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An optimal internal model proportional integral controller to improve pressure tracking profile of artificial ventilator

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2023年第7期61卷 1807-1820页

作者： Acharya, Debasis Das, Dushmanta Kumar Natl Inst Technol Nagaland Chumukedima India

Mechanical ventilator (MV) is a breathing support medical equipment used during medical surgery or in an intensive care unit (ICU) for critical patients to reduce the breathing work of the patient under ventilation. In this work, a piston-driven mechanical ventilator is studied. Such types of ventilators are generally used as anaesthesia ventilators with volume-controlled modes. A modern ventilator can operate in different modes, such as volume control, pressure control, or both. Pressure-controlled mode may be better suited for some cases, such as patients undergoing laparoscopic surgery, as compared to volume-controlled mode because it may increase compliance during pneumoperitoneum, enhance oxygenation, and lower the stress response postoperatively. The pressure profile of a PCV must exactly track the pressure profile of a patient under ventilation to avoid ventilator-induced diaphragmatic dysfunction. Therefore, an optimal internal model control base proportional integral (IMC-PI) controller is proposed. The optimum parameter of the filter component of the IMC-PI controller is found by an enhanced class topper optimization (ECTO) algorithm. The performance of the proposed controller is analyzed in different scenarios and compared with existing results.

关键词： Artificial ventilation system (AVS) optimization algorithm Pressure control ventilator (PCV) Positive end-expiratory pressure (PEEP) Internal model control (IMC)

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Optimal Torque Sharing Function Control for Switched Reluctance Motors Based on Active Disturbance Rejection Controller

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第5期28卷 2600-2608页

作者： Feng, Liyun Sun, Xiaodong Yang, Zebin Diao, Kaikai Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

In this article, an optimal torque sharing function (TSF) control method incorporated with improved linear active disturbance rejection control (LADRC) and modified coyotea* optimization algorithm (MCOA) is introduced and applied in switched reluctance motors (SRMs). First, the piecewise TSF is employed to reduce the torque ripple, which exhibits little effects on the rate of current change and the current peak value. The improved linear extended state observer with the variable gain in speed controller using LADRC is designed to guarantee better antidisturbance ability and torque ripple reduction simultaneously. Then, the MCOA is introduced to automatically tune the parameters including the turn-on angle, conduction angle, and coefficients for better comprehensive performances. Finally, comparisons among the conventional TSF with LADRC, the piecewise TSF with LADRC, and the proposed TSF with improved LADRC are conducted both in the simulations and experiments. The results indicate that the proposed method behaves best in aspects of torque ripple suppression and anti-interference ability among the three methods.

关键词： Active disturbance rejection control (ADRC) optimization algorithm switched reluctance motor (SRM) torque sharing function (TSF)

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Multi-layered coding-based study ona* optimization algorithms for automobile production logistics scheduling

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2021年 170卷 120889-120889页

作者： Yue, Guo Guo Tailai Dan, Wei Ningbo Inst Technol Ningbo 315211 Peoples R China SHU UTS SILC Business Sch Shanghai 200444 Peoples R China Changan Univ Xian 710064 Peoples R China

With the acceleration of economic globalization, competition among manufacturing industries has become increasingly fierce. Automobile manufacturing has always been a critical investment and development industry in different countries. For the automobile manufacturing industry, the logistics scheduling problem of automobile production is affects automobile manufacturing enterprises' ability to compete. This paper discusses disruptive technologies, such as AI, IoT, Big data, etc., to solve production problems. Therefore, production logistics systems research is essential to automobile manufacturing enterprises, to improve production efficiency, reduce production costs, and increase enterprises' economic benefits. We present three kinds of mathematical models designed and calculated by a genetic algorithm, aimed at the Pareto solution set to solve multi-objective optimization, as well as designs for a new contrast flow, which can quickly find the optimal solution and simulate the algorithm.

关键词： Automobile production Logistics scheduling Multi-layered coding Disruptive technologies optimization algorithm

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Learning Rate Dropout

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第11期34卷 9029-9039页

作者： Lin, Huangxing Zeng, Weihong Zhuang, Yihong Ding, Xinghao Huang, Yue Paisley, John Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Columbia Univ Dept Elect Engn New York NY 10027 USA Columbia Univ Data Sci Inst New York NY 10027 USA

optimization algorithms are of great importance to efficiently and effectively train a deep neural network. However, the existinga* optimization algorithms show unsatisfactory convergence behavior, either slowly converging or not seeking to avoid bad local optima. Learning rate dropout (LRD) is a new gradient descent technique to motivate faster convergence and better generalization. LRD aids the optimizer to actively explore in the parameter space by randomly dropping some learning rates (to 0);at each iteration, only parameters whose learning rate is not 0 are updated. Since LRD reduces the number of parameters to be updated for each iteration, the convergence becomes easier. For parameters that are not updated, their gradients are accumulated (e.g., momentum) by the optimizer for the next update. Accumulating multiple gradients at fixed parameter positions gives the optimizer more energy to escape from the saddle point and bad local optima. Experiments show that LRD is surprisingly effective in accelerating training while preventing overfitting.

关键词： Training Convergence optimization Neural networks Perturbation methods Informatics Adaptation models Neural network optimization algorithm regularization

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Weaponizing Favorite Test Functions for Testing Globala* optimization algorithms: An illustration with the Branin-Hoo Function

Weaponizing Favorite Test Functions for Testing Global Optim...

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AIAA Aviation Forum

作者： Jekel, Charles F. Haftka, Raphael T. Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Some popular functions used to test globala* optimization algorithms, such as the Branin-Hoo and Himmelblau functions, have multiple local optima, all with the same value of the objective function. That is all local optima are also global optima. This renders them easy to optimize, because it is impossible for the algorithm to get stuck in a local optimum that is not the global one. Such functions actually present an opportunity to create challenging problems fora* optimization algorithms, because, as illustrated here, it is easy to convert them to functions with competitive local optima by adding a localized bump at the location of one of the optima. This process is illustrated here for the Branin-Hoo function, which has three global optima. We use the popular Python SciPy differential evolution (DE) optimizer for the illustration, because its wide use is likely to imply a well written code. DE also allows the use of the gradient-based BFGS local optimizer for final convergence. By making a large number of replicate runs we establish the probability of reaching a global optimum with the original and weaponized Branin-Hoo. With the original function we find 100% probability of success with a moderate number of function evaluations. With the weaponized version, we found that the probability of getting trapped in a non-global optimum can be made small only with a much larger number of function evaluations.

关键词： optimization algorithm Python Particle Swarm optimization Adaptive Sampling algorithms Search algorithm Annealing

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Hybrid metaheuristic algorithm methods and econometric models in prediction of dogecoin price

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JOURNAL OF MODELLING IN MANAGEMENT 2025年第3期20卷 1030-1080页

作者： Farahani, Milad Shahvaroughi Babaei, Shiva Kharazan, Zahra Sadat Bai, Ali Rahmati, Zahra Ghasemi, Ghazal Alipour, Fardin Farrokhi-Asl, Hamed Khatam Univ Dept Finance Tehran Iran Univ Northern Iowa Coll Business Cedar Falls IA USA Univ Wisconsin Milwaukee Lubar Coll Business Milwaukee WI USA Islamic Azad Univ Dept Law Shahreza Iran Khatam Univ Dept Finance Tehran Iran Univ Wisconsin Milwaukee Cofrin Sch Business Green Bay WI USA

Purpose - This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models. Design/methodology/approach - An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between cryptoprice and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision. Findings - The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models. Originality/value - The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies' prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.

关键词： Cryptocurrency Artificial intelligence optimization algorithm Econometric methods Curve-fitting

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Assembly Level Topology optimization Towards a Part Consolidation algorithm for Additive Manufacturing

Assembly Level Topology Optimization Towards a Part Consolid...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Crispo, Luke Kim, Il Yong Queens Univ Dept Mech & Mat Engn Kingston ON Canada

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

As the adoption of additive manufacturing continues to grow in the aerospace industry, part consolidation is an emerging design technique aimed at decreasing assembly cost. Significant research is focused on design for additive manufacturing principles and their integration into design generation tools such as topology optimization, while part consolidation research has been limited to heuristic guidelines. This work presents the extension of topology optimization to assembly design for the simultaneous optimization of structural performance and connection layout. This methodology uses multiple domains occupying the same space along with a single joining domain to represent the assembly design. The proposed approach allows for future extensions with the calculation of additive manufacturing part costs on an individual domain level. The methodology is tested on a numerical example demonstrating the variation in part geometry and number of parts as the emphasis on joining cost is varied.

关键词： Topology optimization Aerospace Industry Stiffness Matrices Elastic Modulus Messerschmitt Bolkow Blohm optimization algorithm Structural optimization Manufacturing Cost Material Properties MATLAB

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Analysis of Tow-steered Laminates of Composites Using a Mixed-fidelity Neural Network Model

Analysis of Tow-steered Laminates of Composites Using a Mixe...

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AIAA SciTech Forum

作者： Liu, Xin Liu, Bangde Tian, Su Yu, Wenbin Univ Texas Arlington Dept Mech & Aerosp Engn Arlington TX 76019 USA Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA AnalySwift LLC W Lafayette IN 47906 USA Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47906 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Tow-steered composites, with their curvilinear fiber paths, have the potential to significantly enhance structural performance. Nevertheless, their development is impeded by the absence of dedicated computational tools and the considerable computational effort required to probe the vast design space of aerospace structures. To address these issues, an integrated design tool is developed as a plug-in code in commercial finite element (FE) software packages. A new machine learning (ML) module is developed in the plug-in code to provide a cost-effective surrogate model to diminish computational demands. To reduce the computational cost, a mixed-fidelity model and a transfer learning model are developed, both of which utilize varying mesh densities within FE modeling to create coarse mesh, i.e., low-fidelity datasets, and fine mesh, i.e., high-fidelity datasets. The accuracy and efficiency of these ML models are evaluated against a high-fidelity FE model. The findings reveal that the mixed-fidelity and transfer learning models achieve similar accuracy to a high-fidelity FE model and markedly enhance efficiency over the single-fidelity ML model.

关键词： Laminated Composites Neural Networks Bayesian optimization Reduced Order Modelling Aerospace Structure Nastran Design optimization Finite Element Analysis Structural Analysis optimization algorithm

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Artificial Intelligence in Geopolymer Concrete Mix Design: A Comprehensive Review of Techniques and Applications

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2025年 1-33页

作者： Mushthofa, Malik Thedy, John Teguh, Mochamad Purwanto Pratama, Adjie Gemilang Han, Ay Lie Department of Civil Engineering Islamic University of Indonesia Yogyakarta 55584 Indonesia Department of Bioenvironmental Systems Engineering National Taiwan University Taipei 10617 Taiwan Department of Civil Engineering Diponegoro University Semarang 50275 Indonesia

This systematic review explores the application of Artificial Intelligence (AI) in optimizing the mix design of fly ash-based geopolymer concrete (FABGC). Analyzing studies published between 2014 and 2025, it examines key methodologies, including machine learning models,a* optimization algorithms, and multi-criteria decision-making approaches. Critical aspects such as data preprocessing, AI model selection, hyperparameter tuning, explainable AI (XAI), and optimization strategies are synthesized to provide a comprehensive perspective on AI-driven FABGC research. The review identifies Deep Residual Networks (ResNet) and Extreme Gradient Boosting (XGB) as the most accurate models for predicting FABGC strength, consistently outperforming others due to their lower error metrics. Backpropagation Neural Networks (BPNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) also demonstrate competitive performance, while Random Forest (RF) and Decision Tree (DT) models excel in computational efficiency with shorter training times. Despite being the most widely implemented, Artificial Neural Networks (ANN) rarely achieve the highest predictive accuracy. Traditional regression methods, though straightforward, lag behind in performance. These findings underscore the need for standardized datasets, enhanced collaboration, and innovative AI-driven approaches to improve FABGC mix design optimization. Addressing these challenges will facilitate more reliable and efficient AI applications in sustainable concrete technology. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Artificial intelligence Fly ash Geopolymer concrete Mix design optimization algorithm

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Multi-objectives nonlinear structure optimization for actuator in trajectory correction fuze subject to high impact loadings

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Defence Technology（防务技术） 2021年第4期17卷 1338-1351页

作者： Jiang-hai Hui Min Gao Ming Li Ming-rui Li Hui-hui Zou Gang Zhou Northwest Institute of Nuclear Technology Xi’an710025China Shijiazhuang Campus Army Engineering UniversityShijiazhuang050003China

This paper presents an actuator used for the trajectory correction fuze,which is subject to high impact loadings during launch.A simulation method is carried out to obtain the peak-peak stress value of each component,from which the ball bearings are possible failures according to the ***,three schemes against impact loadings,full-element deep groove ball bearing and integrated raceway,needle roller thrust bearing assembly,and gaskets are utilized for redesigning the actuator to effectively reduce the bearings’***,multi-objectives optimization still needs to be conducted for the gaskets to decrease the stress value further to the yield *** gasket’s structure parameters and three bearings’peak-peak stress are served as the four optimization variables and three objectives,*** Latin hypercube design is used for generating sample points,and Kriging model selected according to estimation result can establish the relationship between the variables and objectives,representing the simulation which is ***,twoa* optimization algorithms work out the Pareto solutions,from which the best solutions are selected,and verified by the simulation to determine the gaskets optimized structure *** can be concluded that the simulation and optimization method based on these components is effective and efficient.

关键词： Actuator Trajectory correction fuze Impact loadings Optimized Latin hypercube design Kriging model optimization algorithm

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