• Korean Journal of Heritage: History & Science
• /
• v.51 no.3
• /
• pp.4-31
• /
• 2018

It is difficult to ascertain exactly when the Hongjuupseong (Town Wall) was first constructed, due to it had undergone several times of repair and maintenance works since it was piled up newly in 1415, when the first year of the reign of King Munjong (the 5th King of the Joseon Dynasty). Parts of its walls were demolished during the Japanese occupation, leaving the wall as it is today. Hongseong region is also susceptible to historical earthquakes for geological reasons. There have been records of earthquakes, such as the ones in 1978 and 1979 having magnitudes of 5.0 and 4.0, respectively, which left part of the walls collapsed. Again, in 2010, heavy rainfall destroyed another part of the wall. The fortress walls of the Hongjuupseong comprise various rocks, types of facing, building methods, and filling materials, according to sections. Moreover, the remaining wall parts were reused in repair works, and characteristics of each period are reflected vertically in the wall. Therefore, based on the vertical distribution of the walls, the Hongjuupseong was divided into type I, type II, and type III, according to building types. The walls consist mainly of coarse-grained granites, but, clearly different types of rocks were used for varying types of walls. The bottom of the wall shows a mixed variety of rocks and natural and split stones, whereas the center is made up mostly of coarse-grained granites. For repairs, pink feldspar granites was used, but it was different from the rock variety utilized for Suguji and Joyangmun Gate. Deterioration types to the wall can be categorized into bulging, protrusion of stones, missing stones at the basement, separation of framework, fissure and fragmentation, basement instability, and structural deformation. Manually and light-wave measurements were used to check the amount and direction of behavior of the fortress walls. A manual measurement revealed the sections that were undergoing structural deformation. Compared with the result of the light-wave measurement, the two monitoring methods proved correlational. As a result, the two measuring methods can be used complementarily for the long-term conservation and management of the wall. Additionally, the measurement system must be maintained, managed, and improved for the stability of the Hongjuupseong. The measurement of Nammunji indicated continuing changes in behavior due to collapse and rainfall. It can be greatly presumed that accumulated changes over the long period reached the threshold due to concentrated rainfall and subsequent behavioral irregularities, leading to the walls' collapse. Based on the findings, suggestions of the six grades of management from 0 to 5 have been made, to manage the Hongjuupseong more effectively. The applied suggested grade system of 501.9 m (61.10%) was assessed to grade 1, 29.5 m (3.77%) to grade 2, 10.4 m (1.33%) to grade 3, 241.2 m (30.80%) and grade 4. The sections with grade 4 concentrated around the west of Honghwamun Gate and the east of the battlement, which must be monitored regularly in preparation for a potential emergency. The six-staged management grade system is cyclical, where after performing repair and maintenance works through a comprehensive stability review, the section returned to grade 0. It is necessary to monitor thoroughly and evaluate grades on a regular basis.

• Analytical Science and Technology
• /
• v.33 no.3
• /
• pp.151-158
• /
• 2020

Alcohol, which can easily be obtained in the same way as ordinary beverages, is harmful enough to cause death due to excessive drinking and chronic alcohol intake, so it is important to maintain a proper amount of drinking and healthy drinking habits. In addition, the incidence of behavioral disturbances and impaired judgments that can be caused by chronic alcohol drinking of more than adequate amounts of alcohol is also significant. Accordingly it is very useful for forensic science to check whether the person involved is drunken or is alcoholism state in various accidents. Currently, in Korea, alcohol consumption is determined by detecting the level of alcohol or alcohol metabolism 'ethyl glucuronide (EtG)' in blood or urine samples. However, analysis of alcohol or EtG in blood or urine can only provide information about the current state of alcohol consumption because of a narrow window of detection time. Therefore, it is important to analyze the EtG as a long-term direct alcohol metabolite bio-marker in human hair and to investigate relationship between alcohol consumption and EtG concentration for the evaluation of chronic ethanol consumption. In this study, we established an analytical method for the detection of EtG in Korean hair efficiently and validated selectivity, linearity, limits of detection (LOD), limits of quantification (LOQ), matrix effect, recovery, process efficiency, accuracy and precision using liquid chromatography tandem mass spectrometry (LC-MS/MS). In addition, the assay performance was evaluated in Korean social drinker's hair and the postmortem hair of a chronic alcoholism. The results of this study can be useful in monitoring the alcohol abuse of Korean in clinical cases and legal procedures related to custody and provide a useful tool to evaluate postmortem diagnosis of alcoholic ketoacidosis in forensics.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.33 no.4
• /
• pp.317-324
• /
• 2015

GNSS was firstly proposed for application in weather forecasting in the mid-1980s. It has continued to demonstrate the practical uses in GNSS meteorology, and other relevant researches are currently being conducted. Precipitable Water Vapor (PWV), calculated based on the GNSS signal delays due to the troposphere of the Earth, represents the amount of the water vapor in the atmosphere, and it is therefore widely used in the analysis of various weather phenomena such as monitoring of weather conditions and climate change detection. In this study we calculated the PWV through the meteorological information from an Automatic Weather Station (AWS) as well as GNSS data processing of a Continuously Operating Reference Station (CORS) in order to analyze the heavy snowfall of the Ulsan area in early 2014. Song’s model was adopted for the weighted mean temperature model (T_m), which is the most important parameter in the calculation of PWV. The study period is a total of 56 days (February 2013 and 2014). The average PWV of February 2014 was determined to be 11.29 mm, which is 11.34% lower than that of the heavy snowfall period. The average PWV of February 2013 was determined to be 10.34 mm, which is 8.41% lower than that of not the heavy snowfall period. In addition, certain meteorological factors obtained from AWS were compared as well, resulting in a very low correlation of 0.29 with the saturated vapor pressure calculated using the empirical formula of Magnus. The behavioral pattern of PWV has a tendency to change depending on the precipitation type, specifically, snow or rain. It was identified that the PWV showed a sudden increase and a subsequent rapid drop about 6.5 hours before precipitation. It can be concluded that the pattern analysis of GNSS PWV is an effective method to analyze the precursor phenomenon of precipitation.

• Anxiety and mood
• /
• v.15 no.2
• /
• pp.61-67
• /
• 2019

Objective : This study aimed to investigate the general process from the symptom onset to the psychiatric treatment in Korean panic patients and the effect of improved public awareness on it. Methods : This study has a retrospective design. The subjects were the new patients with panic disorder who visited the psychiatric outpatient clinic in twelve university-affiliated hospitals all across Korea. The medical chart was reviewed retrospectively and the data were collected including chief complaints of symptoms, recent stressors, the time to visit the psychiatric outpatient clinic, and visit of other departments and diagnostic approaches for their symptoms. Results : A total of 814 participants were included in the study. The most common department other than psychiatry the panic patients visited were cardiology (28.3%), general internal medicine (16.0%) and neurology (11.4%). The most frequently used diagnostic tests were a echocardiography (17.9%), 24-hour Holter monitoring (11.2%), and brain MRI (8.2%). Only 37.3% of participants visited psychiatric clinic directly. About 80% of participants visited psychiatric department within 1 year after their first panic symptoms and it took $13.8{\pm}13.7weeks$ on average. Comparing before and after 2012, the number of participants increased who visit directly the psychiatric clinic without visiting other departments (p=0.002) and without visiting emergency room (p<0.001). Conclusions : Our results suggest that a substantial number of patients visit departments other than psychiatry when they experience first panic symptoms. However, most patients begin psychiatric treatment within 1 year after their first symptoms and the number of patient are increasing who visit psychiatric department directly without visiting other departments.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.89-106
• /
• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.