• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.828-835
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• 2023

Vessel traffic service operators (VTSOs) need to quickly and accurately analyze the maritime traffic situation in the vessel traffic service (VTS) area and provide information to the vessels. However, if traf ic increases rapidly, the workload of VTSOs increases, and they may not be able to provide adequate information. Therefore, it is essential to develop VTSO support technologies that can reduce their workload and provide consistent information. In this paper, we propose a model for automatically detecting abnormal vessels in the VTS area. The proposed model consists of a positional model and a contextual model and is specifically optimized for the traffic characteristics of the target area. The implemented model was tested by using real-world data collected at a test center (Daesan Port VTS). Our experiments confirmed that the model could automatically detect various abnormal situations, and the results were validated through expert evaluation.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.88-94
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• 2023

The seating postures of passengers in the automated driving vehicle are possible in atypical forms such as rear-facing and lying down. It is necessary to improve devices such as airbags and seat belts to protect occupants from injury in accidents of the automated driving vehicle, and collision safety evaluation tests must be newly developed. The purpose of this study is to define representative types of head-on collision accidents to develop collision standards for autonomous vehicles that take into account changes in driving behavior and occupants' postures. 150 frontal collision cases remained by filtering (accident videos, images, AIS 2+, passenger car, etc…) and random sampling from approximately 320,000 accidents claimed by a major insurance company over the past 5 years. The most frequent accident type is a head-on collision between a vehicle going straight and a vehicle turning left from the opposite side, accounting for 54.7% of all accidents, and most of these accidents occur in permissive left turns. The next most common frontal collision is the center-lane violation by drowsy driving and careless driving, accounting for 21.3% of the total. For the two types above, data such as vehicle speed, contact point/area, and PDOF at the moment of impact are obtained through accident reconstruction using PC-Crash. As a result, two types of autonomous vehicle crash safety test scenarios are proposed: (1) a frontal oblique collision test based on the accident types between a straight vehicle and a left-turning vehicle, and (2) a small overlap collision test based on the head-on accidents of center-lane violation.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1173-1183
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• 2023

Over the past 20 years, Korea's overall import and export cargo volume has increased at an average annual rate of approximately 5.3%. About 99% of the cargo is still being transported by sea. Due to recent increases in maritime cargo volume, congestion in maritime logistics has become challenging due to factors such as the COVID-19 pandemic and conflicts. Continuous monitoring of ports has become crucial. Various ground observation systems and Automatic Identification System (AIS) data have been utilized for monitoring ports and conducting numerous preliminary studies for the efficient operation of container terminals and cargo volume prediction. However, small and developing countries' ports face difficulties in monitoring due to environmental issues and aging infrastructure compared to large ports. Recently, with the increasing utility of artificial satellites, preliminary studies have been conducted using satellite imagery for continuous maritime cargo data collection and establishing ocean monitoring systems in vast and hard-to-reach areas. This study aims to visually detect ships docked at berths in the Busan New Port using high-resolution satellite imagery and quantitatively evaluate berth utilization rates. By utilizing high-resolution satellite imagery from Compact Advanced Satellite 500-1 (CAS500-1), Korea Multi-Purpose satellite-3 (KOMPSAT-3), PlanetScope, and Sentinel-2A, ships docked within the port berths were visually detected. The berth utilization rate was calculated using the total number of ships that could be docked at the berths. The results showed variations in berth utilization rates on June 2, 2022, with values of 0.67, 0.7, and 0.59, indicating fluctuations based on the time of satellite image capture. On June 3, 2022, the value remained at 0.7, signifying a consistent berth utilization rate despite changes in ship types. A higher berth utilization rate indicates active operations at the berth. This information can assist in basic planning for new ship operation schedules, as congested berths can lead to longer waiting times for ships in anchorages, potentially resulting in increased freight rates. The duration of operations at berths can vary from several hours to several days. The results of calculating changes in ships at berths based on differences in satellite image capture times, even with a time difference of 4 minutes and 49 seconds, demonstrated variations in ship presence. With short observation intervals and the utilization of high-resolution satellite imagery, continuous monitoring within ports can be achieved. Additionally, utilizing satellite imagery to monitor changes in ships at berths in minute increments could prove useful for small and developing country ports where harbor management is not well-established, offering valuable insights and solutions.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.323-330
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• 2016

Communication, Ocean and Meteorological Satellite (COMS) was launched in 2010 with three payloads that include Ka-band communication payload developed by Ministry of Science, ICT and Future Planning (MSIP) and Electronics and Telecommunications Research Institute (ETRI). This study introduces a real-time remote vessel monitoring system built in the Socheongcho Ocean Research Station using the Ka-band communication satellite. The system is composed of three steps; real-time data collection, transmission, and processing/visualization. We describe hardware (H/W) and software systems (S/W) installed to perform each step and the whole procedure that made the raw data become vessel information for a real-time ocean surveillance. In addition, we address functional requirements of H/W and S/W and the important considerations for successful operation of the system. The system is now successfully providing, in near real-time, ship information over a VHF range using AIS data collected in the station. The system is expected to support a rapid and effective surveillance over a huge oceanic area. We hope that the concept of the system can be fully used for real-time maritime surveillance using communication satellite in future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.4
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• pp.483-491
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• 2021

The identification of regional collision risks in water areas is significant for the safety of navigation. This paper introduces a new method of collision risk assessment that incorporates a clustering method based on the distance factor - hierarchical clustering - and uses real-time data in case of several surrounding vessels, group methodology and preliminary assessment to classify vessels and evaluate the basis of collision risk evaluation (called HCAAP processing). The vessels are clustered using the hierarchical program to obtain clusters of encounter vessels and are combined with the preliminary assessment to filter relatively safe vessels. Subsequently, the distance at the closest point of approach (DCPA) and time to the closest point of approach (TCPA) between encounter vessels within each cluster are calculated to obtain the relation and comparison with the collision risk index (CRI). The mathematical relationship of CRI for each cluster of encounter vessels with DCPA and TCPA is constructed using a negative exponential function. Operators can easily evaluate the safety of all vessels navigating in the defined area using the calculated CRI. Therefore, this framework can improve the safety and security of vessel traffic transportation and reduce the loss of life and property. To illustrate the effectiveness of the framework proposed, an experimental case study was conducted within the coastal waters of Mokpo, Korea. The results demonstrated that the framework was effective and efficient in detecting and ranking collision risk indexes between encounter vessels within each cluster, which allowed an automatic risk prioritization of encounter vessels for further investigation by operators.

• Journal of Trauma and Injury
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• v.24 no.1
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• pp.1-6
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• 2011

Purpose: Even though traumatic pancreatic injuries occur in only 0.2% to 4% of all abdominal injuries, the morbidity and the mortality rates associated with pancreatic injuries remain high. The aim of this study was to evaluate the clinical outcomes of traumatic pancreatic injuries and to identify predictors of mortality and morbidity. Methods: We retrospectively reviewed the medical records of 26 consecutive patients with a pancreatic injury who underwent a laparotomy from January 2000 to December 2010. The data collected included demographic data, the mechanism of injury, the initial vital signs, the grade of pancreatic injury, the injury severity score (ISS), the revised trauma score (RTS), the Glasgow Coma Scale (GCS), the number of abbreviated injury scales (AIS), the number of associated injuries, the initial laboratory findings, the amount of blood transfusion, the type of operation, the mortality, the morbidity, and others. Results: The overall mortality rate in our series was 23.0%, and the morbidity rate was 76.9%. Twenty patients (76.9%) had associated injuries to either intra-abdominal organs or extra-abdominal organs. Two patients (7.7%) underwent external drainage, and 18 patients (69.3%) underwent a distal pancreatectomy. Pancreaticoduodenectomies were performed in 6 patients (23.0%). Three patients underwent a re-laparotomy due to anastomosis leakage or postoperative bleeding, and all patients died. The univariate analysis revealed 11 factors (amount of transfusion, AAST grade, re-laparotomy, associated duodenal injury, base excess, APACHE 11 score, type of operation, operation time, RTS, associated colon injury, GCS) to be significantly associated with mortality (p<0.05). Conclusion: Whenever a surgeon manages a patient with traumatic pancreatic injury, the surgeon needs to consider the predictive risk factors. And, if possible, the patient should undergo a proper and meticulous, less invasive surgical procedure.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.848-857
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• 2018

Recently, around the world, active development of new renewable energy sources including solar power, waves, and fuel cells, etc. has taken place. Particularly, floating offshore wind farms have been developed for saving costs through large scale production, using high-quality wind power and minimizing noise damage in the ocean area. The development of floating wind farms requires an evaluation of the Maritime Safety Audit Scheme under the Maritime Safety Act in Korea. Floating wind farms shall be assessed by applying the line and area concept for systematic development, management and utilization of specified sea water. The development of appropriate evaluation methods and standards is also required. In this study, proper standards for marine traffic surveys and assessments were established and a systemic treatment was studied for assessing marine spatial area. First, a marine traffic data collector using AIS or radar was designed to conduct marine traffic surveys. In addition, assessment methods were proposed such as historical tracks, traffic density and marine traffic pattern analysis applying the line and area concept. Marine traffic density can be evaluated by spatial and temporal means, with an adjusted grid-cell scale. Marine traffic pattern analysis was proposed for assessing ship movement patterns for transit or work in sea areas. Finally, conceptual design of a Marine Traffic and Safety Assessment Solution (MaTSAS) was competed that can be analyzed automatically to collect and assess the marine traffic data. It could be possible to minimize inaccurate estimation due to human errors such as data omission or misprints through automated and systematic collection, analysis and retrieval of marine traffic data. This study could provides reliable assessment results, reflecting the line and area concept, according to sea area usage.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Journal of Trauma and Injury
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• v.18 no.2
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• pp.101-106
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• 2005

Purpose: The number of the deceased from free-fall is increasing nowadays. Free-fall comes to a great social problem in that even the survivor will be suffering for cord injury or brain injury, and so on. We analyzed the cases of free-fall patients to find out whether the injury severity is mainly correlated with the height of fall. Methods: We retrospectively investigated the characteristics of patients, who fall from the height above 2m from January 2000 to August 2004. We excluded the patients who transferred to other hospital, transferred from other hospital, and not known the height of fall. 145 patients were evaluated. Variables included in data analysis were age, height of fall, injury severity score (ISS), the being of barrier, and the survival or not. To find out the correlation between height of fall and death, we used receive operating characteristics (ROC) curve analysis. Results: The mean age of patients was $36.5{\pm}19.4$ years old. 110 were male and 35 were female. Mean height of fall was $11.1{\pm}8.5m$. 51 patients (35.2%) were died and 30 patients of them (58.9%) got emergency room on dead body. The mean height of fall is $8.9{\pm}5.8m$ for 94 survivors and $15.2{\pm}11.0m$ for the 51 deceased (p<0.001). The area under the ROC curve was 0.646, which means the height of fall was not adequate factor for predicting for death. At 13.5m, as cut?off value, sensitivity is 52.9%, specificity is 86.2%, positive predictive value is 67.5% and negative predictive value is 77.1%. There were statistical differences in mortality rate and ISS between 'below 13.5m group' and 'above 13.5m group', but there was not statistical difference in head and neck AIS. Conclusion: The height of fall is not adequate factor for prediction of death. So other factors like intoxication or not, the being of barrier or protection device need to be evaluated for predicting of free-fall patient's death.