• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5B
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• pp.487-495
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• 2010

Soil moisture is a crucial factor in hydrological system which influences runoff, energy balance, evaporation, and atmosphere. United States National Aeronautic and Space Administration (NASA) and Department of Agriculture (USDA) have established Soil Moisture Experiment (SMEX) since 2002 for the global observations. SMEX provides useful data for the hydrological science including soil moisture and hydrometeorological variables. The purpose of this study is to investigate the relationship between remotely sensed soil moisture data from aircraft and satellite and ground based experiment. C-band of Polarimetric Scanning Radiometer (PSR) that observed the brightness temperature provides soil moisture data using a retrieval algorithm. It was compared with the In-situ data for 2-30 cm depth at four sites. The most significant depth is 2-10 cm from the correlation analysis. Most of the sites, two data are similar to the mean of data at 10 cm and the median at 7 cm and 10 cm at the 10% significant level using the Rank Sum test and t-test. In general, soil moisture data using the C-band of the PSR was established to fit the Normal, Log-normal and Gumbel distribution. Soil moisture data using the aircraft and satellites will be used in hydrological science as fundamental data. Especially, the C-band of PSR will be used to prove soil moisture at 7-10 cm depths.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.202-222
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• 2023

This study traces the growth and changes in the spatial distribution and characteristics of media cluster in Seoul by focusing on the co-evolution of film and TV production. To identify the spatial distribution and aggregation of film and broadcast TV production, we measure their spatial auto-correlation based on Moran's I and LISA, using the data from the Census on Establishments of the National Statistical Office. In addition, the eleven semi-structured interviews conducted with workers in the media industries, such as film crews and TV drama producers, help to clarify the complexity and dynamics of diverse factors that affect spatial distribution of media cluster. This multi-method study shows the increasing polycentricity of media cluster in the last decade. Gangnam, Mapo, Yeouido, Gangseo-Yeongdeungpo, and Seongsu have emerged as key hubs for media industries, particularly in light of changes in the transportation system and the real estate market. The finding indicates the co-evolution of film and broadcast TV production, demonstrating how the characteristics of the creative industry and metropolitan changes are intertwined with each other in shaping the geographical pattern of the media cluster.

• Journal of Music and Human Behavior
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• v.21 no.1
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• pp.49-68
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• 2024

This study aims to acoustically analyze Korean language characteristics and convert these findings into musical elements, providing foundational data for evidence-based music-language rehabilitation. We collected voice data from thirty men and thirty women aged 19-25, each providing six-syllable prosodic units composed of two accentual phrases, including both declarative and interrogative sentences. Analyzing this data with Praat, we extracted syllabic acoustic properties and conducted statistical analyses based on acoustic properties, sentence type, gender, and particle presence. Significant differences were found in syllable frequency and duration based on accentual phrases and prosodic units (p < .001), with interrogative showing higher frequencies and declaratives longer durations (p < .001). Female frequencies were significantly higher than males' (p < .001), with longer durations observed (p < .001). Particle syllables also showed significantly stronger intensities (p < .001). Finally, we presented melodies converted from these acoustic properties into musical scores based on pitch, duration, and accent. The insights from this analysis of six-syllable Korean sentences will guide further research on developing a system for melodizing large-scale Korean speech data, expected to be crucial in music-based language rehabilitation.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.87-97
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• 2024

This study analyzed the water shortage hotspot areas in South Korea using spatial clustering analysis for water shortage estimates in 2030 of the Master Plans for National Water Management. To identify the water shortage cluster areas, we used water shortage data from the past maximum drought (about 50-year return period) and performed spatial clustering analysis using Local Moran's I and Getis-Ord Gi*. The areas subject to spatial clusters of water shortage were selected using the cluster map, and the spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The results indicated that one cluster (lower Imjin River (#1023) and neighbor) in the Han River basin and two clusters (Daejeongcheon (#2403) and neighbor, Gahwacheon (#2501) and neighbor) in the Nakdong River basin were found to be the hotspot for water shortage, whereas one cluster (lower Namhan River (#1007) and neighbor) in the Han River Basin and one cluster (Byeongseongcheon (#2006) and neighbor) in the Nakdong River basin were found to be the HL area, which means the specific area have high water shortage and neighbor have low water shortage. When analyzing spatial clustering by standard watershed unit, the entire spatial clustering area satisfied 100% of the statistical criteria leading to statistically significant results. The overall results indicated that spatial clustering analysis performed using standard watersheds can resolve the variable spatial unit problem to some extent, which results in the relatively increased accuracy of spatial analysis.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.8 no.1
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• pp.5-24
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• 2001

The purpose of this study was to develop a parenting intervention program and determine the efficacy of the program with low-birth weight infants and their mothers. Nine dyads for the experimental group and twelve dyads for the control group discharged from the Neonatal Intensive Care Unit of a University Hospital in Seoul were recruited for the study. For the intervention group, programmed education and support which focused on the maternal sensitivity of the infant's behavior. rearing environment. motherinfant interaction and infant care were given to each subject. Individual counseling and home visits were provided at discharge, one week after discharge. and one and three months of corrected age in every infant. Structured questionaires were administered and feeding interactions were videotaped and coded by a blinded certified observer. A Quasi-experimental design was conducted for this study. Postpartum depression, maternal self esteem. infant care burden, HOME. mother-infant interaction, and infant development were measured. Results were in favor of the intervention versus the control group. On the Beck depression inventory, intervention mothers showed decreasing trends in depressive symptom vs control mothers although, there were statistically no significant differences between the two groups at each time. The mean score of experimental group was 11.55(mild depression state) at discharge and became 8,6(normal state) at 1 month of corrected age. On the other hand, the mean score of the control group was 13.92(mild depression state) at discharge and became 14.0. Maternal self esteem in both groups improved over time. Infant care burden in both groups was also shown to increase over time. There was a significant difference between the two groups in HOME(p=.0340) at 3 months of corrected age. HOME scores of the experimental group and the control's were 31.10 and 25.58, respectively. Mothers' emotional and language responses were significantly high in the intervention group compared with the control group(p=.0155). Intervention group (53.33) showed a significantly high quality of motherinfant interaction compared with the in control group (42.80)(p =.0340). Intervention group mothers appeared have a better quality of mother-infant interaction behaviors. On the other hand, there was no statistical difference in the infant part between groups. Intervention group infants had higher trends in a general developmental quotient: although, there was no statistical difference between groups. The general developmental quotient of intervention infants was 102.56 and control's was 91.28. However, the developmental quotient of the domain of 'individuality-sociality' was higher in the intervention group infants compared with the control's(p=.0155). The concerns identified by parents revealed two domains of an infants' health management -knowledge and skills in caregiving of lowbirthweight-infants, characteristics of lowbirthweight infants, identifying a developmental milestone, coping with emergency situations and relaxation strategies of mothers from the infant care burden. Interview data with the mothers of low-birth weight infants can be used to develop intervention program contents. Limited intervention time and frequency due to time and cost limitations of this study should be modified. The intervention should be continuously implemented when low-birth weight infants become three years old. An NNNS demonstration appeared to be a very effective intervention for the mothers to improve the quality of mother-infant interactions. Therefore intervening in the mothers of low-birth weight infants as early after delivery as possible is desirable. This study has shown that home visit interventions are worthwhile for mothers only beyond the approach as an essential factor in ability of facilitating a growth fostering environment. In conclusion. the intervention program of this study was very effective in enhancing the parenting for the mothers of low-birth weight infants, resulting in health promotion of low-birth weight infants. The home-visit outreach intervention program of this study will contribute to the health delivery system in this country where there is a lack of continuous follow-up programs for low-birth weight infants after discharge from NICU, if it is activated as part of the home visit programs in community health systems.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.69-85
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• 2016

Successful relationship with loving partners is one of the most important factors in life. In psychology, there have been some previous researches studying the factors influencing romantic relationships. However, most of these researches were performed based on statistical analysis; thus they have limitations in analyzing complex non-linear relationships or rules based reasoning. This research analyzes commitment and persistence in heterosexual involvement according to styles of loving using a datamining technique as well as statistical methods. In this research, we consider six different styles of loving - 'eros', 'ludus', 'stroge', 'pragma', 'mania' and 'agape' which influence romantic relationships between lovers, besides the factors suggested by the previous researches. These six types of love are defined by Lee (1977) as follows: 'eros' is romantic, passionate love; 'ludus' is a game-playing or uncommitted love; 'storge' is a slow developing, friendship-based love; 'pragma' is a pragmatic, practical, mutually beneficial relationship; 'mania' is an obsessive or possessive love and, lastly, 'agape' is a gentle, caring, giving type of love, brotherly love, not concerned with the self. In order to do this research, data from 105 heterosexual couples were collected. Using the data, a linear regression method was first performed to find out the important factors associated with a commitment to partners. The result shows that 'satisfaction', 'eros' and 'agape' are significant factors associated with the commitment level for both male and female. Interestingly, in male cases, 'agape' has a greater effect on commitment than 'eros'. On the other hand, in female cases, 'eros' is a more significant factor than 'agape' to commitment. In addition to that, 'investment' of the male is also crucial factor for male commitment. Next, decision tree analysis was performed to find out the characteristics of high commitment couples and low commitment couples. In order to build decision tree models in this experiment, 'decision tree' operator in the datamining tool, Rapid Miner was used. The experimental result shows that males having a high satisfaction level in relationship show a high commitment level. However, even though a male may not have a high satisfaction level, if he has made a lot of financial or mental investment in relationship, and his partner shows him a certain amount of 'agape', then he also shows a high commitment level to the female. In the case of female, a women having a high 'eros' and 'satisfaction' level shows a high commitment level. Otherwise, even though a female may not have a high satisfaction level, if her partner shows a certain amount of 'mania' then the female also shows a high commitment level. Finally, this research built a prediction model to establish whether the relationship will persist or break up using a decision tree. The result shows that the most important factor influencing to the break up is a 'narcissistic tendency' of the male. In addition to that, 'satisfaction', 'investment' and 'mania' of both male and female also affect a break up. Interestingly, while the 'mania' level of a male works positively to maintain the relationship, that of a female has a negative influence. The contribution of this research is adopting a new technique of analysis using a datamining method for psychology. In addition, the results of this research can provide useful advice to couples for building a harmonious relationship with each other. This research has several limitations. First, the experimental data was sampled based on oversampling technique to balance the size of each classes. Thus, it has a limitation of evaluating performances of the predictive models objectively. Second, the result data, whether the relationship persists of not, was collected relatively in short periods - 6 months after the initial data collection. Lastly, most of the respondents of the survey is in their 20's. In order to get more general results, we would like to extend this research to general populations.

• Journal of radiological science and technology
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• v.39 no.1
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• pp.51-57
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• 2016

Acoustic noise during magnetic resonance imaging (MRI) is the main source for patient discomfort. we report our preliminary experience with this technique in neuroimaging with regard to subjective and objective noise levels and image quality. 60 patients(29 males, 31 females, average age of 60.1) underwent routine brain MRI with 3.0 Tesla (MAGNETOM Tim Trio; Siemens, Germany) system and 12-channel head coil. Q-$T_2$ and $T_2$ sequence were performed. Measurement of sound pressure levels (SPL) and heart rate on Q-$T_2$ and $T_2$ was performed respectively. Quantitative analysis was carried out by measuring the SNR, CNR, and SIR values of Q-$T_2$, $T_2$ and a statistical analysis was performed using independent sample T-test. Qualitative analysis was evaluated by the eyes for the overall quality image of Q-$T_2$ and $T_2$. A 5-point evaluation scale was used, including excellent(5), good(4), fair(3), poor(2), and unacceptable(1). The average noise and peak noise decreased by $15dB_A$ and $10dB_A$ on $T_2$ and Q-$T_2$ test. Also, the average value of heartbeat rate was lower in Q-$T_2$ for 120 seconds in each test, but there was no statistical significance. The quantitative analysis showed that there was no significant difference between CNR and SIR, and there was a significant difference (p<0.05) as SNR had a lower average value on Q-$T_2$. According to the qualitative analysis, the overall quality image of 59 case $T_2$ and Q-$T_2$ was evaluated as excellent at 5 points, and 1 case was evaluated as good at 4 points due to a motion artifact. Q-$T_2$ is a promising technique for acoustic noise reduction and improved patient comfort.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• The Journal of Korean Academy of Prosthodontics
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• v.51 no.4
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• pp.315-322
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• 2013

Purpose: To evaluate the axial displacement of implant-abutment assembly after cyclic loading in internal tapered connection system. Materials and methods: External butt-joint connection implant and internal tapered connection implant were connected with three types of abutment for cement-retained prostheses, i.e. external type abutment (Ext group), internal tapered 1-piece abutment (Int-1 group), and internal tapered 2-piece abutment (Int-2 group). For each group, 7 implants and abutments were used. The implantabutments assemblies were clamped into the implant holder for vertical loads. A dynamic cyclic loading was applied for $150{\pm}10N$ at a frequency of 4 Hz. The amount of axial displacement of the abutment into the implant was calculated at each cycle of 0, 5, 10, 50, 100, 1,000, 5,000, and 10,000. A repeated measures analysis of variance (ANOVA) for the overall effect of cyclic loading and the pattern analysis by linear mixed model were used for statistical analysis. Differences at P<.05 were considered statistically significant. Results: The mean axial displacement after 10,000 cycles were $0.714{\pm}0.488{\mu}m$ in Ext group, $5.286{\pm}1.604{\mu}m$ in Int-1 group, and $11.429{\pm}1.902{\mu}m$ in Int-2 group. In the pattern analysis, Int-1 and Int-2 group showed continuous axial displacement at 10,000 cycles. There was no declining pattern of axial displacement in the Ext group. Conclusion: The pattern of linear mixed model in Ext group showed no axial displacement. There were continuous axial displacements in abutment-implant assemblies in the Int-1 and Int-2 group at 10,000 cycles. More axial displacement was found in Int-2 group than in Int-1 group.