• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.3
• /
• pp.159-168
• /
• 2021

As the information age enters, the use of biometrics using the body is gradually increasing because it is very important to accurately recognize and authenticate each individual's identity for information protection. Among them, finger vein authentication technology is receiving a lot of attention because it is difficult to forge and demodulate, so it has high security, high precision, and easy user acceptance. However, the accuracy may be degraded depending on the algorithm for identification or the surrounding light environment. In this paper, we designed and manufactured a side-type finger vein recognizer that is highly versatile among finger vein measuring devices, and authenticated using the deep learning model of DenseNet-201 for high accuracy and recognition rate. The performance of finger vein authentication technology according to the influence of the infrared light source used and the surrounding visible light was analyzed through simulation. The simulations used data from MMCBNU_6000 of Jeonbuk National University and finger vein images taken directly were used, and the performance were compared and analyzed using the EER.

• Journal of Korean Academy of Nursing
• /
• v.51 no.4
• /
• pp.478-488
• /
• 2021

Purpose: This study aimed to develop and examine the effects of a prenatal program on environmental health behavior using cartoon comics among Korean pregnant women. Methods: This study used a non-equivalent control group pre-test/post-test design. The program used cartoon comics to explore environmental health behaviors during pregnancy. The program consisted of the following four components: environmental toxicants during pregnancy, avoiding particulate matter during pregnancy, environmental toxicants during baby care, and making a healthy environment for children. In total, 35 pregnant women participated in the study: 18 in the experimental group and 17 in the control group. Data collection and program adaptation were conducted between November 3, 2020 and January 19, 2021. The effect of the prenatal education program was evaluated by t-test and repeated measures ANOVA. Results: Learning experience (t = - 2.35, p = .025), feasibility (t = - 2.46, p = .019), satisfaction (t = - 2.23, p = .032) were higher in the experimental group than in the control group in the first post-test. Feasibility (t = - 2.40, p = .022) was higher in the experimental group than in the control group in the second post-test. Repeated-measures ANOVA showed significant interactions between time and group in environmental susceptibility (F = 9.31, p < .001), self-efficacy (F = 3.60, p = .033), and community behavior (F = 5.41, p = .007). Conclusion: This study demonstrates the need for a prenatal education program to promote environmental health perceptions and behavior during pregnancy. We suggest a prenatal class adopting the creative cartoon comics to promote the maternal environmental health behaviors.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.4
• /
• pp.541-547
• /
• 2022

Realistic education using virtual reality compared to traditional learning can enhance students' understanding of knowledge through immersion and interaction. In previous studies, VR education is mainly focused on experiences, and it is difficult to find its applications for environmental education. Environmental issues are a global problem, and environmental education is essential for the future. In this research, we developed an immersive virtual reality-based environmental education simulation designed to help students recognize the importance of environmental education and participate in environmental-friendly actions. This simulation is based on the virtual ecosystem model, which maintains a casual relationship among environmental factors, spatio-temporal connection, and persistent state. Users intuitively recognize environmental problems and is motivated to solve the problem while experiencing the results of interaction related to environmental factors in virtual environment.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.4
• /
• pp.67-74
• /
• 2022

The purpose of this study was to develop and apply a mental simulation practice program using standardized patients for nursing students and to verify the effectiveness. This study is a single-group pre- and post-design study, and a structured questionnaire was provided to 186 nursing students at a university in J for data collection. The collected data were analyzed using SPSS/WIN 27.0 program. As a result of the study, the mental simulation practice education program using standardized patients showed the subjects' communication ability (t=-2.575, p=001), learner self-efficacy (t=-2.228, p=.026) and problem-solving ability (t=-2.298, p=.017) was found to be effective. As a result of this study, it is necessary to develop and apply a simulation practice education program that creates an environment similar to the actual situation and applies various cases using the necessary resources to improve the field adaptation ability of nursing students.

• The Journal of Industrial Distribution & Business
• /
• v.13 no.6
• /
• pp.19-36
• /
• 2022

Purpose: The purpose of this study is to analyze the effects of COVID 19. The effects of COVID 19 are grouped into 5; economic stress, mental stress, health stress, task concern, self-confidence. We introduce the concept of personal ambidexterity that is necessary power for cabin crews to provide appropriate and efficient service to passengers. Ambidexterity consists of exploiting existing resources to sustain and exploring the new including method of performing task, customer, market etc. The former is necessary to maintain present condition while the latter is necessary to prepare for the future. Also motive is considered as a stimulating factor for task. Previous studies show that motive affects ambidexterity and we try to analyze whether COVID 19 effects influence this relationship. Research design, data, and methodology: Considering the relationship between the variables, we designed to measure the influence of the effects of COVID 19 by analyzing the moderating effects of them. For empirical analysis we distributed survey questionnaire and collected. Total of 361 samples are used fo the analysis. For analysis program, SPSS version 23 was used. Regression analysis and moderating effect analysis were conducted. Results: Study results show that first, the variables of economic stress, mental stress, health stress, task concern, self confidence affects personal ambidexterity and service provision. Also ambidexterity affects service provision significantly. Among COVID 19 effects, economic stress, task concern, and self confidence has moderating effects. On the other hand, new work environment does not have moderating effect. Conclusions: In conclusion, the effects of COVID 19 are wide and various. Among them the most serious effect is that COVID 19 is depriving workers of self confidence and passion toward the work. To remedy stresses and restore self confidence and passion, each worker should make his/her own efforts, such as, learning more to become more competitive, also firms should do make efforts to protect employees and to rebuild trust between firm and employees in every respect. Especially firms should realize that economic stress can be treated by economic compensation as the situation turns to normal but trust as well as self confidence and passion is not easy to restore.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.25 no.5
• /
• pp.1-13
• /
• 2022

The damages caused by landslides are increasing worldwide due to climate change. In Korea, damages from landslides occur frequently, making it necessary to develop the effective response strategies. In particular, there is a lack of countermeasures against landslides in cultural heritage areas. The purpose of this study was to spatially analyze the relationship between Buyeo-gun's cultural heritage and landslide susceptible areas in Buyeo-gun, Chungcheongnam-do, which has a long history. Nine spatial distribution models were used to evaluate the landslide susceptibility, and the ensemble method was applied to reduce the uncertainty of individual model. There were 17 cultural heritages belonging to the landslide susceptible area. As a result of calculating the area ratio of the landslide susceptible area for cultural heritages, the cultural heritages with 100% of the area included in the landslide susceptible area were "Standing statue of Maae in Hongsan Sangcheon-ri" and "Statue of King Seonjo." More than 35% of "Jeungsanseong", "Garimseong", and "Standing stone statue of Maitreya Bodhisattva in Daejosa Temple" belonged to landslide susceptible areas. In order to effectively prevent landslide damage, the application of landslide prevention measures should be prioritized according to the proportion belonging to the landslide susceptible area. Since it is very difficult to restore cultural properties once destroyed, preventive measures are required before landslide damage occurs. The approach and results of this study provide basic data and guidelines for disaster response plans to prevent landslides in Buyeo-gun.

• Geomechanics and Engineering
• /
• v.28 no.6
• /
• pp.599-611
• /
• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• Journal of Platform Technology
• /
• v.11 no.2
• /
• pp.15-25
• /
• 2023

This study analyzes the limitations of the insider threat datasets used for insider threat detection research and compares and analyzes the solution-based insider threat data with public insider threat data using a security solution to overcome this. Through this, we design a data format suitable for insider threat detection and implement a system that can safely share insider threat information between different institutions and companies using blockchain technology. Currently, there is no dataset collected based on actual events in the insider threat dataset that is revealed to researchers. Public datasets are virtual synthetic data randomly created for research, and when used as a learning model, there are many limitations in the real environment. In this study, to improve these limitations, a private blockchain was designed to secure information sharing between institutions of different affiliations, and a method was derived to increase reliability and maintain information integrity and consistency through agreement and verification among participants. The proposed method is expected to collect data through an outflow threat collector and collect quality data sets that posed a threat, not synthetic data, through a blockchain-based sharing system, to solve the current outflow threat dataset problem and contribute to the insider threat detection model in the future.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.9
• /
• pp.93-104
• /
• 2023

In modern society, the service method is tended to prefer the non-face-to-face method rather than the face-to-face method. However, services that recommend products such as shoes will inevitably be face-to-face method. In this paper, for the purpose of non-face-to-face service, a system that a foot size is automatically measured and some shoes are recommended based on the measurement result is proposed. To analyze the performance of the proposed method, size measurement error rate and recommendation performance were analyzed. In the recommendation performance experiments, a total of 10 methods for similarity calculation were used and the recommendation method with the best performance among them was applied to the system. From the experiments, the error rate the foot size was small and the recommendation performance was possible to derive significant results. The proposed method is at the laboratory level and needs to be expanded and applied to the real environment. Also, the recommendation method considering design could be needed in the future work.

• Journal of Advanced Navigation Technology
• /
• v.25 no.6
• /
• pp.576-581
• /
• 2021

Compared to a low concentration, a high concentration clearly entails limitations in terms of predictive performance owing to differences in its frequency and environment of occurrence. To resolve this problem, in this study, an artificial intelligence neural network algorithm was used to classify low and high concentrations; furthermore, two prediction models trained using the characteristics of the classified concentration types were used for prediction. To this end, we constructed training datasets using weather and air pollutant data collected over a decade in the Cheonan region. We designed a DNN-based classification model to classify low and high concentrations; further, we designed low- and high-concentration prediction models to reflect characteristics by concentration type based on the low and high concentrations classified through the classification model. According to the results of the performance assessment of the prediction model by concentration type, the low- and high-concentration prediction accuracies were 90.38% and 96.37%, respectively.