电力保护装置自动测试系统设计与研究

电力保护装置在出厂前要进行严格的板卡测试,传统板卡测试系统自动化程度较低,功能不够强大,且开发成本高,通用性不好。针对以上不足,设计了一种基于虚拟仪器的板卡测试系统,该系统主要由测试程序和上位机两部分组成,测试程序主要完成对板卡设备的模块化测试,并给出测试结果;上位机通过以太网与测试板卡通信,借助FTP和telnet技术,完成整个测试程序的发送、执行、测试结果的获取等功能。以电力保护装置内的CPU板卡为研究对象,结果表明,该测试系统可对板卡进行高效率自动测试,测试结果可靠,故障信息可追踪,可有效提高电力保护装置调试的通过率,保证企业产品的可靠性。     

Probabilistic learning on manifolds (PLoM) with partition

The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved difficult supervised problems for the “small data” limit where the number N of points in the training set is small. Many extensions have since been proposed, making it possible to deal with increasingly complex cases. However, the performance limit has been observed and explained for applications for which N is very small and for which the dimension of the diffusion-map basis is close to N. For these cases, we propose a novel extension based on the introduction of a partition in independent random vectors. We take advantage of this development to present improvements of the PLoM such as a simplified algorithm for constructing the diffusion-map basis and a new mathematical result for quantifying the concentration of the probability measure in terms of a probability upper bound. The analysis of the efficiency of this extension is presented through two applications.     

机器翻译译文质量估计综述

机器翻译译文质量估计(Quality Estimation,QE)是指在不需要人工参考译文的条件下,估计机器翻译系统产生的译文的质量,对机器翻译研究和应用具有很重要的价值。机器翻译译文质量估计经过最近几年的发展,取得了丰富的研究成果。该文首先介绍了机器翻译译文质量估计的背景与意义;然后详细介绍了句子级QE、单词级QE、文档级QE的具体任务目标、评价指标等内容,进一步概括了QE方法发展的三个阶段: 基于特征工程和机器学习的QE方法阶段,基于深度学习的QE方法阶段,融入预训练模型的QE方法阶段,并介绍了每一阶段中的代表性研究工作;最后分析了目前的研究现状及不足,并对未来QE方法的研究及发展方向进行了展望。     

快速近似计算Shapley值的归因解释方法

Shapley值归因解释方法虽然能更准确量化解释结果, 但过高的计算复杂度严重影响了该方法的实用性. 本文引入KD树重新整理待解释模型的预测数据, 通过在KD树上插入虚节点, 使之满足TreeSHAP算法的使用条件, 在此基础上提出了KDSHAP方法. 该方法解除了TreeSHAP算法仅能解释树结构模型的限制, 将该算法计算Shapley值的高效性放宽到对所有的黑盒模型的解释中, 同时保证了计算准确度. 通过实验对比分析, KDSHAP方法的可靠性, 以及在解释高维输入模型时的适用性.     

Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Prediction of fish quality level with machine learning

In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was <5 log cfu per g, and as ‘not fresh’ when it was >7 log cfu per g. They were uploaded on a web-based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested.     

面向小语种机器翻译的平行语料库构建方法

神经机器翻译模型的训练效果在很大程度上取决于平行语料库的规模和质量。除了一些常见语言外,汉语与小语种间高质量平行语料库的建设一直处于滞后状态。现有小语种平行语料库多采用自动句子对齐技术利用网络资源构建而成,在文本质量和领域等方面有诸多局限性。采用人工翻译的方式可以构建高质量平行语料库,但是缺乏相关经验和方法。文中从机器翻译实践者和研究者角度出发,介绍了经济高效的人工构建小语种平行语料库的工作,包括其总体目标、实施过程、流程细节和最后结果。在构建过程中尝试并积累了各种经验,形成了小语种到汉语平行语料库构建方法、建议的总结。最终,成功构建了波斯语到汉语、印地语到汉语、印度尼西亚语到汉语各50万条高质量平行语料。实验结果表明,所构建的平行语料库有较好的质量,提高了小语种神经机器翻译模型的训练效果。     

饱和块状混合回填土地基的一维固结分析

用块状渣土置换软弱地基和回填低洼谷地等是处置工程渣土的有效途径。为了分析饱和块状混合回填土地基的固结性状，运用混合物理论建立了其一维固结模型。首先，假定块状土固相和充填土固相之间满足等应变条件，获得了饱和块状混合回填土中各相应变与块状土孔隙变形和充填土孔隙变形的关系式。其次，在小应变条件下，根据自由能势函数方程建立了饱和块状混合回填土的一维线弹性本构方程，再结合达西定律和应力平衡方程获得了一维固结控制方程。再次，利用分离变量法得到一维固结解析解，通过退化本文模型与已有模型进行对比，验证了本文模型的正确性。最后，基于所得解析解，分析了充填土孔隙渗透系数、块状土孔隙渗透系数以及流体交换参数等因素对饱和块状混合回填土地基固结性状的影响。分析结果表明：充填土孔隙渗透系数对饱和块状混合回填土地基整体固结性状起主导作用；在固结初期，块状土超孔压会有一定程度的上升，且3个参数具有相似的作用机理。     

Method for calculating horizontal drain induced non-linear and large strain degree of consolidation

Consolidating dredged clay slurries using a combination of vacuum pressure and prefabricated horizontal drains (PHDs) is widely used in practice. This is a large strain problem, but there is no existing large strain theory for PHD induced consolidation. A method with explicit equations has been proposed to consider both mechanical and geometrical non-linearities in analyzing PHD induced consolidation. The method considers stepwise variation of properties using imaginary time concept. An imaginary time is determined by the condition of continuity of degree of consolidation before and after changing the properties. Then the method was applied to analyze a large scale model test of vacuum consolidation with PHD. Two analyses were conducted. One considered both the mechanical and geometrical non-linearity (large strain), and another only considered mechanical non-linearity (small strain). The results of large strain analysis agree with the measured settlement curve and excess pore pressures well. While the small strain analysis under-estimated the rate of consolidation significantly. The results from this study indicate that for analyzing PHD induced consolidation of clay slurries considering the effect of large strain is important, and the proposed method can be a useful design tool.