• The Korean Journal of Ecology
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• v.16 no.1
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• pp.93-114
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• 1993

The vegetation of Mt. Mudeung was investigated from April, 1991 to September, 1992. The units of vegetation were classified 10 units by the Braun-Blanquet's phytosociological method. The forest vegetation was classified into 10 communities, Pinus densiflora, Pinus vigida, Chamaecyparis obtusa afforestation, Quercus mongolica, Q. variabilis, Q. serrata, Q. acutissima, Miscanthus sinensis var. purpurascens, Hylomecon hylomeconoides and Drosera rotundifolia community. Based on the classification, the actual vegetation map and degree of green naturality were drawn in 1:50,000 scale. The vertical distribution of the main component species was investigated based on the vegetation data of the EN slope and SW slope of Mt. Mudeung from altitude 200m to top.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1527-1543
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• 2017

MiRNA is a biological short sequence, which plays a crucial role in almost all important biological process. MiRNA patterns are common sequence segments of multiple mature miRNA sequences, and they are of significance in identifying miRNAs due to the functional implication in miRNA patterns. In the proposed approach, the primary miRNA patterns are produced from sequence alignment, and they are then cut into short segment miRNA patterns. From the segment miRNA patterns, the candidate miRNA patterns are selected based on estimated probability, and from which, the potential miRNA patterns are further selected according to the classification performance between authentic and artificial miRNA sequences. Three parameters are suggested that bi-nucleotides are employed to compute the estimated probability of segment miRNA patterns, and top 1% segment miRNA patterns of length four in the order of estimated probabilities are selected as potential miRNA patterns.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.549-555
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• 2013

Human influence on soil formation has dramatically increased as the development of human civilization and industry. Increase of anthropogenic soils induced research of those soils; classification, chemical and physical characteristics and plant growth of anthropogenic soils. However there have been no reports on soil pore properties from the anthropogenic soils so far. Therefore the objectives of this study were to test computer tomography (CT) to characterize physical properties of an anthropogenic paddy field soil and to find differences between natural and anthropogenic paddy field soils. Soil samples of a natural paddy field were taken from Ansung, Gyeonggi-do (Ansung site), and samples of an anthropogenic paddy field were from Gumi in Gyeongsangnam-do (Gasan) where paddy fields were remodeled in 2011-2012. Samples were taken at three different depths and analyzed for routine physical properties and CT scans. CT scan provided 3 dimensional images to calculate pore size, length and tortuosity of soil pores. Fractal analysis was applied to quantify pore structure within soil images. The results of measured physical properties (bulk density, porosity) did not show differences across depths and sites, but hardness and water content had differences. These differences repeated within the results of pore morphology. Top soil samples from both sites had greater pore numbers and sizes than others. Fractal analyses showed that top soils had more heterogeneous pore structures than others. The bottom layer of the Gasan site showed more degradation of pore properties than ploughpan and bottom layers from the Ansung site. These results concluded that anthropogenic soils may have more degraded pore properties as depth increases. The remodeled paddy fields may need more fundamental remediation to improve physical conditions. This study suggests that pore analyses using CT can provide important information of physical conditions from anthropogenic soils.

• Journal of KIISE
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• v.44 no.9
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• pp.887-892
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• 2017

Brain-computer interface (BCI) is a technology that controls computer and transmits intention by measuring and analyzing electroencephalogram (EEG) signals generated in multi-channel during mental work. At this time, optimal EEG channel selection is necessary not only for convenience and speed of BCI but also for improvement in accuracy. The optimal channel is obtained by removing duplicate(redundant) channels or noisy channels. This paper propose a dual filter-based channel selection method to select the optimal EEG channel. The proposed method first removes duplicate channels using Spearman's rank correlation to eliminate redundancy between channels. Then, using F score, the relevance between channels and class labels is obtained, and only the top m channels are then selected. The proposed method can provide good classification accuracy by using features obtained from channels that are associated with class labels and have no duplicates. The proposed channel selection method greatly reduces the number of channels required while improving the average classification accuracy.

• Journal of KIISE
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• v.43 no.9
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• pp.1008-1014
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• 2016

Data such as Twitter, Facebook, and customer reviews belong to the informal document group, whereas, newspapers that have grammar correction step belong to the formal document group. Finding consistent rules or patterns in informal documents is difficult, as compared to formal documents. Hence, there is a need for additional approaches to improve informal document analysis. In this study, we classified Twitter data, a representative informal document, into ten categories. To improve performance, we revised and expanded features based on LDA(Latent Dirichlet allocation) word distribution. Using LDA top-ranked words, the other words were separated or bundled, and the feature set was thus expanded repeatedly. Finally, we conducted document classification with the expanded features. Experimental results indicated that the proposed method improved the micro-averaged F1-score of 7.11%p, as compared to the results before the feature expansion step.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1074-1088
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• 2016

Successful Visual Story Telling(VST) of desire is a crucial key for the success of animation because desire is the leading power of story development of animation. An analysis of the desire of VST using the top 5 successful American feature film animations is carried out. Totally, 147 desire shots are extracted by using the proposed Objective Selection of Desire Shots(OSDS) method based on the theory of Makee's conflict and desire pursuing modeling, Maslow's 20 desire types, Greimas's actant model, and the 17 narrative process classification. In addition to them, the 5 Beat(5B) model of a scene is proposed. Five image specialists have evaluated VST of the selected 147 desire shots. For each shot, the desire type among the 20 desires and the strength are obtained. Among them, the top 3 desires(affiliation, achievement, and nurturance) appearing 51.8% are analyzed. The composition elements of shots affecting the desire type and the strength have found. These can be used for better VST of preproduction and production of animation.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.12-16
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• 2007

The function of a protein is closely co-related with its subcellular location(s). Given a protein sequence, therefore, how to determine its subcellular location is a vitally important problem. We have developed a new prediction method for protein subcellular location(s), which is based on n-gram feature extraction and k-nearest neighbor (kNN) classification algorithm. It classifies a protein sequence to one or more subcellular compartments based on the locations of top k sequences which show the highest similarity weights against the input sequence. The similarity weight is a kind of similarity measure which is determined by comparing n-gram features between two sequences. Currently our method extract penta-grams as features of protein sequences, computes scores of the potential localization site(s) using kNN algorithm, and finally presents the locations and their associated scores. We constructed a large-scale data set of protein sequences with known subcellular locations from the SWISS-PROT database. This data set contains 51,885 entries with one or more known subcellular locations. Our method show very high prediction precision of about 93% for this data set, and compared with other method, it also showed comparable prediction improvement for a test collection used in a previous work.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.89-101
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• 2019

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.185-198
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• 2017

Since the year 2000, the growth rate of domestic manufacturing has declined and the sales and employment have decreased. Major developed countries have established a variety of strategies to strengthen their manufacturing competitiveness, and promote manufacturing innovation through the convergence of technology and ICT. The key to manufacturing innovation is to reduce the time and cost for developing new products using modeling and simulation (M&S) technology in the product design stage. M&S industries, which belong to the top sector of the industry value chain, have a huge ripple effect across other industries. On the other hand, the competitiveness of the domestic M&S industry is weak compared to developed countries and even the definition and classification of domestic M&S companies have not been made. In this paper, by analyzing the Korea Standard Industry Classification (KSIC), five fine industry classifications included in M&S service companies were derived. In addition, the 307 M&S service companies were derived in accordance with the selection procedure of 3 steps from the 11,822 related companies. To analyze the capabilities of domestic M&S service companies, the current status of the selected M&S service companies was investigated. Considering the Korean economy's high dependence on the manufacturing industry, strengthening the competitiveness of manufacturing industry is required by enhancing the capacities and building an ecosystem in domestic M&S services for future sustainable economic growth.