• Applied Biological Chemistry
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• v.38 no.3
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• pp.239-247
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• 1995

The improvement of distribution system and freshness prolongation of crisphead lettuce were carried out through vacuum cooling and distribution under the low temperature. Lettuce that vacuum-cooled and transported by cold storage car was shown better freshness than that distributed by conventional method when they arrived at cunsuming area. And it took $10{\sim}17$ hours until their temperatures arrived at same temperatures when they were stored at $0{\sim}15^{\circ}C$ cold storage room. It was cooled to $1^{\circ}C$ after 27 minutes with vacuum cooling apparatus. The weight loss of lettuce that vacuum cooled and transported by cold storage car was below 5% after 30 days cold storage. And ascorbic acid and chlorophyll retentions were 86% and 52%, respectively. The shelf-life of crisphead lettuce, distributed by vacuum cooling and cold storage car transportation, was 5 days at $15^{\circ}C$ and over 40 days at $0^{\circ}C$, respectively. However, when it was distributed by conventional method, it was only 3 days at $15^{\circ}C$ and 20 days at $0^{\circ}C$, respectively.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.699-708
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• 2005

It becomes easy and generalized to track the cellular phone users and vehicles according to the Progress of wireless telecommunication, the spread of network, and the miniaturization of terminal devices. It has been constantly studied to provide location based services to furnish suitable services depending on the positions of customers. Various vehicle tracking and management systems are developed to utilize and manage the vehicle locations to relieve the congestion of traffic and to smooth transportation. However the designed previous work can not evaluated in real world, because most of previous work is only designed not implemented and it is developed for simple model to handle a point, a line, a polygon object. Therefore, we design a moving object query language and implement a vehicle management system to search the positions and trajectories of vehicles and to analyze the cost of transportation effectively. The designed query language based on a SQL can be utilized to get the trajectories between two specific places, the departure time, the arrival time of vehicles, and the predicted uncertainty positions, etc. In addition, the proposed moving object query language for managing transportation vehicles is useful to analyze the cost of trajectories in a variety of moving object management system containing transportation.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.29-42
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• 2012

Recently, traffic congestion has become serious due to increase of private car usages. Carsharing or other innovative public transportation systems were developed to alleviate traffic congestion and carbon emissions. These measures can make the traffic environment more comfortable, and efficient. Cloud Transportation System (CTS) is a recent carsharing model. User can rent an electronic vehicles with various traffic information through the CTS. In this study, a concept, vision and scenarios of CTS are introduced. And, authors analyzed the location of CTS rental stations and estimated CTS demands. Firstly, we analyze the number of the population, employees, students and traffic volume in study areas. Secondly, the frequency and utilization time are examined. Demand for CTS in each traffic zone was estimated. Lastly, the CTS rental station location is determined based on the analyzed data of the study areas. Evaluation standard of the determined location includes accessibility and density of population. And, the number of vehicles and that of parking zone at the rental station are estimated. The result suggests that Haewoondae Square parking lot would be assigned 11 vehicles and 14.23 parking spaces and that Dongbac parking lot be assigned 7.9 vehicles and 10.29 parking spaces. Further study requires additional real-time data for CTS to increase accuracy of the demand estimation. And network design would be developed for redistribution of vehicles.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.6
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• pp.620-627
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• 2017

The importance of "Golden time", the early hours for saving lives in case of an accident, is being increasingly recognized day by day. Especially for marine accidents, it may take several hours for a rescue team to arrive, depending on location. Therefore, captains should always be prepared to handle situations independently. In this paper, in order to make better use of Golden Time in an emergency, we determined what the first consideration should be when selecting a ship evacuation route from perspective of the ship operator. To achieve this, we used maritime accident judgments and ship emergency response manual to identify ship evacuation priorities. AHP analysis (decision-making hierarchy analysis) was conducted for ship operators to determine consideration priorities. As a result, it was found that ship operator consider the safety of people about 6 times more important than that of the actual ship. In order to select an evacuation route, the location of coast guard ships, port of refuge, emergency anchorage, surrounding vessels, drifting and beaching factor are taken into consideration. By using these priority considerations, the decision-making processes of ship operators in emergency situations can be improved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.235-239
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• 2019

High-grade liver injury is associated with high morbidity and mortality rates. We report successful emergency angioembolization and early interventional radiology support to manage a high-grade liver injury in a 29-year-old man who presented following a fall during parachute training. Upon arrival, his blood pressure was 80/40 mmHg, and emergency ultrasonography showed a liver injury with perihepatic fluid collection. The patient's blood pressure reduced to 60/40 mmHg, and emergency angiography was performed without computed tomography (CT) (door to puncture time 36 min). After angioembolization, his blood pressure returned to 120/77 mmHg. Subsequent CT revealed no additional bleeding or hollow viscus injury. He was admitted to the Intensive Care Unit and discharged without complications 30 days after admission. In this case, emergency angioembolization (without performing CT) could successfully and safely treat a hemodynamically unstable patient with a high-grade liver injury.

• Journal of IKEEE
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• v.25 no.2
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• pp.243-251
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• 2021

In this paper, we propose a measurement technique of indoor location based on markerless applicable to AR. The proposed technique has the following originality. The first is to extract feature points and use them to generate local patches to enable faster computation by learning and using only local patches that are more useful than the surroundings without learning the entire image. Second, learning is performed through deep learning using the convolution neural network structure to improve accuracy by reducing the error rate. Third, unlike the existing feature point matching technique, it enables indoor location measurement including left and right movement. Fourth, since the indoor location is newly measured every frame, errors occurring in the front side during movement are prevented from accumulating. Therefore, it has the advantage that the error between the final arrival point and the predicted indoor location does not increase even if the moving distance increases. As a result of the experiment conducted to evaluate the time required and accuracy of the measurement technique of indoor location based on markerless applicable to AR proposed in this paper, the difference between the actual indoor location and the measured indoor location is an average of 12.8cm and a maximum of 21.2cm. As measured, the indoor location measurement accuracy was better than that of the existing IEEE paper. In addition, it was determined that it was possible to measure the user's indoor location in real time by displaying the measured result at 20 frames per second.

• Journal of Platform Technology
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• v.11 no.2
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• pp.26-38
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• 2023

This paper is a study on a transport robot capable of autonomously driving to a destination using a QR code in an indoor environment. The transport robot was designed and manufactured by attaching a lidar sensor so that the robot can maintain a certain distance during movement by detecting the distance between the camera for recognizing the QR code and the left and right walls. For the location information of the delivery robot, the QR code image was enlarged with Lanczos resampling interpolation, then binarized with Otsu Algorithm, and detection and analysis were performed using the Zbar library. The QR code recognition experiment was performed while changing the size of the QR code and the traveling speed of the transport robot while the camera position of the transport robot and the height of the QR code were fixed at 192cm. When the QR code size was 9cm × 9cm The recognition rate was 99.7% and almost 100% when the traveling speed of the transport robot was less than about 0.5m/s. Based on the QR code recognition rate, an experiment was conducted on the case where the destination is only going straight and the destination is going straight and turning in the absence of obstacles for autonomous driving to the destination. When the destination was only going straight, it was possible to reach the destination quickly because there was little need for position correction. However, when the destination included a turn, the time to arrive at the destination was relatively delayed due to the need for position correction. As a result of the experiment, it was found that the delivery robot arrived at the destination relatively accurately, although a slight positional error occurred while driving, and the applicability of the QR code-based destination self-driving delivery robot was confirmed.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.91-103
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• 2022

Determination of sound source characteristics such as: sound volume, direction and distance to the source is one of the important techniques for unmanned systems like autonomous vehicles, robot systems and AI speakers. There are multiple methods of determining the direction and distance to the sound source, e.g., using a radar, a rider, an ultrasonic wave and a RF signal with a sound. These methods require the transmission of signals and cannot accurately identify sound sources generated in the obstructed region due to obstacles. In this paper, we have implemented and evaluated a method of detecting and identifying the sound in the audible frequency band by a method of recognizing the volume, direction, and distance to the sound source that is generated in the periphery including the invisible region. A cross-shaped based sound source recognition algorithm, which is mainly used for identifying a sound source, can measure the volume and locate the direction of the sound source, but the method has a problem with "blind spots". In addition, a serious limitation for this type of algorithm is lack of capability to determine the distance to the sound source. In order to overcome the limitations of this existing method, we propose a QRAS-based algorithm that uses rectangular-shaped technology. This method can determine the volume, direction, and distance to the sound source, which is an improvement over the cross-shaped based algorithm. The QRAS-based algorithm for the OSSD uses 6 AITDs derived from four microphones which are deployed in a rectangular-shaped configuration. The QRAS-based algorithm can solve existing problems of the cross-shaped based algorithms like blind spots, and it can determine the distance to the sound source. Experiments have demonstrated that the proposed QRAS-based algorithm for OSSD can reliably determine sound volume along with direction and distance to the sound source, which avoiding blind spots.

• 한국노년학
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• v.30 no.4
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• pp.1281-1292
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• 2010

This study was performed to identify older adults' self-reported difficulties in understanding and utilizing health information and their relationships with health status and to investigate the differences between age groups and among education levels. Data were collected from July 1 to August 31 in 2007 from older adults in senior centers located in Daegu, Kyungpook, and Busan area. A total of 103 subjects participated in the study. The level of understanding health information in older adults was 50 on average (possible score 15-75). The most difficult items to understand were patient educational materials, written information provided by health care providers, and medical forms. The lower level of difficulty in utilizing health information was associated with better physical and mental health status. There were differences in their self-reported difficulties between the young-old and the old-old as well as among different education levels. Health care providers may need to tailor educational materials and medical forms to the cognitive ability of older adults under the consideration of their age and education levels.

• Smart Media Journal
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• v.12 no.10
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• pp.71-84
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• 2023

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results.