A Knowledge-Based Search Strategy for Optimally Structuring the Terrain Dependent Rational Function Models

This paper describes how a computational system for designing can learn useful, reusable, generalized search strategy rules from its own experience of designing. It can then apply this experience to transform the design process from search based (knowledge lean) to knowledge based (knowledge rich). The domain of application is the design of spatial layouts for architectural design. The processes of designing and learning are tightly coupled.

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STATISTICAL METHOD TO OVERCOME OVERFITTING ISSUE IN RATIONAL FUNCTION MODELS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w4-23-2017 ◽

2017 ◽

Vol XLII-4/W4 ◽

pp. 23-26

Author(s):

S. H. Alizadeh Moghaddam ◽

M. Mokhtarzade ◽

A. Alizadeh Naeini ◽

S. A. Alizadeh Moghaddam

Keyword(s):

Rational Function ◽

Tikhonov Regularization ◽

Satellite Images ◽

Statistical Approach ◽

Real Data ◽

Significance Test ◽

Statistical Test ◽

Data Sets ◽

Geometric Correction ◽

Function Models

Rational function models (RFMs) are known as one of the most appealing models which are extensively applied in geometric correction of satellite images and map production. Overfitting is a common issue, in the case of terrain dependent RFMs, that degrades the accuracy of RFMs-derived geospatial products. This issue, resulting from the high number of RFMs’ parameters, leads to ill-posedness of the RFMs. To tackle this problem, in this study, a fast and robust statistical approach is proposed and compared to Tikhonov regularization (TR) method, as a frequently-used solution to RFMs’ overfitting. In the proposed method, a statistical test, namely, significance test is applied to search for the RFMs’ parameters that are resistant against overfitting issue. The performance of the proposed method was evaluated for two real data sets of Cartosat-1 satellite images. The obtained results demonstrate the efficiency of the proposed method in term of the achievable level of accuracy. This technique, indeed, shows an improvement of 50–80% over the TR.

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The double-edged effect of knowledge search on innovation generations

European Journal of Innovation Management ◽

10.1108/ejim-04-2018-0072 ◽

2019 ◽

Vol 23 (1) ◽

pp. 156-176 ◽

Cited By ~ 2

Author(s):

Chunhsien Wang ◽

Min-Nan Chen ◽

Ching-Hsing Chang

Keyword(s):

Knowledge Acquisition ◽

Search Strategy ◽

High Tech ◽

Content Type ◽

External Knowledge ◽

Alliance Networks ◽

Network Position ◽

Knowledge Based ◽

Alliance Network ◽

Knowledge Search

Purpose The purpose of this paper is to investigate alliance partner diversity (APD) as a driving force that potentially enhances firms’ innovation generation (IG) in interfirm open alliance contexts. The authors propose that APD enhances IG but that the effects depend on both alliance network position and the double-edged external knowledge search strategy. Building on the knowledge-based view and social capital theory, the authors formally model how external knowledge search strategies can lead to productive or destructive acquisitions of external knowledge in interfirm open alliance networks. The authors theorize that when an individual firm adopts a central position in a complex interfirm open alliance network, its propensity toward beneficial IG depends on its knowledge search strategy (i.e. its breadth and depth) due to the joint influence of network position and knowledge search strategy on innovation. Design/methodology/approach Using an original large-scale survey of high-tech firms, this study shows that the relationship between partner diversity and IG is contingent on a firm’s network position and knowledge search strategy. The authors also offer an original analysis of how knowledge search strategy (i.e. its breadth and depth) in network centrality (NC) affects the efficacy of knowledge acquisition in interfirm open alliance networks. Empirically, the authors provide an original contribution to the open innovation literature by integrating social capital and knowledge-based theory to rigorously measure firm IG. Findings Overall, our findings suggest that the knowledge search strategy imparts a double-edged effect that may promote or interfere with external knowledge in IG in the context of the diversity of alliance partners. Research limitations/implications The work has important limitations, such as its analysis of a single industry in the empirical models. Therefore, further studies should consider multiple industries that may provide useful insights into innovation decisions. Practical implications External knowledge search is valuable, particularly in the high-tech industry, as external knowledge acquisition generates innovation output. This study serves to raise managers’ awareness of various approaches to external knowledge searches and highlights the importance of network position in knowledge acquisition from interfirm open alliance collaborations. Originality/value This paper is the first to investigate the double-edged effect of knowledge search on interfirm open alliance networks. It also contributes to the theoretical and practical literature on interfirm open alliance networks by reflecting on external knowledge search and underlying network centrality and APD factors.

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