• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.171-181
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• 2012

Outdoor mobile robots are faced with various terrain types having different characteristics. To run safely and carry out the mission, mobile robot should recognize terrain types, physical and geometric characteristics and so on. It is essential to control appropriate motion for each terrain characteristics. One way to determine the terrain types is to use non-contact sensor data such as vision and laser sensor. Another way is to use contact sensor data such as slope of body, vibration and current of motor that are reaction data from the ground to the tire. In this paper, we presented experimental results on terrain classification using contact sensor data. We made a mobile robot for collecting contact sensor data and collected data from four terrains we chose for experimental terrains. Through analysis of the collecting data, we suggested a new method of terrain feature extraction considering physical characteristics and confirmed that the proposed method can classify the four terrains that we chose for experimental terrains. We can also be confirmed that terrain feature extraction method using Fast Fourier Transform (FFT) typically used in previous studies and the proposed method have similar classification performance through back propagation learning algorithm. However, both methods differ in the amount of data including terrain feature information. So we defined an index determined by the amount of terrain feature information and classification error rate. And the index can evaluate classification efficiency. We compared the results of each method through the index. The comparison showed that our method is more efficient than the existing method.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.4
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• pp.65-77
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• 2007

Site response analyses were performed based on equivalent linear technique using shear wave velocity profiles of 162 sites collected around the Korean peninsula. The site characteristics, particularly the shear wave velocities and the depth to the bedrock, are compared to those in the western United States. The results show that the site-response coefficients based on the mean shear velocity of the top 30m ($V_{S30}$) suggested in the current code underestimates the motion in short-period ranges and overestimates the motion in mid-period ranges. The current Korean code based on UBC is required to be modified considering site characteristics in Korea for the reliable estimation of site amplification. From the results of numerical estimations, new regression curves were derived between site coefficients ($F_{a}\;and\;F_{v}$) and the fundamental site periods, and site coefficients were grouped based on site periods with reasonable standard deviations compared to site classification based on $V_{S30}$. Finally, new site classification system and modification of design response spectra are recommended considering geotechnical characteristics in Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1289-1295
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• 2015

Electromagnetic wave signals radiated from an antenna are reflected by targets and received through the same antenna. These received signals show different characteristics according to various target materials having different dielectric constants. Therefore, target characteristics can be recognized if we can utilize these return signals efficiently. this method can be applied for discrimination and classification of hazardous materials. In this paper, utilizing these experimentally obtained signals, correlation characteristics are obtained and analyzed for classification and discrimination purposes. Although the correlation method requires the storage of reference signals, it shows very promising results. this correlation method can be applied for classification and discrimination of hazardous materials.

• Journal of Korea Technology Innovation Society
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• v.11 no.4
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• pp.592-612
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• 2008

The convergence of technology as a major breakthrough in technology innovation has been generated and developed. With the advent of the 21st century characterized by the knowledge-based economy, it is happening even more frequently and vigorously than ever. However, study on classification system or definition of notions is necessary as the convergence of technology is still in the early stage. The existing classification system has limited application to the convergence of technology. With no standard available for the classification of the technology convergence, there has been much concern for duplicative R&D investment. On this ground, a new and reformed classification system for the convergence of technology and definition of notions are needed. Previous studies regard base technologies used in the technology convergence and new convergence technologies as important. However, this paper views convergence of technology as a dynamic phenomenon and puts an emphasis on the cause of technology convergence, not on the new technology itself and examines whether base technologies go back to their original state after the completion of technology convergence. This kind of approach will classify technology convergence by characteristics. Although more researches including quantitative analysis are necessary, this paper expects to offer help with further researches on classification system.

• Food Science of Animal Resources
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• v.41 no.6
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• pp.936-949
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• 2021

This study discriminated fatty acid profile and flavor characteristics of Beijing You Chicken (BYC) as a precious local breed and Dwarf Beijing You Chicken (DBYC) eggs. Fatty acid profile and flavor characteristics were analyzed to identify differences between BYC and DBYC eggs. Four classification algorithms were used to build classification models. Arachidic acid, oleic acid (OA), eicosatrienoic acid, docosapentaenoic acid (DPA), hexadecenoic acid, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), unsaturated fatty acids (UFA) and 35 volatile compounds had significant differences in fatty acids and volatile compounds by gas chromatography-mass spectrometry (GC-MS) (p<0.05). For fatty acid data, k-nearest neighbor (KNN) and support vector machine (SVM) got 91.7% classification accuracy. SPME-GC-MS data failed in classification models. For electronic nose data, classification accuracy of KNN, linear discriminant analysis (LDA), SVM and decision tree was all 100%. The overall results indicated that BYC and DBYC eggs could be discriminated based on electronic nose with suitable classification algorithms. This research compared the differentiation of the fatty acid profile and volatile compounds of various egg yolks. The results could be applied to evaluate egg nutrition and distinguish avian eggs.

• Journal of the Korean Society for information Management
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• v.33 no.2
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• pp.33-59
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• 2016

This study examined the factors affecting the performance of automatic classification for the domestic conference papers based on machine learning techniques. In particular, In view of the classification performance that assigning automatically the class labels to the papers in Proceedings of the Conference of Korean Society for Information Management using Rocchio algorithm, I investigated the characteristics of the key factors (classifier formation methods, training set size, weighting schemes, label assigning methods) through the diversified experiments. Consequently, It is more effective that apply proper parameters (${\beta}$, ${\lambda}$) and training set size (more than 5 years) according to the classification environments and properties of the document set. and If the performance is equivalent, I discovered that the use of the more simple methods (single weighting schemes) is very efficient. Also, because the classification of domestic papers is corresponding with multi-label classification which assigning more than one label to an article, it is necessary to develop the optimum classification model based on the characteristics of the key factors in consideration of this environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.3
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• pp.107-116
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• 2003

This study investigated how appropriate the classification methods based on conventional spectral characteristics are for high resolution imagery. A supervised classification mixing parametric and non-parametric rules, a method in which fuzzy theory is applied to such classification, and an unsupervised method were performed and compared to each other for accuracy. In addition, comparing the result screen-digitized through interpretation to the classification result using spectral characteristics, this study analyzed the conformity of both methods. Although the supervised classification to which fuzzy theory was applied showed the best performance, the application of conventional classification techniques to high resolution imagery had some limitations due to there being too much information unnecessary to classification, shadows, and a lack of spectral information. Consequently, more advanced techniques including integration with other advanced remote sensing technologies, such as lidar, and application of filtering or template techniques, are required to classify land cover/use or to extract useful information from high resolution imagery.

• Journal of Korean Library and Information Science Society
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• v.32 no.3
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• pp.397-417
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• 2001

The purpose of this study is to suggest the guidelines for developing the effective classification scheme of woman studies on the Internet. In order to do that, fuve search engines and three subject web databases are analyzed based on the characteristics, problems of their classification schemes. In addition their classification schemes are measured in terms of coverage of subject fields and systematic logic. The results suggest the guidelines far classification scheme reflecting the characteristics of women's studies that ice interdisciplinary and multidisciplinary fields.

• Geotechnical Engineering
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• v.14 no.4
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• pp.163-176
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• 1998

To apply piezocone test to soil classification, several classification charts based on correlations between its results and basic characteristics of soils have been developed. However, it is necessary to investigate their applicability to Korean soil, since they were the results obtained from the limited number of foreign sites. In this study, 41 piezocone penetration bests for various sixtes spreading widely over Korean territory were carried out. Correlations between piezocone teat results and basic characteristics and the applicability of existing classification charts were investigated. Conclusively, new classification charts based, on Unified Soil Classification System were developed with a local confidence.

• KCID journal
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• v.17 no.2
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• pp.17-27
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• 2010

In order to manage the water quality in reservoir, it is necessary to understand the temporal and spatial variation of reservoirs and to classify the reservoirs. In this research, agricultural reservoirs are classified according to physical characteristics (depth, residence time, shape of the reservoir etc) and water quality using multivatriate analysis (PCA and CA). CA (Cluster Analysis) method classify reservoirs into several groups as a similarity of the reservoirs, but it is difficult to indicate a full list to the one table. In case of PCA (Principle Component Analysis) method, it has the advantage for the classification on the reservoirs depending on the water quality similarity and also it is useful to analyze the relationship between related factors through correlation analysis. However PCA is limited to classify into several groups based on the characteristics of the reservoirs and each user should be classified as randomly subjective according to the relative position of the reservoir in the figure. In conclusions, compared to conventional reservoirs classification methods, both CA and PCA methods are considered to be a classification method that describes the nature of the reservoir well, but classification results has a restriction on use, so further research will be needed to complement.