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ASME 2023 International Mechanical engineering Congress and Exposition, IMECE 2023

作者： Mauk, Michael G. Kavitha, Arjuna Karthikeyan Senthilvel Vasudevan, Nijanthan Tseng, Tzu-Liang Bill Chiou1, Richard Y. Department of Engineering Leadership and Society Engineering Technology Program Drexel University PhiladelphiaPA United States Department of Industrial Manufacturing and Systems Engineering University of Texas at El Paso El PasoTX United States

ISBN: (纸本)9780791887653

We describe an undergraduate engineering project to give students hands-on experience and integrate diverse knowledge and skills in robotics, thermal imaging, rapid prototyping, machine learning and artificial intelligence, and plastics engineering, and materials testing. A robot is programmed for ultrasonic welding of 3D-printed or laser-machined plastic parts or soldering of components to printed circuit boards. The process is monitored with a thermal camera, and the infrared images and video are analyzed by machine learning and artificial intelligence algorithms for process optimization, based on criteria tensile strength and pressure leak tests. A pre-trained machine learning model can monitor and analyze the thermal joining processes. © 2023 by ASME.

关键词： Machine learning

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Involution symmetry quantification using recurrences

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Physical Review E 2024年第2期110卷 024203-024203页

作者： Gabriel Marghoti Thiago de Lima Prado Sergio Roberto Lopes Yoshito Hirata Physics Department Degree Program in Computer Science Interdisciplinary Center for Science Technology and Innovation CICTI Departament of Physics - Telegraphenberg 14473 Potsdam Germany Institute of Systems and Information Engineering

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot. By analyzing the statistics of small recurrence matrices, our approach delves into microscale dynamics, facilitating the identification of symmetric time series segments through diagonal macroscale structures on a recurrence plot. We validate our approach by successfully quantifying involution symmetries for three-dimensional dynamical models, specifically, order-2 rotational symmetry in the Lorenz '63 model, and inversion symmetry in the Chua circuit. Our quantifier also detects symmetry breaking in the modified Lorenz model for El Niño phenomenon. The method can be applied in a versatile manner, not only to three-dimensional trajectories but also to univariate time series. Symmetry quantification in time series is promising for enhancing dynamical system modeling and profiling.

关键词： Chaos Dynamical systems Time series analysis

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Image Integrity Analysis under Defense Approaches Against Adversarial Patches

Image Integrity Analysis under Defense Approaches Against Ad...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Siritanawan, Prarinya Chunhachatchawhankhun, Kasidit Kotani, Kazunori Graduate School of Science and Technology Department of Engineering Informatics and Interdisciplinary Systems Division Shinshu University Nagano Japan Division of Transdisciplinary Science JAIST Ishikawa Japan Faculty of Transdisciplinary Science Institute of Philosophy in Interdisciplinary Science Kanazawa University Ishikawa Japan

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

Adversarial patches pose a significant threat to image recognition systems, exploiting small, maliciously crafted patterns to mislead models into incorrect *** traditional adversarial examples, adversarial patches operate under real-world constraints, making defense against them a challenging *** study introduces an alternative defense strategy that employs partial masking to mitigate the impact of adversarial patches without relying on precise patch *** global masking patterns were evaluated to determine the best balance between adversarial robustness and image *** proposed method effectively reduces attack success rates while maintaining reasonable classification accuracy, demonstrating its potential to ensure protection in real-world applications without dependency on the detection methods. © 2024 SPIE.

关键词： Defense and security Image classification Image restoration Image analysis Visualization Image processing Data modeling systems modeling

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Semi-Blind Multi-Modulus Approach Using Embedded Pilot for MIMO-OFDM Communications 58

Semi-Blind Multi-Modulus Approach Using Embedded Pilot for M...

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58th Asilomar Conference on Signals, systems and Computers, ACSSC 2024

作者： Aliyu, Kabiru N. Abed-Meraim, Karim Zerguine, Azzedine Lawal, Abdulmajid Kfupm Electrical Engineering Department The Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Univ. Orléans Prisme lab. France

ISBN: (纸本)9798350354058

This work proposes an effective source separation technique based on a semi-blind multi-modulus approach for an embedded pilot scenario in a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) communication system. A jointly hybrid cost function is developed based on the Multi-Modulus (MM) and Least Squares (LS) for a semi-blind channel estimation. A straightforward yet effective semi-blind block gradient descent optimization approach is used. In addition, a technique for optimizing the step size is introduced to improve the convergence rate of the proposed algorithm. The proposed semi-blind technique for a superimposed pilot scheme is compared to the Time-division Multiplexed pilots (i.e., block pilot arrangement) in terms of the symbol error rate and the throughput. The extensive simulation results demonstrate that the inherent ambiguity in the blind estimation is preserved, and the result shows excellent performance and bandwidth trade-offs. © 2024 IEEE.

关键词： Cost functions

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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

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Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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Unraveling the potential of 3D bioprinted immunomodulatory materials for regulating macrophage polarization:State-of-the-art in bone and associated tissue regeneration

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Bioactive Materials 2023年第10期28卷 284-310页

作者： Sayan Deb Dutta Tejal V.Patil Keya Ganguly Aayushi Randhawa Ki-Taek Lim Department of Biosystems Engineering Kangwon National UniversityChuncheon24341Republic of Korea Institute of Forest Science Kangwon National UniversityChuncheon24341Republic of Korea Interdisciplinary Program in Smart Agriculture Kangwon National UniversityChuncheon24341Republic of Korea

Macrophage-assisted immunomodulation is an alternative strategy in tissue engineering, wherein the interplay between pro-inflammatory and anti-inflammatory macrophage cells and body cells determines the fate of healing or inflammation. Although several reports have demonstrated that tissue regeneration depends on spatial and temporal regulation of the biophysical or biochemical microenvironment of the biomaterial, the underlying molecular mechanism behind immunomodulation is still under consideration for developing immunomodulatory scaffolds. Currently, most fabricated immunomodulatory platforms reported in the literature show regenerative capabilities of a particular tissue, for example, endogenous tissue (e.g., bone, muscle, heart, kidney, and lungs) or exogenous tissue (e.g., skin and eye). In this review, we briefly introduced the necessity of the 3D immunomodulatory scaffolds and nanomaterials, focusing on material properties and their interaction with macrophages for general readers. This review also provides a comprehensive summary of macrophage origin and taxonomy, their diverse functions, and various signal transduction pathways during biomaterial-macrophage interaction, which is particularly helpful for material scientists and clinicians for developing next-generation immunomodulatory scaffolds. From a clinical standpoint, we briefly discussed the role of 3D biomaterial scaffolds and/or nanomaterial composites for macrophage-assisted tissue engineering with a special focus on bone and associated tissues. Finally, a summary with expert opinion is presented to address the challenges and future necessity of 3D bioprinted immunomodulatory materials for tissue engineering.

关键词： Macrophage Immunomodulation Biomaterials 3D bioprinting Tissue engineering

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Expert System for Diagnosis of Diphtheria Disease Using Fuzzy Logic 6

Expert System for Diagnosis of Diphtheria Disease Using Fuzz...

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6th International Seminar on Research of Information Technology and Intelligent systems, ISRITI 2023

作者： Isnanto, R.Rizal Mustafid Prasetijo, Agung Budi Fitria Ali, Safira Isna Diponegoro University Information Systems Doctoral Program Semarang Indonesia Diponegoro University Computer Engineering Department Semarang Indonesia

ISBN: (纸本)9798350358346

Diphtheria is an infectious disease that affects the upper respiratory system and throat, arising suddenly and caused by Corynebacterium diphtheriae. The symptoms of diphtheria such as fever, swollen neck and slimy nose are often interpreted as influenza or laryngitis. Diphtheria is a contagious and deadly disease that requires specialized and rapid treatment and care. In today's technological era, it can be overcome with a system that can diagnose diphtheria like a doctor's diagnosis ability commonly called an expert system. This study describes the application of an expert system to diagnose diphtheria disease. The research method used is the web-based Expert System Development Life Cycle (ESDLC). The method used to process input values is Forward Chaining. Fuzzy Tsukamoto to calculate the input value from the user. The research has been tested with Blackbox testing showing the results that the expert system works well and accuracy testing with 92% results. The results show effectiveness with a satisfactory level of accuracy in detecting diphtheria disease. This research contributes to the development of expert systems in the health sector with a focus on diagnosing diphtheria disease using a fuzzy logic approach. © 2023 IEEE.

关键词： Diagnosis

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Gastritis Diagnosis Expert System Using Android-Based Certainty Factor Method 6

Gastritis Diagnosis Expert System Using Android-Based Certai...

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6th International Seminar on Research of Information Technology and Intelligent systems, ISRITI 2023

作者： Isnanto, R. Rizal Purwanto Windarto, Yudi Eko Setiawan, Annas Diponegoro University Information Systems Doctoral Program Semarang Indonesia Diponegoro University Computer Engineering Department Semarang Indonesia

ISBN: (纸本)9798350358346

In the modern era, an increasing number of diseases are emerging because of human lifestyle choices and bacterial transmission. Gastritis, characterized by inflammation in the stomach lining leading to frequent abdominal pain, is one such ailment. Recognizing the significance of addressing these health issues, there is a need for an expert system application specifically designed for diagnosing gastritis. The primary objective is to disseminate information to the public regarding the early symptoms of gastritis, promoting awareness from a young age. The research method used is the web-based Expert System Development Life Cycle (ESDLC). The diagnostic process employs the Certainty Factor (CF) method to ensure accurate results. By utilizing symptom CF values chosen by the user, the Certainty Factor approach facilitates a diagnosis based on calculations established by medical specialists. The research culminates in the development of an expert system application capable of accurately diagnosing the disease Android based. The accuracy rates for the first, second, and third test cases are reported as 98.848%, 99.8464%, and 99.99115264%, respectively. System testing, conducted through black-box testing, validates the application's functionality, demonstrating a 100% success rate for each component. © 2023 IEEE.

关键词： Expert systems

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Deep learning framework for wind speed prediction in Saudi Arabia

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Neural Computing and Applications 2025年第5期37卷 3685-3701页

作者： Alabdulhadi, Arwa Ahmed Rehman, Shafiqur Ali, Amjad Shafiullah, Md Physics Department College of Science and Humanities Imam Abdulrahman Bin Faisal University Jubail3065 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Increased utilization of conventional energy sources severely impacts the environment by increasing the global temperature and contributing to global warming. Sustainable energy sources could contribute to handling the increment while also providing cheap, clean, and abundant energy. Wind energy is considered one of the most reliable sources of sustainable energy due to its abundance and availability during the day and night compared to other renewable resources. However, effective forecasting of such intermittent resources is considered a key challenge for power system operators. This paper develops a novel deep learning framework for forecasting the wind speed in Dhahran City, Saudi Arabia, using two years of data acquired at different heights from the light detection and ranging device, an active remote sensing wind monitoring system. It also segments the data and removes wrong measurements through data pre-processing. The article identifies the best configuration for deep learning models, such as long short-term memory, through a systematic approach. The presented results confirm the efficacy of the developed models against the selected statistical performance measures. The developed model performs better with large data volumes than with lower volumes. Finally, the comparative analysis with the literature-reported results provides confidence in the competency of the proposed model in predicting wind speed for various periods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Clean energy

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Optimized photonic integrated circuit components for optical coherence tomography at 1060 nm

Optimized photonic integrated circuit components for optical...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Hao, Senyue Adkins, Aaron J. Zhang, Yitian Zhou, Chao Department of Electrical & Systems Engineering Washington University Saint Louis United States Imaging Science PhD Program Washington University Saint Louis United States Department of Biomedical Engineering Washington University Saint Louis United States

To implement optical coherence tomography on photonic chips, we developed optimized and experimentally validated silicon nitride-based components for photonic integrated circuits in order to achieve high sensitivity a... 详细信息

关键词： Photonic integrated circuits

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