用于社交媒体的中文命名实体识别

社交领域的中文命名实体识别(NER)是自然语言处理(NLP)中一项重要的基础任务。目前基于词粒度信息或者外部知识的中文命名实体识别方法,都会受到中文分词(CWS)和溢出词(OOV)等问题的影响。因此,该文提出了一种基于字符的使用位置编码和多种注意力的对抗学习模型。联合使用位置编码和多头注意力能够更好地捕获字序间的依赖关系,而使用空间注意力的判别器则能改善对外部知识的提取效果。该文模型分别在Weibo2015 数据集和Weibo2017数据集上进行了实验,实验结果中的F₁值分别为56.79%和60.62%。与多个基线模型相比,该文提出的模型性能更优。     

A Version Space Approach to Learning Context-free Grammars

In principle, the version space approach can be applied to any induction problem. However, in some cases the representation language for generalizations is so powerful that (1) some of the update functions for the version space are not effectively computable, and (2) the version space contains infinitely many generalizations. The class of context-free grammars is a simple representation that exhibits these problems. This paper presents an algorithm that solves both problems for this domain. Given a sequence of strings, the algorithm incrementally constructs a data structure that has nearly all the beneficial properties of a version space. The algorithm is fast enough to solve small induction problems completely, and it serves as a framework for biases that permit the solution of larger problems heuristically. The same basic approach may be applied to representations that include context-free grammars as special cases, such as And-Or graphs, production systems, and Horn clauses.     

激光相变硬化及激光熔覆技术可提高模具使用寿命

介绍了激光相变硬化及激光熔覆技术的原理、性能及应用实例。     

Incremental Induction of Decision Trees

This article presents an incremental algorithm for inducing decision trees equivalent to those formed by Quinlan's nonincremental ID3 algorithm, given the same training instances. The new algorithm, named ID5R, lets one apply the ID3 induction process to learning tasks in which training instances are presented serially. Although the basic tree-building algorithms differ only in how the decision trees are constructed, experiments show that incremental training makes it possible to select training instances more carefully, which can result in smaller decision trees. The ID3 algorithm and its variants are compared in terms of theoretical complexity and empirical behavior.     

人工智能辅助设计：基于深度学习的 风景园林平面识别与渲染

采用风景园林学与人工智能的跨学科研究方式,开发了一种将深度学习模型——生成对抗网络(Generative Adversarial Network,GAN)用于风景园林平面图用地识别与图像渲染的新应用场景。以325张细致标注的平面方案图建立用于深度学习的数据集,训练循环生成对抗网络(CycleGAN)实现平面图不同用地类型地块的提取任务,以及平面色块图到色彩肌理图的渲染生成。进一步从图片质量、正确规范性和色彩表达等方面评价模型的识别与渲染结果。该训练模型有潜力被应用于风景园林案例的用地类型分析及平面渲染,帮助设计师提升分析及制图效率。     

化工设备设计课程中实例教学的应用

实例教学比传统的课堂教学更适合于理论与实际紧密联系的工科类专业课程。作者结合化工设备设计课程的教学实践,论述了实例教学在化学工程专业教学中的作用,提出了开展实例教学的有效途径;对实例教学的具体方法,以及如何有效地评价实例教学的教学效果提出了一些见解和思路。     

辐射井射水取土纠偏法在楼房纠偏中的应用分析

结合具体工程实例,介绍了复杂地质条件下建筑物的纠偏及加固技术,并针对施工中的难点问题,提出了具体的施工技术措施,总结了该技术在实际应用中的优点。     

Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

With multiple microgrids (MGs) integrated into power distribution networks in a distributed manner, the penetration of renewable energy like photovoltaic (PV) power generation surges. However, the operation of power distribution networks is challenged by the issues of multiple power flow directions and voltage security. Accordingly, an efficient voltage control strategy is needed to ensure voltage security against ever-changing operating conditions, especially when the network topology information is absent or inaccurate. In this paper, we propose a novel data-driven voltage profile improvement model, denoted as system-wide composite adaptive network (SCAN), which depends on operational data instead of network topology details in the context of power distribution networks integrated with multiple MGs. Unlike existing studies that realize topology identification and decision-making optimization in sequence, the proposed end-to-end model determines the optimal voltage control decisions in one shot. More specifically, the proposed model consists of four modules, Pre-training Network and modified interior point methods with adversarial networks (Modified IPMAN) as core modules, and discriminator generative adversarial network (Dis-GAN) and Volt convolutional neural network (Volt-CNN) as ancillary modules. In particular, the generator in SCAN is trained by the core modules in sequence so as to form an end-to-end mode from data to decision. Numerical experiments based on IEEE 33-bus and 123-bus systems have validated the effectiveness and efficiency of the proposed method.     

多工位模具托起机构的设计

通过多个项目的实践,总结出几种结构比较稳定、常用的托起机构。通过实例介绍多工位模具托起机构的设计。