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Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm

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Petroleum Science 2022年第6期19卷 2879-2894页

作者： Qi-Hong Feng Shan-Shan Li Xian-Min Zhang Xiao-Fei Gao Ji-Hui Ni School of Petroleum Engineering China University of Petroleum(East China)Qingdao266580ShandongChina Shandong Institute of Petroleum and Chemical Technology Dongying257061ShandongChina CNOOC China Limited Shenzhen BranchShenzhen518000GuangdongChina

Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.

关键词： Well production Optimization efficiency Streamline simulation Streamline feature objective function Bayesian adaptive direct search algorithm

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban conditions.Predicting pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical simulations.Deep learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind flow.However,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow images.This study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow prediction.In the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial characteristics.Detailed information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency characteristics.These spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced predictions.Experimental results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,respectively.We also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind flow.SFGAN reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding buildings.The enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind comfort.The proposed spatial-frequency loss term is general and can be flexibly inte

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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A Novel Separability objective function in CNN for Feature Extraction of SAR Images

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Chinese Journal of Electronics 2019年第2期28卷 423-429页

作者： GAO Fei WANG Meng WANG Jun YANG Erfu ZHOU Huiyu Department of Electronic and Information Engineering Beihang University Department of Design Manufacture and Engineering Management (DMEM) University of Strathclyde Department of Informatics University of Leicester

Convolutional neural network(CNN) has become a promising method for Synthetic aperture radar(SAR) target recognition. Existing CNN models aim at seeking the best separation between classes, but rarely care about the separability of them. We performs a separability measure by analyzing the property of linear separability, and proposes an objective function for CNN to extract linearly separable features. The experimental results indicate the output features are linearly separable,and the classification results are comparable with the other state of the art techniques.

关键词： Synthetic aperture radar(SAR) Convolution neural network(CNN) Classification Linear separability objective function

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A Differential Game Based Approach Against objective function Attack in Cognitive Networks

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Chinese Journal of Electronics 2018年第4期27卷 879-888页

作者： FENG Guangsheng LIN Junyu ZHAO Qian WANG Huiqiang LYU Hongwu ZHAO Xiaoyu HAN Jizhong LI Bingyang College of Computer Science and Technology Harbin Engineering University Institute of Information Engineering Chinese Academy of Sciences School of Computer and Information Engineering Harbin University of Commerce

We study the problem of defense strategy against objective function attack(OFA) in Cognitive networks(CNs), where the network can be operated at the optimal state by adapting the parameters of its objective function. An OFA attacker can disrupt the parameter adaptation by interfering measures, which results in some operating parameters of the objective function deviating from their optimal settings. We first model the interactive process between the OFA attacker and defense system using differential game theory, and propose a defense strategy by introducing a new metric, namely, threat factor. Then,we obtain the optimal defense strategy by proving the existence of the saddle point of the proposed model. Moreover,this defense strategy is scale-free such that it can conquer a large number of OFAs in a decentralized way. Finally, we conduct extensive simulations to show that the proposed approach is effective.

关键词： Cognitive networks objective function Operating parameter Saddle point

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FUZZY IDENTIFICATION METHOD BASED ON A NEW objective function

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Chinese Journal of Aeronautics 2000年第3期13卷 162-166页

作者：王宏伟贺汉根黄柯棣 Dept. of Automatic Control Natl. Univ. of Defense Technology Changsha 410073 China

A method of fuzzy identification based on a new objective function is proposed. The method could deal with the issue that input variables of a system have an effect on the input space while output variables of the system do not exert an influence on the input space in the proposed objective functions of fuzzy clustering. The method could simultaneously solve the problems about structure identification and parameter estimation; thus it makes the fuzzy model become optimal. Simulation example demonstrates that the method could identify non linear systems and obviously improve modeling accuracy.

关键词： objective function fuzzy clustering fuzzy identification non linear systems

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Modularity-like objective function in annotated networks

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Frontiers of physics 2017年第6期12卷 109-113页

作者： Jia-Rong Xie Bing-Hong Wang Department of Modern Physics University of Science and Technology of China Hefei 230026 China

We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a lin- ear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Conversely, when it is weak, the detection may correspond to another partition. Between the two, there is a transition. This paper provides a concept for expanding the scope of modularity methods.

关键词： community structure annotated networks modularity objective function

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Randomized objective function Linear Programming in Risk Management

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Journal of Applied Mathematics and Physics 2021年第3期9卷 391-402页

作者： Dennis Ridley Felipe Llaugel Inger Daniels Abdullah Khan School of Business and Industry Florida A&M University Tallahassee FL USA Department of Scientific computing Florida State University Tallahassee FL USA College of Engineering Universidad Dominicana O&M Santo Domingo Dominican Republic School of Business Claflin University Orangeburg SC USA

The traditional linear programming model is deterministic. The way that uncertainty is handled is to compute the range of optimality. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each objective function coefficient, one at a time. This yields the range of optimality within which the decision variables remain constant. This sensitivity analysis is useful for helping the analyst get a sense for the problem. However, it is unrealistic because objective function coefficients tend not to stand still. They are typically profit contributions from products sold and are subject to randomly varying selling prices. In this paper, a realistic linear program is created for simultaneously randomizing the coefficients from any probability distribution. Furthermore, we present a novel approach for designing a copula of random objective function coefficients according to a specified rank correlation. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendency, spread, skewness and extreme values for the purpose of risk analysis. This enables risk analysis and business analytics, emerging topics in education and preparation for the knowledge economy.

关键词： Linear Programming Random objective function Profit Distribution Risk Monte Carlo Simulation

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A Novel Approach to Optimize the objective function Based on VC Dimension and Structural Risk Minimization

A Novel Approach to Optimize the Objective Function Based on...

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第三十届中国控制会议

作者： QIU Xintao,FU Dongmei,YANG Tao School of Automation,University of Science and Technology Beijing,Beijing 100083,P.R.China

＜正＞So far,lots of algorithms and learning methods are taking the empirical risk for the optimization goal.In this paper,we propose a new way to optimize the objective function based on VC dimension and structural risk minimization.The optimized function F is firstly defined by us,and some effective design forms of it will also be given.Then we fulfill a useful criterion—the balance minimum optimization principle.This principle considers not only the empirical risk,but also considers the VC dimension of learning machine.Balancing the two factors can avoid the underfitting problem and overfitting problem.Experimental results show that the method we proposed is effective to improve the property of algorithm efficiency,convergence and generalization of the learning machine.Also,the proposed principle for optimization is a new criterion,which is not a practical method for a particular problem of improvement.Therefore,this method is suitable to be applied in many practical situations, which may bring a good generalization performance and efficiency in some learning problems.

关键词： VC Dimension objective function Structural Risk Minimization Balance Minimum Optimization Principle

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Hierarchical model updating strategy of complex assembled structures with uncorrelated dynamic modes

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Chinese Journal of Aeronautics 2022年第3期35卷 281-296页

作者： Chengwei FEI Haotian LIU Rhea PATRICIA LIEM Yatsze CHOY Lei HAN Department of Aeronautics and Astronautics Fudan UniversityShanghai 200433China Department of Mechanical and Aerospace Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Department of Mechanical Engineering Hong Kong Polytechnic UniversityHong Kong 999077China

In structural simulation and design,an accurate computational model directly determines the effectiveness of performance evaluation.To establish a high-fidelity dynamic model of a complex assembled structure,a Hierarchical Model Updating Strategy(HMUS)is developed for Finite Element(FE)model updating with regard to uncorrelated modes.The principle of HMUS is first elaborated by integrating hierarchical modeling concept,model updating technology with proper uncorrelated mode treatment,and parametric modeling.In the developed strategy,the correct correlated mode pairs amongst the uncorrelated modes are identified by an error minimization procedure.The proposed updating technique is validated by the dynamic FE model updating of a simple fixed–fixed beam.The proposed HMUS is then applied to the FE model updating of an aeroengine stator system(casings)to demonstrate its effectiveness.Our studies reveal that(A)parametric modeling technique is able to build an efficient equivalent model by simplifying complex structure in geometry while ensuring the consistency of mechanical characteristics;(B)the developed model updating technique efficiently processes the uncorrelated modes and precisely identifies correct Correlated Mode Pairs(CMPs)between FE model and experiment;(C)the proposed HMUS is accurate and efficient in the FE model updating of complex assembled structures such as aeroengine casings with large-scale model,complex geometry,high-nonlinearity and numerous parameters;(D)it is appropriate to update a complex structural FE model parameterized.The efforts of this study provide an efficient updating strategy for the dynamic model updating of complex assembled structures with experimental test data,which is promising to promote the precision and feasibility of simulation-based design optimization and performance evaluation of complex structures.

关键词： Aeroengine casings Assembled structures Correlated mode pair Hierarchical model updating objective function Uncorrelated modes

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An Improved RPL Algorithm for Low-Power and Lossy Networks

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China Communications 2023年第1期20卷 140-152页

作者： Yanan Cao Hao Yuan Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal UniversityTianjin 300387China Chinese People’s Liberation Army Unit 61846 Zhuozhou 07250China

The routing protocol for low-power and lossy networks(RPL),standardized by Internet Engineering Task Force(IETF),is mainly designed to use for Low-power and Lossy Networks(LLNs).To solve the problems of several important routing metrics are not evaluated,the optimal path may contain long single hop links,lack of scientific multi-routing metrics evaluation method and mechanism to balance the parent child number(especially the parent with one hop away from root),this paper proposes an improved RPL algorithm for LLN(I-RPL).First of all,we propose the evaluated routing metrics:child number of parent,candidate parent number,hop count,ETX and energy consumption index.Meanwhile,we improve the path ETX calculation method to avoid selecting optimal path containing long single hop links.Then we design a novel lexical method to synthetically evaluate candidate parents.Meanwhile,based on the evaluation results of candidate parents,we design a novel objective function and a new calculation node rank method which can also be used for selecting the optimal path.Finally,evaluation results show that I-RPL outperforms ETXOF and several other improvements in terms of packet delivery ratio,latency,etc.

关键词： routing protocol RPL routing metric lexical method objective function

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