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arXiv 2025年

作者： Ryzhii, V. Tang, C. Ryzhii, M. Otsuji, T. Shur, M.S. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai980-8578 Japan School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Center of Excellence ENSEMBLE3 Warsaw01-919 Poland International Research Institute of Disaster Science Tohoku University Sendai980-0845 Japan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Electronics of the Future Inc. ViennaVA22181-6117 United States

We propose and evaluate the characteristics of the terahertz (THz) detectors based on perforated graphene layers (PGLs). The PGL structures constitute the interdigital in-plane arrays of the graphene microribbons (GMRs) connected by the sets of narrow constrictions, which form the graphene nanoribbon (GNR) bridges. The PGL detector operation is associated with the rectification and hot-carrier bolometric mechanisms. The excitation of plasmonic oscillations in the GMRs can reinforce these mechanisms. . The room temperature PGL detector responsivity and detectivity are calculated as function of the radiation frequency and device structure parameters. The effects of the rectification and hot-carrier mechanisms are compared. The PGL THz detectors under consideration can exhibit highly competitive values of responsivity and detectivity. © 2025, CC BY.

关键词： Terahertz wave detectors

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The Art of Finding the Optimal Scattering center(s) (Advanced Optical Materials 4/2025)

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Advanced Optical Materials 2025年第4期13卷

作者： Alexander V. Kildishev Karim Achouri Daria Smirnova Elmore Family School of Electrical and Computer Engineering and Birck Nanotechnology Center Purdue University 1205 W State St West Lafayette IN 47907 USA Nanophotonics and Metrology Laboratory Institute of Electrical and Microengineering École Polytechnique Fédérale de Lausanne Route Cantonale 1015 Lausanne Switzerland ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS) Research School of Physics Australian National University Canberra ACT 2601 Australia

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Realization of Nonlinear Adaptive Compliance: Towards Energy Efficiency in Cyclic Tasks

Realization of Nonlinear Adaptive Compliance: Towards Energy...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Rezvan Nasiri Arjang Ahmadi Majid Nili Ahmadabadi Cognitive Systems Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

Due to the nonlinearity of dynamical equations in robotic systems, nonlinear compliance has more potential for energy consumption reduction. Moreover, the legged robots require adaptive structures so as to maximize their efficiency w.r.t. the new environments and gaits. Therefore, having nonlinear adaptive compliance is essential in legged robots. In this paper, we take one step towards the realization of nonlinear compliances with adaptable torque-deflection profiles. So as to achieve this goal, we present a mechanism which consists of four linear extension springs. Changing the arrangement of linear springs can adapt the compliance profile to the desired one. In addition, to optimize the arrangement of linear springs in an online manner, an adaptation rule is presented. The proposed adaptation rule adapts the compliance profile such that the actuator's torque and consequently the energy consumption are reduced. The performance of presented adaptation rule for actuator's torque minimization is analyzed by means of simulations. In simulations, we observed that the adaptation rule not only minimizes the energy consumption but also improves the tracking performance of the controller. Finally, to realize nonlinear adaptive compliance, a prototype of the presented mechanism is designed and constructed.

关键词： Torque Springs Robots Actuators Energy consumption Task analysis Mathematical model

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Towards advancing the earthquake forecasting by machine learning of satellite data

arXiv

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arXiv 2021年

作者： Xiong, Pan Tong, Lei Zhang, Kun Shen, Xuhui Battiston, Roberto Ouzounov, Dimitar Iuppa, Roberto Crookes, Danny Long, Cheng Zhou, Huiyu Institute of Earthquake Forecasting China Earthquake Administration Beijing China National Institute of Natural Hazards Ministry of Emergency Management of China Beijing China School of Informatics University of Leicester Leicester United Kingdom School of Computer Science and Engineering Nanyang Technological University Singapore Department of Physics University of Trento Trento Italy National Institute for Nuclear Physics the Trento Institute for Fundamental Physics and Applications Trento Italy Center of Excellence in Earth Systems Modeling & Observations Chapman University OrangeCA United States School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom School of Electrical Engineering Nantong University Nantong China

Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1,371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databas

关键词： Earthquakes

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A Model-Free Approach for Predictive Direct Power Control of Grid-Connected Converters

A Model-Free Approach for Predictive Direct Power Control of...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Mohammad Khalilzadeh Sadegh Vaez-Zadeh Alireza Jabbarnejad School of electrical and computer engineering Colledge of engineering University of Tehran Tehran Iran Advanced Motion Systems Research Lab The Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering Colledge of Engineering University of Tehran Tehran Iran

This paper presents a model-free finite control set predictive direct power control for grid-connected converters. As the proposed method does not use the model parameters, their uncertainties do not cause prediction error and inappropriate voltage vector selection, unlike the conventional predictive direct power control method. A new form of cost function is adopted in the method that directly uses the time-derivatives (slopes) of the active and the reactive powers. The slopes are predicted free of model parameters with low computational burden and without additional sensors. The method is evaluated through simulation and its performance is compared with the one of the conventional model predictive direct power control method. The results show the effectiveness of the proposed method and its superiority over the conventional method.

关键词： Reactive power Predictive models Cost function Mathematical model Power control Voltage control Computational modeling

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Software module clustering: An in-depth literature analysis

arXiv

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arXiv 2020年

作者： Sarhan, Qusay I. Ahmed, Bestoun S. Bures, Miroslav Zamli, Kamal Z. Department of Computer Science University of Duhok Duhok Kurdistan Region Iraq Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Karlovo nam. 13 Praha 2121 35 Czech Republic IBM Center of Excellence Faculty of Computer Systems and Software Engineering University Malaysia Pahang 26 300 Gambang KuantanPahang Malaysia

Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities’ structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Virtual-Flux-Based DPC of Grid Connected Converters with Fast Dynamic and High Power Quality

Virtual-Flux-Based DPC of Grid Connected Converters with Fas...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Alireza Jabbarnejad Sadegh Vaez-Zadeh Mohammad Khalilzadeh Advanced Motion Systems Research Lab School of electrical and computer engineering Collage of engineering University of Tehran Tehran Iran Advanced Motion Systems Research Lab The Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Grid connected voltage source converters (VSCs) are unavoidable means for developing and supporting power systems. Nevertheless, their harmonic injection may cause deterioration of power quality and current THD. Therefore, injected harmonics through VSCs must observe the corresponding standards, while lower switching frequencies and rapid transient responses are required. This paper, presents a sensorless virtual flux direct power control of VSCs based on a novel modified switching table for fast dynamic response, high power quality and low current THD. An analysis is carried out to determine the best voltage vector for virtual flux direct power control of VSCs at transient and steady states. It mathematically proves that the vector is oriented along the grid voltage vector, which is perpendicular to the grid virtual flux vector, at transient state. Moreover, the modified switching table uses a new logic for selecting the voltage vectors under steady to provide smooth performance. In comparison with the conventional direct power control, the proposed method enjoys faster transient response and lower power ripples and current THD due to utilization of the modified switching table, without requiring grid voltage sensors.

关键词： Power conversion Transient analysis Switches Steady-state Reactive power Voltage control

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Power Quality Improvement Using Virtual Flux Combined Control of Grid Connected Converters under Balanced and Unbalanced Grid Operation

Power Quality Improvement Using Virtual Flux Combined Contro...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Alireza Jabbarnejad Sadegh Vaez-Zadeh Advanced Motion Systems Research Lab School of Electrical and Computer Engineering Collage of engineering University of Tehran Tehran Iran Advanced Motion Systems Research Lab The Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Three phase grid connected power electronic converters (GCCs) are unavoidable means for developing and supporting power systems todays. However, their harmonic injection and high frequency switching may lead to reduction of power quality. Thus, GCCs injected harmonics should respect to the corresponding standards. In this paper, a novel control method is proposed for the GCCs to improve the power quality and current THD under both balanced and unbalanced grid operation. The method employs a virtual flux in connection with a combined control for the first time. Compared to the conventional virtual flux direct power control, the method provides much better power quality, reduced THD, plus less grid parameters sensitivity. In comparison with the conventional combined control method, it does not have grid voltage sensors and cumbersome controller tuning. In addition, the proposed method well respect the standards using the appropriate current references without positive and negative sequence extraction under unbalanced grid conditions.

关键词： Voltage control Reactive power Power quality Switches Pulse width modulation Harmonic analysis

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Personalized learning in hybrid education

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Scientific reports 2025年第1期15卷 18176页

作者： Alaa O Khadidos Hariprasath Manoharan Adil O Khadidos Mohammad N Alanazi Fuhid Alanazi Shitharth Selvarajan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia. Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia. Department of Electronics and Communication Engineering Panimalar Engineering College Poonamallee Chennai India. Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia. College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 13318 Saudi Arabia. Faculty of Computer and Information Systems Islamic University of Madinah Madinah 42351 Saudi Arabia. Department of Computer Science Kebri Dehar University Kebri Dehar 250 Ethiopia. ShitharthS@kdu.edu.et. Department of Computer Science and Engineering Chennai Institute of Technology Chennai India. ShitharthS@kdu.edu.et. Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India. ShitharthS@kdu.edu.et.

The process of teaching and learning during the pandemic has been evolving globally, with many institutions transforming their approaches to enhance the teaching and learning experience. Despite the presence of improved frameworks due to the varied learning capabilities of students, it remains quite challenging to analyse individual characteristic features. Consequently, this research provides clear insights into the integration of the Personalised Learning Approach (PLA) to foster effective interaction with students. However, many existing methods suggest different techniques for evaluating learners in a hybrid mode, where obtaining clear data sets can be difficult. In the teaching and learning approach, if the defined data set from experts is clear, decisions regarding the learning characteristics of students can be made in a shorter period. In the proposed method the PLA framework categorizes learners into four engagement-based clusters using a three-dimensional sensor model and machine learning classifiers. A dual-controller mechanism (master-slave) dynamically adjusts communication intervals and optimizes video transmission, reducing latency and packet loss. The methodology is validated using MATLAB-based simulations with a dataset of 1,700-5,000 learners, analyzing throughput, delay, packet loss, and cost efficiency. The test results clearly demonstrate that the PLA outperforms the conventional method, not only with the parameters mentioned above but also in terms of cost-effectiveness using master and slave controllers.

关键词： Machine learning (ML) Personalized learning approach (PLA) Teaching learning Video classes

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Explainable Natural Language Processing with Matrix Product States

arXiv

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arXiv 2021年

作者： Tangpanitanon, Jirawat Mangkang, Chanatip Bhadola, Pradeep Minato, Yuichiro Angelakis, Dimitris G. Chotibut, Thiparat Bangkok Thailand Thailand Center of Excellence in Physics Ministry of Higher Education Science Research and Innovation Bangkok Thailand Chula Intelligent and Complex Systems Department of Physics Faculty of Science Chulalongkorn University Bangkok Thailand Centre for Theoretical Physics & Natural Philosophy Nakhonsawan Studiorum for Advanced Studies Mahidol University Nakhonsawan Campus Thailand Blueqat Inc. Japan School of Electrical and Computer Engineering Technical University of Crete Chania Greece Centre for Quantum Technologies National University of Singapore Singapore

Despite empirical successes of recurrent neural networks (RNNs) in natural language processing (NLP), theoretical understanding of RNNs is still limited due to intrinsically complex non-linear computations. We systematically analyze RNNs’ behaviors in a ubiquitous NLP task, the sentiment analysis of movie reviews, via the mapping between a class of RNNs called recurrent arithmetic circuits (RACs) and a matrix product state (MPS). Using the von-Neumann entanglement entropy (EE) as a proxy for information propagation, we show that single-layer RACs possess a maximum information propagation capacity, reflected by the saturation of the EE. Enlarging the bond dimension beyond the EE saturation threshold does not increase model prediction accuracies, so a minimal model that best estimates the data statistics can be inferred. Although the saturated EE is smaller than the maximum EE allowed by the area law, our minimal model still achieves ∼ 99% training accuracies in realistic sentiment analysis data sets. Thus, low EE is not a warrant against the adoption of single-layer RACs for NLP. Contrary to a common belief that long-range information propagation is the main source of RNNs’ successes, we show that single-layer RACs harness high expressiveness from the subtle interplay between the information propagation and the word vector embeddings. Our work sheds light on the phenomenology of learning in RACs, and more generally on the explainability of RNNs for NLP, using tools from many-body quantum *** Codes 15A69 © 2021, CC BY.

关键词： Recurrent neural networks

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