• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3597-3609
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• 2012

Purpose: This study was done to develop the multidimensional scale of addictive behavior for adolescents. Methods: The process involved construction of a conceptual framework, initial items, verification of content validity, selection of secondary items, and extraction of final items. The participants were 636 adolescents in six middle schools and four high schools. Results: Seventy items were selected for the final scale, and categorized 8 factors explaining 56.5% of total variance. The factors were labeled as game addictive behavior, shopping addictive behavior, mobile phone addictive behavior, nicotine addictive behavior, television addictive behavior, gambling addictive behavior, alcohol addictive behavior, and internet addictive behavior. The scores for the scale were significantly correlated with addictive personality and self-control. Cronbach's alpha coefficient for the 70 items was .94. Scale scores identified adolescents as addictive behavior group, risk group, and average group. Conclusion: The above findings indicate that the multidimensional scale of addictive behavior for adolescents has good validity and reliability when used with adolescents. More importantly, it provides the first step toward developing a addiction prevention program. Additionally the scales provide an education or guideline, and proper physical and mental health management of youth in research and practice for the promotion of education.

• The Journal of Korean Institute of Information Technology
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• v.15 no.12
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• pp.157-164
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• 2017

Mobile ECG signal measurement is a technique to measure small signals of several mV, and many studies have been conducted to remove noise including wandering scheme. Removal of the equipotential line noise caused by shaking or movement of the electrode cable is one of the core research contents for the electrocardiogram measurement. In this study, we proposed a modified step-size of combined NLMS(normalized least squares) and DLMS(delayed least squares) adaptive filter to eliminate baseline noise from ECG signals. The proposed method mainly adjusts initial filter step-size to reduce distortion of original ECG signals characteristic after eliminating baseline noise. The modified filter step-size is scaled by filter order size and distortion minimization factor. This method is suitable for portable ECG device with a small processor and less power consumption. This technique also decreases computation time which is essential for real-time filtering. The proposed filter also increase the signal to noise ratio (SNR) compared to conventional NLMS filter.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.520-525
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• 2019

Mines are an important infrastructure for securing resources, but safety problems can arise in the course of operation. Recently, the mining process is very complicated due to the large scale and mechanization. Therefore, it is necessary to construct accurate geospatial information on mine for systematic and safe mine operation. The geospatial information construction using the existing total station has a disadvantage that a lot of work time is required because the target must be collimated and measured. In this study, the data of the mines were acquired with the total station and the 3D laser scanner, and the mine spatial information was constructed by using the shape based registration method. By using the static scanner data of some area applying the reference point surveying result of the total station, it was possible to construct the accurate result on the wide area acquired by the mobile scanner effectively. Also, the accuracy of the constructed geospatial information was evaluated and the deviation of mean 0.083m was shown. Point cloud products constructed through the research can contribute to the efficiency improvement of mine management by enabling quantitative analysis such as visualization of mine shape, distance, area and slope, and automation of drawing creation for cross section shape.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.52 no.5
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• pp.461-467
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• 2019

Several studies investigating the prevention and treatment of external parasites in farmed olive flounder Paralichthys olivaceus have found that the anticoccidial agent toltrazuril sulfone is an effective antiparasitic. Prior to undertaking a full-scale study, we developed analytical methods to detect the levels of toltrazuril and toltrazuril sulfone in farmed flounder samples using liquid chromatography-electrospray ionization tandem mass spectrometry (LC-MS/MS). This analysis showed that LC-MS/MS changed the mobile phase and collision energy of toltrazuril and toltrazuril sulfone. This was validated using established conditions. Sample pre-treatment for this process involved extraction with dichloromethane and purification by liquid-liquid extraction in formic acid, acetonitrile, and h-hexane, followed by determination of all compounds by LC-MS/MS. Separation was achieved within 10 min by gradient elution using a Capcell Pak C18 ($3.0{\mu}m$, $100{\times}2.0mm$) analytical column (Shiseido UG 120V) with a mixture of 0.1% (v/v) formic acid and acetonitrile. Multiple reaction monitoring was used for selective detection of toltrazuril and toltrazuril sulfone. This method yields satisfactory results for linearity, precision, and limits of quantification. Therefore, the method established in our study will serve as a basis for further research on parasite control by toltrazuril and toltrazuril sulfone.

• Convergence Security Journal
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• v.20 no.4
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• pp.27-34
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• 2020

Recently, the fourth industrial revolution technology has not only changed everyday life greatly through technological development, but has also become a major keyword in the establishment of defense policy. In particular, Internet of Things, cloud, big data, mobile and cybersecurity technologies, called ICBMS, were selected as core leading technologies in defense information policy along with artificial intelligence. Amid the growing importance of the fourth industrial revolution technology, research is being carried out to develop the C4I system, which is currently operated separately by the Joint Chiefs of Staff and each military, including the KJCCS, ATCIS, KNCCS and AFCCS, into an integrated system in preparation for future warfare. This is to solve the problem of reduced interoperability for joint operations, such as information exchange, by operating the C4I system for each domain. In addition, systems such as the establishment of an integrated C4I system and the U.S. military's Risk Management Framework (RMF) are essential for efficient control and safe operation of weapons systems as they are being developed into super-connected and super-intelligent systems. Therefore, in this paper, the intelligent cyber threat detection, management of users' access to information, and intelligent management and visualization of cyber threat are presented in the future C4I system based on big data/cloud.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.283-290
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• 2022

In this study, an IoT inspection system that can be linked with a server was developed using a gas timer and ESP-01 Wi-Fi module installed on a gas valve in the home. The server environment of the gas leak IoT inspection system was installed with APM (Apache, PHP, MySQL) to collect gas pressure data by generation so that leakage checks could be performed. In order to control the gas leak IoT inspection system, the app inventory was used to manage the gas leak check value in real time. In addition, user convenience has been enhanced so that membership management, WiFi settings, and leakage check values can be checked through mobile apps. In order to manage subscribers by region, the user list was checked by logging in in in the administrator mode so that the information on whether or not the leak test was conducted and the results could be provided. In addition, when the user presses the gas leak check button, the pressure is automatically checked, and the measured value is stored in the server, and when a gas leak occurs, the leakage check is performed after alarm and repair so that it can be used if normal. In addition, in order to prevent overlapping membership, membership management can be performed based on MAC addresses.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.27-30
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• 2022

The plasma process, which has many advantages in terms of efficiency and environment compared to conventional process methods, is widely used in semiconductor manufacturing. Plasma Sheath is a dark region observed between the plasma bulk and the chamber wall surrounding it or the electrode. The Plasma Sheath Monitoring Sensor (PSMS) measures the difference in voltage between the plasma and the electrode and the RF power applied to the electrode in real time. The PSMS data, therefore, are expected to have a high correlation with the state of plasma in the plasma chamber. In this study, a model for predicting the state of nitrogen ions in the plasma chamber is training by a deep learning machine learning techniques using PSMS data. For the data used in the study, PSMS data measured in an experiment with different power and pressure settings were used as training data, and the ratio, flux, and density of nitrogen ions measured in plasma bulk and Si substrate were used as labels. The results of this study are expected to be the basis of artificial intelligence technology for the optimization of plasma processes and real-time precise control in the future.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.37-47
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• 2022

The purpose of this study was to research the effects of Korean beginner learners' perception of vocabulary learning and comparison of vocabulary learning methods between the use of vocabulary application Classcard and rote learning with writing. This experiment was conducted by 24 beginner Korean learners who were at the 1st and 2nd level in Institute of Korean Language of D University. These students were dividing into 2 groups for the test - 12 in the control group and 12 in the experimental group. They took the vocabulary tests which were designed for examining the differences between before and after learning. Study results showed that vocabulary learning using the application Classcard significantly improved learners' vocabulary scores which were statistically significant. These results demonstrated that the method using Classcard has a greater learning effect than the method of rote learning with writing. Furthermore, the survey showed that learners did not feel uncomfortable in learning vocabulary using the application Classcard, and they thought it was one of the ways to learn vocabulary easily, which means that vocabulary learning using this application has a positive effect on learners' learning achievement. This study is significant in that it suggested a vocabulary learning method that can increase learners' interest and vocabulary by proving the effect of vocabulary learning using applications.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.49 no.4
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• pp.291-298
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• 2023

In this study, a quantitative analysis method was developed using high-performance liquid chromatography (HPLC) to analyze the content of ceramide NP in lotion, cream, and cleanser formulations in cosmetics. The analysis was performed using a C18 column, and the mobile phase was set at a ratio of 70 : 30 for acetonitrile and methanol, the flow rate was set to 0.8 mL/min, and the column temperature was set to 20 ℃. The method was verified by analyzing specificity, linearity, limit of detection, limit of quantitation, accuracy, and precision in accordance with the ICH guidelines. As a result of validating the method, the linearity of the calibration curve was excellent (R² = 0.99984). The accuracy of the lotion, cream, and cleanser formulations was confirmed with a recovery rate ranging from 95.11% to 100.48%. The precision analysis showed a low relative standard deviation (RSD) of less than 0.26%. The limit of detection was 0.902 ㎍/mL, and the limit of quantitation was 2.733 ㎍/mL. Through this quantitative analysis method of ceramide NP applied in cosmetics, it is expected to assist in the quality control of products by enabling measurement even when it is difficult to separate the main peak due to the influence of interfering substances.