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Highly-efficient(>70%)and Wide-spectral(400-1700 nm)sub-micron-thick InGaAs photodiodes for future high-resolution image sensors

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Light(science & Applications) 2024年第12期13卷 3224-3235页

作者： Dae-Myeong Geum Jinha Lim Junho Jang Seungyeop Ahn SeongKwang Kim Joonsup Shim Bong Ho Kim Juhyuk Park Woo Jin Baek Jaeyong Jeong SangHyeon Kim School of Electrical Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea

This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors(PDs)designed for high-resolution imaging from the visible to short-wavelength infrared(SWIR)*** approaches encounter challenges such as low resolution and crosstalk issues caused by a thick absorption layer(AL).Therefore,we propose a guided-mode resonance(GMR)structure to enhance the quantum efficiency(QE)of the InGaAs PDs in the SWIR region with only sub-micron-thick *** TiOx/Au-based GMR structure compensates for the reduced AL thickness,achieving a remarkably high QE(>70%)from 400 to 1700 nm with only a 0.98μm AL InGaAs PD(defined as 1μm AL PD).This represents a reduction in thickness by at least 2.5 times compared to previous results while maintaining a high ***,the rapid transit time is highly expected to result in decreased electrical *** effectiveness of the GMR structure is evident in its ability to sustain QE even with a reduced AL thickness,simultaneously enhancing the transit *** breakthrough offers a viable solution for high-resolution and low-noise broadband image sensors.

关键词： thick resolution image

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Provision of Ramp-rate Limitation as Ancillary Service from Distribution to Transmission System:Definitions and Methodologies for Control and Sizing of Central Battery Energy Storage System

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1507-1518页

作者： Spyros I.Gkavanoudis Kyriaki-Nefeli D.Malamaki Eleftherios O.Kontis Aditya Shekhar Umer Mushtaq Sagar Bandi Venu Charis S.Demoulias Department of Electrical&Computer Engineering Aristotle University of ThessalonikiThessalonikiGreece Electrical Engineering Mathematics&Computer Science DepartmentDelft University of TechnologyDelftNetherlands FENECON Gmbh DeggendorfGermany

The variability of the output power of distributed renewable energy sources(DRESs)that originate from the fastchanging climatic conditions can negatively affect the grid ***,grid operators have incorporated ramp-rate limitations(RRLs)for the injected DRES power in the grid *** the DRES penetration levels increase,the mitigation of high-power ramps is no longer considered as a system support function but rather an ancillary service(AS).Energy storage systems(ESSs)coordinated by RR control algorithms are often applied to mitigate these power ***,no unified definition of active power ramps,which is essential to treat the RRL as AS,currently *** paper assesses the various definitions for ramp-rate RR and proposes RRL method control for a central battery ESS(BESS)in distribution systems(DSs).The ultimate objective is to restrain high-power ramps at the distribution transformer level so that RRL can be traded as AS to the upstream transmission system(TS).The proposed control is based on the direct control of theΔP/Δt,which means that the control parameters are directly correlated with the RR requirements included in the grid *** addition,a novel method for restoring the state of charge(So C)within a specific range following a high ramp-up/down event is ***,a parametric method for estimating the sizing of central BESSs(BESS sizing for short)is *** BESS sizing is determined by considering the RR requirements,the DRES units,and the load mix of the examined *** BESS sizing is directly related to the constant RR achieved using the proposed ***,the proposed methodologies are validated through simulations in MATLAB/Simulink and laboratory tests in a commercially available BESS.

关键词： Battery energy storage system distributed renewable energy resource sizing distribution system transmission system ramp-rate limitation state of charge

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Recent progress of artificial intelligence for liquid-vapor phase change heat transfer

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npj Computational Materials 2024年第1期10卷 2552-2565页

作者： Youngjoon Suh Aparna Chandramowlishwaran Yoonjin Won Department of Mechanical and Aerospace Engineering University of CaliforniaIrvineCAUSA Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCAUSA

Artificial intelligence(AI)is shifting the paradigm of two-phase heat transfer *** innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past,for making their insights available to other domains,and for solving for physical quantities based on first principles for phasechange thermofluidic *** review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation *** technologies for meta-analysis,data extraction,and data stream analysis are described with their potential challenges,opportunities,and alternative ***,we offer outlooks and perspectives regarding physics-centered machine learning,sustainable cyberinfrastructures,and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer.

关键词： transfer meaningful shifting

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An Ultra-Wideband Quad-Mode SIW Cavity-Backed Slot Antenna for NextG Communication and Sensing Arrays

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3605-3615页

作者： Muppala, Aditya Varma Sarabandi, Kamal University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States

This paper presents a novel Substrate Integrated Waveguide (SIW) Cavity-Backed Slot Antenna (CBSA) to enable the next generation of communication and sensing infrastructure. The antenna achieves a bandwidth of 40%, with a low profile (c), high front to back ratio, good polarization purity, high isolation, and scalability to large active arrays such as phased arrays and Reconfigurable Intelligent Surfaces (RIS). To achieve wideband operation, four resonant characteristic modes are excited in a compact T-shaped cavity with a rectangular slot aperture. The interaction of these four modes is shown to create fictitious shorts along the length of the slot that result in multiple resonances. Characteristic Mode Analysis (CMA) and optimization are used to place 4 such fictitious short modes at the right frequencies in order to cover a fractional bandwidth of 40%, which is shown to be close to the theoretical bandwidth limit of a single slot with broadside radiation. The antenna is integrated into a 2×1 back-to-back array with a novel ultra-wideband out-of-phase power divider to achieve a symmetric beam pattern. This unit cell is used to demonstrate a high isolation, full-duplex, broadband antenna system. Next, the antenna is integrated into a 1×6 electronically scanned array to demonstrate 1-D beam-steering over the entire band. Finally, 2-D scalability is investigated by prototyping a 2×2 triangular lattice array. All the antennas presented are fabricated in a standard 4-layer printed circuit board to cover the 20 - 30 GHz band. © 1963-2012 IEEE.

关键词： Slot antennas

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A Novel Deep Learning Approach Toward Efficient and Accurate Recommendation Using Improved Alternating Least Squares in Social Media

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 657-675页

作者： Dhawan, Sanjeev Singh, Kulvinder Batra, Amit Choi, Anthony Choi, Ethan Department of Computer Science and Engineering University Institute of Engineering and Technology Kurukshetra University Haryana India Department of Electrical and Computer Engineering Mercer University MaconGA United States

The increasing number of electronic transactions on the Internet has given rise to the design of recommendation systems. The main objective of these systems is to give recommendations to the users about the items (i.e., movies, music, books, etc.) they are likely to purchase in near future. These recommendations are on the foundation of the response provided by the other individuals about these products in the terms of ratings, which are in the form of a five-star rating system. Plenty of algorithms have been proposed in the past for building recommender systems. The most popular of these algorithms comprise content-centered and collaborative filtering recommendation methodologies. Recently, matrix factorization has appeared as a technique in recommender systems. Several algorithms centered on matrix factorization have been suggested in the recent past. In this paper, an improved alternating least squares algorithm has been proposed for building more efficient and accurate recommender systems which improves the performance of matrix factorization. The performance of the model has been evaluated using metrics like root-mean-square-error and execution time. However, in general, the time complexity of both algorithms is O(n^3), where n is the number of users or items. This is because both IALS and ALS involve iteratively solving a system of linear equations, and the time complexity of solving a system of linear equations is O(n^3). However, IALS can achieve significant performance improvements over ALS, particularly for large datasets, due to its incorporation of stochastic gradient descent and parallelization techniques. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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Medicare fraud detection: a comparative study on the effectiveness of one-class and binary classification models

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International Journal of computers and Applications 2025年第3期47卷 323-328页

作者： Leevy, Joffrey L. Hancock, John Khoshgoftaar, Taghi M. Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL United States

Research into Medicare fraud detection that utilizes machine learning methodologies is of great national interest due to the significant fiscal ramifications of this type of fraud. Our big data analysis pertains to three Medicare datasets: Part B, Part D, and DMEPOS. In this paper, One-Class Classification (OCC) and binary classification approaches are evaluated with six different classifiers. We note that when a practitioner is faced with a high cost or unreasonable wait associated with acquiring a second label for binary classification, OCC may intuitively seem like the better alternative. The evaluative metrics used in our study are Area Under the Receiver Operating Characteristic Curve (AUC) and Area Under the Precision-Recall Curve (AUPRC). As a result of our investigation, binary classification was found to be more effective than OCC in detecting Medicare fraud. Additionally, we determined that gradient-boosting algorithms, specifically CatBoost and XGBoost, yield the most robust performance. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Contrastive Learning

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Optimized Distribution Systems Reconfiguration Considering Protection Devices Constraints

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IEEE Transactions on Power Delivery 2025年第3期40卷 1267-1278页

作者： Kahnamouei, Ali Shakeri Lotfifard, Saeed Rostami, Mohammadali Washington State University School of Electrical Engineering and Computer Science PullmanWA99164 United States

This paper presents a method for the optimized reconfiguration of radial distribution systems that explicitly considers the protection systems constraints. A fully automated method based on graph analysis is proposed to automatically identify the primary-backup relation between each pair of protection devices in different network topologies. Accordingly, protection systems constraints are derived automatically and incorporated into the optimization problem. The linearized and convexified nonlinear power flow equations, along with protection system coordination constraints, are solved using the second-order cone programming (SOCP) that assures the feasibility of the results while accelerating the convergence rate. The effectiveness of the proposed methodology is demonstrated through the simulation of the 33-node IEEE test system. © 1986-2012 IEEE.

关键词： Second-order cone programming

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More than just a name?From magnetic to optical skyrmions and the topology of light

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Light(science & Applications) 2025年第1期14卷 26-29页

作者： Jian Chen Andrew Forbes Cheng-Wei Qiu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore School of Physics University of the WitwatersrandJohannesburgSouth Africa

Topology is usually perceived intrinsically immutable for a given *** argue that optical topologies do not immediately enjoy such ***'optical skyrmions'as an example,we show that they will exhibit varying textures and topological invariants(skyrmion numbers),depending on how to construct the skyrmion vector when projecting from real to parameter *** demonstrate the fragility of optical skyrmions under a ubiquitous scenario-simple reflection off an optical *** topology is not without benefit,but it must not be assumed.

关键词： numbers space. topology

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Unsupervised anomaly detection and localization via bidirectional knowledge distillation

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Neural Computing and Applications 2024年第29期36卷 18499-18514页

作者： Wang, Xiaoming Wang, Yongxiong Pan, Zhiqun Wang, Guangpeng School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Knowledge distillation has demonstrated significant potential in addressing the challenge of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher–student (T-S) model provides evidence for anomaly detection and localization. However, the teacher model is pretrained for classification, while the anomaly scores in the distillation-based anomaly detection method are indirectly derived from the classification scores. The mismatch between the two tasks can hinder the optimization of the model. To tackle this issue, we propose an innovative bidirectional knowledge distillation model. In this approach, forward knowledge distillation is pivotal in bolstering the model’s capacity for generalization. Simultaneously, backward knowledge distillation promotes diversity in representing anomalies. This reciprocal knowledge exchange effectively wards off potential performance declines due to target inconsistency. Through bidirectional knowledge distillation, we establish a more encompassing and resilient framework for knowledge transfer. Additionally, we introduce a novel data augmentation strategy to simulate anomalies and effectively eliminate unnecessary noise. In experiments on the MVTec AD, the proposed model achieves competitive results compared to state-of-the-art methods, 97.47% on image-level AUC, 98.23% on pixel-level AUC, and 94.77% on instance-level PRO. These results demonstrate the practicality of our approach in anomaly detection and localization. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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A Review of Anonymization Algorithms and Methods in Big Data

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Annals of Data science 2025年第1期12卷 253-279页

作者： Shamsinejad, Elham Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this study, we examined various data anonymization methods, including differential privacy, advanced encryption, and strong access controls. In addition, the operation, advantages, disadvantages, and use of these methods, the challenges of adapting these methods to big data, and possible solutions for them were also examined. Our results show that traditional data anonymization methods lack scalability, leading to privacy breaches and data loss. When faced with large volumes of data, these methods may not be able to fully process the data. Also, these methods may be ineffective against re-identification attacks, linkage attacks, and inference attacks. We introduced emerging methods that are capable of providing improved privacy with minimal data loss. These methods have scalability for big data. Finally, we examined future research works and raised important questions that can help improve existing algorithms or develop new methods, better manage the complexity and scale of unstructured data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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