• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.195-205
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• 2001

The objective of the paper is to analyze the traffic characteristics for freeway merging area. Freeway merging area is different from basic section due to ramp vehicles. Therefore, to understand the traffic characteristics of (leeway merging area, this study focused on two factors including critical time headway required in merging maneuver and maximum possible merging volume. In this paper, new model that adopts critical time headway instead of critical time gap in calculating the maximum possible merging volume based on probability function was developed In previous studies, for calculating the maximum possible merging volume, it was considered that merging vehicles could merge freely if a given time gap was greater than the critical time gap. Also, the critical time gap was used as the same value in all traffic flow conditions. But, a time gap required in merging maneuver could be changed, even to the same driver, because difference of relative speed varies in different traffic flow conditions. So, in some cases, the critical time gap could be insufficient value in merging maneuver. Therefore, in this study. a calculating procedure for critical time headway in all traffic flow conditions was presented. Also, the maximum possible merging volume and capacity for freeway merging area were calculated by using the previously found critical time headway.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.543-548
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• 2011

Entropy measuring the richness in details of the image is generally obtained by using the histogram of gray levels in an image, and has been widely used as an index for thresholding of the image. In this paper, we propose an entropy-based thresholding method, where the entropy is obtained not by the histogram but by the variance of the gray levels, to binalize a given image. The effectiveness of the proposed method is demonstrated by thresholding experiments on nine test images and comparison with conventional two thresholding methods, that is, Otsu method and entropy-based method using the histogram.

• Journal of the Korean earth science society
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• v.31 no.4
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• pp.301-312
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• 2010

Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.491-500
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• 2014

To estimate the severity of streamflow drought, this study introduced the concept of streamflow drought index based on threshold level method and Seomjingang Dam inflow was applied. Threshold levels used in this study are fixed, monthly and daily threshold, The $1^{st}{\sim}3^{rd}$ analysis results of annual drought, the severe hydrological droughts were occurred in 1984, 1988 and 1995 and the drought lasted for a long time. Annual compared to extreme values of total water deficit and duration, the drought occurred in 1984, 1988, 1995 and 2001 was serious level. In the results of study, because a fixed threshold level is not reflect seasonal variability, at least the threshold under seasonal level was required. Threshold levels determined by the monthly and daily were appropriate. The proposed methodology in this study can be used to forecast low-flow and determine reservoirs capacity.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.485-492
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• 2007

In this paper, we propose the receiver block error detection of the video codec and the image concealment algorithm using fuzzy inference. The proposed error detection and concealment algorithm gets SSD(Summation of Squared Difference) and BMC(Boundary Matching Coefficient) using the temporal and spatial similarity between corresponded blocks in the two successive frames. Proportional constant, ${\alpha}$, for threshold value, TH1 and TH2, is decided after fuzzy data is generated by each parameter. To examine the propriety of the proposed algorithm, random errors are inserted into the QCIF Susie standard image, then the error detection and concealment performance is simulated. To evaluate the efficiency of the algorithm, image quality is evaluated by PSNR for the error detection and concealed image by the existing VLC table and by the proposed method. In the experimental results, the error detection algorithm could detect all of the inserted error, the image quality is improved over 15dB after the error concealment compare to existing error detection algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.475-481
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• 2014

Recently, it is used to apply various improved methods to determine the cause of traffic accidents. However, most of vehicle black box usually start to store the video information by an event trigger in case that the impact value at that time exceeds the threshold impact value as compared the threshold impact value saved in advance with the current impact value. there are problems with above method that a lot video information should be saved in the memory card of the vehicle black box, and the user should delete the unwanted video information every time because of unclassified video store. In this paper, we propose the ontology-based context aware algorithm that the vehicle black box recognize the situation, and then remove the video data with a low weighting factor by itself for efficient memory management.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.787-795
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• 2014

This paper presents an image registration using Triangulation-based Local Transformation (TLT) applied to the remaining matched points after elimination of the matched points with gross error. The corners extracted using geometric mean-based corner detector are matched using Pearson's correlation coefficient and then accepted as initial matched points only when they satisfy the Left-Right Consistency (LRC) check. We finally accept the remaining matched points whose RANdom SAmple Consensus (RANSAC)-based global transformation (RGT) errors are smaller than a predefined outlier threshold. After Delaunay triangulated irregular networks (TINs) are created using the final matched points on reference and sensed images, respectively, affine transformation is applied to every corresponding triangle and then all the inner pixels of the triangles on the sensed image are transformed to the reference image coordinate. The proposed algorithm was tested using KOMPSAT-2 images and the results showed higher image registration accuracy than the RANSAC-based global transformation.

• The KIPS Transactions:PartC
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• v.12C no.7 s.103
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• pp.1007-1014
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• 2005

A generation of rules or patterns for detecting attacks from network is very difficult. Detection rules and patterns are usually generated by Expert's experiences that consume many man-power, management expense, time and so on. This paper proposes statistical methods that effectively detect intrusion and attacks without expert's experiences. The methods are to select useful measures in measures of network connection(session) and to detect attacks. We extracted the network session data of normal and each attack, and selected useful measures for detecting attacks using relative entropy. And we made probability patterns, and detected attacks using likelihood ratio testing. The detecting method controled detection rate and false positive rate using threshold. We evaluated the performance of the proposed method using KDD CUP 99 Data set. This paper shows the results that are to compare the proposed method and detection rules of decision tree algorithm. So we can know that the proposed methods are useful for detecting Intrusion and attacks.

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.11-18
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• 2014

In This paper, we propose the unsupervised learning and fuzzy logic-based coupled data classification method base on ART. The unsupervised learning-based data classification helps improve the grouping technique, but decreases the processing efficiency. However, the data classification requires the decision technique to induce high success rate of data classification with optimal threshold. Therefore it is also necessary to solve the uncertainty of the threshold decision. The proposed method deduces the optimal threshold with the designing of fuzzy parameter and rules. In order to evaluate the proposed method, we design the simulation model with the GPCR(G protein coupled receptor) data in cloud computing environment. Simulation results verify the efficiency of our method with the high recognition rate and low processing time.