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74th International Astronautical Congress, IAC 2023

作者： Clauss, Margot Cho, Mengu Bursachi, Noé Laufer, René Weiss, Bernd Ronnbäck, Anna Öhrwall Department of Computer Science Electrical and Space Engineering Luleå University of Technology Rymdcampus Bengt Hultqvists väg 1 Kiruna981 92 Sweden Department of Space Systems Engineering Kyushu Institute of Technology Kitakyushu8048550 Japan Centre National d'étude Spatiale 18 Av. Edouard Belin Toulouse31400 France Department of Social Sciences Technology and Arts Luleå University of Technology Laboratorievägen 14 LULEÅSE-971 87 Sweden

Sustainability is a major subject for society and industries. Circularity seems to be the next step in this development. Regarding the space industry, implementing sustainability poses different challenges, and many pieces of knowledge and technologies are still missing: to capture a spacecraft in space, the infrastructure to process it, the policy on sustainable space, etc. Nevertheless, those are emerging with risk-taking and non-traditional satellite development and management methods such as Lean satellites. The space community must foresee the requirements to reuse a spacecraft after its end of life. It should answer questions such as how can one reuse a spacecraft? Is it even possible? Can the space industry use findings from other industries to implement reusability? For example, can we implement solar panel end-of-life processes used on Earth in space? This paper will study the reusability of solar panels since they are a common part of most satellites and are composed of rare materials. The capability to reuse them in space might be a first step for space circularity since it will maximize the value of a spacecraft mission to its customers by giving value to something considered nowadays as debris. The concept of reusing spacecraft solar panels can be integrated into a Lean satellite project. Nonetheless, reusing solar panels in space requires understanding their degradation due to the space environment, investigating the applicability of Earth solar panels' end-of-life processes in space, and their potential performances after those processes. It requires also to design reusable solar panels either by making them easier to reprocess or by making them transferable from one spacecraft to another spacecraft. Some missions such as the Hubble Space Telescope, i.e., HST, mission present reusability characteristics in their design. HST was designed to be maintained in space and its solar panels were replaced twice by astronauts during the time of the space shuttl

关键词： Materials Reusability Reuse Solar panels Spacecraft

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FetchAid: Making Parcel Lockers More Accessible to Blind and Low Vision People With Deep-learning Enhanced Touchscreen Guidance, Error-Recovery Mechanism, and AR-based Search Support

arXiv

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arXiv 2024年

作者： Guan, Zhitong Xiong, Zeyu Fan, Mingming Computational Media and Arts Thrust The Hong Kong University of Science and Technology Guangzhou China Division of Integrative Systems and Design Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Parcel lockers have become an increasingly prevalent last-mile delivery method. Yet, a recent study revealed its accessibility challenges to blind and low-vision people (BLV). Informed by the study, we designed FetchAid, a standalone intelligent mobile app assisting BLV in using a parcel locker in real-time by integrating computer vision and augmented reality (AR) technologies. FetchAid first uses a deep network to detect the user's fingertip and relevant buttons on the touch screen of the parcel locker to guide the user to reveal and scan the QR code to open the target compartment door and then guide the user to reach the door safely with AR-based context-aware audio feedback. Moreover, FetchAid provides an error-recovery mechanism and real-time feedback to keep the user on track. We show that FetchAid substantially improved task accomplishment and efficiency, and reduced frustration and overall effort in a study with 12 BLV participants, regardless of their vision conditions and previous experience. © 2024, CC BY-NC-ND.

关键词： Augmented reality

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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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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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Machine Vision Based Local Hyacinth Bean Breed Recognition Using Convolutional Neural Network

Machine Vision Based Local Hyacinth Bean Breed Recognition U...

引用

Advances in Computing,Communication, electrical, and Smart Systems (iCACCESS), International Conference on

作者： Md. Abbas Ali Khan Md. Ataur Rahman Md Liton Hossain Md. Tarek Habib Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Bachelor of Science in Electrical and Electronic Engineering Center for Innovation and Technology University of Liberal Arts Bangladesh Dhaka Bangladesh

The classification is a significant one. This paper proposes a CNN-based Local Hyacinth Bean Breed Recognition (CNN-LHBR) approach along with a machine vision approach. Among the 52 breeds, we have taken only eight categories, namely Bashpaki Faridpur, Chaina Sada Patla Chela, Gochi, Kajoli, Katla, Lati, Noldub, and Pudi Aishna. There are many works done before about breed detection of different fruits as well as disease recognition. But no research has done such a work as LHBR, especially in Bangladesh. The interest of this research is the reason for the high protein and vitamin B complex; besides, each bean has a separate test, yield, seed, and nutrition level. More importantly, we have implemented 3 CNN models for the experiment of the breed recognition of 8 Hyacinth Bean specimens. For model accuracy, we have considered training, validation, and testing accuracy. As for performance evaluation, the confusion matrix has bean applied. Among the models, the customized CNN model gives the best accuracy. The customized CNN model's accuracy is 97.50%.

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Security Challenges for Cloud or Fog computing-Based AI Applications

arXiv

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arXiv 2023年

作者： Pakmehr, Amir Asmuth, Andreas Neumann, Christoph P. Pirkl, Gerald Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Electrical Engineering Media and Computer Science Ostbayerische Technische Hochschule Amberg-Weiden Amberg Germany

Security challenges for cloud or fog-based machine learning services pose several concerns. Securing the underlying cloud or fog services is essential, as successful attacks against these services, on which machine learning applications rely, can lead to significant impairments of these applications. Because the requirements for Artificial Intelligence applications can also be different, we differentiate according to whether they are used in the cloud or in a fog computing network. This then also results in different threats or attack possibilities. For cloud platforms, the responsibility for security can be divided between different parties. Security deficiencies at a lower level can have a direct impact on the higher level where user data is stored. While responsibilities are simpler for fog computing networks, by moving services to the edge of the network, we have to secure them against physical access to the devices. We conclude by outlining specific information security requirements for Artificial Intelligence applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Fog

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mediating The Marginal: A Quantitative Analysis of Curated LGBTQ+ Content on Instagram 25

Mediating The Marginal: A Quantitative Analysis of Curated L...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Souza, Garrett Lutz, Nina Turner, Katlyn M Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Human Centered Design and Engineering University of Washington Seattle WA United States Media Lab Massachusetts Institute of Technology Cambridge MA United States

ISBN: (纸本)9798400713941

Control and curation of dominant visual culture - rendering who and what is visible - is central to identity formation, particularly for LGBTQ+ communities relying on digital spaces for safe self-expression. In this work, we analyze Instagram as a site of algorithmic visual curation, performing a quantitative analysis of algorithmically mediated image feeds delivered to a gay-coded user. Our persona account exclusively followed #gay and #instagay feeds, and engaged in content within these discursive spaces to seed algorithmic content promotion to a normative gay user. We present an analysis of skin tone presentations, emoji usage, and engagement metrics alongside analysis of generative outputs of dominant visual trends within the #gay search and Explore feeds. We observe content depicting darker-skinned individuals has higher engagement yet less algorithmic promotion relative to lighter skin tones, while hypermasculine and homonormative content is heavily promoted. These results suggest that, while marginalized positionalities have certainly been rendered more visible through social media platforms, this visibility is increasingly contingent on assimilation to normative ideals through algorithmically determined modes that are not necessarily consistent with user choices, preferences, or realities. © 2025 Copyright held by the owner/author(s).

关键词： LGBTQ+ Visual Content Recommendation Algorithms Social Networks

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Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data

arXiv

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arXiv 2025年

作者： Jeon, Eun Som Choi, Hongjun Buman, Matthew P. Turaga, Pavan Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Lawrence Livermore National Laboratory LivermoreCA94550 United States College of Health Solutions Arizona State University PhoenixAZ85004 United States Geometric Media Lab School of Arts Media and Engineering School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States

The analysis of wearable sensor data has enabled many successes in several applications. To represent the high-sampling rate time-series with sufficient detail, the use of topological data analysis (TDA) has been considered, and it is found that TDA can complement other time-series features. Nonetheless, due to the large time consumption and high computational resource requirements of extracting topological features through TDA, it is difficult to deploy topological knowledge in machine learning and various applications. In order to tackle this problem, knowledge distillation (KD) can be adopted, which is a technique facilitating model compression and transfer learning to generate a smaller model by transferring knowledge from a larger network. By leveraging multiple teachers in KD, both time-series and topological features can be transferred, and finally, a superior student using only time-series data is distilled. On the other hand, mixup has been popularly used as a robust data augmentation technique to enhance model performance during training. Mixup and KD employ similar learning strategies. In KD, the student model learns from the smoothed distribution generated by the teacher model, while mixup creates smoothed labels by blending two labels. Hence, this common smoothness serves as the connecting link that establishes a connection between these two methods. Even though it has been widely studied to understand the interplay between mixup and KD, most of them are focused on image based analysis only, and it still remains to be understood how mixup behaves in the context of KD for incorporating multimodal data, such as both time-series and topological knowledge using wearable sensor data. In this paper, we analyze the role of mixup in KD with time-series as well as topological persistence, employing multiple teachers. We present a comprehensive analysis of various methods in KD and mixup, supported by empirical results on wearable sensor data. We observe that app

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Analysis of Trie-Based Lan File Search Engine

Analysis of Trie-Based Lan File Search Engine

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： C Shanmugasundaram C Umamaheswari V Suriyaa Kartikeyan TR Department of Electrical and Electronics Engineering Manakula Vinayagar Institute of Technology Department of Computer Science with Data Science St. Thomas College of Arts and Science Department of Computer Technology Anna University Chennai

Searching and Accessing files in a computer device is one of the most important and basic task performed by the computer today. Each Operating System has a dedicated search bar for this, But however, In a network encompassing a multitude of systems, retrieving a specific file from various systems can pose a formidable task. Put differently, users frequently find it challenging to recollect the precise location where they've stored a particular file. This undertaking offers a user-friendly application interface wherein the user inputs the file's name, subsequently receiving both the precise file path and the IP address of the computer device within the network as output. We can access the file by clicking on the path generated. Accessing Files from multiple systems in a network is quite difficult and a Slow process and there are not many Applications which serve this purpose. We aim to overcome this problem by providing an efficient Trie Data Structure based Searching Algorithm to search and access files in a network from any node in the Local Area Network.

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

SSRN

引用

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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