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In this research, we focus on the collaboration efficiency that arises from the research and development (RD however, only a few focus on the efficiency. Thus, this study investigates the effect of collaboration and collaboration type in terms of innovation efficiency for SMEs. First, we evaluate and compare the innovation efficiency of collaboration by applying data envelopment analysis-based global Malmquist productivity analysis. Then, we characterized the collaboration typology through cluster analysis and analyzed the features of the configured collaboration types. Finally, we analyze innovation efficiency to evaluate the performance of the collaboration types. More specifically, we examine the stepwise effect of collaboration for the phases of innovation, and we confirmed that collaboration had different effects on R&D and commercialization. Throughout this research, we found that DEA can be applied to innovation management area in the sense of developing collaborated innovation strategies and collaboration modes, in fact we confirm that the innovation efficiency resulting from DEA may generate suitable managerial strategies for collaboration. Also, four collaboration types identified in this study also showed considerably different innovation characteristics and efficiency. In addition, the results imply that differentiated collaboration strategy is required considering the technological intensity.

Korea, mandatory Food Traceability System (FTS) regulation on dietary supplements has been enforced since the end of 2014, which has allowed government to collect electronic data to trace f...

As quality of life has improved, the need for high-performance building materials that meet specific technological requirements has increased. Residential environments have also changed owing to climate change. A technology roadmap could define and systematically reflect a timeline for the development of future core technologies. The purpose of this research is to build a technology roadmap that could be utilized for the development of technology in the eco-friendly building material industry. This research is composed of multiple analysis processes—patent analysis, Delphi, and analytic hierarchy process analysis—that minimize the uncertainty caused by the lack of information in the eco-friendly construction industry by securing objective future forecast data. Subsequently, the quality function deployment test is implemented to verify the feasibility of the technology roadmap that is constructed. The design of various types of functional, low-carbon building materials could reduce carbon emissions and save energy by ensuring a hazardous-material-free market in the future. This design development roadmap is required to complement this technology roadmap.

The integrated circuits (ICs) on wafers are highly vulnerable to defects generated during the semiconductor manufacturing process. The spatial patterns of locally clustered defects are likely to contain information related to the defect generating mechanism. For the purpose of yield management, we propose a multi-step adaptive resonance theory (ART1) algorithm in order to accurately recognise the defect patterns scattered over a wafer. The proposed algorithm consists of a new similarity measure, based on the p-norm ratio and run-length encoding technique and pre-processing procedure: the variable resolution array and zooming strategy. The performance of the algorithm is evaluated based on the statistical models for four types of simulated defect patterns, each of which typically occurs during fabrication of ICs: random patterns by a spatial homogeneous Poisson process, ellipsoid patterns by a multivariate normal, curvilinear patterns by a principal curve, and ring patterns by a spherical shell. Computatio...

Industrial revolution which is represented by specialization, standardization, and simplification significantly improves productivity, however it makes tasks in production line more simple and repetitive. This monotonous work environment affects most factory workers to be suffered from lack of motivation and boredom, so consequently makes workers to perceive their job unsatisfied and meaningless. We believe that gamification approach can make this tedious workplace more playful and motivating. In this context, a case study was conducted for a bolt-tightening task in the automotive assembly line. Especially, we explored our five-step design framework which can be useful as a basic procedure for the manufacturing gamification: (1) target system analysis; (2) goal and constraints identification; (3) concept generation; (4) concept evaluation; and (5) scenario development. Based on this design framework, a gamified interface for a bolt-tightening task was developed. The effectiveness of gamified interface was evaluated by lab-based experiment with semi-structured interview, and lessons learnt and related design suggestions are also dealt with.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.