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Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

computer Modeling in engineering & Sciences 2024年第7期140卷 409-430页

作者： Zhuoqun Xia Hangyu Hu Wenjing Li Qisheng Jiang Lan Pu Yicong Shu Arun Kumar Sangaiah School of Computer and Communication Engineering Changsha University of Science and TechnologyChangshaChina Hunan Province People’s Hospital(The First Affiliated Hospital of Hunan Normal University) ChangshaChina The First Affiliated Hospital of Guangxi Medical University NanningChina International Graduate Institute of AI National Yunlin University of Science and TechnologyYunlinChina Department of Electrical and Computer Engineering Lebanese American UniversityBayrutLebanon

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible *** research has failed to fully explore effective DR lesion information in fundus ***,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion ***,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis ***,the LLM generates a global attention map based on ***,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion *** paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

关键词： DDR dataset diabetic retinopathy lesion localization multi-level patch attention mechanism

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Predictive World Models from Real-World Partial Observations 1

Predictive World Models from Real-World Partial Observations

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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Karlsson, Robin Carballo, Alexander Fujii, Keisuke Ohtani, Kento Takeda, Kazuya Nagoya University Graduate School of Informatics Nagoya Japan Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Nagoya University Tier Iv Graduate School of Informatics Nagoya Japan

ISBN: (纸本)9798350319958

Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learning such simulations is called predictive world modeling. Recently, reinforcement learning (RL) agents leveraging world models have achieved SOTA performance in game environments. However, understanding how to apply the world modeling approach in complex real-world environments relevant to mobile robots remains an open question. In this paper, we present a framework for learning a probabilistic predictive world model for real-world road environments. We implement the model using a hierarchical VAE (HVAE) capable of predicting a diverse set of fully observed plausible worlds from accumulated sensor observations. While prior HVAE methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only. We experimentally demonstrate accurate spatial structure prediction of deterministic regions achieving 96.21 IoU, and close the gap to perfect prediction by 62 % for stochastic regions using the best prediction. By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications. Code is available at https://***/robin-karlsson0/predictive-world-models. © 2023 IEEE.

关键词： Mobile robots

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Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning

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IEEE Access 2025年 13卷 87672-87692页

作者： Zhang, Kai Lou, Tun-Chieh Wang, Chung-Che Jang, Jyh-Shing Roger Li, Henian Lin, Lang Chang, Norman Yiwu Industrial and Commercial College Zhejiang Yiwu322000 China National Taiwan University Graduate Institute of Network and Multimedia Taipei106 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taipei106 Taiwan University of Florida Department of Electrical and Computer Engineering GainesvilleFL32611 United States Ansys Inc CanonsburgPA15317 United States

This study applies machine learning to side-channel attacks and proposes an iterative transfer learning method for deep learning models. This study leverages the similarity in training patterns across bytes by first training on a single byte and then using the resulting model as a pretrained foundation for the remaining bytes. This approach enables effective model training with smaller amounts of data while reducing the measurement-to-disclosure (MTD, i.e., the minimum number of traces needed for successful key recovery) in the attack phase. With sufficient data, iterative transfer learning reduces MTD from 55 to 54 using MLP and from 125 to 83 using CNN. Even under limited data conditions, it successfully breaks AES-128 while reducing training samples from 13,600 to 2,000, achieving an average MTD of 635, whereas traditional methods fail. Experimental results demonstrate that the iterative transfer learning approach addresses the persistent data scarcity challenge in deep learning, significantly expanding the applicability of deep learning methods in side-channel attack scenarios. © 2013 IEEE.

关键词： Deep learning

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Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting 38

Introducing Spectral Attention for Long-Range Dependency in ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kang, Bong Gyun Lee, Dongjun Kim, HyunGi Chung, DoHyun Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Future Automotive Mobility Seoul National University Korea Republic of

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results. © 2024 Neural information processing systems foundation. All rights reserved.

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Frequency-Voltage-var Function for VFD in Oil and Gas Platform with Wind Power 8

Frequency-Voltage-var Function for VFD in Oil and Gas Platfo...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： De Sousa Medeiros, Kassiane Da Rocha, Lorrana Faria Soares Callegari, João Marcus Brandão, Danilo Iglesias Universidade Federal de Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte Brazil Norwegian University of Science and Technology Department of Electric Energy Trondheim Norway

ISBN: (纸本)9798350321128

Renewable energy resources emerge as a sustainable alternative to augmenting the energy supply of Floating Production Storage and Offloading (FPSO) platforms. However, the increased generation based on converter-interfaced energy decreases the system-equivalent inertia constant, which becomes more susceptible to frequency deviations. This paper proposes and evaluates the performance of the combined frequency-voltage-var control to mitigate frequency variation in a typical FPSO unit with penetration of wind power systems. The control functions are communication-free and embedded in two active front-end variable frequency drives (AFE- VFDs) set on water injection pumps. The FPSO's electrical power system model is developed in MATLAB/Simulink®. Comparative results with the AFE-VFD equipped with volt-var, freq-var, and combined freq-volt-var functions are shown to highlight the merits of the proposed solution. © 2023 IEEE.

关键词： Floating production storage and offloading freq-var control isolated power system power quality volt-var control

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving engineering Design Problems

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computer Modeling in engineering & Sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Role of Consumption Pattern in Optimal Allocation of Distributed Generators in Electric Power and Energy Systems

Role of Consumption Pattern in Optimal Allocation of Distrib...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Mahdavi, Meisam Jurado, Francisco Schmitt, Konrad Chamana, Manohar Bayne, Stephen University of Jaén Linares Department of Electrical Engineering Jaén Spain Texas Tech University Electrical & Computer Engineering Department LubbockTX United States Texas Tech University Renewable Energy Program LubbockTX United States

ISBN: (纸本)9781665490719

The placement of distributed generation (DG) units in power systems is an efficient way for energy loss reduction, especially when the penetration of DG in modern systems is growing due to their impacts on environmental sustainability. On the other hand, load variations and methods of electricity consumption affect energy losses amount. Therefore, power demand variations have an essential role in the determination of energy loss amount and optimal generation of DG. However, considering the variability of load level in the DG allocation problem increases the burden and computational time, and neglecting it causes the energy losses to be calculated inaccurately. Therefore, this paper aims to evaluate the effect of load patterns on renewable DG allocation plans in order to find out the importance of considering load variations in energy loss minimization via DG placement. The analysis has been conducted on 7-, 12-, 16-, 28-, 30-, 33-, 59-, 69-, 70-, 84-, and 119-bus distribution systems by a classic optimization tool named AMPL. © 2023 IEEE.

关键词： Distributed power generation

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Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

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IEEE Access 2024年 12卷 109230-109254页

作者： Khan, Hira Javaid, Nadeem Bashir, Tariq Akbar, Mariam Alrajeh, Nabil Aslam, Sheraz Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Cyprus University of Technology Limassol3036 Cyprus

Cardiovascular Diseases (CVDs) have emerged as a significant physiological condition, being a primary contributor to mortality. Timely and precise diagnosis of heart disease is crucial to safeguard patients from additional harm. Recent studies show that the usage of data driven approaches, such as Deep Learning (DL) and Machine Learning (ML) techniques, in the field of medical science is highly useful in accurately diagnosing heart disease in less time. However, statistical learning and traditional ML approaches require feature engineering to generate robust and effective features from data, which are then used in the prediction models. In the case of large complex data, both processes pose many challenges. Whereas, DL techniques are capable of learning features automatically from the data and are effective at handling large and intricate datasets while outperforming the ML models. This study focuses on the accurate prediction of CVDs, considering the patient’s health and socio-economic conditions while mitigating the challenges presented by imbalanced data. The Adaptive Synthetic Sampling Technique is used for data balancing, while the Point Biserial Correlation Coefficient is used as a feature selection technique. In this study, two DL models, Ensemble based Cardiovascular Disease Detection Network (EnsCVDD-Net) and Blending based Cardiovascular Disease Detection Network (BlCVDD-Net), are proposed for accurate prediction and classification of CVDs. EnsCVDD-Net is made by applying an ensemble technique to LeNet and Gated Recurrent Unit (GRU), and BlCVDD-Net is made by blending LeNet, GRU and Multilayer Perceptron. SHapley Additive exPlanations is used to provide a clear understanding of the influence different factors have on CVD diagnosis. The network’s performance is evaluated on the basis of various performance metrics. The results indicate that the EnsCVDD-Net outperforms all base models with 88% accuracy, 88% F1-score, 91% precision, 85% recall, and 777s execu

关键词： Heart

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Simultaneous Dual-region Functional Imaging in Miniaturized Two-photon Microscopy

Simultaneous Dual-region Functional Imaging in Miniaturized ...

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Optics and the Brain, BRAIN 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Zhang, Zixiao Liu, Shing-Jiuan Mattison, Ben Yang, Weijian Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Biomedical Engineering University of California Davis DavisCA95616 United States Biomedical Engineering Graduate Group University of California Davis DavisCA95616 United States

We demonstrate simultaneous dual-region in-vivo imaging of brain activity in mouse cortex through a miniaturized spatial-multiplexed two-photon microscope platform, which doubles the imaging speed. Neuronal signals fr... 详细信息

ISBN: (纸本)9781957171340

关键词： Brain imaging Femtosecond lasers In vivo imaging Multiphoton microscopy Spatial resolution Ultrafast lasers

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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