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.     

北京地面沉降与地下水开采时空演变对比

区域地面沉降是世界上很多城市都要面对的一种"城市病"。地下水过量开采是这一问题最普遍的原因,北京也因此出现了严重的地面沉降问题。为了全面认识地下水开采格局变化下的地面沉降发育分布的新特点,推动实现《北京地面沉降防治规划(2013-2020年)》中的"控沉目标",对北京市从解放初期至今60多年间的地下水开采和地面沉降演变进行了时间和空间上的定性定量对比分析。结果表明:地面沉降的形成发展阶段与地下水开发利用阶段高度吻合;地面沉降中心区与地下水位降落漏斗区的时空变化高度相关;随着地下水开采深度整体上向地表以下更深层发展,主沉降层逐渐向深部(100m以下)地层转移,为地面沉降防控决策制定提供了依据。     

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。     

DVC中基于块小波变换的边信息生成算法

在运动补偿内插生成的初始边信息基础上,根据图像的时空相关性,自适应地判断块小波变换方式,传送不同地小波系数辅助解码端生成边信息。实验结果表明,对于不同的视频序列,该算法在一定程度上能有效改进边信息的生成质量。     

Exploring the Interactions of Storylines from Informative News Events

Today＇s news readers can be easily overwhelmed by the numerous news articles online. To cope with information overload, online news media publishes timelines for continuously developing news topics. However, the timeline summary does not show the relationship of storylines, and is not intuitive for readers to comprehend the development of a complex news topic. In this paper, we study a novel problem of exploring the interactions of storylines in a news topic. An interaction of two storylines is signified by informative news events that play a key role in both storylines. Storyline interactions can indicate key phases of a news topic, and reveal the latent connections among various aspects of the story. We address the coherence between news articles which is not considered in traditional similarity-based methods, and discover salient storyline interactions to form a clear, global picture of the news topic. User preference can be naturally integrated into our method to generate query-specific results. Comprehensive experiments on ten news topics show the effectiveness of our method over alternative approaches.     

基于贝叶斯网络的新媒体事件分类模型

为了对新媒体事件进行准确有效分类,提出一种将K-means算法和贝叶斯网络相结合的混合算法。该算法首先运用K-means算法将训练样本聚类,再根据聚类的结果,运用改进的贝叶斯网络对新媒体事件进行分类。其中,层次贝叶斯网络模型的构建,避免了贝叶斯网络在参数学习时陷入局部寻优;同时引入隐藏节点,更大程度满足了贝叶斯网络的条件独立假设。实验结果表明该算法效果明显。