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39th Chinese Control Conference (CCC)

作者： Lu, Jian Yue, Shihong Ma, Haitao Yem, Sidolla Wang, Huaxiang Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

Electrical tomographic imaging (ETI) is an advanced technique that has been widely applied in many fields. But it's application is greatly limited by its low spatial resolution which mainly caused by two inherent problems: ill-posed solution and "soft field" effect. Even many optimizing methods have been applied to overcome these problems and have made some contributions to increase the ETI spatial resolution. Meanwhile, the 'prior information' method has been demonstrated to be efficient and feasible. In this paper, we introduced the 'prior information' method for the optimization of the spatial resolution of ET! process. To do this, we first recovered rich types of the prior information in an ETI process, and then introduced them to the ETI process. Based on the Tikhonov regularization and a traditional optimization method, this assessable prior information can greatly increase spatial resolution for typical flow patterns. Moreover, the reconstructed ETI optimal solution is more robust than the solutions achieved by other existing methods. Both theoretical analysis and experimental results strongly confirmed the priority of this new method. It also laid an important foundation for further wide usage of the prior information to improve the spatial resolution in other applications.

关键词： Electrical impedance imaging optimization algorithm Image reconstruction

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Assessing the Significance of Nesting Order in optimization Using Multiple Dominance Relations

Assessing the Significance of Nesting Order in Optimization ...

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AIAA Aviation Forum

作者： Phillips, Samuel T. Jarrett, Jerome P. Univ Cambridge Dept Engn Cambridge CB2 1PZ England

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

A promising alternative to the traditional objectives-and-constraints formulation for optimization that uses multiple dominance relations has recently been developed. Optimal designs according to one dominance relation are sought within the set of designs that are optimal according to some other relation. This problem formulation allows the designer to express more accurately what it is they want from an optimization, resulting in more efficient use of computational resources by focussing the search on designs that are of interest. A key feature of this new formulation is the nesting of multiple dominance relations, and this work assesses whether the ordering of dominance relations within this hierarchical structure is significant. A two-dimensional transonic airfoil design problem is used for this purpose, with design parameters classified as being primary objectives, desirable features, or design requirements. These three classifications result in six possible nesting arrangements, each of which is applied to scenarios comprising one and two primary objectives. Results show that the ordering of dominance relations is significant, with a well chosen arrangement actively assisting the scheme by preventing it from wasting a substantial proportion of the available computational budget searching regions of design space that are of little interest to the designer.

关键词： Airfoil Pitching Moment Computational Fluid Dynamics Angle of Attack Skin Friction Coefficient optimization algorithm Computer algorithm Aerospace Designs

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Discrete Adjoint Constrained Design optimization Approach for Unsteady Transonic Aeroelasticity and Buffet

Discrete Adjoint Constrained Design Optimization Approach fo...

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AIAA Aviation Forum

作者： Thomas, Jeffrey P. Dowell, Earl H. Duke Univ Dept Mech Engn & Mat Sci Durham NC 27708 USA

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

We present the details of two primary research topics in the area of aeroelastic optimization that we are currently working on. The first topic is a new discrete adjoint aeroelastic design optimization solution methodology, which has a computational cost that is independent of the number of structural degrees-of-freedom of the aeroelastic model. In recent years, we have developed a discrete adjoint nonlinear harmonic balance frequency-domain unsteady aeroelastic design optimization methodology, which is able to compute aeroelastic geometric design gradients, that can then be used with an optimization solver to compute an alternate geometric design, which will delay the onset of flutter. This methodology has proven to be robust, however it can be computationally costly since the computational cost is proportional to the number of structural degrees-of-freedom. The new methodology we present does not have that issue, and only a single adjoint solution computation is required when design gradients are required by whatever optimization algorithm one happens to be using. The reason this is possible is that we have recently developed a new nonlinear harmonic balance frequency-domain based aeroelastic solution technique where both the aeroelastic solver and computational flow solver can be marched to convergence simultaneously. With our original aeroelastic solution methodology, we run the aeroelastic solver and flow solver sequentially, one after the other. With the ability to march the aeroelastic and flow solvers simultaneously, we automatically satisfy the constraints that the aeroelastic residual and flow solver residual are zero once the combined aeroelastic solver and flow solver is converged. The second topic discussed in this abstract are new transonic aeroelastic flutter and transonic buffet design optimization results that are computed using a constrained optimization solver. Previously, our transonic aeroelastic flutter and transonic buffet design optimization

关键词： Transonic Aeroelasticity Lift Over Drag Ratio Harmonic Balance Frequency Domain optimization algorithm Computing Angle of Attack Freestream Mach Number NACA 0012 Wind Tunnels

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Design of structure and control system of semiconductor refrigeration box

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Chinese Journal of Chemical Engineering 2020年第11期28卷 2792-2798页

作者： Meng Yu Zhiyun Zou Academy of Military Sciences Beijing 102205China

In order to create low temperature environment for the valve testing,a new type of semiconductor refrigeration box based on semiconductor refrigeration chip and programmable logic controller(PLC)control system is *** power of the semiconductor refrigeration chip is determined by calculating the heat dissipation characteristics of the semiconductor refrigeration *** natural convection heat dissipation with forced air cooling,the heat sink of semiconductor refrigeration chip is *** the control strategy,switch control is combined with an intelligent control *** single neuron optimization algorithm based on quadratic optimization is adopted to adjust and optimize the parameters of the proportional-integral-derivative(PID)controllers in real *** into account the limited hardware capabilities of the PLC,the Jacobian information in parameter adjustment is redesigned into a simplified form of *** actual test results of refrigeration box show good control performance.

关键词： Semiconductor refrigeration Structure design PLC optimization algorithm

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Simulation of Chlorophyll a Concentration in Donghu Lake Assisted by Environmental Factors Based on Optimized SVM and Data Assimilation

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WATER 2022年第15期14卷 2353-2353页

作者： Tang, Xiaodong Huang, Mutao Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China

Lake eutrophication is a global water environmental problem and has become a research focus nowadays. Chlorophyll a concentration is an important index in terms of evaluating lake eutrophication. The aim of this study was to build an effective and universal empirical model for simulation of chlorophyll a concentration in Donghu Lake. On the basis of the relationship between chlorophyll a concentration and dissolved oxygen (DO), water temperature (T), total nitrogen (TN), and total phosphorus (TP), models for simulating chlorophyll a concentration were built by using simulated annealing (SA), genetic algorithm (GA), artificial bee colony (ABC), and particle swarm optimization (PSO) to optimize parameters of support vector machine (SVM). Moreover, a collaborative mode (Col-SVM model) was built by introducing data assimilation, and meanwhile, accuracy and universality of the model were studied. Modeling results showed that the application of optimization algorithms and data assimilation improved the performance of modeling based on SVM. Model simulation results demonstrated that the Col-SVM model has high accuracy, decent stability, and good simulation effect;the root mean square error (RMSE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency coefficient (NSE), bias, and mean relative error (MRE) between simulated values and observed values were 10.07 mu g/L, 0.31, 0.96, -0.050, and 0.15, respectively. In addition, model universality analysis results revealed that the Col-SVM model has good universality and can be used to simulate the chlorophyll a concentration of Donghu Lake at different times. Overall, we have built an effective and universal simulation model of chlorophyll a concentration that provides a new idea and method for chlorophyll a concentration modeling.

关键词： chlorophyll a concentration support vector machine optimization algorithm data assimilation Donghu Lake

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Analyzing the Societal Cost of Electric Roads Compared to Batteries and Oil for All Forms of Road Transport

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ENERGIES 2022年第5期15卷 1925页

作者： Coban, Hasan Huseyin Rehman, Aysha Mohamed, Abdullah Ardahan Univ Dept Elect Engn TR-75002 Ardahan Turkey Univ Gujrat Dept Math Gujrat 50700 Pakistan Future Univ Egypt Univ Res Ctr New Cairo 11745 Egypt

This article is designed to demonstrate that electric roads are an affordable way to electrify all forms of road transport-not only cars, but also buses and trucks. Electric roads represent a way to power electric vehicles without relying solely on batteries. The idea is that when an electric vehicle reaches an electric road, it stops using power from the battery and instead uses power directly from the road itself. The primary challenge for electric vehicles is still the perception of a compromised quality of life in owning an electric vehicle due to a limited range compared with petrol and diesel cars, today. This paper introduces a new technology, currently experiencing rapid development, that can not only overcome range anxiety but make electric vehicles better, in terms of range, than petrol and diesel cars today. Furthermore, not only can this research help to arrange this, but it can also help, for the first time, to cost-effectively electrify heavy-duty transport, such as trucks and buses, which would be a huge breakthrough in terms of sustainability, as it is very important to start supplying electricity to heavy-duty vehicles. The case study provides a very hypothetical example of a trip with and without an electric road, covering a total of 26,011 km of highways and main roads. The results indicate that building electric roads is cheaper than many other alternatives. If a large battery is replaced with a smaller battery for each new vehicle sold, after 3 years, enough savings will be made to electrify all highways and main roads in Turkey. This paper can help transport operators and policymakers develop strategies to accelerate the adoption of electric vehicles by appropriately implementing electric road infrastructure.

关键词： electric road system battery-electric car electric vehicle sustainable transport profitability charging infrastructure optimization algorithm

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A novel hybrid algorithm based on Biogeography-Based optimization and Grey Wolf Optimizer

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APPLIED SOFT COMPUTING 2018年 67卷 197-214页

作者： Zhang, Xinming Kang, Qiang Cheng, Jinfeng Wang, Xia Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Technol Res Ctr Comp Intelligence & Data Min Xinxiang Henan Peoples R China

In order to obtain a Biogeography-Based optimization (BBO) algorithm with strong universal applicability, this paper presents a novel hybrid algorithm based on BBO and Grey Wolf Optimizer (GWO), named HBBOG. Firstly, BBO and GWO are improved respectively. For BBO, the mutation operator is got rid of and a differential mutation operation is merged into the migration operator to enhance the global search ability. The original migration operation is replaced by a multi-migration operation to enhance the local search ability. For GWO, the opposition-based learning approach is merged to prevent the algorithm from falling into the local optima to some degree. Then, the improved BBO and the opposition learning based GWO are hybridized by a new strategy, named single-dimensional and all-dimensional alternating strategy, to formulate HBBOG. HBBOG can effectively maximize the two algorithms' advantages and overall balance exploration and exploitation, therefore, it can obtain strong universal applicability. We make a large number of experiments on a set of various kinds of benchmark functions and CEC2014 test set and apply HBBOG to clustering optimization. The experimental results show that HBBOG outperforms quite a few state-of-the-art algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Evolutionary algorithm Biogeography-Based optimization Grey Wolf Optimizer Opposition-based learning approach

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Using Boundary Condition and Topology optimization to Design an Airplane Component

Using Boundary Condition and Topology Optimization to Design...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Persson, Johan A. Wiberg, Anton Linkoping Univ Management & Engn S-58183 Linkoping Sweden

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

This paper demonstrates a method that can be used to combine topology optimization with optimization of the boundary conditions. The method utilizes design of experiments and surrogate models to model how the boundary conditions affect the potential mass of the component. A demonstration of the method is made by applying it to design an airplane component and comparing the result to other approaches. The best design is then manufactured using additive manufacturing to verify that it is feasible.

关键词： Topology optimization Aeroplane Surrogate Model Design of Experiments Structural optimization optimization algorithm Finite Element Method Shape optimization Trusses Fuel Consumption

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Semantic communication-based convolutional neural network for enhanced image classification

Franklin Open

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Franklin Open 2024年 9卷

作者： Guler, Nivine Hazem, Zied Ben Department of Computer Science University of Central Asia Naryn 722918 Kyrgyzstan Automation and Sustainability Research Centre (ASRC) Department of Mechatronics Engineering College of Engineering (COE) University of Technology Bahrain Salmabad Bahrain

In AI-IoT environments, the traditional centralized cloud computing approach leads to high network transmission volumes and communication delays, negatively affecting intelligent task performance. This study addresses these issues by introducing an innovative semantic communication system model for intelligent tasks, leveraging deep learning techniques. The research focuses on image classification tasks constrained by bandwidth and delay in AI-IoT scenarios. The proposed model features a tailored semantic communication network architecture, where image feature maps are extracted on IoT devices. These semantic relations are then compressed based on the extracted feature maps to reduce power consumption on IoT devices and mitigate communication transmission pressures. Simulations and comparative analyses of various network performance metrics show that the proposed semantic communication system improves image classification accuracy by 90%% at low signal-to-noise ratios compared to traditional methods. With an 80%% compression ratio, the classification accuracy loss is minimal—within 2%%—when the signal-to-noise ratio exceeds 0. Additionally, at a signal-to-noise ratio of 20, the semantic compression transmission scheme enhances classification accuracy by 30%% compared to random compression. Moreover, the proposed system outperforms traditional approaches in execution time by approximately 80%%. © 2024 The Author(s)

关键词： CNN IoT optimization algorithm Semantic communication

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A Framework of gradient-based shape optimization using feature-based CAD parameterization

A Framework of gradient-based shape optimization using featu...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Sun, Liang Yao, Weigang Robinson, Trevor T. Marques, Simao Armstrong, Cecil G. Queens Univ Belfast Sch Mech & Aerosp Engn Belfast BT9 5AH Antrim North Ireland Univ Surrey Dept Mech Sci Guildford GU2 7XH Surrey England

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

This paper presents a framework of performing aerodynamic shape optimization using the feature-based CAD system, which is widely used in aerospace industry. An interface is set up to communicate between the system installs the CAD package and the system installs the CFD solver. Mesh is adapted to fit the new CAD model after each parametric update by performing surface mesh deformation and volume mesh deformation in sequence. The proposed framework can be easily integrated into the industrial product design and analysis process.

关键词： Shape optimization Aerodynamic Shape optimization CAD System Aerospace Industry Lift Coefficient Mesh Generation Open Source Software Pressure Coefficient Linear Elasticity optimization algorithm

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