• STI Policy Review
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• v.8 no.2
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• pp.75-93
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• 2017

Recently, global competition in the manufacturing sector is driving firms in the manufacturing sector to conduct product innovation projects to maintain their competitive edge. The key points of product innovation projects are 1) what the purpose of the project is and 2) what expected results in the target market can be achieved by implementing the innovation. Therefore, this study focuses on the performance of innovation projects with a business viewpoint. In this respect, this study proposes the "achievement rate" of product innovation projects as a measurement of project performance. Then, this study finds the best strategies from various innovation activities to optimize the achievement rate of product innovation projects. There are three major innovation activities for the projects, including three types of R&D activities: Internal, joint and external R&D, and five types of non-R&D activities - acquisition of machines, equipment and software, purchasing external knowledge, job education and training, market research and design. This study applies decision tree modeling, a kind of data-mining methodology, to explore effective innovation activities. This study employs the data from the 'Korean Innovation Survey (KIS) 2014: Manufacturing Sector.' The KIS 2014 gathered information about innovation activities in the manufacturing sector over three years (2011-2013). This study gives some practical implication for managing the activities. First, innovation activities that increased the achievement rate of product diversification projects included a combination of market research, new product design, and job training. Second, our results show that a combination of internal R&D, job training and training, and market research increases the project achievement most for the replacement of outdated products. Third, new market creation or extension of market share indicates that launching replacement products and continuously upgrading products are most important.

• The Journal of the Petrological Society of Korea
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• v.17 no.4
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• pp.231-237
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• 2008

Famous foreign mining companies in Argentina are Xstrata, Barrick Gold, Yamana Gold, and Anglogold Asaanti respectively. Among these companies firstly a profile of Xstrata including company history, current and future mining projects, production, and financial condition are introduced in this paper for the Korean mining companies those are sincerely looking for reliable collaborative partners not to make serious mistake in investment.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.37-38
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• 2015

A number of documents containing important and useful knowledge have been generated over time in the construction industry. Such text-based knowledge plays an important role in the construction industry for decision-making and business strategy development by being used as best practice for upcoming projects, delivering lessons learned for better risk management and project control. Thus, practical and usable knowledge creation from construction documents is necessary to improve business efficiency. This study proposes a knowledge creating system from construction documents using text mining and the design comprises three main steps - text mining preprocessing, weight calculation of each term, and visualization. A system prototype was developed as a pilot study of the system design. This study is significant because it validates a knowledge creating system design based on text mining and visualization functionality through the developed system prototype. Automated visualization was found to significantly reduce unnecessary time consumption and energy for processing existing data and reading a range of documents to get to their core, and helped the system to provide an insight into the construction industry.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.39-40
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• 2015

As international construction market increases, the importance of risk management in international construction project is emphasized. Unfortunately, current risk management practice does not sufficiently deal with project risks. Although a lot of risk analysis techniques have been introduced, most of them focus on project's external unexpected risks such as country conditions and owner's financial standing. However, because those external risks are difficult to manage and take preemptive action, we need to concentrate on project inherent risks. Based on this premise, this paper proposes a project-oriented risk mining approach which could detect and extract project risk factors automatically before they are materialized. This study presents a methodology regarding how to extract potential risks which exist in owner's project requirements and project tender documents using state of the art data analysis method such as text mining. The project-oriented risk mining approach is expected to effectively reflect project characteristics to the project risk management and could provide construction firms with valuable business intelligence.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.453-458
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• 2018

Geomechanical parameters are important factors for engineering projects during design, construction and support stages of tunnel and dam projects. Geostatistical estimation methods are known as one of the most significant approach at estimation of Geomechanical parameters. In this study, Azad dam headrace tunnel is chosen to estimate Geomechanical parameters such as Rock Quality Designation (RQD) and uniaxial compressive strength (UCS) by ordinary kriging as a geostatistical method. Also Rock Mass Rating (RMR) distribution is presented along the tunnel. Main aim in employment of geostatistical methods is estimation of points that unsampled by sampled points.To estimation of parameters, initially data are transformed to Gaussian distribution, next structural data analysis is completed, and then ordinary kriging is applied. At end, specified distribution maps for each parameter are presented. Results from the geostatistical estimation method and actual data have been compared. Results show that, the estimated parameters with this method are very close to the actual parameters. Regarding to the reduction of costs and time consuming, this method can use to geomechanical estimation.

• Geomechanics and Engineering
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• v.17 no.3
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• pp.279-285
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• 2019

The process of slope stability analysis is one of the most important stages in design of some civil and mining projects. Bimslopes are made from bimrocks (block-in-matrix rocks) where rocky blocks are distributed in a bonded matrix of finer texture. These kind of slopes are often seen in weathered and near-surface depths. Previous studies have shown that VBP (Volumetric Block Proportion) is one of the most significant factors affecting bimrocks strength and consequently the stability of bimslopes. In this paper, the influence of block inclinations on bimslope stability have been investigated. For this purpose, 180 theoretical models have been made with various VBPs, all of them have a specified block size distribution. These bimslopes contain blocks with differing dips relative the slope inclination. Also for each kind of block inclination, 10 different blocks arrangements have been modeled. The Finite Element Method (FEM) was used to analysis the stability of these bimslopes models. The results showed the inclination of blocks has a strong impact on the Safety Factor and stability of bimslopes. When the difference in angle of dip of blocks relative to the slope angle is maximum, the Safety Factor of bimslopes tends to be a maximum compared with the matrix-only state. Furthermore, with increasing VBP of bimslopes stability increases. The graphs obtained from this study could be used for preliminary guidance in the projects design with bimslopes.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.366-374
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• 2014

This study assessed and analysed a result of a product design experiment through Product Data Analytics (PDA), to find reasons for failure of some projects in the experiment. PDA is a computer-based data analysis that uses Product Data Management (PDM) databases as its operational databases. The study examines 20 product design projects in the experiment, which are prepared to follow same product development process by using an identical PDM system. The design result in the PDM database is assessed and analysed by On-Line Analytical Processing (OLAP) and data mining tools in PDA. The assesment and analysis reveals the lateness in creation of 3D CAD models as the main reason of the failure.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.4
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• pp.247-258
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• 2021

To understand restoration and conservation projects conducted in Korea for endangered freshwater fishes and amphibians/reptiles, information about Request for Protocols-related studies on restoration, breeding, and release were collected. Trends of studies were visualized via word clouds and VOSviewer program using a text mining technique. Analysis of restoration projects for endangered freshwater fishes elucidated that most research studies conducted to date were focused on genetics and release through captive breeding that could be classified into captive breeding and habitat environments. As for research projects related to amphibians/reptiles, monitoring projects had the highest number, followed by genetic, translocation, and monitoring studies. In addition, restoration projects for amphibians/reptiles included a large number of post-capture translocation projects. Thus, many projects were confirmed by public institutions rather than by the Ministry of Environment. Network analysis revealed that it was largely classified into capture, translocation, and Kaloula borealis. Based on these results, limitations, achievements, and challenges associated with projects conducted thus far are highlighted. Research directions for future restoration and conservation of endangered freshwater fishes and amphibians/reptiles in South Korea are also suggested.

• Tunnel and Underground Space
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• v.32 no.3
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• pp.173-193
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• 2022

In this study, international standardization trend of smart mining technology was analyzed focusing on the guidelines developed by GMG (Global Mining Guidelines Group). GMG is a non-profit organization that unites the global mining community. It was established to promote mining safety, innovation and sustainability. Currently, GMG's working group consists of artificial intelligence, asset management, autonomous mining, cybersecurity, data access and usage/interoperability, the electric mine, mineral processing, underground mining, and sustainability. Guideline development projects related to smart mining technology are being conducted in artificial intelligence, autonomous mining, cybersecurity, data access and usage/interoperability, and underground mining. As of April 2022, eight types of smart mining-related guidelines have been published through pre-launch, launch, guideline definition, contents generation, technical editing/layout/final review, and voting process. It is judged that the GMG guidelines can be an important reference for the development of domestic smart mining technology standards.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.89-96
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• 2016

Most data generated in construction projects is unstructured text data. Unstructured data analysis is very needed in order for effective analysis on large amounts of text-based documents, such as contracts, specifications, and RFI. This study analysed previously performed project's bid related documents (bidder inquiry) in overseas construction projects; as a results of the analysis frequent words in documents, association rules among the words, and various document topics were derived. This study suggests effective text analysis approach for massive documents with short time using text mining technique, and this approach is expected to extend the unstructured text data analysis in construction industry.