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Distributionally robust system identification for continuous fermentation nonlinear switched system under moment uncertainty of experimental data

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APPLIED MATHEMATICAL MODELLING 2024年 127卷 679-695页

作者： Yuan, Jinlong Lin, Sida Zhang, Shaoxing Liu, Chongyang Dalian Maritime Univ Sch Sci Dept Appl Math Dalian 116026 Peoples R China Shandong Technol & Business Univ Sch Math & Informat Sci Yantai 264005 Peoples R China

In this paper, we consider a nonlinear switched dynamical system (NSDS) with unknown system parameters in the context of uncertain experimental data. This system is employed to model the continuous fermentation for the production of 1,3-propanediol through glycerol bioconversion. The uncertain experimental data points are regarded as stochastic variables and only their first-order moment information of the probability distributions is available. The target of this paper is to optimize these system parameters under the environment of uncertain experimental data. Taking these factors into account, we propose a distributionally robust system identification (DRSI) problem (i.e., a bi-level system identification problem) governed by the NSDS. The objective functional comprises two level objectives: (i) the inner-level objective aims to maximize the expectation of the relative error between the solution of the NSDS and the uncertain experimental data with respect to their probability distributions at approximately stable time;and (ii) the outer-level objective is to minimize the expectation with respect to these system parameters. The DRSI problem is equivalently transformed into a single-level system identification (SLSI) problem with non-smooth term through the application of the duality theory in the probability space. A smoothing technique is employed to approximate the non-smooth term in the SLSI problem. Subsequently, an error analysis of the employed smoothing technique is derived. The gradients of the objective functional in the SLSI with respect to these system parameters are obtained. An optimization algorithm is designed to solve the SLSI problem. Finally, the paper concludes with simulation results.

关键词： Distributionally robust system identification Uncertain experimental data Switched dynamical system Error analysis optimization algorithm

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Day-ahead optimal scheduling considering thermal and electrical energy management in smart homes with photovoltaic-thermal systems

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APPLIED ENERGY 2024年 374卷

作者： Fiorotti, Rodrigo Fardin, Jussara F. Rocha, Helder R. O. Rua, David Lopes, Joao Abel Pecas IFES Fed Inst Espirito Santo Sao Mateus Brazil UFES Fed Univ Espirito Santo Vitoria Brazil INESC TEC INESC Technol & Sci Porto Portugal Univ Porto FEUP Fac Engn Porto Portugal

The environmental impact on the energy sector has become a significant concern, necessitating the implementation of Home Energy Management Systems (HEMS) to enhance the energy efficiency of buildings, reduce costs and greenhouse gas emissions, and ensure user comfort. This paper presents a novel approach to provide optimal day-ahead energy management plans in smart homes with Photovoltaic/Thermal (PVT) systems, aiming to achieve a balance between energy cost and user comfort. This multi-objective problem employs the Non-dominated Sorting Genetic algorithm III as the optimization algorithm and the Nonlinear Auto-regressive with External Input to forecast the day-ahead meteorological variables, which serve as inputs to predict the PVT electrical and heat production in the thermal resistance model. The HEMS benefits from the time-of-use tariff due to the flexibility provided by the energy storage from a battery bank and a boiler. Furthermore, it performs a load scheduling for 10 controllable loads based on three feature parameters to characterize occupant behavior. A study case analysis revealed a cost reduction of approximately 66% in the solution close to the knee of the Pareto curve (S3 solution). The environmental impact on the energy sector has become a The PVT heat production was sufficient to meet the thermal demand of the showers. The proposed hybrid battery management model effectively eliminated the export of electricity to the grid, reducing consumption during peak periods and the maximum peak demand.

关键词： Demand side management Day-ahead forecasting Battery energy storage system PVT system Occupant behavior modeling optimization algorithm

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Rocket Landing Guidance Based on Linearization-Free Convexification

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第2期47卷 217-232页

作者： Yang, Runqiu Liu, Xinfu Song, Zhengyu Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China China Acad Launch Vehicle Technol Beijing 100076 Peoples R China

The landing problem of a reusable rocket is generally a highly constrained and nonconvex optimization problem. In this paper, we will convexify the problem without relying on any linearization. Specifically, we propose to quickly determine some of the state variables in advance so that the nonlinearity of the dynamics is greatly reduced, and then we accurately convexify the problem by change of variables and transformation of the optimization objective. This convexification process enables us to design an iterative algorithm that can converge very reliably, and the convergence does not rely on any trust region constraint. It should be highlighted that the algorithm is very efficient, generally taking just milliseconds to converge on a personal computer. Furthermore, by ensuring the recursive feasibility of calling the iterative algorithm in each guidance cycle, we can design a landing guidance algorithm that is able to achieve precise landing under various uncertainties and disturbances. Numerical examples are provided to demonstrate the high performance of the iterative algorithm and the landing guidance algorithm.

关键词： Rockets Guidance and Navigational algorithms Flight Path Angle Aerodynamic Coefficients Soft Landing optimization algorithm Convex optimization Optimal Control Problem Reusable Launch Vehicle Powered Descent Guidance

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Development of advanced AI-based segmentation and prediction method for air entrainment in plunging water jets

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PROGRESS IN NUCLEAR ENERGY 2024年 177卷

作者： Zhou, Wen Miwa, Shuichiro Yamashita, Susumu Okamoto, Koji Univ Tokyo Sch Engn Dept Nucl Engn & Management 7-3-1 HongoBunkyo Ku Tokyo 1138654 Japan Japan Atom Energy Agcy Nucl Sci & Engn Ctr 2-4 Tokai Mura Mito Ibaraki 3191195 Japan

Understanding air entrainment phenomena induced by plunging water jets is critical in the fields of nuclear and hydraulic engineering. Air entrainment is one of the key safety design parameters for nuclear systems. However, most existing studies rely on empirical correlations or curve-fitting models to estimate bubble penetration depth, and no agreed-upon calculation principle exists for varying jet conditions. To address these limitations, this research developed two advanced AI approaches: an improved YOLOv5 for segmenting air entrainment and the NSGA-III-BPNN method for predicting penetration depth. The improved YOLOv5 enables real-time segmentation and extraction of air entrainment motion and dynamics under diverse conditions, demonstrating high scalability and robustness. The penetration depth estimated using the improved YOLOv5 shows greater accuracy compared to conventional empirical correlationsand is more efficient than traditional image post-processing techniques for classifying shape regimes based on dynamic air entrainment patterns. To overcome the limitations of object segmentation, which typically relies on video or image data, the NSGA-III-BPNN method predicts maximum penetration depths with greater accuracy than YOLOv5, offering a more effective prediction model for air entrainment penetration depth. By leveraging advanced AI techniques, the research not only provides valuable segmentation data for refining computational fluid dynamics (CFD) modeling but also paves the way for significant advancements in both nuclear and hydraulic engineering.

关键词： Jet penetration Semantic segmentation Computer vision Neural network optimization algorithm

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Modified Fitness Dependent Optimizer for Solving Numerical optimization Functions

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IEEE ACCESS 2022年 10卷 83916-83930页

作者： Salih, Jumaa Fatih Mohammed, Hardi M. Abdul, Zrar Kh Charmo Univ Appl Comp Dept KRG Sulaymaniyah 46023 Chamchamal Iraq

The Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that was developed in 2019. It is one of the metaheuristic algorithms that has been used by researchers to solve various applications especially for engineering design problem. In this paper, a comprehensive survey conducted about FDO and its applications. Consequently, despite of having competitive performance of FDO, it has two major problems including low exploitation and slow convergence. Therefore, a modification of FDO (MFDO) is proposed for solving FDO issues. MFDO used two methods to enhance the performance of FDO: firstly, optimizing the range of weight factor between 0 and 0.2 which is used for finding fitness weight. Secondly, using sine cardinal mathematical function to update fitness weight and pace which is referred to the speed of the bees. To evaluate the performance of MFDO, 19 classical benchmark functions and CEC2019 benchmark functions are used. MFDO compared against all the enhancement of FDO and also it is compared with Grey Wolf optimization (GWO), Chimp optimization algorithm (ChOA), Genetic algorithm (GA), and Butterfly optimization algorithm (BOA). Statistical results proved that MFDO achieved significant performance compared to other algorithms. Finally, MFDO is used to solve three applications: Welded Beam Design (WDB), Pressure Vessel Design (PVD), and Spring Design Problem. Results proved that MFDO outperformed well in solving these applications against FDO, Gravitational Search algorithm (GSA), GA, and Grasshopper optimization algorithm (GOA).

关键词： Metaheuristics optimization Mathematical models Convergence Behavioral sciences Search problems Heuristic algorithms Modified fitness dependent optimizer fitness dependent optimizer metaheuristic algorithm optimization algorithm

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Dynamic generalized normal distribution optimization for feature selection

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NEURAL COMPUTING & APPLICATIONS 2022年第20期34卷 17355-17370页

作者： Tubishat, Mohammad Rawshdeh, Zainab Jarrah, Hazim Elgamal, Zenab Mohamed Elnagar, Ashraf Alrashdan, Maen T. Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Prince Sultan Univ Coll Business Adm Riyadh Saudi Arabia Skyline Univ Coll Sch Informat Technol Sharjah U Arab Emirates Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Jadara Univ Fac Sci & Informat Technol Irbid Jordan

High dimensionality of data represents a major problem that affects the accuracy of the classification. This problem related with classification is mainly resulted from the availability of irrelevant features. Feature selection represents a solution to a problem by selecting the most informative features and discard the irrelevant features. Generalized normal distribution optimization (GNDO) represents a newly developed optimization that confirmed its outperformance in comparison with well-known optimization algorithms on parameter extraction for photovoltaic models. As an optimization algorithm, however, GNDO suffers from degraded performance when dealing with a problem with a high dimensionality. The main problems of GNDO include exploitation problem by falling into local optima problem. Also, GNDO has solutions diversity problem when it deals with data with high dimensionality. To alleviate the drawbacks of this algorithm and solve feature selection problems, a local search algorithm (LSA) is used. The new algorithm is called dynamic generalized normal distribution optimization (DGNDO), which includes the following main improvements to GNDO: it can improve the best solution to solve the local optima problem, it can improve solution diversity by improving the randomly selected solution, and it can improve both exploration and exploitation combined. To confirm the outperformance and efficiency of the new DGNDO algorithm, DGNDO algorithm is applied on 20 benchmarked datasets from UCI repository of data. In addition, DGNDO algorithm results are compared with seven well-known optimization algorithms using number of evaluation metrics including classification, accuracy, fitness, the number of selected features, statistical results using Wilcoxon test and convergence curves. The obtained results reveal the superiority of DGNDO algorithm over all other competing algorithms.

关键词： Generalized normal distribution optimization Feature selection optimization algorithm

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A hybrid model for diabetic retinopathy and diabetic macular edema severity grade classification

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INTERNATIONAL JOURNAL OF DIABETES IN DEVELOPING COUNTRIES 2025年 1-15页

作者： Sabeena, A. S. Jeyakumar, M. K. Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kanyakumari 629180 Tamil Nadu India Noorul Islam Ctr Higher Educ Dept Comp Applicat Kanyakumari 629180 Tamil Nadu India

BackgroundDiabetic retinopathy (DR) is a common reason for permanent vision loss, particularly among the elderly population worldwide;diabetic macular edema (DME) is characterized by the accumulation of fluid or swelling in the macula, which can occur in any phase of DR progression. New studies have deepened our knowledge of the needs in eye care to improve methods for recognizing, addressing, diagnosing, and treating retinal *** work intends to present a scheme for disease severity classification following the processes: pre-processing, segmentation, and disease severity classification. Firstly, pre-processing is done by employing a median filter, followed by GD-STFA-based SwinUNet for segmentation of lesions into soft and hard exudates, hemorrhages, and ***, DR and DME severity classification is employed by AlexNet-DQN, which is proposed by combining AlexNet and DQN models. Moreover, the proposed AlexNet-DQN is trained using the Exponential Gradient Descent-Sea Turtle Foraging algorithm (ExpGD-STFA), which is developed by including the exponential weighted moving average (EWMA) concept in *** presented mechanism attained better results when compared to traditional *** DR severity classification, the proposed mechanism attained an accuracy of 98.3%, sensitivity of 98.9%, specificity of 97.8%, and precision of 97.6%. Meanwhile, for DME severity classification, the presented mechanism attained an accuracy of 98.1%, sensitivity of 98.8%, specificity of 97.5%, and precision of 97.3%.

关键词： Diabetic retinopathy Diabetic macular edema Segmentation Severity grade classification optimization algorithm Deep learning

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Research on Calibration Method of Triaxial Magnetometer Based on Improved PSO-Ellipsoid Fitting algorithm

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ELECTRONICS 2025年第9期14卷 1778-1778页

作者： Guan, Jun Chen, Zhihui Jiang, Guilin Jiangsu Univ Sci & Technol Sch Automat Zhenjiang 212100 Peoples R China Nanjing Univ Sci & Technol Natl Key Lab Transient Phys Nanjing 210094 Peoples R China Huaihai Ind Grp Co Ltd Changzhi 046012 Peoples R China

To address the measurement accuracy degradation of triaxial magnetometers caused by manufacturing errors and environmental interference, and the limited robustness of traditional calibration methods, this study proposes a Dynamic Hierarchical Elite-guided Particle Swarm optimization (DHEPSO)-based ellipsoid fitting algorithm. First, an error model for the triaxial magnetometers is established. Next, the DHEPSO algorithm is utilized to fit the ellipsoid parameters by integrating a dynamic hierarchical mechanism, elite guidance strategy, and adaptive inertia weight adjustment, thereby balancing global exploration and local exploitation to efficiently optimize the parameters. Finally, error compensation and precise calibration are achieved using the optimized parameters. The simulation results show that, compared to the Least Squares Method (LSM), it reduces the absolute distance between the simulated data and the ellipsoid by 63.10% and the post-calibration total magnetic field intensity standard deviation by 60% under outlier interference. Against the traditional PSO, TSLPSO, MPSO, and AWPSO, DHEPSO achieves total distance reductions of 48.52%, 47.74%, 56.71%, and 33.09%, respectively, with faster convergence. The statistical analysis of 60 trials confirms DHEPSO's stability, exhibiting lower median error and interquartile range. The results validate DHEPSO's high precision and robustness in high-noise environments, offering theoretical support for engineering applications.

关键词： triaxial magnetometer error calibration ellipsoid fitting least squares method optimization algorithm particle swarm optimization

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Virtual Target Selection for a Multiple-Pursuer-Multiple-Evader Scenario

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第5期22卷 391-400页

作者： Weintraub, Isaac E. Von Moll, Alexander Casbeer, David W. Manyam, Satyanarayana G. Air Force Res Lab Control Sci Ctr Wright Patterson AFB OH 45433 USA InfoSciTex Corp 4027 Col Glenn Hwy Dayton OH 45431 USA

This paper considers an N-pursuer-M-evader scenario involving L virtual targets. The virtual targets serve as an intermediary target for the pursuers, allowing the pursuers to delay their final assignment to the evaders. However, upon reaching the virtual target, the pursuers must decide which evader to capture. It is assumed that there are more pursuers than evaders and that the pursuers are faster than the evaders. The objective is two-part: first, assign each pursuer to a virtual target and evader such that the pursuer team's energy is minimized, and, second, choose the virtual targets' locations for this minimization problem. The approach taken is to consider the Apollonius geometry between each pursuer's virtual target location and each evader. Using the constructed Apollonius circles, the pursuer's travel distance and maneuver at a virtual target are obtained. These metrics serve as a gauge for the total energy required to capture a particular evader and are used to solve the joint virtual target selection and pursuer-evader assignment problem. This paper provides a mathematical definition of this problem, the solution approach taken, and an example. Following the example, a Monte Carlo analysis is performed, demonstrating the efficacy of the algorithm and its suitability for real-time applications.

关键词： Mixed Integer Linear Programming Optimal Control Theory optimization algorithm Weapon Target Assignment Monte Carlo Analysis Guidance, Navigation, and Control Systems Missile Defense

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A deep learning based model for diabetic retinopathy grading

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SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Akhtar, Samia Aftab, Shabib Ali, Oualid Ahmad, Munir Khan, Muhammad Adnan Abbas, Sagheer Ghazal, Taher M. Virtual Univ Pakistan Dept Comp Sci Lahore 54000 Pakistan Appl Sci Univ Coll Arts & Sci Comp Sci Dept POB 5055 Manama Bahrain Natl Coll Business Adm & Econ Sch Comp Sci Lahore 54000 Pakistan Korea Univ Dept Biostat Coll Med Seoul 02841 South Korea Gachon Univ Fac Artificial Intelligence & Software Dept Software Seongnam 13557 South Korea Prince Mohammad Bin Fahd Univ Dept Comp Sci Al Khobar 34754 Dhahran Saudi Arabia Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Dept Networks & Cybersecur Amman Jordan

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim of this research is to develop an automated and efficient system for early detection and accurate grading of diabetic retinopathy severity with less time consumption. In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). The dataset utilized in this study is Messidor-1. In preprocessing, we have used Histogram Equalization to improve image contrast and denoising techniques to remove noise and artifacts which enhanced the clarity of the fundus images. We applied data augmentation techniques to our preprocessed images in order to tackle class imbalance issues. Augmentation techniques involve flipping, rotation, zooming and adjustment of color, contrast and brightness. The proposed RSG-Net model contains convolutional layers to perform automatic feature extraction from the input images and batch normalization layers to improve training speed and performance. The model also contains max pooling, drop out and fully connected layers. Our proposed RSG-Net model achieved a testing accuracy of 99.36%, specificity of 99.79% and a sensitivity of 99.41% in classifying diabetic retinopathy into 4 grades and it achieved 99.37% accuracy, 100% sensitivity and 98.62% specificity in classifying DR into 2 grades. The performance of RSG-Net is also compared with other state-of-the-art methodologies where it outperformed these methods.

关键词： Diabetic retinopathy Convolutional neural network Deep learning Augmentation optimization algorithm

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