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IEEE International Conference on Industrial engineering and engineering Management

作者： Marvin Voß Jonas Kallisch Maxim Runge Tobias Theus Karl-Heinz Niemann Christoph Wunck Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Emden/Leer Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing.

关键词： Industries Supply chain management Federated learning Supply chains Collaboration Particle measurements Manufacturing Security Risk analysis Information exchange

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Corsetto: A Kinesthetic Garment for Designing, Composing for, and Experiencing an Intersubjective Haptic Voice 23

Corsetto: A Kinesthetic Garment for Designing, Composing for...

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Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

作者： Ozgun Kilic Afsar Yoav Luft Kelsey Cotton Ekaterina R. Stepanova Claudia Núñez-Pacheco Rebecca Kleinberger Fehmi Ben Abdesslem Hiroshi Ishii Kristina Höök MIT Media Lab Massachusetts Institute of Technology United States Media Technology and Interaction Design KTH Royal Institute of Technology Sweden Division of Interaction Design at Department of Computer Science and Engineering Chalmers University of Technology Sweden Sweden School of Interactive Arts and Technology Simon Fraser University Canada College of Art Media & Design Northeastern University United States and MIT Media Lab Massachusetts Institute of Technology United States RISE SICS Computer Systems Sweden

ISBN: (纸本)9781450394215

We present a novel intercorporeal experience – an intersubjective haptic voice. Through an autobiographical design inquiry, based on singing techniques from the classical opera tradition, we created Corsetto, a kinesthetic garment for transferring somatic reminiscents of vocal experience from an expert singer to a listener. We then composed haptic gestures enacted in the Corsetto, emulating upper-body movements of the live singer performing a piece by Morton Feldman named Three Voices. The gestures in the Corsetto added a haptics-based ‘fourth voice’ to the immersive opera performance. Finally, we invited audiences who were asked to wear Corsetto during live performances. Afterwards they engaged in micro-phenomenological interviews. The analysis revealed how the Corsetto managed to bridge inner and outer bodily sensations, creating a feeling of a shared intercorporeal experience, dissolving boundaries between listener, singer and performance. We propose that ‘intersubjective haptics’ can be a generative medium not only for singing performances, but other possible intersubjective experiences.

关键词： voice micro-phenomenology haptics machine learning Robotic textiles shape changing interfaces somaesthetic interaction design

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Alzheimer's disease Development and Classification using MRI 23

Alzheimer's disease Development and Classification using MRI

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： CHITRA P Naganandhini S Ramkumar R Merin Stepha J Jessy K Hemalatha S Department of Computer Science_School of Sciences GITAM University India Department of Computer Applications Arulmigu Palaniandavar Arts and Science College for Women India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan University India Department of Electrical and Electronics Engineering St.Josephs Institute of Technology India

ISBN: (纸本)9798400709418

Abstract: Alzheimer's disease (AD) is among the neurological diseases (dementia) that afflict the elderly most frequently. We introduce a novel machine learning-based approach in this research to differentiate individuals with the early AD classification. Preprocessing, feature selection, training data, and classifiers all affect the outcomes of machine learning-based methods for classifying AD. A novel composite comprehensive MRI development of Alzheimer's disease is provided in this chapter (AD-DCP-MRI). The results were analyzed in terms of accuracy, precision, recall, and F1-score using the data package that included T1-weighted MRI clinical OASIS temporal data. Our recommendation model is effective for AD categorization, as evidenced by its increased accuracy. These methods can also be successfully applied in the medical field to help with the early identification and diagnosis of disease.

关键词： Alzheimer disease

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Guiding Wireless Signals with Arrays of Metallic Linear Fresnel Reflectors: A Low-cost, Frequency-versatile, and Practical Approach

Guiding Wireless Signals with Arrays of Metallic Linear Fres...

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IEEE Conference on Vehicular technology (VTC)

作者： Hieu Le Oguz Bedir Mostafa Ibrahim Jian Tao Sabit Ekin Electrical and Computer Engineering Texas A&M University College Station Texas USA Engineering Technology and Industrial Distribution Texas A&M University College Station Texas USA School of Performance Visualization and Fine Arts Texas A&M University College Station Texas USA Engineering Technology and Electrical and Computer Engineering Texas A&M University College Station Texas USA

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

This study presents a novel mechanical metallic reflector array to guide wireless signals to the point of interest, thereby enhancing received signal quality. Comprised of numerous individual units, this device, which acts as a linear Fresnel reflector (LFR), facilitates the reflection of incoming signals to a desired location. Leveraging geometric principles, we present a systematic approach for redirecting beams from an Access Point (AP) toward User Equipment (UE) positions. This methodology is geared towards optimizing beam allocation, thereby maximizing the number of beams directed towards the UE. Ray tracing simulations conducted for two 3D wireless communication scenarios demonstrate significant increases in path gains and received signal strengths (RSS) by at least 50 dB with strategically positioned devices.

关键词： Wireless communication Vehicular and wireless technologies Solid modeling Three-dimensional displays Systematics Reconfigurable intelligent surfaces Ray tracing Reflection Resource management Gain

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A Novel Method for Attendance Marking System Using Hybrid LSTM and RNN Based Networks 7

A Novel Method for Attendance Marking System Using Hybrid LS...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Manigandan, E. Al Ansari, Mohammed Saleh Nuthakki, Praveena Bhuvneshwari, S. Kala Priyadharshini, G. Ponnusamy, Muruganantham Saveetha College of Liberal Arts and Sciences SIMATS Department of Computer Applications Tamilnadu Thandalam India College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Koneru Lakshmaiah Education Foundation Department of CSE Andhra Pradesh Guntur India KSR Institute for Engineering and Technology Department of Mathematics Tamilnadu Tiruchengode India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Indian Institute of Information Technology Kalyani West Bengal Nadia India

ISBN: (纸本)9798350340600

The suggested system uses face detection and identification algorithms to automate the attendance marking and management processes. Face recognition refers to the process of identifying a person by their distinctive facial traits. At the moment, face recognition software is the most rapidly developing field in IT. This suggested system seeks to replace manual attendance tracking with an automated system that uses facial recognition technology to keep track of which students are present in the classroom at any given time. The primary goal of this endeavor is to create an automatic, user-friendly system for recording and managing attendance. The suggested method involves analyzing data, selecting features to employ, and assessing the model's efficiency. The suggested method employs Gaussian blur, segmentation, and scaling for preprocessing. PCA and LDA are used for feature selection and extraction, respectively. The LSTM-KNN hybrid method is used for model training. When compared to LSTM and KNN, two existing approaches, the proposed methodology performs exceptionally well. © 2023 IEEE.

关键词： Principal component analysis

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Federated Conformal Predictors for Distributed Uncertainty Quantification

arXiv

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arXiv 2023年

作者： Lu, Charles Yu, Yaodong Karimireddy, Sai Praneeth Jordan, Michael I. Raskar, Ramesh MIT Media Lab Massachusetts Institute of Technology Cambridge United States Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley United States

Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models. In this paper, we extend conformal prediction to the federated learning setting. The main challenge we face is data heterogeneity across the clients — this violates the fundamental tenet of exchangeability required for conformal prediction. We propose a weaker notion of partial exchangeability, better suited to the FL setting, and use it to develop the Federated Conformal Prediction (FCP) framework. We show FCP enjoys rigorous theoretical guarantees and excellent empirical performance on several computer vision and medical imaging datasets. Our results demonstrate a practical approach to incorporating meaningful uncertainty quantification in distributed and heterogeneous environments. We provide code used in our experiments https://***/clu5/federated-conformal. © 2023, CC BY-NC-ND.

关键词： Medical imaging

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Data-Driven Ground-Fault Location Method in Distribution Power System with Distributed Generation

SSRN

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SSRN 2023年

作者： Caporuscio, Mauro Dupuis, Antoine Löwe, Welf Department of Computer Science and Media Technology Linnaeus University Växjö Sweden Department of Electrical Engineering Uppsala University Uppsala Sweden Department of Medicine and Optometry

The recent increase in renewable energy penetration at the distribution level introduces a multi-directional power flow that outdated traditional fault location techniques. To this extent, the development of new methods is needed to ensure fast and accurate fault localization and hence strengthen power system reliability. This paper proposes a data-driven ground fault location method for the power distribution system. An 11-bus 20 kV power system is modeled in Matlab/Simulink to simulate ground faults. The faults are generated at different locations and under various system operational states. Time-domain faulted three-phase voltages at the system substation are then analyzed with discrete wavelet transform. Statistical quantities of the processed data are eventually used to train an Artificial Neural Network (ANN) for finding a mapping between computed voltage features and faults. Specifically, three ANNs allow the prediction of faulted phase, faulted branch, and fault distance from the system substation separately. According to the results, the method shows good potential, with a total relative error of 0,4\% for fault distance prediction. To test robustness, the method is applied to datasets with unknown system states. © 2023, The Authors. All rights reserved.

关键词： Neural networks

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Iot Based Condition Monitoring of electrical Equipment 2

Iot Based Condition Monitoring of Electrical Equipment

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Raja, J. Kalyani, N. Singh, Mukul Kumar Garg, Akriti Sarojwal, Atul Devi, S. Rukmani Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering School of Computing Tamil Nadu Chennai600062 India School of Computing Sastra Deemed University Tamil Nadu Thirumalaisamudram613401 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Simats Deemed to be University Department of Computer Science Saveetha College of Liberal Arts and Sciences Tamil Nadu Chennai600124 India

ISBN: (纸本)9798350364699

In recent years, the surveillance of the health of electrical appliances has been transformed by the Internet of Things (IoT). This investigation offers a comprehensive account of a system that employs the Internet of Things (IoT) to conduct predictive maintenance and analysis of electrical equipment. The network of interconnected sensors in the proposed system enables the real-time monitoring of a variety of operating characteristics, including temperature, current, voltage, and vibration. Upon the identification of equipment health indicators, the data collected by these sensors is transmitted to a centralised cloud platform. Sophisticated analytics and machine learning algorithms analyse the data on this platform to detect any patterns. The system's predictive warnings and real-time insights facilitate the implementation of preventive maintenance, reduce downtime, and enhance the reliability and efficacy of electrical equipment. The article explores the benefits of utilising Internet of Things (IoT) condition monitoring in industrial and utility settings. It also addresses pertinent topics, including system architecture, data processing methodologies, and other pertinent topics. © 2024 IEEE.

关键词： Predictive maintenance

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An Effective Detection of Ransomware Using Improved Long Short-Term Memory 3

An Effective Detection of Ransomware Using Improved Long Sho...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Vijayalakshmi, V. Fallahhusein, Muhamed Balamurugan, D. Suresh, T. Muthu Krishnan, K. Lakshmi Bangaru Arts and Science College Department of Computer Science with Artificial Intelligence Melmaruvathur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Computer Science and Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Electrical and Electronics Engineering Mamallapuram India

ISBN: (纸本)9798331508456

Ransomware detection focuses on recognizing the unusual system activities which indicate an unauthorized data access and restriction. It continuously monitors the system behavior continuously to detect potential threats before significant damage occurs. By identifying anomalies and patterns, it effectively distinguishes ransomware attacks from normal operation. However, Ransomware attacks cause a serious threat because of far-reaching effects and its zero-day variants are even more hazardous. This research proposes Improved Long Short-Term Memory (LSTM) to detect the ransomware effectively and accurate detection of Ransomware. The proposed improved LSTM learns the sequential patterns in system behavior and detects subtle abnormalities over time. The enhanced model improves detection accuracy by learning past data and adapts to new attack variants including zero-day threats. By identifying anomalies, the proposed method enables timely detection and reduces the impact of ransomware attacks. To select the appropriate features, the Dung Beetle Optimization (DBO) is used to optimize the feature subset for better detection performance. The proposed improved LSTM obtains a better accuracy of 95% when compared to existing method like cost sensitive Pareto Ensemble classifier (CSPE-R). © 2025 IEEE.

关键词： Zero-day attack

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Perceptual Judgments of Simulated Low Temperatures in LCD based Vehicle Displays

Perceptual Judgments of Simulated Low Temperatures in LCD ba...

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SID International Symposium Digest of Technical Papers, 2023

作者： Göreke, Hande D. Djupsjöbacka, Anders Schenkman, Bo N. Andrén, Börje Hermann, David S. Brunnström, Kjell Industrial Systems RISE Research Institutes of Sweden AB Stockholm Sweden Informatics and Media Uppsala University Uppsala Sweden Speech Music and Hearing KTH Royal Institute of Technology Stockholm Sweden Display Systems Volvo Car Corporation Göteborg Sweden Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden

A well-known drawback with LCD-displays in cold is a slow pixel response leading to poor picture quality. Low temperatures can constitute a hazard in viewing important displays in cars. Perceptual experiments with 20 test-persons were conducted to find clear and acceptable ranges on screens simulating distortions in low temperatures. The results showed perception over clear and acceptable image quality was impaired beyond -20°C for the LCD-screen in the experiments. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Liquid crystal displays

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