• Journal of the Economic Geographical Society of Korea
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• v.21 no.1
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• pp.20-33
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• 2018

Population distribution in urban space varies with transportation flow changing along time of day. Transportation flow is directly affected by the activities of urbanites and the distribution of related facilities, since the flow is the result of moving to the point where the facilities associated with their activities are located. It is thus necessary to analyze the spatio-temporal characteristics of the urban population distribution by integrating the distribution of activity spaces related to the daily life of urbanites and the flow of transportation. The purpose of this study is to analyze the population distribution in urban space with daily and weekly time bases using the building database and T-card database in the city of Seoul, which is rich in information on land use and transportation flow. For a time-based analysis that is difficult to grasp by general statistical techniques, a four-dimensional visualization method combining time and space using a Java program is devised. Dynamic visualization in the four-dimensional space and time allows intuitive analysis and makes it possible to understand more effectively the spatio-temporal characteristics of population distribution. For this purpose, buildings are classified into three activity groups: residential, working, and commercial according to their purpose, and the number of passengers traveling to and from each stop site of bus and subway networks in the T-card database for one week is calculated in one-minute increments, Visualizing these and integrating transportation and land use, we analyze spatio-temporal characteristics of the population distribution in Seoul. As a result, it is found that the population distribution of Seoul displays distinct spatio-temporal characteristics according to land use. In particular, there is a clear difference in the population distribution pattern along the time axis according to the mixed aspects of working, commercial, and residential activities. The results of this study can be very useful for transportation and location planning of city facilities.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.453-469
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• 2016

Many image fusion quality assessment techniques, which include Wald's, QNR and Khan's protocols, have been proposed. A total procedure for the quality assessment has been defined as the quality assessment protocol. In this paper, we compared the performance of the three protocols using KOMPSAT-2/3/3A satellite images, and tested the applicability to the fusion quality assessment of the KOMPSAT satellite images. In addition, we compared and analyzed the strengths and weaknesses of the three protocols. We carried out the qualitative and quantitative analysis of the protocols by applying five fusion methods to the KOMPSAT test images. Then we compared the quantitative and qualitative results of the protocols from the aspects of the spectral and spatial preservations. In the Wald's protocol, the results from the qualitative and quantitative analysis were almost matched. However, the Wald's protocol had the limitations 1) that it is timeconsuming due to downsampling process and 2) that the fusion quality assessment are performed by using downsampled fusion image. The QNR protocol had an advantage that it utilizes an original image without downsampling. However, it could not find the aliasing effect of the wavelet-fused images in the spectral preservation. It means that the spectral preservation assessment of the QNR protocol might not be perfect. In the Khan's protocol, the qualitative and quantitative analysis of the spectral preservation was not matched in the wavelet fusion. This is because the fusion results were changed in the downsampling process of the fused images. Nevertheless, the Khan's protocol were superior to Wald's and QNR protocols in the spatial preservation.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.2
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• pp.201-208
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• 2008

Statement of problem: A new implant impression technique which use abutments as impression coping, and use resin cement as a splinting material was described. Accuracy of this technique was compared with conventional closed tray and resin splinted open tray technique for a $15^{\circ}$ angled 3-implant model Material and methods: A dental stone master model with 3 linearly positioned implant analogue and a reference framework which was passively fitted to it were fabricated. The center analogue was perpendicular to the plane of model and the outer analogues had a $15^{\circ}$angulation forward or backward. 10 closed tray impressions, 10 resin splinted open tray impressions, 10 abutment-resin framework cementation impressions and 10 abutment-metal framework cementation impressions were made with additional silicone material and poured with dental stone. A light microscope with image processing was used to record the vertical gap dimension between reference framework and analogue of duplicated cast made with each 4 impression techniques. Statistical analysis used one-way ANOVA with post-hoc tests Tukey test of .05 level of significance Results: Significant difference in the vertical gap dimension was found between closed tray technique; 74.3 (${\pm}33.4$)${\mu}m$ and resin splinted open tray technique, and two other new technique. (P<.05) Abutment-metal framework cementation technique;42.5 (${\pm}11.9$)${\mu}m$ was significantly different from resin splinted open tray technique. (P<.05) Abutmentresin framework cementation technique;51.0 (${\pm}14.1$)${\mu}m$ did not differ significantly from resin splinted open tray technique;50.3 (${\pm}16.9$)${\mu}m$. (P>.05) Conclusion: Within limitations of this study, the accuracy of implant level impressions of resin splinted open tray technique was superior to that of closed tray technique. A new technique using abutment and metal framework cementation was more accurate than resin splinted open tray technique.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.331-350
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• 2022

This study aims to understand topics of incivility related to COVID-19 from analyzing Twitter posts including COVID-19-related hate speech. To achieve the goal, a total of 63,802 tweets that were created between December 1st, 2019, and August 31st, 2021, covering three targets of hate speech including region and public facilities, groups of people, and religion were analyzed. Frequency analysis, dynamic topic modeling, and keyword co-occurrence network analysis were used to explore topics and keywords. 1) Results of frequency analysis revealed that hate against regions and public facilities showed a relatively increasing trend while hate against specific groups of people and religion showed a relatively decreasing trend. 2) Results of dynamic topic modeling analysis showed keywords of each of the three targets of hate speech. Keywords of the region and public facilities included "Daegu, Gyeongbuk local hate", "interregional hate", and "public facility hate"; groups of people included "China hate", "virus spreaders", and "outdoor activity sanctions"; and religion included "Shincheonji", "Christianity", "religious infection", "refusal of quarantine", and "places visited by confirmed cases". 3) Similarly, results of keyword co-occurrence network analysis revealed keywords of three targets: region and public facilities (Corona, Daegu, confirmed cases, Shincheonji, Gyeongbuk, region); specific groups of people (Coronavirus, Wuhan pneumonia, Wuhan, China, Chinese, People, Entry, Banned); and religion (Corona, Church, Daegu, confirmed cases, infection). This study attempted to grasp the public's anti-citizenship public opinion related to COVID-19 by identifying domestic COVID-19 hate targets and keywords using social media. In particular, it is meaningful to grasp public opinion on incivility topics and hate emotions expressed on social media using data mining techniques for hate-related to COVID-19, which has not been attempted in previous studies. In addition, the results of this study suggest practical implications in that they can be based on basic data for contributing to the establishment of systems and policies for cultural communication measures in preparation for the post-COVID-19 era.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.

• The Korean Journal of Pesticide Science
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• v.20 no.1
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• pp.14-22
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• 2016

A trial of combining and quantifying the effects of food processing on various pesticides was carried out using a meta-analysis. In this study, weighted mean response ratios and confidence intervals about the reduction of pesticide residue levels in fruits and vegetables treated with various food processing techniques were calculated using a statistical tool of meta-analysis. The weighted mean response ratios for tap water washing, peeling, blanching (boiling) and oven drying were 0.52, 0.14, 0.34 and 0.46, respectively. Among the food processing methods, peeling showed the greatest effect on the reduction of pesticide residues. Pearsons's correlation coefficient (r=0.624) between weighted mean response ratios and octanolwater partition coefficients ($logP_{ow}$) for twelve pesticides processed with tap water washing was confirmed as having a positive correlation in the range of significance level of 0.05 (p=0.03). This means that a pesticide having the higher value of $logP_{ow}$ was observed as showing a higher weighted mean response ratio. These results could be used effectively as a reference data for processing factor in risk assessment and as an information for consumers on how to reduce pesticide residues in agricultural products.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Life Science
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• v.24 no.4
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• pp.361-369
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• 2014

Broad-range and specific 16S rRNA gene PCR is used for detection and identification of bacterial pathogens in clinical specimens from patients with a high suspicion for infection. We describe the development of a broad-range and specific PCR primer, based on bacterial 16S rRNA, for use in routine diagnostic clinical microbiology services. The primers were designed by using conservative regions of 16S rRNA sequences from 10 strains. Ninety-eight clinical strains were isolated from clinical patient specimens. A total of 98 strains of bacteria were identified by phenotypic methods; PCR with newly designed primers and universal primers. All purified PCR products were sequenced using both forward and reverse primers on an automated DNA analyzer. In this study, we evaluated the usefulness of the newly designed primers and the universal primers for the detection of bacteria, and both these techniques were compared with phenotypic methods for bacteria detection. When we also tested 98 strains of clinical isolates with newly designed primers, about 778 bp DNA fragments were amplified and identified from all strains. Of the 98 strains, 94 strains (95.9%) correspond in comparison with phenotypic methods. The newly designed primers showed that the identities of 98 (100%) strains were the same as those obtained by universal PCR primers. The overall agreement between the newly designed primers and universal primers was 100%. The primer set was designed for rapid, accurate, and cheap identification of bacterial pathogens. We think the newly designed primer set is useful for the identification of pathogenic bacteria.

• Tuberculosis and Respiratory Diseases
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• v.46 no.6
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• pp.836-845
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• 1999

Background : Obesity is present in the majority of adult patients with obstructive sleep apnea(OSA) and is considered to be a major risk factor for its development. A reduction in body weight has been associated with substantial improvement in the severity of apnea. However, a variety of treatment strategies for obesity have yielded limited sucess. This study was done to determine resting energy expenditure(REE) in patients with obstructive sleep apnea and the correlation between the severity of sleep apnea and REE, and to investigate whether leptin influences REE and correlated with the severity of sleep apnea in 39 patients with OSA and 45 controls matched for obesity. Method : Overnight polysomnography was performed on all subjects using standard techniques. Measurements of REE were made using a Sensormedic Vmax 229 and a canopy system. Serum leptin concentration was measured by human leptin RIA kit of LINCO Research INC. Results : REE was greater in patients with OSA compared with controls, but there was no difference between the two groups on REE%. And also there was no significant correlation between anthropometric data, polysomnographic data and REE%. Serum leptin was linearly related to body mass index(BMI), apnea index, apnea hypopnea index and lowest arterial oxygen saturation($SaO_2$) but not related to REE%. Conclusion : This study suggests the followings. Firstly patients patients with sleep apnea have a pattern of obesity characterized by energy homeostasis at an elevated body weight set-point. In order to achieve a lower body weight in these patients, it may be necessary to increase energy expenditure by increasing physical activity. Secondly leptin level was not correlated with REE, suggesting that leptin may predominantly regulate body fat by altering eating behavior rather than calorigenesis. Lastly leptin level was significantly correlated with the severity of sleep apnea. These elevated level of leptin in patients of sleep apnea may be related to the obesity, however it needs further studies to determine the relationship between the severity of sleep apnea and serum leptin.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.4
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• pp.304-312
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• 2023

Plant breeding is a time-consuming process, mainly due to the limited annual generational advancement. A speed breeding system, using LED light sources, has been applied to accelerate generational progression in various crops. However, detailed protocols applicable to soybeans are still insufficient. In this study, we report the optimized protocols for a speed breeding system comprising 12 soybean varieties with various maturity ecotypes. We investigated the effects of two light qualities (RGB ratio), three levels of light intensity (PPFD), and two soil conditions on the flowering time and development of soybeans. Our results showed that an increase in the red wavelength of the light spectrum led to a delay in flowering time. Furthermore, as light intensity increased, flowering time, average internode length, and plant height decreased, while the number of nodes, branches, and pods increased. When compared to agronomic soil, horticultural soil resulted in an increase of more than 50% in the number of nodes, branches, and pods. Consequently, the optimal conditions were determined as follows: a 10-hour short-day photoperiod, an equal RGB ratio (1:1:1), light intensity exceeding 1,300 PPFD, and the use of horticultural soil. Under these conditions, the average flowering time was found to be 27.3±2.48 days, with an average seed yield of 7.9±2.67. Thus, the speed breeding systems reduced the flowering time by more than 40 days, compared to the average flowering time of Korean soybean resources (approximately 70 days). By using a controlled growth chamber that is unaffected by external environmental conditions, up to 6 generations can be achieved per year. The use of LED illumination and streamlined facilities further contributes to cost savings. This study highlights the substantial potential of integrating modern crop breeding techniques, such as digital breeding and genetic editing, with generational shortening systems to accelerate crop improvement.