• Journal of Practical Agriculture & Fisheries Research
• /
• v.23 no.1
• /
• pp.89-104
• /
• 2021

Based on the TF-IDF weighted value that evaluates the importance of words that play a key role, the semantic network analysis(SNA) was conducted on the self-introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The top three words calculated by TF-IDF weights were agriculture, mathematics, study (Q. 1), clubs, plants, friends (Q. 2), friends, clubs, opinions, (Q. 3), mushrooms, insects, and fathers (Q. 4). In the relationship between words, the words with high betweenness centrality are reason, high school, attending (Q. 1), garbage, high school, school (Q. 2), importance, misunderstanding, completion (Q.3), processing, feed, and farmhouse (Q. 4). The words with high degree centrality are high school, inquiry, grades (Q. 1), garbage, cleanup, class time (Q. 2), opinion, meetings, volunteer activities (Q.3), processing, space, and practice (Q. 4). The combination of words with high frequency of simultaneous appearances, that is, high correlation, appeared as 'certification - acquisition', 'problem - solution', 'science - life', and 'misunderstanding - concession'. In cluster analysis, the number of clusters obtained by the height of cluster dendrogram was 2(Q.1), 4(Q.2, 4) and 5(Q. 3). At this time, the cohesion in Cluster was high and the heterogeneity between Clusters was clearly shown.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.50 no.5
• /
• pp.30-39
• /
• 2022

This study aimed to evaluate the awareness of conservation areas and green resources and analyze their characteristics by utilizing accumulated blog data created for specific places and objects. Among all the conservation areas and resources located in the Seoul metropolitan area, places that can be evaluated were classified, and sites were evaluated by dividing them into ten categories based on the number of blog posts written. As a result of the study, the users' awareness of forests was the highest, and the awareness of conservation areas and green resources was higher in urban areas than suburban areas. The result shows that the conservation areas and green resources located around the metropolitan area serve as natural tourist destinations while being the object of conservation for users. In addition, these results are in the same vein as the research results in domestic and foreign studies on the importance of ecosystem services in urban areas. Unlike existing research methods, this study is meaningful in that it identified the level of user awareness through social media analysis and applied it to evaluating conservation areas and green resources. It can be used as basic data to prepare a management plan considering public interest and awareness or to establish a development plan to increase awareness. In addition, the cumulative amount of blog content used in the study is meaningful in that it can identify and monitor users' interest in the space. However, it was not possible to examine the contents of each blog in detail because it was evaluated based on the amount of social media content. In addition, in the case of conservation areas and green resources, it is necessary to review and supplement the evaluation contents by adding keyword analysis and content analysis for the site to be evaluated as content other than the pure viewpoint of users may be mixed with development issues.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1723-1735
• /
• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.3
• /
• pp.247-262
• /
• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.

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

• Spatial Information Research
• /
• v.6 no.2
• /
• pp.133-152
• /
• 1998

This article considers the experimental foundations of geographical phenomena for the distribution of lodging houses and the development of lodging Information Systems in Shin-Chon Area. This system allows the rural students to find their lodging houses conveniently. We examine the geographical reality of lodging houses in Shin-chon area and explores the lodging information system, reflecting how students select the lodging houses. Criteria for selection of lodging houses are travel time to school, interior facilities, rent fee, members, owners of lodging houses, which are collected by field swvey. The lodging information system is built in integration of Visual Basic with spatial data which are created in Mapinfo and Arcview through MapObject, component GIS software. This system provide query tools to efficiently investigate data as well as interactive map display. Also it displays the characteristics of a selected lodging houses using the identify tool on the map.

• Korean Journal of Clinical Laboratory Science
• /
• v.53 no.3
• /
• pp.225-232
• /
• 2021

This study sought to investigate the distribution, antimicrobial resistance rate, and bacterial co-infection frequency of non-tuberculous mycobacteria (NTM) in a single center in Incheon, South Korea. A total of 8,258 specimens submitted for tuberculosis (TB)/NTM real-time PCR tests during the years 2015 to 2020 were retrospectively reviewed. In total, 296 specimens (3.6%) were NTM positive, and the positivity increased from 2.5% (30/1,209) in 2015 to 3.8% (66/1,740) in 2020. Of 296 NTM specimens, 54.7% (162/296) were identified as the Mycobacterium avium complex (MAC) followed by the Mycobacterium abscessus complex (MABC) 20.9% (62/296), M. fortuitum 6.4% (19/296) and M. flavescens 3.4% (10/296). Of the NTM-positive specimens, 76.7% (227/296) were tested for drug resistance. The results showed multidrug-resistant NTM in 40.1% (91/227) and extensively drug-resistant NTM in 59.9% (136/227) of these specimens. Of the 145 isolates taken for bacterial culture, bacteria/fungi co-infection with NTM accounted for 43.4% (63/145), in which the most common bacterial species was Klebsiella pneumonia (23.8%, 15/63). This study is the first report on the distribution and antimicrobial resistance of NTM in Incheon. As the proportion of NTM infections increases, active treatment and thorough infection control are required for effective management.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.221-241
• /
• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.28 no.1
• /
• pp.35-42
• /
• 2018

Objectives: The objectives of this study are to research the usage characteristics of asbestos-containing building materials and to conduct exposure risk assessment by applying no. 2016-230 "Methods of Risk Assessment of Asbestos-Containing Buildings" from the Ministry of Environment. Methods: One hundred buildings located in the Seoul and Gyeonggi-Incheon area were chosen, with 29 in Seoul, 20 in Incheon, and 51 in Gyeonggi-do Province. The year of construction was divided between three buildings in the 1970s, 11 buildings in the 1980s, 42 buildings in the 1990s, and 44 buildings in the 2000s. The bulk samples were analyzed by using a polarizing microscope after a pre-process using a stereomicroscope in a hood with an HEPA filter. This study defined ACMs(asbestos-containing materials) as asbestos when the content percentage was over 1% in the analysis result. Methods and standards of risk assessment of asbestos-containing building materials were conducted by refering to no. 2016-230 "Method of Risk Assessment of Asbestos-Containing Buildings" from the Ministry of Environment. The risk of exposure to ACMs was rated by a score based on three categories(high, middle, low risk of asbestos exposure). Results: In this study, 30 of the 100 buildings and 36 of the 416 bulk samples(8.6%) were found to have had asbestos. Asbestos was detected at a high rate, in 18 out of 42, in buildings constructed in the 1990s and at the lowest rate(7 out of 44) for buildings constructed in the 2000s. As a result of the evaluation according to no. 2016-230 "Method of Risk Assessment of Asbestos-Containing Buildings" of the Ministry of Environment, the risk assessment level of two asbestos-containing building materials was found to be "Medium", and 28 buildings materials were found to be at the "Low" level. Conclusion: As asbestos is regulated by the government, it is required to conduct active management and implemention by introducing methods of risk assessment of asbestos exposure that are supported by data from various situations. In the case of buildings owned by individuals, building owners should be aware of the risk of exposure to asbestos.

• Korean Security Journal
• /
• no.48
• /
• pp.9-33
• /
• 2016

The purpose of the present study was to find out cause-and-effect relationship between job requirements and job resources, with job burnout as a mediator variable, and the effects of these variables on organizational effectiveness. The population in the present study was private security guards employed by 13 private security companies in Seoul and Gyeonggi-do areas, and a survey was conducted on 500 security guards selected using purposive sampling technique. Out of 460 questionnaires distributed, 429 responses, excluding 31 outliers or insincere responses, were used for data analysis. For analysis, data were coded and entered into SPSS 18.0 and AMOS 18.0, which were used to analyze the data. Descriptive analyses were performed to find out sociodemographic characteristics of the respondents. The exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to test the validity of the measurement tool, and the Cronbach's Alpha coefficients were calculated to test the reliability. To find out the significance of relationships among variables, Pearson's correlation analysis was performed. Covariance Structure Analysis (CSA) was performed to test the relationship among latent factors of a model for job requirements, job resources, job burnout, and organizational effectiveness of the private security guards, and the fitness of the model analyzed with CSA was determined by the goodness-of-fit index ($x^2$, df, p, RMR, GFI, CFI, TLI, RMSEA). The level of significance was set at .05, and the following results were obtained. First, even though the effect of job requirements on job burnout was not statistically significant, it had a positive influence overall, and this result can be considered such that the higher the perception of job requirements by the member of the organization, the higher the perception of job burnout. Second, the influence of job resources on job burnout was negative, which can be considered that the higher the perception of job resources, the lower the perception of job burnout. Third, even though the influence of job requirements on organizational effectiveness was statistically nonsignificant, it had a negative influence overall, and this result can be considered that the higher the perception of job requirements, the lower the perception of organizational effectiveness. Fourth, job resources had a positive influence on organizational effectiveness, and it can be considered that the higher the perception of job resources, the higher the perception of organizational effectiveness. Fifth, the results of the analysis between job burnout and organizational effectiveness revealed that, even though the influence of job burnout on organizational effectiveness was statistically nonsignificant, it had partial negative influences on sublevels of organizational effectiveness, and this may suggest that the higher the perception of job burnout by the organization members, the lower the organizational effectiveness. Sixth, the analysis of mediating role in the relationship between job requirements and organizational effectiveness, job burnout was taking partial mediating role between job requirements and organizational effectiveness. These results suggest that reducing job burnout by managing job requirements, organizational effectiveness that leads to job satisfaction, organizational commitment, and turnover intention can be maximized. Seventh, the analysis of mediating role in the relationship among job requirements, job resources, and organizational effectiveness, job burnout was assuming a partial mediating role in the relationships among job requirements, job resources, and organizational effectiveness. These results suggest that organizational effectiveness can be maximized by either lowering job requirements or burnout management through reorganizing job resources.