基于凝聚子群法的专家团队聚合

阐述了目前虚拟咨询企业发展优势,通过分析其服务现状发现虚拟咨询企业服务力需进一步提高,主要体现在应该通过与行业专家合作加强自身问题解决能力;以绩效管理为例,介绍了如何运用社会网络中的凝聚子群法实现专家团队的聚合,为企业选择合适的专家提供参考。

子群法与特征线法结合的中子共振计算

传统的中子共振自屏计算方法采用了有理近似,局限于处理简单的共振模型,在处理复杂燃料栅元/组件时会引入较大误差。为提高复杂情况下共振计算的精度,将子群法共振模型与特征线方法结合,推导了子群法-特征线法方程。基于WIMSD格式的69群数据库,编制了可用于任意二维几何中子共振计算的SGMOC程序。通过数值验证表明,该程序计算结果与MCNP程序计算结果吻合良好,具有较高的计算精度与几何通用性。

共振自屏计算的子群法与共振干涉效应研究

对以子群法与特征线法相结合的中子共振自屏计算方法进行了研究,编制了共振计算程序(SGMOC);程序采用WIMSD格式的多群数据库。数值验证表明,SGMOC的计算结果与MCNP程序计算结果吻合良好,具有较高的计算精度与几何通用性。以SGMOC为基础,对子群法共振干涉效应修正计算的两种方法进行了研究分析。条件概率法对UO2燃料栅元无限增殖系数(kinf)计算的修正约为0.0003～0.0018;借助NJOY程序的方法对UO2燃料栅元kinf计算的修正约为0.0002～0.0013。

煤炭资源资本化高质量发展理论框架的构建——基于凝聚子群分析法的应用

中国经济向高质量发展阶段转型,需要有配套的理论予以指导。论文的研究目标就是构建煤炭资源资本化管理的高质量发展理论框架。论文首先以问题为导向,以高质量发展理论为指引,然后利用ROSTCM 6、UCINET软件聚焦煤炭资源资本化和高质量发展的核心内容,采用"凝聚子群分析法"构建煤炭资源资本化高质量发展理论框架,得出煤炭资源资本化高质量发展理论由概念性基础理论和分支理论组成,是以资本化为核心的凝聚子群紧密聚集的框架理论(体系)。煤炭资源资本化管理的高质量理论包括:概念性基础理论、资本化节约理论、资本化环保理论、资本化和谐共生理论、资本化生态产业理论、资本化循环发展理论等。论文的主要创新性在于,首次将凝聚子群分析法用于构建理论框架,由凝聚子群分析法揭示的煤炭资源资本化高质量发展的6大理论系统内在联系性、逻辑性强,对煤炭资源资本化管理的实践具有重要指导意义。

ESSM和Tone方法在熔盐堆共振计算中的适用性分析

基于ThorLAT栅格计算程序,以统一慢化方程形式实现了子群法、嵌入式自屏法(Embedded Self-Shielding Method,ESSM)、Tone和考虑不同燃料区影响的Tone-N共振计算方法。采用SHEM361能群结构,通过反应堆应用虚拟环境(Virtual Environment for Reactor Applications,VERA)组件基准题和熔盐堆燃料栅元基准题对ThorLAT中实现的各类共振计算方法进行了验证和分析。在VERA组件基准题中,有效增殖因子keff及棒功率与参考解均符合较好。对于熔盐堆燃料栅元基准题,基于均匀共振积分表插值的ESSM和Tone方法计算精度较高,且在存在温度分布的算例中计算精度和效率均优于不相关模型和全相关模型的子群法。Tone-N与Tone方法得到的熔盐堆燃料栅元基准题计算结果基本相同,说明了在Tone方法中使用均匀截面假设的合理性。相比XMAS172能群结构,SHEM361能群结构通过体现可分辨共振能区更精细的能谱来有效改进精度。上述结果初步表明,SHEM361能群结构下基于均匀共振积分表插值的ESSM和Tone方法可用于熔盐堆共振计算。

HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.

Driving fatigue fusion detection based on T-S fuzzy neural network evolved by subtractive clustering and particle swarm optimization

In order to improve the accuracy and reliability of the driving fatigue detection based on a single feature, a new detection algorithm based on multiple features is proposed. Two direct driver＇s facial features reflecting fatigue and one indirect vehicle behavior feature indicating fatigue are considered. Meanwhile, T-S fuzzy neural network（TSFNN）is adopted to recognize the driving fatigue of drivers. For the structure identification of the TSFNN, subtractive clustering（SC） is used to confirm the fuzzy rules and their correlative parameters. Moreover, the particle swarm optimization （PSO）algorithm is improved to train the TSFNN. Simulation results and experiments on vehicles show that the proposed algorithm can effectively improve the convergence speed and the recognition accuracy of the TSFNN, as well as enhance the correct rate of driving fatigue detection.

Construction of Early-warning Model for Plant Diseases and Pests Based on Improved Neural Network

By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.

Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

Dynamic compensation for sensors based on particle swarm optimization and realization on LabVIEW

In shock wave＇s pressure testing,a dynamic compensation digital filter is designed based on particle swarm optimization （PSO） algorithm.Dynamic calibration experiment and simulation are conducted for the pressure sensor.PSO algorithm is applied on Matlab platform to achieve optimization according to input and output data of the sensor as well as the reference model,and the global optimal values got by optimization become the parameters of the compensator.Finally,the dynamic compensation filter is established on LabVIEW platform.The experimental results show that the data after processing with the compensation filter truly reflects the input signal.

High-order generalized screen propagator migration based on particle swarm optimization

Various migration methods have been proposed to image high-angle geological structures and media with strong lateral velocity variations; however, the problems of low precision and high computational cost remain unresolved. To describe the seismic wave propagation in media with lateral velocity variations and to image high-angle structures, we propose the generalized screen propagator based on particle swarm optimization （PSO-GSP）, for the precise fitting of the single-square-root operator. We use the 2D SEG/EAGE salt model to test the proposed PSO-GSP migration method to image the faults beneath the salt dome and compare the results to those of the conventional high-order generalized screen propagator （GSP） migration and split-step Fourier （SSF） migration. Moreover, we use 2D marine data from the South China Sea to show that the PSO-GSP migration can better image strong reflectors than conventional imaging methods.

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

PSO algorithm for Young's modulus reconstruction

To get the quantitive value of abnormal biological tissues, an inverse algorithm about the Young＇s modulus based on the boundary extraction and the image registration technologies is proposed. With the known displacements of boundary tissues and the force distribution, the Young＇s modulus is calculated by constructing the unit system and the inverse finite element method （IFEM）. Then a tough range of the modulus for the whole tissue is estimated referring the value obtained before. The improved particle swarm optimizer （PSO） method is adopted to calculate the whole Yong＇s modulus distribution. The presented algorithm overcomes some limitations in other Young＇s modulus reconstruction methods and relaxes the displacements and force boundary condition requirements. The repetitious numerical simulation shows that errors in boundary displacement are not very sensitive to the estimation of next process; a final feasible solution is obtained by the improved PSO method which is close to the theoretical values obtained during searching in an extensive range.

Application of QPSO-KM Algorithm in Wine Quality Classification

Since there are many factors affecting the quality of wine, total 17 factors were screened out using principle component analysis. The difference test was conducted on the evaluation data of the two groups of testers. The results showed that the evaluation data of the second group were more reliable compared with those of the first group. At the same time, the KM algorithm was optimized using the QPSO algorithm. The wine classification model was established. Compared with the other two algorithms, the QPSO-KM algorithm was more capable of searching the globally optimum solution, and it could be used to classify the wine samples. In addition,the QPSO-KM algorithm could also be used to solve the issues about clustering.

Modified particle swarm optimization-based antenna tilt angle adjusting scheme for LTE coverage optimization

In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is proposed based on the modified particle swarm optimization（ MPSO） algorithm.The number of mobile stations（ MSs） served by e NBs, which is obtained based on the reference signal received power（RSRP） measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s.

Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems

A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.

Inversion of self-potential anomalies caused by simple polarized bodies based on particle swarm optimization

Prticle swarm optimization(PSO)is adopted to invert the self-potential anomalies of simple geometry.Taking the vertical semi-infinite cylinder model as an example,the model parameters are first inverted using standard particle swarm optimization(SPSO),and then the searching behavior of the particle swarm is discussed and the change of the particles’distribution during the iteration process is studied.The existence of different particle behaviors enables the particle swarm to explore the searching space more comprehensively,thus PSO achieves remarkable results in the inversion of SP anomalies.Finally,six improved PSOs aiming at improving the inversion accuracy and the convergence speed by changing the update of particle positions,inertia weights and learning factors are introduced for the inversion of the cylinder model,and the effectiveness of these algorithms is verified by numerical experiments.The inversion results show that these improved PSOs successfully give the model parameters which are very close to the theoretical value,and simultaneously provide guidance when determining which strategy is suitable for the inversion of the regular polarized bodies and similar geophysical problems.

Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.

Turnout fault diagnosis based on DBSCAN/PSO-SOM

In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.

A composite particle swarm algorithm for global optimization of multimodal functions

During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.