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检索条件"任意字段=Proceedings of the 2020 2nd International Conference on Robotics Systems and Vehicle Technology"

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Energy Conservation for Environment Monitoring System in an IoT based WSN 2

Energy Conservation for Environment Monitoring System in an ...

引用

2nd international conference on Smart Technologies, Communication and robotics, STCR 2022

作者： Satya Sreedhar, Siva Anitha, R. Rachel, Priya Suganya, S. Ramesh Babu Durai, C. Uthayakumar, G.S. Seshadri Gudlavalleru Engineering College India S A Engineering College India School of Engineering United States CMR Institute of Technology Tamilnadu India Dhanalakshmi College of Engineering Tamilnadu India St. Josephs Institute of Technology India

ISBN: (纸本)9781665460477

Energy distribution is vital in an IoT-based Wireless Sensor Network (WSN).There is no other fuel source for WSN since they deal with battery systems. This means that when the battery runs out, they have no option except to replace it on a regular basis, which isn't always possible. Information may be lost during transmission as another problem with WSNs. Despite the fact that information disasters are rare, it remains a constant threat. The greatest danger lies in a loss of data. B) CH-to-sink data lost. This article saves energy by forecasting missing node values. © 2022 IEEE.

关键词： Wireless sensor networks

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Multi-Object Tracking Based on Prediction of Invisible Object Trajectories 2

Multi-Object Tracking Based on Prediction of Invisible Objec...

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2nd international conference on robotics, Artificial Intelligence and Intelligent Control, RAIIC 2023

作者： Du, Baoqing Wu, Sidong Fu, Kechang Ren, Liuquan School of Automation Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798350328004

In previous 3D Multi-Object Tracking (3DMOT) methods terminated tracking when the tracked object was not associated with new detected object with in few frames (usually less than 5 frames). However, this can lead to premature termination of a tracker if an object simply became dimmer, such as being temporarily occluded by other objects or leaving the field of view. This became a major cause of identity switches in tracking. Additionally, premature termination of trajectory tracking is the main cause of identity switching in modern 3D MOT systems. To address this problem, we propose a method to preserve vanished object trajectories using trajectory prediction: when the life cycle management module performs trajectory management, unlike previous 3DMOT methods, when a tracked object is not matched by association within a few frames, instead of identifying the tracked object as vanishing and terminating its trajectory, our method predicts the vanishing tracked object state by the 3D Kalman Filter, which in turn comes to preserving its trajectory. With this approach, the experimental results on the nuScenes dataset show that the 3D multi-object tracking algorithm proposed in this paper performs excellently, with an Average Multi-Object Tracking Accuracy (AMOTA) of 73.7% at the frame rate of 2hz, and the Identity Switches (IDS) are also significantly reduced compared to the current excellent 3D multi-object tracking algorithms (e.g., about 50% less than SimpleTrack). © 2023 IEEE.

关键词： Trajectories

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Development of Product Quality with Enhanced Productivity in Industry 4.0 with AI Driven Automation and Robotic technology 2

Development of Product Quality with Enhanced Productivity in...

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2nd international conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Lal, Bechoo Vishnu Sakravarthy, N. Kumar, Marrapu Aswini Chinthamu, Narender Pokhriyal, Sumit Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram Guntur India Sri Eshwar College of Engineering Department of Mechanical Engineering Tamilnadu Coimbatore India Centurion University of Technology and Management Department of Computer Science and Engineering Andhra Pradesh Vizianagaram India DallasTX United States Graphic Era Hill University Department of Physics Dehradun India

ISBN: (纸本)9798350325799

Automation and robotics have advanced significantly with the introduction of Industry 4.0, revolutionizing the manufacturing environment. This study examines the evolution of product quality in the context of Industry 4.0, concentrating on how robotic and AI-driven automation technologies help to increase productivity. Automation systems can efficiently analyze enormous volumes of data by integrating AI algorithms and machine learning capabilities, allowing for real-time monitoring and production process optimization. Utilizing AI-driven automation, manufacturers may increase task precision and accuracy, improving the quality of the final product. By enhancing human capacities and doing monotonous or physically taxing activities, robotic technology plays a significant role in Industry 4.0. AI-enabled advanced robots can collaborate with human workers, adapt to changing production conditions, and carry out intricate tasks quickly and accurately. Together, humans and robots are more productive and make fewer mistakes, which enhances the quality of the final output. Additionally, real-time defect or deviation detection and correction is possible with AI-driven quality control systems. These systems can detect quality problems at an early stage and take quick corrective action by analyzing sensor data and machine vision. Manufacturers can achieve uniform and standardized product quality while minimizing human error by automating quality control operations. Furthermore, proactive equipment monitoring and a lower chance of unforeseen breakdowns are made possible by the use of AI in predictive maintenance. AI algorithms can forecast maintenance requirements by examining sensor data and historical performance, enabling prompt repairs or replacements. In addition to increasing production by reducing downtime, this predictive strategy also helps to improve product quality by ensuring that machinery is running at peak efficiency. © 2023 IEEE.

关键词： Industry 4.0

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3D Brain Tumor Segmentation on U-Net Classifier 2

3D Brain Tumor Segmentation on U-Net Classifier

引用

2nd international conference on Sustainable Computing and Data Communication systems, ICSCDS 2023

作者： Suja, G.P. Kaleeswari, P. Raajan, P. Ram Prasad, A.V.S. Aarthi, K. Gopatoti, Anandbabu Muslim Arts College Department of Computer Science Thiruvinthancode Kanyakumari629174 India Bme Psr Engg College Sivakasi India Manonmaiam Sundaranar University Muslim Arts College Department of Computer Science Thiruvinthancode Tirunelveli627 012 India Mechanical Engineering K L e F Kongu Engineering College Department of Ece Erode638052 India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9781665491990

For deep learning segmentation approaches, it can be difficult to extract the multimodal features that are present in multimodal glioma pictures, which provide a variety of aspects of tumour borders based on MRI. The application of deep learning (DL) and multimodal MRI images in the investigation of brain tumour segmentation has become increasingly useful as a result of the rapid development of current technology. In this paper, we suggest using DL to study multimodal MRI image segmentation to improve the treatment and therapeutic accurateness of brain tumour segmentation. Moreover, this research builds a computer aided medical diagnosis for brain tumours, extracts features from MRI images using discrete wavelet transform (DWT) and GLCM, and finally employs a U-Net to make the diagnosis;UNet outperforms on 3D MRI scanned pictures, as shown by extensive experimental results with 97.8% accuracy on the BraTS 2020 and 2021 datasets. © 2023 IEEE.

关键词： Magnetic resonance imaging

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The improved inertial S-subgradient projection algorithm for non-convex split feasibility problems 2

The improved inertial S-subgradient projection algorithm for...

引用

2nd international conference on Optical Communication and Optical Information Processing, OCOIP 2024

作者： Zhao, Yue Li, Meixia Tan, Jingjing College of Mathematics and Systems Science Shandong University of Science and Technology Shandong Qingdao China School of Mathematics and Statistics Weifang University Shandong Weifang China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

Split feasibility problem is a kind of very important common mathematical problem, which is applied in many fields such as signal processing, image restoration, and intensity modulated radiation therapy. With the development of society and the needs of production practice, the application of split feasibility problem in various fields is no longer limited to convex sets. However, there is still relatively a little research on non-convex situations, and the algorithms related to subgradient projection for solving non-convex split feasibility problem lack the validation of numerical experiments. In this paper, we explore non-convex split feasibility problem and propose an improved inertial S-subgradient projection algorithm. We prove the weak convergence of the algorithm and provide step length conditions independent of the matrix spectral radius. At the same time, we verify the feasibility and effectiveness of the algorithms through numerical experiments. © 2025 SPIE.

关键词： Radiotherapy

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MBSE-Based Guidance System Simulation of Launch vehicle 2nd

MBSE-Based Guidance System Simulation of Launch Vehicle

引用

2nd international conference of Mechanical System Dynamics, ICMSD 2023

作者： Ma, Guowei Wang, Qingtao Zhang, Wenfeng Yu, Rendong Liu, Xiong School of Science Nanjing University of Science and Technology Nanjing China Institute of Launch Dynamics Nanjing University of Science and Technology Nanjing China National Key Laboratory of Complex Multibody System Dynamics Nanjing University of Science and Technology Nanjing China Shanghai Aerospace Systems Engineering Institute Shanghai China

ISBN: (纸本)9789819980475

In the traditional development process of the launch vehicle guidance system, the system is designed repeatedly in the earlier stage and the verification of the system is conducted in the later stage. This development mode may cause some problems, such as low design efficiency and high cost. In order to improve the shortcomings of the traditional development process, in this paper the MBSE (Model Based System Engineering) method is introduced in the design phase of the launch vehicle guidance system, and the MBSE-based design model is used to carry out system verification in real time. The application of this method greatly improves the design efficiency of the launch vehicle guidance system. The MBSE-based simulation model of the launch vehicle guidance system is established, the system perturbation guidance simulation is carried out, and the result is compared with the standard design trajectory to verify the correctness of the guidance system designed based on MBSE. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Launch vehicles

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Design and Experimental Analysis of an Adaptive Cruise Control 1

引用

2nd international conference on robotics, Computer Vision and Intelligent systems (ROBOVIS)

作者： Alomari, Khaled Sundermann, Stephan Goehring, Daniel Rojas, Raul Free Univ Berlin Dahlem Ctr Machine Learning & Robot Arnimallee 7 D-14195 Berlin Germany

ISBN: (数字)9783031196508

ISBN: (纸本)9783031196492;9783031196508

Nowadays, cars are equipped with quite a few Advanced Driver Assistant systems(ADAS) to increase comfortability and safety while driving. Adaptive Cruise Control (ACC) is one of these ADAS systems that keeps a certain distance to a heading vehicle in front by smoothly adapting the vehicle's speed. Usually, this is implemented using a separate PID controller for the velocity and distance or a MIMO system. This paper proposes a novel Fuzzy Logic approach for an autonomous model car called Autominy. The AutoMiny platform was developed at Dahlem Center for Machine Learning and robotics at Freie Universitat Berlin. It is equipped with a software stack for fully autonomous driving with custom modules for localization, control, and navigation. AutoMiny navigates using a pre-build vector force field approach. The proposed Fuzzy Logic controller can handle two states with different profiles. We extend the evaluating between our approach against a standard PID controller approach.

关键词： Advanced Driver Assistance systems Adaptive Cruise Control Fuzzy logic Vector force field

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An Intrusion Detection System for Identifying Simultaneous Attacks using Multi-Task Learning and Deep Learning 2

An Intrusion Detection System for Identifying Simultaneous A...

引用

2nd international conference on Computing and Information technology, ICCIT 2022

作者： Albelwi, Saleh A. University of Tabuk Industrial Innovation and Robotics Center Faculty of Computing and Information Technologhy Tabuk Saudi Arabia

ISBN: (纸本)9781665436052

Though both machine learning and deep learning have made great progress in developing intrusion detection systems (IDSs), two critical problems remain. The first is the ever-evolving nature of malicious activity. Data flows and packets can contain several types of attacks simultaneously, while machine learning and deep learning algorithms can only learn a single task at a time. Second, the publicly available datasets are created to evaluate only one type of attack. In order to address these issues, this paper proposes a Multi-Task Learning (MTL) model for an IDS based on deep neural networks (DNNs), which has the ability to detect several types of attacks simultaneously. To do this, we concatenated each corresponding sample in both the UNSW-NB15 and CICIDS2017 datasets into one feature vector. This guarantees that a portion of the data flows, in both the training and testing sets, contains two threats at one time. The experimental results demonstrate that the proposed system maximizes performance. © 2022 IEEE.

关键词： Linearization

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Research on neural network temperature compensation for embedded weighing systems 2

Research on neural network temperature compensation for embe...

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2nd international conference on Electrical, Electronics, and Information Engineering, EEIE 2023

作者： Gan, Hui Lv, Meini Zhan, Liping Yan, Zelin Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou China Wuzhou Medical College Wuzhou China

ISBN: (纸本)9781510672840

The accuracy of weighing systems is easily affected by sensors, conversion circuits, temperature, and other factors. Aiming at the problem of significant temperature impact during the operation of the weighing system, which leads to a decrease in weighing accuracy. This article proposes a BP neural network method for temperature compensation of the weighing system to further improve the weighing accuracy. Firstly, a weighing system is built based on STM32 to collect system temperature and weighing data. Then, a BP neural network is built using MATLAB, and the calibration data is imported to train the optimal model of the BP neural network. Finally, the model parameters are imported into an embedded system to achieve real-time temperature compensation for the STM32 weighing system. The experimental results show that the accuracy and stability of the weighing system have been improved, and it has good application value. © 2024 SPIE.

关键词： Embedded systems

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Rapid Modeling and Model Order Reduction of Launch vehicle Based on Digital Twin 2nd

Rapid Modeling and Model Order Reduction of Launch Vehicle B...

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2nd international conference of Mechanical System Dynamics, ICMSD 2023

作者： Huang, Tianyou Wang, Qingtao Liu, Xiong Liu, Zhendong Wang, Qixiao School of Science Nanjing University of Science and Technology Nanjing China Institute of Launch Dynamics Nanjing University of Science and Technology Nanjing China National Key Laboratory of Complex Multibody System Dynamics Nanjing University of Science and Technology Nanjing China Shanghai Aerospace Systems Engineering Institute Shanghai China

ISBN: (纸本)9789819980475

In this paper, a dynamic analysis technique for complex launch vehicle models using digital twin technology is introduced. A parameterized model of the launch vehicle is established and simulations are conducted on a multi-physics simulation platform. The resulting analysis file is then integrated into a system-level multi-physical domain digital twin platform. To simplify the model, singular value decomposition (SVD) method is employed to reduce its complexity. Numerical comparison and error analysis are performed between the reduced model and the original model. By comparing and analyzing the reduced order models with different levels of error, the effectiveness of the digital twin approach is demonstrated. Furthermore, the reduced order model within the digital twin framework is encapsulated. This encapsulation enables the realization of comprehensive life cycle configuration management for the launch vehicle equipment. Overall, this technique offers a promising approach for analyzing and managing complex models of the context of launch vehicles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Launch vehicles

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