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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Park, Jagang Cho, Hyukjoon Lee, Seojoo Lee, Kyungmin Lee, Kanghee Park, Hee Chul Ryu, Jung-Wan Park, Namkyoo Jeon, Sanggeun Min, Bumki Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Centerfor Theoretical Physics of Complex Systems Institute of Basic Science Daejeon34126 Korea Republic of Department ofElectrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of School of Electrical Engineering Korea University Seoul02841 Korea Republic of

We experimentally confirm the non-Hermitian band structures of a photonic temporal crystal in the microwave. These non-Hermitian band structures are shown to be two measurable distinct subsets of complex eigenfrequenc... 详细信息

ISBN: (纸本)9781957171258

关键词： Band structure

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3-inch, 3000-ppi Silicon Nitride Masks for Direct Patterning of OLED Microdisplays

3-inch, 3000-ppi Silicon Nitride Masks for Direct Patterning...

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International Conference on Display Technology, ICDT 2022

作者： Dong, Shou-Cheng Deng, Sunbin Yang, Jiye Jiang, Yibin Tang, Ching W. State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Institute for Advanced Study Hong Kong University of Science and Technology Hong Kong

3-inch, 3000-ppi silicon nitride masks (SiNMs) have been achieved for the direct patterning of ultrahigh-resolution RGB side-by-side OLED microdisplays. © 2022, John Wiley and Sons Inc. All rights reserved.

关键词： Silicon nitride

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Dynamically Reconfigurable Perception using Dataflow Parameterization of Channel Attention

Dynamically Reconfigurable Perception using Dataflow Paramet...

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Asilomar Conference on Signals, Systems & computers

作者： Yujunrong Mal Kshitij Nikha Jani Boutellier Benjamin Riggan Shuvra S. Bhattacharyya Department of Electrical and Computer Engi-neering University of Maryland USA Department of Electrical and Computer Engineering University of Nebraska-Lincoln USA School of Technology and Innovation University of Vaasa Finland Department of Electrical and Computer Engi-neering University of Nebraska-Lincoln USA Department of Electrical and Computer Engineering Institute for Advanced Computer Studies University of Maryland College Park USA

Despite the increasing performance of deep learning models for intelligent perception tasks, the size and complex-ity of state-of-the-art models have become increasingly large, which makes them difficult to deploy in resource-constrained edge computing environments. Moreover, the conditions and constraints under which these models operate may be im-possible to fully anticipate at design time, and may undergo significant changes during inference. In this paper, we address these challenges by developing a new parameterized design approach for image-based perception that enables efficient and dynamic reconfiguration of convolutions using channel attention. Compared to switching among sets of multiple complete neural network models, the proposed reconfiguration approach is much more streamlined in terms of resource requirements, while providing a high level of adaptability to handle unpredictable and dynamically-varying operational scenarios. The efficiency and adaptability of the proposed approach are demonstrated on ResNetSO integrated with the Convolutional Block Attention Module (CBAM) concept.

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Reinforcement Learning Driven Smart Charging Algorithms to Enhance Battery Lifespan and Grid Sustainability

Reinforcement Learning Driven Smart Charging Algorithms to E...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Sivakumar, S. Yadav, Mali Ram Bharathi, A. Akila, D. Dineshkumar, P. Banupriya, V. Vel Tech Rangarajan Dr.Sagunthala R And D Institute Of Science And Technology Department Of Electrical And Electronics Engineering Chennai India Lovely Professional University Department Of Physical Education Punjab India Vels Institute Of Science Technology And Advanced Studies Department Of Information Technology Pallavaram India Saveetha College Of Liberal Arts And Sciences Simats Department Of Computer Applications Chennai India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India M. Kumarasamy College Of Engineering Department Of Computer Science And Business Systems Karur India

ISBN: (纸本)9798350371567

This study investigates the use of RL-driven smart charging algorithms, particularly the Deep Deterministic Policy Gradients (DDPG) model, to maximise grid sustainability and battery lifetime. The current state of this field's study is lacking in real-time adaptation to different settings and in handling the ever-changing nature of charging profiles. By comparison, the suggested approach demonstrates a significant efficiency boost through the use of RL principles, which allow the model to learn and adjust its charging tactics. In specifically, the strategy contributes to a 10% improvement in total grid efficiency while cutting peak demand by 25%, minimising charge-discharge cycles by 20%, and reducing battery degradation rates by 15%. Not only do these measurements show that the suggested method works, but they also show how unique it is in offering a smart charging solution that is both sensitive and adaptive. This research fills in the gaps left by other studies and proposes an optimisation technique that is more dynamic and works in real-time. Its influence on the health of individual batteries and the sustainability of the grid as a whole is noteworthy. © 2024 IEEE.

关键词： Reinforcement learning

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Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

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npj Flexible Electronics 2024年第1期8卷 192-203页

作者： Hee Kyu Lee Sang Uk Park Sunga Kong Heyin Ryu Hyun Bin Kim SangHoon Lee Danbee Kang Sun Hye Shin Ki Jun Yu Juhee Cho Joohoon Kang Il Yong Chun Hye Yun Park Sang Min Won Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Department of Clinical Research Design and Evaluation SAIHSTSungkyunkwan UniversitySeoulRepublic of Korea Patient-Centered Outcomes Research Institute Samsung Medical CenterSeoulRepublic of Korea Center for Clinical Epidemiology Samsung Medical CenterSeoulRepublic of Korea Division of Pulmonary and Critical Care Medicine Department of MedicineSamsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea School of Electrical and Electronic Engineering Yonsei UniversitySeoulRepublic of Korea School of Advanced Materials Science and Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Center for Neuroscience Imaging Research Institute for Basic ScienceSuwonRepublic of Korea

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning(ML)algorithm *** findings from these previous studies have established a strong foundation for the analysis of highresolution,intricate signals,typically through frequency domain *** this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 *** interface provides real-time data visualization and communication with the ML server,creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.

关键词： system diagnosis mounted

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Automatic classification of fatty acid amide hydrolase polymorphism genotype based on EEG signal: Automatic classification of fatty acid amide hydrolase polymorphism…: Reza Javanshir et al.

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Soft Computing 2024年第21期28卷 12575-12585页

作者： Javanshir, Reza Sedghi, Mohammadreza Esmaeili, Mahdad Charsouei, Saeid Anvar, Leila Hosseinzadeh Ahmadalipour, Ali Department of Medical Bioengineering Faculty of Advanced Medical Sciences Tabriz University of Medical Sciences Tabriz Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Neurology Faculty of Medicine Tabriz University of Medical Sciences Tabriz Iran Student Research Committee Tabriz University of Medical Sciences Tabriz Iran Research Center of Psychiatry and Behavioral Sciences Tabriz University of Medical Sciences Tabriz Iran Aging Research Institute Tabriz University of Medical Sciences Tabriz Iran

Epilepsy is a brain abnormality neurological disorder and is life-threatening, affecting the behavior and lifestyle of many people worldwide. Neurologists commonly use an electroencephalogram (EEG) to manually interpret the brain's electrical activity. Some patients respond differently to drugs as the effective doses will be ineffective in some patients or cause adverse drug reactions in others and genetic factors seem to be involved in variable responses in some cases. On the other hand, performing genetic tests to detect the genotype of patients is usually invasive, expensive, and time-consuming. Estimating the patient's genotype using data such as information obtained from EEG, can be considered a significant achievement. Genes involved in the functioning of the endocannabinoid system are known to be a critical element in the physiological function the brain and nerves perform, and the defects in the activity of this system have been confirmed in the pathobiology of diseases such as epilepsy. Levels of endocannabinoids can be influenced by gene polymorphisms such as fatty acid amide hydrolase (FAAH) gene single nucleotide polymorphism. Because the FAAH controls the endocannabinoids levels by hydrolyzing and terminating the activity of the anandamide within the central nervous system (CNS) and also the origin of seizures which is the electrical storm of brain neurons, it is thought that there can be a relation between the FAAH enzyme and the brain oscillations. Identifying genotypes that are related to EEG variation in epilepsy is crucial for clinical epilepsy monitoring and controlling brain oscillations. In this paper, we show the FAAH rs2295633 polymorphism can be detected (classified) using EEG signals (as a common diagnostic tool) following the convolutional neural network (CNN) classification method. The multichannel time series data of EEG collected through a sliding window technique was given as input into a deep CNN model to find a probable relationship b

关键词： Electroencephalography

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Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks

Human-machine Hierarchical Networks for Decision Making unde...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Chen Quan Baocheng Geng Yunghsiang S. Han Pramod K. Varshney Department of Electrical Engineering and Computer Science Syracuse University Syracuse USA Department of Computer Science University of Alabama at Birmingham Alabama USA Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

This paper proposes a belief-updating scheme in a human-machine collaborative decision-making network to com-bat Byzantine attacks. A hierarchical framework is used to realize the network where local decisions from physical sensors act as reference decisions to improve the quality of human sensor decisions. During the decision-making process, the belief that each physical sensor is malicious is updated. The case when humans have side information available is investigated, and its impact is analyzed. Simulation results substantiate that the proposed scheme can significantly improve the quality of human sensor decisions, even when most physical sensors are malicious. Moreover, the performance of the proposed method does not necessarily depend on the knowledge of the actual fraction of malicious physical sensors. Consequently, the proposed scheme can effectively defend against Byzantine attacks and improve the quality of human sensors' decisions so that the performance of the human-machine collaborative system is enhanced.

关键词： Simulation Decision making Collaboration Sensor systems Sensors Man-machine systems

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Balancing Fairness and Accuracy for Predictive Models in Criminal Justice Applications Using Multi-Objective Optimization Methods

Balancing Fairness and Accuracy for Predictive Models in Cri...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaowen Qi Yujungrong Ma Kiminori Nakamura Shuvra S. Bhattacharyya Department of Electrical and Computer Engineering and Institute for Advanced Computer Studies University of Maryland College Park MD USA Department of Criminology and Criminal Justice University of Mary-land College Park MD USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of predictive modeling for criminal justice applications, the dual challenges of ensuring fairness and maintaining interpretability are crucial. This paper addresses these challenges by introducing a new approach to optimizing decision trees using evolutionary algorithms (EAs). Our approach focuses on refining decision trees to achieve a balance between accuracy and algorithmic fairness, a task complicated by potential bias present in historical data. By leveraging the principles of multi-objective optimization, our model systematically trades off prediction accuracy and fair-ness. The evolutionary process characterized by selection, crossover, and mutation is tailored to fit the decision tree structure, ensuring that model development is not only accurate but also promoting measurable fairness. Experimental results demonstrate the effectiveness of our approach in providing interpretable and fair predictive models that can be considered for high-stakes applications in criminal justice. More broadly, this research makes a significant contribution to the field of explainable machine learning, providing a powerful framework for engineering systems that are transparent, fair, and adaptable to different data environments.

关键词： Accuracy Decision making Semantics Refining Optimization methods Evolutionary computation Predictive models Prediction algorithms Spatiotemporal phenomena Decision trees

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Thermal management challenges and approaches for liquid hydrogen-fueled aircraft

Thermal management challenges and approaches for liquid hydr...

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AIAA Aviation Forum and ASCEND, 2024

作者： Ordonez, Juan C. Sailabada, Chaianan Guo, Wei Cooley, Lance Cheetham, Peter Kim, Chul Zheng, Jim P. Gladin, Jonathan Cattafesta, Louis FAMU-FSU College of Engineering Department of Mechanical Engineering Center for Advanced Power Systems and Energy and Sustainability Center Florida State University TallahasseeFL32310 United States Electrical and Computer Engineering FAMU-FSU College of Engineering and Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Center for Advanced Power Systems FAMU-FSU College of Engineering and Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Department of Electrical Engineering The State University of New York at Buffalo BuffaloNY14260 United States School of Aerospace Engineering Aerospace Systems Design Laboratory Georgia Institute of Technology AtlantaGA30332 United States Department of Mechanical Materials and Aerospace Engineering Illinois Institute of Technology ChicagoIL60616 United States

ISBN: (数字)9781624107160

ISBN: (纸本)9781624107160

This paper presents an overview of thermal management challenges associated with aircraft that rely on liquid hydrogen as the sole fuel source. The paper discusses strategies that can enhance the overall cooling capacity and enable a larger contribution of fuel cells to the overall power. Design considerations for heat exchangers to integrate cryogenic hydrogen with secondary thermal management loops, outer mold line cooling, and additional thermal management strategies are presented. Cooling requirement estimates for a blended-wing aircraft (112 passengers, 2400 nautical miles) are reported. A finite element simulation using RANS suggests a cooling capacity rate of the order of 10 kW/m2 is available through the Outer Mold Line (OML). The paper also reports on the cooling capacity available by using hydrogen as a heat sink to cool the power train. For the IZEA concept, the hydrogen stream to the fuel cell can handle close to 6% of the fuel cell heat generation. © 2024, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hydrogen fuels

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Benchmarking Large Language Models for Persian: A Preliminary study Focusing on ChatGPT

arXiv

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arXiv 2024年

作者： Abbasi, Nesa Sani, Samin Mahdizadeh Abaskoh, Amirhossein Sadeghi, Pouya Babalou, Mohammad Hadi Barun, Sara Masoudi, Mostafa Kamahi, Sepehr Namazifard, Danial Edalat, Ali Yaghoobzadeh, Yadollah Naghavian, Nikoo Computer Science Department University of British Columbia Vancouver Canada School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Institute for Advanced Studies Khatam University Tehran Iran

This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian language tasks. Our primary focus is on GPT-3.5-turbo, but we also include GPT-4 and OpenChat-3.5 to provide a more holistic evaluation. Our assessment encompasses a diverse set of tasks categorized into classic, reasoning, and knowledge-based domains. To enable a thorough comparison, we evaluate LLMs against existing task-specific fine-tuned models. Given the limited availability of Persian datasets for reasoning tasks, we introduce two new benchmarks: one based on elementary school math questions and another derived from the entrance exams for 7th and 10th grades. Our findings reveal that while LLMs, especially GPT-4, excel in tasks requiring reasoning abilities and a broad understanding of general knowledge, they often lag behind smaller pre-trained models fine-tuned specifically for particular tasks. Additionally, we observe improved performance when test sets are translated to English before inputting them into GPT-3.5. These results highlight the significant potential for enhancing LLM performance in the Persian language. This is particularly noteworthy due to the unique attributes of Persian, including its distinct alphabet and writing styles. We have made our codes, prompts, and data available here: https://***/Ipouyall/Benchmarking_ChatGPT_for_Persian. © 2024, CC BY.

关键词： Benchmarking

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