• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.94-101
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• 2016

With increasing global concerns for environmental impacts, efforts have been made to encourage a modal shift from road freight to an eco-friendly transport system such as rail freight. In Korea, the government has set master plans for a green transport system but has not taken any substantial action to promote rail freight transport. In developing policies and actions to promote rail freight, quantitative studies on environmental impacts among transportation means are essential. This study examined the air pollutant emissions and greenhouse gas (GHG) emissions per unit freight transported by road and rail, respectively. To improve the accuracy, we analyzed emission data and freight transport mileage of rail freight considering diesel locomotives and electric locomotives separately. The results show that unit air pollutant emissions (except SO2) from road freight are about 7~15 times more than those from rail freight. In addition, the GHG emission unit of road freight is about 4 times higher than that of rail freight.

• Journal of Korean Neurosurgical Society
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• v.30 no.sup2
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• pp.294-299
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• 2001

Objectives : CT-guided stereotactic evacuation for spontaneous intracerebral hemorrhage can minimize the brain damage and can be performed safely and simply under local anesthesia. But that procedure is time consuming and has a risk of rebleeding because of the stress during head pin fixation. So authors describe easy and precise guidelines for FHA of putaminal hemorrhage without stereotactic instrument. Methods and Materials : We analyzed the data of 298 patients who underwent CT-guided stereotactic aspiration of putaminal hematoma in our hospital between January 1990 and December 2000. We divided the patients into three groups according to the location of hematoma : anterior portion, middle portion and posterior portion of putamen. Total number of catheters inserted into the hematoma were 345 and there were with regard to the direction and depth of catheters. Results : Proposed guidelines of catheter insertion to putaminal hemorrhage in our institution. 1) hematoma at the anterior portion of putamen ; Direction of catheter was the midpupillary line of the eye and the point intersecting a line drawn from the burr hole to a point between external auditory meatus(EOM) and 1cm posterior to EOM. Depth of catheter was 6-6.5cm. 2) hematoma at the middle portion of putamen ; Direction of catheter was the midpupillary line of the the eye and the point intersecting a line drawn from the burr hole to a point between 1cm and 2cm posterior to EOM. Depth of catheter was 6.5-7cm. 3) hematoma at the posterior portion of putamen ; Direction of catheter was 15 degree laterally from the midpupillary line of the eye and the point intersecting a line drawn from the burr hole to a point between 2cm and 3cm posterior to EOM. Depth of catheter was 7-7.5cm. We have performed FHA of putaminal hemorrhage in 48 cases according to this guideline. All catheter were inserted exactly at the center of hematoma and average operation time was about 30 minutes. Conclusion : Our proposed guidelines for putaminal hemorrhage are considered to be safe and simple method with similar accuracy and rapid decompression compared with traditional stereotactic method. Main advantages of this technique were unnecessity of stereotactic frame application and less time requirement for hematoma removal.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.427-435
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• 2005

With the accumulation of protein and its related data on the Internet, many domain based computational techniques to predict protein interactions have been developed. However, most of the techniques still have many limitations to be used in real fields. They usually suffer from a low accuracy problem in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we reevaluate a domain combination based protein interaction prediction method and develop an interaction possibility ranking method for multiple protein pairs. Probability equations are devised and proposed in the framework of domain combination based protein interaction prediction method. Using the ranking method, one can discern which protein pair is more probable to interact with each other than other protein pairs in multiple protein pairs. In the validation of the ranking method, we revealed that there exist some correlations between the interacting probability and the precision of the prediction in case of the protein pair group having the matching PIP(Primary Interaction Probability) values in the interacting or non interacting PIP distributions.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.473-478
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• 2017

Drones require altitude holding in order to achieve flight objectives. The altitude holding of the drone is to repeat the operation of raising or lowering the drone according to the altitude information being measured in real-time. When the drones are maintained altitude, the drone's altitude will continue to change due to external factors such as imbalance in thrust due to difference in motor speed or wind. Therefore, in order to maintain the altitude of drone, we have to exactly measure the continuously changing altitude of the drone. Generally, the acceleration sensor is used for measuring the height of the drones. In this method, there is a problem that the measured value due to the integration error accumulates, and the drone's vibration is recognized by the altitude change. To solve the difficulty of the altitude measurement, commercial drones and existing studies are used for altitude measurement together with acceleration sensors by adding other sensors. However, most of the additional sensors have a limitation on the measurement distance and when the sensors are used together, the calculation processing of the sensor values increases and the altitude measurement speed is delayed. Therefore, it is necessary to accurately measure the altitude of the drone without considering additional sensors or devices. In this paper, we propose a measurement algorithm that improves general altitude measurement method using acceleration sensor and show that accuracy of altitude holding and altitude measurement is improved as a result of applying this algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.433-438
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• 2016

This paper proposes a grade prediction method to measure meat quality in Hanwoo (Korean Native Cattle) using classification and feature extraction algorithms. The applied classification algorithm is an AdaBoost and the texture features of the given ultrasound images are extracted using SFTA. In this paper, as an initial phase, we selected ultrasound images of Hanwoo for verifying experimental results; however, we ultimately aimed to develop a diagnostic decision support system for human body scan using ultrasound images. The advantages of using ultrasound images of Hanwoo are: accurate grade prediction without butchery, optimizing shipping and feeding schedule and economic benefits. Researches on grade prediction using biometric data such as ultrasound images have been studied in countries like USA, Japan, and Korea. Studies have been based on accurate prediction method of different images obtained from different machines. However, the prediction accuracy is low. Therefore, we proposed a prediction method of meat quality. From the experimental results compared with that of the real grades, the experimental results demonstrated that the proposed method is superior to the other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.599-604
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• 2005

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.107-116
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• 2005

The information on travel time in providing the information of traffic to drivers is one of the most important data to control a traffic congestion efficiently. Especially, this information is the major element of route choice of drivers, and based on the premise that it has the high degree of confidence in real situation. This study developed a vehicle arrival time prediction algorithm called as "VAT-DV" for 6 corridors in total 6.1Km of "Nam-san area trffic information system" in order to give an information of congestion to drivers using VMS, ARS, and WEB. The spatial scope of this study is 2.5km~3km sections of each corridor, but there are various situations of traffic flow in a short period because they have signalized intersections in a departure point and an arrival point of each corridor, so they have almost characteristics of interrupted and uninterrupted traffic flow. The algorithm uses the information on a demand volume and a queue length. The demand volume is estimated from density of each points based on the Greenburg model, and the queue length is from the density and speed of each point. In order to settle the variation of the unit time, the result of this algorithm is strategically regulated by importing the AVI(Automatic Vehicle Identification), one of the number plate matching methods. In this study, the AVI travel time information is composed by Hybrid Model in order to use it as the basic parameter to make one travel time in a day using ILD to classify the characteristics of the traffic flow along the queue length. According to the result of this study, in congestion situation, this algorithm has about more than 84% degree of accuracy. Specially, the result of providing the information of "Nam-san area traffic information system" shows that 72.6% of drivers are available.

• Journal of The Korean Ophthalmological Society
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• v.59 no.12
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• pp.1173-1180
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• 2018

Purpose: We evaluated the postoperative accuracy of intraocular lens power prediction for patients undergoing phacotrabeculectomy and identified preoperative factors associated with refractive outcome in those with primary open-angle glaucoma (POAG). Methods: We retrospectively reviewed the medical records of 27 patients who underwent phacotrabeculectomy to treat POAG. We recorded all discrepancies between predicted and actual postoperative refractions. We compared the data to those of an age- and sex-matched control group that underwent uncomplicated cataract surgery during the same time period. Preoperative factors associated with the mean absolute error (MAE) were identified via multivariate regression analyses. Results: The mean refractive error of the 27 eyes that underwent phacotrabeculectomy was comparable to that of the 27 eyes treated via phacoemulsification (+0.02 vs. -0.01 D, p = 0.802). The phacotrabeculectomy group exhibited a significantly higher MAE (0.65 vs. 0.35 D, p = 0.035) and more postoperative astigmatism (-1.07 vs. -0.66 D, p = 0.020) than the phacoemulsification group. The preoperative anterior chamber depth (ACD) and the changes in the postoperative intraocular pressure (IOP) were significantly associated with a greater MAE after phacotrabeculectomy. Conclusions: POAG treatment via combined phacoemulsification/trabeculectomy was associated with greater error in terms of final refraction prediction, and more postoperative astigmatism. As both a shallow preoperative ACD and a greater postoperative change in IOP appear to increase the predictive error, these two factors should be considered when planning phacotrabeculectomy.

• Journal of agriculture & life science
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• v.46 no.3
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• pp.47-58
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• 2012

This study was carried out to develop assessment model for the optimal site prediction of Dendropanax morbifera, Evergreen broad-leaved trees in warm temperate zone according to climate change. It was created criterion for assessment model of the optimal site prediction by quantification method to possible analysis of quantitative and qualitative data, through study relationship between growth of tree and site environmental factors. A program of the optimal site prediction was developed using program version 3.2, an Avenue and Dialog Designer tool of ESRI as GIS(geographic information system) engine. Developed program applied to test accuracy of the optimal site prediction in study area of Wando, Jeollanam-do, having a various evergreen broad-leaved trees of warm temperate zone. In the results from analysis of the optimal site prediction on Dendropanax morbifera, the characteristics of optimal site were analyzed site environmental features with 401~500m of altitude, $15^{\circ}$ of slope, hillside of local topography, alluvium of deposit type, convex of slope type and south of aspect. The mapping area per grade of the optimal site prediction in the Dendropanax morbifera showed 1,487.2ha(25.4%) of class I, 1,020.3ha(17.4%) of class II, 2,231.8ha(38.2%) of class III and 1,110.5ha(19.0%) of class IV.