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2024 Laser Science, LS 2024

作者： Bartos, Jan Ba Tis, Taleb Park, Wounjhang Huang, Shu-Wei Department of Electrical Computer and Energy Engineering University of Colorado Boulder80309 United States Material Science and Engineering Program University of Colorado Boulder80309 United States

We present progress towards an imaging magnetometer using a magneto-optical polymer nanocomposite thin film, experimentally demonstrating 29nT/√Hz sensitivity with © 2024 The Author(s). © Optica Publishing ... 详细信息

关键词： Heterodyne detection

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On-chip meta-lens based Adaptive Integrated Photonic Imager

On-chip meta-lens based Adaptive Integrated Photonic Imager

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Kim, Jong Ryul Jeong, Heijun Gu, Tingyi Quantum Science and Engineering Program University of Delaware NewarkDE19711 United States Electrical and Computer Engineering University of Delaware NewarkDE19711 United States

ISBN: (纸本)9781957171395

关键词： Adaptive optics

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CNN Performances for Stunting Face Image Classification 4

CNN Performances for Stunting Face Image Classification

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4th International Conference on Electrical engineering and computer Science, ICECOS 2024

作者： Yunidar Roslidar Yusni Nasaruddin Fitriarnia Faculty of Engineering Universitas Syiah Kuala Doctoral Program School of Engineering Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Faculty of Engineering Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368253

Detecting stunting in children is an important issue currently faced by healthcare providers to follow up efforts to reduce the number of stunted children in a region. Emerging research has applied deep learning to many aspects of stunting, including classifying stunting in children through facial images. This study aims to extend previous work on CNN benchmarking in classifying facial images of stunted children and normal children. Here, we trained, validated, and tested five more popular CNN architectures, namely ShuffleNetV2, InceptionV3, MobileNetV2, EfficientNetB0, and VGG19;the research was applied to the same dataset built in previous work. We presented the training and validation loss graphs, and the confusion matrices of all architectures. We measured CNN's accuracy, precision, recall, and F1-measures of the testing stage. Simulation results indicated that three CNN models, namely MobileNetV2, VGG19, and EfficientNetB0 performed comparably or better than previous CNNs in classifying children's facial images. VGG19 and EfficientNetB0 achieved testing accuracies of 100%. © 2024 IEEE.

关键词： Convolutional neural networks

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Comments on "relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems"

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IEEE Transactions on Automatic Control 2025年第6期70卷 4233-4236页

作者： Şahan, Gökhan Trenn, Stephan Izmir Institute of Technology Department of Mathematics and Computational Science and Engineering Program Urla Izmir Turkey University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen9722BA Netherlands

This study addresses the deficiencies in the assumptions of the results in Chen and Yang, 2017 [1] due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then we prove why they are wrong in that form and show the errors in the proof of the main result of [1]. Next, we compare the assumptions and related results of [1] with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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Production-Oriented Design for High Parallel Test Efficiency 21

Production-Oriented Design for High Parallel Test Efficiency

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Shin, Jaehwan Lee, Young-Woo Inha University School of Electrical and Computer Engineering Incheon Korea Republic of Inha University Program in Semiconductor and Device Incheon Korea Republic of

ISBN: (纸本)9798350377088

As the importance and complexity of System-on-Chip (SoC) testing increase, research to enhance test efficiency is being conducted. Among these, research is ongoing to reduce the number of pins used in testing through internal test modules in the Device Interface Board (DIB) application area. However, the existing test modules were designed without considering the actual mass production environment, making them unsuitable for application in the actual chip fabrication process. In this paper, we propose a method that offers enhanced error detection capabilities and high parallelism, implementing additional functionalities required for mass production, making it applicable to actual manufacturing processes. © 2024 IEEE.

关键词： System-on-chip

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CAWA: Correlation-Based Adaptive Weight Adjustment via Effective Fields for Debiasing 13

CAWA: Correlation-Based Adaptive Weight Adjustment via Effec...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Noh, Kyoungrae Kim, San Kim, Jaekwang Sungkyunkwan University Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Convergence Program for Social Innovation Suwon Korea Republic of

ISBN: (纸本)9798350373332

In real-world image classification tasks, neural networks often encounter significant challenges due to unexpected or deceptive correlations and biases inherent in the dataset. These biases can emerge from disproportionate data distributions, causing models to generalize poorly to new, unseen data. Such data distribution issues are particularly problematic compared to more balanced datasets because they lead the model to rely on bias attributes rather than intrinsic attributes. Ideally, the model should classify based on intrinsic attributes, but due to the influence of bias attributes, frequent misclassification occurs. Such biases compromise the fairness and accuracy of the model, especially in critical scenarios such as medical diagnosis, autonomous driving, or criminal justice, where misclassification can have significant consequences. To address these challenges, we propose an innovative two-stage approach aimed at mitigating bias more effectively and efficiently. © 2024 IEEE.

关键词： Image classification

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Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm

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Energy Reports 2023年 10卷 3113-3124页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Chemical Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir 5019 Tunisia

This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The approach unfolds in three integral phases. Firstly, the establishment of interval centers and ranges, employing upper and lower bounds, effectively manages the inherent uncertainties arising from noise and measurement errors intrinsic to the wind system. Subsequently, the dataset undergoes processing via the Sine-Cosine Optimization Algorithm (SCOA), enabling the extraction of the most pertinent attributes. The culmination of predictive precision and classification performance is achieved through the integration of the refined dataset into an ensemble learning paradigm, harmonizing bagging, boosting techniques, and an artificial neural network classifier. The principal aim of the IEL-SCOA approach is to discern the spectrum of operational conditions within WEC systems, encompassing a healthy mode alongside six distinct faulty modes. These anomalies, encompassing short circuits, open circuits, and wear-out incidents, are deliberately induced at diverse locations and facets of the system, notably the generator and grid sides. Empirical results underscore the robustness and efficiency of the proposed methodology, showcasing an exceptional accuracy rate of 99.76 %. These outcomes definitively establish the IEL-SCOA approach as a potent and efficacious tool for precise fault diagnosis in uncertain WEC systems. © 2023

关键词： Fault detection

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ForTT-Gen: Network Traffic Generator for Malware Forensics Analysis Training 12

ForTT-Gen: Network Traffic Generator for Malware Forensics A...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Bistene, Jonas Vasconcelos Das Chagas, Clayton Escouper Dos Santos, Anderson Fernandes Pereira De Souza Dias, Gabriela Moutinho Salles, Ronaldo Moreira Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil Military Institute of Engineering Department of Computer Engineering Rio de Janeiro Brazil Ciicesi Estg Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350330366

The generation and replication of network traffic are essential tasks for testing, analyzing, simulating, and evaluating the behavior and efficiency of systems, protocols, applications, and network services. However, it faces various challenges and limitations when generating or replicating network traffic in a realistic, responsive, and scalable manner, using appropriate models. This work proposes a modeling of a program for generating and replicating network traffic, called Forensic Training Traffic Generator (ForTT-Gen), aims to address the challenge associated with generating and replicating network traffic generated by malware by creating files that can be used in the analysis of network packets inherent to forensic analysts' training. ForTT-Gen is a tool that can generate and replicate network traffic using a hybrid model that combines the replication of real data and the generation of synthetic data through statistical techniques. Experimental results demonstrate the model's ability to reliably. Accurately reproducing the statistical patterns of the original traffic, is evidenced by a determination coefficient of 1 and a Pearson coefficient of 0.9, all within a 0,58 confidence interval with 95% certainty. © 2024 IEEE.

关键词： Malware

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning 6

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Abd-Elaziz, Mariam M. Elshafie, Basma M. Abdullah, Hagar Gerges, Karen Y. Ghanem, Ingy A. Fouad, Khaled M. El-Ghamry, Amir Moawad, Ibrahim F. El-Metwally, Sara New Mansoura University Faculty of Computer Science and Engineering Dakahlia Egypt Mansoura University New Mansoura University Faculty of Engineering Computer and Communication Program Faculty of Computer Science and Engineering Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt Mansoura University New Mansoura University Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science University of Hertfordshire Gaf Egypt Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning © 2024 IEEE.

关键词： Adversarial machine learning

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Supporting the Automated Generation of Acceptance Tests of Process-Aware Information Systems 19

Supporting the Automated Generation of Acceptance Tests of P...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Paiva, Tales M. Oliveira, Toacy C. Pillat, Raquel M. Alencar, Paulo S.C. Computer and Systems Engineering Program Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Software Engineering Program Universidade Federal do Pampa Alegrete Brazil Cheriton School of Computer Science University of Waterloo Waterloo Canada

ISBN: (纸本)9789897586729

Software quality assurance is a crucial process that ensures software products meet specified requirements and quality standards. Achieving an exhaustive test coverage is essential for quality assurance, particularly in complex and dynamic Process-Aware Information Systems (PAIS) built upon the Business Process Model and Notation (BPMN). Manual testing in such systems is challenging due to many execution paths, dependencies, and external interfaces. This paper proposes a model-based testing strategy that uses BPMN models and build specifications as input to generate a Robotic Process Automation (RPA) script that automates a comprehensive User Acceptance Test procedure. Leveraging on Robotic Process Automation (RPA) to automate user interactions allows for reducing the need for testers to manually input PAIS-related information when handling user forms. We also present a Case Study to demonstrate the feasibility of our approach. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Acceptance tests

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