Discovering Structure in Design Databases Through Functional and Surface Based Mapping

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Katherine Fu ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky ◽  
Kristin Wood
Keyword(s):  
Semantic Analysis ◽  
Computational Design ◽  
Functional Similarity ◽  
Transfer Of Knowledge ◽  
Structural Form ◽  
Design Data ◽  
Cross Domain ◽  
Starting Point ◽  
Surface Content ◽  
Content Similarity

This work presents a methodology for discovering structure in design repository databases, toward the ultimate goal of stimulating designers through design-by-analogy. Using a Bayesian model combined with latent semantic analysis (LSA) for discovering structural form in data, an exploration of inherent structural forms, based on the content and similarity of design data, is undertaken to gain useful insights into the nature of the design space. In this work, the approach is applied to uncover structure in the U.S. patent database. More specifically, the functional content and surface content of the patents are processed and mapped separately, yielding structures that have the potential to develop a better understanding of the functional and surface similarity of patents. Structures created with this methodology yield spaces of patents that are meaningfully arranged into labeled clusters, and labeled regions, based on their functional similarity or surface content similarity. Examples show that cross-domain associations and transfer of knowledge based on functional similarity can be extracted from the function based structures, and even from the surface content based structures as well. The comparison of different structural form types is shown to yield different insights into the arrangement of the space, the interrelationships between the patents, and the information within the patents that is attended to—enabling multiple representations of the same space to be easily accessible for design inspiration purposes. In addition, the placement of a design problem in the space effectively points to the most relevant cluster of patents in the space as an effective starting point of stimulation. These results provide a basis for automated discovery of cross-domain analogy, among other implications for creating a computational design stimulation tool.

Discovering Structure in Design Databases Through Functional and Surface Based Mapping

2011 ◽  
Author(s):  
Katherine Fu ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky ◽  
Kristin Wood
Keyword(s):  
Design Space ◽  
Semantic Analysis ◽  
Computational Design ◽  
Design Data ◽  
Cross Domain ◽  
Design By Analogy ◽  
Automated Discovery ◽  
Surface Similarity ◽  
Design Databases ◽  
Functional Content

This work presents a methodology for discovering structure in design repository databases, toward the ultimate goal of stimulating designers through design-by-analogy. Using a Bayesian model combined with Latent Semantic Analysis for discovering structural form in data, an exploration of inherent structural forms, based on the content and similarity of design data, is undertaken to gain useful insights into the nature of the design space. In this work, the approach is applied to uncover structure in the U.S. patent database. More specifically, the functional content and surface content of the patents are processed and mapped separately, yielding structures that have the potential to develop a better understanding of the functional and surface similarity of patents. These results may provide a basis for automated discovery of cross domain analogy, among other implications for creating a computational design stimulation tool.

An Introduction to Nonlinear Chemical Dynamics

1998 ◽  
Author(s):  
Irving R. Epstein ◽  
John A. Pojman
Keyword(s):  
Flow Reactor ◽  
Chemical Dynamics ◽  
Design Data ◽  
Chemical Oscillators ◽  
Starting Point ◽  
Hands On ◽  
Undergraduate Laboratory ◽  
History Of ◽  
Mechanistic Basis ◽  
Nonlinear Chemical Dynamics

Just a few decades ago, chemical oscillations were thought to be exotic reactions of only theoretical interest. Now known to govern an array of physical and biological processes, including the regulation of the heart, these oscillations are being studied by a diverse group across the sciences. This book is the first introduction to nonlinear chemical dynamics written specifically for chemists. It covers oscillating reactions, chaos, and chemical pattern formation, and includes numerous practical suggestions on reactor design, data analysis, and computer simulations. Assuming only an undergraduate knowledge of chemistry, the book is an ideal starting point for research in the field. The book begins with a brief history of nonlinear chemical dynamics and a review of the basic mathematics and chemistry. The authors then provide an extensive overview of nonlinear dynamics, starting with the flow reactor and moving on to a detailed discussion of chemical oscillators. Throughout the authors emphasize the chemical mechanistic basis for self-organization. The overview is followed by a series of chapters on more advanced topics, including complex oscillations, biological systems, polymers, interactions between fields and waves, and Turing patterns. Underscoring the hands-on nature of the material, the book concludes with a series of classroom-tested demonstrations and experiments appropriate for an undergraduate laboratory.

Near Optimal Approach to General Manufacturing Feature Extraction

1995 ◽  
Author(s):  
Jian (John) Dong ◽  
Sreedharan Vijayan
Keyword(s):  
Feature Extraction ◽  
Process Planning ◽  
Planning System ◽  
Manufacturing Cost ◽  
Design Data ◽  
Feature Extraction Method ◽  
Automatic Feature Extraction ◽  
Starting Point ◽  
Planning Function ◽  
Optimal Approach

Abstract Computers are being used increasingly in the process planning function. The starting point of this function involves interpreting design data from a CAD model of the designed component Feature-based technology is becoming an important tool for this. Automatic recognition of features and extraction of feature information from CAD data can be used to drive a process planning system. In this paper a new approach to automatic feature extraction called the Blank-Surface Concave-edge (BS-CE) approach is illustrated. This approach attempts to remove as much of the blank material with a given machine setup as possible. Hence intuitively one can say that the manufacturing cost of material removal may be minimized if this technique is employed. This feature extraction method is explained along with examples of its implementation. An analysis of alternate feature extraction results is performed and the cost of manufacture is compared to demonstrate the near optimal performance of this technique.

Enriching Topic Coherence on Reviews for Cross-Domain Recommendation

2020 ◽  
Author(s):  
Mala Saraswat ◽  
Shampa Chakraverty
Keyword(s):  
Topic Modeling ◽  
Semantic Analysis ◽  
User Generated Content ◽  
Core Concept ◽  
Semantic Clustering ◽  
Cross Domain ◽  
Semantic Coherence ◽  
Reference Corpus ◽  
Multiple Domains ◽  
Explicit Semantic Analysis

Abstract With the advent of e-commerce sites and social media, users express their preferences and tastes freely through user-generated content such as reviews and comments. In order to promote cross-selling, e-commerce sites such as eBay and Amazon regularly use such inputs from multiple domains and suggest items with which users may be interested. In this paper, we propose a topic coherence-based cross-domain recommender model. The core concept is to use topic modeling to extract topics from user-generated content such as reviews and combine them with reliable semantic coherence techniques to link different domains, using Wikipedia as a reference corpus. We experiment with different topic coherence methods such as pointwise mutual information (PMI) and explicit semantic analysis (ESA). Experimental results presented demonstrate that our approach, using PMI as topic coherence, yields 22.6% and using ESA yields 54.4% higher precision as compared with cross-domain recommender system based on semantic clustering.

Policy implementation in the era of accelerating projectification: Synthesizing Matland’s conflict–ambiguity model and research on temporary organizations

2017 ◽  
Vol 33 (4) ◽  
pp. 447-465 ◽  
Author(s):  
Christian Jensen ◽  
Staffan Johansson ◽  
Mikael Löfström
Keyword(s):  
Policy Implementation ◽  
Public Administration ◽  
Public Policies ◽  
Structural Form ◽  
Project Organization ◽  
The Public ◽  
Temporary Organizations ◽  
Starting Point ◽  
Project Organizations ◽  
Entity Relationship

It has become increasingly common to use the project as a form of organization when implementing public policies. Previous research has identified political, administrative and organizational motives behind this trend towards more project-based organizations within the public administration. The problem is that project-based organization carries inherent problems and special challenges when these projects are supposed to be implemented in permanent agencies and organizations. The purpose of this paper is to identify problems and challenges that public administrations face when ‘the project organization’ is used as a structural form of organization in implementing different kinds of public policies. The article takes its starting point in the policy implementation research and especially in Matland’s conflict-ambiguity model. This research tradition is complemented by a review of research on temporary organizations, which draws attention to some inherent and significant characteristics of project organizations, that is the concepts of entity, relationship and time. Our analysis shows that the use of project organization puts special demands on the players involved, and if these are not taken into account, there is a high risk that projects designed to bring about social change will not produce the effects that policymakers and citizens expect.

Computational Methods Applied to Modelica Simulation Models in a Web Based Framework

2005 ◽  
Author(s):  
Bjo¨rn Johansson
Keyword(s):  
Design Optimization ◽  
Computational Methods ◽  
Probabilistic Analysis ◽  
Service Oriented Architecture ◽  
Simulation Models ◽  
Computational Design ◽  
Design Data ◽  
Web Based ◽  
Actuation System ◽  
Service Oriented

In this paper, it is illustrated how computational design methods such as design optimization and probabilistic analysis is applied to system simulation models in a web based framework. Special emphasis is given models defined in the Modelica modeling language. An XML-based information system for representation and management of design data for use together with Modelica models as well as other types of models is proposed. This approach introduces a separation between the model of the system and data related to the design of the product. This is important in order to facilitate the use of computational methods in a generic way. A web based framework for integration of simulation models and computational methods is further illustrated. The framework is based on open standards for distributed computing and enables so-called service oriented architecture. Finally, an example is presented, where design optimization and probabilistic analysis is carried out on a Modelica model of an aircraft actuation system using the proposed and implemented tools and methods.

scholarly journals Development of High Affinity and High Specificity Inhibitors of Matrix Metalloproteinase 14 through Computational Design and Directed Evolution

2017 ◽  
Vol 292 (8) ◽  
pp. 3481-3495 ◽  
Author(s):  
Valeria Arkadash ◽  
Gal Yosef ◽  
Jason Shirian ◽  
Itay Cohen ◽  
Yuval Horev ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Surface Display ◽  
High Specificity ◽  
Computational Design ◽  
Computational Method ◽  
Yeast Surface Display ◽  
Mmp Inhibitor ◽  
Starting Point ◽  
Yeast Surface

Degradation of the extracellular matrices in the human body is controlled by matrix metalloproteinases (MMPs), a family of more than 20 homologous enzymes. Imbalance in MMP activity can result in many diseases, such as arthritis, cardiovascular diseases, neurological disorders, fibrosis, and cancers. Thus, MMPs present attractive targets for drug design and have been a focus for inhibitor design for as long as 3 decades. Yet, to date, all MMP inhibitors have failed in clinical trials because of their broad activity against numerous MMP family members and the serious side effects of the proposed treatment. In this study, we integrated a computational method and a yeast surface display technique to obtain highly specific inhibitors of MMP-14 by modifying the natural non-specific broad MMP inhibitor protein N-TIMP2 to interact optimally with MMP-14. We identified an N-TIMP2 mutant, with five mutations in its interface, that has an MMP-14 inhibition constant (Ki) of 0.9 pm, the strongest MMP-14 inhibitor reported so far. Compared with wild-type N-TIMP2, this variant displays ∼900-fold improved affinity toward MMP-14 and up to 16,000-fold greater specificity toward MMP-14 relative to other MMPs. In an in vitro and cell-based model of MMP-dependent breast cancer cellular invasiveness, this N-TIMP2 mutant acted as a functional inhibitor. Thus, our study demonstrates the enormous potential of a combined computational/directed evolution approach to protein engineering. Furthermore, it offers fundamental clues into the molecular basis of MMP regulation by N-TIMP2 and identifies a promising MMP-14 inhibitor as a starting point for the development of protein-based anticancer therapeutics.

scholarly journals Prawnie istotne aspekty interpretacji językoznawczej – przykład 2: zakres znaczeniowy leksemu zboczeniec (i czy jest nim homoseksualista?)

2021 ◽  
Vol 28 (1) ◽  
pp. 153-170
Author(s):  
Michał Szczyszek
Keyword(s):  
Forensic Science ◽  
Semantic Analysis ◽  
Legal Aspects ◽  
Court Case ◽  
Traditional Methods ◽  
Communication Situation ◽  
Starting Point ◽  
Linguistic Analyses

In the article, I discuss the legal aspects of language: using linguistic analyses for the benefit of the courts. I discuss linguists’ court communication situation and the expectations towards them. The starting point is one exemplary court case in which an expert linguist was appointed to issue an opinion on the evidence. The conclusions fall into two categories: linguistics and forensic science. Linguistic conclusions, developed in accordance with traditional methods of lexicographic analysis and lexicological and semantic analysis, are not necessarily (because they would not have to be) innovative for linguists. It was more important to show the situation of a linguist in court, the structure of judicial opinion and the procedures for building the linguistic response to a process inquiry as seen from the forensic perspective. 

scholarly journals Non-canonical Very as a Degree Modifier of NPs in English

2020 ◽  
Vol 9 (3) ◽  
pp. 1
Author(s):  
Marcus V. R. Vieira ◽  
Luciana Sanchez-Mendes
Keyword(s):  
Semantic Analysis ◽  
Lexical Entry ◽  
English Speaker ◽  
Starting Point ◽  
British National Corpus ◽  
The Difference ◽  
Degree Modifiers ◽  
Scalar Properties ◽  
Exhaustive Interpretation ◽  
National Corpus

The aim of this paper is to investigate the meaning of constructions with a non-canonical use of very inside NPs and to propose a unified formal semantic analysis for the degree modifier very. We adopt the notion of scalar properties and take as a starting point the fact that very is a typical degree modifier that boosts the degree of open-scale adjectives (e.g. tall) (cf. Kennedy & McNally, 2005). In this work, we focus on what we name non-canonical very: the modification of very on NPs (e.g. the very house John lived in). Our methodology consists of three major steps: firstly, we selected sentences with non-canonical very from The British National Corpus. Then, we selected sentences from five American and British novels published in the 19th and 20th centuries, comparing the sentences with their translations into Portuguese. Based on a first analysis of these sentences and on Matthewson’s (2004) methodology, we proceed to controlled elicitation of contexts with the participation of a native-English speaker of Wales. Data collected present definite DPs and complex NPs, what supports a proposal that consider modification of a scale that is closed and contextually dependent. We argue in favor of an analysis that gives a uniform lexical entry to very and assume that the difference on interpretation of canonical and non-canonical modification is due to scalar properties of the modified predicate. Canonical very modifies lexical open scales whereas non-canonical very modifies contextual closed scales of precision and produces an exhaustive interpretation. The study reveals the importance of logical scalar properties for the semantic investigation of degree modifiers.

scholarly journals CLASSIFYING COMPONENT FUNCTION IN PRODUCT ASSEMBLIES WITH GRAPH NEURAL NETWORKS

2021 ◽  
pp. 1-18
Author(s):  
Vincenzo Ferrero ◽  
Bryony DuPont ◽  
Kaveh Hassani ◽  
Daniele Grandi
Keyword(s):  
Product Design ◽  
Expert Knowledge ◽  
Ground Truth ◽  
Tier 2 ◽  
Design Data ◽  
Knowledge Based ◽  
Starting Point ◽  
Graph Neural Networks ◽  
Tier 3 ◽  
Component Function

Abstract Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Function- based design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learn- ing from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F1-score of 0.832 for tier 1 (broad), 0.756 for tier 2, and 0.783 for tier 3 (specific) functions. Given the imbalance of data features, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems, and Design-for-X consideration in function-based design.

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