Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

Dynamic spatiotemporal correlation coefficient based on adaptive weight

Risk management is an important aspect of financial research because correlations among financial data are essential in evaluating portfolio risk.Among various correlations,spatiotemporal correlations involve economic entity attributes and are interrelated in space and time.Such correlations have therefore drawn increasing attention in financial risk management.However,classical correlation measurements are typically based on either time series correlations or spatial dependence;they cannot be directly applied to financial data with spatiotemporal correlations.The spatiotemporal correlation coefficient model with adaptive weight proposed in this paper can(1)address the absolute quantity,dynamic quantity,and dynamic development of financial data and(2)be used for risk grading,financial risk evaluation,and portfolio management.To verify the validity and superiority of this model,cluster analysis results and portfolio performance are compared with a classical model with time series correlation or spatial correlation,respectively.Empirical findings show that the proposed coefficient is highly effective and convenient compared to others.Overall,our method provides a highly efficient financial risk management method with valuable implications for investors and financial institutions.

LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

Unsupervised Functional Data Clustering Based on Adaptive Weights

In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.

一种改进的加权K-prototypes算法

为了提高K-prototypes算法的聚类准确度,解决其随机选取聚类中心初始值导至的聚类精度较低和聚类结果不稳定的问题。通过对混合属性数据聚类算法的研究,对K-prototypes算法做了进一步的改进。提出了混合属性聚类的初始聚类中心确定方法,并且通过加权算法改进了相异度计算公式。最后用UCI数据集对算法进行检验,结果表明,改进的加权K-prototype算法更加稳定,并具有较高的聚类精度。

Fast Adaptive Support-Weight Stereo Matching Algorithm

Adaptive support-weight (ASW) stereo matching algorithm is widely used in the field of three-dimensional (3D) reconstruction owing to its relatively high matching accuracy.However,since all the weight coefficients need to be calculated in the whole disparity range for each pixel,the algorithm is extremely time-consuming.To solve this problem,a fast ASW algorithm is proposed using twice aggregation.First,a novel weight coefficient which adapts cosine function to satisfy the weight distribution discipline is proposed to accomplish the first cost aggregation.Then,the disparity range is divided into several sub-ranges and local optimal disparities are selected from each of them.For each pixel,only the ASW at the location of local optimal disparities is calculated,and thus,the complexity of the algorithm is greatly reduced.Experimental results show that the proposed algorithm can reduce the amount of calculation by 70% and improve the matching accuracy by 6% for the 15 images on Middlebury Website on average.

Research on Data Fusion of Adaptive Weighted Multi-Source Sensor

Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.

Supply Chain Finance Credit Risk Evaluation Method Based on Self-Adaption Weight

Credit risk is the core issue of supply chain finance. In the supply chain, problems happened in different enterprises can influent the whole to different degrees through transferring, thus statuses of all enterprises and their different influences should be considered when evaluating the supply chain’s credit risk. We examine the characters of supply chain network and complex network, use the local growing complex network to simulate the real supply chain, use cluster analysis to classify the company into several levels;Introducing each level’s self-adaption weight formula according to the company’s quantity and degrees of this level and use the weight to improve the credit evaluation method. The research results indicate that complex network can be used to simulate the supply chain. The credit risk evaluation (CRE) of an enterprise level with bigger note degrees has a greater weight in the supply chain system’s CRE, thus has greater effect on the whole chain. Considering different influences of different enterprise levels can improve credit risk evaluation method’s sensitivity.

Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.

Terrain classification based on adaptive weights with airborne LiDAR data for mining area

The fast high-efficiency inspection for mining subsidence of mine area is a reliable way for forecasting accident and evaluating losing expense. In order to monitor mining subsidence of exploitation mine efficiently, LiDAR data were used and a novel strip division method was brought forward based on separating-treatment theory, which divided the mass of discrete three-dimensional point cloud data into a series of parallel strips and reduced the dimension in each strip. Polynomial fitting algorithm based on the adaptive weights, which located in the range of the strip, was used for classification complex terrain data of mine-area. The results show that LiDAR datamation can be greatly reduced. In the mean time, the time spending for calculation is shortened, and computational complexity is simplified. Therefore, high-efficiency terrain classification of LiDAR point cloud method can be great beneficial to monitoring environment of mine area.

Wide-Area Delay-Dependent Adaptive Supervisory Control of Multi-machine Power System Based on Improve Free Weighting Matrix Approach

The paper demonstrates the possibility to enhance the damping of inter-area oscillations using Wide Area Measurement (WAM) based adaptive supervisory controller (ASC) which considers the wide-area signal transmission delays. The paper uses an LMI-based iterative nonlinear optimization algorithm to establish a method of designing state-feedback controllers for power systems with a time-varying delay. This method is based on the delay-dependent stabilization conditions obtained by the improved free weighting matrix (IFWM) approach. In the stabilization conditions, the upper bound of feedback signal’s transmission delays is taken into consideration. Combining theoriesof state feedback control and state observer, the ASC is designed and time-delay output feedback robust controller is realized for power system. The ASC uses the input information from Phase Measurement Units (PMUs) in the system and dispatches supplementary control signals to the available local controllers. The design of the ASC is explained in detail and its performance validated by time domain simulations on a New England test power system (NETPS).

An Active Anti-Jamming Approach for Frequency Diverse Array Radar with Adaptive Weights

Due to the rapid development of electronic countermeasures(ECMs),the corresponding means of electronic counter countermeasures(ECCMs)are urgently needed.In this paper,an act-ive anti-jamming method based on frequency diverse array radar is proposed.By deriving the closed form of the phase center in a uniform line array FDA,we establish a model of the FDA signal based on adaptive weights and derive the effect of active anti-jamming in this regime.The pro-posed active anti-jamming method makes it difficult for jammers to detect or locate our radar.Fur-thermore,the effectiveness of the two frequency increment schemes in terms of anti-jamming is ana-lyzed by comparing the deviation of phase center.Finally,the simulation results verify the effective-ness and superiority of the proposed method.

Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences

In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.

AN ADAPTIVE-WEIGHTED TWO-DIMENSIONAL DATA AGGREGATION ALGORITHM FOR CLUSTERED WIRELESS SENSOR NETWORKS

In this paper,an Adaptive-Weighted Time-Dimensional and Space-Dimensional(AWTDSD) data aggregation algorithm for a clustered sensor network is proposed for prolonging the lifetime of the network as well as improving the accuracy of the data gathered in the network.AWTDSD contains three phases:(1) the time-dimensional aggregation phase for eliminating the data redundancy;(2) the adaptive-weighted aggregation phase for further aggregating the data as well as improving the accuracy of the aggregated data; and(3) the space-dimensional aggregation phase for reducing the size and the amount of the data transmission to the base station.AWTDSD utilizes the correlations between the sensed data for reducing the data transmission and increasing the data accuracy as well.Experimental result shows that AWTDSD can not only save almost a half of the total energy consumption but also greatly increase the accuracy of the data monitored by the sensors in the clustered network.

基于一致性图的权重自适应多视角谱聚类算法

随着移动设备和互联网的普及,多视角数据的采集和分享变得更加容易,其可以从多个视角更准确地描述数据。目前,一些多视角聚类算法忽略了不同视角间的一致性潜在知识和不同视角的重要性。针对该问题,提出一种平衡视角间一致性信息的多视角聚类算法。首先通过调节视角权重学习视角间一致的共享相似度矩阵,提升共享矩阵的一致性,其中相关性强的视角具有的一致性信息更多,视角权重越大,在一致性学习中发挥的作用越大,而差异性大的视角其权重越小,在学习中发挥的作用越小。其次学习视角间的一致性样本嵌入以及不同视角的特征嵌入,并将特征嵌入中包含的多样性特征信息迁移到样本嵌入中,以此促进样本嵌入的一致性表达。在不同视角特征中包含多样性信息,可补充上述共享相似度矩阵学习中单一样本关系的不足。因此,采用二部图协同聚类,通过建立样本数据、样本嵌入和特征嵌入的关系图,学习样本的特征嵌入,并将其迁移到样本嵌入中。最后将图学习、谱聚类和特征嵌入学习整合到统一的框架中进行联合优化,得到最优的样本嵌入。实验结果表明,通过对样本嵌入进行K-means聚类,将该算法运行于5个真实数据集并与7种聚类算法对比,其中在3-Sources、Yale、MRSCV1数据集上的正确率均高于对比算法5%以上,验证了该算法的有效性。

ADASYN与类别逆比例加权法在阿尔茨海默病不平衡数据中的应用

目的利用自适应合成抽样(adaptive synthetic sampling,ADASYN)与类别逆比例加权法处理类别不平衡数据,结合分类器构建模型对阿尔茨海默病(alzheimer′s disease,AD)患者疾病进程进行分类预测。方法数据源自阿尔茨海默病神经影像学计划(Alzheimer′s disease neuroimaging initiative,ADNI),经随机森林填补缺失值,弹性网络筛选特征子集后,利用ADASYN与类别逆比例加权法处理类别不平衡数据。分别结合随机森林(random forest,RF)、支持向量机(support vector machine,SVM)构建四种模型:ADASYN-RF、ADASYN-SVM、加权随机森林(weighted random forest,WRF)、加权支持向量机(weighted support vector machine,WSVM),与RF、SVM比较分类性能。模型评价指标为宏观平均精确率(macro-average of precision,macro-P)、宏观平均召回率(macro-average of recall,macro-R)、宏观平均F1值(macro-average of F1-score,macro-F1)、准确率(accuracy,ACC)、Kappa值和AUC(area under the ROC curve)。结果ADASYN-RF的分类性能最优(Kappa值为0.938,AUC为0.980),ADASYN-SVM次之。利用ADASYN-RF预测得到的重要分类特征分别为CDRSB、LDELTOTAL、MMSE,在临床上均可得到证实。结论ADASYN与类别逆比例加权法都能辅助提升分类器性能,但ADASYN算法更优。

自适应相似图联合优化的多视图聚类

相比于单一视图学习,多视图学习往往可以获得学习对象更全面的信息,因而在无监督学习领域,多视图聚类受到了研究者的极大关注,其中基于图的多视图聚类,近年来取得了很大的研究进展.基于图的多视图聚类一般是先从各个视图原始数据学习相似图,再进行视图间相似图的融合来获得最终聚类结果,因此,多视图聚类的效果是由相似图质量和相似图融合方法共同决定的.然而,现有基于图的多视图聚类方法几乎都聚焦在视图间相似图的融合方法研究上,而缺乏对相似图本身质量的关注.这些方法大多数都是孤立地从各视图的原始数据中学习相似图,并且在后续图融合过程中保持相似图不变.这样得到的相似图不可避免地包含噪声和冗余信息,进而影响后续的图融合和聚类.而少量考虑相似图质量的研究,要么相似图构造和图融合过程是直接联立迭代的,要么在预定义相似图过程中提前利用秩约束进一步初始化,要么就是利用相似图存在的一些底层结构来获取融合图的.这些方法对相似图本身改进很小,最终聚类性能提升也十分有限.同时现有基于图的多视图聚类流程也缺乏对各视图间一致性和不一致性的综合考虑,这也会严重影响最终的多视图聚类性能.为了避免低质量预定义相似图对聚类结果的不利影响以及综合考虑视图间一致性与不一致性来提升最终聚类效果,本文提出了一种自适应相似图联合优化的多视图聚类方法.首先通过Hadamard积来获得视图间高质量一致性部分信息,再将每个预定义相似图和这部分信息对标,重构各个视图的预设相似图.这个过程强化了各视图间的一致性部分,弱化了不一致性部分.其次设计了相似图重构改进和图融合联合迭代优化框架,实现了相似图的自适应改进,最终达到相似图和聚类结果共同提升的效果.该方法将相似图改进过程与图融合过程联合起来进行自适应迭代优化,并且在迭代优化中不断强化各视图间的一致性,弱化视图间的不一致性.此外,本文的方法也集成了现有多视图聚类方法的一些优点,自加权以及无需额外聚类步骤等.在九个基准数据集上与八个对比方法的实验验证了本文方法的有效性与优越性.

基于现场可编程门阵列的矩阵求逆算法设计

由于自适应抗干扰算法在更新最优权值时存在时间延时,导致很难满足动态环境下的权值更新率要求。针对该情况已有学者对如何实现快速采样矩阵求逆算法进行研究,但仍存在只适用于低维矩阵,且权值更新率慢的问题。为解决上述问题,提出了一种基于Cholesky分解的采样矩阵求逆算法实现架构。该实现架构主要包括协方差矩阵计算模块、Cholesky分解模块、计算下三角矩阵L的逆矩阵模块、三角矩阵相乘和权值计算模块。设计采用流水线加状态机实现结构有效地解决了因高阶采样矩阵求逆运算量大产生的权值更新率慢的问题。仿真结果表明,在现场可编程门阵列(field programmable gate array,FPGA)的硬件平台上,对于56阶采样矩阵,在100 MHz工作频率下,一次权值的更新时间仅需要1.2 ms。本文所提的实现架构为自适应抗干扰快速求解权值提供了一种切实可行的解决方案,对存在类似需求的权值求解系统具有一定的参考价值。