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Retinal disorder diagnosis based on hybrid deep learning models

Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Computationally Efficient VBR-Based Sensitivity Analysis of Induction Motors

Computationally Efficient VBR-Based Sensitivity Analysis of ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Koti, Hossein Nejadi Valipoor, Zahra Demerdash, Nabeel A. O. Rockwell Automation Inc. Motion Control Team Mequon United States Marquette University Electrical and Computer Engineering Department Milwaukee United States

ISBN: (纸本)9798350316445

Sensitivity analysis is a powerful tool that can be utilized for reducing the size of the design space at hand to explore and significantly reduce the computational burden of the optimization process. This is achieved by 'smart' design variable selection. The term 'smart' indicates fixing the un-conflicting design variables that do not require exploration to their maximum or minimum values, depending on their enhancing or degrading effect on the objective functions and allowing the conflicting design variables to be explored within their allowable dynamic range. Intelligent optimization is a critical part of induction motor design process. In this paper, computationally efficient sensitivity analysis is performed to effectively reduce the number of the design variables that will be actively explored in the optimization process of the induction motors. This goal is achieved through the VBR-based TS-FEA together with Design of Experiment and Response Surface Methodology. In addition, the motor estimated losses, including the calculated core losses are fed to a thermal network model to estimate the temperature of different motor components. © 2023 IEEE.

关键词： Core Losses Induction Motor Temperature Thermal Calibration VBR-based FEA

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Modeling a Single Camera System based on Predicted Bounding Box and Pan-Tilt Angles 13

Modeling a Single Camera System based on Predicted Bounding ...

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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Putri, Adi Novitarini Maria, Fabiola Hidayat, Egi Mahayana, Dimitri Machbub, Carmadi Institut Teknologi Bandung Control and Computer Research Group School of Electrical Engineering and Informatics Bandung Indonesia Institut Teknologi Sains Bandung Bekasi Indonesia

ISBN: (纸本)9798350340891

The control schemes that are related to visual servoing systems are often done through trial and error. Based on the classical control concept, it is necessary to have a mathematical model of the plant before designing the controller. Therefore, this research focuses on producing a mathematical model of the pan-tilt system. We propose the combination of the computer vision method with the classic system identification method, namely N4SID to obtain the dynamics model of a pan-tilt system. In this research, the system identification procedure was successful in producing a state-space model of a visual servoing system using a pan-tilt system and a single camera. This study merged traditional system identification techniques with computer vision to create a discrete-time linear state-space model. When the system order value equals three, the approximation with the minimum MSE value is obtained. © 2023 IEEE.

关键词： Visual servoing

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Beam-line parameter image processing optimization and maintenance for beam alignment in the National Ignition Facility 7

Beam-line parameter image processing optimization and mainte...

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High Power Lasers for Fusion Research VII 2023

作者： Awwal, Abdul A.S. Leach, Richard Lowe-Webb, Roger Kamm, Vicki Miller Integrated Computer Control System National Ignition Facility Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94551 United States

ISBN: (纸本)9781510659070

The National Ignition Facility (NIF) employs 192 laser beams to achieve inertial confinement fusion by irradiating a mm scale fusion target. Automatic alignment (AA) image processing algorithms are used to align 192 beams to the NIF target chamber center. Cameras placed along the beam path supply the images that are analyzed by AA algorithms to provide beam location and alignment information. NIF has the capability of using beam-specific database parameters. This allows beam line images to be processed using optimized algorithms tailored to individual beam alignment needs. For a given segment of alignment in the NIF beam path, 192 different versions of the algorithm can be run simply by changing data base parameters. This capability is vital to alignment precision in a system as complex and mature as NIF. Since optical components and devices age, laser parameters and beam alignment quality can and do change. Constant beam-by-beam monitoring of alignment performance is needed in order to mitigate any issues caused by such changes. The objective of this work is to evaluate how periodic AA algorithm beam parameter changes might better maintain alignment requirements over time in the NIF facility. We show examples from final optics assembly (FOA) and harmonic generator (THG, SHG) loops. © 2023 SPIE.

关键词： Alignment

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Balancing Human-robot Collaborative Disassembly Line by Using Dingo Optimization Algorithm 4

Balancing Human-robot Collaborative Disassembly Line by Usin...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Guo, Xiwang Zhou, Liangbo Zhou, Mengchu Wang, Weitian Wang, Jiacun Qi, Liang Liaoning Petrochemical University Information And Control Engineering Fushun China Liaoning Petrochemical University Information And Control Fushun China New Jersey Institute Of Technology Electrical And Computer Engineering NewarkNJ United States Montclair State University School Of Computing Montclair United States Monmouth University Computer Science And Software Eng. West Long BranchNJ United States Shandong University Of Sci. And Tech. Computer Intelligent Sci. And Tech. Qingdao China

ISBN: (纸本)9798350315790

Disassembling and recycling scrapped products play important roles in effectively reducing environmental pollution and improving resource sustainability. A multi-product human-robot collaborative disassembly-line-balancing problem (MHDP) arises from doing so and represents an important yet to be solved in the remanufacturing field. This work explores how to use optimization algorithms to schedule tasks between humans and robots to maximize remanufacturing profit in human-robot collaboration contexts. Specifically, we investigate MHDP and develop a mixed integer programming model considering various system constraints. We propose a dingo optimization algorithm that simulates wild dogs' social hierarchy and chasing processes to solve MHDP. We compare the proposed algorithm with an exact solution finder, i.e., IBM CPLEX and a well-known intelligent optimization method, i.e., Genetic Algorithm to show its competitive performance in terms of solution accuracy and efficiency. © 2024 IEEE.

关键词： Genetic algorithms

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Fed-Autoformer: A Federated Learning-Based Battery Anomaly Detection Approach for Electric Vehicle Charging with Privacy Preservation 30th

Fed-Autoformer: A Federated Learning-Based Battery Anomaly D...

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Yan, Xuhan Yang, Shunbo Guo, Yiguo Fu, Yimu Wu, Sixing Li, Jianbin School of Control and Computer Engineering North China Electric Power University Beijing China Economic Technology Research Institute State Grid Shandong Electric Power Company Shandong Jinan City China

ISBN: (纸本)9783031829062

The battery management system ensures the safe operation of electric vehicles (EVs) by detecting abnormalities in the battery energy storage system. However, the extensive and widely dispersed data in EV battery data, along with the presence of users’ private details, raise a demand for effective anomaly detection and privacy protection. To tackle those two challenges, we propose Fed-Autoformer, a distributed anomaly detection architecture based on real vehicle charging data to detect battery anomalies and protect privacy. Thus, we first propose a novel anomaly detection model for EV charging data based on the Autoformer encoder architecture. In particular, it enables an autocorrelation attention mechanism to extract and consider the intrinsic correlation of battery data features (e.g., voltage, current, state of charge (SOC), etc.) for anomaly detection. In addition, we introduce a federated learning architecture to protect the privacy of battery charging data and improve the federated learning training process based on the differences between vehicle batteries in charging stations to ensure detection accuracy while protecting the privacy of EV charging data. Finally, we comprehensively analyze the feasibility and effectiveness of our proposed architecture using a real EV charging dataset. Our proposed federated learning approach for EV battery anomaly detection not only effectively protects privacy through federated learning but also improves the accuracy by 3.2% compared to most mainstream time series anomaly detection models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charging stations

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Document-level Event Extraction combined with Writing Style and Argument Role RelationA Document-level Event Extraction model DEEWR 4

Document-level Event Extraction combined with Writing Style ...

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4th International Conference on Artificial Intelligence and computer engineering, ICAICE 2023

作者： Zhao, Jingsheng Yan, Shuai Ni, Qihui Wang, Yongzheng Zhu, Qiaoming School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China School of Computer Science and Technology Soochow University Suzhou215006 China

ISBN: (纸本)9798400708831

Document-level event extraction is an important task in natural language processing, which faces the challenges of arguments-scattering and multi-events. The current mainstream method is to decompose document-level event extraction into three subtasks: entity extraction, event type recognition, and argument-filling. However, existing methods flew consider the impact of writing style on event extraction, and have complex models with high training costs. To address these problems, this paper proposes a document-level event extraction model that incorporates both writing style and argument role relation. First, Roberta is used to encode the document sentences to obtain semantic features, and BiLSTM-CRF is used to identify candidate entities. Second, the writing style is modeled as the relative distribution of argument roles in the document and is used to supervise the event type recognition results. Third, argument-filling is simplified to the relation judgment between the argument roles. This method effectively addresses the problem of scattered arguments by constructing event clusters based on the relation of argument roles. It also deals with the multi-event problem through event cluster division. Compared with previous path expansion-based methods, experimental results show that the model has lower training costs and performs better in low training epochs. © 2023 ACM.

关键词： Filling

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Uncertainty-Aware Path Planning with Multiple Constraints for Autonomous Vehicles Based on Nonlinear Reduced-Order Model Predictive control

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IEEE Transactions on Consumer Electronics 2024年

作者： Badiei, Mohammad Hossein Dashtaki, Saeed Mohammadi Vafamand, Navid Piran, Md. Jalil University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran Shiraz University Department of Power and Control Engineering Shiraz Iran Sejong University Department of Computer Science and Engineering Korea Republic of

In this paper, we present a Risk-Aware Nonlinear Reduced-Order Model Predictive control framework that utilizes real-time state estimation, reduced-order modeling, and optimization for reliable and efficient path planning with low energy consumption. Our method employs filtering techniques to estimate the system's state by correcting for process and measurement noise, providing an accurate representation of the vehicle's position and environment. A reduced-order model predicts future states based on the estimated input, while the optimizer adjusts control inputs to meet trajectory and safety constraints, such as avoiding obstacles. By incorporating adapted filtering techniques and optimized cost functions, our approach minimizes computational overhead while maintaining precise control over vehicle navigation. In the experiments, we introduce communication and process noises, allowing the robot to operate without knowing the precise location of obstacles and resulting in potential near-collisions. This involves redefining the constrained optimal control problem for a reduced-order model, addressing input constraints amidst uncertainties. Our method achieves over 94% success in collision-free navigation within the first 50 steps, while maintaining precise trajectory alignment and adapting to environmental changes. It also improves prediction speed up to 15% per cycle compared to NMPC and demonstrates 91% overall effectiveness in avoiding collisions and reaching the target. © 1975-2011 IEEE.

关键词： Cost functions

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FAST FOURIER TRANSFORM ALGORITHM FOR EXTRACTION OF ATTENUATION, PHASE, AND SCATTERING IMAGES IN PHASE-CONTRAST IMAGING

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2023年第4期85卷 137-150页

作者： Ciobanu, Ionuț-Cristian Safca, Nicoleta Anghel, Elena Rășinar, Cezara Popescu, Dan Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania Extreme Light Infrastructure – Nuclear Physics Magurele Romania University POLITEHNICA of Bucharest Bucharest Romania Faculty of Chemical Engineering and Biotechnologies University POLITEHNICA of Bucharest Romania

Phase contrast imaging is based on the refraction of the X-ray beam as it passes through the material and shows excellent results for biological tissues. In this paper, we present a new approach for a Python algorithm that can filtrate phase contrast images (attenuation, phase, and scattering) and improve the identification of the interest object. The images used in this work were obtained with a 5.66 m long Talbot-Lau interferometer with a record angular sensitivity of 0.82 µradians, a phase stepping procedure of 16 steps, and a mean energy of 30 keV. The sample used was 0.89 mm diameter fiber from an accredited mammographic phantom. The proposed algorithm extracts the attenuation, phase, and scattering images from the dataset using Fast Fourier Transform. For filtration, we implemented a non-linear filter (median) to eliminate the noise generated by the acquisition system and the projection processing and to improve the contrast. The results were analyzed by calculation of the contrast-to-noise ratio (CNR). © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Fast Fourier transforms

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An Attention-ReXNet Network for Long Tail Road Scene Classification 13

An Attention-ReXNet Network for Long Tail Road Scene Classif...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Guo, Yuan H. Zhu, Jun R. Yang, Chang C. Yang, B. School of Computer Science and Artificial Intelligence Changzhou University Changzhou213000 China School of Microelectronics and Control Engineering Changzhou University Changzhou213000 China

ISBN: (纸本)9798350315196

With the development of assisted driving and autonomous driving, road scene classification has become increasingly important due to its ability to provide environmental information for drivers and autonomous driving, which is beneficial to reduce traffic accidents. However, road scene classification is limited by dataset imbalance and running speed, making it difficult to apply practically. To solve the problems mentioned above, we proposed an Attention-ReXNet network to conduct road scene classification. A balance Softmax cross-entropy is introduced to solve the class imbalance problem in road scene classification. The proposed method is evaluated in the Road Surface Classification Dataset (RSCD), which contains 27 categories and 1 million images with class imbalance. Experimental results show that Attention-ReXNet and balance Softmax cross entropy can achieve the best classification performance, with accuracy and top-1 being 87.67% and 88.52%, respectively. © 2023 IEEE.

关键词： Autonomous vehicles

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