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Articles 901 - 930 of 1335
Full-Text Articles in Computer Engineering
Decomposition Lstm With Dual Multi-Head Self-Attention For Wind Turbine Drivetrain State Forecasting, Haikun Jia, Huini Sun, Shuang Bai
Decomposition Lstm With Dual Multi-Head Self-Attention For Wind Turbine Drivetrain State Forecasting, Haikun Jia, Huini Sun, Shuang Bai
Turkish Journal of Electrical Engineering and Computer Sciences
Due to the clean and renewable nature of wind energy, accurate prediction of rotor loads and operating states for wind turbine units has become of paramount importance. Currently, traditional methods relying on expert analysis combined with instrument testing for qualitative reasoning are both time-consuming and labor-intensive, and their accuracy guarantees are limited. In response to wind farm data entailing the interweaving of data from multiple sources and the diverse interrelations across various features and time steps, this study introduces a method for predicting rotor loads and operating states. Initially, we employ an iterative multi-scale seasonal-trend decomposition block to capture latent …
Fuzzy-Virtual Inertia Control To Improve The Frequency Response Of Multi-Area Power Systems, Nourelhouda Djaraf, Yacine Daili, Abderrahim Zemmit, Abdelghani Harrag
Fuzzy-Virtual Inertia Control To Improve The Frequency Response Of Multi-Area Power Systems, Nourelhouda Djaraf, Yacine Daili, Abderrahim Zemmit, Abdelghani Harrag
Turkish Journal of Electrical Engineering and Computer Sciences
Virtual inertia control (VIC) is essential for power systems dominated by electronic devices to compensate for the lack of inertia and ensure frequency regulation. However, most existing VICs often focus solely on optimizing the virtual inertia parameter to adapt to the high penetration of renewable energy sources (RESs) without considering the damping factor. This oversight can lead to significant fluctuations and power mismatches, especially in interconnected systems where the coordination between MGs is sensitive and essential, and there is a risk of propagation of deviations between MGs, which makes the control more complex. To address these issues, this paper presents …
Multikernel Embedded Fusion Unet (Mkef-Unet): A Robust Deep Learning Approach For Accurate Segmentation Of Chagas Parasites, Preet Kumar, Carlos Brito-Loeza, Lavdie Rada
Multikernel Embedded Fusion Unet (Mkef-Unet): A Robust Deep Learning Approach For Accurate Segmentation Of Chagas Parasites, Preet Kumar, Carlos Brito-Loeza, Lavdie Rada
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces a novel approach for segmenting Chagas parasites on stained blood smear samples from mice during the acute phase of infection with Trypanosoma cruzi utilizing a U-Net-based deep learning model named multikernel embedded fusion UNet (MKEF-UNet). Our proposed model incorporates DenseNet-121 for feature extraction, a classifier module for predicting parasite information, and a segmentation decoder with multiscale feature fusion to generate precise segmentation results. Notably, the integration of the embedded vector module, multikernel convolutions with dilations, and advanced data augmentation techniques significantly enhance the model’s robustness and generalization capabilities. In extensive experiments on the Chagas dataset, MKEF-UNet achieves …
Enhancing Spatial-Temporal Video Prediction With Ts-Vq-Vae: A Novel Encoder-Processor-Decoder Approach, Mei Feng, Fan Li
Enhancing Spatial-Temporal Video Prediction With Ts-Vq-Vae: A Novel Encoder-Processor-Decoder Approach, Mei Feng, Fan Li
Turkish Journal of Electrical Engineering and Computer Sciences
Video prediction is a significant and actively researched area within the data science community. Its primary objective is to generate future video frames based on historical frames, finding applications in diverse domains such as human motion prediction, climate change analysis, and traffic flow forecasting. Traditional methods combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex correlations in spatial-temporal signals. Recent methods improve video prediction accuracy by introducing external information such as optical flow, semantic maps, and human pose data. However, these methods have limitations, such as not fully exploring the intermediate states of learning representations, overlooking …
Enhancement Of Hardware-In-Loop Simulation Ability For Homing Guidance Through Adaptive Field-Of-View Method, Shizheng Wan, Yu Cheng, Xu Zhang, Xuwei Fan
Enhancement Of Hardware-In-Loop Simulation Ability For Homing Guidance Through Adaptive Field-Of-View Method, Shizheng Wan, Yu Cheng, Xu Zhang, Xuwei Fan
Journal of System Simulation
Abstract: Considering homing guidance test in the hardware-in-loop simulation, commands of flight simulator and antenna array are likely to exceed their ranges when the target vehicle maneuvers with a large cross range. To solve this problem, the adaptive field-of-view method is proposed to enhance simulation ability in laboratory. Inflight aircraft attitudes and missile-target line-of-sight angles are chosen as state parameters, and the optimal performance function can be established with maximum servo angle of both flight simulator and antenna array. Gradient descent algorithm is applied to acquire the optimal bias angles between the laboratory coordinate system and the launch inertial coordinate …
Economic Optimal Scheduling Of Microgrid Considering Elastic Recovery, Jianghong Chen, Kanghao Shi, Jiahui Hu, Xiaohan Zhao
Economic Optimal Scheduling Of Microgrid Considering Elastic Recovery, Jianghong Chen, Kanghao Shi, Jiahui Hu, Xiaohan Zhao
Journal of System Simulation
Abstract: To enhance the ability of microgrids (MGs) to withstand extreme disaster events, this paper proposes a multi-objective scheduling model considering resilience restoration and economic performance, based on the traditional concepts of power system resilience and reliability. Resilience is specifically quantified. The model integrates energy storage into the objective function and includes reliability indicators as constraints, building on traditional microgrid economic dispatch. The optimization problem is solved using an improved white shark optimizer (WSO) and multi-objective fuzzy programming, where different weights are assigned to each objective function, and the optimal weights are determined through case studies. A microgrid scheduling scheme …
Electric Vehicle Dispatching Strategy And Incentive Evaluation Based On Virtual Energy Storage, Shuo Chen, Hao Hu, Huimin Fang, Haiwei Wang, Xiaolong Chen, Chengcheng Mei, JiaʹNan Zhu, Qian Ai
Electric Vehicle Dispatching Strategy And Incentive Evaluation Based On Virtual Energy Storage, Shuo Chen, Hao Hu, Huimin Fang, Haiwei Wang, Xiaolong Chen, Chengcheng Mei, JiaʹNan Zhu, Qian Ai
Journal of System Simulation
Abstract: To address the multifaceted challenges arising from the widespread integration of electric vehicles into the power grid, harnessing the dispatchability features of electric vehicles becomes imperative. This paper based on a virtual energy storage aggregation model, optimizes the charging scheduling of electric vehicles and assesses their charging incentives through a composite weighting methodology. It establishes a framework for the participation of flexible loads in distribution network scheduling, formulates a second-order cone relaxation optimal power flow model, and develops dispatch strategies. By quantifying the contribution of electric vehicles concerning their flexibility and system stability, and simulating user charging preferences using …
Research On Robot Path Planning Based On Improved Harris Hawks Algorithm, Yuxin Bai, Zhenya Chen, Ruitao Shi, Weitao Su, Zhuoqiang Ma, Shangjin Yang
Research On Robot Path Planning Based On Improved Harris Hawks Algorithm, Yuxin Bai, Zhenya Chen, Ruitao Shi, Weitao Su, Zhuoqiang Ma, Shangjin Yang
Journal of System Simulation
Abstract: In order to improve the convergence accuracy of the HHO algorithm, this paper proposes a GSHHO(gold sine harris hawks optimization) algorithm based on multi-strategies. An infinite iterative chaotic map is used to initialize the population, and an elite reverse learning strategy is used to improve population quality; A convergence factor adjustment strategy is used to recalculate prey energy, balancing the global exploration and local development capabilities of the algorithm; In the development phase of Harris Eagle, the golden sine strategy was introduced to replace the original position update method and improve the local development ability of the algorithm; Experiments …
Fine-Grained Traffic Flow Inference Model Based On Dynamic Back Projection Network, Ming Xu, Guangyao Qi, Geqi Qi
Fine-Grained Traffic Flow Inference Model Based On Dynamic Back Projection Network, Ming Xu, Guangyao Qi, Geqi Qi
Journal of System Simulation
Abstract: To solve the problem of large errors in the inference results of existing fine-grained urban flow inference models in complex traffic areas, a fine-grained traffic flow inference model based on dynamic back-projection network is proposed. The multi-dimensional interaction between the input coarse-grained traffic flow and external factors is calculated, and the interaction results are dynamically and adaptively fused with the coarse-grained traffic flow, so that the features can interact and adjust each other to assist model reasoning. Combining deep convolution and self-attention mechanism to learn local information and global information, and improve the understanding of input data by subsequent …
Research On Transformer Fault Diagnosis Method Based On Digital Twin, Lun Jiang, Dajiang Wang, Wenlei Sun, Shenghui Bao, Han Liu, Saike Chang
Research On Transformer Fault Diagnosis Method Based On Digital Twin, Lun Jiang, Dajiang Wang, Wenlei Sun, Shenghui Bao, Han Liu, Saike Chang
Journal of System Simulation
Abstract: Aiming at the inability of existing intelligent algorithms for transformer fault diagnosis to quickly and efficiently identify transformer faults, resulting in fault misdetection and untimely detection, this paper proposes a transformer fault diagnosis method using the improved sparrow optimization algorithm to optimize the two-layer fault diagnostic model of XGBoost combined with the digital twin technology. The method adopts advanced sensors to collect oil and gas data and temperature data of the transformer, uses 5G module to transmit the real-time data to the digital twin system. The system monitors the temperature data in real-time by setting the equipment alarm threshold; …
Vibration Simulation And Multivariate Statistical Analysis Method Of Composite Structures, Bo Guo, Ming Tie, Wenhui Fan
Vibration Simulation And Multivariate Statistical Analysis Method Of Composite Structures, Bo Guo, Ming Tie, Wenhui Fan
Journal of System Simulation
Abstract: To investigate the natural frequency characteristics of composite laminates under parametric uncertainties and the different degree of influence of these parameters on the natural frequency under different boundary conditions and different vibration orders, a two-dimensional anisotropic medium-thick plate material model and a three-dimensional anisotropic cylindrical thin-shell material vibration model are established. Aiming at the uncertainty of structural parameters of these composite materials, the composite material vibration simulation and multivariate statistical analysis software are developed to simulate the structural vibration of composite materials. A multivariate statistical analysis method for natural frequency uncertainty of composite materials is presented. Through principal component …
An Intelligent Ambulance Regulation Model Based On Online Reinforcement Learning Algorithm, Lei Zhang, Xuechao Zhang, Chao Wang, Xianglei Bo
An Intelligent Ambulance Regulation Model Based On Online Reinforcement Learning Algorithm, Lei Zhang, Xuechao Zhang, Chao Wang, Xianglei Bo
Journal of System Simulation
Abstract: In emergency scenarios where ambulances are used to evacuate casualties, it is necessary to fully coordinate the rescue capability of the ambulance with the real-time status of the casualties in the scenario to achieve the best rescue results. Such problems are generally non-deterministic polynomial problems, and the traditional deterministic scheduling algorithms are less effective. This paper aimed at the modeling research of the real-time regulation of ambulances in emergency scenarios, an online reinforcement learning DNQ algorithm frameworks based on the data enhancement method is proposed and applied to the solution of the ambulances control model. To solve the problems …
Research On Pedestrian Avoidance Strategy For Agv Based On Deep Reinforcement Learning, He Wang, Jianing Xu, Guangyu Yan
Research On Pedestrian Avoidance Strategy For Agv Based On Deep Reinforcement Learning, He Wang, Jianing Xu, Guangyu Yan
Journal of System Simulation
Abstract: To ensure the safety and comfort of pedestrians during Automated Guided Vehicle (AGV) obstacle avoidance in smart factory environments, a deep reinforcement learning-based end-to-end obstacle avoidance method is proposed. The YOLOv8 module is introduced to extract pedestrian pose information, and a visual-based state space is designed. A reinforcement learning mechanism is formulated based on personal space theory, penalizing AGV behaviors such as entering pedestrian comfort space and collisions. A virtual simulation system is constructed, utilizing PPO algorithm along with LSTM network layer for obstacle avoidance strategy training and simulation experiments. Simulation results indicate that this obstacle avoidance strategy, under …
City Regional Traffic Flow Prediction Based On Spatiotemporal Multi-View Attention Residual Network, Jing Chen, Guowei Yang, Zhaochong Zhang, Wei Wang
City Regional Traffic Flow Prediction Based On Spatiotemporal Multi-View Attention Residual Network, Jing Chen, Guowei Yang, Zhaochong Zhang, Wei Wang
Journal of System Simulation
Abstract: However, efficiently and comprehensively capturing the complex spatiotemporal correlations within urban traffic flow presents a key challenge. Existing research methods struggle to fully capture these spatiotemporal dependencies. To address these issues, we propose a novel end-to-end deep learning framework called the spatiotemporal multi-view attention residual network (ST-MVAR) for predicting traffic flow in urban areas. we integrate the proximity, periodicity, trend, and external factors of traffic flow as inputs to the network. This network employs skip connections to form a multi-layer nested residual network structure. Additionally, we design a Multi-View Extension module to capture spatial dependencies of traffic flow at …
Parallel Task Transmission And Processing Optimization Scheme For Uav-Assisted Internet Of Vehicles, Chao Yang, Ruiqun Zheng, Zhen Li, Hongwei Zhang, Yanqun Tang, Dongze Li
Parallel Task Transmission And Processing Optimization Scheme For Uav-Assisted Internet Of Vehicles, Chao Yang, Ruiqun Zheng, Zhen Li, Hongwei Zhang, Yanqun Tang, Dongze Li
Journal of System Simulation
Abstract: To address the increasing of computation demands of internet of vehicles (IoV) users due to the sudden traffic congestion, unmanned aerial vehicles (UAVs) are introduced to the intelligent transportation systems (ITS) to construct an UAV-assisted IoV network. The UAV limited energy and computing resources lead to the current traditional UAV coverage strategy with one by one less efficiency. We propose a parallel task transmission and processing optimization strategy, considering the line-of-sight communication links and fast moving characteristics of UAV. After receiving the tasks from vehicles in the service point, UAV can fly to the next point and perform task …
Three-Way Decision Clustering Algorithm Fusion Of Mutant Fireflies Algorithm, Zhaobin Li, Jun Ye, Haoyan Zhou, Yixin Wang, Yuzhen Han
Three-Way Decision Clustering Algorithm Fusion Of Mutant Fireflies Algorithm, Zhaobin Li, Jun Ye, Haoyan Zhou, Yixin Wang, Yuzhen Han
Journal of System Simulation
Abstract: To address problems such as the premature phenomenon in the three-way clustering algorithm caused by the random selection of initial cluster centers and the need for repeated experiments to determine the value of q in the q-nearest neighbor concept, a three-way clustering algorithm optimized by a variant of the firefly algorithm is proposed. The firefly algorithm is employed to solve the problem of sensitivity to initial cluster centers. The target function value is taken as the brightness intensity of firefly to search the clustering center point, and the optimal solution is taken as the clustering center of the algorithm …
Research On Improved A* Algorithm Path Planning Based On Global Key Point Extraction, Guijuan Lin, Zihan Li, Yu Wang
Research On Improved A* Algorithm Path Planning Based On Global Key Point Extraction, Guijuan Lin, Zihan Li, Yu Wang
Journal of System Simulation
Abstract: To address the limitations of the traditional A* algorithm in large and complex scenes, including traversing a large number of nodes, long computation times, and susceptibility to U-shaped traps, this paper proposes an improved A* algorithm incorporating the jump point search (JPS) concept and image processing techniques to extract key points from the global map. The proposed method preprocesses the global map to identify corner points located one grid diagonally from obstacles, constructs a key point list, and replaces the nodes traditionally traversed by the A* algorithm with these global key points, significantly reducing computational overhead. The neighbor nodes …
Real-Time Nonlinear Economic Model Predictive Control Of Wind Energy Conversion System, Wenwen Wang, Xiangjie Liu, Xiaobing Kong
Real-Time Nonlinear Economic Model Predictive Control Of Wind Energy Conversion System, Wenwen Wang, Xiangjie Liu, Xiaobing Kong
Journal of System Simulation
Abstract: To address the new challenges of economic control and real-time requirements in wind energy conversion systems (WECS), this study proposes a nonlinear economic model predictive control (NEMPC) strategy. This strategy aims to maximize power generation and while reducing fatigue loads on critical structures, such as towers and gearboxes. Additionally, a moving horizon estimator (MHE) has been designed to provide an effective initialization for optimization. By exploiting the similarity of nonlinear programs between adjacent sampling moments, the algorithm achieves real-time iterative (RTI) solutions. Using a 5 MW wind turbine as the research object, the proposed strategy is implemented in the …
Tohf: A Feature Extractor For Resource-Constrained Indoor Vslam, Ruoqing Li, Yaochi Zhao, Zhuhua Hu, Wenlu Qi, Guangfeng Liu
Tohf: A Feature Extractor For Resource-Constrained Indoor Vslam, Ruoqing Li, Yaochi Zhao, Zhuhua Hu, Wenlu Qi, Guangfeng Liu
Journal of System Simulation
Abstract: To address the issues of sensitivity to texture and lighting variations, excessive local dependence caused by feature point redundancy, and storage overhead under hardware resource constraints in existing VSLAM feature extractors in indoor environments, We propose the Texture- Oriented and Homogenized FAST Feature Extractor (TOHF), which integrates HVS (Human Visual System) for enhanced texture analysis. TOHF employs a two-stage thresholding strategy and dynamically adjusts feature point distribution, balancing computational efficiency and storage needs. We conducted experimental verification based on the ORB-SLAM3 framework on dataset from resource-limited device and the EuRoc dataset, focusing on matching rate, reprojection error, absolute trajectory …
Design And Verification Of Display And Control System Based On Mbse And Vaps For Civil Helicopter, Xi Cao, Bo Liu, Bingzhi Su, Tao Nie
Design And Verification Of Display And Control System Based On Mbse And Vaps For Civil Helicopter, Xi Cao, Bo Liu, Bingzhi Su, Tao Nie
Journal of System Simulation
Abstract: Aiming at the challenges of difficulties in tracing requirements, detecting interaction design defects, and achieving early system design verification, this paper proposes a design and verification for the display and control system (DCS) of civil helicopters based on model-based systems engineering (MBSE) and VAPS. The method begins with capturing stakeholder requirements to form system requirements, followed by the allocation of these requirements to system use cases. Black-box activity diagrams and sequence diagrams are constructed to conduct "requirement-function analysis" from the top down, describing the functional flow of the DCS. A running black-box statechart diagram is further established to verify …
Cae Simulation Optimization Method Based On Dynamic Coupling Model, Xue Chen, Jianwen Cao
Cae Simulation Optimization Method Based On Dynamic Coupling Model, Xue Chen, Jianwen Cao
Journal of System Simulation
Abstract: In order to solve the optimization problem of designing complex equipment under multi-factor coupling scene, a CAE simulation optimization method based on dynamic coupling model and multibranch parallel inference strategy is proposed. The dynamic hierarchical DEVS model is used to construct the automatic coupling model from pre-processing, numerical solution and post-processing phases of CAE software adaptively. Aiming at the key parameters of CAE model, multi-branch instance models with multi-factor constraints are constructed based on greedy algorithm. The multi-task parallel inference strategy is used to compute the multi-branch simulation results efficiently. The scheme optimization is realized based on the evaluation …
Visual Slam Algorithm Based On Feature Point Selection In Dynamic Scenes, Limei Jiang, Xinwei Chen
Visual Slam Algorithm Based On Feature Point Selection In Dynamic Scenes, Limei Jiang, Xinwei Chen
Journal of System Simulation
Abstract: To address low positioning accuracy and robustness in traditional visual SLAM algorithms under dynamic conditions, this paper proposes an improved dynamic SLAM algorithm based on feature point selection. Built upon the ORB-SLAM3 framework, it incorporates dynamic region partitioning and feature point filtering. The dynamic region partitioning module utilizes an enhanced RT-DETR object detection algorithm to detect dynamic objects in the images and divides the dynamic regions based on the detection boxes. The feature point selection module utilizes epipolar constraints and optical flow methods to filter out feature points on moving objects, retaining stationary dynamic objects and background points within …
Reinforcement Learning Modeling Of Missile Penetration Decision Based On Combat Simulation, Bin Zhang, Yonglin Lei, Qun Li, Yuan Gao, Yong Chen, Jiajun Zhu, Chenlong Bao
Reinforcement Learning Modeling Of Missile Penetration Decision Based On Combat Simulation, Bin Zhang, Yonglin Lei, Qun Li, Yuan Gao, Yong Chen, Jiajun Zhu, Chenlong Bao
Journal of System Simulation
Abstract: Penetration capability is a primary measure of missile systems. In response to the shortcomings of traditional knowledge-based decision-making methods that are difficult to adaptively evolve, an intelligent penetration decision-making based on combat simulation and DRL is proposed. A missile intelligent decision-making training environment is constructed based on the WESS system. Taking missile maneuver penetration decision-making as an example, a maneuver penetration decisionmaking network model is designed and trained based on the SAC-discrete algorithm and the test of intelligence is conducted. Experimental results show that the intelligent decision model derived from machine learning has a better combat outcome than traditional …
Research On Air Target Threat Assessment Technology Based On Deep Learning, Dawei Jiang, Yangyang Dong, Lidong Zhang, Xiao Lu, Chunxi Dong
Research On Air Target Threat Assessment Technology Based On Deep Learning, Dawei Jiang, Yangyang Dong, Lidong Zhang, Xiao Lu, Chunxi Dong
Journal of System Simulation
Abstract: In order to realize the effective assessment of air combat targets, a deep learning-based air target threat assessment method is proposed. According to threat characteristics of the air target, the threat attributes of air target faced by electronic countermeasure operation are analyzed from the two perspectives of platform layer and equipment layer, the air target threat assessment index system is constructed, and the air target threat assessment index data set is established. Based on convolutional neural network, a residual structure is introduced to optimize the network, a threat assessment model is established, and the threat ranking of air targets …
Multi-Strategy Hybrid Mountain Gazelle Optimizer For Robot Path Planning, Xu Jin, Yuanbin Mo
Multi-Strategy Hybrid Mountain Gazelle Optimizer For Robot Path Planning, Xu Jin, Yuanbin Mo
Journal of System Simulation
Abstract: Aiming at the problems of local optimum and premature convergence in the design of optimization path of robot navigation system, a multi-strategy hybrid MGO(HMGO) improved algorithm based on the mountain gazelle optimizer(MGO) is proposed. The algorithm uses the quasi-reverse learning strategy to optimize the population initialization ensuring its diversity, introduces the dynamic adaptive density factor to adjust the parameters of the optimization mechanism, and integrates arithmetic optimization and sine-cosine strategies for random perturbations. Through ablation experiments, 13 benchmark test functions, and simulation experiments on the solution of two-dimensional and threedimensional space robot path planning problems, the results demonstrate that …
Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty
Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty
Faculty Publications
To build AI systems capable of decision-support assistance, such as AI-assisted healthcare, it is essential to develop user-centric decision-making processes that are robust, interpretable, and capable of effectively processing and acting on natural language interactions. Instruction-based prompting of large language models has demonstrated considerable success in supporting humans with information assistance tasks, including creative writing and content generation. However, recent studies reveal that language models exhibit limitations in performing complex reasoning and planning tasks, such as constructing compositional or hierarchical plans involving multiple reasoning steps. To address these challenges, we propose a neurosymbolic framework that integrates large language models with …
Msbzip55 Regulates Salinity Tolerance By Modulating Melatonin Biosynthesis In Alfalfa, Tingting Wang, Jiaqi Yang, Jiamin Cao, Qi Zhang, Huayue Liu, Peng Li, Yizhi Huang, Wenwu Qian, Xiaojing Bi, Hui Wang, Yunwei Zhang
Msbzip55 Regulates Salinity Tolerance By Modulating Melatonin Biosynthesis In Alfalfa, Tingting Wang, Jiaqi Yang, Jiamin Cao, Qi Zhang, Huayue Liu, Peng Li, Yizhi Huang, Wenwu Qian, Xiaojing Bi, Hui Wang, Yunwei Zhang
Faculty Publications
No abstract provided.
Design And Realization Of Concurrent Cryptosystem For Medical Image Privacy On Reconfigurable Hardware, Vinoth Raj R
Design And Realization Of Concurrent Cryptosystem For Medical Image Privacy On Reconfigurable Hardware, Vinoth Raj R
Theses and Dissertations
The protection of medical image privacy plays a crucial role in maintaining confidentiality for the secure storage and transmission of patient’s sensitive healthcare data. Medical images are the widely used data type in the e-healthcare sector. Traditional cryptographic algorithms have limitations when applied to large-scale medical image datasets due to their high computational requirements. The primary goal of this research work is to design and implement indigenous algorithms to provide confidentiality for grayscale and color DICOM (Digital Imaging and Communications in Medicine) images through an encryption process. The research leverages the benefits of reconfigurable hardware, namely the Field-Programmable Gate Arrays …
Emg-Based Intraoperative Neuromonitoring Using Advanced Machine Learning Approaches, Abdalla Nabil Elsharkawy, Nourhan Zayed
Emg-Based Intraoperative Neuromonitoring Using Advanced Machine Learning Approaches, Abdalla Nabil Elsharkawy, Nourhan Zayed
Mechanical Engineering
Intraoperative neuromonitoring (IONM) plays a critical role in minimizing nerve damage during surgeries by providing real-time feedback on neural integrity. This study evaluated models associated with deep learning and machine learning models for electromyography classification of signal during intraoperative neuromonitoring (IONM). The CNNLSTM model achieved the highest accuracy (85.2%), outperforming traditional models like KNN (53%), RF (62%), and CNN (76%). This demonstrates the degree to which the CNN-LSTM model can gain insight into temporal and spatial dependencies throughout the EMG signals, which makes it optimal for real-time classification in IONM applications. This implies that deep learning techniques can improve surgical …
Neurophisology Biosignals Of Cognitive Training Classification In Virtual Reality Environment Using Deep Learning Model, Nourhan Zayed, Mohamed Reda
Neurophisology Biosignals Of Cognitive Training Classification In Virtual Reality Environment Using Deep Learning Model, Nourhan Zayed, Mohamed Reda
Mechanical Engineering
This research investigates the potential of neurophysiological biosignals fusion, such as electroencephalogram (EEG) and Eye Tracking signals (ET), to classify cognitive states during virtual reality (VR) training, specifically for the rehabilitation of neurodegenerative diseases. By analyzing EEG data collected from participants engaged in VR-based cognitive exercises, we aim to identify patterns associated with different cognitive states and develop a robust classification system. A Convolutional Neural Network (CNN) model was developed to predict task performance utilizing neurophysiological inputs in an immersive world. This system could be used to monitor cognitive function, assess treatment efficacy, and provide real-time feedback to adapt the …