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Two-stage Optimal Scheduling of Community Integrated Energy system Considering Operation Sequences of Hydrogen Energy Storage systems

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Journal of Modern Power systems and Clean Energy 2025年第1期13卷 276-288页

作者： Wei Kong Kai Sun Jinghong Zhao School of Electrical Engineering Naval University of EngineeringWuhanChina Department of Electrical Engineering State Key Laboratory of Power System Operation and ControlTsinghua UniversityBeijingChina

The hydrogen energy storage system(HESS)integrated with renewable energy power generation exhibits low reliability and flexibility under source-load *** address the above issues,a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems(CIESs)with power to hydrogen and heat(P2HH)*** aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for ***,the refined operation model of HESS is established,and its operation model is linearized according to the operation domain of HESS,which simplifies the difficulty in solving the optimization problem under the premise of maintaining high approximate ***,considering the flexible start-stop of alkaline electrolyzer(AEL)and the avoidance of multiple energy conversions,the operation sequences of HESS are ***,a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established,and the model is simulated and verified using the source-load prediction data of typical days in each *** simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14%while maintaining similar operating cost to the day-ahead economic optimal ***,by formulating the operation sequences of HESS,the operating cost of CIES is reduced by up to about 4.4%.

关键词： Community integrated energy system(CIES) hydrogen storage operation sequence two-stage optimal scheduling

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MCHIAO: a modified coronavirus herd immunity-Aquila optimization algorithm based on chaotic behavior for solving engineering problems

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Neural Computing and Applications 2024年第22期36卷 13381-13465页

作者： Selim, Heba Haikal, Amira Y. Labib, Labib M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

This paper proposes a hybrid Modified Coronavirus Herd Immunity Aquila Optimization Algorithm (MCHIAO) that compiles the Enhanced Coronavirus Herd Immunity Optimizer (ECHIO) algorithm and Aquila Optimizer (AO). As one of the competitive human-based optimization algorithms, the Coronavirus Herd Immunity Optimizer (CHIO) exceeds some other biological-inspired algorithms. Compared to other optimization algorithms, CHIO showed good results. However, CHIO gets confined to local optima, and the accuracy of large-scale global optimization problems is decreased. On the other hand, although AO has significant local exploitation capabilities, its global exploration capabilities are insufficient. Subsequently, a novel metaheuristic optimizer, Modified Coronavirus Herd Immunity Aquila Optimizer (MCHIAO), is presented to overcome these restrictions and adapt it to solve feature selection challenges. In this paper, MCHIAO is proposed with three main enhancements to overcome these issues and reach higher optimal results which are cases categorizing, enhancing the new genes’ value equation using the chaotic system as inspired by the chaotic behavior of the coronavirus and generating a new formula to switch between expanded and narrowed exploitation. MCHIAO demonstrates it’s worth contra ten well-known state-of-the-art optimization algorithms (GOA, MFO, MPA, GWO, HHO, SSA, WOA, IAO, NOA, NGO) in addition to AO and CHIO. Friedman average rank and Wilcoxon statistical analysis (p-value) are conducted on all state-of-the-art algorithms testing 23 benchmark functions. Wilcoxon test and Friedman are conducted as well on the 29 CEC2017 functions. Moreover, some statistical tests are conducted on the 10 CEC2019 benchmark functions. Six real-world problems are used to validate the proposed MCHIAO against the same twelve state-of-the-art algorithms. On classical functions, including 24 unimodal and 44 multimodal functions, respectively, the exploitative and explorative behavior of the hybrid

关键词： Coronavirus

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DMDRDF: diabetes mellitus and retinopathy detection framework using artificial intelligence and feature selection

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Soft Computing 2024年第19期28卷 11393-11420页

作者： Balaha, Hossam Magdy El-Gendy, Eman M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Diabetes mellitus is one of the most common diseases affecting patients of different ages. Diabetes can be controlled if diagnosed as early as possible. One of the serious complications of diabetes affecting the retina is diabetic retinopathy. If not diagnosed early, it can lead to blindness. Our purpose is to propose a novel framework, named DMDRDF, for early and accurate diagnosis of diabetes and diabetic retinopathy. The framework consists of two phases, one for diabetes mellitus detection (DMD) and the other for diabetic retinopathy detection (DRD). The novelty of DMD phase is concerned in two contributions. Firstly, a novel feature selection approach called Advanced Aquila Optimizer Feature Selection (A2OFS) is introduced to choose the most promising features for diagnosing diabetes. This approach extracts the required features from the results of laboratory tests while ignoring the useless features. Secondly, a novel classification approach (CA) using five modified machine learning (ML) algorithms is used. This modification of the ML algorithms is proposed to automatically select the parameters of these algorithms using Grid Search (GS) algorithm. The novelty of DRD phase lies in the modification of 7 CNNs using Aquila Optimizer for the classification of diabetic retinopathy. The reported results concerning the DMD datasets shows that AO reports best performance metrics in the feature selection process with the help of modified ML classifiers. The best achieved accuracy is 98.65% with the GS-ERTC model and max-absolute scaling on the "Early Stage Diabetes Risk Prediction Dataset" dataset. Also, from the reported results concerning the DRD datasets, the AOMobileNet is considered a suitable model for this problem as it outperforms the other modified CNN models with accuracy of 95.80% on the "The SUSTech-SYSU dataset" dataset. © The Author(s) 2024.

关键词： Feature Selection

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Employing of machine learning and wearable devices in healthcare system: tasks and challenges

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Neural Computing and Applications 2024年第29期36卷 17829-17849页

作者： Saad, Hend S. Zaki, John F. W. Abdelsalam, Mohamed M. Computers Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Disease outbreaks are nowadays a critical issue despite the development and rapid growth of technology. One of the major challenges facing healthcare professionals and healthcare industries is disease prevention and control by technology. Due to busy work schedules, maintaining a healthy lifestyle has become impossible, so the solution to these previous challenges is the intelligent health monitoring system. Over the past few years, a lot of research has been done on the use of Machine learning (ML) techniques in healthcare applications. With wearable devices, ML greatly helps in tracking human activities and vital signs as well as helping to monitor and diagnose patients’ health so it plays a huge role in elderly care. Research and development of more applications for wearable devices has been underway in the past five years, due to significant technological advances in medical sensors. Despite the widespread use of wearable devices, there is little research on machine learning applications of these devices. This paper presents a review of the different areas of recent ML research for healthcare wearable devices. It also discusses the different challenges facing ML applications on wearable devices. © The Author(s) 2024.

关键词： Machine learning

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IHHO: an improved Harris Hawks optimization algorithm for solving engineering problems

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Neural Computing and Applications 2024年第20期36卷 12185-12298页

作者： Akl, Dalia T. Saafan, Mahmoud M. Haikal, Amira Y. El-Gendy, Eman M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO. © The Author(s) 2024.

关键词： Optimization

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Accurate breast cancer diagnosis strategy (BCDS) based on deep learning techniques

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Neural Computing and Applications 2025年第6期37卷 4617-4650页

作者： Ibrahim, Taghreed S. Saraya, M.S. Saleh, Ahmed I. Rabie, Asmaa H. Computers and Control Department Faculty of Engineering Mansoura University Mansoura Egypt

Breast cancer (BC) survival rates and the patient's quality of life are boosted by early detection and timely therapy. It is the most prominent cancer and the primary trigger for deaths due to cancer in women around the world. As a result, a variety of artificial intelligence-based computer-assisted procedures are being included in the conventional diagnostic workflow. This study proposes an accurate Breast Cancer Diagnosis Strategy (BCDS) based on deep learning techniques. A framework for BCDS will be presented to consolidate and improve BC detection by defining three stages of BCDS: (i) Preprocessing Stage (PS), (ii) Classification Stage (CS), and (iii) Ensemble Voting Stage (EVS). In PS, three preprocessing operations which are image resizing using bilinear interpolation, data augmentation using Conditional- Convolutional Generative Adversarial Network (C-DCGAN) with Adversarial Feedback Loop (AFL) and data enhancement using Multiscale Retinex with Color Restoration (MSRCR) algorithm will be performed to enhance images and increase the performance of diagnostic model. In CS, an ensemble learning-based technique that includes three classifiers called Xception, Inception-ResNet-V2, and Visual Geometry Group (VGG16) will be applied to accurately diagnose BC patients. Finally, in EVS, majority voting and weighted random forest based on accurate voting techniques will be provided to get the most optimal diagnosis. In the benchmark BreakHis dataset, test results illustrated that the three fine-tuned classifiers (Xception, Inception-ResNet-V2, and VGG16) of BCDS provide accuracy values equal 97%, 98%, and 99.28% for multi-classification. These fine-tuned classifiers yield accuracy scores of 99%, 99%, and 100% based on binary jobs. Results indicate that the BCDS model achieves 100% accuracy for binary tasks and 99.89% accuracy for multi-classification tasks. Physicians can utilize BCDS as a decision-support framework, especially in nations of poverty when resources and k

关键词： Diseases

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Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system

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Neural Computing and Applications 2025年 1-18页

作者： Khater, A. Aziz Fekry, Mohamed El-Bardini, Mohammad El-Nagar, Ahmad M. Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32852 Egypt

In this paper, a novel adaptive fuzzy controller based on deep reinforcement learning (DRL) is introduced for electro-hydraulic servo systems. The controller combines the strengths of fuzzy proportional–integral (PI) control and deep Q-learning network (DQLN) to achieve real-time adaptation and improve the control performance. The purpose of this paper is to address the challenges of controlling electro-hydraulic servo systems by developing an adaptive controller that can dynamically adjust its control parameters based on the system’s state. The traditional fuzzy PI controller is enhanced with DRL techniques to enable automatic adaptation and compensation for changing online conditions. The proposed adaptive controller utilizes a DQLN to dynamically adjust the scaling factors of the input/output membership functions. By using the DQLN algorithm, the controller learns from a variety of system data to determine the optimal control parameters. The update equation of the weights for the Q-network is derived using the Lyapunov stability (LS) theorem, which overcomes the limitations of gradient descent (GD) methods such as instability and local minima trapping. To evaluate the effectiveness of the proposed controller, it is practically implemented to regulate an electro-hydraulic servo system. The controller’s performance is compared against other existing controllers, and its enhancements are demonstrated through experimental evaluation. © The Author(s) 2025.

关键词： Deep reinforcement learning

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Economically optimized heat exchanger design: a synergistic approach using differential evolution and equilibrium optimizer within an evolutionary algorithm framework

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Neural Computing and Applications 2024年第24期36卷 14999-15026页

作者： Moharam, Amal Haikal, Amira Y. Elhosseini, Mostafa Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO’s tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of shell-and-tube heat exchangers (STHEs) and photovoltaic module parameters. The CP-EODE algorithm leverages DE's exploration strengths to enhance EO’s performance adopting a cooperative parallel approach that divides the population into EO and DE sub-populations for a more effective search space exploration. The validation of CP-EODE’s effectiveness commenced with its application to 29 benchmark functions, where a Friedman test yielded an Fr value of 22.19, significantly surpassing the critical value of 7.81, and a Wilcoxon signed rank test confirmed statistical improvements with a z-score beyond − 1.96. Four quality metrics were discussed to provide a detailed assessment of CP-EODE’s performance highlighting its robust exploration and exploitation capabilities. In practical applications, CP-EODE demonstrated significant advancements in STHE design achieving up to 19.706% reduction in heat exchanger surface area compared to traditional and other algorithmic designs, showcasing its potential to enhance engineering design efficiency and cost-effectiveness. A critical examination of the algorithm's convergence rate revealed its swift optimization capability, with rapid attainment of optimal solutions in various test cases, further establishing CP-EODE’s advantage in handling complex optimization challenges. Through rigorous statistical analysis and performance metrics, CP-EODE emerges as a superior solution for complex optimization challenges, underscoring the value of hybrid algorithmic strategies in engineering optimizations. © The Author(s), under exclusive licence to Springer-

关键词： Cost effectiveness

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Development of sliding mode control based on diagonal recurrent neural network for coupled tank system

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Neural Computing and Applications 2024年第25期36卷 15279-15293页

作者： El-Nagar, Ahmad M. Abdo, Mohamed I. Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menof32852 Egypt

This study presents the development of sliding mode control (SMC) using the diagonal recurrent neural network (DRNN) for nonlinear systems. Firstly, the SMC for linear systems is developed for nonlinear coupled tank system. Second, the DRNN is used to design the equivalent part of the SMC law, which is performed to approximate the dynamics of a controlled process. Third, the sliding surface for the switching control is developed using the DRNN. The DRNN parameters are tuned using Lyapunov function to achieve the controlled process stability. For the developed scheme, discontinuous signum function is used to compensate the chattering phenomenon. The developed scheme is applied for controlling the uncertain nonlinear coupled tank system. The simulation results indicate that the developed scheme can respond to the effects of system uncertainties compared to other existing schemes. © The Author(s) 2024.

关键词： Lyapunov functions

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GY-YOLO: ghost separable YOLO for pedestrian detection

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Neural Computing and Applications 2025年 1-27页

作者： Elhenidy, Ali M. Labib, Labib M. Haikal, Amira Y. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Engineering Mansoura National University Gamasa Egypt

In recent years, there has been impressive development in human detection. The main challenge in pedestrian detection is the training data. To assess detectors in crowd scenarios more effectively, a novel dataset in this study called the HEP dataset (Hybrid Egyptian Pedestrian dataset) is introduced. The HEP dataset is extensive, has comprehensive annotations, and is highly diverse. The dataset images are collected by two different means. Most of the images are collected from different mobile cameras for people crossing the street in high crowded streets in Egypt, and the rest of the images are collected from the web. That is why the dataset is called hybrid. The collected dataset is more suitable for pedestrian detection as the whole images focus on pedestrian scenarios for people outdoors crossing the street. This outperforms the previous benchmarks such as CrowdHuman and WiderPerson which collect data from the web and surveillance cameras with lots of images for indoor people. GS-YOLO also is proposed to address the real-time performance and the occlusion in the crowd scenes issues. GS-YOLO is a novel pedestrian detection model that utilizes efficient Ghost and depth separable convolution modules. GS-YOLO replaces all the convolution layers in the backbone and the head of the original YOLOv8 with Ghost and depth separable modules, respectively. A deformable to-features module is proposed to enrich features for the different feature pyramid networks. GS-YOLO is trained and tested over the collected dataset and other benchmarks like CrowdHuman and WiderPerson datasets. GS-YOLO achieves competitive results over the state-of-the-art models such as YOLOv5 and YOLOv8. GS-YOLO achieves 92.8% mAp on the HEP dataset, while YOLOv5 achieves 90.3% mAp and YOLOv8 achieves 91.1% mAp. © The Author(s) 2025.

关键词： Computer vision Deep learning Pedestrian detection YOLO

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