• The Journal of the Korea institute of electronic communication sciences
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• 제8권12호
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• pp.1883-1890
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• 2013

Location detection technology (LDT) is one of the core techniques for location based service (LBS) in wireless communication for improving resource management and quality of services. The location of a mobile station (MS) is estimated using the time of arrival (TOA) technique based on three circles with centers corresponding to coordinates of three base stations (BSs) and radius corresponding to distances between MS and BSs. For accurately estimating the location of MS, three circles should meet at a point for the trilateration method, but they generally do not meet a point because the radius is increased depending on the number of time delay for estimating the distance between MS and BS and the carrier frequency. The increased three circles intersect at six points and the three intersection points among them should be generally placed close to coordinate of the location for the specific MS. In this paper, we propose the shortest distance algorithm for TOA trilateration method, to select three interior intersection points from entire six points. The proposed approach selects three intersection points with the shortest distances between coordinates of MS and intersection points and determines the averaged coordinate of the selected three points, as the location of the specific MS. We demonstrate the performance of the proposed algorithm using a typical computer simulation example.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 2010년도 한국컴퓨터종합학술대회논문집 Vol.37 No.1(A)
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• pp.5-6
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• 2010

• Journal of Korean Society of Transportation
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• 제32권4호
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• pp.410-420
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• 2014

This research suggests a new tram signal priority model which determines signal timings and tram intersection waiting time using analytical method. This model can calculate the signal timings for Early Green and Green Extension among the active tram signal priority techniques by tram detection time of upstream detector. Moreover, it can determine the tram intersection waiting time that means tram intersection travel time delay from a vantage point of tram travel. Under the active tram signal priority condition, priority phases can bring additional green time from variable green time of non-priority phases. In this study, the signal timing and tram intersection waiting time calculation model was set up using analytical methods. In case studies using an isolated intersection, this study checks tram intersection waiting time ranged 12.7 to 29.4 seconds when variable green times of non-priority phases are 44 to 10 seconds under 120 seconds of cycle length.

• Journal of the Korea Society of Computer and Information
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• 제18권9호
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• pp.53-62
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• 2013

This paper proposes a method of automatically predicting the vanishing point for the purpose of detecting the road region from natural images. The proposed method stably detects the vanishing point in the road environment by analyzing the dominant orientation of the image and predicting the vanishing point to be at the position where the feature components of the image are concentrated. For this purpose, in the first stage, the image is partitioned into sub-blocks, an edge sample is selected randomly from within the sub-block, and RANSAC is applied for line fitting in order to analyze the dominant orientation of each sub-block. Once the dominant orientation has been detected for all blocks, we proceed to the second stage and randomly select line samples and apply RANSAC to perform the fitting of the intersection point, then measure the cost of the intersection model arising from each line and we predict the vanishing point to be located at the average point, based on the intersection point model with the highest cost. Lastly, quantitative and qualitative analyses are performed to verify the performance in various situations and prove the efficiency of the proposed algorithm for detecting the vanishing point.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제57권5호
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• pp.902-907
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• 2008

Shape of the pulse waveform is affected by the visco-elasticity characteristics of the arterial wall and the reflection waves generated at the bifurcations of arterial branches. This study was designed to improve the accuracy for the extraction of pulse wave features, then proved the superiority of the developed algorithm by clinical evaluation. Upstroke point of the pulse wave was used as an extraction feature since it is minimally affected by the waveform variation. R-peak of the ECG was used as a reference to decide the minimum level, then intersection of the least squares of regression line was used as an upstroke point. Developed algorithm was compared with the existing minimum value detection algorithm and tangent-intersection algorithm using data obtained from 102 subjects. Developed algorithm showed the least standard deviation of $0.29{\sim}0.44\;m/s$ compared with that of the existing algorithms, $0.91{\sim}3.66\;m/s$. Moreover, the rate of standard deviation of more than 1.00m/s for the PWV values reduced with the range of $29.0{\sim}42.4%$, which proved the superiority of the newly developed algorithm.

• Journal of Internet Computing and Services
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• 제12권5호
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• pp.101-111
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• 2011

Most researches in WSN-based parking management system used wireless sensors to monitor the events in a car parking area. However, the problem of car collisions in car parks was not discussed by previous researches. The car position details over time are vital in analyzing a collision event. This paper proposes a collision verification method to detect and to analyze the collision event in the parking area, and then notifies car owners. The detection uses the information from motion sensors for comprehensive details of position and direction of a moving car, and the verification processes an object tracking technique with a fast OBB intersection test. The performance tests show that the location technique is more accurate with additional sensors and the OBB collision test is faster compared to a normal OBB intersection test.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제7권3호
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• pp.38-47
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• 2008

A vehicle counting system at intersection is designed and implemented using analyzing a video stream from a camera. To separate foreground image from background, we compare three different methods, among which Li's method is chosen. Blobs are extracted from the foreground image using connected component analysis and the blobs are tracked by a blob tracker, frame by frame. The primary tracker use only the size and location of blob in foreground image. If there is a collision between blobs, the mean-shift tracking algorithm based on color distribution of blob is used. The proposed system is tested using real video data at intersection. If some huristics is applied, the system shows a good detection rate and a low error rate.

• Journal of Digital Convergence
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• 제11권2호
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• pp.215-220
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• 2013

They are well-known geometric properties of a circle that the perpendicular bisector of a chord passes through the center of a circle, and the intersection of the perpendicular bisectors of any two chords is its center. This paper is related to a fast pupil detection method capable of detecting the center and the radius of a pupil using these geometric properties at high speed when detecting the pupil region for iris segmentation. The proposed method is characterized as rapidly detecting the center and the radius of the pupil, extracting the candidate points of the circle in human eye images using morphological operations, and finding two chords using four points on the circular edge, and taking the intersection of the perpendicular bisectors of these two chords for its center. The proposed method can not only detect the center and the radius of a pupil rapidly but also find partially occluded pupils in human eye images.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제15권3호
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• pp.52-59
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• 2016

COSMOS system which was developed in Seoul for real-time signal control was designed to judge traffic condition for practicing signal operation. However, it occurs efficiency problem that stop line detection and queue length detection could not judge overflow saturation of street. For that reason, following research process GPS data of Seoul city's corporationowned taxi to calculate travel speed that excluded existing system of stop line detection and queue length detection. Also, "Research of calculating queue length by GPS data" which was progressed with following research expressed queue length. It is based on establishing algorithm of judging congestion situation. The algorithm was applied to a few areas where appeared congestion situation consistently to confirm real time traffic condition with established network. [Entrance of the National Sport Institute ${\rightarrow}$ Gangnam station Intersection, Yuksam station intersection ${\rightarrow}$ National Sport Institute.

• Journal of Korean Neurosurgical Society
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• 제66권1호
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.