Yangtze Power Co. Ltd. Yichang China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

To achieve corrosion detection of hydraulic metal structures based on deep learning, reduce subjectivity in the corrosion annotation process, and enhance annotation efficiency, this study proposes a corrosion detection method for hydraulic metal structures based on a YOLOv10 serial architecture that incorporates a Transformer module. The serial architecture first uses an object detection model to locate corrosion, followed by a classification model to assess corrosion severity. In terms of F1-score, the proposed corrosion detection network shows a 1% improvement over the baseline. For accuracy, after transfer learning, the proposed corrosion classification model outperforms the baseline by 8.8%. This method was implemented in real-world scenarios, successfully enabling corrosion detection and severity classification of hydraulic metal structures, and verifying the feasibility of the serial architecture.

关键词： Deep learning YOLO Accuracy Annotations Corrosion Transfer learning Metals Hydraulic systems computer architecture Transformers

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Revolutionizing Car Assembly Line Efficiency Using Multi-Object Detection and Tracking 3

Revolutionizing Car Assembly Line Efficiency Using Multi-Obj...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Peters, Abhishikt Edward Juliet, Sujitha Anitha, J. Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore641114 India

ISBN: (纸本)9798350372120

This paper presents a novel approach to address the challenges of manual labor reliance and inefficiencies in the automotive manufacturing industry through the implementation of computer vision-based object detection and tracking. By leveraging advanced technologies, such as AI-powered object tracking, the proposed system aims to streamline production processes, enhance quality control, and reduce the need for manual intervention. This study explores the integration of cutting-edge technologies, such as BYTETrack, YOLOv8, and Roboflow Supervision, to revolutionize multi-object detection and tracking in automotive production lines. Through a comparative analysis of these technologies, this study highlights their unique features and capabilities, highlighting their potential to optimize efficiency and productivity in the manufacturing sector. Overall, this research contributes to ongoing efforts to enhance automation and drive progress in the automotive manufacturing industry. © 2024 IEEE.

关键词： Quality control

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A Path Planning for Unmanned Helicopter in Urban Environment 3

A Path Planning for Unmanned Helicopter in Urban Environment

引用

3rd international conference on Artificial Intelligence and computer Information Technology, AICIT 2024

作者： Zhai, Pengfei Tian, Yuxin Lei, Yuanlong Sheng, Shouzhao College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350365443

For the safety and reliability requirements of unmanned helicopter path planning in urban environments, this paper conducts research on path planning for unmanned helicopters in three-dimensional urban environments. First, a DEM raster method is used to model the three-dimensional urban environment. Secondly, the heuristic function of the ant colony algorithm is designed according to the constraints of path planning in urban environments. To address the issues of low path search efficiency and poor quality of the paths planned by traditional ant colony algorithms, improvements are made. The improved ant colony algorithm is then used for path planning in the three-dimensional urban environment. Finally, simulations are conducted to verify the feasibility of the modeling and path planning algorithm. © 2024 IEEE.

关键词： Ant colony optimization

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VIoT: A Voice and IoT Controlled Unmanned Automated Vehicle 3

VIoT: A Voice and IoT Controlled Unmanned Automated Vehicle

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Rahman, Naimur Sadi, Muhammad Sheikh Saeed, Md. Abu Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350388282

VIoT, a Voice and IoT Controlled Unmanned Automated Vehicle (UAV), pioneers a blend of Voice, IoT, and Bluetooth technologies to redefine remote-controlled robotics. Powered by Rock Pi 3a and an Android app, users seamlessly control the car using Bengali voice commands transmitted through IoT for long-range communication. It also uses Bluetooth for short-range operations if the vehicle is within approximately 20 meters of range from the controlling app. The dual-mode control mechanism offers flexibility, allowing users to steer the robotic car through the Android app effortlessly. Leveraging Google Speech Recognition API for Bengali voice input, this paper establishes a new standard for user-friendly robotic interfaces. Using WebSocket instead of an actual database like Firebase as the medium of data passing for IoT communications significantly solves latency problems. The comprehensive hardware setup includes Rock Pi 3a, L293D motor driver, and basic components, setting a milestone in human-robot interaction and expanding adaptability across various applications. © 2024 IEEE.

关键词： Remote control

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A Classification Method for Unordered Steel Scrap Images Based on Hybrid Convolutional Networks 24

A Classification Method for Unordered Steel Scrap Images Bas...

引用

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

作者： Yu, Yan School of Mechatronics Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798400710834

With the development of deep learning and image recognition technologies, these methods have found widespread applications across various fields. However, the annotation of industrial application-specific data, such as steel scrap images, remains challenging due to the unordered nature of the images, which often requires domain experts to perform the annotation. To address this, we propose a feature learning method for unordered images based on hybrid convolutional networks. Utilizing a preprocessed steel scrap dataset, we employ a cascaded convolutional network to capture deep correlation features within such unordered images while enhancing their fine-grained structures. A self-supervised approach is used to train the encoder, which is subsequently fine-tuned using a minimal set of labeled samples to complete the classification and annotation of steel scrap. Experimental results on four different categories of steel scrap annotation demonstrate that the proposed method achieves accuracies of 60.47%, 61.24%, 23.45%, 62.46%, respectively, verifying the effectiveness of the method. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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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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A multi-agent system for indirect reciprocity with optional interaction 3

A multi-agent system for indirect reciprocity with optional ...

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3rd international conference on Intelligent Mechanical and Human-computer Interaction Technology, IHCIT 2024

作者： Yu, Ying Luo, Shili Chen, Tingwen He, Yanru Wu, Jiahui School of Mechatronic Engineering and Automation Shanghai University Shanghai200000 China

ISBN: (纸本)9781510683105

Indirect Reciprocity (IR) is the main mechanism to promote cooperation. A multi-agent system is proposed in this paper to simulate IR with optional interactions so that more specific experiments could be carried out. Comparative experimental results verify the rationality and feasibility of the simulation model structure, and more parameters’ effects on the optional IR are further analyzed. © 2024 SPIE.

关键词： Chatbots

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