检索结果-内蒙古大学图书馆

Rocket Landing Guidance Based on Linearization-Free Convexification

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

来源：评论

学校读者我要写书评

暂无评论

Dynamic generalized normal distribution optimization for feature selection

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A deep learning based model for diabetic retinopathy grading

引用

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

来源：评论

学校读者我要写书评

暂无评论

Improved Predictive Current Controller for Variable Flux Memory Machine Drives Considering Magnetization State Manipulating Operations

引用

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2025年第2期13卷 1406-1417页

作者： Liu, Xing Yang, Hui Cai, Shun Lin, Heyun Yu, Feng Yang, Yong Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Southeast Univ Jiangsu Prov Key Lab Smart Grid Technol & Equipmen Nanjing 210096 Peoples R China UCL Dept Mech Engn London E20 2AE England Nantong Univ Sch Elect & Informat Engn Nantong 226019 Peoples R China Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China

Variable flux memory machine (VFMM) is recognized as a competitive counterpart to traditional permanent magnet synchronous machine (PMSM) for electrified transportation, due to the advantages in terms of extended speed range and improved global efficiency. Regarding the control aspect of VFMM drives, the most crucial challenge refers to the design of current controller with emphasis on the exclusive magnetization state (MS) manipulation, which is realized via short d-axis current pulse injection. This article proposes an improved finite-control-set model-free predictive current control (FCS-MFPCC) for VFMM drives. In order to ensure simultaneously the dq-axis current tracking performances during MS manipulating processes, a novel hybrid optimization algorithm is proposed. In particular, a sliding mode-based regulation dedicated to the q-axis current is additionally designed in the proposed algorithm, and by doing so, it is guaranteed that the q-axis current is regulated toward its reference. Finally, experiments on a VFMM test rig are conducted to validate the effectiveness and superiority of the proposed FCS-MFPCC method.

关键词： Current control Robustness optimization Prediction algorithms Torque Prototypes Predictive models Power electronics Mathematical models Heuristic algorithms Finite-control-set model-free predictive current control (FCS-MFPCC) optimization algorithm permanent magnet synchronous machine (PMSM) variable flux memory machine (VFMM)

来源：评论

学校读者我要写书评

暂无评论

A Wideband and Low Sidelobe Magnetoelectric Dipole Antenna Array With Embedded Resistors

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

引用

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2025年第3期6卷 749-758页

作者： Huang, Jianhui Luk, Kwai-Man City Univ Hong Kong Dept Elect Engn State Key Lab Terahertz & Millimeter Waves Hong Kong Peoples R China

A simple and effective method, by using '0-1' excitations, to reduce the sidelobe level (SLL) for a wideband magnetoelectric (ME) dipole antenna array is proposed. First of all, the multiple-population genetic algorithm (MPGA) is utilized to search optimal '0-1' excitations for SLL reductions. Then, the '0' and '1' excitation can be achieved by incorporating the absorbing element embedded with a resistor and an ME dipole antenna element, respectively. Different from the traditional tapered excitation techniques, the proposed array utilizes an equal power divider to distribute the power to each element. Finally, a planar 16 x 16 stripline-fed antenna array according to the optimized array configuration is designed, fabricated, and measured. An overlapped impedance bandwidth of 57% (10-18 GHz) is achieved with the standing wave ratio (SWR) less than 2, and the SLLs are lower than -17.5 dB across 40% bandwidth both in E- and H-planes. In addition, the measured realized gain of this array prototype is up to 26.2 dBi with a high realized aperture efficiency ranging from 61% to 73%. The proposed lightweight, high-gain and high-integration Ku-band antenna array with low SLL characteristics shows great potential in satellite communications.

关键词： Antenna arrays Wideband Apertures Feeds Dipole antennas Antenna measurements optimization Resistors Power dividers Genetic algorithms Low sidelobe level (SLL) magnetoelectric (ME) dipole stripline Ku-band wideband optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimization of Electric Pumps for Hybrid Rocket Engines

Multi-objective Optimization of Electric Pumps for Hybrid Ro...

引用

AIAA Aviation Forum

作者： Ferrero, A. Gedda, G. Masseni, F. Pastrone, D. Politecn Torino Dept Mech & Aerosp Engn Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

A multi-objective optimization of an electric pump for hybrid rockets is carried out through an evolutionary algorithm. The pump is modeled by means of a meanline flow method. For given values of required head and flow rate, some of the pump design parameters are varied to minimize the power and the torque to be delivered by the motor. The two minima do not coincide because of the dependence of pump power losses on shaft speed for different geometries;Pareto-optimal solutions are found near the upper bound of the chosen speed range.

关键词： Hybrid Rocket Engines Particle Swarm optimization Hybrid Rocket Drive Pump Liquid Propellant Rocket Propellant Electric Power Multidisciplinary optimization optimization algorithm Mass Flow Rate

来源：评论

学校读者我要写书评

暂无评论

Resource optimization algorithm Based on Evaluation Decision mModel of Information Entropy

Resource Optimization Algorithm Based on Evaluation Decision...

引用

International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Huang, Limin He, Guosong Chen, Zhi Hubei Univ Sci & Technol Modern City & Dev Res Inst Coll Resources & Environm Sci Xianning 437100 Peoples R China

ISBN: (纸本)9783038351153

Xianning is located in the fourth pole of economic growth core area of The Central Triangle, through the location entropy analysis method we can know that, tourism industry is an advantaged industry in Xianning and the adjacent region of The Central Triangle, and have their own advantages and characteristics of tourism resources. According to the space constraint issues on function realization of advantaged tourism resources in Xianning, space breakthrough strategy based on The Central Triangle pattern has been presented.

关键词： Information entropy optimization algorithm Resource utilization Function division Weighted objective Radiation

来源：评论

学校读者我要写书评

暂无评论

Calculation of the mechanical properties of high-performance concrete employing hybrid and ensemble-hybrid techniques

引用

STRUCTURAL CONCRETE 2024年第5期25卷 3765-3787页

作者： Zhang, Leilei Zhao, Yuwei Zhengzhou Shengda Univ Zhengzhou 450000 Henan Peoples R China Sun Life Co EED Baku Azerbaijan

This study aims to execute machine learning methods to predict the mechanical properties containing TS and CS of HPC. They are essential parameters for the durability, workability, and efficiency of concrete structures in civil engineering. In this regard, obtaining the estimation of the mechanical properties of HPC is complex energy and time-consuming. Due to this, an observed database was compiled, including 168 datasets for CS and 120 for TS. This database trained and validated two machine learning models: SVR and RT. The models combine the prediction outputs from the meta-heuristic algorithms to build hybrid and ensemble-hybrid models, which include dwarf mongoose optimization, PPSO, and moth flame optimization. According to the observed outputs, the ensemble models have great potential to be a recourse to deal with the overfitting problem of civil engineering, thus leading to the development of more supportable and less polluting concrete structures. This research significantly improves the efficiency and accuracy of predicting vital mechanical properties in high-performance concrete by integrating machine learning and metaheuristic algorithms, offering promising avenues for enhanced concrete structure design and development.

关键词： compressive and tensile strength high-performance concrete optimization algorithm regression tree support vector regression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：