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Reference Path Generation for Autonomous Driving in Off-road Environment 2022

Reference Path Generation for Autonomous Driving in Off-road...

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2nd international conference on robotics and Control Engineering, RobCE 2022

作者： Zhang, Song Liang, Huawei Zhao, Pan Li, Zhiyuan Su, Tao Hefei230031 China University of Science and Technology of China Hefei230026 China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei230031 China Innovation Research Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Hefei230031 China

ISBN: (纸本)9781450395854

This paper proposes a reference path generation method for Autonomous vehicles(AVs) in the off-road environment. Since obtaining sufficient and reliable prior information in an off-road environment is difficult, we adopt the Astar strategy to complete global routing based on the satellite topology map. Then planning result is corrected based on vehicle localization to obtain the initial reference path. Because the road geometry changes according to the road environment, we will use the centerline of the drivable area extracted with Probabilistic Neural Networks(PNN) to generate the final reference path. While the drivable area is invalid, the reference path planner is applied based on the initial reference path to form the final reference path. In the real off-road scenarios, the proposed method is tested on our experimental vehicle. Experimental results demonstrated that the proposed algorithm could generate reliable reference paths effectively and improve the performance and robustness of local trajectory planning. © 2022 Owner/Author.

关键词： Autonomous vehicles

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Wavelet Transform and Deep Learning for Breast Cancer Neoadjuvant Chemotherapy Efficacy Prediction 2

Wavelet Transform and Deep Learning for Breast Cancer Neoadj...

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2nd international conference on Biomedical and Intelligent systems, IC-BIS 2023

作者： Xiao, Hanguang Zhu, Xiuhong Wei, Yujia Li, Huanqi Xia, Qingling College of Artificial Intelligent Chongqing University of Technology Chongqing China

ISBN: (纸本)9781510666733

The purpose of this study was to investigate the value of wavelet features in machine learning radiomics and deep learning for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. We retrospectively analyzed 199 BC patients from the Duke-Breast-Cancer-MRI dataset. First, five machine learning classification models were constructed based on the combination of texture features and wavelet features extracted from the tumor mask region for the prediction of NAC response. Then, a deep learning model based on wavelet transform (WCNN) with a classical backbone of VGG16 was proposed to effectively predict the pCR of NAC using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans taken before NAC treatment. The prediction performance was evaluated by using the area under the receiver operating characteristic (ROC) curve (AUC). Results show that both radiomics and deep learning models with wavelet features perform much better than the models without wavelet features. In addition, the proposed WCNN model outperforms all machine learning-based radiomics and deep learning models built in this study, which indicates that the wavelet-based deep learning model has great potential to provide information for breast cancer patients planning to undergo NAC treatment. © 2023 SPIE.

关键词： Wavelet transforms

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Analysis of Energy Loss Through a Flow Divider Valve in Different Applications Using Various Hydraulic Drive systems 2nd

Analysis of Energy Loss Through a Flow Divider Valve in Diff...

引用

proceedings of the 2nd international conference on Future Technologies on Manufacturing, Automation, Design and Energy 2021

作者： Kumar, Dharmendra Mahato, Anil C. Department of Mechanical Engineering Birla Institute of Technology Mesra Ranchi835215 India

ISBN: (纸本)9789819912872

Flow divider valve (FDV) is a flow control valve used in power hydraulic system to split the pump flow in dual paths: primary path and auxiliary path. The flow rate ratio in each divided flow path is either predetermined or can be adjustable as per the loading conditions. Each flow path supplies flow to operate different loads to perform different work simultaneously. The FDV makes the system compact and efficient by reducing various components present in the system. This valve can be used in two different applications: a system that performing dual functions against dual loads simultaneously, e.g., steering system in automobile and another is in wind turbine, to reduce the power fluctuation in the absence of generator. In present study, two different hydraulic systems (wind turbine system and steering system) are identified to analyze the power loss through the FDV. The simulation results indicate that the power loss through the FDV is lower when it is used in wind turbine application for reducing the power fluctuation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wind turbines

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Research on Blockchain's Role in Promoting the Digital Economy Among the Belt and Road Countries 2

Research on Blockchain's Role in Promoting the Digital Econo...

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2nd international conference on Networking systems of AI, INSAI 2022

作者： Li, Jingxuan Gao, Fei Zhu, Bo Gu, Ziyu China Academy of Information and Communications Technology Beijing China China Uicom Digital Technology Company Limited Beijing China Beijing Normal University Beijing China

ISBN: (纸本)9781665491785

Digital economy has become the key in reshaping the global trade structure and changing the global competitive landscape. As a key technology for digital transformation and an important impetus for the development of digital economy, blockchain has been paid much more attention by global major economies. More and more countries regard blockchain as an important role in promoting their digital economy, especially among the Belt and Road countries. This paper analyzes the features of blockchain technology and its applications in empowering the digital economy. This paper also analyzes the development status of blockchain in key countries through three dimensions, including the national strategic layout, technical standards formulation and infrastructure construction, and application development and industry ecosystem. Furthermore, it analyzes the opportunities and challenges facing China's digital Belt and Road in Southeast Asia in blockchain-based digital economic cooperation and proposes suggestions for international cooperation of blockchain empowering digital economy. © 2022 IEEE.

关键词： Blockchain

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Reinforcement learning for multi-robot system: A review 2

Reinforcement learning for multi-robot system: A review

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2nd international conference on Computing and Data Science, CDS 2021

作者： Yang, Xudong School of Mechanical and Power Engineering East China University of Science and Technology Shanghai China

ISBN: (纸本)9781665404280

The optimization control of multi-robot systems based on Reinforcement Learning is the frontier field of robotics and distributed Artificial Intelligence in recent years. Multi-robot systems have the characteristics of distribution, heterogeneity, and high-dimensional spatial continuity, which makes the research of reinforcement learning for multi-robot systems face a series of challenges. This paper reviews the challenges in four practical problems of the multi-robot system which are distributed collaborative driving of multiple vehicles, mobile sensing robot team, multi-robot collaborative monitoring, and multi-UAV cooperative task planning and the latest solutions of them. Methods based on Deep Reinforcement Learning and Multi-Agent Reinforcement Learning are also described. This review may be useful to guide researchers and technologists from the industry in their choice of better cope with the multi-robot system's problems. © 2021 IEEE.

关键词： Multipurpose robots

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Autonomous Number Plate Detection System for vehicle Identification and Tracking

Autonomous Number Plate Detection System for Vehicle Identif...

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Recent Trends in Microelectronics, Automation, Computing and Communications systems (ICMACC), international conference on

作者： Neha Sri Nareddy Abhinaya Polneni Rekha Baithi Pratyusha Kunchamwar Sunil Kumar Suvarna Durgabhavani Battu Naveena Budda Vikas Agarwal Premchander Munimanda AI IoT and Robotics Lab (AIR Lab) AGVs Group Autonomous Robotics Systems Limited Hyderabad India Department of Information Technology Malla Reddy Engineering College for Women Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta USA School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy.

关键词： Visualization Accuracy Law enforcement System performance Transportation Artificial neural networks Real-time systems Security License plate recognition Videos

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An energy-efficient V2X Resource Allocation for User Privacy Protection: A Learning-Based Approach 22

An energy-efficient V2X Resource Allocation for User Privacy...

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2nd international conference on Control and Intelligent robotics, ICCIR 2022

作者： Chai, Xinyue Cheng, Mengqian Chen, Quan Song, Xiaoqin Song, Tiecheng College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics China Shenzhen Research Institute Southeast University China

ISBN: (纸本)9781450397179

In Intelligent Transportation systems(ITS), vehicles are mainly considered to travel in vehicle groups on highways, and in cities, C-V2X is more prone to data eavesdropping when communi-cating at vehicle convergence sections such as intersections, and with limited spectrum resources, communication quality needs to be guaranteed and enhanced. It is a great challenge to improve the spectrum efficiency (SE) and energy efficiency (EE) of the V2X network while satisfying the C-V2X confidentiality rate. To solve this problem, this paper proposes a deep reinforcement learning based SE and EE enhancement algorithm. It establishes an objective optimization function that considers both SE and EE, and uses the secrecy rate of C-V2X as the key constraint of this function. The optimization problem is transformed into a spec-trum and transmission power selection problem for V2V and V2I links using the Deep-Q-Network ( DQN ). The simulation results show that the overall efficiency and V2V link secrecy rate of the proposed algorithm is significantly higher than that of the ran-dom algorithm when the number of vehicles is between 20 and 40, with an average secrecy rate increase of 82.86%. © 2022 ACM.

关键词： Energy efficiency

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System Modeling and Machine Learning in Prediction of Metastases in Lung Cancer

System Modeling and Machine Learning in Prediction of Metast...

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16th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2023

作者： Swierniak, Andrzej Kozlowska, Emilia Fujarewicz, Krzysztof Borys, Damian Wilk, Agata Smieja, Jaroslaw Suwinski, Rafal Department of Systems Biology and Engineering Silesian University of Technology Akademicka 16 Gliwice44-100 Poland The 2nd Radiotherapy and Chemotherapy Clinic M. Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze Armii Krajowej 15 Gliwice Poland

The aim of this paper is to present goals and preliminary results of our project devoted to system engineering approach in prediction of metastases in lung cancer. More specifically we consider existing and develop new methods of system modeling, machine learning, signal processing and intelligent control to find biomarkers enabling prediction of risk of tumor spread and colonization of distant organs in non-small-cell lung carcinoma basing on clinical data and medical images. The results could bring us knowledge about the dynamics and origin of metastatic dissemination of lung cancer. By dynamics, we understand when and where a tumor will disseminate, and by origin we mean dissemination path (directly from original tumor or through lymphatic nodes). This information is very valuable for clinicians, as it could guide the personalized treatment of lung cancer patients. The results will elucidate important issues concerning prediction of individual progress of cancer and treatment outcome in oncology. They will provide both theoretical and simulation tools to support decision making and diagnostics in oncology, on the basis of individual patient state. © 2023 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Lung cancer

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2nd international conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

2nd International Conference on Sustainable Development Usin...

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2nd international conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

ISBN: (纸本)9783031717284

The proceedings contain 38 papers. The special focus in this conference is on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT. The topics include: Advancing Sustainable Security: AI-Driven Embedded Hardware for Mobile robotics;Evaluating Cyber-Crime Using Machine Learning and AI Approach for Environmental Sustainability;Fostering Social Relationships in Higher Education Institutions Through AI-Powered Solutions for Sustainable Development;impact of Artificial Intelligence and Machine Learning in the Sustainable Transformation of the Pharma Industry;Integrating Robot-Based Resources to Foster Computational Thinking and Communication Skills in EFL Education: A Path Towards Sustainable Development;Sustainable Development in Cancer Diagnosis: Empowering Precision Medicine with Artificial Intelligence and CRC Detection Tools;sustainable Development Through Digital Twin technology: Optimizing the Supply Chain;fostering Sustainable Growth via the Fusion of Artificial Intelligence and Computer Vision;Sustainable Digital and Enterprise Transformation for Omni-Channel Experiences Empowered by Data and AI/ML;sustainable Finance Beyond Banking Shaping the Future of Financial technology;Sustainable Safeguarding: Implementing Fraud Defense systems for Authorized Push Payments Through robotics Process Automation and AI Integration;Advancing Sustainable Development: Harnessing AI for Efficient and Eco-Friendly Customer Support in IoT Solutions;enabling Sustainable Interaction - A Novel Approach to Hand Gesture-Based Action Recognition for Intelligent systems;exploring Network Privacy Measures in Mobile Networks;exploring Sustainable Financial Network Analytics: Opportunities and Challenges in Systemic Risk Management and Investment Analysis;fostering Sustainable Circular Economy Practices in India: A Bibliometric Review of IoT Based Technologies;Leveraging Block-DAO Integration for Sustainable Smart Manufacturing: Operational Efficiency, Supply Chai

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Comparison of Two Video-Based Methods for Knee Joint Angle Measurement: A Preliminary Study 2

Comparison of Two Video-Based Methods for Knee Joint Angle M...

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2nd Edition IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Ceriola, Luca Mileti, Ilaria Taborri, Juri Donati, Marco Rossi, Stefano Patanè, Fabrizio University Niccoló Cusano Department of Engineering Rome00166 Italy Viterbo Italy Motustech-Sport & Health Technology Marilab RM Roma00121 Italy

ISBN: (纸本)9798350300802

The measurement of the range of motion of human lower limb joints is of fundamental importance for biomechanical assessment. To date, there are numerous video-based methodologies for joint angle analysis, such as vision systems with passive markers and vision systems using deep learning algorithms. This study conducted a comparative analysis between two video-based measurement methods for analyzing the knee angle in the sagittal plane in locomotion activities. A healthy adult subject was involved in this study. Two motor conditions were analyzed: slow walking (3.6 km/h) and fast walking (5,0 km/h) on a treadmill. Two RGB cameras were used, one placed sagittally to the subject and the other at 45°. A 6-camera optoelectronic system was used as the gold standard. The knee angle in the sagittal plane was analyzed using a deep learning method (TC-Former) and a passive marker method (Blob Analysis). The results for slow walking on the treadmill show a relative error expressed in percentages of 14.7±11.0 and 7.5±3.5 for the first and second methods respectively. The results for the fast walking task show a relative error of 20.0±7.7 and 8.9±4.0 for the first and second methods, respectively. © 2023 IEEE.

关键词： gait analysis markerless metrological assessment video-based

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