Applying deep learning to right whale photo identification

Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time-consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport-like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities.     

Test procedure for evaluating the human–machine interface of vehicles with automated driving systems

Abstract

Objective: The human–machine interface (HMI) is a crucial part of every automated driving system (ADS). In the near future, it is likely that—depending on the operational design domain (ODD)—different levels of automation will be available within the same vehicle. The capabilities of a given automation level as well as the operator’s responsibilities must be communicated in an appropriate way. To date, however, there are no agreed-upon evaluation methods that can be used by human factors practitioners as well as researchers to test this.

Methods: We developed an iterative test procedure that can be applied during the product development cycle of ADS. The test procedure is specifically designed to evaluate whether minimum requirements as proposed in NHTSA’s automated vehicle policy are met.

Results: The proposed evaluation protocol includes (a) a method to identify relevant use cases for testing on the basis of all theoretically possible steady states and mode transitions of a given ADS; (b) an expert-based heuristic assessment to evaluate whether the HMI complies with applicable norms, standards, and best practices; and (c) an empirical evaluation of ADS HMIs using a standardized design for user studies and performance metrics.

Conclusions: Each can be used as a stand-alone method or in combination to generate objective, reliable, and valid evaluations of HMIs, focusing on whether they meet minimum requirements. However, we also emphasize that other evaluation aspects such as controllability, misuse, and acceptance are not within the scope of the evaluation protocol.     

基于灰色支持向量机的湿天然气集输管道腐蚀研究

湿天然气集输管道系统运行时间长,管道腐蚀严重,失效泄漏事故频发,其系统风险评价面临诸多问题,因而研究其腐蚀率预测有重要意义。基于灰色支持向量机(GSVM)方法,综合考虑管道材质及其各种影响因素,对其进行灰色相关分析,并根据结果选取有较高相关度的影响因子作为输入变量,将腐蚀率作为目标输出函数,建立湿天然气集输管道腐蚀预测模型。并通过实证分析比较,发现用该模型计算出的管道腐蚀率平均相对误差较小,其预测结果与实际值吻合程度较高,使预测精度得到提高。     

Study on Lockout Procedures for the Safety of Workers Intervening on Equipment in the Municipal Sector in Québec

In Québec, workers intervening in hazardous zones of machines, equipment and processes during maintenance, repairs and unjamming activities have to apply lockout procedures. Lockout procedures involve shutting down the equipment, isolating it, applying individual locks, releasing residual energies and verifying the absence of energies. Lockout has mostly been linked to industrial sectors. However, the municipal sector also faces challenges when it comes to controlling hazardous energies. The objectives of this research are to study serious accidents linked to our subject, study the application of lockout in different municipalities in Québec, identify the specificities for the municipal sector and propose some means to support the application of lockout. We will show that lockout procedures are required in different locations in municipalities and that they are currently being implemented in the municipal sector in Québec. Moreover, we propose a model which aims at facilitating the implementation of lockout procedures in the municipal sector.     

3D simulation of hydrogen ignition in a rapid compression machine

Low-temperature (at T < 900–950 K) ignition delays of hydrogen–air mixtures are mainly measured in rapid compression machines (RCM). This communication is aimed at numerical simulation of ignition delays of hydrogen–air mixtures in the RCM by means of a coupled three-dimensional (3D) Unsteady Reynolds-Averaged Navier–Stokes (URANS) – Particle Method (PM) simulation of RCM operation capable of catching turbulence–chemistry interaction. The study indicates that the time history of piston motion in an RCM affects the final state of a test mixture at the end of compression stroke and therefore influences the phenomena relevant to test mixture ignition. More specifically, the calculations show that different laws of piston motion at a fixed average piston velocity (i.e., fixed piston displacement and fixed compression time) and fixed compression ratio result in different evolution of mean pressure, temperature and velocity fields in the RCM test section leading to different ignition behavior. The reasons for the arising differences lie in the fact that the local instantaneous piston velocity determines the roll-up vortex structure, strength and turbulence dissipation in it, heat transfer in test-section walls, and mass leakage through piston rings.     

电动汽车用电机及其控制器振动试验方法的探讨

振动试验是汽车产品设计验证和质量检验重要试验方法。本文主要从试验标准、试验条件、振动试验中主要参数的计算推导及应用、试验工装的设计、试验中应注意的问题等方面对电动汽车电机及其控制器的振动试验进行介绍。     

基于数据填补-机器学习的煤与瓦斯突出预测效果研究*

为解决煤与瓦斯突出事故数据集少,数据缺失严重的问题,提出将多重插补（MI）和随机森林填补(MF)应用于填补缺失参数,并将填补前和填补后的数据输入SVM,ELM,RF 3种机器学习算法进行训练,构建9种耦合模型。采用总体准确率、局部准确率、运行时间这3种指标评价模型性能。研究结果表明:采用数据填补算法后,由于训练样本增大,煤与瓦斯突出事故预测的总体准确率提高,运行时间增长;MF-RF模型的总体准确率与事故预测准确率最高,分别为97.90%和98.93%;RD-ELM模型的运行时间最短,为0.24 s;多重插补使得煤与瓦斯突出预测的总体准确率提高0.98%~1.11%,随机森林填补总体准确率提高5.13%~7.50%,随机森林填补的效果好于多重插补。     

木工机床切削机构产生噪声的原因及刀具降低噪声新技术

我国的木材加工企业普遍存在严重的噪声污染 ,极大地损害了工作者的身心健康 ,是迫切需要解决的重要问题。为了控制木工机床的噪声 ,分析和讨论了产生噪声的原因 ,指出切削机构是噪声的主要声源。探讨了降低噪声的途径 ,综合评述了国外木工刀具降噪新技术。在工业发达的国家 ,现代木工刀具的设计与制造均考虑了降噪措施 ,其技术原理集中于控制气流在刀槽或齿槽中涡流产生的空气动力性噪声和防止刀具振动时产生的机械噪声两方面。这些新技术已用于生产中 ,降噪效果明显 ,值得借鉴。笔者强调 ,开发低噪声木工刀具 ,保障劳动者的人体健康 ,是我国木材加工工业文明发展的标志     

PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS1

ABSTRACT: The cascade correlation neural network was used to predict the two-year peak discharge (Q₂) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q₂ by regression. Our research shows Q₂ was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds.     

烃类沸点的定量构效关系研究

应用CODESSA软件计算296种烃类物质的分子结构描述符,分别用启发式回归（HM）和最佳多元线性回归（B-MLR）筛选计算出的所有分子描述符,并建立沸点的线性回归模型。用B-MLR方法筛选出的4个描述符作为支持向量机（SVM）的输入建立了非线性模型。预测结果表明：所建立的模型稳健,泛化能力强,预测误差小。非线性模型（R2=0.9905,RMSE=10.2295）的性能优于线性回归模型（HM：R2=0.9819,RMSE=14.0606;B-MLR：R2=0.9842,RMSE=13.1058）,预测效果令人满意。