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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kumar, A. Kishore Rajesh Kumar, T. Balamanigandan, R. Kumar, A. Sasi Mahaveerakannan, R. Department of Robotics and Automation Sri Ramakrishna Engineering College Tamilnadu Coimbatore641022 India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Surathkal India

ISBN: (纸本)9798350397376

Industrial Internet of Things (IIoT) may allow for the state of equipment to be monitored and defects to be detected before they become serious, helping smart factories keep up with the rising demands for safety and efficiency brought on by worldwide competition. In the case of Cloud Computing, data-driven approaches implemented in the cloud are used to first train the model using a vast quantity of raw data, and then end-machines send their real-time readings for processing at the cloud data centre. It is possible, however, that this will impose a significant delay and a high computational cost. In this study, we take into account a hierarchical structure in which edge-PLCs are used to collect sensed data locally and lessen the burden on long-distance connections. Due to the fact that a single responsibility may be associated with multiple swaying topographies, we aim to first reduce the sum of features required to identify a fault, and then identify the smallest possible set of edge-PLCs that still accounts for all of the most important ones, all while keeping deployment costs to a minimum. To address this, we present an Enhanced Harris Hawkins Optimization (EHHO) approach to tackling the features selection issue, followed by the assortment of edge-PLCs through the set coverage problematic. The model named Residual Attention Neural Network (RANN) is proposed the in this study to forecast the fault detection procedure. Residual Neural Network (ResNet) is used by RANN to help diagnose problems, alongside the knowledge of experts. We evaluate the performance of our technique in comparison to existing AI-based approaches, such as the lregression model and its expansions, by simulating on a real data trail. The findings confirm competence and presentation of the suggested approach, which meets or even surpasses the correctness of approaches employing the entire collection of data. © 2023 IEEE.

关键词： Internet of things

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Implementing SLAM for Robot Navigation with Lidar in ROS Platform 4

Implementing SLAM for Robot Navigation with Lidar in ROS Pla...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Verma, Gaurav Kumar Ray, Ranjit Swar, Surya Narayan Meena, Gagan Deep Dept. of Electrical Engineering National Institute of Technology Patna India Underwater Robotics & Autonomous Group CSIR-Central Mechanical Engineering Research Institute Durgapur India Department of Computer Science & Engineering Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (纸本)9798331528614

This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the SLAM. SLAM is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely. © 2024 IEEE.

关键词： SLAM robotics

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Fisherman Location Store, Updating, and Alert System using Cloud AWS with Satellite

Fisherman Location Store, Updating, and Alert System using C...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Kiruthiga Devi, M. Parthasarathy, R. Unnamalai, K. Perinba Athiban, S. Manikandan, K. Ramya, N. Dr. MGR Educational and Research Institute Chennai Department of Information Technology India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Adarsh College for Women Department of Computer Science Anna India VIT Artificial Intelligence and Robotics Chennai India Sona College of Technology Department of Biomedical Engineering Salem India Sri Sai Ram Engineering College Department of CSE Chennai India

ISBN: (纸本)9798331522988

he successful demonstration of their location tracking alert system for fishermen, Satellites will employ Amazon Web Services (AWS). Using cloud-based computing technology, the activities and whereabouts of fishermen are monitored in real time through this system. The ACS sat will send a vast amount of information in short always-shrinking cycle and store data very efficiently, only the location data for live updates on every moves by the fishermen. Here using this project we are stressing on the need to analyze data and push alerts according protocols, for timely raise alarm which definitely saves Fishermen life. The aim is to develop a Real Time Location Tracking and Monitoring GIS Application, which can trace the fishermen real-time location in marine activities. Analyze location information with the best edge of the sword such as cloud computing and AI /ML. The GIS App will have a simple user interface along with extensive monitoring abilities. Building a GIS App to aid marine safety by improving the tracking and monitoring of fishing operations using cloud technology. In the end, with these benchmarks in tow this could mark a step change and benchmark for maritime operations;as we finally get, safety and productivity combined. © 2024 IEEE.

关键词： Benchmarking

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A Translation-Tolerant Place Recognition Method by Viewpoint Unification 27

A Translation-Tolerant Place Recognition Method by Viewpoint...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zheng, Linwei Hu, Xiangcheng Ma, Fulong Zhao, Guoyang Qi, Weiqing Ma, Jun Liu, Ming The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong

ISBN: (纸本)9798331505929

Place recognition serves as a fundamental component in tasks like loop closure detection and relocalization for mobile robots. Polar coordinate representations, such as Scan Context, which align with the data structure of range sensors, have become the most common data structure for point cloud descriptors in place recognition. While polar representations demonstrate the rotation invariance, they remain susceptible to translation variations. In this study, we introduce a novel approach: shifting the viewpoint of the original point cloud to construct the unified Scan Context, thereby mitigating translation variance. Our key concept focuses on identifying a stable, unified viewpoint for a given place and then pre-translating the point cloud accordingly. This naturally results in a descriptor devoid of translation variance. Importantly, within a given place, the viewpoint unification process tends to relocate the viewpoint to a similar position, irrespective of the original sensor perspective. In other words, the unified Scan Context becomes more closely associated with the place's structural characteristics than the physical location of the sensor. We validate our method through a series of comprehensive experiments encompassing synthetic scenarios and real-world datasets, showcasing its robustness in effectively handling translation variations. © 2024 IEEE.

关键词： Data structures

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Robot-Assisted Wide-Area Photoacoustic System

Robot-Assisted Wide-Area Photoacoustic System

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Gao, Shang Ma, Xihan Zhang, Haichong K. Worcester Polytechnic Institute Department of Robotics Engineering Worcester United States Worcester Polytechnic Institute Department of Biomedical Engineering Worcester United States Worcester Polytechnic Institute Department of Computer Science Worcester United States

ISBN: (纸本)9798350346459

Liver ischemia and reperfusion injury (IRI) is a vascular complication that can lead to hepatic functional impairment. IRI is relevant in liver transplantation and during liver surgery performed with intermittent vascular inflow occlusion. Detecting intraoperative liver oxygenation impairment and ischemia could help prevent IRI, which is challenging partially due to the multiple hepatic vascular inflows. We propose a robot-assisted wide-area photoacoustic (PA) imaging system to monitor the liver oxygenation level intraoperatively. PA imaging has been widely used for oxygenation detection in preclinical research. We used a robotic arm to actuate the 2D PA probe and scan across the liver surface. An accurate volumetric tomography can be reconstructed via registering 2D images to the recorded robot trajectory. An ex vivo porcine liver sample was scanned using the proposed system. The result suggested that the robot-assisted wide-area PA tomography has the potential to be incorporated into clinical practice to evaluate liver SO2 intraoperatively. © 2023 IEEE.

关键词： Photoacoustic effect

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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

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SN computer science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN computer vision Deep learning Smart glasses Smoke monitoring

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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Machine Learning In Sustainable Development-An Overview 1

Machine Learning In Sustainable Development-An Overview

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Altrjman, Chadi Gupta, Richa Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Turkey University of Kyrenia Faculty of Engineering Kyrenia Turkey Waterloo University Canada Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338027

I have studied how artificial intelligence (AI) or machine learning (ML) is applied in a sustainable system called "Eugenie"for my work, project, and thesis. In this project, I get to discuss Eugenie's role in ensuring the sustainability of large environmental business organizations. I also get to discuss how it aids operation managers in making predictions about when their machines will break down days, weeks, and months in advance, allowing them to schedule maintenance cycles more effectively. In other words, Eugenie provides plant operators with data so they can prioritize repairs. Eugenie executes this process by utilizing real-time data from numerous sensors attached to large machinery and equipment in the process industry, metals and mining, oil and gas, and smart cities industries. Two products make up this program;the first is named RAY-FINN and the second is called PAPILLON. Eugenie assists businesses in a variety of ways, including:•Aids in the attainment of operational excellence by process organizations•Always raise overall profitability.•Contributes to a greener and safer future. © 2023 IEEE.

关键词： Sustainable development

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The Influence of Human Factors in Ensuring Cybersecurity in Organizations: Effectiveness of PDPA and GDPR Implementation 3

The Influence of Human Factors in Ensuring Cybersecurity in ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sharifi, Ahmad Zia Walid, Md. Abul Ala Momand, Mohammad Dawood Safi, Ihsan Ul Haq Safi, Hamidullah Khanzai Hossain, Sadia Nangarhar University Faculty of Computer Science Department of Networking and Communication Afghanistan Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science and Engineering Gazipur Bangladesh Lautsprecher Teufel GMbH Berlin Germany Bangabandhu Sheikh Mujibur Rahman Digital University Department of Internet of Things and Robotics Engineering Gazipur Bangladesh

ISBN: (纸本)9798331518592

Modern organizations face rising levels of cyber risks, making cybersecurity a top priority for safeguarding sensitive data and maintaining operational continuity. Recent studies find human factors as the most critical contributors to cyber risks. These human-related risks include user misuse, user mistakes, and user malice. A large proportion of cyber risks arise when hackers use social engineering or phishing techniques to manipulate employees into disclosing confidential information. While some employees knowingly engage in these actions, some incidents occur accidentally due to a lack of awareness about cybersecurity protocols. As a result, governments around the world are enacting data protection legislation aimed at enhancing employees' cyber risk. This study explores the perception of US employees on the effectiveness of data protection legislation in countering cyber threats. It surveyed 30 randomly selected employees from various industries. The results showed that employees perceive data protection legislation as effective in mitigating employeerelated cyber risks. © 2024 IEEE.

关键词： Phishing

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A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

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computers, Materials & Continua 2023年第2期74卷 2817-2838页

作者： Noureldin Eissa Uswah Khairuddin Rubiyah Yusof Ahmed Madani Centre for Artificial Intelligence&Robotics Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Computer Engineering Arab Academy for ScienceTechnology and Maritime TransportCairo2033Egypt Department of Mechanical Precision Engineering Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia

Epigenetics is the study of phenotypic variations that do not alter DNA *** epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human *** of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in ***,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA ***,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying *** each DNA methylation site,a beta value(β)is used to reflect the methylation ***,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user *** study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation *** efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research *** performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data *** NBDC-GA outperformed the IGA in terms of convergence ***,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,*** proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities th

关键词： Cancer clustering DNA methylation feature selection metaheuristic technique the cancer genome atlas

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