• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.69-83
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• 2006

This paper describes an image disparity estimation method using a segmented-region based stereo matching. Segmented-region based disparity estimation yields a disparity map as the unit of segmented region. However, there is a problem that it estimates disparity imprecisely. The reason is that because it not only have matching errors but also apply an identical way to disparity estimation, which is not considered each type of matched regions. To solve this problem, we proposes a disparity estimation method which is considered the type of matched regions. That is, the proposed method classifies whole matched regions into similar-matched region, dissimilar-matched region, false-matched region and miss-matched region by analyzing the type of matched regions. We then performs proper disparity estimation for each type of matched regions. This method minimizes the error in estimating disparity which is caused by inaccurate matching and also improves the accuracy of disparity of the well-matched regions. For the purpose of performance evaluations, we perform tests on a variety of scenes for synthetic, indoor and outdoor images. As a result of tests, we can obtain a dense disparity map which has the improved accuracy. The remarkable result is that the accuracy of disparity is also improved considerably for complex outdoor images which are barely treatable in the previous methods.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.173-190
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• 2015

The purpose of this study is to improve bankruptcy prediction models by using a novel hybrid under-sampling approach. Most prior studies have tried to enhance the accuracy of bankruptcy prediction models by improving the classification methods involved. In contrast, we focus on appropriate data preprocessing as a means of enhancing accuracy. In particular, we aim to develop an effective sampling approach for bankruptcy prediction, since most prediction models suffer from class imbalance problems. The approach proposed in this study is a hybrid under-sampling method that combines the k-Reverse Nearest Neighbor (k-RNN) and one-class support vector machine (OCSVM) approaches. k-RNN can effectively eliminate outliers, while OCSVM contributes to the selection of informative training samples from majority class data. To validate our proposed approach, we have applied it to data from H Bank's non-external auditing companies in Korea, and compared the performances of the classifiers with the proposed under-sampling and random sampling data. The empirical results show that the proposed under-sampling approach generally improves the accuracy of classifiers, such as logistic regression, discriminant analysis, decision tree, and support vector machines. They also show that the proposed under-sampling approach reduces the risk of false negative errors, which lead to higher misclassification costs.

• Journal of radiological science and technology
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• v.38 no.4
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• pp.375-381
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• 2015

Digital Radiography is a big part of diagnostic radiology. Because uncorrected digital radiography image supported false effect of Patient's health care. We must be manage the correct digital radiography image. Thus, the artifact images can have effect to make a wrong diagnosis. We report types of occurrence by analyzing the artifacts that occurs in digital radiography system. We had collected the artifacts occurred in digital radiography system of general hospital from 2007 to 2014. The collected data had analyzed and then had categorize as the occurred causes. The artifacts could be categorized by hardware artifacts, software artifacts, operating errors, system artifacts, and others. Hardware artifact from a Ghost artifact that is caused by lag effect occurred most frequently. The others cases are the artifacts caused by RF noise and foreign body in equipments. Software artifacts are many different types of reasons. The uncorrected processing artifacts and the image processing error artifacts occurred most frequently. Exposure data recognize (EDR) error artifacts, the processing error of commissural line, and etc., the software artifacts were caused by various reasons. Operating artifacts were caused when the user didn't have the full understanding of the digital medical image system. System artifacts had appeared the error due to DICOM header information and the compression algorithm. The obvious artifacts should be re-examined, and it could result in increasing the exposure dose of the patient. The unclear artifact leads to a wrong diagnosis and added examination. The ability to correctly determine artifact are required. We have to reduce the artifact occurrences by understanding its characteristic and providing sustainable education as well as the maintenance of the equipments.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.9-18
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• 2018

Recently, the use of image stitching technology has been increasing as the number of contents based on virtual reality increases. Image Stitching is a method for matching multiple images to produce a high resolution image and a wide field of view image. The image stitching is used in various fields beyond the limitation of images generated from one camera. Image Stitching detects feature points and corresponding points to match multiple images, and calculates the homography among images using the RANSAC algorithm. Generally, corresponding points are needed for calculating conversion relation. However, the corresponding points include various types of noise that can be caused by false assumptions or errors about the conversion relationship. This noise is an obstacle to accurately predict the conversion relation. Therefore, RANSAC algorithm is used to construct an accurate conversion relationship from the outliers that interfere with the prediction of the model parameters because matching methods can usually occur incorrect correspondence points. In this paper, we propose an algorithm that extracts more accurate inliers and computes accurate transformation relations by using correspondence point relation information used in RANSAC algorithm. The correspondence point relation information uses distance ratio between corresponding points used in image matching. This paper aims to reduce the processing time while maintaining the same performance as RANSAC.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.97-111
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• 2009

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a signal intersection and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, we intend to present a technology capable of overcoming problems in which advanced existing technologies exhibited limitations in handling real-time due to large data capacity such as object separation of vehicles and tracking, which pose difficulties due to environmental diversities and changes at a signal intersection with complex traffic situations, as pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian complex model analytical method which has been considered the best among well-known environmental obstacle reduction methods. To prove that the technology developed by this research has performance advantage over existing automatic traffic accident recording systems, a test was performed by entering image data from an actually operating crossroad online in real-time. The test results were compared with the performance of other existing technologies.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.329-337
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• 2019

It is true that many tales delivered in an 'Story-telling' auditorium or theater have not so much exquisite and refined forms as distorted and deteriorated ones. Furthermore, when false interpretations of tale-performers added into the category of the texts of tales, the problems can be made worse. In case of oral folk tales, there can be discordance between the standpoint of a tale-performer and the contents of a tale. This thesis is directly aimed at pointing out the 'Behavior Economics' problems concerned with the reading and interpretation of tales through investigating the missing parts of a text in reading tales. Man's rationality is meant to be confined to bounded rationality. Instead of making best choices, bounded rationality leads consumers to make a decision which they think suffices themselves to the point requiring no more consideration on the given item. It is the very Heuristic that does work in the process of this simplified decision making process. Heuristic utilizes established empirical notion and specific information, and that's why there can be cognitive 'Biases' sometimes leading to inaccurate judgment. As Oral Literature is basically based on heavy guesswork and perceptual biases of general public, it is imperative to contemplate oral literature in the framework of Heuristic of behavior economics. This thesis deals with thinking types and behavioral patterns of the general public in the perspective of heuristic by examining 'Story-tellings' on the basis of personal or public memory. In addition, heuristic involves how to deal with significant but intangible content such as the errors of oral story teller, the deviations of the story, and responses of the audience.

• Korean Journal of Heritage: History & Science
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• v.45 no.4
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• pp.92-103
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• 2012

The Cheomsungdae was built in the Silla dynasty during the reign of queen Seondeok. It has a round cylindrical structure with a flowing curved fa ade. The identity of the Cheomsungdae has not been revealed since there is not much historical evidence or documents about the building. This study is trying to investigate the building technique and method from the technical point of view of the past when it was constructed. There have been much work and studies done for the Cheomsungdae, but not much were focusing on the technical aspects of the building. In addition there are many questions and doubts about the hypothesis of the building technique of Cheomsungdae since there aren't any remaining documents or historical evidence supporting it. Among many questions, we think that the discussion on falsework technique is not considering traditional construction method of the Dujabee (a traditional construction technique using various tools and equipment for the stability of the building) technique. Therefore, it is hard to identify them as reliable historical facts. As the result of the study, we want to provide the basic data on the construction techniques of Korean traditional architecture and broaden the study scope of technical history by narrowing the errors. The study could be summarized into three points. 1. The historical architecture Cheomseongdae was constructed by using traditional crane techniques such as a Noklo (pulley ladder). Cheomseongdae was re-evaluated as a high level technology for the history of architecture. 2. The benchmark method on Cheomseongdae construction has been applied with a precise scientific method based on the geometrical principals using the central axis. 3. In terms of the history of Korean traditional architecture technology, as there aren't many studies done we proposed various basic data for the traditional crane techniques and criteria of Korean traditional architecture technology. We could expect various and active studies for the technical approach of the history of architecture.

• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.117-139
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• 2021

The way of change detection in which presence of a different item is determined between memory and test arrays with a brief in-between time interval resembles how visual search is done considering that the different item is searched upon the onset of a test array being compared against the items in memory. According to the resemblance, the present study examined whether varying the probability of change occurrence in a visual short-term memory-based change detection task can influence the aspect of response-decision making (i.e., change prevalence effect). The simple-feature change detection task in the study consisted of a set of four colored boxes followed by another set of four colored boxes between which the participants determined presence or absence of a color change from one box to the other. The change prevalence was varied to 20, 50, or 80% in terms of change occurrences in total trials, and their change detection errors, detection sensitivity, and their subsequent RTs were analyzed. The analyses revealed that as the change prevalence increased, false alarms became more frequent while misses became less frequent, along with delayed correct-rejection responses. The observed change prevalence effect looks very similar to the target prevalence effect varying according to probability of target occurrence in visual search tasks, indicating that the background principles deriving these two effects may resemble each other.