基于复杂网络时空特性的泡沫图像动态纹理特征分析

基于机器视觉的浮选过程监控方法已经被广泛应用于浮选过程中,泡沫表面纹理特征是过程监控的关键视觉特征之一。当前静态纹理特征只能从空间维度描述图像特征,在时间维度上刻画图像序列的内在变化特性存在不足,不能准确反映浮选泡沫浮选过程动态特性。提出了基于复杂网络时空特性的泡沫图像序列动态纹理特征方法。通过将每帧图像的像素点映射到网络各节点,利用邻接矩阵建立复杂网络模型和网络权值动态演化反应不同时刻的图像特征,基于复杂网络时空特性提取泡沫图像序列的动态纹理特征。结合实际生产数据进行仿真验证,实验结果表明该方法可准确识别浮选动态状况,为浮选生产过程的实时调节提供重要的指导信息。     

基于机器视觉的煤泥浮选泡沫图像分割方法

针对煤泥浮选泡沫图像中存在的噪声干扰和气泡粘连等问题,提出了一种基于机器视觉的分割模型,采用煤泥浮选泡沫图像分割算法对实验室采集到的煤泥浮选泡沫图像进行图像分割试验。结果表明,基于机器视觉的煤泥浮选泡沫图像分割模型主要包括ROF模型去除图像噪声和基于顶帽、底帽变换的粘连泡沫分割。ROF模型基于有界全变分函数空间,该空间中函数的不连续正好与图像中物体的边缘轮廓对应,非常适于泡沫图像去噪处理;顶帽变换使得泡沫与泡沫之间的间隙变大,解决了气泡粘连问题;最后采用分水岭算法完成对泡沫的分割。理论分析及实验室研究均表明,该方法可准确、快速地分割出复杂煤泥浮选泡沫图像中的泡沫,分割准确率高达81.1%,验证了分割方法的有效性。     

煤泥浮选泡沫图像纹理特征的提取及泡沫状态的识别

用煤泥浮选泡沫数字图像获取系统获取了51幅煤泥精矿泡沫图像；引入了空间灰度相关矩阵和邻域灰度相关矩阵来提取泡沫的纹理特性，并提取基于这两种算法的一系列特征参数来描述泡沫的结构；分析了各泡沫特征参数随浮选时间(泡沫纹理)的变化关系，定性地指出了各泡沫特征参数与泡沫纹理的相关性；并利用自组织神经网络对煤泥浮选泡沫的状态进行了识别，分类识别的平均正确率达76.5%.     

基于图像空间结构统计分布的浮选泡沫状态识别

通过泡沫图像统计建模,实现了基于图像空间结构感知的浮选泡沫状态自动识别与客观评价。首先,采用Weibull分布建立了泡沫图像各方向边缘响应结构的统计分布模型,有效获取了泡沫图像空间结构的统计分布细节;然后,通过统计学习获得各典型工况状态下的泡沫图像边缘响应统计分布的混合高斯（MoG）模型;最后,通过简单的贝叶斯推理推断出测试泡沫图像对应的工况状态。结果表明：所提出的方法因有效获取了与浮选生产性能直接相关的泡沫空间结构的统计分布特征,可以实时监视泡沫空间结构的变化情况,泡沫生产状态识别准确率高。     

稀土矿浮选泡沫大小与回收率的相关性

以白云鄂博稀土矿为研究对象,针对浮选过程中浮选泡沫大小与回收率的相关性,结合计算机图像处理技术,利用Matlab数学分析软件,进行算法设计编写,对泡沫图像进行预处理、阈值分割、Canny算子边缘提取,提取泡沫边缘特征信息,并通过像素网格标定,对泡沫边缘进行精确分割,从而确定泡沫大小、统计泡沫大小分布规律。在实际浮选过程中浮选槽中泡沫形态呈动态变化,且存在兼并破裂等现象,在对浮选面积进行统计时,采用PDF泡沫概率统计方法,优化泡沫表征分析算法,分析泡沫大小与回收率之间的相关性。结果表明:通过计算统计整个浮选过程中的泡沫面积概率分布,并使用BP神经网络建立预测模型,对浮选泡沫面积与回收率相关性进行样本训练,即可对稀土矿物的浮选回收率进行预测。     

基于视觉纹理度的WBCT岩心图像压缩

针对传统的基于小波变换的压缩方法具有方向选择性差的缺陷,将Contourlet变换与小波变换相结合,并提出图像纹理度的概念,结合人眼视觉特性实现Contourlet方向变换的最优分解,并在此基础上采用SPECK算法对岩心图像进行压缩编码。实验结果表明:改进后算法的压缩效果明显好于SPECK算法,并能更有效地保留岩心图像的边缘和纹理特征。     

基于图像的皮肤纹理评价算法研究

皮肤表面纹理或微轮廓的量化评价对抗皱宣称的化妆品功效评价有重要意义。基于皮肤美容领域的应用需求和日常生活的实际需要,结合图像处理领域的相关算法,对皮肤的基础纹理特征展开了研究。首先,将实验实测的皮肤图像转为灰度图像,再通过对比度受限的自适应直方图均衡化对图像进行增强,之后通过高斯滤波去除图像的噪声,再采用维纳滤波对纹理的细节信息进行增强,得到纹理清晰的皮肤图像。通过实验确定适合于皮肤纹理评价的灰度共生矩阵的灰度级数和距离,基于灰度共生矩阵算法对皮肤纹理进行统计分析,提出了基于4个纹理特征参数的综合指标数学模型,并应用该模型对全部皮肤图像进行了纹理特征定量评价,同时也由专家对这些皮肤图像进行视觉盲评,2种评价方法一致性良好。     

基于EfficientNet和LSTM的井下设备定位技术

针对矿井设备在基于视觉定位时存在由井下环境复杂而导致的定位可靠性低、参数调整困难等问题，本文提出了一种基于EfficientNet深度学习模型和LSTM时序模型的井下相对定位模型，以提高设备定位算法在井下复杂工作环境中的精确度和鲁棒性。算法训练过程中通过设计光照强度随机和边缘平滑的图像预处理模式，使训练得到的深度学习网络对图像的照度和纹理敏感度降低。实验结果表明：在测试数据集上，该模型能够进行精确的井下定位。该研究将端对端的深度学习应用于井下定位技术，并为矿井视觉定位的发展提供理论参考。     

基于灰度形态学和区域生长的轮胎气泡图像分割

针对轮胎气泡图像复杂的背景斑点纹理,提出基于灰度图像的形态学处理和区域生长的气泡图像分割方法。利用腐蚀后的灰度图像与原图像叠加,增强气泡与背景的对比度,有效地去除了图像中的斑点纹理,在此基础上选取种子点,并通过区域生长法将气泡有效分割。该方法分割出来的气泡位置和面积准确。     

基于加权深度支持向量数据描述的工业过程故障检测

传统支持向量数据描述（SVDD）方法本质上采用浅层学习框架,难以有效监控非线性工业过程的复杂故障。针对此问题,提出一种基于加权深度支持向量数据描述（WDSVDD）的故障检测方法。该方法一方面在深度学习框架下重新定义SVDD优化目标函数,构建基于深度特征的深度SVDD监控模型（DSVDD）,并利用核密度估计法计算监控指标的统计控制限;另一方面,考虑到深度特征的故障敏感度差异特性,在DSVDD监控模型中设计特征加权层,分别从静态和动态信息分析角度给出权重因子的计算方法,利用权重因子突出故障敏感特征的影响以提高故障检测率。应用于一个典型化工过程的测试结果表明,所研究的方法能够比传统SVDD方法更有效地监控过程中复杂故障的发生。     

CHARACTERIZATION OF FLOTATION PROCESSES WITH SELF-ORGANIZING NEURAL NETS

Flotation processes are difficult to describe fundamentally, owing to the stochastic nature of the froth structures and the ill-defined chemorheology of the froth. Considerable information on the process is reflected by the structure of the froth. In previous work it has been shown that structural features extracted from flotation froths can be related to the behavior of flotation processes in a qualitative way through the identification of certain behavioral regimes or classes by using a supervised neural net as classifier. Although useful as an aid to control decisions, this method is less suitable for quantitative or dynamic analysis of the behavior of flotation plants. In this paper a new method for the analysis of flotation plants is consequently proposed, based on the use of order preserving maps of features extracted from digitized images of the froth phase. The construction of these maps by means of a self-organizing neural net is demonstrated by way of examples concerning the analysis of industrial copper and platinum flotation plants.     

DTCWT-based zinc fast roughing working condition identification

The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process. The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform (DTCWT) is proposed for process monitoring of zinc fast roughing. Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification (iRFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.     

State evaluation of copper flotation process based on transfer learning and a layered and blocked framework

State evaluation is vital to ensure the process operating optimality for copper flotation processes. Specifically, the froth image is the comprehensive embodiment of raw ore properties and process operations, which is one of the key factors to realize condition recognition and state evaluation. Firstly, a feature mosaic technique-based neural network framework is proposed. The input image features are extracted from the different network structures, which can achieve higher precision in condition recognition and state evaluation than a single neural network framework. Then, an improved deep convolutional generative adversarial networks (DCGAN) model based on feature matching and maximize mean discrepancy (MMD) distance is investigated so that the froth images with high similarity, integrity, and balance to the original images can be generated. Therefore, the problem of small image sets and the lack of labelled images for some sub-processes can be solved. Finally, a layered and blocked state evaluation model is constructed based on the improved DCGAN model and transfer learning (TL) so that the state evaluation of the copper flotation process with multiple sub-processes, long process, and small image sets of some sub-processes is solved. The effectiveness of the proposed method is verified through a series of data experiments on a copper flotation industrial process.     

Development of a vision‐based online soft sensor for oil sands flotation using support vector regression and its application in the dynamic monitoring of bitumen extraction

Extraction from oil sands is a crucial step in the industrial recovery of bitumen. It is challenging to obtain online measurements of process outputs such as bitumen grade and recovery. Online measurements are a prerequisite for innovating better process control solutions for process efficiency and cost reduction. We have developed a soft sensor to provide online measurements of bitumen grade and recovery in a flotation‐based oil sand extraction process. Continuous froth images were captured using a VisioFroth camera system on a batch flotation unit. A support vector regression (SVR) model with a Gaussian kernel was constructed to develop a soft sensor for bitumen grade and recovery using froth image features as the inputs. The model was trained and validated for batch flotation of different grades of oil sands ore at industry‐relevant process conditions. A Dean‐Stark analyzer was used to obtain offline grade and recovery measurements that were used to calibrate the soft sensor. Mean squared errors (MSE) of 62 and 74 were achieved for grade (%) and recovery (%), respectively, and this was obtained using 5‐fold cross validation. The developed soft sensor model has been applied successfully in the real‐time dynamic monitoring of flotation grade and recovery for different grades of ore and operating conditions.

     

基于灰度共生矩阵和支持向量机的气液两相流流型识别

根据灰度共生矩阵具有较好的纹理表达能力的特性，提出了一种基于图像灰度共生矩阵和支持向量机相结合的气液两相流流型识别的新方法。该方法利用高速摄影系统获取水平管道内气液两相流的流动图像，经过图像处理后，提取图像灰度共生矩阵的纹理特征，进而建立流型图像的灰度共生矩阵纹理特征向量，并以此特征向量作为流型样本对支持向量机进行训练，实现了对流动图像的流型智能化识别。实验结果表明，支持向量机能够快速准确地识别水平管道内的7种典型流型，整体识别率达到100%，每幅流型图像的判别时间约为1.7 s，为流型在线识别提供一种新方法。     

Determination of steel quality based on discriminating textural feature selection

Image data can be acquired from a product surface in real time by image sensor systems in chemical plants. For quality determination based on these image datasets, effective texture classification methodology is essential to handle highly dimensional images and to extract quality-related information from these product surface images.Wavelet texture analysis is useful for reducing the dimension and extracting textural information from images. Although wavelet texture analysis extracts only textural characteristics from images, the extracted features still contain unnecessary information for classification. The texture analysis method can be improved by retaining only class-dependent features and removing common features. In previous works, best basis and local discriminant basis are the most popular techniques for selecting an important basis from the wavelet packet basis. However, feature selection based on wavelet texture analysis has been studied for texture classification. Because previous methods are designed for wavelet coefficients with features for analysis, their performance is poor with wavelet texture analysis.We propose a novel texture classification methodology for quality determination based on feature selection using wavelet texture analysis. The proposed methodology applies the sequential forward floating selection (SFFS) algorithm as a feature selection strategy to select discriminating wavelet signatures using wavelet texture analysis. The proposed methodology is validated through quality determination for industrial steel surfaces. The results show that the proposed method has fewer classification errors with fewer number of features than previous methods.     

基于图像动态纹理特征的气固流化床流型识别

准确识别流型是气固流化床二相流参数检测的重要内容,文中提出一种基于图像光流法和动态纹理特征相 结合的气固流化床流型识别的新方法.实验是在气固流化床二相流实验系统上利用高速摄影系统获取流型图像.流型图像分别为鼓泡床,节涌床,湍动床,快速流化床,稀相输送等5种典型流型.首先对获取的不同流型图像分别进行去噪和对比度拉伸...     

基于长短时记忆神经网络和支持向量数据描述的间歇过程监测方法

间歇过程操作是化工过程中的一种重要生产方式.与连续过程不同,间歇生产不是在一个稳定的工作状态运行,而是根据设定的原料比例、操作条件所对应的操作轨迹运行.因此间歇过程数据具有多阶段性、动态时变性和非线性等特性,传统的监测方法难以应用于对间歇过程生产运行状态的监测.为了解决这个问题,提出了一种新的间歇过程监测策略.首先基于...