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| 1 | AN AGGREGATE FUNCTION METHOD FOR NONLINEAR PROGRAMMING显示文摘This paper presents a new method, called the 'aggregate function' method, for solvingnonlinear programming problems. At first, we use the 'maximum' constraint in place of theoriginal constraint set to convert a multi-constrained optimization problem to a non-smoothbut singly constrained problem; we then employ the surrogate constraint concept and themaximum entropy principle to derive a smooth function, by which the non-smooth maximumconstraint is approximated and the original problem is converted to a smooth and singly con-strained problem; furthermore, we develop a multiplier penalty algorithm. The presentalgorithm has merits of stable and fast convergence and ease of computer implementation,and is particularly suitable to solving a nonlinear programming problem with a large num-ber of constraints. | 李兴斯 | 1991 | Science China Mathematics1991，34，12： | 30 |
| 2 | Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization显示文摘For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space(the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO) algorithms. | Zhiming Lv Linqing Wang Zhongyang Han Jun Zhao Wei Wang | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，3： | 13 |
| 3 | Discriminated sgRNAs-Based SurroGate System Greatly Enhances the Screening Efficiency of Plant Base-Edited Cells显示文摘The development of CRISPR/Cas9-mediated base editing has made genomic modification more efficient. However, selection of genetically modified cells from millions of treated cells, especially plant cells, is still challenging. In this study, an efficient surrogate reporter system based on a defective hygromycin resistance gene was established in rice to enrich base-edited cells. After step-by-step optimization, the Discriminated sgRNAs-based SurroGate system (DisSUGs) was established by artificially differentiating the editing abilities of a wild-type single guide RNA (sgRNA) targeting the surrogate reporter gene and an enhanced sgRNA targeting endogenous sites. The DisSUGs enhanced the efficiency of screening base-edited cells by 3- to 5-fold for a PmCDA1-based cytosine-to-tyrosine base editor (PCBE), and 2.5- to 6.5-fold for an adenine base editor (ABE) at endogenous targets. These targets showed editing efficiencies of <25% in the conventional systems. The DisSUGs greatly enhanced the frequency of homozygous substitutions and expanded the activity window slightly for both a PCBE and an ABE. Analyses of the total number of single-nucleotide variants from whole-genome sequencing revealed that, compared with the no-enrichment PCBE strategy, the DisSUGs did not alter the frequency of genome-wide sgRNA-independent off-target mutations, but slightly increased the frequency of target-dependent off-target mutations. Collectively, the DisSUGs developed in this study greatly enhances the efficiency of screening plant base-edited cells and will be a useful system in future applications. | Wen Xu Yongxing Yang Ya Liu Guiting Kang Feipeng Wang Lu Li Xinxin Lv Si Zhao Shuang Yuan Jinling Song Ying Wu Feng Feng Xiaoqing He Chengwei Zhang Wei Song Jiuran Zhao Jinxiao Yang | 2020 | Molecular Plant2020，13，1： | 8 |
| 4 | Multi-disciplinary design optimization with variable complexity modeling for a stratosphere airship显示文摘This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere airship. Firstly, MDO based on the Concurrent SubSpace Optimization(CSSO) strategy is improved for handling the subsystem coupling problem in stratosphere airship design which contains aerodynamics, structure, and energy. Secondly, the VCM method based on the surrogate model is presented for reducing the computational complexity in high-fidelity modeling without loss of accuracy. Moreover, the global-to-local optimization strategy is added to the architecture to enhance the process. Finally, the result gives a prominent stratosphere airship general solution that validates the feasibility and efficiency of the optimization architecture. Besides, a sensitivity analysis is conducted to outline the critical impact upon stratosphere airship design. | Shi YIN Ming ZHU Haoquan LIANG | 2019 | Chinese Journal of Aeronautics2019，32，5： | 7 |
| 5 | Skin toxicity predicts efficacy to sorafenib in patients with advanced hepatocellular carcinoma显示文摘AIM:To study the relationship between adverse events(AEs),efficacy,and nursing intervention for sorafenibtherapy in patients with hepatocellular carcinoma(HCC).METHODS:We enrolled 37 consecutive patients withadvanced HCC who received sorafenib therapy.Relationships among baseline characteristics as well as AEoccurrence and tumor response,overall survival(OS),and treatment duration were analyzed.The nursingintervention program consisted of education regardingself-monitoring and AEs management,and telephoneRESULTS:A total of 37 patients were enrolled in the study,comprising 30 males(81%) with a median age of 71 years.The disease control rate at 3 mo was 41%,and the median OS and treatment duration were 259 and 108 d,respectively.Nursing intervention was given to 24 patients(65%).Every patient exhibited some kinds of AEs,but no patients experienced G4 AEs.Frequently observed AEs > G2 included anorexia(57%),skin toxicity(57%),and fatigue(54%).Factors significantly associated with longer OS in multivariate analysis demonstrated that age ≤ 70 years,presence of > G2 skin toxicity,and absence of > G2 hypoalbuminemia.The disease control rate in patients with > G2 skin toxicity was 13/20(65%),which was significantly higher compared with that in patients with no or G1 skin toxicity.Multivariate analysis revealed that nursing intervention and > G2 skin toxicity were independent significant predictors for longer treatment duration.CONCLUSION:Skin toxicity was associated with favorable outcomes with sorafenib therapy for advanced HCC.Nursing intervention contributed to better adher-ence,which may improve the efficacy of sorafenib. | Masako Shomura Tatehiro Kagawa Koichi Shiraishi Shunji Hirose Yoshitaka Arase Tetsuya Mine Jun Koizumi | 2014 | World Journal of Hepatology2014，6，9： | 6 |
| 6 | Optimal Design and Force Control of a Nine-Cable-Driven Parallel Mechanism for Lunar Takeoff Simulation显示文摘Traditional simulation methods are unable to meet the requirements of lunar takeo simulations, such as high force output precision, low cost, and repeated use. Considering that cable-driven parallel mechanisms have the advantages of high payload to weight ratio, potentially large workspace, and high-speed motion, these mechanisms have the potential to be used for lunar takeo simulations. Thus, this paper presents a parallel mechanism driven by nine cables. The purpose of this study is to optimize the dimensions of the cable-driven parallel mechanism to meet dynamic workspace requirements under cable tension constraints. The dynamic workspace requirements are derived from the kinematical function requests of the lunar takeo simulation equipment. Experimental design and response surface methods are adopted for building the surrogate mathematical model linking the optimal variables and the optimization indices. A set of dimensional parameters are determined by analyzing the surrogate mathematical model. The volume of the dynamic workspace increased by 46% after optimization. Besides, a force control method is proposed for calculating output vector and sinusoidal forces. A force control loop is introduced into the traditional position control loop to adjust the cable force precisely, while controlling the cable length. The e ectiveness of the proposed control method is verified through experiments. A 5% vector output accuracy and 12 Hz undulation force output can be realized. This paper proposes a cable-driven parallel mechanism which can be used for lunar takeo simulation. | Wangmin Yi Yu Zheng Weifang Wang XiaoqiangTang Xinjun Liu Fanwei Meng | 2019 | Chinese Journal of Mechanical Engineering2019，32，4： | 5 |
| 7 | Health diagnosis of concrete dams using hybrid FWA with RBF-based surrogate model显示文摘Structural health monitoring is important to ensuring the health and safety of dams.An inverse analysis method based on a novel hybrid fireworks algorithm (FWA) and the radial basis function (RBF) model is proposed to diagnose the health condition of concrete dams.The damage of concrete dams is diagnosed by identifying the elastic modulus of materials using the displacement changes at different reservoir water levels.FWA is a global optimization intelligent algorithm.The proposed hybrid algorithm combines the FWA with the pattern search algorithm, which has a high capability for local optimization.Examples of benchmark functions and pseudo-experiment examples of concrete dams illustrate that the hybrid FWA improves the convergence speed and robustness of the original algorithm.To address the time consumption problem, an RBF-based surrogate model was established to replace part of the finite element method in inverse analysis.Numerical examples of concrete dams illustrate that the use of an RBF-based surrogate model significantly reduces the computation time of inverse analysis with little influence on identification accuracy.The presented hybrid FWA combined with the RBF network can quickly and accurately determine the elastic modulus of materials, and then determine the health status of the concrete dam. | Si-qi Dou Jun-jie Li Fei Kang | 2019 | Water Science and Engineering2019，12，3： | 3 |
| 8 | Jini技术Surrogate体系结构研究显示文摘文章首先在深入研究Jini技术工作机制的基础上,指出了Jini技术在应用中的局限性;然后介绍了Surrogate体系结构,从运行机制的角度剖析了其改进Jini技术局限性的机理;最后,通过讨论Surrogate体系结构的设计目标总结了其优越性。 | 魏振春 韩江洪 张建军 张利 | 2003 | 计算机工程与应用2003，39，8： | 3 |
| 9 | 基于Surrogate的预装式储能电站布局优化显示文摘为解决预装式储能电站内部布局优化的问题,同时兼顾集装箱内部通风散热效果最好与储能容量最大,提出一种基于Surrogate的预装式储能电站布局优化方案,以进、出风口半径、通风道宽度为决策变量,利用拉丁超立方抽样法生成样本,设计箱体内部设备排布方案及通风口方案,利用有限元软件ANSYSWorkbench仿真计算箱体内温度分布情况;基于热分析结果,使用Surrogate建模方法构建优化模型,采用粒子群算法求解优化模型,得到最佳布局及散热方案。最后,算例验证了方法的适用性。此方法的提出,解决了当前预装式储能电站优化方案中存在的主观性偏强或求解不优的问题,有利于推动预装式储能电站设计的进一步发展。 | 袁铁江 杨南 张昱 车勇 李爱魁 | 2021 | 高电压技术2021，47，4： | 3 |
| 10 | 解0-1线性规划Surrogate对偶的一个方法显示文摘0—1线性观划不难化为以下形式: (P)minc^Tx s.t.Ax≤b,x∈X这里X={(x_1,…,x_n)~T|x_i=0,1,i=1,…,n},A是m×n矩阵,c^T=(c_1,…,c_n),c_i≤0,(i=1,…,n),b∈R^m.假定(P)是适定的,称x是决策变量,A、b、c是参数变量. 设非负乘子V∈R^m。 | 倪明放 | 1989 | 高等学校计算数学学报1989，11，3： | 3 |
| 11 | Proton nuclear magnetic resonance-based metabonomic models for non-invasive diagnosis of liver fibrosis in chronic hepatitis C:Optimizing the classification of intermediate fibrosis显示文摘AIM To develop metabonomic models(MMs), using 1 H nuclear magnetic resonance(NMR) spectra of serum, to predict significant liver fibrosis(SF: Metavir ≥ F2), advanced liver fibrosis(AF: METAVIR ≥ F3) and cirrhosis(C: METAVIR = F4 or clinical cirrhosis) in chronic hepatitis C(CHC) patients. Additionally, to compare the accuracy of the MMs with the aspartate aminotransferase to platelet ratio index(APRI) and fibrosis index based on four factors(FIB-4). METHODS Sixty-nine patients who had undergone biopsy in the previous 12 mo or had clinical cirrhosis were included. The presence of any other liver disease was a criterion for exclusion. The MMs, constructed using partial least squares discriminant analysis and linear discriminant analysis formalisms, were tested by cross-validation, considering SF, AF and C. RESULTS Results showed that forty-two patients(61%) presented SF, 28(40%) AF and 18(26%) C. The MMs showed sensitivity and specificity of 97.6% and 92.6% to predict SF; 96.4% and 95.1% to predict AF; and 100% and 98.0% to predict C. Besides that, the MMs correctly classified all 27(39.7%) and 25(38.8%) patients with intermediate values of APRI and FIB-4, respectively. CONCLUSION The metabonomic strategy performed excellently in predicting significant and advanced liver fibrosis in CHC patients, including those in the gray zone of APRI and FIB-4, which may contribute to reducing the need for these patients to undergo liver biopsy. | Andrea Dória Batista Carlos Jonnatan Pimentel Barros Tássia Brena Barroso Carneiro Costa Michele Maria Goncalves de Godoy Ronaldo Dionísio Silva Joelma Carvalho Santos Mariana Montenegro de Melo Lira Norma ThoméJucá Edmundo Pessoa de Almeida Lopes Ricardo Oliveira Silva | 2018 | World Journal of Hepatology2018，10，1： | 2 |
| 12 | Multi-objective optimization of a flow straightener in a large capacity firefighting water cannon显示文摘In the present study,a multi-objective optimization of a flow straightener in a firefighting water cannon is performed by using the surrogate modeling and a hybrid multi-objective genetic algorithm to increase the jet range of the water cannon.Based on analysis using the three-dimensional Reynolds-averaged Navier-Stokes equations,the optimization is carried with a surrogate model and the radial basis neural network.Three geometric design variables,i.e.,the lengt扎 the thickness of the blade,and the radius of the outer pipe of the flow straightener,are selected for the optimization.The pressure drop through the water can non and the area-averaged turbulent kinetic energy at the outlet of the water cannon,which are closely related to the jet range of the water cannon,are selected as the objective functions to be minimized.The design space is determined through a parametric study,and the Latin hypercube sampling method is used to select the design points in the design space.The Pareto-optimal solutions are obtained through the optimization.Five representative Pareto-optimal solutions are selected to study the trade-off between two objectives. | Qing-jiang Xiang Lin Xue Kwang-Yong Kim Zhe-fu Shi | 2019 | Journal of Hydrodynamics2019，31，1： | 2 |
| 13 | Machine-Learning-Assisted Optimization and Its Application to Antenna Designs: Opportunities and Challenges显示文摘With the rapid development of modern wireless communications and radar, antennas and arrays are becoming more complex, therein having, e.g., more degrees of design freedom, integration and fabrication constraints and design objectives. While fullwave electromagnetic simulation can be very accurate and therefore essential to the design process, it is also very time consuming, which leads to many challenges for antenna design, optimization and sensitivity analysis(SA). Recently, machine-learning-assisted optimization(MLAO) has been widely introduced to accelerate the design process of antennas and arrays. Machine learning(ML) methods, including Gaussian process regression, support vector machine(SVM) and artificial neural networks(ANNs), have been applied to build surrogate models of antennas to achieve fast response prediction. With the help of these ML methods, various MLAO algorithms have been proposed for different applications. A comprehensive survey of recent advances in ML methods for antenna modeling is first presented. Then, algorithms for ML-assisted antenna design, including optimization and SA, are reviewed. Finally, some challenges facing future MLAO for antenna design are discussed. | Qi Wu Yi Cao Haiming Wang Wei Hong | 2020 | China Communications2020，17，4： | 2 |
| 14 | Active learning of deep surrogates for PDEs:application to metasurface design显示文摘Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components.However,the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables.For photonic-device models,we find that this training becomes especially challenging as design regions grow larger than the optical wavelength.We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples.Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve,and we demonstrate how this can be exploited to accelerate large-scale engineering optimization. | Raphaël Pestourie Youssef Mroueh Thanh V.Nguyen Payel Das Steven G.Johnson | 2020 | npj Computational Materials2020，，1： | 2 |
| 15 | An experimental study on spray auto-ignition of RP-3 jet fuel and its surrogates显示文摘Jet fuel is widely used in air transportation,and sometimes for special vehicles in ground transportation.In the latter case,fuel spray auto-ignition behavior is an important index for engine operation reliability.Surrogate fuel is usually used for fundamental combustion study due to the complex composition of practical fuels.As for jet fuels,two-component or three-component surrogate is usually selected to emulate practical fuels.The spray auto-ignition characteristics of RP-3 jet fuel and its three surrogates,the 70%mol n-decane/30%mol 1,2,4-trimethylbenzene blend(Surrogate 1),the 51%mol n-decane/49%mol 1,2,4-trimethylbenzene blend(Surrogate 2),and the 49.8%mol n-dodecane/21.6%mol iso-cetane/28.6%mol toluene blend(Surrogate 3)were studied in a heated constant volume combustion chamber.Surrogate 1 and Surrogate 2 possess the same components,but their blending percentages are different,as the two surrogates were designed to capture the H/C ratio(Surrogate 1)and DCN(Surrogate 2)of RP-3 jet fuel,respectively.Surrogate 3 could emulate more physiochemical properties of RP-3 jet fuel,including molecular weight,H/C ratio and DCN.Experimental results indicate that Surrogate 1 overestimates the auto-ignition propensity of RP-3 jet fuel,whereas Surrogates 2 and 3 show quite similar auto-ignition propensity with RP-3 jet fuel.Therefore,to capture the spray auto-ignition behaviors,DCN is the most important parameter to match when designing the surrogate formulation.However,as the ambient temperature changes,the surrogates matching DCN may still show some differences from the RP-3 jet fuel,e.g.,the first-stage heat release influenced by low-temperature chemistry. | Yaozong DUAN Wang LIU Zhen HUANG Dong HAN | 2021 | Frontiers in Energy2021，15，2： | 2 |
| 16 | 基于Jini技术的智能家居系统集成研究显示文摘家庭网络具有软硬件资源复杂和动态的特点,如何集成这些异构系统并开发新的应用,成为智能家居系统中亟待解决的问题。本文分析比较了UPnP、OSGi、HAVi和Jini等家庭网络中常用的几种中间件,对Jini技术进行了较为深入的研究,说明了Jini技术在智能家居系统中具有广阔应用前景。同时针对Jini自身的特点,提出了一套家居系统的Jini解决方案,可较好地解决智能家居系统集成问题。 | 李中堂 王波 | 2007 | 建筑电气2007，26，12： | 2 |
| 17 | Applying Knowledge of Users with Similar Preference to Construct Surrogate Models of IGA显示文摘Interactive genetic algorithms(IGAs) are effective methods of solving optimization problems with qualitative indices. The problem of user fatigue resulting from the user's evaluations has a negative influence on the performance of these algorithms. Employing various surrogate models to evaluate(a part of) individuals instead of a user is a feasible approach to solve the problem. Previous studies have not fully utilized knowledge provided by users with a similar preference when constructing these models.The problem of constructing surrogate models by using the knowledge of users with a similar preference was focused in this study. Users with a similar preference participating in the evolution were identified by using the collaborative filtering algorithm based on the nearest neighbor, and the individuals evaluated by these users were chosen as a part of samples for training the surrogate model of the current user's cognition. The proposed method was applied to an evolutionary fashion design system, and the experimental results showed that the proposed method can improve the capability in exploration on the premise of greatly alleviating user fatigue. | GONG Dunwei YANG Lei SUN Xiaoyan | 2015 | Chinese Journal of Electronics2015，24，3： | 2 |
| 18 | Multikinase inhibitor-associated hand-foot skin reaction as a predictor of outcomes in patients with hepatocellular carcinoma treated with sorafenib显示文摘AIM To investigate the relationship between the onsets of multikinase inhibitor(MKI)-associated hand-foot skin reaction(HFSR) and prognosis under intervention by pharmacists after the introduction of sorafenib.METHODS We conducted a retrospective study involving 40 patients treated with sorafenib. Intervention by pharmacists began at the time of treatment introduction and continued until the appearance of symptomatic exacerbation or non-permissible adverse reactions. We examined the relationship between MKI-associated HFSR and overall survival(OS) after the initiation of treatment.RESULTS The median OS was 10.9 mo in the MKI-associated HFSR group and 3.4 mo in the no HFSR group, showing a significant difference in multivariate analysis. A multivariate analysis of the time to treatment failure indicated that the intervention by pharmacists and MKI-associated HFSR were significant factors. The median cumulative dose and the mean medication possession ratio were significantly higher in the intervention group than in the non-intervention group. A borderline significant difference was observed in terms of OS in this group.CONCLUSION Intervention by pharmacists increased drug adherence. Under increased adherence, MKI-associated HFSR was an advantageous surrogate marker. Intervention by healthcare providers needs to be performed for adequate sorafenib treatment. | Masanori Ochi Toshiro Kamoshida Atsushi Ohkawara Haruka Ohkawara Nobushige Kakinoki Shinji Hirai Akinori Yanaka | 2018 | World Journal of Gastroenterology2018，24，28： | 1 |
| 19 | THE INFLUENCE OF THE DIFFERENT DISTRIBUTEDPHASE-RANDOMIZED ON THE EXPERIMENTAL DATA OBTAINEd IN DYNAMIC ANALYSIS显示文摘In this paper the influence of the differently distributed phase-randontized to the data obtained in dynamic analysis for critical value is studied.The calculation results validate that the sufficient phase-randomized of the different distributed random numbers are less influential on the critical value . This offers the theoretical foundation of the feasibility and practicality of the phase-randomized method. | 马军海 陈予恕 刘曾荣 | 1998 | Applied Mathematics and Mechanics(English Edition)1998，19，11： | 1 |
| 20 | Surrogate model-assisted interactive genetic algorithms with individual’s fuzzy and stochastic fitness显示文摘We propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract a user’s cognition on evaluated individuals in order to alleviate user fatigue in interactive genetic algorithms with an individual’s fuzzy and stochastic fitness. We firstly present an approach to construct a directed fuzzy graph of an evolutionary population according to individuals’ dominance relations, cut-set levels and interval dominance probabilities, and then calculate an individual’s crisp fitness based on the out-degree and in-degree of the fuzzy graph. The approach to obtain training data is achieved using the fuzzy entropy of the evolutionary system to guarantee the credibilities of the samples which are used to train the surrogate model. We adopt a support vector regression machine as the surrogate model and train it using the sampled individuals and their crisp fitness. Then the surrogate model is optimized using the traditional genetic algorithm for some generations, and some good individuals are submitted to the user for the subsequent evolutions so as to guide and accelerate the evolution. Finally, we quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to find the satisfactory individuals, and also apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency. | Xiaoyan SUN, Dunwei GONG (School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China) | 2010 | 控制理论与应用(英文版)2010，8，2： | 1 |
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