• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• 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 Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.225-253
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• 2015

The standard is a nation's one of the most important industrial issues that improve the social and economic efficiency and also the basis of the industrial development and trade liberalization. This research analyzes the enactment and the utilization of Korean industrial standards(KS) of various industries. This paper examines Korean industries' KS utilization status based on the KS possession, enactments and inquiry records. First, we implement multidimensional scaling method to visualize and group the KS possession records and the nation's institutional issues. We develop several hypothesis to find the decision factors of how each group's KS possession status impacts on the standard enactment activities of similar industry sectors, and analyzes the data by implementing regression analysis. The results show that the capital intensity, R&D activities and sales revenues affect standardization activities. It suggests that the government should encourage companies with high capital intensity, sales revenues to lead the industry's standard activities, and link the policies with the industry's standard and patent related activities from R&D. Second, we analyze the impacts of each KS data's inquiry records, the year of enactments, the form and the industrial segment on the utilization status by implementing statistical analysis and decision tree method. The results show that the enactment year has significant impact on the KS utilization status and some KSs of specific form and industrial segment have high utilization records despite of short enactment history. Our study suggests that government should make policies to utilize the low-utilized KSs and also consider the utilization of standards during the enactment processes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.105-112
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• 2018

To determine the position of receiver, the GPS (Global Positioning System) uses position information of satellites and pseudo ranges based on signals. These are reflected by surrounding structures and multipath errors occur. This paper proposes a method for multipath error reduction using digital images to enhance the accuracy. The goal of the study is to calculate the shielding environment of receiver using image processing and apply it to GPS positioning. The proposed method, firstly, performs a preprocessing to reduce the effect of noise on images. Next, it uses hough transform to detect the outline of building roofs and determines mask angles and permissible azimuth range. Then, it classifies the satellites according to the condition using the image processing results. Finally, base on point positioning, it computes the receiver position by applying a weight model that assigns different weights to the classified satellites. We confirmed that the RMSE (Root Mean Square Error) was reduced by 2.29m in the horizontal direction and by 15.62m in the vertical direction. This paper showed the potential for the hybrid of GPS positioning and image processing technology.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.165-177
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• 2022

The purpose of this study is to present the direction of employment guidance for long-term service through the analysis of the cause of employment of employed students who graduated from specialized high school. In particular, the purpose is to present student guidance plans for long-term service by analyzing personal reasons for students graduating from commercial high schools and policy factors for individual, school, company, and government service after employment. To this end, a survey was conducted for graduates of commercial high schools nationwide, and the validity, reliability, and causality of the survey data were analyzed by applying Exploratory Factor Analysis, Cronbach's Alpha, and decision tree analysis techniques. We found that personal goal setting for employment is an important factor for working for more than 1 year, personal relationships at work and personal characteristics are important factors for working for more than 3 years. In addition, we found that the reason for getting a job is that personal reasons and school recommendations are great, special lectures on employment, camps, and 'advice from seniors and teachers' programs are helpful in finding a job, and accounting and computer related subjects are helpful for long-term employment. Accordingly, in specialized high schools, it is required to prepare specific instructional measures for education such as setting personal goals and the formation of human relationships that are the basis of social life, and to actively operate the above subjects and programs to help with employment and longevity.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.138-148
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• 2022

Purpose This study evaluated the rates and annual trends of pediatric CT scans in South Korea using a nationwide population-based database. Materials and Methods Data regarding pediatric CT scan usage between 2012 and 2017 were retrieved from the health insurance review and assessment service. Data on the age, sex, diagnosis, and the anatomical area of involved patients were also extracted. Results A total of 576376 CT examinations were performed among 58527528 children aged below 18 years (9.8 scans/1000 children), and the number of CT examinations per 1000 children was noted to have increased by 23.2% from 9.0 in 2012 to 11.0 in 2017. Specifically, the number of CT examinations increased by 32.9% for the 6-12 years of age group (7.4/1000 to 9.8/1000) and by 34.0% for the 13-18 years of age group (11.4/1000 to 15.3/1000). Moreover, majority of the CT scans were limited to the head (39.1%), followed by the extremities (32.5%) and the abdomen (13.7%). Notably, the number of extremity CT scans increased by 83.6% (2.3/1000 to 4.2/1000), and its proportion as compared to other scans increased from 25.3% to 37.7%. Conclusion CT scans in the pediatric population increased continuously from 2012 to 2017 at an annual rate of 4.4%. Therefore, physicians should balance the benefits of CT with its potential harms from associated radiation exposure in pediatric patients.