• Current Research on Agriculture and Life Sciences
• /
• v.13
• /
• pp.45-53
• /
• 1995

This research was conducted to investigate morphological and genetic variation of Allium victorialis val. platyphyllum which growed wild in Mt. Hambaek, Mt. Odae, and Ullungdo. The tree layer of Allium victorialis var. platyphyllum community in Mt. Hambaek and Mt. Odae was dominated by Quercus mongolica. The tree layer of Ullungdo generally consist of Fagus crenata var. multinervis, Acer triflorum, Sorbus commixta, and Tilia insularis. In the herb layer, Rumohra standishii, Trillium tschonoskii, and Lilium hansonii are common at Allium victorialis var. platyphyllum community in Ullungdo. The vegetation in Ullungdo was widely different from those in Mt. Hambaek and Mt. Odae by species composition. The result of Principal Component Analysis(PCA) and Canonical Discriminent Analysis of by the 8 characters showed that Allium victorialis var. platyphyllum could be classified into 2 groups: (one ; Mt. Hambaek and Mt. Odae, the other ; Ullungdo). In PCA, the major factors in the first principal component group was angle of leaf apex. Variation of band by isozyme GOT(glautamate oxaloaccetate transaminase) is similar between Mt. Hambaek and Mt. Odae. However, Ullungdo differed from Mt. Hambaek and Mt. Odae in variation of bands.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.921-926
• /
• 2022

Sugar solutions (5%, 10%, 15% and 20%) were tested by seven sensors of Astree E-Tongue for selecting a sensor for sweetness. NMS sensor was chosen as a sensor for sweetness among two sensors (PKS and NMS sensors selected in first stage) by considering precision, linearity and accuracy. Sugar, fructose, glucose and xylitol (5%, 10% and 15%) were tested by E-tongue. The principal component analysis (PCA) result by E-Tongue with seven sensors at 5% concentration level of four sweetners was not satisfactory (Discrimination index was -0.1). On the other hand, the relative NMS sensor response values were derived as 1.08 (fructose), 0.99 (glucose) and 1.00 (xylitol) comparing to sugar. Only the E-Tongue relative glucose response 0.99 was different from 0.5~0.75 of the relative sweetness range reported as the human sensory test results. Considering the excellent precision (%RSD, 1.53~3.64%) of E-Tongue using NMS single sensor for three types of sweeteners compared to sugar in the concentration range of 5% to 15%, replacing sensory test of sweetened beverages by E-Tongue might be possible for new product development and quality control.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.spc
• /
• pp.1-10
• /
• 2022

As the volume of marine hazardous and noxious substances (HNSs) transported in domestic and overseas seas increases, the risk of HNS spill accidents is gradually increasing. HNS leaked into the sea causes destruction of marine ecosystems, pollution of the marine environment, and human casualties. Secondary accidents accompanied by fire and explosion are possible. Therefore, various types of HNSs must be rapidly detected, and a control strategy suitable for the characteristics of each substance must be established. In this study, the ground HNS spill experiment process and application result of detection algorithms were presented based on hyperspectral remote sensing. For this, styrene was spilled in an outdoor pool in Brest, France, and simultaneous observation was performed through a hyperspectral sensor. Pure styrene and seawater spectra were extracted by applying principal component analysis (PCA) and the N-Findr method. In addition, pixels in hyperspectral image were classified with styrene and seawater by applying spectral matching techniques such as spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), and spectral angle mapper (SAM). As a result, the SDS and SSV techniques showed good styrene detection results, and the total extent of styrene was estimated to be approximately 1.03 m². The study is expected to play a major role in marine HNS monitoring.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.3
• /
• pp.173-178
• /
• 2024

This study presents a technique for assessing the dimensional quality of assembly parts in Prefabricated Steel Structures (PSS) using a stereo vision sensor. The stereo vision system captures images and point cloud data of the assembly area, followed by applying image processing algorithms such as fuzzy-based edge detection and Hough transform-based circular bolt hole detection to identify bolt hole locations. The 3D center positions of each bolt hole are determined by correlating 3D real-world position information from depth images with the extracted bolt hole positions. Principal Component Analysis (PCA) is then employed to calculate coordinate axes for precise measurement of distances between bolt holes, even when the sensor and structure orientations differ. Bolt holes are sorted based on their 2D positions, and the distances between sorted bolt holes are calculated to assess the assembly part's dimensional quality. Comparison with actual drawing data confirms measurement accuracy with an absolute error of 1mm and a relative error within 4% based on median criteria.

• Korean Journal of Mineralogy and Petrology
• /
• v.37 no.2
• /
• pp.23-34
• /
• 2024

It is essential to know the mineral composition of core samples to assess the possibility of gas hydrate (GH) in sediments. During the exploration of gas hydrates (GH), mineral composition values were obtained from each core sample collected in the Ulleung Basin using X-ray diffraction (XRD). Based on this data, machine learning was performed with 3100 input values representing XRD peak intensities and 12 output values representing mineral compositions. The 488 data points were divided into 307 training samples, 132 validation samples, and 49 test samples. The random forest (RF) algorithm was utilized to obtain results. The machine learning results, compared with expert-predicted mineral compositions, revealed a Mean Absolute Error (MAE) of 1.35%. To enhance the performance of the developed model, principal component analysis (PCA) was employed to extract the key features of XRD peaks, reducing the dimensionality of input data. Subsequent machine learning with the refined data resulted in a decreased MAE, reaching a maximum of 1.23%. Additionally, the efficiency of the learning process improved over time, as confirmed from a temporal perspective.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.19 no.4
• /
• pp.315-326
• /
• 2013

This study investigated the temporal-spatial distribution and variations in water quality parameters (temperature, salinity, pH, DO, COD, SPM, DIN, DIP, silicate, TN, TP, and chlorophyll-a) in the coastal area of Jeju, Korea, adjacent to aquaculture ponds (Aewol-ri, Haengwon-ri, Pyosun-ri, and Ilkwa-ri). Data were collected bimonthly from February 2010 to December 2011. A principal component analysis (PCA) identified three major factors controlling variations in water quality during the sampling period. Aquaculture effluent water led to large changes in nutrient levels. The highest nutrient values were observed during the investigation period. The relatively large increase in organic matter at the sampling stations coupled with sea area runoff events during the summer rainy period. Variation in chlorophyll-a concentration was mainly driven by meteorological factors such as air temperature and rainfall in the coastal areas of Aewol and Haengwon. In the coastal areas of Pyosun and Ilkwa, pollution was caused by anthropogenic factors such as discharge of aquaculture effluent water. High nutrient concentrations at the majority of the coastal stations indicate eutrophication of coastal waters, especially within a distance of 300 m and depth of 10m from drainage channels. Coastal eutrophication driven by aquaculture effluent may be harmful inshore. Events such as eutrophication may potentially influence water pollution in aquaculture ponds when seawater intake is detected because of aquaculture effluent water.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.2
• /
• pp.411-417
• /
• 2021

The field that studies human-computer interaction is called HCI (Human-computer interaction). This field is an academic field that studies how humans and computers communicate with each other and recognize information. This study is a study on hand gesture recognition for human interaction. This study examines the problems of existing recognition methods and proposes an algorithm to improve the recognition rate. The hand region is extracted based on skin color information for the image containing the shape of the human hand, and the center of gravity profile is calculated using principal component analysis. I proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. We proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. The existing center of gravity profile has shown the result of incorrect hand gesture recognition for the deformation of the hand due to rotation, but in this study, the center of gravity profile is used and the point where the distance between the points of all contours and the center of gravity is the longest is the starting point. Thus, a robust algorithm was proposed by re-improving the center of gravity profile. No gloves or special markers attached to the sensor are used for hand gesture recognition, and a separate blue screen is not installed. For this result, find the feature vector at the nearest distance to solve the misrecognition, and obtain an appropriate threshold to distinguish between success and failure.

• Journal of Life Science
• /
• v.17 no.9 s.89
• /
• pp.1217-1223
• /
• 2007

I investigated the reproduction system of nine natural populations of P. discolor in Korea and two Mongolian P. conferta populations. The measurements of 19 quantitative or qualitative morphological characters were taken on each of total individuals directly from their natural habitats. Multivariate principal component analyses (PCA) were conducted to detect differences among populations consid-ering several characters simultaneously of variances using the statistical analysis system. 19 morpho-logical characteristics between Korean Potentilla species and Mongolian Potentilla species showed a slight heterogeneity of variance. The length of internodes (LFL and LSI) and characteristics of root (LLR and NOR) were shown a significant difference between two species (P<0.05). The number of ra-mets in P. conferta decreased with increasing geographic distance from viviparity. However, P. discolor has most ramets at distance intervals $60{\sim}80$ cm. In light conditions, P. discolor was significantly less resilience than P. conferta. In drought conditions, although there was not shown significant difference, P. conferta was less resilience than P. discolor. The core analysis indicates that P. conferta is the more resistant species than P. discolor and usually propagates by clonal growth during several strong envi-ronmental disadvantages such as drought events.

• Journal of the Korea Convergence Society
• /
• v.9 no.7
• /
• pp.1-7
• /
• 2018

Feature selection, one of data preprocessing techniques, is one of major research areas in many applications dealing with large dataset. It has been used in pattern recognition, machine learning and data mining, and is now widely applied in a variety of fields such as text classification, image retrieval, intrusion detection and genome analysis. The proposed method is based on a genetic algorithm which is one of meta-heuristic algorithms. There are two methods of finding feature subsets: a filter method and a wrapper method. In this study, we use a wrapper method, which evaluates feature subsets using a real classifier, to find an optimal feature subset. The training dataset used in the experiment has a severe class imbalance and it is difficult to improve classification performance for rare classes. After preprocessing the training dataset with SMOTE, we select features and evaluate them with various machine learning algorithms.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.20 no.6
• /
• pp.55-62
• /
• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.