基于灰色关联分析的电网安全事故关键致因分析

探讨和研究电网安全事故的影响因素及控制已成为预防电网安全事故发生的热点问题。为揭示电网安全事故发生的深层机理,基于事故致因理论,采用质性分析和灰色关联分析的方法,分析中国电网安全事故的关键致因。研究发现,电网安全事故的影响因素主要包括主体因素（认知因素、行为因素和身体因素）、过程因素（组织氛围因素和组织过程因素）、对象因素（设备可靠性低和设备管理松懈）及情境因素（作业环境因素和非作业环境因素）。灰色关联分析表明：电网安全事故的致因因素可以分为红色危险因素（r≥0.8）、橙色警备因素（0.7≤r＜0.8）和黄色警告因素（r＜0.7）,其中,组织氛围因素、认知因素和行为因素是电网安全事故的关键致因因素。在此基础上提出电网安全事故管理的政策建议,以期推动中国电网安全发展进程。     

A framework for variable content document generation with multiple actors

ContextAdvances in customization have highlighted the need for tools supporting variable content document management and generation in many domains. Current tools allow the generation of highly customized documents that are variable in both content and layout. However, most frameworks are technology-oriented, and their use requires advanced skills in implementation-related tools, which means their use by end users (i.e. document designers) is severely limited.ObjectiveStarting from past and current trends for customized document authoring, our goal is to provide a document generation alternative in which variants are specified at a high level of abstraction and content reuse can be maximized in high variability scenarios.MethodBased on our experience in Document Engineering, we identified areas in the variable content document management and generation field open to further improvement. We first classified the primary sources of variability in document composition processes and then developed a methodology, which we called DPL – based on Software Product Lines principles – to support document generation in high variability scenarios.ResultsIn order to validate the applicability of our methodology we implemented a tool – DPLfw – to carry out DPL processes. After using this in different scenarios, we compared our proposal with other state-of-the-art tools for variable content document management and generation.ConclusionThe DPLfw showed a good capacity for the automatic generation of variable content documents equal to or in some cases surpassing other currently available approaches. To the best of our knowledge, DPLfw is the only framework that combines variable content and document workflow facilities, easing the generation of variable content documents in which multiple actors play different roles.     

面向口语统计语言模型建模的自动语料生成算法

在资源相对匮乏的自动语音识别(Automatic speech recognition, ASR)领域, 如面向电话交谈的语音识别系统中, 统计语言模型(Language model, LM)存在着严重的数据稀疏问题. 本文提出了一种基于等概率事件的采样语料生成算法, 自动生成领域相关的语料, 用来强化统计语言模型建模. 实验结果表明, 加入本算法生成的采样语料可以缓解语言模型的稀疏性, 从而提升整个语音识别系统的性能. 在开发集上语言模型的困惑度相对降低7.5%, 字错误率(Character error rate, CER)绝对降低0.2个点; 在测试集上语言模型的困惑度相对降低6%, 字错误率绝对降低0.4点.     

Data-driven hair segmentation with isomorphic manifold inference

Hair segmentation is challenging due to the diverse appearance, irregular region boundary and the influence of complex background. To deal with this problem, we propose a novel data-driven method, named Isomorphic Manifold Inference (IMI). The IMI method assumes the coarse probability map and the binary segmentation map as a couple of isomorphic manifolds and tries to learn hair specific priors from manually labeled training images. For an input image, firstly, the method calculates a coarse probability map. Then it exploits regression techniques to obtain the relationship between the coarse probability map of the test image and those of training images. Finally, this relationship, i.e., a coefficient set, is transferred to the binary segmentation maps and a soft segmentation of the test image will be achieved by a linear combination of those binary maps. Further, we employ this soft segmentation as a shape cue and integrate it with color and texture cues into a unified segmentation framework. A better segmentation is achieved by the Graph Cuts optimization. Extensive experiments are conducted to validate effectiveness of the IMI method, compare contributions of different cues and investigate the generalization of IMI method. The results strongly encourage our method.     

从"特富龙事件"再议绿色化学在高等教育中的重要性

高等院校是国家培养人才的重要场所,为保证新世纪我国经济的可持续发展,本文就"特富龙事件"分析了在高等教育中实施绿色化学教育的必要性,并提出了实施绿色化学教育的具体建议.     

Automatic annotation of tennis games: An integration of audio,vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.     

A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times

Injection molding is an ideal manufacturing process for producing high volumes of products from both thermoplastic and thermo setting materials. Nevertheless, in some cases, this type of manufacturing process decelerates the production rate as a bottleneck. Thus, layout optimization plays a crucial role in this type of problem in terms of increasing the efficiency of the production line. In this regard, a novel computer simulation–stochastic data envelopment analysis (CS-SDEA) algorithm is proposed in this paper to deal with a single row job-shop layout problem in an injection molding process. First, the system is modeled with discrete-event-simulation as a powerful tool for analyzing complex stochastic systems. Then, due to lack of information about some operational parameters, theory of uncertainty is imported to the simulation model. Finally, an output-oriented stochastic DEA model is used for ranking the outputs of simulation model. The proposed CS-SDEA algorithm is capable of modeling and optimizing non-linear, stochastic, and uncertain injection process problems. The solution quality is illustrated by an actual case study in a refrigerator manufacturing company.     

基于事件驱动方式的高速串口通信方案的设计

针对串口通信中系统资源利用不足的问题,提出了一种基于多线程技术和事件驱动方式的串口通信方案。该方案在读取串口数据、对串口通信的错误及通信事件进行监视时,用OVERLAPPED结构体中的hEvent参数指定I/O操作完成后所触发的事件对象。然后,用等待函数检查事件对象的当前状态。最后,当事件对象为有信号状态时完成后续操作。在Win7操作系统的Visual C++开发平台下,结合Windows通信API控制串口的编程方法进行仿真测试。测试结果表明该方案能有效解决采用查询方式时因数据高速涌入造成数据丢失的问题。