• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.1-11
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• 2021

Recently, in Korea, the risk of meteorological disasters is increasing due to climate change, and the damage caused by rainfall is being emphasized continuously. Although the current weather forecast provides quantitative rainfall, there are several difficulties in predicting the extent of damage. Therefore, in order to understand the impact of damage, the threshold rainfall for each watershed is required. The damage caused by rainfall occurs differently by region, and there are limitations in the analysis considering the characteristic factors of each watershed. In addition, whenever rainfall comes, the analysis of rainfall-runoff through the hydrological model consumes a lot of time and is often analyzed using only simple rainfall data. This study used GIS data and calculated the threshold rainfall from the threshold runoff causing flooding by coupling two hydrologic models. The calculation result was verified by comparing it with the actual case, and it was analyzed that damage occurred in the dangerous area in general. In the future, through this study, it will be possible to prepare for flood risk areas in advance, and it is expected that the accuracy will increase if machine learning analysis methods are added.

• The Korean Journal of Elementary Counseling
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• v.4 no.1
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• pp.215-236
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• 2005

The Purpose of this study is that self-growth program searches effect self-concept and peer-relationship with elementary school student, and advance following assumption to achieve this purpose and achieved study. First, self-concept point of experiment group students who execute self-growth program will be improved is meaning than self-concept point of control group students. Second, peer-relationship point of experiment group students who execute self-growth program will be improved is meaning than peer-relationship point of control group students. Third, effect that self-growth program gets to self-concept may be meaning difference according to sex. Fourth, difference that impact that self-growth program gets in peer- relationship is meaning according to sex may join. Chose fifth-year student 2 class 68 people (experiment group n=34, control group n=34) in I primary school locating to Jeonrabuk-do Iksan-si to verify above construction for study target. Disposal about experiment group executed over 10th for 60-80 minute 2 times in a week because investigator uses reconstructing self-growth program with virtue research paper such as learning program for own growth of Lee-Hyeong-Deuk (1998). In order to verify the effect after experiment, 1 collected materials for estimation by providing the subject children with questionaires about self-concept and peer-relationship before and after the experiment, and then analyzed the average differences in number of marks between the experiment group and the control group before and after the experiment through and by using One-Way ANOVA, and SPSS 11.0 program. The following is the result what I obtained from the above study. First, there was significant difference is between average difference before and after of experiment group and control group which execute self-growth program in self-concept elevation ($F_{(1,66)} =28.734$, p <.001). From the sub-variable, there was significant difference in academic self ($F_{(1,66)}=6.423$, p<.05), Social Self ($F_{(1,66)}=48.331$, p<.001), Physical Self ($F_{(1,66)}=11.074$, p <.01), sentimental self ($F_{(1,66)}=9.402$, p <.01) Second, there was significant difference is average difference before and after of experiment group and control group which execute self-growth program in peer-relationship promotion ($F_{(1,66)}=24.109$, p <.001). From the sub-variable there W3S Significant difference in trust ($F_{(1,66)}=14.507$, p<.001), respect ($F_{(1,66)}=15.271$, p <.001). Third, there was expose that significant difference does not exist in average self-concept before and after by sex of experiment group which executes self-growth program, and was not shown significant difference in sub-vairable. Fourth, there was expose that significant difference of whole peer- relationship and in respect of sub-variable in average peer-relationship before-after by sex of experiment group which execute self-growth program, but significant difference did not appear in trust. Could get conclusion that self-growth program is effect in elementary school student self-concept elevation and peer-relationship promotion according to these study finding, and confirmed possibility that self-growth program may contribute to change emotional special quality of children positively in education spot.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.74-99
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• 2022

Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.96-105
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• 1998

The aim of this paper is to propose the method of WDHMM(Weighted DHMM), using the MLP-VQ for the improvement of speaker-independent connect-digit recognition system. MLP neural-network output distribution shows a probability distribution that presents the degree of similarity between each pattern by the non-linear mapping among the input patterns and learning patterns. MLP-VQ is proposed in this paper. It generates codewords by using the output node index which can reach the highest level within MLP neural-network output distribution. Different from the old VQ, the true characteristics of this new MLP-VQ lie in that the degree of similarity between present input patterns and each learned class pattern could be reflected for the recognition model. WDHMM is also proposed. It can use the MLP neural-network output distribution as the way of weighing the symbol generation probability of DHMMs. This newly-suggested method could shorten the time of HMM parameter estimation and recognition. The reason is that it is not necessary to regard symbol generation probability as multi-dimensional normal distribution, as opposed to the old SCHMM. This could also improve the recognition ability by 14.7% higher than DHMM, owing to the increase of small caculation amount. Because it can reflect phone class relations to the recognition model. The result of my research shows that speaker-independent connected-digit recognition, using MLP-VQ and WDHMM, is 84.22%.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.282-294
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• 2017

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.

• Journal of Digital Convergence
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• v.18 no.1
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• pp.301-307
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• 2020

In recent years, a recommendation system is introduced in many different fields with the beginning of the 5G era and making a considerably prominent appearance mainly in books, movies, and music. In such a recommendation system, however, the preference degrees of users are subjective and uncertain, which means that it is difficult to provide accurate recommendation service. There should be huge amounts of learning data and more accurate estimation technologies in order to improve the performance of a recommendation system. Trying to solve this problem, this study proposed a movie recommendation system based on Fuzzy-AHP and Word2vec. The proposed system used Fuzzy-AHP to make objective predictions about user preference and Word2vec to classify scraped data. The performance of the system was assessed by measuring the accuracy of Word2vec outcomes based on grid search and comparing movie ratings predicted by the system with those by the audience. The results show that the optimal accuracy of cross validation was 91.4%, which means excellent performance. The differences in move ratings between the system and the audience were compared with the Fuzzy-AHP system, and it was superior at approximately 10%.

• Journal of Korean Academy of Nursing
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• v.9 no.2
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• pp.63-72
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• 1979

This study was performed to investigated of characteristics of male diploma nursing students. Data were collected by means of a mailed questionnaire. The sample included 29 respondents from 3 diploma nursing colleges during the period of Nov. 1st-15th, 1978. Major findings included. 1 ) Motive by which the objects of this investigation have chosen the science of nursing. Twenty-one (72.4%) responded, “As nursing occupation is a public welfare work, ”the highest rate, eighteen (62.3%) chose on advices of their parents and acquaintances. Seventeen (58.6%) reflected as a means of life with an occupation in hope of employment abroad. 2) Appreciation of nursing occupation. Twenty-two (75.9%) of opinions that the nursing job is called for by society was pre-dominent. While eighteen (62.1%) replied, “It is the job fit for the male sex, too.”“It is admitted as specialized occupation.”, or“It needs various human relation.”3) Degree of satisfaction wilt the science of nursing. Fifteen (51.7%) responded neither satisfied nor dissatisfied, while eight (27.6％) indicated as “satisfied”and four (13.8%) as“dissatisfied.”4) Degree of satisfaction with the faculty. Sixteen (55.2%) replied, “common, ”the highest, while ten (34.5%) indicated as“dissatisfied, ”two (6.9%) as“satisfied.”The reason for dissatisfaction with the faculty; The responses regarding dissatisfaction was twenty-three (79.3%) as insufficiency of the faculty. Thirteen (44.8%) indicated“the lack of personal cultivation of the faculty.”, And eleven (37.9%) indicated as“the quantitive shortage of the faculty, ”or“the vagueness of learning estimation.”5) Degree of satisfaction with the clinical, training. Eight (27.6%) responded as“common, ”or “dissatisfied, ”while seven (24.1%) indicated as“satisfied.”Reason for dissatisfaction with the training ; Twenty (69.0％) indicated“deficiency of personal treatment to the students of the men of business in the hospital”with respect to the reason, eighteen (62.1％) was indicated as gap between theory and practice, while eleven (37.9%) indicated“insufficiency of the equipment and materials of the hospital.”6) Interest in employment after graduation. Twenty-five (86.2%) indicated“going abroad”while fifteen (51.7％) indicated “education of nursing, ”which were the highest responses. Thirteen (44.8%) chose“Community Health Nursing (Health Center, Industrial Health).”7) Interest an employment during clinical nursing. Sixteen (55.2%) was interested in an operating room or the department of anesthesia, while fifteen (51.7%) was indicated “psychiatry, ”Eight (21.6%) chose a intensive care unit or a emergency room.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.119-125
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• 2015

A concentration meter is widely used at purification plants, sewage treatment plants and waste water treatment plants to sort and transfer high concentration sludge and to control the amount of chemical dosage. When the strange substance is contained in the sludge, however, the attenuation of ultrasonic wave could be increased or not be transmitted to the receiver. At that case, the value of concentration meter is higher than the actual density value or vibrated up and down. It has also been difficult to automate the residuals treatment process according to the problems as sludge attachment or damage of a sensor. Multi-beam ultrasonic concentration meter has been developed to solve these problems, but the failure of the ultrasonic beam of a specific concentration measurement value degrade the performance of the entire system. This paper proposes the method to improve the accuracy of sludge concentration rate by choosing reliable sensor values and learning them by proposed algorithm. The prediction algorithm is chosen as neuro-fuzzy model, which is tested by the various experiments.