Fault handling in large water networks with online dictionary learning

Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking the network topology into account; second, we use incoming sensor data to build a network model through online dictionary learning. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time. This brings the benefit of continuous integration of new data into the existing network model, either in the beginning for training or in production when new data samples are gathered. The proposed algorithms show good performance in our simulations on both small and large-scale networks.     

Object-oriented DBMS requirements

The requirements on an object-oriented DBMS for management of information in a large, complex enterprise are presented. These requirements aid in the achievement of an environment characterized by data sharing, open architectures, application and data portability, and assurance of data integrity. They were defined from the point of view of a user of the DBMS; therefore they describe the expected functionality of the DBMS and do not specify the method of implementation to achieve this functionality. They encompass requirements on the data model, query and data manipulation languages, the system architecure, interfaces to the system, change management, and transaction management.     

O—O数据模型中实体联系的表达方法

具有对实体和实体间联系的描述能力，是数据库的基本，吼是对数据库进逻辑设计的基本出发点。Ｏ－Ｏ数据模型不具备显式描术这实体联系的手段。且优于关系模型。     

High‐error approximate dictionary search using estimate hash comparisons

A method for finding all matches in a pre‐processed dictionary for a query string q and with at most k differences is presented. A very fast constant‐time estimate using hashes is presented. A tree structure is used to minimize the number of estimates made. Practical tests are performed, showing that the estimate can filter out 99% of the full comparisons for 40% error rates and dictionaries of up to four million words. The tree is found to be efficient up to a 50% error rate. Copyright © 2006 John Wiley & Sons, Ltd.     

灵活运用数据库设计理论

简介了关系模型、键、完整性规则、E-R模型、函数依赖、模式分解和关系模式的范式等数据库技术基础和规范化设计理论，从键和完整性规则的实现、数据冗余的特殊情况和字典设计等方面论述了在实际设计工作中如何灵活运用关系数据库设计理论。     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

Unseen users, unknown systems: Computer design for a scholar's dictionary

The Dictionary of Old English computing systems have provided access since the 1970s to a database of approximately three million running words. These systems, designed for a variety of machines and written in a variety of languages, have until recently been planned with computing center billing algorithms in mind. With personal workstations emphasis has shifted to building more elegant user interfaces and to providing the entire DOE database to editors around the world. While the shift from sequential files to random access files and the provision of extensive development tools have changed some of the design process, error checking and protection of the database against accidental intrusion have remained as central issues.Richard L. Venezky is Unidel Professor of Educational Studies and professor of Computer and Information Sciences at the University of Delaware. He was formerly professor and chair of Computer Sciences at the University of Wisconsin. His research interests include writing systems, literacy, knowledge representation, and computer-assisted instruction. Among his recent publications are The Subtle Danger: Reflections on the Literacy Abilities of America's Young Adults (Princeton, NJ: ETS, 1987), and Steps Towards a Modern History of American Reading Instruction (Review of Research in Education, 1986, vol. 13, 129–70).     

电网故障处置预案文本中的命名实体识别研究

电网故障处置预案是电网故障处置的重要参考,对电网故障处置预案文本中各类电力设备、名称编号等细粒度的关键实体信息进行抽取,是实现计算机学习理解预案内容并进一步支撑故障处置智能化的重要基础。文中提出一种基于深度学习的电网故障处置预案文本命名实体识别技术,首先采用字向量表征预案文本,然后将注意力机制以及双向长短期记忆网络相结合,有所侧重地提取实体词深层字符特征,最后采用条件随机场求解最优序列化的标注。算例表明:文中所提预案文本命名实体识别模型不依赖人工特征,能够自动高效地提取文本特征,准确识别预案文本中细粒度的实体词,满足预案文本中关键实体信息精确定位和识别的要求。     

无理数字典码的测试数据压缩方案

提出了一种无理数字典码的测试数据压缩方案,能有效地压缩芯片测试数据。方案利用无理数序列建立字典,编码时只需进行相容性检查,无需填充无关位,简化了操作步骤;同时,选择局部压缩率最大的一组数据作为最终编码结果,保证压缩效果可以达到局部最优。对ISCAS 89标准电路Mintest集的实验结果显示,该方案的压缩效果优于传统压缩方法,如Golomb码、FDR码、混合定变长码等。     

稀疏卷积非负矩阵分解的语音增强算法

非平稳噪声和低信噪比条件下提高增强语音质量一直以来都是语音增强研究的难题。近年来,卷积非负矩阵分解在语音增强算法中成功应用,本文进一步考虑语音信号在时频域的稀疏性,提出了稀疏卷积非负矩阵分解(Sparse Convolutive Nonnegative Matrix Factorization, SCNMF)的语音增强算法。该算法包括训练和增强两个阶段。训练阶段通过SCNMF算法分别对纯净语音和噪声的频谱进行训练,得到纯净语音和噪声字典,并将其作为增强阶段的先验信息。增强阶段首先通过SCNMF算法对带噪语音的频谱进行分解,然后利用纯净语音和噪声联合字典对语音编码矩阵进行估计,重构增强语音。本文通过实验仿真分析了稀疏因子对增强语音质量的影响。实验结果表明,在非平稳噪声和低信噪比条件下,本文算法增强效果均优于多带谱减、非负矩阵分解、卷积非负矩阵分解等传统的算法。