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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Dhibi, Khaled Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha23874 Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh11586 Saudi Arabia Chemical Engineering Program Texas A&M University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The main objective of this work is to develop novel fault diagnosis techniques using ensemble learning and multivariate statistical techniques. The proposed methods are capable of identifying and classifying PV faults. First, an ensemble learning (EL) method that combines several base models is proposed. Next, kernel principal components analysis (KPCA) and reduced KPCA are proposed to extract and select the pertinent characteristics from raw data. Then, the extracted significant characteristics are transmitted to the EL model for classification purposes. The main idea behind these proposals is to provide the best accuracy and also improve the results in terms of computation time. The diagno-sis results demonstrated the efficiency of the proposed frameworks. © 2022 IEEE.

关键词： Multivariant analysis

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Using Yolo for Mask Recognition

Using Yolo for Mask Recognition

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Demidchik, N.N. Tulepbergenov, D.B. Kintonova, A. Zh. Glazyrina, N.S. Furayeva, I.I. Senkovskaya, A.A. L.N. Gumilyov Eurasian National University Department Of 'Computer And Software Engineering' Nur-Sultan Kazakhstan L.N. Gumilyov Eurasian National University Department Of 'Department Of Artificial Intelligence Technologies' Nur-Sultan Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

This project is designed to recognize face masks on an image using TensorFlow, Keras machine learning tools, YOLO-based Convolutional Neural Network family of models and a selection of 852 photos. This technology will enable the automated verification of masks on real-time video from surveillance cameras, which will contribute to the implementation of the requirements of the Ministry of Healthcare of the Republic of Kazakhstan on the compliance with the mask regime. This application is designed to monitor wearing compliance with medical mask requirements at L.N. Gumilyov Eurasian National University in Nur-Sultan. © 2022 IEEE.

关键词： Machine learning

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A Secure and Energy-Efficient Cross-Layer Framework for Internet of Things

A Secure and Energy-Efficient Cross-Layer Framework for Inte...

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International Conference on Information Networking

作者： Rashid Mustafa Nurul I Sarkar Mahsa Mohaaghegh Shahbaz Pervez Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Department of Information Technology Whitecliffe College of Art and Design Auckland New Zealand

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

This study addresses the problem of maximizing the power consumption of IoT (Internet of Things) devices while protecting them against cyberattacks. A layered architecture can be useful for addressing the unique challenges of security and energy efficiency in IoT systems. To this end, we propose a secure and energy-efficient cross-layer framework for IoT applications. The proposed cross-layer framework is based on collaboration among physical (sensor), network, and application layers. We examine the need for a three-layered approach in achieving the balance between security and energy efficiency. The proposed approach aims to provide a major leap in the areas of security and energy efficiency in IoT deployments. The system performance is validated by test-bed measurements with a focus on resource-constrained IoT settings. Using Cooja simulator we assess application-layer vulnerabilities and energy efficiency. The findings reported in this paper provide some insights into the design and deployment of IoT systems using a cross-layer design approach.

关键词： Cross layer design Collaboration Zigbee computer architecture Energy efficiency User experience Internet of Things

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Implementation of Code Integrity Check Module for RISC-V Architecture with AES

Implementation of Code Integrity Check Module for RISC-V Arc...

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Theoretical and Applied computer Science and engineering (ICTASCE), International Conference on

作者： Şafak Tülemez Deniz Turgay Altilar Computer Engineering Department Software Engineer Aselsan Istanbul Technical University Istanbul Turkey Computer Engineering Department Istanbul Technical University Istanbul Turkey

Embedded applications are commonly used in different fields. Different kinds vulnerabilities can occur in embedded systems because of various reasons such as coding bugs or security attacks. Most of security vulnerabilities can occur during coding phase of an embedded application. During the design phase of a program, different types of coding bugs can be inserted to executable. On the other hand, even if the executable program is very well coded, many different kinds of vulnerabilities can also occur at runtime. In this thesis, we aim to provide a security application which controls integrity of running program in computer. Executable binary of a program consists of data and text sections. CPU fetches and executes instructions in the text section of a program. Each instruction block is firstly placed into instruction cache of CPU. Instruction block hashes are generated after compiler phase of a program. Then, during the runtime phase instruction block hashes are recalculated and compared with compiler phase hash. We aim to prevent running of a corrupted or manipulated program during runtime phase.

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Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anxiety Notification System 9th

Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anx...

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9th International Conference on Computational Science and Technology, ICCST 2022

作者： Abdulmanab, Muhammad Rahiman Nor’Azman, Nur Faidz Hazirah Meon, Hasni Razak, Chempaka Seri Abdul Ab Hamid, Siti Hafizah Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Communication Faculty of Communication Visual Art and Computing Universiti Selangor Bestari Jaya Malaysia School of Pre-University Studies Taylor’s College Lakeside Campus Subang Jaya Malaysia

ISBN: (纸本)9789811984051

As anxiety becomes increasingly prevalent among youths especially university students, early prevention via anxiety disorder profiling is crucial. Nevertheless, most screening tools to date are not automated, labour-intensive and time-consuming. Mental health detection in online social networks (OSNs) using artificial intelligence (AI) can provide means for capturing overlooked behavioural attributes, but few are centred around anxiety detection. Internet of Things (IoT)’s increasing popularity enables bio-signals monitoring to aid in mental health care. However, minimum research has been devoted to IoT-based assistive technologies for anxiety, which currently are only used by individuals already diagnosed and actively monitored. This study proposes a novel approach to anticipatory anxiety detection for Malaysian university students aged between 18 and 25 inclusive as an early notification system using AI and IoT for bilingual Malay and English tweets and vital signs analysis respectively. Our result shows that Logistic Regression with Bag-of-Words (BoW) is the overall best performing model with 81.06% accuracy. The Arduino smartwatch sensor readings from the MAX30102 pulse oximeter sensor are used to determine different body conditions using a rule-based approach. The comparison test result between the prototype and conventional measuring devices shows the prototype’s ability to accurately distinguish vital signs with a relatively low Mean Absolute Percent Error (MAPE) rate of 10.92% and 1.51% for pulse rate and temperature respectively. Together, our proof-of-concept results indicate the potential of AI and IoT in deriving new measures of anxiety, by analysing social data combined with bio-signals monitoring to alert users regarding their anxiety state. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Using Machine Learning and Neural Networks to Analyze and Predict Chaos in Multi-Pendulum and Chaotic Systems

arXiv

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arXiv 2025年

作者： Ramachandruni, Vasista Reddy Nara, Sai Hruday Lalu, Geo Yang, Sabrina Kumar, Mohit Ramesh Jain, Aarjav Mehta, Pratham Koo, Hankyu Damonte, Jason Akl, Marx Department of Computer Science and Engineering Aspiring Scholars Directed Research Program 46249 Warm Springs Blvd. FremontCA94539 United States

A chaotic system is a highly volatile system characterized by its sensitive dependence on initial conditions and outside factors. Chaotic systems are prevalent throughout the world today: in weather patterns, disease outbreaks, and even financial markets. Chaotic systems are seen in every field of science and humanities, so being able to predict these systems is greatly beneficial to society. In this study, we evaluate 10 different machine learning models and neural networks [1] based on Root Mean Squared Error (RMSE) and R^2 values for their ability to predict one of these systems, the multi-pendulum. We begin by generating synthetic data representing the angles of the pendulum over time using the Runge Kutta Method for solving 4th Order Differential Equations (ODE-RK4) [2]. At first, we used the single-step sliding window approach, predicting the 50st step after training for steps 0-49 and so forth. However, to more accurately cover chaotic motion and behavior in these systems, we transitioned to a time-step based approach. Here, we trained the model/network on many initial angles and tested it on a completely new set of initial angles, or "in-between" to capture chaotic motion to its fullest extent. We also evaluated the stability of the system using Lyapunov exponents. We concluded that for a double pendulum, the best model was the Long Short Term Memory Network (LSTM)[3] for the sliding window and time step approaches in both friction and frictionless scenarios. For triple pendulum, the Vanilla Recurrent Neural Network (VRNN)[4] was the best for the sliding window and Gated Recurrent Network (GRU) [5] was the best for the time step approach, but for friction, LSTM was the best. © 2025, CC BY.

关键词： Runge Kutta methods

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Transmission Line Age Determination with Statistical Health Index and Failure Probability Curve Fitting Technique

Transmission Line Age Determination with Statistical Health ...

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International Conference on Condition Monitoring and Diagnosis, CMD

作者： Cattareeya Suwanasri Ittiphong Yongyee Thanapong Suwanasri Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mechatronics Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Electrical and Software Systems Engineering The Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9788986510225

ISBN: (纸本)9798350353877

The deterioration of transmission lines has a profound effect on the reliability and safety of the power grid. Accurate estimation of their age is critical for effective maintenance and investment planning. This paper proposes a method that combines the percentage statistical health index (%SHI) with failure probability curve-fitting (FPCF) to estimate the ages of overhead transmission lines. %SHI assesses equipment condition through scoring and weighting, while FPCF correlates SHI with the probability of failure. Evaluation of 924 towers situated in various terrains across Thailand reveals discrepancies between estimated and actual ages, influenced by environmental factors. To enhance reliability, proactive maintenance strategies such as regular inspections and advanced monitoring are recommended.

关键词： Power transmission lines Poles and towers Probability Inspection Maintenance Safety Planning Curve fitting Monitoring Investment

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Fully-adaptive Model for Broadcasting with Universal Lists

Fully-adaptive Model for Broadcasting with Universal Lists

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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Saber Gholami Hovhannes A. Harutyunyan Department of Computer Science and Software Engineering Concordia University Montreal Quebec Canada

In classical broadcasting, a piece of information must be transmitted to all entities of a network as quickly as possible, starting from a particular member. Since this problem has an enormous number of applications and is proven to be NP-Hard, several models are defined in the literature while trying to simulate real-world situations and relax several constraints. A well-known branch of broadcasting utilizes a universal list throughout the process. That is, once a vertex is informed, it must follow its corresponding list, regardless of the originator and the neighbor it received the message. The problem of broadcasting with universal lists could be categorized into two sub-models: non-adaptive and adaptive. In the latter model, a sender will skip the vertices on its list from which it has received the message, while those vertices will not be skipped in the first *** this study, we will present another sub-model called fully adaptive. Not only does this model benefit from a significantly better space complexity compared to the classical model, but, as will be proved, it is faster than the two other sub-models. Since the suggested model fits real-world network architectures, we will design optimal broadcast algorithms for well-known interconnection networks such as trees, grids, and cube-connected cycles under the fully-adaptive model. We also present a tight upper bound for tori under the same model.

关键词： Adaptation models Upper bound Scientific computing Multiprocessor interconnection Computational modeling Broadcasting Network architecture

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Boosting Segmentation Performance across Datasets using Histogram Specification with Application to Pelvic Bone Segmentation

Boosting Segmentation Performance across Datasets using Hist...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Jois, Prabhakara Subramanya Manjunath, Aniketh Fevens, Thomas Concordia University Department of Computer Science and Software Engineering Montréal Canada University of Southern California Department of Computer Science Los Angeles United States

ISBN: (纸本)9781665401265

Accurate segmentation of the pelvic computerized tomography (CT) imaging is crucial for the clinical diagnosis of pelvic bone diseases and for planning patient-specific hip surgeries. Several methodologies have been proposed for such segmentation tasks with the emergence and advancements of deep learning for digital healthcare. But in a low data scenario, the lack of abundant data needed to train a Deep Neural Network is a significant bottleneck. In this work, we propose a methodology based on modulation of image tonal distributions and deep learning to boost the performance of networks trained on limited data. The strategy involves pre-processing of test data through histogram specification. This simple yet effective approach can be viewed as a style transfer methodology. The segmentation task uses a U-Net configuration with an EfficientNet-B0 backbone, optimized using an augmented BCE-IoU loss function. This configuration is validated on a total of 284 images taken from two publicly available CT datasets, The Cancer Imaging Archive (TCIA) and the Visible Human Project (VHBD). The average performance measures for the Dice coefficient and Intersection over Union are 95.7% and 91.9%, respectively, which give strong evidence for the effectiveness of the approach, which is highly competitive with state-of-the-art methodologies. © 2021 IEEE.

关键词： computerized tomography

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