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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Modeling of Transformer Differential Protection with Different Electronic Components Using SimInTech Environment

Modeling of Transformer Differential Protection with Differe...

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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Vaytelenok, L. Smolensk branch of National Research University 'Moscow Power Engineering Institute' Department of Electrical Power Systems Smolensk Russia

Mathematical modeling tools are often used to analyze proper functioning, as well as to design relay protection. Currently, most of the electric power facilities are operated by digital security, however, there are still facilities where protection is performed using electromechanical relays. Therefore, it is important to consider the issues of modeling protections performed by various electronic components, including the possibility of analyzing the transition from electromechanical relays to microprocessor-based relays. This paper considers the SimInTech dynamic simulation environment capabilities to implement the model of differential protection of three-winding transformer. Plus, two options of protection are considered: on the basis of electromechanical restraint relay DZT-11 and on the basis of microprocessing protection. The developed models make it possible to visually examine the operation of these protections, as well as to compare protections on different electronic components for the object under consideration. © 2024 IEEE.

关键词： Transformer windings

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Performance-Guaranteed Solutions for Multi-Agent Optimal Coverage Problems using Submodularity, Curvature, and Greedy Algorithms 63

Performance-Guaranteed Solutions for Multi-Agent Optimal Cov...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Welikala, Shirantha Cassandras, Christos G. Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Boston University Division of Systems Engineering and Center for Information and Systems Engineering BrooklineMA02446 United States

ISBN: (纸本)9798350316339

We consider a class of multi-agent optimal coverage problems in which the goal is to determine the optimal placement of a group of agents in a given mission space so that they maximize a coverage objective that represents a blend of individual and collaborative event detection capabilities. This class of problems is extremely challenging due to the non-convex nature of the mission space and of the coverage objective. With this motivation, greedy algorithms are often used as means of getting feasible coverage solutions efficiently. Even though such greedy solutions are suboptimal, the submodularity (diminishing returns) property of the coverage objective can be exploited to provide performance bound guarantees. Moreover, we show that improved performance bound guarantees (beyond the standard (1-1 / e) performance bound) can be established using various curvature measures of the coverage problem. In particular, we provide a brief review of all existing popular applicable curvature measures, including a recent curvature measure that we proposed, and discuss their effectiveness and computational complexity, in the context of optimal coverage problems. We also propose novel computationally efficient techniques to estimate some curvature measures. Finally, we provide several numerical results to support our findings and propose several potential future research directions. © 2024 IEEE.

关键词： Optimal detection

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AlGaN/GaN Split-Electrode Sectorial Sensor Array for Ultra-Low Magnetic Field Detection at 8 μT 22

AlGaN/GaN Split-Electrode Sectorial Sensor Array for Ultra-L...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Xia, Lingxi Liang, Yung C. National University Of Singapore Department Of Electrical And Computer Engineering Singapore Research Institute China

ISBN: (纸本)9784886864352

Low magnetic field sensing has been a long-term focus because of its broad application potential not only in motion devices but also in vivo. In this work, we report the magnetic sensor design and array formation on the AlGaN/GaN platform to extend the detection limit for ultra-low magnetic field sensing. The proposed sensor is based on the split-electrode sectorial Hall-effect mechanism that intrinsically provides a linear magnetic response around zero-field and shows great potential in sensing small magnetic fields. Two approaches are used to extend the detection limit. First, the single high-sensitivity magnetic field sensor has been realized by device geometry optimization. The highest sensitivity of 57.42 %T-1 by the fabricated prototype was obtained in the laboratory. Further, this work also features the first array formation consisting of seven sectorial sensors, which can detect the magnetic field as low as 8 μT, and the sensing response is linear over three decades in field range. © 2023 IEEJ.

关键词： AlGaN/GaN HEMT high sensitivity semiconductor magnetic sensor

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Probabilistic Model Checking of Temporal Interaction Dynamics in the Supreme Court 11th

Probabilistic Model Checking of Temporal Interaction Dynami...

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11th International Symposium on From Data to Models and Back, DataMod 2023

作者： Das, Susmoy Sharma, Arpit Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (纸本)9783031872167

The Supreme Court plays an extremely critical role in ensuring adherence to the rule of law and in strengthening the democracy. Due to this reason, modeling and analysis of small group interactions in the courtroom setting is an important task as it can help in understanding court decision-making. We apply probabilistic model checking for the modeling and analysis of temporal interaction dynamics in the context of the Supreme Court of the United States. We have used the transcripts of the oral arguments of cases from the Supreme Court for constructing a discrete-time Markov reward model (DTMRM). Next, we formulate interesting queries over interaction by using probabilistic computation tree logic (PCTL) and PCTL with rewards and verify them using a probabilistic symbolic model checker (PRISM). Our experimental results show that probabilistic model checking is very effective in identifying trends, hidden patterns, and how justices behave during the trials. These results not only provide valuable feedback to the justices but may also be used by the advocates and law students for finding better ways to present their arguments in the court. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Coil Optimization of a Planar Omnidirectional WPT System

Coil Optimization of a Planar Omnidirectional WPT System

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yu, Xipei Feng, Junjie Zhu, Liyan Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering Blacksburg United States

ISBN: (纸本)9798350376067

Recently, omnidirectional wireless power transfer (WPT) has gained widespread popularity since 2010s especially in consumer electronics application benefiting from ease of integration with furniture. In such applications, angular misalignment occurs frequently. Obviously, it will affect the stable operation of system significantly. However, the coil optimization is not investigated accurately. In other words, the modelling and effects under angular misalignment are not investigated completely. Targeting at this, this article proposes a planar omnidirectional wireless power transfer system with three sets of planar coils. Afterwards, the modelling process is conducted for the planar omnidirectional WPT system under angular misalignment and Ns effect is investigated. Afterwards, the coil optimization is conducted to increase its usability in real applications. Finally in the experiment, three sets of coils are fabricated and the transfer efficiency are measured under orientation and angular misalignment at 6.78MHz. When charging the mobile phone and electronic watch receivers, the overall efficiency is 69%-78% and 55%-65%, considering the orientation and angular misalignment. © 2024 IEEE.

关键词： Inductive power transmission

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Nonlinear Optimal Regulation of SIR Epidemic Model via Finite-Horizon SDRE 26

Nonlinear Optimal Regulation of SIR Epidemic Model via Finit...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Paul, Sudipta Islam, Samiul Naidu, Desineni Subbaram Abu Hanif Pramanik, Md. Rensselaer Polytechnic Institute Department of Electrical Computer & Systems Engineering New York United States Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Bangladesh University of Minnesota Duluth Department of Electrical Engineering United States

ISBN: (纸本)9798350359015

A Susceptible-Infectious-Recovered (SIR) model is a popular and fundamental epidemiological model often used to assess the efficacy of disease prevention and control measures. SIR disease model, with the implementation of various optimal control methods, is investigated to determine the optimal ways to lessen the effects of an infectious disease. In this research work, a non-linear, closed-loop, finite-horizon, optimal regulation with state-dependent Riccati equation (FH-SDRE) is implemented in an epidemic model called SIR model with vaccination to minimize the infected and susceptible population. Given the nonlinear dynamics of the SIR epidemic model, initially, this method converts the nonlinear equations into a state-dependent coefficient (SDC) matrix, which holds the nonlinearities of the dynamical system. After that, SDRE is solved at every small time instant over a specified finite time period. The core idea here is to utilize an approximate analytical approach that transforms the differential Riccati equation (DRE) into a differential Lyapunov equation (DLE) during every time period. Then, the real-time solution of the DLE at each of these time steps will result in the control law required to achieve the desired system performance. The effectiveness of the FH-SDRE regulation for the SIR model is demonstrated by minimizing the infected and susceptible population within a desired 30-day time period. © 2023 IEEE.

关键词： finite-horizon regulation SIR epidemic model state-dependent Riccati equation

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A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

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Journal of Modern Power systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang Li School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems Engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Just wing it: near-optimal estimation of missing mass in a Markovian sequence

The Journal of Machine Learning Research

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The Journal of Machine Learning research 2024年第1期25卷 15100-15142页

作者： Ashwin Pananjady Vidya Muthukumar Andrew Thangaraj Schools of industrial and systems engineering and electrical and computer engineering georgia institute of technology atlanta Schools of Electrical and Computer Engineering and Industrial and Systems Engineering Georgia Institute of Technology Atlanta Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

We study the problem of estimating the stationary mass--also called the unigram mass-- that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem has several applications--for example, estimating the stationary missing mass is critical for accurately smoothing probability estimates in sequence models. While the classical Good-Turing estimator from the 1950s has appealing properties for i.i.d. data, it is known to be biased in the Markovian setting, and other heuristic estimators do not come equipped with guarantees. Operating in the general setting in which the size of the state space may be much larger than the length n of the trajectory, we develop a linear-runtime estimator called Windowed Good-Turing (WINGIT) and show that its risk decays as Õ(Tmix/n), where Tmix denotes the mixing time of the chain in total variation distance. Notably, this rate is independent of the size of the state space and minimax-optimal up to a logarithmic factor in n/Tmix. We also present an upper bound on the variance of the missing mass random variable, which may be of independent interest. We extend our estimator to approximate the stationary mass placed on elements occurring with small frequency in the trajectory. Finally, we demonstrate the efficacy of our estimators both in simulations on canonical chains and on sequences constructed from natural language text.

关键词： missing mass good-turing Markov chains minimax optimal

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