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Improved Detection of Transmission Tower Equipment Using YOLOv7 with Saliency Data Augmentation

Improved Detection of Transmission Tower Equipment Using YOL...

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Clean Energy and Electric Power Engineering (ICCEPE), international conference on

作者： Yingying Xu Chunhe Song Yong Sun Shimao Yu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

Transmission towers are critical infrastructure for power transmission, and the reliable operation of their equipment is essential to ensure electricity supply. However, the detection of transmission tower equipment faces challenges such as target overlap, large variations in size, small targets, and class imbalance due to the characteristics of the equipment. To tackle the identified issues, this study introduces a method based on YOLOv7 for the detection of transmission tower components, utilizing saliency-based data augmentation. Initially, an approach focusing on importance is employed to augment the sample size in categories with limited numbers, thereby enhancing the effectiveness of detection. Secondly, an adaptive adjustment module is introduced into the multi-head self-attention mechanism to dynamically adjust attention allocation based on the actual size of the targets and integrate contextual information, thus better handling targets with large size variations and overlaps. Finally, incorporating dynamic convolution within the backbone network significantly improves the ability to extract features from small objects. the outcomes of the experiments confirm the efficiency of the suggested approach in the smart surveillance of devices within intricate transmission settings, offering a reliable solution for maintaining power supply dependability.

关键词： Surveillance Poles and towers Data enhancement Power transmission Object detection Manuals Data augmentation Feature extraction Reliability Saliency detection

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Application of fuzzy logic for detecting incidents at signalized highway intersections

Application of fuzzy logic for detecting incidents at signal...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： S. Lee J. Yen R.A. Krammes Texas A&M ITS Research Center of Excellence Texas A and M University USA Center for Fuzzy Logic Robotics and Intelligent Systems Texas A and M University USA

Traffic incidents-accidents, cargo spills, and stalled vehicles-are a major cause of urban congestion. Research in incident detection for signalized arterial streets is at a very initial stage. Existing algorithms are far from being robust in dealing with the difficulties related to data availability and the multi-dimensional nature of the incident detection problem. this paper discusses a fuzzy-logic based incident detection model for real-time implementation at signalized diamond interchanges. the model overcomes the boundary condition problem inherent in conventional threshold-based methods. the model captures system-wide incident effects utilizing multiple measures for more accurate and reliable detection, and is incorporated into a real-time traffic adaptive diamond interchange control system. the model is scaleable and expandable for larger systems of arterial streets. Simulation results suggest that the fuzzy-logic-based approach to incident detection at signalized diamond interchanges is promising.

关键词： Fuzzy logic Signal detection Road transportation Traffic control Real time systems control systems Adaptive control Vehicle detection Intelligent robots Road vehicles

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A Descriptive Research on Internet of things Implementation in Healthcare Sector

A Descriptive Research on Internet of Things Implementation ...

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international conference on Contemporary Computing and informatics (IC3I)

作者： Vivek Veeraiah G. K. Ravi Kumar Devesh Pratap Singh Akshita Chaudhary Ghada Elkady Dhyana Sharon Ross Department of Computer Science Adichunchanagiri University Mandya Karnataka India Department of Computer Science and Engineering Adichunchanagiri University Mandya Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Application RCCV Girls College Ghaziabad Uttar Pradesh India College of International transport & Logistics Arab academy for Science Technology & Maritime Transport Cairo Egypt Loyola Institute of Business Administration (LIBA) Chennai Tamil Nadu India

the Web of things (IoT) is critical for the virtual change in medication because it provides innovative business models to facilitate and promote improvements in practice operations, control costs, enhance productivity, and improve patients’ experiences. the use of innovative IoT techniques in personalized health, however, remains incredibly challenging due to several issues, such as incoherent IoT program structures, diversity among connected wearable devices, a lack of accurate and affordable smart medical sensors, the complexity of the generated data, and an increased need for interoperability. this research delves at the history of IoT in healthcare, with a focus on how these technologies are being used to the rapidly expanding area of personalized medicine. It covers both common health scenarios and cutting-edge, intricate methods generated from the Internet of things. It highlights the scientific, ethical, and economic challenges of developing a better medical system for early illness detection. At long last, the Web of things (IoT) is permitting present-day medication to answer the requests rapidly of the time by creating a platform for communication across different health sectors, providing digital help at every stage, and so on.

关键词： Wearable computers Telemedicine Medical services Real-time systems Internet of things Biomedical monitoring Medical diagnostic imaging

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Design of a novel 6-DoF robotic system for pituitary tumor removal under MRI guidance

Design of a novel 6-DoF robotic system for pituitary tumor r...

引用

international conference on control, automation and Systems ( ICCAS)

作者： Muhammad Umar Farooq Kyoungrae Cha Dae Kyung Sohn Heon Yoo Seong Young Ko Medical Robotics and Intelligent Control Laboratory (MeRIC-Lab) Chonnam National University Gwangju Korea Medical Device Development Center Daegu Gyeongbuk Medical Innovation Foundation Center for Colorectal Cancer National Cancer Center Neuro Oncology Clinic National Cancer Center

Magnetic resonance imaging (MRI) provide superior visuals that might be suitable for complicated surgical procedures in complex parts of the human body like brain and heart. Brain tumor removal is one of the trickiest surgical procedures and the pituitary tumor is one of the most common type of intracranial tumors. In this paper, we present an MRI compatible robotic system for pituitary tumor removal via trans-nasal access to the surgical site. the robotic system is designed to operate in a Siemens® 3T MRI scanner. A 5mm diameter and 6-DOF design specific manipulator is developed to access the pituitary glands and remove the tumor using the commercially available tools. the actuation system is designed to fit inside the MRI bore and operate just above the nasal entry point. the manipulator is designed to work in a 20×20mm workspace of the pituitary gland and this workspace is validated by experiments. the presented robotic system is expected to decrease the complexity by accessing the surgical site through the nasal cavity and improve the quality of tumor removal by providing better image quality under MRI guidance.

关键词： Magnetic resonance imaging Manipulators Tumors Electron tubes Shafts Actuators

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Hidden Markov models & principal component analysis for multispectral palmprint identification

Hidden Markov models & principal component analysis for mult...

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international conference on Information and Communication Technology and Accessibility (ICTA)

作者： Abdallah Meraoumia Maarouf Korichi Salim Chitroub Ahmed Bouridane Laboratory of Mathematics Informatics and Systems (LAMIS) University of Tebessa Tebessa ALGERIA Electronics and Computer Science Faculty Laboratory of Intelligent and Communication Systems Engineering (LISIC) Algiers ALGERIA Department of Computer Science and Digital Technologies Northumbria University Newcastle Newcastle upon Tyne UK

Automatic personal identification from their physical and behavioral traits, called biometrics technologies, is now needed in many fields such as: surveillance systems, access control systems, physical buildings and many more applications. In this paper, we propose an efficient online personal identification system based on Multi-Spectral Palmprint images (MSP) using Hidden Markov Model (HMM) and Principal Components Analysis (PCA). In this study, the band image {RED, BLUE, GREEN and Nearest-InfraRed (NIR)} is rotated with different orientations then applying the PCA technique to each oriented image, to decorrelate the image columns, and concentrate the information content on the first components of the transformed vectors. thus, the observation vector is formed by concatenate some components of the transformed vectors for all orientations. Subsequently, we use the HMM for modeling the observation vector of each MSP. Finally, log-likelihood scores are used for MSP matching. Our experimental results show the effectiveness and reliability of the proposed approach, which brings both high identification and accuracy rate.

关键词： Hidden Markov models Biometrics (access control) Feature extraction Principal component analysis Databases Computational modeling Transforms

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Object Recognition for Autonomous Driving in Adverse Weather Condition Using Polarized Camera

Object Recognition for Autonomous Driving in Adverse Weather...

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international conference on control, Mechatronics and automation (ICCMA)

作者： Min-Ho Ha Chan-Hoe Kim Tae-Hyoung Park Industrial AI Research Center Chungbuk National University Cheongju South Korea Dept. Control and Robot Engineering Chungbuk National University Cheongju South Korea Dept. Intelligent Systems and Robotics Chungbuk National University Cheongju South Korea

ISBN: (纸本)9781665490498

Autonomous driving refers to a vehicle driving by itself without human intervention. An autonomous vehicle must recognize the environment with an attached sensor, make judgments appropriate to the situation, and drive toward its destination. Although many studies have been conducted on autonomous driving in clear weather, the response to bad weather is insufficient because the sensor encounters difficulties recognizing the environment when it is backlight or raining. this study proposes a method for recognizing objects (e.g., vehicles, pedestrians, lanes) based on polarization cameras for safe autonomous driving in backlight conditions or rain. First, the camera’s light input is adjusted, altering its aperture backlight conditions. then, the polarized image from the camera is synthesized and inputted into the object recognition network. the object detection network used Yolo v5, while the lane detection network used an ultrafast lane detection network. By extracting features from the same backbone, the inference speed of the object recognition network increased. On average, a speed of 23 fps and an object recognition accuracy of over 90% were shown in the Xavier environment.

关键词： Rain Lane detection Roads Vehicle driving Cameras Feature extraction Reduced order systems

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An Improved Method for the Error Modeling of a 5-DoF Parallel Machining Robot Based on Measurement by a Laser Tracker

An Improved Method for the Error Modeling of a 5-DoF Paralle...

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IEEE international conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Xuan Luo Fugui Xie Xin-Jun Liu Zenghui Xie Zaihua Yang State Key Laboratory of Tribology & Tsinghua University (DME) Siemens Joint Research Center for Advanced Robotics and Beijing Key Lab of Precision Tsinghua University Beijing China Beijing Institute of Spacecraft Environment Engineering Beijing China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Kinematic calibration of 5-DoF parallel robots is very challenging, due to the difficulty in accomplishing the efficient and precise measurement of the end-effector's pose errors, and the situation that the terms of the error mapping Jacobian matrix (EMJM) in various units will restrict the identification accuracy. this paper proposes an improved error modeling method based on measurement by a laser tracker to deal with these problems for a 5-DoF parallel machining robot, DiaRoM-II. Two steps of modeling modification are developed as: (1) reselecting the robot's geometric errors in a uniform unit; (2) substituting the pose errors of the end-effector with the position errors of two points along the axis of the spindle. Accordingly, the dimensionless EMJM is established. On this basis, the identification simulation experiments are conducted, and the results verify that the improved error modeling method is effective in accurately identifying the geometric errors of DiaRoM-II.

关键词： Frequency modulation conferences automation control systems Intelligent systems 5G mobile communication

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Review of Deep Learning for Physiological Signal-based Healthcare Applications

Review of Deep Learning for Physiological Signal-based Healt...

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international conference on Contemporary Computing and informatics (IC3I)

作者： Ramakrishnan Raman Jaymin Bhalani B. Sumathy Shivaji Bothe Anita Gehlot M. Kalyan Chakravarthi Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of PIT Parul University Gujarat India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India Department of IT & Business Analytics Sri Balaji University Pune (SBUP) Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttaranchal India School of Electronics Engineering VIT-AP University Amaravathi India

Deep Learning has been considered as the successful approach for generated as well as discriminative task which is recently proved with medical analysis of two-dimensional images and it is highly potential even though the physiological data are in the form of signals of single dimension the beneficially should be analyzed using the novel approach of various healthcare applications based on the physiological signals using deep learning. the studies include recent scientific researchers done on deep learning based on the physiological signal data that includes the electrocardiogram (ECG), electromyogram (EMG), electrooculogram and electroencephalogram. the main goal of the research is the study of deep learning for the physiological signal-based model for various healthcare applications and development of physiological signal-based model for stress detection in working people and wearable physiological system. It helps to compare, categorize and comprehend the parameters off deep learning approaches for the analysis of physiological signals. Dataset resources, deep learning model, type of input data and healthcare applications are compared. In the perspective of signal data analyzing the physiology for healthcare applications and data modality as well as the concept of system CNN architecture based on deep learning and using data set.

关键词： Deep learning Computational modeling Human factors Medical services Mental health Brain modeling Physiology

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A cognitive architecture for a humanoid robot: a first approach

A cognitive architecture for a humanoid robot: a first appro...

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IEEE-RAS international conference on Humanoid Robots

作者： C. Burghart R. Mikut R. Stiefelhagen T. Asfour H. Holzapfel P. Steinhaus R. Dillmann Learning and CoOperation Multimodal Robots Collaborative Research Centre on Humanoid Robots Karlsruhe Germany University of Karlsruhe Germany

Future life pictures humans having intelligent humanoid robotic systems taking part in their everyday life. thus researchers strive to supply robots with an adequate artificial intelligence in order to achieve a natural and intuitive interaction between human being and robotic system. Within the German Humanoid Project we focus on learning and cooperating multimodal robotic systems. In this paper we present a first cognitive architecture for our humanoid robot: the architecture is a mixture of a hierarchical three-layered form on the one hand and a composition of behaviour-specific modules on the other hand. Perception, learning, planning of actions, motor control, and human-like communication play an important role in the robotic system and are embedded step by step in our architecture

关键词： Humanoid robots Cognitive robotics Intelligent robots Human robot interaction Robot sensing systems Cognition Artificial intelligence Intelligent systems Motor drives Psychology

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Intelligent energy audit and machine management for energy-efficient manufacturing

Intelligent energy audit and machine management for energy-e...

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IEEE conference on Cybernetics and Intelligent Systems

作者： Chee Khiang Pang Cao Vinh Le Oon Peen Gan Xiang Min Chee Dan Hong Zhang Ming Luo Hian Leng Chan Frank L. Lewis Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Institute of Manufacturing Technology ASTAR (Agency for Science Technology & Research) Singapore Automation & Robotics Research Institute University of Texas Arlington Fort Worth TX USA

To reduce energy consumption for sustainable and energy-efficient manufacturing, a good understanding of the dynamic energy consumption patterns on the manufacturing shop floor is essential. In this paper, we introduce a novel approach to address the challenge of missing operation context information during in-situ energy data measurement. Finite-State Machines (FSMs) are used to model the engineering processes, and a two-stage framework for online classification of real time energy measurement data in terms of machine operation states is proposed for energy audit and machine management. the first stage uses advanced signal processing techniques to reduce noise while preserving important features, and the second stage uses intelligent pattern recognition algorithms to cluster energy consumption patterns. Our proposed two-stage framework is evaluated on an industrial injection moulding system using a Savizky-Golay (SG) filter and a Neural Network (NN), and our experimental results show a 95.85% accuracy in identification of machine operation states.

关键词： Energy consumption Artificial neural networks Injection molding Power demand Monitoring Energy measurement

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