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Thermal Model Validation of a Power Electronics Module for Digital Twin Application

Thermal Model Validation of a Power Electronics Module for D...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zoller, Svenja Cabre-Piqueras, Claudia Schilder, Boris Filba-Martinez, Alber Acker, Lukas Frankfurt University of Applied Science Faculty of Computer Science and Engineering Frankfurt Germany Power Systems Dept. Barcelona Spain

ISBN: (纸本)9798350364446

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Human Body Action Recognition based on 3D Feature Fusion over RGB Scenes 11

Human Body Action Recognition based on 3D Feature Fusion ove...

引用

11th International Conference on Information Technology, ICIT 2023

作者： Nsour, Heba Mustafa, Zaid Al-Sendah, Maria Al-Balqa Applied University Dept. of Computer Science Albalqa Jordan Al-Balqa Applied University Dept. of Computer Information Systems Albalqa Jordan Kuwait

ISBN: (纸本)9798350320060

Human body action recognition (HBAR) is an important area of research in machine learning and image processing due to its vast range of applications. Similarly, estimating various components of human anatomy from RGB scenes are essential to human action tracking. The present study involves the implementation of human key body point detection for pose estimation. During abstracted silhouettes and vital human points, the proposed system has extracted two main features, such as 3D features and distance features. Once the relevant attributes have been incorporated, executing precise tasks and strategies is critical to achieving maximum features. Therefore, we used the t- SNE-based data refinement technique for optimal feature selection. Finally, for training, a classification model random forest is utilized. The presented system is verified on a recognized benchmark dataset, i.e., the UCF sports database. The experimental settings have revealed that our proposed system has attained better performance and outperformed other present state-of-the-art methods regarding mean recognition rate. © 2023 IEEE.

关键词： Stochastic systems

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A New Projective Point in Convex Polygon Test with O(log N) Complexity 17

A New Projective Point in Convex Polygon Test with O(log N) ...

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17th IEEE International Scientific Conference on Informatics, INFORMATICS 2024

作者： Skala, Vaclav University of West Bohemia Faculty of Applied Sciences Dept. of Computer Science and Engineering Pilsen Czech Republic

ISBN: (纸本)9798350387674

This contribution describes a novel and fully projective algorithm for a point-in-convex polygon test with computational complexity of O(log N) in E2. The polygon vertices and tested points can be given in projective space, i.e., with the homogeneous coordinate w ≠ 0, without conversion to Euclidean space. It is independent of the polygon orientation. It uses vector-vector operations and is therefore aimed for implementation using SSE or AVX instructions. The algorithm is simple and robust, easy to implement. © 2024 IEEE.

关键词： Vectors

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Generative Adversarial Networks as Weight Generators in Ensemble Classification 4

Generative Adversarial Networks as Weight Generators in Ense...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Yehia, Asmaa Fahmy, Sherif Fahmy, Ahmed AASTMT Basic Applied Science Dept. Giza Egypt AASTMT Computer Engineering Dept. Cairo Egypt

ISBN: (纸本)9798350348248

Ensemble classifiers are constructed from various component classifiers by aggregating the results of the individual classifiers in some manner. While there are many ways to do this, one of the least computationally intensive is to use a voting ensemble, where the vote of each classifier is weighted by a number representing its performance. Choosing the appropriate weights is an involved process, that ranges from using trial and error to using sophisticated machine learning algorithms. In this paper, we proposed a novel technique that uses generative adversarial networks to customize the weights used for each individual input to the ensemble. Specifically, we propose to use conditional generative adversarial networks to generate the weights, with the input image acting as the label to the network and the appropriate weight being the output. We test the proposed system on an image classification task and show that the proposed method produces better results than using fixed weights by 2.43 percent. © 2023 IEEE.

关键词： Image recognition

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Automatic transformation of UML-RT models into nuXmv model checker 7

Automatic transformation of UML-RT models into nuXmv model c...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Schorr, Ruth Frankfurt Unviersity of Applied Sciences Dept. of Computer Science and Engineering Frankfurt Germany

ISBN: (纸本)9798350383065

The use of diagram-based development approach especially Model Driven Development (MDD), where the executable code can be generated directly from the diagrammatic system model, has gained much popularity in the recent years. The use of automated code generation allows to focus primarily on the core design aspects of system design without having to deal with the developmental challenges. Moreover, the approach also avoids any gap between the system design and the developed system, which can be very common when code development is manual. However, at present, these generated programs have to be manually checked for semantics correctness and hence, the possibility of an automated model level verification of such MDD artifacts is the next big step in this domain. There are multiple tools today that implement MDD and are being used by the research world for exploring the possible verification alternatives. In this paper we look closely into the verification of one such standardized profile, UML for Real-Time (UML-RT), developed particularly for MDD of Real-Time systems. Eclipse Papyrus-RT is an open-source modeling tool that supports UML-RT for specifying software systems. nuXmv is a model checker, that can verify system models against a set of properties defined using temporal logics. This paper presents a system that automatically translates Papyrus-RT models into a behaviorally equivalent nuXmv model. © 2023 IEEE.

关键词： Real time systems

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Unleashing Machine Learning for Hepatitis C Prediction: A Holistic Exploration of Clinical Insights

Unleashing Machine Learning for Hepatitis C Prediction: A Ho...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Al-Amain Janin, Fatema Tuj Ahmed Robin, Faizul Ahmed, Shifat Mohi Uddin, Khandaker Mohammad Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Hamburg University of Applied Sciences Dept. of Information and Electrical Engineering Hamburg Germany

ISBN: (纸本)9798331529765

Chronic diseases, characterized by long-term effects and persistent symptoms, pose significant challenges to human health and well-being. The hepatitis C virus (HCV) is the cause of chronic hepatitis C, which affects 58 million people globally. It also causes 290,000 deaths annually due to liver cancer and cirrhosis, two conditions connected to HCV. Taking advantage of machine learning (ML) algorithms' ability to analyze large datasets and identify patterns and relationships that aid in diagnosis, this effort focuses on employing algorithms for machine learning to identify and forecast hepatitis C precisely. Various techniques were utilized, including Random Forest, Cat Boost, SVM, and others to create predictive models using blood values from 615 patients, including both hepatitis patients and healthy blood donors. The dataset underwent comprehensive preprocessing, including handling missing values, scaling, and dimensionality reduction via Principal Component Analysis (PCA). Various ML algorithms were evaluated, and the Cat Boost classifier achieved the highest accuracy of 99.17%, recall of 92.00%, precision of 100.00% and F1-score is 96.00%. This study demonstrates the potential of ML in enhancing HCV diagnosis, offering less invasive and cost-effective alternatives to traditional methods, and providing predictive insights for personalized treatment plans. The findings underscore the importance of advanced data preprocessing and feature selection in improving model efficiency. This study adds to the ongoing efforts to lessen the prevalence of hepatitis C worldwide, aligning with the World Health Organization's goal to significantly decrease new HCV cases and related deaths by 2030. © 2024 IEEE.

关键词： Adversarial machine learning

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Secure Risk Management Approach for Enhancing the Performance of Cloud-based Cooperative Spectrum Sensing CRNs 22

Secure Risk Management Approach for Enhancing the Performanc...

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Balogun, Victor Paquette, Xavier Sarumi, Oluwafemi A. Dept. of Applied Computer Science University of Winnipeg WinnipegMB Canada Institute of Computer Science Heinrich-Heine-University Duesseldorf DuesseldorfD-40215 Germany

ISBN: (纸本)9798331510183

The increasing demand for radio spectrum, spurred by an upsurge in interconnected devices, necessitates innovative management solutions. Cognitive Radio Networks (CRN) offer a promising approach to dynamically allocate spectrum resources. This study examines the Cooperative Spectrum Sensing (CCSS) architecture, leveraging cloud computing and big data analytics, to enhance CRN efficacy. We present a risk analysis identifying key vulnerabilities, explore regulatory considerations within the Canadian spectrum landscape, and propose an augmented architecture incorporating preventative and defensive strategies. We also discuss challenges encountered in the implementation phase and suggest future research trajectories for cloud-assisted CCSS systems. ©2024 IEEE.

关键词： Risk assessment

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Vehicle To Vehicle and Vehicle To Ground Communication - Speech Encryption Algorithm

Vehicle To Vehicle and Vehicle To Ground Communication - Spe...

引用

2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Al-Hazaimeh, Obaida M. Al-Smadi, Ma'moun A. Al-Balqa Applied University Dept. of Computer Science Irbid Jordan Al-Balqa Applied University Dept. of Electrical Engineering Irbid Jordan

ISBN: (纸本)9798350322972

Vehicle to vehicle (V2V) communication has many applications in intelligent transportation system (ITS) like smart grid systems, fleet management and traffic light control. It aims to interconnect several components like road infrastructures, wireless networks, sensors, and vehicles. Therefore, it can be utilized to reduce congestion, accidents and fuel consumption. This paper propose a speech encryption system based on a one-dimensional logistic map for vehicle to vehicle communication (V2V) communication in intelligent transportation systems (ITS) to improve road safety and efficiency, where encryption and security play a major role. This study employs a symmetric key cryptography technique for chaotic ciphering. In this approach, a random key table lookup criterion was paired with a one-dimensional chaotic map in order to encrypt speech samples with a high level of security and acceptable speed. XOR encryption and speech row shuffling are used in the proposed technique. Using the difference between the old and new row locations, a combination of variable rotation and inversion was performed to each shuffled row in order to increase the level of security. As a consequence of the testing, the proposed approach was shown to be both efficient and usable. Combining key table and logistic map illustrates the advantages of both high-level security and large random key space. The encrypted speech produced is suitable for practical usage especially in vehicle to vehicle and vehicle to base transmission. © 2023 IEEE.

关键词： Roads and streets

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sysBERT: Improved Behavioral Malware Detection using BERT Trained on sys2vec Embeddings 58

sysBERT: Improved Behavioral Malware Detection using BERT Tr...

引用

58th Hawaii International Conference on System sciences, HICSS 2025

作者： Carter, John Mancoridis, Spiros Protopapas, Pavlos Dept. of Computer Science Drexel University United States Institute for Applied Computational Science Harvard University United States

ISBN: (纸本)9780998133188

As malware becomes increasingly stealthy and more difficult to detect, behavioral malware detection has become the preferred method of detection, which uses representative run-time data from the device to determine if an infection has occurred. In this work, we collected kernel-level system calls from a router serving IoT devices during periods of benign behavior and periods of known malware infection. The system calls were processed using our custom-trained sys2vec model, which created contextual embeddings for each system call observed. We then subjected the data to a classifier using a Gated Recurrent Unit (GRU) with an Attention layer. Although this pipeline performed well for noisy, easy-to-detect malware, it struggled with stealthier malware. To combat this, we trained a classifier that uses a custom-trained BERT encoder in place of the GRU/Attention layers, which results in much better detection at a usable false positive rate (FPR) ≤ 1 × 10-5,. © 2025 IEEE computer Society. All rights reserved.

关键词： computer viruses

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YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesion Classification Using the HAM10000 Dataset

YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesio...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Saha, Utsha Ahamed, Imtiaj Uddin Imran, Md Ashique Ahamed, Imam Uddin Hossain, Al-Amin Gupta, Ucchash Das Dept. of Computer Science North Dakota State University FargoND United States Agricultural Data Analytics Unit North Dakota State University FargoND United States Dept. of Computer Science University of South Dakota VermillionSD United States Dept. of Applied Computer Science The University of Winnipeg Winnipeg Canada Dept. of Computer Science Liverpool John Moores University Liverpool United Kingdom

ISBN: (纸本)9798350350548

Skin cancer is a prevalent and potentially fatal disease that requires early detection for effective treatment. We trained and evaluated five YOLOv8 classification model variants (YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, and YOLOv8x-cls) on the HAM10000 dataset, which contains 10,015 dermatoscopic images of common pigmented skin lesions. The models were trained for 30 epochs using data augmentation techniques to enhance generalization. Performance was assessed using metrics including accuracy, precision, recall, F1-score, and inference time. The YOLOv8x-cls model achieved the highest accuracy of 86.2% and precision of 82.1%, while the YOLOv8lcls model demonstrated the best balance with the highest F1-score of 77.0%. Compared to previous ensemble approaches, our single YOLOv8 models achieved superior performance with lower computational overhead. The YOLOv8n-cls variant showed the fastest inference time of 0.5 ms, making it suitable for real-time applications. Our results demonstrate the potential of YOLOv8-based models for accurate and efficient skin lesion classification, which could aid in early skin cancer detection and improve patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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