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Tour Design Using Resonant-Orbit Invariant Manifolds in Patched Circular Restricted Three-Body Problems

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第1期44卷 106-119页

作者： Anderson, Rodney L. CALTECH Jet Prop Lab 4800 Oak Grove DrM-S 301-121 Pasadena CA 91109 USA

It is increasingly desirable to incorporate multibody effects in tour design early in the process to both discover new solutions and reduce the number and size of required maneuvers. Flybys have previously been designed using the heteroclinic connections of resonant orbits in the circular restricted three-body problem (CRTBP), but tour design often requires the consideration of additional moons, especially within the Jovian system. In this study, the heteroclinic connections of multiple resonant orbits are chained together within separate CRTBP models to perform multiple flybys that advance through desired resonances. Methods for patching these trajectories together are explored, and sample trajectories are computed.

关键词： Ganymede Orbital Period Computing optimization algorithm Numerical optimization Keplerian Orbit Libration Points Astrodynamics Astronomy Earth

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Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification

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IEEE ACCESS 2021年 9卷 36019-36037页

作者： El-Kenawy, El-Sayed M. Mirjalili, Seyedali Ibrahim, Abdelhameed Alrahmawy, Mohammed El-Said, M. Zaki, Rokaia M. Eid, Marwa Metwally Delta Higher Inst Engn & Technol DHIET Dept Commun & Elect Mansoura 35111 Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Mansoura Univ Fac Engn Comp Engn & Control Syst Dept Mansoura 35516 Egypt Mansoura Univ Fac Comp & Informat Dept Comp Sci Mansoura 35516 Egypt Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt Delta Higher Inst Engn & Technol DHIET Mansoura 35111 Egypt Benha Univ Shoubra Fac Engn Dept Elect Engn Banha 11629 Egypt

The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search optimization algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network's connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases.

关键词： X-ray imaging Feature extraction Diseases optimization Lung COVID-19 Classification algorithms Chest X-ray transfer learning convolutional neural network squirrel search optimization multilayer perceptron optimization algorithm

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Group-based multiple pipe routing method for aero-engine focusing on parallel layout

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Frontiers of Mechanical Engineering 2021年第4期16卷 798-813页

作者： Hexiang YUAN Jiapeng YU Duo JIA Qiang LIU Hui MA School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System(Ministry of Education) Northeastern UniversityShenyang 110819China Shenyang Engine Research Institute of AECC Shenyang 110015China School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China

External pipe routing for aero-engine in limited three-dimensional space is a typical nondetermini-stic polynomial hard problem,where the parallel layout of pipes plays an important role in improving the utilization of layout space,facilitating pipe assembly,and *** paper presents an automatic multiple pipe routing method for aero-engine that focuses on parallel *** compressed visibility graph construction algorithm is proposed first to determine rapidly the rough path and interference relationship of the pipes to be *** on these rough paths,the information of pipe grouping and sequencing are obtained according to the difference degree and interference degree,***,a coevolutionary improved differential evolution algorithm,which adopts the coevolutionary strategy,is used to solve multiple pipe layout optimization *** using this algorithm,pipes in the same group share the layout space information with one another,and the optimal layout solution of pipes in this group can be obtained in the same evolutionary ***,to eliminate the minor angle deviation of parallel pipes that would cause assembly stress in actual assembly,an accurate parallelization processing method based on the simulated annealing algorithm is ***,the simulation results on an aero-engine demonstrate the feasibility and effectiveness of the proposed method.

关键词： multiple pipe routing optimization algorithm aero-engine pipe grouping parallel layout

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Comparison of Electrified Aircraft Propulsion Drive Systems with Different Electric Motor Topologies

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JOURNAL OF PROPULSION AND POWER 2021年第5期37卷 733-747页

作者： Sirimanna, Samith Thanatheepan, Balachandran Lee, Dongsu Agrawal, Shivang Yu, Yangxue Wang, Yuyao Anderson, Aaron Banerjee, Arijit Haran, Kiruba Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Urbana IL 61801 USA Hitet LLC Champaign IL USA

Y Electric aircraft propulsion is a growing research area that looks into achieving propulsion through fully electric or hybrid electric systems while achieving low CO2 emissions. The system-level benefit gained by different electric and hybrid-electric propulsion schemes depends heavily on the performance of system-level components in the electric drive-train, including the electric motor, gear box, motor drive, protection systems, as well as the thermal management system. When comparing motor topologies, it is important to understand performance measures such as efficiency and specific power on a drive system level. Many different motor types have been qualitatively compared and can be found in the literature. To guide appropriate component selection, this paper presents details of a quantitative study for a given electric propulsion drive system. A Pareto optimal front for a notional drive system of a 1.5 MW electrical propulsor with different motor types is generated and compared. An optimization algorithm coupled with an electromagnetic finite element analysis software tool was used to optimize the induction motor, switched reluctance motor, wound rotor synchronous motor, permanent magnet synchronous motor (PMSM), slotless PMSM, permanent-magnet-assisted synchronous reluctance motor, brushless DC motor, and brushless doubly fed reluctance motor types for efficiency and specific power. Overall advantages considering system-level efficiency, specific power, and a few other key metrics such as origin of losses, cooling complexity, manufacturing tolerance, and fault tolerance are discussed. This gives an indication of the relative performance of different motor types and confirms the overall advantage of PM motor topologies in aircraft propulsion.

关键词： Aircraft Propulsion System Switched Reluctance Motor Finite Element Analysis Electric Propulsion optimization algorithm Thermal Management System Fault Tolerance Software Tool Hybrid Electric Aircraft Torque Ripple

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Brain tumor grade identification using deep Elman neural network with adaptive fuzzy clustering-based segmentation approach

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第16期80卷 25139-25169页

作者： Sankaran, K. Sakthidasan Thangapandian, M. Vasudevan, N. Hindustan Inst Technol & Sci Dept Elect & Commun Engn Chennai Tamil Nadu India

A brain tumor is a collection of anomalous evolution of harmful and unnecessary nerve cells in the brain. For advanced treatment, it is essential to categorize the tumors from the MRI (magnetic resonance imaging). In this paper, develop the adaptive fuzzy Tsallis entropy (FTE) clustering with the improved cuckoo search optimization (ICS) procedure, and the effective feature selection mechanism is proposed in this paper. Initially, the input MRI is pre-processed using the anisotropic diffusion filter and non-parametric region-based techniques to eliminate noise removal and skull stripping. After that, the tumor regions are segmented using the adaptive FTE clustering method with the ICS algorithm. The robust features are attained using the first-order statistical, discrete wavelet transform (DWT), the histogram of oriented gradients (HOG), and intensity histogram. After that, the set of optimal features are selected from the mined features using the black widow optimization (BWO) algorithm in the feature selection (FS) process. Finally, a deep Elman neural network (DENN) structure is utilized to categorize the grade of brain tumor. The suggested approach is simulated using the Matlab environment using the BRATS 2012, BRATS 2019 and BRATS 2020 datasets. The different performances are evaluated, and it is related to other existing classifiers and other approaches. The simulation results verified that the accuracy of developed approach is 99.8% for BRATS 2012, 98.7% for BRATS 2019 and 98% for BRATS 2020 as compared to other classifiers and other existing approaches.

关键词： Segmentation Feature selection Brain tumor identification MRI Classifiers optimization algorithm Classification

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Aeroacoustic and aerodynamic optimization of propeller blades

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Chinese Journal of Aeronautics 2020年第3期33卷 826-839页

作者： Peixun YU Jiahui PENG Junqiang BAI Xiao HAN Xiang SONG School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China Unmanned System Research Institute Northwestern Polytechnical UniversityXi’an 710072China

It is of great significance to develop a high-efficiency and low-noise propeller optimization method for new-generation propeller aircraft *** with free form deformation method,dynamic mesh interpolation technology,optimization algorithm,surrogate model,aerodynamic calculation and aeroacoustic prediction model module,the integrated aerodynamic and aeroacoustic design method of propeller is *** optimization design for the six-blade propeller is carried *** non-reduction in efficiency,thrust coefficient and the minimum of aerodynamic noise is treated as the optimization design *** spatial vorticity distribution of the propeller before and after the design is also analyzed by using unsteady computational fluid dynamics *** results show that the optimized propeller can effectively reduce the aerodynamic noise *** maximum total sound pressure level can be reduced by 5 dB without reducing its aerodynamic *** developed method has good application potential in low-noise optimization design of propeller and other rotating machinery.

关键词： Aeroacoustic Aerodynamic Dynamic mesh interpolation technology Free form deformation method optimization algorithm Propeller Sound pressure level Surrogate model

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Optimizing Urban Electric Vehicle Charging Infrastructure Selection: An Approach Integrating GPS Data, Battery Levels, and Energy Availability

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IFAC-PapersOnLine 2024年第13期58卷 392-397页

作者： Meriem Belaid Said El Beid Anas Hatim CISIEV Team Cadi Ayyad University Marrakech 40160 Morocco TIM team Cadi Ayyad University Marrakech 40160 Morocco

This research paper offers a comprehensive exploration of how to enhance the charging infrastructure for Electric Vehicles (EVs) in urban settings. The analysis assesses three hypothetical charging infrastructures in a city, each with generators connected to the national grid and strategically placed AC and DC chargers. The proposed method employs GPS data, EV battery levels, and energy availability across the charging infrastructures. Using an optimization algorithm implemented via the Pandapower Python library, the most appropriate charging infrastructure is identified for individual drivers. The algorithm considers factors such as GPS data and energy availability to suggest the optimal charging station for each EV’s battery level. This pioneering solution aims to streamline the charging process, enhance user experience, and encourage efficient use of urban charging infrastructure for electric vehicles.

关键词： Electric Vehicle (EV) Charging Infrastructure Urban Environments AC Chargers DC Chargers GPS Data Battery Levels optimization algorithm Pandapower Python Library Sustainable Transportation User Experience

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Reliable Islanded Microgrid Operation Using Dynamic Optimal Power Management

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第2期57卷 1755-1766页

作者： Harasis, Salman Sozer, Yilmaz Elbuluk, Malik Univ Akron Dept Elect & Comp Engn Akron OH 44325 USA

The tradeoff between the operating cost and the reliability of microgrids imposes challenges of having cost-effective power dispatch and reliable operations. The work presented in this article optimally solves this issue in a microgrid that contains photovoltaic (PV), dispatchable generator, and storage battery. Flexible control algorithms are developed to enable high penetration of PV power in islanded microgrids such that the challenge of having a cost-effective and reliable operation can be mitigated. This is achieved by creating a power flow optimization framework that considers all variables associated with the system operation. Particle swarm optimization is utilized to effectively optimize the power flow among the integrated sources and to dynamically schedule the battery state of charge. The proposed controller of each source works to fulfill the requirements of the optimization. The energy index of reliability is employed as a direct measure to quantify the reliability level of the microgrid. The effectiveness of the proposed approach is demonstrated by simulations and validated by experimental studies.

关键词： Batteries Microgrids Reliability State of charge optimization Generators Control systems Economic power-sharing microgrid optimal battery state of charge (SOC) optimal power optimization algorithm particle swarm optimization (PSO) photovoltaic (PV) power flow reliability

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Application of Self-adaptive Vision-Correction algorithm for Water-Distribution Problem

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KSCE JOURNAL OF CIVIL ENGINEERING 2021年第3期25卷 1106-1115页

作者： Lee, Eui Hoon Chungbuk Natl Univ Sch Civil Engn Cheongju 28644 South Korea

Water is one of the essential elements in human life, and the water-distribution system is an important infrastructure that distributes water to the people. The design of the water-distribution system was done manually in the past, but recently, it was conducted by applying optimization algorithms. Various optimization algorithms were developed for the optimal design of the water-distribution system, but there was a disadvantage to perform sensitivity analysis for parameter estimation manually. A vision-correction algorithm (VCA) was developed to emulate the conventional manual vision-correction procedure, and it exhibited a good performance in many mathematical benchmark and civil engineering problems. However, the VCA has limited usefulness because of its large number of parameters, many of which require estimation. In this study, a self-adaptive VCA (SAVCA) was developed to overcome these shortcomings by modifying the parameters of the VCA to be self-adaptive or fixed. The Balerma network - a water-distribution system - was selected as a civil engineering problem. The results of SAVCA were better than those of other methods for the design of Balerma network. The SAVCA exhibited good usability and performance and can be applied to various fields in civil engineering including the water-distribution system.

关键词： Water-distribution system Civil engineering problem Metaheuristic Self-adaptive Vision correction optimization algorithm

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Prediction and analysis of domestic water consumption based on optimized grey and Markov model

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WATER SUPPLY 2021年第7期21卷 3887-3899页

作者： Wang, Zhaocai Wu, Xian Wang, Huifang Wu, Tunhua Shanghai Ocean Univ Coll Informat Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Food Shanghai 201306 Peoples R China Wenzhou Business Coll Sch Informat Engn Wenzhou 325035 Peoples R China

With the rapid development of urbanization and the continuous improvement of living standards, China's domestic water consumption shows a growing trend. However, in some arid and water deficient areas, the shortage of water resources is a crucial factor affecting regional economic development and population growth. Therefore, it is essential important to reliably predict the future water consumption data of a region. Aiming at the problems of poor prediction accuracy and overfitting of non-growth series in traditional grey prediction, this paper uses residual grey model combined with Markov chain correction to predict domestic water consumption. Based on the traditional grey theory prediction, the residual grey prediction model is established. Combined with the Markov state transition matrix, the grey prediction value is modified, and the model is applied to the prediction of domestic water consumption in Shaanxi Province from 2003 to 2019. The fitting results show that the accuracy grade of the improved residual grey prediction model is "good". This shows that the dynamic unbiased grey Markov model can eliminate the inherent error of the traditional grey GM (1,1) model, improve the prediction accuracy, have better reliability, and can provide a new method for water consumption prediction.

关键词： grey model Markov chain optimization algorithm state transition matrix water consumption prediction

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