Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines

Model-based fault diagnosis tends to be too expensive or time-consuming to apply in the mineral processing industries, owing to the complexity and variability of operations. In contrast, data-based methods are inexpensive, but do not exploit the availability of first principle knowledge of plant operations. In this investigation, the use of process causality maps in conjunction with data-based fault diagnosis is considered as a hybrid methodology that can leverage the advantages of both approaches. Extreme learning machine algorithms are used to implement the data-based component of the approach. These algorithms can be deployed rapidly on large-scale systems and have the ability to deal with highly nonlinear systems. Two different variants are considered, viz. one used in combination with principal component analysis, as well as one with a bagging algorithm for fault diagnosis and applied to an industrial concentrator circuit in South Africa. The use of process causality maps led to significantly more effective fault diagnosis, while the use of extreme learning machines in combination with principal component analysis likewise allowed markedly better fault detection and diagnosis. In contrast, fault diagnosis with the bagging approach did not perform particularly well, owing to the high degree of correlation between the variables, which made it difficult to isolate individual causal variables.     

A neural network based technique for vibration characterization using Gaussian laser beams

This paper presents a neural network technique combined with an optical measurement system for the characterization of mechanical vibrations in industrial machinery. In the proposed system, the Gaussian beam of a laser source illuminates on an array of photodetectors. If either the laser source or the photodetector array is coupled with a vibrating system, then the optical powers detected by the photodetectors will vary accordingly, and are expected to reflect the magnitude and frequency of the X–Y planar vibrations of the monitored system. The time-varying optical powers are input to an artificial neural network-based vibration monitoring system which maps the power distributions to the X–Y position of the laser beam center. An experimental setup of the system is built and used for training and testing purposes. The obtained experimental results demonstrate the adequacy of combining optical techniques with neural networks to estimate the vibration frequency and magnitude. Estimated frequencies were within 1% of the actual ones, and the estimated magnitudes were within 29% of the actual magnitudes when using a chirp signal in the training phase. The magnitude estimation percentage error was further reduced below 12% when the neural network was trained with a decaying chirp signal.     

GCAT-U-Net嵌入全局坐标注意力机制的遥感地块分割网络

遥感影像的地块背景特征复杂,当前地块分割方法不能较好地处理模糊的边缘信息,导致分割精度不理想;文章利用注意力机制处理地块特征,提出了一种基于全局坐标注意力机制的遥感地块分割网络:GCAT-U-Net;该方法在U-Net网络基础上嵌入了全局坐标注意力机制,加强了深度神经网络对于遥感影像数据中重要特征的关注度;在公开的GID数据集上的实验结果表明,文章提出的模型将准确率从0.9041提升到了0.9227,比传统U-Net网络提高了2百分点;结合特征自身重要性和特征位置信息的全局坐标注意力机制有助于更精确的目标定位,其输出相较于嵌入单一注意力机制,地块边界更为清晰,提升效果更为显著。     

Elp3 modulates neural crest and colorectal cancer migration requiring functional integrity of HAT and SAM domains

Cell migration is a finely tuned biological process that often involves epithelial-mesenchymal transition (EMT). EMT is typically characterized by the upregulation of mesenchymal markers such as Snail1. This process has been shown to be of critical importance to normal developmental processes, including neural crest migration and invasion. Interestingly, similar mechanisms are utilized in disease processes, such as tumor metastasis and migration. Notably, EMT and EMT-like processes confer tumor cells with the ability to migrate, invade, and adopt stem cell-like properties that largely account for immunosuppression and tumor recurrence. Therefore, identifying sensitive EMT markers may contribute to cancer prognosis and diagnosis in many facets. Previously, we showed that Elp3 plays an essential role during neural crest migration by stabilizing Snail1. In the current study, we further elucidate the molecular mechanism underlying colorectal cancer migration. We found that mElp3 exerted an identical function to xElp3 in modulating neural crest migration, and both HAT and SAM domains are imperative during this migratory process. Interestingly, overexpression of mElp3 in SW480 cells promoted cell migration and invasion, and we further showed that Elp3 stabilized Snail1 requiring integrity of both SAM and HAT domains. Our findings warrant further exploration of the specific target of Elp3 in cancer cells.     

Intuitive Facial Animation Editing Based On A Generative RNN Framework

For the last decades, the concern of producing convincing facial animation has garnered great interest, that has only been accelerating with the recent explosion of 3D content in both entertainment and professional activities. The use of motion capture and retargeting has arguably become the dominant solution to address this demand. Yet, despite high level of quality and automation performance-based animation pipelines still require manual cleaning and editing to refine raw results, which is a time- and skill-demanding process. In this paper, we look to leverage machine learning to make facial animation editing faster and more accessible to non-experts. Inspired by recent image inpainting methods, we design a generative recurrent neural network that generates realistic motion into designated segments of an existing facial animation, optionally following user-provided guiding constraints. Our system handles different supervised or unsupervised editing scenarios such as motion filling during occlusions, expression corrections, semantic content modifications, and noise filtering. We demonstrate the usability of our system on several animation editing use cases.     

基于Flink的海量医学图像检索系统设计与实现

医学图像检索是有效利用医学资源的基础，而医学图像的海量性和增量性为图像检索带来了新的挑战和要求。为了提高医学图像检索过程的效率，设计并实现一种基于Flink的海量医学图像检索系统。首先，系统通过Web应用作为用户操作入口，在后端搭建数据平台和业务集群；其次，系统通过HBase对医学图像数据进行分布式存储，利用深度卷积神经网络模型提取医学图像特征；然后，将所提取的医学图像特征数据进行乘积量化编码，并通过HBase进行存储；最后，通过基于Flink的内存计算对接Kafka进行实时图像检索，以及对批量导入图像的特征索引编码。系统在4个节点的服务器上部署分布式集群，使用真实医学图像数据集进行测试，通过在MapReduce和Spark两种不同技术模块下的对比实验表明本系统具有更好的检索效率表现。     

基于深度语义分割的多源遥感图像海面溢油监测

针对遥感图像海面溢油区域通常受到斑噪声以及强度不均等因素的影响,从而导致溢油区域监测效果较差的问题,本文引入了深度语义分割的方法,将深度卷积神经网络与全连接条件随机场相结合,形成端对端连接。以Resnet结构为基础,首先通过深度卷积神经网络对多源遥感图像粗分割并作为输入,然后经过改进的全连接条件随机场,利用高斯成对势和平均场近似定理,建立条件随机场形成递归神经网络作为输出。通过多源遥感图像对海面溢油区域进行监测,并利用可见光图像估计溢油区域面积。实验在所建立的多源遥感图像数据集上与其它先进模型进行对比,结果表明本文方法提高了溢油区域的分割精度以及精细细节程度,平均交并比为82.1%,监测效果具有明显地改善。     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

基于GRA-GRNN模型的露天金属矿爆破后矿岩界线位移分析

为了避免露天金属矿爆破后导致爆堆边缘矿石品位贫化损失,需要根据最低品位阈值重新计算矿岩边界,而影响矿岩边界发生改变因素众多,需要确定主要影响因素。因此,利用爆堆爆破前地形方向和爆堆地质数据,构建灰色关联-广义回归神经网络模型(GRA-GRNN)分析爆堆矿岩边界变化主要影响因素。首先对爆堆钻孔品位数据使用析取克里金法进行空间插值,并根据矿山工艺最低品位阈值提取爆破前的矿岩边界;其次,将爆破前后的数字DEM模型进行求差,求得爆破后的爆堆数字DEM模型,并构建爆破前后爆堆数字DEM模型空间分布椭圆,从而确定爆堆爆破后的水平形变方向;对影响爆堆爆破后形变的可能因素进行提取,并应用GRA-GRNN模型选取主要影响因素及对其强度进行分析,并将其结果与BP神经网络模型预测结果进行了对比。从实验结果可知,影响爆堆爆破后形变强度排前三的因素为:爆破前地形方向、爆孔排距和列距,强度分别为0.880、0.760和0.755,预测结果优于BP模型。     

Affective Experience Measurement in Outdoor Environment via Machine Visual Cognition

Outdoor environments with quality landscapes can benefit people’s physical and mental health. Real-time assessment on individuals’ environmental affective experience can improve the scientism in measuring the quality of outdoor environments. Existing measurement methods are often insufficient for the cases of a larger site area or sample size. The machine visual cognition of Artificial Intelligence can realize the recognition of facial expressions and the changes in video images, which supports high-precision and long-cycle measurements on individuals’ affective experience in outdoor environments. Taking an urban community square as the study site, this research simultaneously collects participants’ facial data from video images and their electrodermal activity data, wherein Convolutional Neural Network algorithm model is trained with a deep learning algorithm, i.e. codec–SVM optimized model, whose reliability is tested through an additional experiment. The research reveals that: 1) The accuracy rate of the main and additional experiments in measuring individuals’ affective experience is 82.01% and 65.08%, respectively; 2) The additional experiment verifies the application potential of the codec–SVM optimized model; And 3) the model works more effective for outdoor scenarios with varying usage behaviors and open views. Therefore, machine visual cognition can be used for emotion measurement in a larger site area or sample size and contributes to the effectiveness of landscape optimization efforts, especially as an instrumental tool to study the affective experience of the ones who have communication or reading disability. The findings also demonstrate the model’s great potential in building Smart Cities with refined public services.