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A systematic review on state of modeling, clinical issues, and advanced control approaches for artificial ventilator: traditional control to soft computing-based control approach

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2025年第1期13卷 25-25页

作者： Acharya, Debasis Das, Dushmanta Kumar Brainware Univ Dept Comp Sci & Engn Cyber Secur & Data Sci Kolkata India Natl Inst Technol Nagaland Dept Elect & Elect Engn Dimapur India

An artificial ventilator is crucial in medical emergencies, providing severely ill patients with sufficient oxygen to reduce their breathing effort. For critical patients, clinicians must set various parameters on the ventilator to ensure safe and comfortable ventilation. Accurate tracking of the patient's breathing profile by the ventilator is essential for synchronization, reducing the patient's workload and effort. Designing an effective ventilator controller requires proper system modeling and a clear understanding of its purpose. This paper offers a brief review of modeling, physiological and clinical issues, and state-of-the-art advanced control practices, guiding future control design for ventilators. It also presents detailed ventilator modeling and parameters from the literature, along with a comprehensive review of traditional and soft-computing-based control approaches.

关键词： Artificial ventilator Fuzzy logic controller PID controller optimization algorithm Patient-hose ventilator Piston-motor-driven ventilator

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Genetic algorithm optimization of a horizontal axis wind turbine blade section performance equipped with a single dielectric barrier discharge plasma actuator utilizing a direct regression model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第20期236卷 10456-10469页

作者： Fadaei, Mohaddeseh Davari, Ali Reza Sabetghadam, Fereidoun Islamic Azad Univ Ramsar Branch Dept Mech & Aerosp Engn Ramsar *** Iran Islamic Azad Univ Sci & Res Branch Dept Aerosp Engn Ramsar Iran

The usage of a single dielectric barrier discharge plasma actuator (SDBD-PA) device for improving a aerodynamic performance of a locally developed horizontal axis wind turbine blade section is studied with one of the most recent electrostatic models. To characterize this blade section behavior over a wide range of operating conditions of SDBD-PA and to advance its performance with a fully automateda* optimization algorithm, a computationally cost efficient direct regression model is proposed. In this paper, 512 comprehensive numerical simulations are carried out to derive the direct regression model for aerodynamic performance calculation when the PA is in use. However, to obtain highly accurate results, two different models for angle of attacks higher and less than 21 degrees are suggested. The proposed mathematical model within the specified boundary limits allows for a rapid linkage between aerodynamic performance and genetic algorithm which canbe made to acquire optimum results for each case without requiring burdensome numerical simulations. It is identified that superimpose input parameters effects onto each other is not explanatory of the final effect on aerodynamic performance and interaction effects should be seriously taken into consideration at the proposed regression model.

关键词： Direct regression model wind turbine plasma actuator optimization algorithm aerodynamic performance

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Gate-Based Variational Quantum algorithm for Truss Structure Size optimization Problem

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AIAA JOURNAL 2024年第12期62卷 4824-4833页

作者： Xu, Yusheng Wang, Xiaojun Wang, Zhenghuan Beihang Univ Natl Key Lab Strength & Struct Integr Inst Solid Mech Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China

Quantum computing has become a pivotal innovation in computational science, offering novel avenues for tackling the increasingly complex and high-dimensional optimization challenges inherent in engineering design. This paradigm shift is particularly pertinent in the domain of structural optimization, where the intricate interplay of design variables and constraints necessitates advanced computational strategies. In this vein, the gate-based variational quantum algorithm utilizes quantum superposition and entanglement to improve search efficiency in large solution spaces. This paper delves into the gate-based variational quantum algorithm for the discrete variable truss structure size optimization problem. By reformulating this optimization challenge into a quadratic, unconstrained binary optimization framework, we bridge the gap between the discrete nature of engineering optimization tasks and the quantum computational paradigm. A detailed algorithm is outlined, encompassing the translation of the truss optimization problem into the quantum problem, the initialization and iterative evolution of a quantum circuit tailored to this problem, and the integration of classical optimization techniques for parameter tuning. The proposed approach demonstrates the feasibility and potential of quantum computing to transform engineering design and optimization, with numerical experiments validating the effectiveness of the method and paving the way for future explorations in quantum-assisted engineering optimizations.

关键词： Trusses Computing and Informatics Multidisciplinary Design optimization optimization algorithm Evolutionary algorithm Annealing Structural optimization Quantum Computing

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Evaluation of autonomous underwater vehicle motion trajectorya* optimization algorithms

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KNOWLEDGE-BASED SYSTEMS 2023年第1期276卷

作者： Basil, Noorulden Alqaysi, M. E. Deveci, Muhammet Albahri, A. S. Albahri, O. S. Alamoodi, A. H. Mustansiriyah Univ Dept Elect Engn Baghdad Iraq Al Farahidi Univ Dept Med Instruments Engn Tech Baghdad 10021 Iraq Natl Def Univ Turkish Naval Acad Dept Ind Engn TR-34940 Tuzla Istanbul Turkiye Imperial Coll London Royal Sch Mines London SW7 2AZ England Iraqi Commiss Comp & Informat ICCI Baghdad Iraq La Trobe Univ Dept Comp Sci & Informat Technol Melbourne VIC Australia Mazaya Univ Coll Comp Tech Engn Dept Nasiriyah Iraq Univ Pendidikan Sultan Idris UPSI Fac Comp & Meta Technol FKMT Tanjong Malim Perak Malaysia Middle East Univ MEU Res Unit Amman Jordan Natl Def Univ Turkish Acad Dept Ind Engn TR-34940 Tuzla Istanbul Turkiye

The operation of autonomous underwater vehicles (AUVs) relies on three major motions: yaw, theta, and depth, each requiring its own set of proportional integral derivative (PID) controller gain criteria. Thus, different issues arise, including the availability of multiple criteria fora* optimization algorithm evaluation, the importance of these criteria, the trade-off between criterion performance, and criterion critical values. These issues make the evaluation ofa* optimization algorithms for AUV motion control a complex multicriteria decision-making (MCDM) problem. This research proposes a novel selectionintegrated approach for AUVa* optimization algorithms in different motions using two MCDM methods: fuzzy-weighted zero-inconsistency (FWZIC) for criteria weighting and fuzzy decision by opinion score method (FDOSM) fora* optimization algorithm selection. The approach comprises three main phases: development of PID, FWZIC-based criteria weighting, and FDOSM-baseda* optimization algorithm selection. In all three motion types - "depth", "yaw", and "theta"- Kp_& theta;had the highest weight value, with respective weights of 0.143, 0.149, and 0.142. In contrast, Ki_depth consistently received the lowest weight value across all three motion types, with respective weights of 0.057, 0.0598, and 0.057. Regarding the "depth"motion, the Archimedesa* optimization algorithm (AOA) was the highestperforming alternative with a score of 0.077, while the eagle strategy-particle swarma* optimization algorithm was the worst alternative with a score of 0.022. The Cuckooa* optimization algorithm was identified as the best alternative for the "yaw"motion with a score of 0.072, whereas black hole optimization had the lowest score of 0.036. The best alternative for the "theta"motion was AOA with a score of 0.067, and the worst algorithm was sunflower optimization with a score of 0.028. This developed approach was evaluated based on systematic ranking and sensitivity analysis, which confirmed the validity

关键词： Autonomous underwater vehicle FWZIC FDOSM Multicriteria decision-making optimization algorithm Proportional integral derivative gain

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Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 392卷 114616-114616页

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Gandomi, Amir H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo Australia

This paper presents a novel bio-inspired algorithm inspired by starlings' behaviors during their stunning murmuration named starling murmuration optimizer (SMO) to solve complex and engineering optimization problems as the most appropriate application of metaheuristic algorithms. The SMO introduces a dynamic multi-flock construction and three new search strategies, separating, diving, and whirling. The separating search strategy aims to enhance the population diversity and local optima avoidance using a new separating operator based on the quantum harmonic oscillator. The diving search strategy aims to explore the search space sufficiently by a new quantum random dive operator, whereas the whirling search strategy exploits the vicinity of promising regions using a new operator called cohesion force. The SMO strikes a balance between exploration and exploitation by selecting either a diving strategy or a whirling strategy based on the flocks' quality. The SMO was tested using various benchmark functions with dimensions 30, 50, 100. The experimental results prove that the SMO is more competitive than other state-of-the-art algorithms regarding solution quality and convergence rate. Then, the SMO is applied to solve several mechanical engineering problems in which results demonstrate that it can provide more accurate solutions. A statistical analysis shows that SMO is superior to the other contenders. (c) 2022 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Metaheuristic algorithm Bio-inspired algorithm Swarm intelligence algorithm Quantum computing Applied mechanics and engineering problems

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High-Precision Self-Positioning of Inspection Robot When It Is Located at the Edge of the Cell

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wu, Junmin Wu, Peng Liu, Shidong Wang, Xiaoli Xie, Yaqin China Electric Power Research Institute Electric Power Wireless Communication Technology Laboratory Nanjing 210003 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing 210044 China

The high-accuracy self-positioning of the inspection robot is the key to ensure the accuracy of the inspection task. The existing location solutions do not solve the problem of low positioning accuracy when the robot is located at the edge of the cell. The positioning method based on received signal strength (RSS) is a low-cost positioning scheme. However, this method is greatly affected by the fluctuation of the RSS, which leads to the poor positioning accuracy of the target, especially for the target located at the edge of the cell. Meanwhile, the positioning accuracy is also related to the location distribution of the access point (AP). A three-stepa* optimization algorithm based on RSS and AP layout is proposed in this paper to improve the localization accuracy of the inspection robots when it is at the edge of the cell. First, the optimal AP layout with the highest positioning accuracy is selected based on the maximization of Fisher information matrix. Then the matrix is decomposed based on the active set method. Finally, for different initial user estimation position, the positioning accuracy of the user is further improved using an improved extended min-max method. Simulation results show that the optimal AP layout ensures the lowest positioning error compared with the specified and the worst layout scenario. In the case of six optimal AP layouts, the positioning accuracy of the proposed algorithm is improved by 16.07%, 48.35% and 17.54% compared with the min max strategy, the min-max algorithm with area partition strategy and the traditional Taylor series expansion method respectively. © 1963-2012 IEEE.

关键词： access point (AP) layout inspection robot Localization method optimization algorithm received signal strength (RSS)

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Application of Ant Colony optimization Improved Clustering algorithm in Malicious Software Identification

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 1031-1039页

作者： Qian, Yong Sichuan Coll Architectural Technol Int Sch Tech Educ Deyang 618000 Peoples R China

Due to the increasing threat of malware to computer systems and networks, traditional malware detection and recognition technologies face difficulties and limitations. Therefore, exploring new methods to improve the accuracy and efficiency of malware identification has become an urgent need. This study introduces ant colony algorithm to optimize traditional clustering algorithms and algorithm parameters. The experimental results showed that the improvement rates of the improved algorithm in accuracy, echo value, and false alarm rate were 0.253, 0.115, and 0.056, respectively. The accuracy on the training and validation sets continued to increase and the loss curve continued to decrease. In addition, the improved algorithm had stronger modeling ability for data feature relationships and temporal information. This is of great help in improving the recognition ability of virus and worm software. The improved algorithm had a lower occupancy rate of computing resources compared to other algorithms, but it could also effectively monitor device operation. Compared with traditional methods, this method can more accurately identify malicious software and effectively identify malicious software samples from large-scale datasets. This is of great significance for protecting computer systems and network security.

关键词： Ant colony algorithm clustering algorithm malicious software identification computer security optimization algorithm

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A new artificial immune algorithm and its application for optimization problems

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Journal of Harbin Institute of Technology(New Series) 2006年第2期13卷 129-133页

作者：于志刚宋申民段广仁 Aerospace College Harbin Institute of Technology Harbin 150001 China

A new artificial immune algorithm （AIA） simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies＇ evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem, the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount （the iterative times）. Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing muhimodal optimization.

关键词： artificial immune network optimization algorithm preventing premature convergence.

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optimization of terminal area arrival flight sorting based on an improved sparrow search algorithm

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SCIENCE PROGRESS 2024年第1期107卷 368504241238078页

作者： Zhao, Weixi Liang, Te Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming Peoples R China Beihang Univ Fac Instrumentat Sci & Optoelect Engn Beijing Peoples R China Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Peoples R China

At present, airspace congestion and flight delays have become widespread concerns. This study aims to optimize the sequencing of arrival flights in the terminal area of multirunway airports. Considering the constraints of multiple runways, slant intervals and moving flight positions, this article establishes an optimization model for arrival flight sequencing in a multirunway airport terminal area. Accordingly, an improved sparrow search algorithm (ISSA) is proposed based on Chebyshev chaotic mapping, the golden sine strategy, and the variable neighborhood strategy. Through six basic test functions, the ISSA is compared with particle swarm optimization, the whalea* optimization algorithm, the genetic algorithm, and other algorithms to verify its superiority. Finally, two sets of instance data from Kunming Changshui Airport were used for experiments. The results show that the total delay times (TDTs) of small-scale flights (number of aircraft: 29) and large-scale flights (number of aircraft: 147) are 55.3% and 20.5% lower, respectively, than those of the first-come-first-served algorithm. The superiority of the ISSA designed in this article is verified, and it can significantly reduce the TDTs of arrival flights. It is suitable for optimizing arrival flights during peak hours at most airports. This approach provides theoretical support for optimizing the sorting of flights in terminal areas.

关键词： Flight sequence optimization algorithm sparrow algorithm air traffic management

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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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