• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.131-142
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• 2021

Purpose: Clinical practicums via non-face-to-face methods were inevitable due to the COVID-19 pandemic. We developed an online program for emergency nursing care and identified the feasibility of the program and the learning achievements of students. Methods: This was a methodological study. The program was developed by three professors who taught theory and clinical practicum for adult nursing care and clinical experts. Students received four hours of video content and two task activities every week in four-week program. Real-time interactive video conferences were included. Qualitative and qualitative data were collected. Results: A total of 96 students participated in the program. The mean score for overall satisfaction with the online program was 4.72(±1.02) out of 6. Subjects that generally had high learning achievement scores were basic life support care, fall prevention, nursing documentation, infection control, and anaphylaxis care. As a result of a content analysis of 77 reflective logs on the advantages of this program, students reported that "experience in applying nursing process," "case-based learning and teaching method," and "No time and space constraints" were the program's best features. Conclusion: Collaboration between hospitals and universities for nursing is more important than ever to develop online content for effective clinical practicum.

• Hwankyungkyoyuk
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• v.7 no.1
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• pp.46-87
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• 1994

Our country has been consuming a huge amount of energy in the course of industrialization and its demand is expected to increase enormously in the future. However, the deposits of energy resources are so limited that the settlement of energy problem comes up the essential subject. To solve the energy problem, it is requested that new resources to gain energy stably should be developed and also energy should be economized and used effectively. The effective use of energy and an the wisdom of economy in energy are requested to everybody and these things should be habitualized from very young age through education. Nevertheless, almost every school in our country hasn’t been concerned about energy education. Even though they have a concern, they are very short of the energy education materials and the quality of the materials is not so good. Therefore it is very meaningful to the settlement of energy problem of the country to make the students who will lead our country to make the students who will lead our country in the future realize the seriousness of energy problem and to provide them the necessary knowledge and methods to solve this problem so that they practice those things in everyday life. Having these necessities, this research, supported by The Korea Energy Management Corporation(KEMCO), was performed for 8 months from April 17, 1994 to December 17, 1994. Many peoples participated in this study such as 30 staffs of researchers and authors, 5 staffs of photographers and illustrators, and 3 VCR program producers developing an energy education material set for middle school students that includes a printed material for student, a diskette for computer simulation, a teacher's guidebook, VCR material and its guidebook. The following main development direction was established : First, the material for student should be consisted of units that let students know the seriousness of energy problem. Second, the focus should be put on the necessary method and practice to economize energy actually in real life based on the basic knowledge learned in elementary school. Third, material for student should be consisted of modules to be student activity-oriented teaching-learning rather than lecture-oriented one. The activity, to maximize student's interests, should be presented in various forms such as experiments, investigation, play, data interpretation, computer simulation, visits, expression and appreciation, etc. To develop the energy education materials for middle school students, a research plan was made first. After literature review about domestic and foreign energy education materials, several research trips home and abroad, and discussion meetings, the basic theory of energy education such as the principle, objective, contents, teaching-learning method, and evaluation method was established. Material for student was developed through the following procedures : The activities in the existing energy education materials were analysed and were divided into four categories related to energy using places of home, school, community, and country, and which were again divided into three categories related to time of past, present, and future, Considering these division, nine modules which are structure units of material for student were chosen, Each module comprises 2-4 activities. Totally 31 activities were designed in this way. The syllabi were made out for each activity and writing was asked for to experts related to each activity after several discussions and revision. To complement the draft, another several discussions and revision were also made on it and then pictures and illustrations were asked for. All these procedures complete the material for student, titled ; Energy Inquiry of Middle School Students', which totals 129 pages and is all in color. As the manuscript of material for student was fixed, writing for teacher's guidebook was asked for to the same writers. The draft of teacher's guidebook was also complemented through the several concentrated works and discussions. Teacher's guidebook focused on the teaching-learning principle and methods of energy education and on the concrete instruction cases for effective instruction of material for student. It is organized with two parts : the one is 'general outline' which introduces theoretical contents and the other is 'details' which are practically helpful to teaching-learning. It is totally 131 pages including both 'general outline' and 'details'. The VCR material and its guidebook consist of contents that cultivate the good attitude trying to economize energy and raise student's interests with a purpose of strong motivation to recognize the necessity of economy and practice it. After establishing development direction of VCR material through discussion meetings and research trips, its script was made by relevant experts. Then the script was also reviewed two times. The drafted VCR material made by a video material developing expert was examined and modified by previews twice. After completion of VCR material, the VCR guidebook was made. All these procedures led to the development of VCR material which runs 20 minutes in VHS type. The VCR guidebook shows a production purpose of the program, structure of contents, evaluation methods, and contents of the program in detail to give help to instructors when they use this VCR material, When these energy education materials are used, it is desirable that the VCR material should be presented first to induce student's motive, and then material for student is introduced Since the material for student is composed of activity-oriented modules and each module is independent one another in general, and each activity is, too. the necessary module or activity can be chosen and utilized in any order according to school or class conditions. This energy education materials will contribute to the development of student's ability to solve energy problem in everyday life and teacher's ability to teach the fundamental knowledge and method in solving energy problem.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.47-55
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• 2020

This paper presents a real-time autonomous computation of shot numbers and aiming points against multiple soft targets on grounds by applying an unsupervised learning, k-mean clustering and Monte carlo simulation. For this computation, a 100 × 200 square meters size of virtual battlefield is created where an augmented enemy infantry platoon unit attacks, defences, and is scatted, and a virtual weapon with a lethal range of 15m is modeled. In order to determine damage types of the enemy unit: no damage, light wound, heavy wound and death, Monte carlo simulation is performed to apply the Carlton damage function for the damage effect of the soft targets. In addition, in order to achieve the damage effectiveness of the enemy units in line with the commander's intention, the optimal shot numbers and aiming point locations are calculated in less than 0.4 seconds by applying the k-mean clustering and repetitive Monte carlo simulation. It is hoped that this study will help to develop a system that reduces the decision time for 'detection-decision-shoot' process in battalion-scaled combat units operating Dronebot combat system.

• Journal of the Korean Chemical Society
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• v.60 no.1
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• pp.69-81
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• 2016

The purpose of this study was to find out the validation of instructional strategies using the Advanced Laboratory Equipment (ALE class) by investigating science high school students’ perception on ALE in chemistry classrooms and to consider the need for development of teaching materials on ALE class. 7 sessions of ALE including experiments with innovative equipment were developed and applied to 21 students in D Science High School. At the end of the sessions, questionnaire was given to the students. We also collected qualitative data by interviewing 9 students who participated in the questionnaire. We analyzed the data collected by In-depth interviews and students’ experimental reports. The result showed that ALE class was effective to enhance students’ understanding of learning concepts because the experimental time was shortened in real time data processing. Some students showed creative performance on solving scientific problems by using everyday materials in experimental process and developed perceptions of practical inquiry. Through this process, students’ positive attitudes and interests in science and heuristic inquiry skills were also enhanced. Developing ALE lesson materials will be helpful for students to understand science and technology and the domain of science in broader contexts.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.76-86
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• 2016

SQL Injection attacks are the most widely used and also they are considered one of the oldest traditional hacking techniques. SQL Injection attacks are getting quite complicated and they perform a high portion among web hacking. The big data environments in the future will be widely used resulting in many devices and sensors will be connected to the internet and the amount of data that flows among devices will be highly increased. The scale of damage caused by SQL Injection attacks would be even greater in the future. Besides, creating security solutions against SQL Injection attacks are high costs and time-consuming. In order to prevent SQL Injection attacks, we have to operate quickly and accurately according to this data analysis techniques. We utilized data analytics and machine learning techniques to defend against SQL Injection attacks and analyzed the execution plan of the SQL command input if there are abnormal patterns through checking the web log files. Herein, we propose a way to distinguish between normal and abnormal SQL commands. We have analyzed the value entered by the user in real time using the automated SQL Injection attacks tools. We have proved that it is possible to ensure an effective defense through analyzing the execution plan of the SQL command.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.201-207
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• 2020

In this study, the practicality of unmanned aerial vehicle photography information was identified. Therefore, a total of four consecutive surveys were conducted on the field-level survey areas among the areas subject to photography using unmanned aerial vehicles, and the changes in crop conditions were analyzed using pictures of unmanned aerial vehicles taken during each survey. It is appropriate to collect and utilize photographic information by directly taking pictures of the survey area according to the time of the on-site survey using unmanned aerial vehicles in the field layer, which is an area where many changes in topography, crop vegetation, and crop types are expected. And it turned out that it was appropriate to utilize satellite images in consideration of economic and efficient aspects in relatively unchanged rice paddies and facilities. If the survey area is well equipped with systems for crop cultivation, deep learning can be utilized in real time by utilizing libraries after obtaining photographic data for a certain area using unmanned aircraft in the future. Through this process, it is believed that it can be used to analyze the overall crop and shipment volume by identifying the crop status and surveying the quantity per unit area.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.3-14
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• 2022

Autonomous equipment requires a large amount of data from various environments. However, it takes a lot of time and cost for an experiment in a real construction sites, which are difficulties in data collection and processing. Therefore, this study aims to develop a virtual environment for autonomous tower cranes technology development and validation. The authors defined automation functions and operation conditions of tower cranes with three performance criteria: operational design domain, object and event detection and response, and minimum functional conditions. Afterward, this study developed a virtual environment for learning and validation for autonomous functions such as recognition, decision making, and control using the Unity game engine. Validation was conducted by construction industry experts with a fidelity which is the representative matrix for virtual environment assessment. Through the virtual environment platform developed in this study, it will be possible to reduce the cost and time for data collection and technology development. Also, it is also expected to contribute to autonomous driving for not only tower cranes but also other construction equipment.