A method of constructing an inspiration library driven by user-perceived preference evaluation data for biologically inspired design

Biologically inspired design (BID) is one of the common methods for product design. To solve the problem of inaccurate acquisition of inspirational creatures due to the lack of user perception preference analysis, a data-driven intelligent service model for BID considering user perception needs is proposed based on Kansei engineering. Firstly, by extracting the perceptual features of creatures from the semantic source elements of products through mapping and encodes them, we proposed a data acquisition method based on intuitionistic fuzzy sets considering different customer preference distributions, bridging the gap caused by the asymmetry between designers and users. Secondly, the functional relationship between biometric features and user-perceived attributes is identified and predicted, and a predictive model of biodata considering user preferences is obtained by multiple linear regression analysis. Finally, based on the data clustering and reorganization theory to understand the organization and dynamics of the database, the construction of a BID library was completed, and the design resources in the library were used as analyzed knowledge for designers to plan design activities. Taking the bionic design of a UAV product as an example, a prototype of a computer-aided design service system was developed based on the theory proposed in the article, and the analyzed knowledge was used to improve the efficiency and science of the design, effectively verifying the usefulness of this study for design. To a certain extent, this study addresses the problem of cognitive limitations of designers and cognitive differences between designers and users, promotes the application of bioinspiration in product design, and improves the marketability of design solutions.     

Temperature-responsive culture surfaces for insect cell sheets to fabricate a bioactuator

ABSTRACT

For the first time, we have fabricated insect-derived cell sheets by using temperature-responsive culture surfaces having a phase-transition temperature below 25°C. We prepared the temperature-responsive cell culture surfaces (tissue culture polystyrene, TCPS) by grafting a copolymeric gel consisting of hydrophobic N-tert-butylacrylamide (tBAAm) and N-isopropylacrylamide (IPAAm) units. First, to characterize the hydrophilic and hydrophobic properties of the copolymeric gel-grafted surfaces, static water contact angles of each surface were measured at various temperatures. By increasing the amount of tBAAm in the grafted copolymeric gel, the transition temperature of the gel was shifted to lower temperatures. At 25°C, the grafted copolymeric gel was dehydrated, and the insect-derived cells (AeAl2 cells) adhered on all the copolymeric gel-grafted surfaces. At 20°C, AeAl2 cells cannot adhere on the P(IPAAm-1.62tBAAm)-TCPS surface (the initial molar ratio of IPAAm and tBAAm (tBAAm?=?1.62 mol%)) better than on other surfaces (TCPS and tBAAm?=?4.88, 8.17 mol%). These two findings implied that the lower critical solution temperature of the copolymeric gel-grafted-TCPS existed from 20°C to 25°C. The laminin-coated P(IPAAm-1.62tBAAm)-TCPS surface showed temperature-dependent cell attachment and detachment properties, while AeAl2 cells were not detached from the extracellular matrix uncoated P(IPAAm-1.62tBAAm)-TCPS surface. AeAl2 cells and insect muscle cells were harvested as the respective sheets.     

基于桥连接的词典学习方法的语义解析

现阶段的语义解析方法大部分都基于组合语义,这类方法的核心就是词典。词典是词汇的集合,词汇定义了自然语言句子中词语到知识库本体中谓词的映射。语义解析一直面临着词典中词汇覆盖度不够的问题。针对此问题,该文在现有工作的基础上,提出了基于桥连接的词典学习方法,该方法能够在训练中自动引入新的词汇并加以学习,为了进一步提高新学习到的词汇的准确度,该文设计了新的词语—二元谓词的特征模板,并使用基于投票机制的核心词典获取方法。该文在两个公开数据集(WebQuestions和Free917)上进行了对比实验,实验结果表明,该文方法能够学习到新的词汇,提高词汇的覆盖度,进而提升语义解析系统的性能,特别是召回率。     

Semantics for consistent activation in context-oriented systems

ContextContext-oriented programming languages provide dedicated programming abstractions to define behavioral adaptations and means to combine those adaptations dynamically according to sensed context changes. Some of these languages feature programming abstractions to explicitly define interaction dependencies among contexts. However, the semantics of context activation and the meaning of dependency relations have been described only informally, which in some cases has led to incorrect specifications, faulty implementations and inconsistent system behavior.ObjectiveWith the aim of avoiding faulty implementations and inconsistencies during system execution, this paper proposes both a formal and run-time model of contexts, context activation and context interaction.MethodAs a formal and computational basis, we introduce context Petri nets, a model based on Petri nets, which we found to match closely the structure of contexts in context-oriented systems. The operational semantics of Petri nets permits the modeling of run-time context activations. Existing Petri net analyses allow us to reason about system properties. As validation, we carried out small and medium-sized case studies.ResultsIn the cases explored, context Petri nets served effectively as underlying run-time model to ensure that declared context interaction constraints remain consistent during context manipulation. Moreover, context Petri nets enabled us to analyze certain properties regarding the activation state of particular contexts.ConclusionContext Petri nets thus proved to be appropriate to encode and manage the semantics of context activation, both formally and computationally, so as to preserve the consistency of context-oriented systems.     

Domain structure-based transfer learning for cross-domain word representation

Cross-domain word representation aims to learn high-quality semantic representations in an under-resourced domain by leveraging information in a resourceful domain. However, most existing methods mainly transfer the semantics of common words across domains, ignoring the semantic relations among domain-specific words. In this paper, we propose a domain structure-based transfer learning method to learn cross-domain representations by leveraging the relations among domain-specific words. To accomplish this, we first construct a semantic graph to capture the latent domain structure using domain-specific co-occurrence information. Then, in the domain adaptation process, beyond domain alignment, we employ Laplacian Eigenmaps to ensure the domain structure is consistently distributed in the learned embedding space. As such, the learned cross-domain word representations not only capture shared semantics across domains, but also maintain the latent domain structure. We performed extensive experiments on two tasks, namely sentiment analysis and query expansion. The experiment results show the effectiveness of our method for tasks in under-resourced domains.     

Feature-based ontological framework for semantic interoperability in product development

An essential requirement in integrating tasks in product development is to have a seamless exchange of product information through the entire product lifecycle. A key challenge in the integration is the exchange of shape semantics in terms of understandable labels and representations. A unified taxonomy is proposed to represent, classify, and extract shape features. This taxonomy is built using the Domain-Independent Form Feature (DIFF) model as the representation of features. All the shape features in a product model are classified under three main classes, namely, volumetric features, deformation features and free-form surface features. Shape feature ontology is developed using the unified taxonomy, which brings the shape features under a single reasoning framework. One-to-many reasoning framework is presented for mapping semantically equivalent information (label and representation) of the feature to be exchanged to target applications, and the reconstruction of the shape model automatically in that target application. An algorithm has been developed to extract the semantics of shape features and construct the model in the target application. The algorithm developed has been tested for shape models taken from literature and test cases are selected based on variations of topology and geometry. Results of exchanging product information are presented and discussed. Finally, the limitations of the proposed method for exchanging product information are explained.     

Economic Optimization of Distribution Leakage Control

E vry WATER DISTRIBUTION system has an economic optimum level of leakage. To maintain that optimum level of leakage there is a corresponding optimum level of leakage control activity.
Despite much work on this subject, particularly in recent years with publication of DoE Report No. 26¹, many undertakers remain unclear about their economic optimum leakage level and how much effort is required to maintain that level.
This paper describes development of an economic model which attempts to quantify optimum leakage through measurement of parameters which should be readily available or obtainable.
The model was originally developed specifically for routine sounding. However, extension of the basic principles allows development of a general optimization theory. The paper then shows how the theory can be applied to optimization of district metering     

建筑语言演进的动力与规律——建筑创新的认识论基础

从建筑语言的系统性与矛盾性,去分析研究建筑语言演进的动力及其内在规律,并随之指出我们在建筑创作中所应确立和坚守的探索方向。     

生态研究文献关键词分析对风景园林学科的启示

关键词分析是文献计量的一种方法。它能快速、客观和直观地了解某个学术领域的研究热点和趋势。随着人类对自然环境的重视,近年来有关生态的研究逐渐从纯粹的郊野自然走向城市自然。搜集了2004-2013年10年间《景观与城市规划》(Landscape and Urban Planning)期刊刊载的有关风景园林学生态方向的文献,将文献中的关键词按研究对象、研究方法以及研究热点进行统计和分类,提取出高频关键词,并根据文献摘要内容,对有关风景园林学科的生态研究进展进行了综述,总结出近10年期刊中有关风景园林学科生态研究热点,为掌握风景园林学科的发展动态提供依据。     

基于本体的改模方案聚类研究

在分析改模知识的基础上,提出了一种基于本体的聚类分析法来获得典型的改模方案,建立了注塑模改模知识领域本体,确立了用矩阵方法表达改模方案数据的数学模型。通过基于改模知识领域本体之间概念相似度的计算获得改模概念间的语义相似度、用归一化公式计算改模方案间的相似度和簇间相似度,用相似度矩阵表示改模方案间的相似度,并应用凝聚的层次聚类法进行改模方案的聚类,通过改模方案聚类粒度的确定方法获得聚类结果。以企业的改模方案数据为例,验证了该方法的有效性。