Influence of temperature on acoustic emission source location accuracy in underground structure

An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control.     

基于NR-Transformer的集群作业运行时间预测

高性能集群的作业调度通常使用作业调度系统来实现，准确填写作业运行时间能在很大程度上提升作业调度效率。现有的研究通常使用机器学习的预测方式，在预测精度和实用性上还存在一定的提升空间。为了进一步提高集群作业运行时间预测的准确率，考虑先对集群作业日志进行聚类，将作业类别信息添加到作业特征中，再使用基于注意力机制的NR-Transformer网络对作业日志数据建模和预测。在数据处理上，根据与预测目标的相关性、特征的完整性和数据的有效性，从历史日志数据集中筛选出7维特征，并按作业运行时间的长度将其划分为多个作业集，再对各作业集分别进行训练和预测。实验结果表明，相比于传统机器学习和BP神经网络，时序神经网络结构有更好的预测性能，其中NR-Transformer在各作业集上都有较好的性能。     

Reproductive tract disease in Irish grazing dairy cows: Retrospective observational study examining its association with reproductive performance and accuracy of 2 diagnostic tests

The detection of reproductive tract disease (RTD) 3 wk postpartum is important because of its effect on subsequent reproductive outcomes. Numerous methods for the diagnosis of RTD are described, some of which are more practical and instantaneous in terms of diagnosis. Two of these methods involve identification of purulent vaginal discharge (PVD) and evidence of ultrasonographic uterine changes indicative of endometritis (UE). The objectives of our retrospective observational study were (1) to assess the association of PVD or UE score at the prebreeding examination (PBE) with the hazard of pregnancy within the subsequent breeding season; (2) to determine the test sensitivity (Se) and specificity (Sp) at the point of sampling of both tests using a Bayesian latent class model; and (3) to determine the effect of varying positivity thresholds on test accuracy. To achieve these objectives, we analyzed an initial data set of 5,049 PBE from 2,460 spring-calved cows in 8 herds between 2014 and 2018. Each PBE was conducted once between 25 and 86 d in milk. At each PBE, vaginal discharge was obtained with a Metricheck device (Simcro) whereas uterine contents were assessed using transrectal ultrasonography. Purulent vaginal discharge was scored on a scale of 0 to 3 depending on discharge character, and UE was scored on a scale of 0 to 4 depending on the presence and consistency of intraluminal fluid. Cows with scores of ≥2 in either test had received treatment. Fertility data were available from 4,756 PBE after data exclusion. The association between PVD or UE score at the PBE and subsequent hazard of pregnancy was analyzed using a Cox proportional hazards model. Cows with a PVD score of 2 or 3 were less likely to conceive than cows with a PVD score 0 [score 2 hazard ratio (HR) = 0.74; 95% confidence interval (CI): 0.59–0.94; score 3 HR = 0.65; 95% CI: 0.51–0.84]. Cows with a UE score of 1, 2, 3, or 4 were less likely to conceive than cows with a UE score of 0 (score 1 HR = 0.82; 95% CI: 0.73–0.93; score 2 HR = 0.79; 95% CI: 0.62–1.00; score 3 HR = 0.43; 95% CI: 0.43–0.90; score 4 HR = 0.39; 95% CI: 0.26–0.58). To determine the Se and Sp of PVD or UE score for diagnosis of RTD at the time of PBE, a Bayesian latent class model was fitted on 2,460 individual cow PBE. Flat priors were used for the Se and Sp of UE, whereas informative priors were used for PVD Se (mode = 65%, 5th percentile = 45%) and Sp (mode = 90%, 5th percentile = 80%) and RTD prevalence (mode = 20%, 5th percentile = 10%). Posterior estimates (median and 95% Bayesian probability intervals; BPI) were obtained using ‘rjags' (R Studio). The optimal test thresholds (PVD and UE score ≥1) were selected by assessing the effect of different thresholds on test estimates and using a misclassification cost analysis. Based on these, median (95% BPI) Se for PVD and UE score ≥1 were 44% (29–60%) and 67% (33–100%), respectively. Median Sp for PVD and UE score ≥1 were 90% (86–93%) and 91% (86–93%), respectively. Higher scores in both tests were associated with impaired fertility, and UE scoring with a threshold of ≥1 had the highest test Se and Sp estimates although test Se was conditional on days in milk when the PBE occurred.     

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ～1,900,000 animals, and ～5,900 genotyped elite artificial insemination rams. For the ～8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare “partial” and “whole” predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the “official” (routine) evaluation, which is a mixture of both single and multiple trait, and “deletion” of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.     

基于人脸卡口数据的行人共现关系图谱构建

随着人脸识别技术的不断进步以及人脸卡口的大范围且密集的部署, 本文针对团伙犯罪案件侦察这一应用场景, 对人脸卡口数据进行深入挖掘, 探究其中行人间的共现关系, 获取所关注的嫌疑人的现实社交网络, 锁定团伙其余人员. 经过实验比对和论证, 本文使用Chinese Whispers聚类算法对行人节点进行识别, 通过Faiss加速邻接边的构建, 加速图的初始化步骤, 解决其聚类效率低下的问题. 在此基础上, 使用共现频次和Apriori算法中的置信度挖掘行人间的共现关系, 构建行人共现关系图谱.     

Graduate Student Literature Review: Considerations for nutritional grouping in dairy farms

Each cow in a group has different nutritional requirements even if the group is formed by cows of similar age, number of lactations, and lactation stage. Common dairy farm management setup does not support formulating a diet that accurately matches individual nutritional requirements for each cow; therefore, a proportion of cows in the group will be overfed and another proportion underfed. Overfeeding and underfeeding cows increases the risk of metabolic diseases, decreases milk production, and increases nutrient waste. Consequently, profitability of dairy farms and the environment are negatively affected. Nutritional grouping is a management strategy that aims to allocate lactating cows homogeneously according to their nutritional requirements. Groups of cows with more uniform nutritional requirements facilitates the formulation of more accurate diets for the group. Current availability of large data streams on dairy farms facilitates the design of algorithms to implement nutritional grouping. Our review summarizes important factors to consider when grouping cows, describes nutritional grouping approaches, and summarizes benefits of implementing nutritional grouping in dairy farms.     

针对细微特征进行K-means聚类的电台分选识别技术

现代战场中的无线通信设备日益增多，精准获取个体信息已成为研究热点，但也是难点。针对通信电台，提出了一种分选识别技术。该技术从电台物理层特性出发，对其辐射信号的细微特征进行K-means聚类以实现分选，分选的同时提取各个个体的特征属性值，未知信号通过与特征属性值相关运算实现个体识别。该技术无需先验知识，无需训练运算，通过实验验证，其可行、高效，易于工程实现。     

Digital twin-based analysis of volumetric error mapping procedures

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time.This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.