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From Static and Dynamic Perspectives:A Survey on Historical Data Benchmarks of control Performance Monitoring

IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 300-316页

作者： Pengyu Song Jie Wang Chunhui Zhao Biao Huang the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University IEEE the Department of Chemical and Materials Engineering University of Alberta

In recent decades, control performance monitoring(CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical data benchmark(HIS) has garnered the most attention due to its practicality and effectiveness. However, existing CPM reviews usually focus on the theoretical benchmark, and there is a lack of an in-depth review that thoroughly explores HIS-based methods. In this article, a comprehensive overview of HIS-based CPM is provided. First, we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo: static and dynamic properties. The static property portrays time-independent variability in system output, and the dynamic property describes temporal behavior driven by closed-loop feedback. Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two ***, two mainstream solutions for CPM methods are summarized, including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior. Furthermore, this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.

关键词： control performance monitoring (CPM) data-driven method historical data benchmark (HIS) industrial process performance index static and dynamic analysis

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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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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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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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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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CO_(2)methanation over low-loaded Ni-M,Ru-M(M=Co,Mn)catalysts supported on CeO_(2)and SiC

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Carbon Resources Conversion 2025年第1期8卷 124-140页

作者： Chopendra G.Wasnik Maki Nakamura Taiki Shimada Hiroshi Machida Koyo Norinaga Department of Chemical Systems Engineering Graduate School of EngineeringNagoya UniversityAichi 464-8603Japan

The concentration of the major greenhouse gas CO_(2)is rapidly increasing in the atmosphere,leading to global warming and a range of environmental *** efficient circulation and utilization of CO_(2)is critical in the current environmental ***,an exothermic process,emerges as a critical strategy for effective CO_(2)*** this front,there is a significant demand for rational design of catalysts that maintain high activity and methane selectivity over a wide temperature range(250-550℃).The catalyst that can promise a consistent reaction even at 500℃under an atmospheric pressure is thus *** present study investigated bimetallic catalysts with SiC,which is known for its exceptional thermal conductivity,and CeO_(2),which is characterized by its CO_(2)affinity,as base *** incorporated Ni-M and Ru-M(M=Co and Mn)as the active metals,each loaded at 2%.Impressively,with merely 20 mg,the Ni-Co/SiC catalyst achieved a CO_(2)conversion rate of 77%and CH_(4)selectivity of 88%at 500℃,in a fixed-bed tubular reactor system with conditions of H_(2)/CO_(2)=4,a total flow rate of 70 ml min^(-1),and a steady GHSV of 12,000 h^(-1).Moreover,2Ni-2Co/CeO_(2)catalyst demonstrated exceptional performance with a 76%conversion of CO_(2)and a 83%selectivity for CH_(4),all under identical *** catalyst’s durability was confirmed by a subsequent 40-hour stability test,which showed only a 3-5%*** developed catalysts were comprehensively characterized by BET/BJH,CO pulse chemisorption,H_(2)-TPR,HAADF-STEM-EDS,SEM-EDS and XRD *** unveil their physicochemical and surface *** was found that Co and Mn,when integrated,effectively restrained the agglomeration of Ni and Ru particles,ensuring optimal metal dispersion on the *** conclusion,our synthesized bimetallic catalysts shown a sustained catalytic capability,even in the high-temperature environment.

关键词： Greenhouse gas CO_(2)methanation Catalyst activity Methane selectivity Ni-and Ru-catalysts GHSV Catalyst’s durability

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Mechanochemical synthesis of Ag/TiO_(2)@PANI nanocomposites for enhanced toluene photocatalytic degradation under near-ultraviolet light

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Chinese Journal of chemical engineering 2024年第11期75卷 222-229页

作者： Qiulian Zhu Binghao Wang Ying Mo Shengfan Liao Yinfei Chen Hanfeng Lu Innovation Team of Air Pollution Control Institute of Catalytic Reaction EngineeringCollege of Chemical EngineeringZhejiang University of TechnologyHangzhou 310014China Department of Chemical and Biochemical Engineering College of Chemistry and Chemical EngineeringKey Laboratory for Chemical Biology of Fujian ProvinceXiamen UniversityXiamen 361005China

Photocatalytic oxidation technology is a promising green technology for degrading volatile organic compounds(VOCs)due to its non-toxic,environmentally friendly,energy-saving and affordable *** this paper,Ag/TiO_(2)@PANI-MC with high stability and activity was synthesized by the mechanochemical *** designed Ag/TiO_(2)@PANI-MC were of high specific surface area,light absorption capacity and low recombination rate of electronehole pairs,which was demonstrated by various *** applied in photocatalytic toluene oxidation,the conversion is 17%at 20℃under 100 W high-pressure mercury *** photocatalytic performance is with less temperature sensitivity and significantly improved compared with Ag/TiO_(2)or TiO_(2)***,the reaction routine was also confirmed by gas chromatography-mass spectrometry and toluene was mineralized to CO_(2).More importantly,the Ag/TiO_(2)@PANI-MC indicated good reusability after three cycles,which was verified by the Fourier transform-infrared spectroscopy comparison with fresh and used *** work proves a potential way of constructing nanocomposites based on mechanochemical synthesis for enhanced toluene photocatalytic degradation.

关键词： Toluene Photocatalytic degradation Mechanochemical synthesis Nanocomposite

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Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

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Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

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Developing deep learning-based large-scale organic reaction classification model via sigma-profiles

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Green chemical engineering 2025年第2期6卷 181-192页

作者： Wenlong Wang Chenyang Xu Jian Du Lei Zhang State Key Laboratory of Fine Chemical Frontiers Science Center for Smart Materials Oriented Chemical EngineeringInstitute of Chemical Process Systems EngineeringSchool of Chemical EngineeringDalian University of TechnologyDalian116024China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore117585Singapore

Advanced technologies like deep learning have accelerated the discovery of novel chemical reactions,especially in the field of organic *** hundreds of thousands of reactions available for reference,one way to effectively leverage them is by classifying chemical reactions into different clusters based on their specific characteristics,which makes target-guided navigation in the vast chemical space *** previous attempts that apply deep learning to reaction classification tasks have made substantial progress,developing a model with good interpretability as well as high accuracy for large-scale reaction classification tasks remains an open *** this work,a deep learning-based model for a large-scale reaction classification task is first constructed by utilizing pre-trained BERT and ***,the model is trained under the open-source dataset USPTO_TPL which contains recorded reactions of up to 1000 different *** multi-classification accuracy of the model on the testing dataset is 99.382%,showing its great potential for practical ***,a reaction similarity map is presented to correlate the reactions in the USPTO_TPL dataset based on their sigmaprofile-based statistical ***,representative reactions from the testing dataset are provided to illustrate the model's effectiveness on the reaction classification task.

关键词： Reaction classification Reaction fingerprint Sigma-profiles Deep learning

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