• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.57-64
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• 2023

The ROK military faces a significant challenge in its vigilance mission due to demographic problems, particularly the current aging population and population cliff. This study demonstrates the crucial role of the 4th industrial revolution and its core artificial intelligence algorithm in maximizing work efficiency within the Command&Control room by mechanizing simple tasks. To achieve a fully developed military surveillance system, we have chosen multi-object tracking (MOT) technology as an essential artificial intelligence component, aligning with our goal of an intelligent and automated surveillance system. Additionally, we have prioritized data visualization and user interface to ensure system accessibility and efficiency. These complementary elements come together to form a cohesive software application. The CCTV video data for this study was collected from the CCTV cameras installed at the 1st and 2nd main gates of the 00 unit, with the cooperation by Command&Control room. Experimental results indicate that an intelligent and automated surveillance system enables the delivery of more information to the operators in the room. However, it is important to acknowledge the limitations of the developed software system in this study. By highlighting these limitations, we can present the future direction for the development of military surveillance systems.

• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.73-82
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• 2019

The satirical content of the absurd play, centered on Lee Keun-sam's play, represents the family image of a modern capitalist society where only duty is emphasized while the characters are lost in love with the family. They show humans becoming subordinate to economic logic as traditional relationships and family relationships change into material ones due to the rapid development of the economy. The narrator expresses the roles of the performer and the narrator together. It also presents the plot as a characteristic element of epic and absurd dramas, and directs actors as directors. It also foretells the events that will take place in the future, presents the inner consciousness of the characters in the play, and reduces and expands events and times. In terms of conflict, in order to fulfill the financial responsibility of their children, the professor translates them like a machine and the wife distributes the money they earn as they demand. The middle-aged professor and his wife are not willing to make a difference in the real world, so specific conflicts are not revealed. Therefore, no concrete conflict appears within this work. The plot of consisted of 22 epicentre compartments, consisting of a time frame from evening to the next morning. And no special events happen and show only one family's daily life. In addition, materials that show simple repetition of daily life such as newspapers, rice, birthdays, etc. are effectively showing the character of absurdity through repeated structure. The linguistic features of the absurd play focus on expressing anxiety, despair, fantasy and the sense of loss that the object's purpose has disappeared. The stage system avoids detailed portrayals of naturalist plays and creates a thoroughly simplified image that the theme of the play demands, which shows that the stage unit is also an important element that characterizes the absurdity of reflexes.

• Journal of Practical Agriculture & Fisheries Research
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• v.17 no.1
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• pp.125-132
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• 2015

The report about equine anesthesias in Korea are very rare. This paper aimed at the mortality rate during anesthesia in Thoroughbred horses at Equine Hospital of Busan Race Park, KRA in South Korea from 2005 to 2010. Drugs used in anesthesia was IV injection of detomidine hydrochloride (0.01 mg/kg) or xylazine(0.5mg/kg) for sedation and premedication, Guaifenesin(50-100 mg/kg) for muscle relaxation, ketamine hydrochloride(2 mg/kg) for induction of anaesthesia and Inhalational isoflurane(1.3-1.5 %) to maintain anesthesia. Total number of anesthetic cases was 190, 150 of inhalational anesthesia and 40 of general anesthesia, repectively. The purpose of anesthesia was highest in the disorder of musculoskeletal system, followed by urogenital system and respiratory system Mortality case due to anesthesia was one during arthroscopic surgery for removal of osteochondral chip fragments. The time of anesthesia was 150 min, fatal sign was hypoxemia and the reason was improper machine operation of the anesthetist. In conclusion, the perianesthetic mortality rate during anesthesia in Thoroughbred horses at Busan Race Park was 0.52%(1 death per 190 anesthetics).

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.79-85
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• 2022

Recently, a smartphone manufactured on a flexible substrate has been released as an electronic device, and research on a stretchable electronic device is in progress. In this paper, a silicon-based stretchable material is made and used as a substrate to implement and evaluate an optical sensor device using oxide semiconductor. To this end, a substrate that stretches well at room temperature was made using a silicone-based solution rubber, and the elongation of 350% of the material was confirmed, and optical properties such as reflectivity, transmittance, and absorbance were measured. Next, since the surface of these materials is hydrophobic, oxygen-based plasma surface treatment was performed to clean the surface and change the surface to hydrophilicity. After depositing an AZO-based oxide film with vacuum equipment, an Ag electrode was formed using a cotton swab or a metal mast to complete the photosensor. The optoelectronic device analyzed the change in current according to the voltage when light was irradiated and when it was not, and the photocurrent caused by light was observed. In addition, the effect of the optical sensor according to the folding was additionally tested using a bending machine. In the future, we plan to intensively study folding (bending) and stretching optical devices by forming stretchable semiconductor materials and electrodes on stretchable substrates.

• Resources Recycling
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• v.31 no.6
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• pp.60-65
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• 2022

This study presents the carburization process for recycling sludge, which was formed during silicon wafer machining. The sludge used in the carburization process is a mixture of silicon and silicon carbide (SiC) with iron as an impurity, which originates from the machine. Additionally, the sludge contains cutting oil, a fluid with high viscosity. Therefore, the sludge was dried before carburization to remove organic matter. The dried sludge was washed by acid cleaning to remove the iron impurity and subsequently carburized by heat treatment under vacuum to form the SiC powder. The ratio of silicon to SiC in the sludge was varied depending on the sources and thus carbon content was adjusted by the ratio. With increasing SiC content, the carbon content required for SiC formation increased. It was demonstrated that substoichiometric SiC_x (x<1) was easily formed when the carbon content was insufficient. Therefore, excess carbon is required to obtain a pure SiC phase. Moreover, size reduction by high-energy milling had a beneficial effect on the suppression of SiC_x, forming the pure SiC phase.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.159-172
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• 2023

In general, social problem-solving research aims to create important social value by offering meaningful answers to various social pending issues using scientific technologies. Not surprisingly, however, although numerous and extensive research attempts have been made to alleviate the social problems and issues in nation-wide, we still have many important social challenges and works to be done. In order to facilitate the entire process of the social problem-solving research and maximize its efficacy, it is vital to clearly identify and grasp the important and pressing problems to be focused upon. It is understandable for the problem discovery step to be drastically improved if current social issues can be automatically identified from existing R&D resources such as technical reports and articles. This paper introduces a comprehensive dataset which is essential to build a machine learning model for automatically detecting the social problems and solutions in various national research reports. Initially, we collected a total of 700 research reports regarding social problems and issues. Through intensive annotation process, we built totally 24,022 sentences each of which possesses its own category or label closely related to social problem-solving such as problems, purposes, solutions, effects and so on. Furthermore, we implemented four sentence classification models based on various neural language models and conducted a series of performance experiments using our dataset. As a result of the experiment, the model fine-tuned to the KLUE-BERT pre-trained language model showed the best performance with an accuracy of 75.853% and an F1 score of 63.503%.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.189-198
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• 2023

As crimes frequently occur on the street, the spread of CCTV is increasing. However, due to the shortcomings of passively operated CCTV, the need for intelligent CCTV is attracting attention. Due to the heavy system of such intelligent CCTV, high-performance devices are required, which has a problem in that it is expensive to replace the general CCTV. To solve this problem, an intelligent CCTV system that recognizes low-quality images and operates even on devices with low performance is required. Therefore, this paper proposes a Saying CCTV system that can detect threats in real time by using the AWS cloud platform to lighten the system and convert images into text. Based on the data extracted using YOLO v4 and OpenPose, it is implemented to determine the risk object, threat behavior, and threat situation, and calculate the risk using machine learning. Through this, the system can be operated anytime and anywhere as long as the network is connected, and the system can be used even with devices with minimal performance for video shooting and image upload. Furthermore, it is possible to quickly prevent crime by automating meaningful statistics on crime by analyzing the video and using the data stored as text.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.99-113
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• 2023

In a customer-based industry, customer retention is the competitiveness of a company, and improving customer retention improves the competitiveness of the company. Therefore, accurate prediction and management of potential dormant customers is paramount to increasing the competitiveness of the enterprise. In particular, there are numerous competitors in the domestic card industry, and the government is introducing an automatic closing system for dormant card management. As a result of these social changes, the card industry must focus on better predicting and managing potential dormant cards, and better predicting dormant customers is emerging as an important challenge. In this study, the Recurrent Neural Network (RNN) methodology was used to predict potential dormant customers in the card industry, and in particular, Long-Short Term Memory (LSTM) was used to efficiently learn data for a long time. In addition, to redefine the variables needed to predict dormant customers in the card industry, Unified Theory of Technology (UTAUT), an integrated technology acceptance theory, was applied to redefine and group the variables used in the model. As a result, stable model accuracy and F-1 score were obtained, and Hit-Ratio proved that models using LSTM can produce stable results compared to other algorithms. It was also found that there was no moderating effect of demographic information that could occur in UTAUT, which was pointed out in previous studies. Therefore, among variable selection models using UTAUT, dormant customer prediction models using LSTM are proven to have non-biased stable results. This study revealed that there may be academic contributions to the prediction of dormant customers using LSTM algorithms that can learn well from previously untried time series data. In addition, it is a good example to show that it is possible to respond to customers who are preemptively dormant in terms of customer management because it is predicted at a time difference with the actual dormant capture, and it is expected to contribute greatly to the industry.

• Composites Research
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• v.21 no.5
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• pp.23-30
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• 2008

Stab threats using sharp edged or pointed Instruments could be easily encountered by police officers or soldiers. In this study, the shear thickening fluids (STF) was impregnated into Kevlar fabrics to improve the stab protection and the resistance of STF impregnated Kevlar fabrics was experimentally investigated. The puncture and cut resistance were tested using a drop test machine withspike and knife indenters fabricated based on the National Institute of Justice (NIJ) standard. The STF was filled with spherical $SiO_2$ particles having an average diameter of 100nm, 300nm, and 500nm. The effect of particle size on puncture and cut resistance of STF impregnated Kevlar fabrics was also investigated. The measured impact load histories showed that STF impregnation into fabric leads to withstand higher peak loads than that of neat fabrics under spike test. The test results showed that Kevlar impregnated with STF exhibit remarkable improvements in puncture resistance while it is slightly influential on the cut resistance. Specifically, particle size is the one of the dominant factors controlling fabric resistance to puncture under spike impact test.