• Journal of Korean Neurosurgical Society
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• v.64 no.2
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• pp.297-308
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• 2021

Objective : Shunt infection is a common complication while treating hydrocephalus. The antibiotic-impregnated shunt catheter (AISC) was designed to reduce shunt infection rate. A meta-analysis was conducted to study the effectiveness of AISCs in reduction of shunt infection in terms of age, follow-up time and high-risk patient population. Methods : This study reviewed literature from three databases including PubMed, EMBASE, and Cochrane Library (from 2000 to March 2019). Clinical studies from controlled trials for shunt operation were included in this analysis. A subgroup analysis was performed based on the patient's age, follow-up time and high-risk population. The fixed effect in RevMan 5.3 software (Cochrane Collaboration) was used for this meta-analysis. Results : This study included 19 controlled clinical trials including 10105 operations. The analysis demonstrated that AISC could reduce the infection rate in shunt surgery compared to standard shunt catheter (non-AISC) from 8.13% to 4.09% (odds ratio [OR], 0.48; 95% confidence interval [CI], 0.40-0.58; p=0.01; I2=46%). Subgroup analysis of different age groups showed that AISC had significant antimicrobial effects in all three groups (adult, infant, and adolescent). Follow-up time analysis showed that AISC was effective in preventing early shunt infections (within 6 months after implant). AISC is more effective in high-risk population (OR, 0.24;95% CI, 0.14-0.40; p=0.60; I2=0%) than in general patient population. Conclusion : The results of meta-analysis indicated that AISC is an effective method for reducing shunt infection. We recommend that AISC should be considered for use in infants and high-risk groups. For adult patients, the choice for AISC could be determined based on the treatment cost.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.102-116
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• 2021

For the safe operation of autonomous vehicles, the representative technology of the 4^th industrial revolution era, a combination of various technologies such as sensor technology, software technology and car technology is required. An autonomous vehicle is a vehicle that recognizes current location and situation by using the various sensors, and makes its own decisions without depending on the driver. Perfect recognition technology is required for fully autonomous driving. Since the precise road maps provide various road information including lanes, stop lines, traffic lights and crosswalks, it is possible to minimize the cognitive errors that occur in autonomous vehicles by using the precise road maps with location information of the road facilities. In this study, the definition, necessity and technical trends of the precise road map have been analyzed, and the HD(High Definition) map prototype based on the web GIS has been built in the autonomous driving-specialized areas of Daegu Metropolitan City(Suseong Medical District, about 24km), the Happy City of Sejong Special Self-Governing City(about 33km), and the FMTC(Future Mobility Technical Center) PG(Proving Ground) of Seoul National University Siheung Campus using the MMS(Mobile Mapping System) surveying results given by the National Geographic Information Institute. In future research, the built-in precise road map service will be installed in the autonomous vehicles and control systems to verify the real-time locations and its location correction algorithm.

• Journal of The Korean Society of Clinical Toxicology
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• v.19 no.1
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• pp.1-7
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• 2021

Purpose: The purpose of this study was to conduct a systematic review to investigate the socio-economic benefits of the poison control center (PCC) and to assess whether telephone counseling at the poison control center affects the frequency of emergency room visits, hospitalization, and length of stay of patients with acute poisoning. Methods: The authors conducted a medical literature search of the PubMed, EMBASE, and Cochrane Library databases. Two reviewers evaluated the abstracts for eligibility, extracted the data, and assessed the study quality using a standardized tool. Key results such as the cost-benefit ratio, hospital stay days, unnecessary emergency room visits or hospitalizations, and reduced hospital charges were extracted from the studies. When meta-analysis was possible, it was performed using RevMan software (RevMan version 5.4). Results: Among 299 non-duplicated studies, 19 were relevant to the study questions. The cost-benefit ratios of PCC showed a wide range from 0.76 to 36 (average 6.8) according to the level of the medical expense of each country and whether the study included intentional poisoning. PCC reduced unnecessary visits to healthcare facilities. PCC consultation shortened the length of hospital stay by 1.82 (95% CI, 1.07-2.57) days. Conclusion: The systematic review and meta-analysis support the hypothesis that the PCC operation is cost-beneficial. However, when implementing the PCC concept in Korea in the future, it is necessary to prepare an institutional framework to ensure a costeffective model.

• Clean Technology
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• v.27 no.1
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• pp.61-68
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• 2021

Selective catalytic reduction (SCR) is widely used as a method of removing nitrogen oxide in large-capacity thermal power generation systems. Uniform mixing of the injected ammonia and the inlet flue gas is very important to the performance of the denitrification reduction process in the catalyst bed. In the present study, a computational analysis technique was applied to the ammonia injection system design process of a denitrification facility. The applied model is the denitrification facility of an 800 MW class coal-fired power plant currently in operation. The flow field to be solved ranges from the inlet of the ammonia injection system to the end of the catalyst bed. The flow was analyzed in the two-dimensional domain assuming incompressible. The steady-state turbulent flow was solved with the commercial software named ANSYS-Fluent. The nozzle arrangement gap and injection flow rate in the ammonia injection system were chosen as the design parameters. A total of four (4) cases were simulated and compared. The root mean square of the NH3/NO molar ratio at the inlet of the catalyst layer was chosen as the optimization parameter and the design of the experiment was used as the base of the optimization algorithm. The case where the nozzle pitch and flow rate were adjusted at the same time was the best in terms of flow uniformity.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.2
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• pp.137-146
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• 2022

AIS(Automatic Identification System) is a device that automatically transmits and receives ship information and is mounted on the ship. AIS information of ships near the coast can be received on the ground, but when going out to sea more than 50 nautical miles, communication with the ground is cut off. To solve this problem, ship information can be transmitted to the ground through an AIS satellite equipped with an AIS receiver. There is no case of AIS satellite development in Korea yet, and many domestic shipping companies are using overseas AIS services. PNUSAT-1 is a 1U+ CubeSat, developed by Pusan National University, and it is equipped with an AIS receiver for monitoring of ships and transmitting ship information to the ground. Since the mission data of PNUSAT-1 is in text format, the data size is not large. In consideration of this, communication equipment, low-precision sensors, and actuators were selected. In this paper, system preliminary design of PNUSAT-1 was performed, requirements for mission performance, operation scenario and mode design, hardware and software selection, and preliminary design of each subsystem were performed.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.51-58
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• 2022

Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.189-196
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• 2023

This paper analyzes the level of satisfaction of two groups of teachers who were educated about artificial intelligence using App Inventor. The participants were 13 pre-service and 9 in-service elementary school teachers and the data was collected using a questionnaire. As a result of the study, in-service teachers were all more satisfied than pre-service teachers in terms of interest, difficulty, and participation in the education. In addition, the questions investigating whether education helped motivate learning of artificial intelligence and whether there is a willingness to apply it to elementary classes in the future were also more positive for in-service teachers than for pre-service teachers. In general, pre-service teachers had somewhat more negative views than in-service teachers, but they were more positive than in-service teachers in terms of whether the education helped improve their understanding of artificial intelligence and whether they were willing to participate in additional education. Analysis of the Mann-Whitney test to see if there was a significant difference in satisfaction between the two groups showed no significance. This may be because most of the students in the two groups already had block-type or text-type programming experience, so they were able to participate in the education without any special resistance or difficulty with App Inventor, resulting in high levels of satisfaction from both groups. The results of this study can provide basic data for the future development and operation of programs for artificial intelligence education for both pre-service and in-service elementary school teachers.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.371-380
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• 2023

Relation extraction is to extract relationships between named entities from text. Traditionally, relation extraction methods only extract relations between predetermined subject and object entities. However, in end-to-end relation extraction, all possible relations must be extracted by considering the positions of the subject and object for each pair of entities, and so this method uses time and resources inefficiently. To alleviate this problem, this paper proposes a method that sets directions based on the positions of the subject and object, and extracts relations according to the directions. The proposed method utilizes existing relation extraction data to generate direction labels indicating the direction in which the subject points to the object in the sentence, adds entity position tokens and entity type to sentences to predict the directions using a pre-trained language model (KLUE-RoBERTa-base, RoBERTa-base), and generates representations of subject and object entities through probabilistic crossover operation. Then, we make use of these representations to extract relations. Experimental results show that the proposed model performs about 3 ~ 4%p better than a method for predicting integrated labels. In addition, when learning Korean and English data using the proposed model, the performance was 1.7%p higher in English than in Korean due to the number of data and language disorder and the values of the parameters that produce the best performance were different. By excluding the number of directional cases, the proposed model can reduce the waste of resources in end-to-end relation extraction.