• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.107-114
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• 2020

This study was conducted to examine the effects of simulation-based education on the clinical reasoning competence, clinical competence, and satisfaction with simulation experience (SSE). The research design was one group pretest-posttest. Study participants were 89 third-year nursing students from C University in G city, who were engaged the simulation-based education for eight weeks from August to October 2019. Learning scenario titles were blood transfusion reaction patient care with postoperative total hip replacement, hypoglycemia patient care with diabetes mellitus, and hyperkalemia patient care with liver cirrhosis. The data were analyzed by paired t-test using SPSS Win 23.0 program. After applying simulation-based education, nursing students' clinical reasoning competence (t=-17.082, p<.001) and clinical competence(t=-18.40, p<.001) improved significantly. SSE score was 4.65 out of 5 points. The results indicate that the simulation-based education in this study gave the students the experience of providing qualified and secure nursing care under conditions similar to those in the real clinical field. To improve the clinical reasoning competence and clinical competence of nursing students, various cases scenarios are developed and simulation-based education should be applied to more subjects in the nursing curriculum.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.646-653
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• 2016

Outbound market is a rapidly growing global industry, and has evolved into a 11 trillion won trade. A lot of recommender systems, which are based on collaborative and content filtering, target the existing purchase log or rely on studies based on similarity of products. These researches are not highly efficient as data was not obtained in advance, and acquiring the overwhelming amount of data has been relatively slow. The characteristics of an outbound product are that it should be purchased at least twice in a year, and its pricing should be in the higher category. Since the repetitive purchase of a product is rare for the outbound market, the old recommender system which profiles the existing customers is lacking, and has some limitations. Therefore, due to the scarcity of data, we suggest an improved customer-profiling method using web usage mining, algorithm of association rule, and rule-based algorithm, for faster recommender system of outbound product.

• Korea Science and Art Forum
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• v.37 no.5
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• pp.239-248
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• 2019

This study started from the need to improve one of shortcomings of synthetic PVAc adhesives - potential physical harm and environmental hazards to the workers or their users. As a matter of fact, PVAc adhesives are currently mainly used because of their convenience and economy for the production of woodcrafts. The purpose of this study was to develop natural adhesives through research on natural adhesives in step with the current increase of societal attention to environmental friendliness and rapid surge in their demand in the face of such problems. So, the study attempted research on the bases to develop natural adhesives for woodcraft, using cellulose extracts from wood - natural adhesive material. The findings of the study were as follows. Firstly, natural adhesives showed the improved effect in the field of adhesive strength, a basic physical property by 0.2 - 4 times compared with the existing materials and the study confirmed they had the similar or stable pH value. Besides, they had good reversibility, demonstrating their basic physical property as a natural adhesive for woodcraft. While, their durability to ultraviolet ray degradation also showed an excellent result value being better by 1.5 - 8.5 times than the existing materials. The study expects natural adhesives with improved and better performances compared with the existing materials could be developed, if further research on adhesive strength, antibiosis, conservative property were to continue by developing refinery technology for cellulose extracts from wood and rendering the functionality to them.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.140-146
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• 2021

As the environment using intelligent IoT devices increases, various studies are underway to ensure the integrity of information sent and received from intelligent IoT devices. However, all IoT information generated in heterogeneous environments is not fully provided with reliable protocols and services. In this paper, we propose an intelligent-based multi-blockchain model that can extract only critical information among various information processed by intelligent IoT devices. In the proposed model, blockchain is used to ensure the integrity of IoT information sent and received from IoT devices. The proposed model uses the correlation index of the collected information to trust a large number of IoT information to extract only the information with a high correlation index and bind it with blockchain. This is because the collected information can be extended to the n-tier structure as well as guaranteed reliability. Furthermore, since the proposed model can give weight information to the collection information based on blockchain, similar information can be selected (or bound) according to priority. The proposed model is able to extend the collection information to the n-layer structure while maintaining the data processing cost processed in real time regardless of the number of IoT devices.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.63-72
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• 2022

With the advent of cryptocurrencies such as Bitcoin in 2009, the paradigm of money, a means of payment, has been changing significantly. And it has a great impact on our daily lives. Thus central banks have attempted various analyzes on the issuance and impact of digital currencies including electronic payments but a study on which issuance method is suitable is insufficient. In this study, the issuance of digital currency was analyzed compared to the electronic payments which are currently used. As a result, the account-based model did not show any significant differences from the current RTGS(real-time gross settlement systems) and retail payment systems. But the token-based model is expected that it can improve the efficiency of finance and induce technological innovation in the financial field. However, it was analyzed that this model would weaken the intermediary function of financial institutions such as loans due to the characteristics of digital signature technology. In addition, in order to protect consumers against security attacks such as hacking and phishing of CBDCs, legal and institutional supports similar to the current electronic payment method are required, and continuous technology development efforts are also required for the CBDC issuance model to maintain convenience and anonymity equivalent to cash.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.1-9
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• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.1-11
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• 2024

Bridges crack and become damaged due to age and external factors such as earthquakes, lack of maintenance, and weather conditions. With the number of aging bridge on the rise, lack of maintenance can lead to a decrease in safety, resulting in structural defects and collapse. To prevent these problems and reduce maintenance costs, a system that can monitor the condition of bridge and respond quickly is needed. To this end, existing research has proposed artificial intelligence model that use sensor data to identify the location and extent of cracks. However, existing research does not use data from actual bridge to determine the performance of the model, but rather creates the shape of the bridge through simulation to acquire data and use it for training, which does not reflect the actual bridge environment. In this paper, we propose a bridge safety determination edge AI model that detects bridge abnormalities based on artificial intelligence by utilizing acceleration data from bridge occurring in the field. To this end, we newly defined filtering rules for extracting valid data from acceleration data and constructed a model to apply them. We also evaluated the performance of the proposed bridge safety determination edge AI model based on data collected in the field. The results showed that the F1-Score was up to 0.9565, confirming that it is possible to determine safety using data from real bridge, and that rules that generate similar data patterns to real impact data perform better.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1873-1879
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• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.61-68
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• 2024

The majority of high-speed railway bridges along the domestic Gyeongbu and Honam lines feature a PSC-box type structure with a span length ranging from 35 to 40m, which typically exhibits a first bending natural frequency of approximately 4 to 5Hz. When KTX high-speed trains transverse these bridges at speeds ranging from 290 to 310km/h, the vibration induced by the trains approaches the first bending natural frequency of the bridge. Furthermore, with the upcoming operation of a EMU-320 high-speed train and the anticipated increase in the speeds of these high-speed trains, there is a need to analyze the dynamic response of high-speed railway bridges. For this, based on measured responses from actual railway bridges, a numerical model was constructed using a numerical model updating technique. The dynamic response of the updated numerical model exhibited a strong agreement with the measured response from the actual railway bridges. Subsequently, this updated model was utilized to analyze the dynamic response characteristics of the bridges when KTX and EMU-320 trains operate at increased speeds. The maximum vertical displacement and acceleration at the mid-span of the bridges were also compared to those specified in the railway design standard with the increasing speed of KTX and EMU-320.

• Korean Journal of Construction Engineering and Management
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• v.15 no.6
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• pp.26-34
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• 2014

Highway construction is a combination of linear, repetitive, and highly equipment intensive operations. Various types of construction equipment are deployed to ensure undisrupted performance of construction, and thus productivity improvement and cost-saving can be achieved through well-thought-out planning. The selection of construction equipment is dependent upon construction sequence, site conditions, and construction methods. In the process of planning, management should consider various types of construction methods per each type of construction operation. Also, management should map out proper construction equipment operation plan that takes the construction duration and cost measures into consideration. However, limited availability of historic data from the similar types of operations has been a stumbling block to proper construction planning, making the operations performed based upon experience and intuition guided by rules-of-thumb. As a consequence, the planing phase rarely provided an adequate validity in the implementation phase. The researchers developed a process logic for each construction type that management can utilize from early phase of highway construction planning process. Moreover, derived the construction equipment combination optimized for efficiency by using the process simulation technique. The developed method is expected to be useful for the decision-making process that aims to evaluate efficiency of various process plans and to ensure optimal selection of construction equipment for highway construction projects.