Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

Simultaneous Optimization of Incomplete Multi-Response Experiments

This article attempts to develop a simultaneous optimization procedure of several response variables from incomplete multi-response experiments. In incomplete multi-response experiments all the responses (p) are not recorded from all the experimental units (n). Two situations of multi-response experiments considered are (i) on units all the responses are recorded while on units a subset of responses is recorded and (ii) on units all the responses (p) are recorded, on units a subset of responses is recorded and on units the remaining subset of responses is recorded. The procedure of estimation of parameters from linear multi-response models for incomplete multi-response experiments has been developed for both the situations. It has been shown that the parameter estimates are consistent and asymptotically unbiased. Using these parameter estimates, simultaneous optimization of incomplete multi-response experiments is attempted following the generalized distance criterion [1]. For the implementation of these procedures, SAS codes have been developed for both complete (k ≤ 5, p = 5) and incomplete (k ≤ 5, p1 = 2, 3 and p2 = 2, 3, where k is the number of factors) multi-response experiments. The procedure developed is illustrated with the help of a real data set.

Multi-response optimization of MIL-101 synthesis for selectively adsorbing N-compounds from fuels

In this work,MIL-101,a metal organic framework,has been synthesized and examined in the adsorptive denitrogenation process.Due to the importance of adsorption capacity and selectivity,the effects of synthesis parameters including metal type,reagent ratio,time and temperature on the MIL-101 performance were investigated by measuring quinoline(QUI)separation from iso-octane.The optimum conditions were determined using a Taguchi experimental design and the multiresponse optimization(multivariate statistical)method.Based on the arithmetic mean of normalized QUI adsorption capacity and QUI/dibenzothiophene(DBT)selectivity,as the objective function,the optimum value of synthesis parameters were found to be manganese as metal type in the structure,180°C for synthesis temperature,15h for synthesis time and 1.00 for reagent molar ratio.Under these conditions,QUI adsorption capacity and QUI/DBT selectivity were 19.3 mg-N/g-Ads.and 24.6,respectively.Accordingly,the arithmetic mean between normalized values of these measured parameters was equal to 1.10,which is in good agreement with the predicted value.The MIL-101 produced under optimum conditions was characterized by determining its specific surface area,X-ray powder diffraction patterns and Fourier transform infrared spectroscopy.Finally,isotherm and kinetic studies indicate that the Langmuir isotherm and pseudo-first-order model can successfully describe the experimental data.

Parameter optimization of gravity density inversion based on correlation searching and the golden section algorithm

For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversion parameters and subdivision scheme can, not only improve the inversion process efficiency, but also ensure inversion result accuracy. The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method. It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces. In this paper, we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm. Considering that the gold section algorithm is not globally optimized. we present a heuristic method to ensure the inversion result is globally optimized. With a series of model tests, we systematically compare and analyze the inversion result efficiency and accuracy with different parameters. Based on the model test results, we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved.

Statistical analysis of fracture properties based on particle swarm optimization and Pearson correlation coefficient method

Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.

Multi-dimensional and Multi-threshold Airframe Damage Region Division Method Based on Correlation Optimization

In order to obtain the image of airframe damage region and provide the input data for aircraft intelligent maintenance,a multi-dimensional and multi-threshold airframe damage region division method based on correlation optimization is proposed.On the basis of airframe damage feature analysis,the multi-dimensional feature entropy is defined to realize the full fusion of multiple feature information of the image,and the division method is extended to multi-threshold to refine the damage division and reduce the impact of the damage adjacent region’s morphological changes on the division.Through the correlation parameter optimization algorithm,the problem of low efficiency of multi-dimensional multi-threshold division method is solved.Finally,the proposed method is compared and verified by instances of airframe damage image.The results show that compared with the traditional threshold division method,the damage region divided by the proposed method is complete and accurate,and the boundary is clear and coherent,which can effectively reduce the interference of many factors such as uneven luminance,chromaticity deviation,dirt attachment,image compression,and so on.The correlation optimization algorithm has high efficiency and stable convergence,and can meet the requirements of aircraft intelligent maintenance.

Comprehensive Evaluation Method for Traffic Flow Data Quality Based on Grey Correlation Analysis and Particle Swarm Optimization

Nowadays,data are more and more used for intelligent modeling and prediction,and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usable or not.However,the comprehensive evaluation method of data quality mostly contains the subjective factors of the evaluator,so how to comprehensively and objectively evaluate the data has become a bottleneck that needs to be solved in the research of comprehensive evaluation method.In order to evaluate the data more comprehensively,objectively and differentially,a novel comprehensive evaluation method based on particle swarm optimization(PSO)and grey correlation analysis(GCA)is presented in this paper.At first,an improved GCA evaluation model based on the technique for order preference by similarity to an ideal solution(TOPSIS)is proposed.Then,an objective function model of maximum difference of the comprehensive evaluation values is built,and the PSO algorithm is used to optimize the weights of the improved GCA evaluation model based on the objective function model.Finally,the performance of the proposed method is investigated through parameter analysis.A performance comparison of traffic flow data is carried out,and the simulation results show that the maximum average difference between the evaluation results and its mean value(MDR)of the proposed comprehensive evaluation method is 33.24%higher than that of TOPSIS-GCA,and 6.86%higher than that of GCA.The proposed method has better differentiation than other methods,which means that it objectively and comprehensively evaluates the data from both the relevance and differentiation of the data,and the results more effectively reflect the differences in data quality,which will provide more effective data support for intelligent modeling,prediction and other applications.

The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation

In order to overcome the disturbance of noise,this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm,nonlinear blind source separation and cross correlation method.Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline,nonlinear blind source separation is applied.In nonlinear blind source separation,the odd polynomials of higher order are used to fit the nonlinear transformation function,and the mutual information of separation signals is used as the evaluation function.Then the parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm,thus the source signals can be separated from the mixed signals.The two-phase flow signals with noise which are obtained from upstream and downstream sensors are respectively processed by nonlinear blind source separation method so that the noise can be effectively removed.Therefore,based on these noise-suppressed signals,the distinct curves of cross correlation function and the transit times are obtained,and then the velocities of two-phase flow can be accurately calculated.Finally,the simulation experimental results are given.The results have proved that this method can meet the measurement requirements of two-phase flow velocity.

A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

Fracture property identification method based on shrinkage factor particle swarm optimization

In the multi-wave and multi-component seismic exploration,shear-wave will be split into fast wave and slow wave,when it propagates in anisotropic media. Then the authors can predict polarization direction and density of crack and detect the development status of cracks underground according to shear-wave splitting phenomenon. The technology plays an important role and shows great potential in crack reservoir detection. In this study,the improved particle swarm optimization algorithm based on shrinkage factor is combined with the Pearson correlation coefficient method to obtain the fracture azimuth angle and density. The experimental results show that the modified method can improve the convergence rate,accuracy,anti-noise performance and computational efficiency.

Data-driven Based Uncertainty Set Modeling Method for Microgrid Robust Optimization with Correlated Wind Power

Microgrid is considered an important part of the future zero carbon energy systems.However,the uncertainty caused by renewable energy source brings huge challenges to the scheduling of MG and restricts its ability of carbon emission reduction.In this paper,a novel improved multi-ellipsoidal uncertainty set modeling method is proposed to better depict the uncertainty of wind power and reduce the conservativeness of traditional robust optimization.Probabilistic information from historical data is utilized to capture the temporal correlation of forecast error of wind power,as well as the conditional correlation of forecast error with forecast value,making the uncertainty set more data-adaptive to variation of forecast results and more accurate for uncertainty description.A two-stage robust optimization model of a grid-connected microgrid is established based on the proposed uncertainty set and solved by column and constraint generation algorithm.Simulation results based on actual data illustrate the average unbalanced power of microgrid between day-ahead trading and real-time power exchange with utility grid is dropped by nearly 11.16%compared with a deterministic optimization method,11.86%with traditional box uncertainty set-based robust optimization method,and 2.89%with stochastic optimization method.

On Broadband Adaptive Beamforming in the Presence of Correlated Interferences

A novel approach of unitarily interpolated array MVDR （UIA-MVDR） is proposed, aiming at avoiding the signal cancellation caused by broadband signal-correlated interferences. UIA-MVDR belongs to the classic approaches of spectral averaging. However, it is distinguished from the conventional interpolated array MVDR （IA-MVDR） by two points： 1） It imposes a unitary constraint on the transform matrices. 2） It only optimizes the worst-case performance of array manifold approximation. As a result, the restriction on the order of Bessel function expansion is released, so that very accurate approximation can be achieved even in the case of small or middle arrays. Compared with many related approaches, UIA-MVDR destroys the correlation more completely and then achieves better performance. Its excellent performance in both correlated and uncorrelated broadband interferences suppression is confirmed via a n umber of numerical examples.

Particle Swarm Optimized Optimal Threshold Value Selection for Clustering based on Correlation Fractal Dimension

The work on the paper is focused on the use of Fractal Dimension in clustering for evolving data streams. Recently Anuradha et al. proposed a new approach based on Relative Change in Fractal Dimension (RCFD) and damped window model for clustering evolving data streams. Through observations on the aforementioned referred paper, this paper reveals that the formation of quality cluster is heavily predominant on the suitable selection of threshold value. In the above-mentionedpaper Anuradha et al. have used a heuristic approach for fixing the threshold value. Although the outcome of the approach is acceptable, however, the approach is purely based on random selection and has no basis to claim the acceptability in general. In this paper a novel method is proposed to optimally compute threshold value using a population based randomized approach known as particle swarm optimization (PSO). Simulations are done on two huge data sets KDD Cup 1999 data set and the Forest Covertype data set and the results of the cluster quality are compared with the fixed approach. The comparison reveals that the chosen value of threshold by Anuradha et al., is robust and can be used with confidence.

Optimization of Output Properties for Bias Signal-Modulated in a Single-Mode Laser

Defining the quantity K as the signal-to-noise ratio （SNR） and the normalized intensity fluctuation C（O） of a single-mode laser for bias signal modulation driven by color noises with colored correlation, the whole output properties of the laser system is described by K. It is found that there is a maximum in the curves of K versus D, Q, and io. The optimization parameters are gained.

TOPSIS based Taguchi design optimization for CVD growth of graphene using different carbon sources: Graphene thickness, defectiveness and homogeneity

Chemical inhomogeneity of chemical vapor deposition(CVD) grown graphene compromises its usage in highperformance devices. In this study, TOPSIS based Taguchi optimization was performed to improve thickness uniformity and defect density of CVD grown graphene. 1.56% decrease in the mean 2 D/G intensity ratio, 87.96% improvement in the mean D/G intensity ratio, 56.07% improvement in the standard deviation D/G intensity ratio, 25.21%improvement in the standard deviation 2 D/G intensity ratio, and 69.32% improvement in the surface roughness were achieved with TOPSIS based Taguchi optimization. The statistical differences between the copper and silicon substrates have been found significantly in terms of their impacts on the graphene's properties with the0.000 p-value for the mean D/G intensity ratio and with the 0.009 p-value for the mean 2 D/G intensity ratio, respectively. Graphene having 11% lower mean D/G intensity ratio(low defective graphene products) compared to the values given in the literature using single-response optimization was obtained using multi-response optimization.

Performance Improvement in Estimation of Spatially Correlated Rician Fading MIMO Channels Using a New LMMSE Estimator

In most of the previous researches on the multiple-input multiple-output (MIMO) channel estimation, the fading model has been assumed to be Rayleigh distributed. However, the Rician fading model is suitable for microcellular mobile systems or line of sight mode of WiMAX. In this paper, the training based channel es-timation (TBCE) scheme in the spatially correlated Rician flat fading MIMO channels is investigated. First, the least squares (LS) channel estimator is probed. Simulation results show that the Rice factor has no effect on the performance of this estimator. Then, a new linear minimum mean square error (LMMSE) technique, appropriate for Rician fading channels, is proposed. The optimal choice of training sequences with mean square error (MSE) criteria is investigated for these estimators. Analytical and numerical results show that the performance of proposed estimator in the Rician channel model compared with Rayleigh one is much better. It is illustrated that when the channel Rice factor and/or the correlation coefficient increase, the per-formance of the proposed estimator significantly improves.

Optimization of the Structural Parameters of a Plastic Centrifugal Pump in the Framework of a Flow Field Analysis

In order to determine the optimal structural parameters of a plastic centrifugal pump in the framework of an orthogonal-experiment approach,a numerical study of the related flow field has been performed using CFX.The thickness S,outlet angleβ2,inlet angleβ1,wrap angle,and inlet diameter D1 of the splitter blades have been considered as the variable factors,using the shaft power and efficiency of the pump as evaluation indices.Through a parametric analysis,the relative importance of the influence of each structural parameter on each evaluation index has been obtained,leading to the following combinations:β119°,β235°,S 2 mm,wrap angle 154°,and D185 mm(corresponding to the maximum efficiency of 75.48%);β119°,β220°,S 6 mm,wrap angle 158°,and D181 mm(corresponding to the minimum shaft power of 75.48%).Moreover,the grey correlation method has been applied to re-optimize the shaft power and efficiency of the pump,leading to the following optimal combination:β119°,β215°,S 4 mm,D181 mm,and wrap angle 152°(corresponding to the maximum efficiency of 71.81%and minimum shaft power of 2.187 kW).

A New Approach for Structural Optimization with Application to Wind Turbine Tower

This work takes the bionic bamboo tower(BBT)of 2 MW wind turbine as the target,and the nondominated sorting genetic algorithm(NSGA-II)is utilized to optimize its structural parameters.Specifically,the objective functions are deformation and mass.Based on the correlation analysis,the target optimization parameters were determined.Furthermore,the Kriging model of the BBT was established through the Latin Hypercube SamplingDesign(LHSD).Finally,the BBT structure is optimized withmultiple objectives under the constraints of strength,natural frequency,and size.The comparison shows that the optimized BBT has an advantage in the Design Load Case(DLC).This advantage is reflected in the fact that the overall stability of the BBT has increased by 2.45%,while the displacement of the BBT has decreased by 0.77%.In addition,the mass of the tower is decreased by 1.49%.Correspondingly,the steel consumption of each BBT will be reduced by 2789 Kg.This work provides a scientific basis for the structural design of the tower in service.

Systematic Analysis and Optimization of the Agricultural Structure in Ethnic Regions in Northwest Sichuan Province

Based on introducing the basic conditions of the ethnic regions in northwest Sichuan Province,the thesis analyzes its agricultural development status,which can be classified into two aspects--rich natural resources and slow agricultural economic growth.Through the analysis on the agricultural structure of northwest Sichuan Province,it is found that the production efficiency and economic benefits of crop planting in this region are low,animal husbandry,as a major industry in pastoral region,sees high production efficiency,the agricultural production is still at the resource-oriented stage;its agricultural structure is still the farming-pastoral structure relying mainly on animal husbandry,planting is dominated by crop planting and potato planting,animal husbandry mainly produces dairy and beef;the ethnic regions in northwest Sichuan Province is endowed with the advantages to grow crops,beans and fruits as well as to produce beef,mutton and milk,among which,three industries,say fruit,beef and dairy are with increasing location quotient and gradually strengthening industrial comparative advantage,while the location quotient of the other industries is declining and their industrial comparative advantages are more stable.In order to promote the agriculture in northwest Sichuan Province to develop in breadth and depth,the thesis proposes the following measures and suggestions:the first one is to adhere to the development strategy of modern animal husbandry;the second is to appropriately improve the proportion of economic crops;the third is to accelerate the development of green food processing industry.