• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.28-42
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• 2013

Personal Rapid Transit(PRT) is a new unmanned transportation system using electricity. The purpose of the PRT is relieving the congestion of city traffic and connecting between inner city and airport, high-speed railroad. PRT requires to develop devices for the guarantee of safety and reliability. PRT as the mean of rail transportation must be equipped with control system for front rail sensing. Ultra Wide Band(UWB) radar system is suitable for PRT's detection because it has the advantage of low power consumption, low interference and high resolution. In this paper, an improved adaptive Constant False Alarm Rate(CFAR) algorithm is proposed and studied in various noise environments. The proposed algorithm improves performance in various noise environments compared to the Mean Level CFAR algorithms and other adaptive CFAR algorithms.

• Journal of Trauma and Injury
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• v.34 no.2
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• pp.81-86
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• 2021

Purpose: This study investigated the clinical outcomes of trauma patients with blunt thoracic aortic injuries at a single institution. Methods: During the study period, 9,501 patients with traumatic aortic injuries presented to Trauma Center of Gil Medical Center. Among them, 1,594 patients had severe trauma, with an Injury Severity Score (ISS) of >15. Demographics, physiological data, injury mechanism, hemodynamic parameters associated with the thoracic injury according to chest computed tomography (CT) findings, the timing of the intervention, and clinical outcomes were reviewed. Results: Twenty-eight patients had blunt aortic injuries (75% male, mean age, 45.9±16.3 years). The majority (82.1%, n=23/28) of these patients were involved in traffic accidents. The median ISS was 35.0 (interquartile range 21.0-41.0). The injuries were found in the ascending aorta (n=1, 3.6%) aortic arch (n=8, 28.6%) aortic isthmus (n=18, 64.3%), and descending aorta (n=1, 3.6%). The severity of aortic injuries on chest CT was categorized as intramural hematoma (n=1, 3.6%), dissection (n=3, 10.7%), transection (n=9, 32.2%), pseudoaneurysm (n=12, 42.8%), and rupture (n=3, 10.7%). Endovascular repair was performed in 71.4% of patients (45% within 24 hours), and two patients received surgical management. The mortality rate was 25% (n=7). Conclusions: Traumatic thoracic aortic injuries are life-threatening. In our experience, however, if there is no rupture and extravasation from an aortic injury, resuscitation and stabilization of vital signs are more important than an intervention for an aortic injury in patients with multiple traumas. Further study is required to optimize the timing of the intervention and explore management strategies for blunt thoracic aortic injuries in severe trauma patients needing resuscitation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.2
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• pp.13-23
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• 2016

Many traffic centers are highly hesitant in employing existing Automatic Incident Detection Algorithms due to high false alarm rate, low detection rate, and enormous effort taken in maintaining algorithm parameters, together with complex algorithm structure and filtering/smoothing process. Concerns grow over the situation particularly in Freeway Incident Management Area This study proposes a new algorithm and introduces a novel concept, the Displaced Flow Index (DiFI) which is similar to a product of relative speed and relative occupancy for every execution period. The algorithm structure is very simple, also easy to understand with minimum parameters, and could use raw data without any additional pre-processing. To evaluate the performance of the DiFI algorithm, validation test on the algorithm has been conducted using detector data taken from Naebu Expressway in Seoul and following transferability tests with Gyeongbu Expressway detector data. Performance test has utilized many indices such as DR, FAR, MTTD (Mean Time To Detect), CR (Classification Rate), CI (Composite Index) and PI (Performance Index). It was found that the DR is up to 100%, the MTTD is a little over 1.0 minutes, and the FAR is as low as 2.99%. This newly designed algorithm seems promising and outperformed SAO and most popular AIDAs such as APID and DELOS, and showed the best performance in every category.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.25-39
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• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.