[数据驱动的产品设计]基于功能模型和层次分析法的智能产品服务系统概念方案构建

为研究智能产品服务系统概念方案的构建，解决系统的复杂特性和功能耦合特性，提出应用发明问题解决理论（TRIZ）功能模型和层次分析法(AHP)两种工具，在概念设计阶段利用TRIZ功能模型定性地对其进行分析建模，并借助层次分析法对不同服务供应商所提供的服务功能模块进行定量的决策分析，通过择优集成相关功能模块，形成智能产品服务系统概念方案的构建方法。最后以3家服务供应商的服务功能模块构建为例，验证该方法的有效性。     

重庆主城区城市空间结构演变与优化——基于公共服务功能组织视角

城市空间结构与城市公共服务功能组织密切相关。重庆主城区作为典型的"多中心、组团式"城市,在城市公共服务功能的就近组织、公共服务需求的均衡化方面具有先天的优势。本文梳理重庆主城区空间结构的演进历程以及与公共服务需求变化的互动关系,辨识随着城市功能由生产向消费转型,空间面临的结构问题及其内在动因,借鉴马斯洛理论,提出顺应当前及未来公共服务需求层次的分异,城市空间结构应从"多中心、组团式"进一步向"分层多中心、组团式"优化,引导形成相对集中、功能综合的基层"小、微中心"体系,以有序组织公共服务功能,最大限度地减少非必要交通出行,降低居民出行的时耗,提升城市效率,并借鉴恩格尔系数构建衡量城市效率的评价框架,为破解山地城市空间结构问题,推动城市空间转型提供新的思考视角。     

兴隆庄煤矿生态系统服务价值评估

以兖州矿区兴隆庄煤矿为例,基于1990-2018年间的遥感影像数据,从供给、调节、支持和文化4个方面对兴隆庄生态系统服务价值进行测算评估,并分析其动态变化度及敏感性。评估结果表明,总生态服务价值表现为先增加后减少,1990-2010年矿区生态系统服务总价值从18841.5万元增加至29034.2万元,主要原因是采煤塌陷导致耕地向塌陷水体转变以及林地面积的增加;2010-2018年生态总价值减少了2484.8万元,主要在于耕地大规模地转变为建设用地以及区域地下水位下降造成的水域面积减小。敏感性分析显示,耕地和水体是平原矿区生态系统服务价值最敏感的因素,耕地的敏感性指数由1990年的0.79降为2018年的0.28,说明其在生态服务价值中的影响逐年降低。     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。     

基于多元神经网络融合的分布式资源空间文本分类研究

针对实体产业对科技资源的服务需求,以服务效应作为资源文本分类标准,提出一种基于多元神经网络融合的分布式资源空间文本分类模型。设计了包含词嵌入层、卷积层、双向门控循环单元层、注意力机制层和softmax层的多元神经网络通路;在此基础上采用基于需求—效应—资源分类策略,完成了从定性科技资源需求到定量资源服务效应求解,再到定性科技资源输出的映射变换,重点解决了分布式科技资源局部和全局语义特征形式多样、文本长距离依赖特征显著、重要资源信息难以准确识别的问题,进而从分布式科技资源空间中快速准确地获取效应知识,提升实体产业产品研发效率和创新能力;通过万方专利科技资源数据集验证了所提方法的可行性和有效性,为更加全面地挖掘资源文本特征和按需服务实体产业提供了一种新的思路和手段。     

Similarities and differences between male and female novice designers on color‐concept associations for warnings,action required,and signs and equipment status messages

This research examined the male and female novice designers toward color associations for the concepts used for ‘warnings’, ‘action required’, and ‘signs and equipment status’ through a questionnaire‐based study. A total of 178 Hong Kong Chinese final year undergraduate design students (89 males and 89 females) participated in the study. The test used required the participants to indicate their choice of one of nine colors to associations with each of 38 concepts in a color‐concept table, so that any one color could be associated with any one of the concepts. For both male and female groups of novice designers, chi‐square tests revealed a strong color association for each concept tested in this study (P < .05). The results showed males and females agreed on some color‐concept association stereotypes which were therefore gender neutral. The male and female novice designers had the same color associations and similar levels of stereotype strengths for 21 concepts. The nine strongest and therefore most useful color‐concept association stereotypes for both male and female novice designers were: red‐danger, red‐fire, red‐hot, red‐stop, red‐emergency, red‐error, blue‐cold, blue‐male, and green‐exit. However, the male and female novice designers had different color association stereotypes for the standby (green vs. yellow), emergency exit (green vs. red), and toxic (purple vs. black) concepts, and the strengths of the 14 remaining associations for both groups were not at equivalent levels. Overall, it is anticipated that the findings of this study will act as a useful reference for novice designers and other design practitioners to optimize color coding in the design of ‘warnings’, ‘action required’, and ‘signs and equipment status’ messages.     

[数据驱动的智能服务]数据驱动的复杂产品智能服务技术与应用

随着传感器、数据采集装置和其他具备感知能力的模块在复杂产品服务运行阶段的应用，复杂产品运维系统的数字化和智能化程度越来越高，具有实时、多源、异构、海量等特性的数据成为提高复杂产品系统可靠和低成本运行的决策依据，数字孪生技术提供了一种有效途径。介绍了数据驱动的复杂产品智能服务研究进展；分析了数据驱动的智能服务基本特征与框架模型；提出了数据驱动的复杂产品智能服务方法，主要包括面向服务的复杂产品建模与仿真方法、数据驱动的服务需求获取与精准分析预测方法、基于数字孪生的设备故障识别与动态性能预测方法、数据驱动的装备视情维修与备件库存联合多目标决策优化方法、基于数字孪生的复杂产品辅助维修技术、多要素协同的复杂装备能效精准分析预测方法、基于数据挖掘的复杂产品运行优化控制方法等；给出了智能服务系统的应用案例。所提出的框架和方法可为现代制造服务的智能化转型升级提供参考。     

Using provenance to efficiently improve metadata searching performance in storage systems

In cloud storage systems, more than 50% of requests are metadata operations, and thus the file system metadata search performance has become increasingly important to different users. With the rapid growth of storage system scales in volume, traditional full-size index trees cannot offer high-performance metadata search due to hierarchical indexing bottleneck. In order to alleviate the long latency and improve the quality-of-service (QoS) in cloud storage service, we proposed a novel provenance based metadata-search system, called PROMES. The metadata search in PROMES is split into three phases: (i) leveraging correlation-aware metadata index tree to identify several files as seeds, most of which can satisfy the query requests, (ii) using the seeds to find the remaining query results via relationship graph search, (iii) continuing to refine and rerank the whole search results, and sending the final results to users. PROMES has the salient features of high query accuracy and low latency, due to files’ tight and lightweight indexing in relationship graph coming from provenance’s analysis. Compared with state-of-the-art metadata searching schemes, PROMES demonstrates its efficiency and efficacy in terms of query accuracy and response latency.     

A self‐scalable load injection service

Load testing of applications is an important and costly activity for software provider companies. Classical solutions are very difficult to set up statically, and their cost is prohibitive in terms of both human and hardware resources. Virtualized cloud computing platforms provide new opportunities for stressing an application's scalability, by providing a large range of flexible and less expensive (pay‐per‐use model) computation units. On the basis of these advantages, load testing solutions could be provided on demand in the cloud. This paper describes a Benchmark‐as‐a‐Service solution that automatically scales the load injection platform and facilitates its setup according to load profiles. Our approach is based on: (i) virtualization of the benchmarking platform to create self‐scaling injectors; (ii) online calibration to characterize the injector's capacity and impact on the benched application; and (iii) a provisioning solution to appropriately scale the load injection platform ahead of time. We also report experiments on a benchmark illustrating the benefits of this system in terms of cost and resource reductions. Copyright © 2013 John Wiley & Sons, Ltd.     

A proposed expert system for word sense disambiguation: deductive ambiguity resolution based on data mining and forward chaining

One of the major issues in the process of machine translation is the problem of choosing the proper translation for a multi‐sense word referred to as word sense disambiguation (WSD). Two commonly used approaches to this problem are statistical and example‐based methods. In statistical methods, ambiguity resolution is mostly carried out by making use of some statistics extracted from previously translated documents or dual corpora of source and target languages. Example‐based methods follow a similar approach as they also make use of bilingual corpora. However, they perform the task of matching at run‐time (i.e. online matching). In this paper, by looking at the WSD problem from a different viewpoint, we propose a system, which consists of two main parts. The first part includes a data mining algorithm, which runs offline and extracts some useful knowledge about the co‐occurrences of the words. In this algorithm, each sentence is imagined as a transaction in Market Basket Data Analysis problem, and the words included in a sentence play the role of purchased items. The second part of the system is an expert system whose knowledge base consists of the set of association rules generated by the first part. Moreover, in order to deduce the correct senses of the words, we introduce an efficient algorithm based on forward chaining in order to be used in the inference engine of the proposed expert system. The encouraging performance of the system in terms of precision and recall as well as its efficiency will be analysed and discussed through a set of experiments.