检索结果-内蒙古大学图书馆

3rd International Conference on Information Communication and Software Engineering (ICICSE)

作者： Zhang, Wei Shang, Ling Tian, Hongxing Zhang, Yangbo Yichang Res Inst Testing Technol Div 105 Yichang Peoples R China Yichang Res Inst Testing Technol Metrol Dept Yichang Peoples R China

ISBN: (纸本)9798350313048

Acoustic pressure and particle velocity is narrowband filtered to obtain multi-band signal. High-order correlation equations composed of acoustic pressure and particle velocity are solved by optimization algorithm and DOA (Direction of arrive) distribution for different signal in multi-band is counted. Then the DOA of multiple targets can be distinguished and tracked. In order to improve the accuracy, the least square method is used to fit the measurement results and a prediction model is established, and then DOA trajectory is optimized by Kalman filter. The experimental results indicate that DOA of three targets can be distinguished and trajectory tracked, DOA of interference source is also observed by the pseudo color picture of DOA distribution, the accuracy of DOA estimation is less than 5 degrees.

关键词： vector hydrophone DOA estimation least square method Kalman filter optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A multi-objective optimization of electric vehicles energy flows: the charging process

引用

ANNALS OF OPERATIONS RESEARCH 2021年第1-2期296卷 315-333页

作者： Merhy, Ghimar Nait-Sidi-Moh, Ahmed Moubayed, Nazih Univ Picardie Jules Verne LTI Lab St Quentin en Yvelines France Lebanese Univ Fac Engn CRSI LaRGES Beirut Lebanon

As the electrification of vehicles keeps being widespread, and facing the impossibility of storing big amounts of electrical energy, the challenge of controlling and adapting the electricity supply and demand has become a necessity. Therefore, electric vehicles could be an optimal solution for the storage and the retrieval of energy depending on the supply and demand of electricity. Thus, this study proposes an energetic strategy based on a multi-objective and multi-criteria optimization algorithm related to the control of the energy flows between the electric vehicles and the grid, home or building depending on the electricity supply and demand. The main focus at this stage involves the optimization of the vehicles' charging mode. Hence, in this paper, the multi-objective optimization proposed is first implemented through the presentation of its algorithm and the description and modeling of its objective functions and their corresponding constraints. Then, the genetic algorithm optimization approach is adopted for the charging process of the study to find the optimal solution for each objective function. Besides, the weighted sum approach is applied after the normalization of all the fitness functions and several case studies are carried out to find the optimal solutions based on the priority of specific objectives over the others. Once all calculations are done, a simulation via Matlab software is performed and the results are discussed and compared. The simulation of the results has verified the theoretical calculations proving the effectiveness of the proposed methodology. However, as some of the calculated optimal solutions of the system seem to be conflicting, the prioritization of some objectives over others had to be operated in order to figure out a global solution for the multi-objective system. Thus, as a compromise had to be applied for the calculation of quasi-optimal solutions that would converge towards the Pareto-front, the decision maker's preferenc

关键词： Electric vehicles Energy flows Multi-objective optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Buckling and Fundamental Frequency optimization of Tow-Steered Composites Using Layerwise Structural Models

引用

AIAA JOURNAL 2023年第9期61卷 4149-4163页

作者： Sanchez-Majano, Alberto Racionero Pagani, Alfonso Polytech Univ Turin Dept Mech & Aerosp Engn I-10129 Turin Italy

Variable-angle-tow (VAT) composite laminates can eventually improve the mechanical performance of lightweight structures by taking advantage of a larger design space compared to straight-fiber counterparts. Here, we provide a scalable low- to high-fidelity methodology to retrieve the tow angles that maximize the buckling load and the fundamental frequency of VAT plates. A genetic algorithm is used to solve the optimization problem in which the objective function is mimicked using a surrogate model. Both unconstrained and manufactured-constrained problems are solved. The surrogates are built with outcomes from numerical models generated by means of the Carrera unified formulation, which enables to obtain straightforwardly different degrees of accuracy by selecting the order of the structural theory employed. The results show both the validity and flexibility of the proposed design approach. It is shown that, although the optimal design fiber angle orientations are consistently similar, discrepancies in the prediction of the buckling load or fundamental frequency can be found between high-fidelity layerwise and low-to-refined equivalent-single-layer models, of which classical laminated plate or first-shear deformation theories are degenerate examples.

关键词： Buckling Analysis Structural Modeling and Simulation Fiber Volume Fraction Genetic algorithm Finite Element Method optimization algorithm Mechanical and Structural Vibrations Composite Structures Variable Stiffness Composites Variable-angle Tow

来源：评论

学校读者我要写书评

暂无评论

Application of optimization Techniques for Searching Optimal Reservoir Rule Curves: A Review

引用

WATER 2023年第9期15卷

作者： Kangrang, Anongrit Prasanchum, Haris Sriworamas, Krit Ashrafi, Seyed Mohammad Hormwichian, Rattana Techarungruengsakul, Rapeepat Ngamsert, Ratsuda Mahasarakham Univ Fac Engn Maha Sarakham 44150 Thailand Rajamangala Univ Technol Isan Fac Engn Khon Kaen Campus Khon Kaen 40000 Thailand Ubon Ratchathani Univ Fac Engn Ubon Ratchathani 34190 Thailand Shahid Chamran Univ Ahvaz Fac Civil Engn & Architecture Dept Civil Engn Ahvaz 61355 Iran

This paper reviews applications of optimization techniques connected with reservoir simulation models to search for optimal rule curves. The literature reporting the search for suitable reservoir rule curves is discussed and examined. The development of optimization techniques for searching processes are investigated by focusing on fitness function and constraints. There are five groups of optimization algorithms that have been applied to find the optimal reservoir rule curves: the trial and error technique with the reservoir simulation model, dynamic programing, heuristic algorithm, swarm algorithm, and evolutionary algorithm. The application of an optimization algorithm with the considered reservoirs is presented by focusing on its efficiency to alleviate downstream flood reduction and drought mitigation, which can be explored by researchers in wider studies. Finally, the appropriate future rule curves that are useful for future conditions are presented by focusing on climate and land use changes as well as the participation of stakeholders. In conclusion, this paper presents the suitable conditions for applying optimization techniques to search for optimal reservoir rule curves to be effectively applied in future reservoir operations.

关键词： optimal reservoir rule curves optimization algorithm climate change reservoir model stakeholder participation

来源：评论

学校读者我要写书评

暂无评论

Reliability optimization of a Mars Oxygen Production Plant

引用

JOURNAL OF SPACECRAFT AND ROCKETS 2023年第3期60卷 982-990页

作者： Hinterman, Eric D. Khopkar, Piyush P. MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA MathWorks Inc Dept Aeronaut & Astronaut Natick MA 01760 USA

The Mars Oxygen ISRU Experiment (MOXIE) is an instrument onboard NASA's Perseverance rover. On April 20th, 2021, MOXIE generated oxygen on Mars from the carbon dioxide present in the Martian atmosphere, demonstrating, for the first time, in-situ resource utilization (ISRU) on the surface of another celestial body. Learnings from MOXIE on Mars have aided in the design of a scaled-up version of MOXIE. Oxygen generated from this scaled-up system would be used as propellant in a Mars Ascent Vehicle that would enable the crew to return to Earth once their mission was complete, as well as in life support systems. Failure of any of its subsystems would result in a loss of mission due to the inability of the crew to return to Earth. Accordingly, risk analysis is one of the most crucial steps in the design of the scaled-up MOXIE that must be completed and understood before building and launching the system to Mars. The intent of this paper is to present a comprehensive, quantitative analysis of the operational risks associated with this Mars ISRU plant. We then present an approach to optimize the reliability of each subsystem using a modified probabilistic risk assessment and heuristics-based optimization algorithm.

关键词： Space Exploration and Technology Mars Ascent Vehicle Propellant optimization algorithm Life Support System Discrete Probability Distributions Failure Modes and Effects Analysis Mars Missions In Situ Resource Utilization MOXIE

来源：评论

学校读者我要写书评

暂无评论

Coverage Maximization for Heterogeneous Aerial Networks

引用

IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第1期11卷 91-95页

作者： Zhai, Daosen Shi, Qiqi Zhang, Ruonan Tang, Xiao Cao, Haotong Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Shaanxi Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

In this letter, we investigate the network planning of the heterogeneous aerial networks to maximize the network coverage. Specifically, a joint high-altitude aerial base station (HABS) trajectory and low-altitude aerial base station (LABS) position optimization problem is formulated with the objective to maximize the number of covered users. Based on the feature of the air-to-ground channel, we first obtain the optimal altitude of the LABSs through the bisection method. Then, we propose a joint particle-and-fish swarm algorithm to optimize the horizontal position of the LABS. For the uncovered users, we model the user coverage probability in the HABS and devise a random interval contraction based algorithm to optimize the flying radius of the HABS. Simulation results indicate that our algorithms can fully exploit the mobility of the heterogeneous aerial networks to cover more users.

关键词： optimization Unmanned aerial vehicles Base stations Wireless networks Particle swarm optimization NOMA High frequency Aerial networks unmanned aerial vehicle network coverage optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A New Dynamic Optimal M2M RF Interface Setting in Relay Selection algorithm (DORSA) for IoT Applications

引用

IEEE ACCESS 2022年 10卷 5327-5342页

作者： Ghasri, Monireh Allah Gholi Hemmatyar, Ali Mohammad Afshin Sharif Univ Technol Dept Comp Engn Tehran *** Iran

Machine-to-Machine (M2M) communication is an important type of communication in the Internet-of-Things (IoT). How to send data in these high-density communications using relay selection can help improve the performance of this type of communication in various applications. In addition, the possibility of simultaneous use of different Radio Frequency (RF) interfaces helps to use the spectrum more efficiently. In this work, we try to further use machine communication RF equipment and improve the average data rate of networks in some applications such as the IoT, which have their own bandwidth requirements. Therefore, we provide an optimization algorithm for relay selection as well as the simultaneous and dynamic multiple M2M RF interface setting that is called Dynamic Optimal Relay Selection and RF interfaces Setting algorithm (DORSA). The simulation results show that the average data rate of DORSA with three interfaces (DORSA_W-B-Z) can be improved up to 10% compared to the existing methods such as optimal direct transmission and relay selection algorithms with static RF interface setting.

关键词： Radio frequency Relays Heuristic algorithms Machine-to-machine communications Standards Internet of Things Bipartite graph Machine-to-machine (M2M) communications the Internet of Things (IoT) relay selection dynamic RF interface setting multiple RF interfaces optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Surrogates for Optimal 2D Spatial Packaging of Interconnected Systems with Physics Interactions (SPI2)

Machine Learning Surrogates for Optimal 2D Spatial Packaging...

引用

AIAA Aviation Forum

作者： Parrott, Corey M. Peddadat, Satya R. T. Allison, James T. James, Kai A. Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

This work proposes new methods and applications of neural networks in automated design optimization for spatial packaging of interconnected systems with physics interactions (SPI2). The experiments are demonstrated on 2D electrothermal systems with heat-generating components connected via fluid-based cooling pipes. We seek to minimize the bounding box area of the system, while accounting for multiple physics and geometric constraints. We train a multilayer perceptron (MLP) on data generated through state-of-the-art physics-based optimization models, constructed with a finite element method (FEM)-based 2D steady state thermal conduction model and a lumped parameter pipe flow model. With the physics-based optimal designs as the ground truth, the model is tasked with mapping various configuration data, including design starting points, and boundary conditions of the physics model to the optimal system layout. Through training the model for this task, the network effectively acts as a surrogate for both physics-driven simulations and numerical optimization. Capable of real-time design synthesis, the MLP exhibits good generalization performance by generating designs that closely match those attained with the iterative physics-based optimization techniques. Relative to the ground truth physics-based designs, our trained surrogate achieves similar to 91% accuracy while performing the same design tasks orders of magnitude faster. Further, we impose loss functions with respect to the geometric constraints of the physics-based model to more consistently produce feasible designs and match the characteristics of the physics data.

关键词： Reinforcement Learning Generative Adversarial Network Design optimization Finite Element Method Steady State Thermal Conduction Pipe Flow Surrogate Model Topology optimization Stochastic Gradient Descent optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal Energy Storage System Sizing for Peak Shaving in a NU Dormitory Building 15

Optimal Energy Storage System Sizing for Peak Shaving in a N...

引用

15th Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Mohamed, Naseef Thanarak, Prapita Ketjoy, Nipon Prasatsap, Unchittha Naresuan Univ Sch Renewable Energy & Smart Grid Technol Phitanulok Thailand

ISBN: (纸本)9798350318104;9798350318098

An optimal battery energy storage system (BESS) capacity was determined using the modified particle swarm optimization (PSO) algorithm for the NU dormitory Building 13. The BESS management strategy uses peak shaving of the electric load profile in the NU dormitory building. As well, the time-of-use tariff (TOU) was considered, and a mathematical model of BESS management was developed. The optimal BESS sizing and the efficient BESS management strategy can efficiently shave the peak load and reduce the overall electricity cost because the peak load power and the energy cost in the on-peak period are diminished. By using peak shaving in the NU dormitory building, the optimal BESS sizing of 26.68 kWh for the increased load factor criteria was determined. When the BESS was operated, the Load factor was improved by 20%, and the peak power consumption was reduced to 25.70 kW.

关键词： Battery energy storage system optimization algorithm BESS Management Peak shaving

来源：评论

学校读者我要写书评

暂无评论

Research on Operational Risk Monitoring Method of Intelligent Financial System Based on Deep Learning and Improved RPA 6th

Research on Operational Risk Monitoring Method of Intelligen...

引用

6th European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Yuan, Liang Zhu, Hui State Grid Huitong Jincai Beijing Informat Techno Beijing 100031 Peoples R China

ISBN: (纸本)9783031287862;9783031287879

The accuracy of traditional financial system operational risk monitoring is low. Therefore, this paper proposes a method of intelligent financial system operational risk monitoring based on deep learning and improved RPA. Set the financial monitoring index and obtain the warning threshold parameters;Using deep neural network method to mine key risk indicators and obtain reconstruction coefficients of data mining errors of financial system;By improving the RPA method to calculate the fit degree of financial risk, it matches the internal business process of the enterprise;The operational risk monitoring algorithm of financial system is designed to realize the operational risk monitoring of financial system. The experimental results show that the risk monitoring accuracy of the design method is 80.3%, and the overall test threshold of the model is 0.5 after the introduction of non-financial indicators, which shows that it can be applied in practice.

关键词： Deep learning Improve RPA Intelligent financial system Operational risk monitoring optimization algorithm

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：