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Design and Characterization of a Novel High-bandwidth Flexure -guided XY Nanopositioner 9

Design and Characterization of a Novel High-bandwidth Flexur...

9th IFAC Symposium on Mechatronic systems (MECHATRONICS) / 16th International conference on Motion and Vibration control (MoViC)

作者： Khodabakhshi, E. Nikooienejad, N. Maroufi, M. Moheimani, S. O. R. Univ Texas Dallas Erik Jonsson Sch Engn & Comp Sci Richardson TX 75080 USA

Design, mechanical modeling and characterization of a new flexure-guided piezoelectrically actuated nanopositioner are presented in this paper. The scan range of this planar scanner is 5.8 pm in both X- and Y-directions with the first resonance frequency being above 15 kHz. Lateral displacements are measured using an interferometer sensor. In this paper, we focus on presenting the novelty of the mechanical design and characterization of the nanopositioner in terms of system dynamics and sensor noise, with the ultimate goal of controller design for high-speed SPM. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： nanopositioning flexure-guided nanopositioner high-speed nanopositioner piezoelectric actuators interferometer sensor

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2022 IEEE International conference on advanced Robotics and Its Social Impacts, ARSO 2022

2022 IEEE International Conference on Advanced Robotics and ...

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2022 IEEE International conference on advanced Robotics and Its Social Impacts, ARSO 2022

ISBN: (纸本)9781665479660

The proceedings contain 25 papers. The topics discussed include: aerial manipulation using a human-embodied drone interface;healthcare professionalś attitudes towards the organization of care services and the adoption of welfare robots in Norway;design and evaluation of energy charge mechanism for the hip joint motion : -toward the application to a new wearable device for passive walking assistance;metrics for evaluating social conformity of crowd navigation algorithms;motor imitation: bio-inspired vs direct geometric control;a new methodological approach to analyze human roles in human-robot interaction scenarios;increased complexity of a human-robot collaborative task may increase the need for a socially competent robot;effect of robot gazing behavior on human comfort and robot predictability in navigation;and on the development of autonomous assistive robotic system for drinking task for people with disability.

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Mosquito Electronic Counting Device

Mosquito Electronic Counting Device

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advanced Algorithms and control Engineering (ICAACE), International conference on

作者： Apostolos Tsagaris Spiros Mourelatos Department of Industrial Engineering and Management International Hellenic University - IHU Thessaloniki Greece Ecodevolepment SA Thessaloniki Greece

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

The study presents an innovative device for real-time counting and identification of adult mosquitoes, integrating IoT technologies, infrared sensors, and machine learning algorithms. The device enhances the functionality of the existing ECODEV trap by offering high accuracy, low cost, energy autonomy, and the capability for both quantitative and qualitative analysis of mosquito populations. The system's architecture is based on sensors that capture mosquito movements and transmit the data via GSM to a central server for analysis. Users can monitor the measurements in real-time through maps and applications. The device was successfully tested, achieving a counting accuracy of 93.29%, surpassing traditional systems. In the future, this technology could be expanded to other applications, such as agriculture and livestock management.

关键词： GSM Accuracy Costs Infrared sensors Real-time systems sensor systems Livestock Agriculture Optical sensors Monitoring

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Image Recognition and Analysis of Computer Interlock interface Based on OpenCV Technology

Image Recognition and Analysis of Computer Interlock Interfa...

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IEEE advanced Information Management,Communicates,Electronic and Automation control conference (IMCEC)

作者： Xuanmo Chen Qiang Li College of Computer Science and Technology Lanzhoujiaotong University Lanzhou China Key Laboratory of Optoelectronic Technology and Intelligent Control Ministry of Education Province Lanzhou China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

An integrated computerized locking mechanism is vital for upholding the safety of train movements within railway networks. As computer technology progresses swiftly, conventional locking systems are encountering a multitude of difficulties, with the identification and processing of visual interface data being a key challenge. Furthermore, the pace of technological innovation has led to an array of other obstacles for these systems to surmount. The deployment of cutting-edge technologies, including AI and machine learning, has markedly enhanced the functionality and security of these mechanisms. Anticipatedly, the future will witness the development of even more sophisticated and secure railway traffic management systems. These evolving technologies serve as formidable instruments in tackling the prevailing issues and challenges. Specifically, the computerized interface locking system is indispensable in guaranteeing the safe running of trains within railway *** paper applies the image processing capability of OpenCV to preprocess and extract characteristics from the interlocking interface image. Subsequently, the enhanced clustering algorithm of RV-DBSCAN is utilized to identify the text of signaling equipment diagrams, followed by the implementation of the Paddle OCR (Optical Character Recognition) machine learning algorithm to classify and identify the images. The recognition accuracy rate for information related to railway signaling equipment stands at 92.6%. This process offers a dependable approach for extracting and recognizing station information from computerized interlocking interfaces.

关键词： Visualization Technological innovation Image recognition Machine learning algorithms Optical character recognition Software algorithms Clustering algorithms

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Wireless sensor Network Based Train to Train Imparting system

Wireless Sensor Network Based Train to Train Imparting Syste...

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International conference on Trends in Electronics and Informatics (ICEI)

作者： G. Shanmugavadivel Kalaiyarasi M Kaviya T Madhumitha V Monisha A Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur Tamil Nadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Trains are well known as a reliable, affordable, and speedy means of transportation in metropolitan cities in India and around the globe. Though convenient and ever popular, train accidents in India are often blamed on human mistake. With the speed of trains high and the latency in human intervention, it is always a major task to prevent such crashes. This study suggests a proactive method of minimizing railway accidents by using Radio Frequency Identification (RFID) technology. The main goal of the suggested system is to forecast possible train collisions and notify the central control room or train drivers prior to an accident. There are few mechanisms in place to avoid train crashes at present, and thus the use of an advanced Anti-Collision Device (ACD) system is essential. The suggested train collision avoidance system combines RFID technology, Zigbee communication, machine learning, and artificial intelligence (AI) to improve railway safety. Every locomotive has an automated monitoring system, and the railway network is segmented into two-kilometer sections, each having a unique track number. RFID tags placed on the trains are scanned by onboard RFID scanners, and this information is processed by an AI system to check for the existence of other trains on the same path. When there is a possible collision, the system sends alarms to train controllers through Zigbee, and an automatic braking system is activated to halt the crash. This advanced system offers real-time collision detection and avoidance, vastly enhancing railway safety and minimizing the possibility of train collisions.

关键词： Wireless sensor networks Zigbee Railway safety Machine learning Computer crashes Real-time systems Safety Reliability Collision avoidance Radiofrequency identification

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Machine Learning and Wireless sensor Network based Library Automation system 3

Machine Learning and Wireless Sensor Network based Library A...

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3rd International conference on Power, Energy, control and Transmission systems, ICPECTS 2022

作者： Manikandan, J. Sivanesan, T.M. Gunasekaran Ramapraba, P.S. Sri Sairam Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India R&D Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering Tamil Nadu Chennai India Sidhartha Institute of Science and Technology Department of Electronics and Communication Engineering Andhra Pradesh Puttur India Panimalar Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462754

Remote sensor network and radio frequency identification innovation are on the world's outskirts. In this age of data innovation, the library as a repository for information gathering and distribution is critical. Innovations such as the personal computer, the Internet, and universal identification have sped the shift to the digital age. New improvements were integrated to configure Radio Frequency Identification remote organization with Wireless sensor network comprising hubs to work on the intelligent administration of the library, relying on manual and data the board. The three modules that were basically researched were writing seek positioning framework, book full casing framework, and programmed getting and returning entry monitor warning framework. The intelligent library framework based on Wireless sensor organization and Radio Frequency Identification incorporated sophisticated RFID and ZigBee technologies for the simplification and inclusion of writing assets, administration interface, and the library's administrative procedure. The significance of library computerization and smart collaboration was recognized, and the notion of individuals situated was fully explored. According to the findings, the smart library framework, which is based on wireless sensor organization and radio frequency identification, was able to fundamentally understand the smart library executives. To summarize, the framework creates an unpredictable library administration and board framework at any time and in any location. © 2022 IEEE.

关键词： Automation

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Research on Ultrasonic Nondestructive Testing Method Based on Multimodal sensor Fusion and Deep Learning

Research on Ultrasonic Nondestructive Testing Method Based o...

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advanced Algorithms and control Engineering (ICAACE), International conference on

作者： Hao Wang Sino-European School of Technology of Shanghai University Shanghai Univerity Wen zhou Zhejiang Province China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In this paper, an innovative ultrasonic nondestructive testing (NDT) method is proposed, which combines multimodal sensor fusion technology with deep learning algorithms to address the limitations of traditional single-modal ultrasonic testing in complex materials and structures. A multimodal detection system integrating ultrasonic, infrared thermal imaging and vibration sensors is constructed, and a deep learning architecture based on improved residual network is designed, which includes feature extraction branches optimized for each modal characteristic and an innovative cross-modal attention fusion mechanism. Experimental results show that compared with the traditional ultrasonic testing method, the proposed method improves the defect detection accuracy by 15.7 % and reduces the missed detection rate by 12.3%. It shows significant advantages in the detection of four typical defects, namely cracks, delamination, pores and inclusions, with an average accuracy of 93.6%. In particular, the method shows excellent anti-interference ability in a strong noise environment, and can maintain an effective detection rate of 59.2 % even under extremely harsh conditions of -5dB, which is due to the complementary redundancy characteristics of multimodal data and the adaptive weight adjustment ability of the attention fusion mechanism. This study provides a new technical path for high-reliability nondestructive testing in complex industrial environments, which is of great significance for improving the detection efficiency and reliability in practical application scenarios.

关键词： Deep learning Accuracy Multimodal sensors Computational modeling Nondestructive testing Thermal sensors sensor fusion Acoustics Real-time systems Testing

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A Real-Time Driver Assistance system Using Object Detection and Tracking 1

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6th International conference on Advances in Computing and Data Sciences (ICACDS)

作者： Murthy, Jamuna S. Chitlapalli, Sanjeeva S. Anirudha, U. N. Subramanya, Varsha BNM Inst Technol Dept ISE Bangalore India

ISBN: (数字)9783031126413

ISBN: (纸本)9783031126413;9783031126406

ADAS (advanced Driver Assistance system) has become a vital part of the driving experience. In recent years, there have been several advancements in ADAS technology such as parking assistance and lane detection. The proposed work presents a real-time Driver assistance framework by implementing the state-of-the-art object detection algorithm YOLOv4. This paper provides a comparison between and other state-of-the-art object detectors. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three different datasets and one standard dataset. YOLOv4 proves to be faster and more accurate than the other object detection algorithms in the comparison. This framework is used to build an application which helps users make better decisions on the road. This application consists of a simple user interface that displays alerts and warnings.

关键词： Object detection ADAS LIDAR sensor CNN YOLOv4

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A Bionic Textile sensory system for Humanoid Robots Capable of Intelligent Texture Recognition

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advanced MATERIALS 2025年 e2417729页

作者： Zheng, Xianhong Zhang, Runrun Ding, Binbin Zhang, Zhao Shi, Yu Yin, Leang Cao, Wentao Wang, Zongqian Li, Guiyang Liu, Zhi Li, Changlong Liu, Zunfeng Huang, Wei Sun, Gengzhi Anhui Polytech Univ Sch Text & Garment Wuhu 241000 Peoples R China Nanjing Tech Univ NanjingTech Inst Adv Mat IAM Nanjing 211816 Peoples R China Jinling Inst Technol Coll Intelligent Sci & Control Engn Nanjing 211199 Peoples R China Fudan Univ Shanghai Stomatol Hosp Dept Prosthodont Shanghai 201102 Peoples R China Fudan Univ Sch Stomatol Shanghai 201102 Peoples R China Nankai Univ State Key Lab Med Chem Biol Key Lab Funct Polymer Mat Coll ChemTianjin Key Lab Funct Polymer Mat Tianjin 300071 Peoples R China

Artificial tactile perception systems that emulate the functions of slow adaptive (SA) and fast adaptive (FA) cutaneous mechanoreceptors are essential for developing advanced prosthetics and humanoid robots. However, constructing a high-performance sensory system within a single device capable of simultaneously perceiving both static and dynamic forces for surface-texture recognition remains a critical challenge;this contrasts with common strategies integrating individual SA- and FA-mimicking sensors in multi-layered, multi-circuit configurations. Herein, a textile pressure/tactile (PT) sensor is reported based solely on piezoresistive principle alongside high sensitivity and rapid response to both high-frequency vibrations and static forces. These characteristics are attributed to the sensor's 3D multiscale architecture and the corresponding hierarchical structural deformation of its honeycomb-like sensing fabric. As a proof-of-concept application relevant to humanoid robotics and prosthetics, an automated surface-texture-recognition system is constructed by integrating the PT sensor with machine-learning algorithms, a prosthetic device, an industrial robot arm, and a graphical user interface. This artificial sensory system demonstrates the ability to learn distinct object features, differentiate fine surface textures, and subsequently classify unknown textiles with high recognition accuracy (>98.9%) across a wide range of scanning speeds (50-300 mm s(-1)). These results show promise for the future development of interactive artificial intelligence.

关键词： bionic sensory system MXene tactile sensor textile texture recognition

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PoinTap system: a human-robot interface to enable remotely controlled tasks 26

PoinTap system: a human-robot interface to enable remotely c...

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26th IEEE International conference on Emerging Technologies and Factory Automation (ETFA)

作者： Sibona, Fiorella Cheng, Pangcheng David Cen Indri, Marina Di Prima, Danilo Politecn Torino Dipartimento Elettron & Telecomunicaz DET Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9781728129891

In the last decades, industrial manipulators have been used to speed up the production process and also to perform tasks that may put humans at risk. Typical interfaces employed to teleoperate the robot are not so intuitive to use. In fact, it takes longer to learn and properly control a robot whose interface is not easy to use, and it may also increase the operator's stress and mental workload. In this paper, a touchscreen interface for supervised assembly tasks is proposed, using an LCD screen and a hand-tracking sensor. The aim is to provide an intuitive remote controlled system that enables a flexible execution of assembly tasks: high level decisions are entrusted to the human operator while the robot executes pick-and-place operations. A demonstrative industrial case study showcases the system potentiality: it was first tested in simulation, and then experimentally validated using a real robot, in a laboratory environment.

关键词： Industrial manipulators human-robot interface assembly tasks remote control

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