• Journal of the Korea Institute of Building Construction
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• v.11 no.3
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• pp.238-246
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• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2008.04a
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• pp.231-234
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• 2008

Each corporation isn't consistent because of offering specific information for publicity. It spends considerable labor force and time surfing of the material information and choosing need material. That's the reason it's different with material information and choice system between production and marketing corporation. Among lots of material information, some of them are used by offering material information simplify. So it is demanded necessity of reasonable material choice system. That system helps it choose a material more easily. So we studied about the information retrieval system of reasonable material choice through the study about assortment system of material information and material choice method. It's the basic study for the suggestion of reasonable material choice system. According to this study, if that system is suggested and realized, we can overcome from the system of typical and repeatable at every course of architecture during several times. It will be helpful to increase about the productivity of architecture as the minimization of time, labor, resource and cost for the material choice.

• Journal of the Korean Society of Safety
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• v.20 no.4 s.72
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• pp.63-70
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• 2005

It has been used so many kinds of architectural materials and interior products in current building construction, and use of composite architectural materials is increasing with the development of chemical technology. As the green architecture has become the center of public interest, much effort is conducted in advanced countries on the LCA point of view, such as restriction of architectural materials that emitting pollution substances, developing of Non-Toxic architectural materials, and recycling of used materials, etc. with the cooperation of related organizations, material manufacture companies, and construction companies. Because the kinds of materials to be used in building constructions are so various, there might be some possibility of personal and subjective choice at the time of materials selection resulting the missing the requirements of building components and the choice of harmful materials to human. One way to resolve the material problem is to present the green architectural materials which coincide with the quality performance at service and not harmful to man and nature. At this point of view, this study aims to develop the material classification model by investigating the major labelling system about green architectural materials in both domestic and abroad and to implement an efficient material selection system by making a powerful database of environmental standard and quality basis of building requirements.

• Proceedings of the KSRS Conference
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• v.1
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• pp.297-300
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• 2006

The knowledge of fuel load and composition is important for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential of reduction the uncertainty in mapping fuels and offers the best approach for improving our abilities. This paper compared the results of object-oriented classification to a pixel-based classification for fuel type map derived from Hyperion hyperspectral data that could be enable to provide this information and allow a differentiation of material due to their typical spectra. Our methodological approach for fuel type map is characterized by the result of the spectral mixture analysis (SMA) that can used to model the spectral variability in multi- or hyperspectral images and to relate the results to the physical abundance of surface constitutes represented by the spectral endmembers. Object-oriented approach was based on segment based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery

• Tunnel and Underground Space
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• v.7 no.1
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• pp.1-12
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• 1997

Engineering rock mass classification is extensively used to determine the reasonable support system throughout the tunneling process in the field. Selection of support system based on the results of engineering rock mass classification is simple and straight-forward. However, this method cannot consider the effect of in-situ stresses, mechanical properties of support material, and support installation time on the behavior or rock-support system To handle the various conditions encountered in the underground excavation sites rock-support system. To handle the various conditions encountered in th eunderground excavation sites rock-support interaction program has been developed. This program can analyze the interaction between rock mass and support materials and also can simulate the tunnel excavation-support insstallation process by controlling the support installation time and the stiffness of support system. Practical applicability of this program was verfied by comparing the results of support design to those from rock mass classification for virtual underground excavation at the drilling site KD-06 in Geoje island.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.1
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• pp.91-102
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• 2014

Objectives: This study was performed in order to devise a procedure for supplementing the Special Management Materials in the Occupational Safety and Health Act and recommend candidate materials. The results are expected to be used as fundamental data for classification and criteria necessary to manage Special Management Materials in workplaces. Also, they are expected to be used as a basis for selecting target materials to nominate as additional Special Management Material. Methods: We investigated the selection standards for candidates and review ranges of data sources to nominate Special Management Materials. The substances classified as GHS(Globally Harmonized System of classification and labeling of chemicals) category 1A(known to have carcinogenic potential and reproductive toxicity for humans) or 1B(presumed to have carcinogenic potential and reproductive toxicity for humans) carcinogens and reproductive toxicants among the Controlled Hazardous Substances of the Regulation on Occupational Safety and Health Standards and substances with OELs(Occupational Exposure Limits) were inspected as the candidates for Special Management Materials. Conclusions: A seven-step procedure for selecting candidates to designate as Special Management Materials was suggested, including the setting of target chemicals for evaluation, classification of CMR(Carcinogens, Mutagens or Reproductive toxicants) by GHS classification and criteria, suggestion and selection of the candidates, and more. This study recommends 58 chemicals as qualified candidates to supplement the Special Management Materials.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.35 no.2
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• pp.58-64
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• 2003

Because of its tendency of making strong Hoc on the fiber surface with fines, Meogujaengi has not been valued as a material of Hanji. As an attempt to manufacture high value-added products using the material made from Jumchihanji, this study performed morphological classification and chemical component analysis and selection of pulping of Meogujaengi method. As a result, it can be concluded as follows, 1. Meogujaengi is assumed to be a local variety of Broussonetia karinoki and its outward appearance is distinguished from Broussonetia kazinoki. 2. The bast fiber of Meogujaengi is longer and thinner than that of Broussonetia papyrifera or Broussonetia kazinoki. However, because of the coarse linear of fiber tissue, there are many clusters. 3. The cluster phenomenon of Meogujaengi is nothing to do with its chemical components. Although the contents of its chemical components are different from those of Broussonetia kazinoki, no component was found that obstructs pulping. 4. The pretreatment for suppressing the occurrence of clusters of Meogujaengi was effective, and it was necessary to do secondary beating using hollander beater after beating mixed with PAM using knife beater.

• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.43-66
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• 1985

Korea needs proper and rational public policies on conservation and use of forest land and other natural resources because of the accelerating expansion of national land developments in recent years. Unfortunately, there is no systematic planning system to support the needs. Generally, forest land use planning needs suitability analysis based on efficient land classification system. The goal of this study was to classify a forest land using multivariate satistical methods. A case study was carried out in winter of 1983 on a mountainous area higher than 100m above sea level located at Mt. Kwanak in Anyang -city, Kyung-gi-do (province). The study area was 19.80 km$^2$wide and was divided into 1, 383 Operational Taxonomic Units (OTU's) by a 120m$\times$120m grid. Fourteen descriptors were identified and quantified for each OTU from existing national land data : elevation, slope, aspect, terrain form, geologic material, surface soil permeability, topsoil type, depth of the solum, soil acidity, forest cover type, stand size class, stand age class, stand density class, and simple forest soil capability class. For this study, a FORTRAN IV program was written for input and output map data, and the computer statistics packages, SPSS and BMD, were used to perform the multivariate statistical analysis. Fourteen variables were analyzed to investigate the characteristics of their fire quench distribution and to estimate the correlation coefficients among them. Principal component analysis was executed to find the dimensions of forest land characteristics, and factor scores were used for proper samples of OTU throughout the study area. In order to develop the classes of forest land classification based on 102 surrogates, cluster and discriminant analyses of principal descriptor variable matrix were undertaken. Results obtained through a series of multivariate statistical analyses were as follows ; 1) Principal component analysis was proved to be a useful tool for data selection and identification of principal descriptor variables which represented the characteristics of forest land and facilitated the selection of samples.

• The Journal of Advanced Prosthodontics
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• v.9 no.6
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• pp.486-495
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• 2017

The wide use of chairside CAD/CAM restorations has increased the diversity of the restorative material. For the practitioner, the selection of the appropriate material is difficult amongst the variety offered by the market. Information on the characteristics of the products can be difficult to assess due to the lack of up-to-date classification and the lack of reliability of manufacturer's advertising. The purpose of this article is to structure the data on restorative materials provided by various sources in order for the practitioner to choose the product most suited to the clinical situation. The objective is to classify chairside CAD/CAM materials and to define their characteristics and indications.