• Asia pacific journal of information systems
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• v.32 no.4
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• pp.803-829
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• 2022

With the advances in technology and the trend towards increased computer-based experiential learning in education settings, business games are being increasingly used by business educators. This article utilizes Toulmin's Sensemaking Framework to investigate the sensemaking process of business professionals to reveal how they consciously reason about the value of business games for learning complex business concepts and principles. Using the analysis of responses from 43 business professionals, our study identifies key areas where business professionals find value in business games and the limitations of using business games. First, business games are found to be an effective tool when teaching practical business skill sets to business professionals. Second, business games enhance the overall learning process in professional business training. Third, despite the advantages, some pitfalls in applying business games to practice are found. We also found sub-themes, claims, and argument patterns of how business professionals evaluate the value of business games through a grounded theory qualitative analysis method. Analysis results show several ground-warrant patterns exist in the arguments on values of business games including general principle - causal reasoning, personal experience - generalization, and personal projection - generalization. With these findings, we believe this paper contributes to the theory and practice of business game design, development, and the game playing and learning process.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.57 no.4
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• pp.479-488
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• 2024

In recent years, changes in the fishing ground environment have led to reduced catches by fishermen at traditional fishing spots and increased operational costs related to vessel exploration, fuel, and labor. In this study, we developed a deep learning model to classify the fishing activities of drift gillnet fishing boats using AIS (automatic identification system) trajectory data. The proposed model integrates long short-term memory and 1-dimensional convolutional neural network layers to effectively distinguish between fishing (throwing and hauling) and non-fishing operations. Training on a dataset derived from AIS and validation against a subset of CCTV footage, the model achieved high accuracy, with a classification accuracy of 90% for fishing events. These results show that the model can be used effectively to monitor and manage fishing activities in coastal waters in real time.

• Applied Microscopy
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• v.50
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• pp.23.1-23.9
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• 2020

We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain 𝓢 to a target domain 𝓒, where 𝓢 is for our noisy experimental dataset, and 𝓒 is for the desired clear atomic images. Noise2Atom uses two external networks to apply additional constraints from the domain knowledge. This model requires no signal prior, no noise model estimation, and no paired training images. The only assumption is that the inputs are acquired with identical experimental configurations. To evaluate the restoration performance of our model, as it is impossible to obtain ground truth for our experimental dataset, we propose consecutive structural similarity (CSS) for image quality assessment, based on the fact that the structures remain much the same as the previous frame(s) within small scan intervals. We demonstrate the superiority of our model by providing evaluation in terms of CSS and visual quality on different experimental datasets.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.109-124
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• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.48-54
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• 2008

Biochemical weapons, called as a poor nation's nuclear-weapon, are most favorable Weapons of Mass Destruction(WMD). At the beginning of war, these biochemical weapons, which can threaten the operations of our forces and cause the anxiety and chaos of people, should be used to attack our principle facilities. And these attacks might be conducted as a long term scenario over the war. Consequentially, our military training as well as civilian-military joint training have been focused on these circumstances to improve defense capability against the invasion of biochemical weapons. Add to these efforts, there have been a lot of researches to develop advanced decontaminations that can secure our troops and equipments. In this study, applicability of the water-soluble decontaminant for the contaminated aircraft was evaluated. The water-soluble decontaminant has been applied to the military stations and ground weapon systems only. According to the theoretical analyses and published papers, the water-soluble decontaminant has been shown better decontamination capability than commercial cleaner by roughly 50%. Furthermore, as a result of experiment efforts in this study, it was showed that the water-soluble decontaminant can reduce corrosion risk which is primary concern for the aircraft structures.

• Korean Journal of Health Education and Promotion
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• v.22 no.2
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• pp.19-32
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• 2005

Objectives: The purpose of this study was to examine teachers' safety education activities to determine the significant educational diagnosis variables and to identify their needs of safety education in early-child care centers based on the PRECEDE model. Methods: A total of 304 teachers in early-child care centers participated in this study selected by a multi-stage stratified sampling method considering 11 regions in Seoul, Korea. Self-report type questionnaires were posted to all teachers in 220 early-child care centers by ground mailing service and the 304 teachers completed the questionnaires. The participants' responses were anonymously coded into and analyzed in SPSS program. Results: 'Scratch or bite' was the most frequent accident type(78.3%) and the frequent accident places were 'classroom(88.8%)' and 'playground(67.8%)'. The most frequently conducted safety education activities were 'reminding children their safe behaviors at the beginning and the end of daily class' and the next was 'saving a special time for safety education.' For educational diagnosis factors, related to safety education activities, teachers' safety education activity was more frequent when teachers' safety knowledge was high(p<.001), when teachers had good application skills of their knowledge to their teaching activities(p<.001), when they had strong needs on safety training opportunities(p<.05), and their interests on safety education(p<.001). For enabling factors, class preparation by safety education guide-book review(p<.001), by development of educational materials(p<.001), and by search for the related reference (p<.001), and by participation to safety education training programs for teachers(p<.01) were the significant enabling factors on teachers' safety class activities. For the reinforcing factors, the center-wide support of safety education brochures to children (p<.001), the concerns of centers utilizing safety education specialists(p<.001), and the concerns about safety information collection out of centers(p<.001) were significant factors related with teachers' safety education activities. Conclusions: The significant educational and institutional factors on teachers' safety education activities were teachers' concerns on safety education, their interests on safety knowledge, and the strong concerns on child safety education from the centers.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.171-178
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• 2016

An aircraft in flight and ATC(: Air Traffic Controllers) working in the Ground Control Center carry out a voice communication using the radio. Voice signal to be transmitted from the aircraft is received to a plurality of terrestrial sites around the country at the same time. The ATC receives the various quality of voice signal from the aircraft depending on the distance, speed, weather conditions and adjusted condition of the antenna and the radio. The ATC carries out a voice communication with aircraft in the optimal conditions finding the best voice signal. However, the present system chooses the values of the CD(: Carrier Dectect) which is determined to be superior to, based on the input voice level, as optimal channel. Thus this system can not be seen to select the optimal channel because it doesn't consider the effect of the noise which influences on the communication quality. In this paper, after removing the noise in the voice signal, we could give the digitized information and an improved voice signal quality, so that users can select an optimal channel. By using it, when operating the training eavesdropping system or the aircraft control, we can expect prevention accident and improvement of training performance by selecting the improved quality channel.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.105-113
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• 2011

Since existing thematic maps have been made with medium- to low-resolution satellite images, they have several shortcomings including low positional accuracy and low precision of presented thematic information. Digital aerial photo image taken recently can express panchromatic and color bands as well as NIR (Near Infrared) bands which can be used in interpreting forest areas. High resolution images are also available, so it would be possible to conduct precision land cover classification. In this context, this paper implemented object-based land cover classification by using digital aerial photos with 0.12m GSD (Ground Sample Distance) resolution and IKONOS satellite images with 1m GSD resolution, both of which were taken on the same area, and also executed qualitative analysis with ortho images and existing land cover maps to check the possibility of object-based land cover classification using digital aerial photos and to present usability of digital aerial photos. Also, the accuracy of such classification was analyzed by generating TTA(Training and Test Area) masks and also analyzed their accuracy through comparison of classified areas using screen digitizing. The result showed that it was possible to make a land cover map with digital aerial photos, which allows more detailed classification compared to satellite images.