• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.237-246
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• 2020

this paper, a data modeling method based on decision-making conditions is proposed for making combat and battlefield management systems to be intelligent, which are also a decision-making support system. A picture of a robot seeing and perceiving like humans and arriving a point it wanted can be understood and be felt in body. However, we can't find an example of implementing a decision-making which is the most important element in human cognitive action. Although the agent arrives at a designated office instead of human, it doesn't support a decision of whether raising the market price is appropriate or doing a counter-attack is smart. After we reviewed a current situation and problem in control & command of military, in order to collect a big data for making a machine staff's advice to be possible, we propose a data modeling prototype based on decision-making conditions as a method to change a current control & command system. In addition, a decision-making tree method is applied as an example of the decision making that the reformed control & command system equipped with the proposed data modeling will do. This paper can contribute in giving us an insight of how a future AI decision-making staff approaches to us.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.5
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• pp.99-112
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• 2020

This study empirically analyzed the relationship with major influencing factors in enhancing innovation capability of start-ups and their influence on innovation performance. If the existing innovation competency studies were analyzed from a general corporate perspective, In this study, it was analyzed from the perspective of start-up companies with less than 7 years of founding. As a result of a survey on startups, learning competency among the sub-variables of innovation competency, R&D competency and marketing competency are significant positive (+) consistent with both organizational competence related to organizational culture and organizational goals, technology commercialization competency, and close product competency. Has been shown to affect. The technical competence part does not have a significant effect on the product competency. However, it could not be interpreted that the importance of these competencies was low. This is because although technical competence did not directly affect product competency, it was analyzed as a meaningful result in relation to R&D competency. In addition, the characteristics of the company were classified into technology orientation and market orientation, and the relationship between each sub-variable was analyzed. The technical competence of a technology-oriented company did not have a significant effect on the product competency, but it was found that it had an effective effect on the R&D capacity. It is also consistent with the research findings that the initial survival rate is low as the characteristics of start-ups are often based on technology and ideas. Based on these results, There is a difference in major innovation capabilities according to the growth stage of a company. From a practical point of view, I would like to present approaches and implications for strengthening the competence of start-ups.

• Journal of Elementary Mathematics Education in Korea
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• v.18 no.1
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• pp.1-17
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• 2014

Instructions for the commutative property of multiplication at elementary schools tend to be based on checking the equality between the quantities of 'a times b 'and b' times a, ' for example, $3{\times}4=12$ and $4{\times}3=12$. This article critically examined the approaches to teach the commutative property of multiplication from Kant's perspective of mathematical knowledge. According to Kant, mathematical knowledge is a priori. Yet, the numeric exploration by checking the equality between the amounts of 'a groups of b' and 'b groups of a' does not reflect the nature of apriority of mathematical knowledge. I suggest we teach the commutative property of multiplication in a way that it helps reveal the operational schema that is necessarily and generally involved in the transformation from the structure of 'a times b' to the structure of 'b times a.' Distributive reasoning is the mental operation that enables children to perform the structural transformation for the commutative property of multiplication by distributing a unit of one quantity across the other quantity. For example, 3 times 4 is transformed into 4 times 3 by distributing each unit of the quantity 3, which results in $3{\times}4=(1+1+1){\times}4=(1{\times}4)+(1{\times}4)+(1{\times}4)+(1{\times}4)=4+4+4=4{\times}3$. It is argued that the distributive reasoning is also critical in learning the subsequent mathematics concepts, such as (a whole number)${\times}10$ or 100 and fraction concept and fraction multiplication.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.53-61
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• 2022

Recent years have drawn a great attention to generation MZ and Metaverse, due to 4th industrial revolution and the development of digital environment that blurs the boundary between reality and virtual reality. Generation MZ approaches the information very differently from the existing generations and uses distinguished communication methods. In terms of learning, they have different motivations, types, skills and build relationships differently. Meanwhile, Metaverse is drawing a great attention as a teaching method that fits traits of gen MZ. Thus, the current research aimed to investigate how to increase the use of Metaverse in Educational Technology. Specifically, this research examined the antecedents of popularity of Gather Town, a platform of Metaverse. Big data of news articles have been collected and analyzed using the Bigkinds system provided by Korea Press Foundation. The analysis revealed, first, a rapid increasing trend of media exposure of Gather Town since July 2021. This suggests a greater utilization of Gather Town in the field of education after the COVID-19 pandemic. Second, Word Association Analysis and Word Cloud Analysis showed high weights on education related words such as 'remote', 'university', and 'freshman', while words like 'Metaverse', 'Metaverse platform', 'Covid19', and 'Avatar' were also emphasized. Third, Network Analysis extracted 'COVID19', 'Avatar', 'University student', 'career', 'YouTube' as keywords. The findings also suggest potential value of Gather Town as an educational tool under COVID19 pandemic. Therefore, this research will contribute to the application and utilization of Gather Town in the field of education.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• Korean Security Journal
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• no.50
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• pp.11-32
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• 2017

The Civil Contingencies Act 2004 in the United Kingdom provides a comprehensive definition of "Emergency", calling upon the Uk's emergency management to deal with any disaster risk regardless of cause or source. Old contingency plans for civil defense and peacetime emergencies have been integrated into current integrated emergency management. In the UK, emergencies are managed by emergency services and other responders at the local level without direct involvement of central government. On top of this, a classified assessment of the risks of civil emergencies is also conducted on a regular basis, not only at the local level but also at the national level. This research looks into the Uk's emergency management system, including recent changes, and its risk assessment systems. Finally, the research draws policy implications for the development of Korea's disaster management mechanism as follows: 1) Korea should adopt an integrated emergency management system and combine civil defense with peacetime emergency planning, 2) it should create inter-operability between emergency responding organizations such as police, fire and ambulance, and finally 3) it must develop risk evaluating tools, such as a Community Risk Register and National Risk Register, both at the local and the national level. Last but not least, the UK emergency management system cannot be directly lifted from the UK and applied to risks and hazards faced by South Korea. Therefore, cross-cultural synthesis of many national approaches to emergency management is further required particularly for customizing policy to the particular needs of Korea.

• Journal of KIISE
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• v.43 no.1
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• pp.87-95
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• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.211-218
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• 2022

Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.

• Journal of Korean Home Economics Education Association
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• v.35 no.2
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• pp.133-144
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• 2023

The goal of this study was to improve environmental awareness by systematically developing a hands-on online lesson for adults on making drink bags by upcycling discarded umbrella cloth. The lesson was developed using an ADDIE model. During the analysis stage, the instructional design direction was established based on the findings of previous studies. In the design stage, the operation of practical classes in the online environment was specifically planned. The contents of education and the training time were also determined. The materials developed during the development stage included a kit and theoretical information containing images to raise awareness of environmental pollution and the significance of upcycling, as well as videos and photos. During the implementation stage, two sessions were held three months apart. A total of 36 adults participated, with 18 participants in each session. In the evaluation stage, the first session participants provided feedback on class satisfaction, which led to improvements. Positive feedbacks were received from the second session participants, who expressed satisfaction with the smooth communication and easy approaches to the learning materials. In both instances, the surveys on environmental consciousness and attitudes yielded an overall average score of 4.27, indicating a generally positive evaluation.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.