• Journal of the Korean Operations Research and Management Science Society
• /
• v.38 no.4
• /
• pp.11-33
• /
• 2013

We analyzed the structures and properties of the global financial market networks using social network analysis approach. The Minimum Spanning Tree (MST) lengths and networks of the global financial markets based on the correlation coefficients have been analyzed. Firstly, similar to the previous studies on the global stock indices using MST length, the diversification effects in the global multi-asset portfolio can disappear during the crisis as the correlations among the asset class and within the asset class increase due to the system risks. Second, through the network visualization, we found the clustering of the asset class in the global financial markets network, which confirms the possible diversification effect in the global multi-asset portfolio. Meanwhile, we found the changes in the structure of the network during the crisis. For the last one, in terms of the degree centrality, the stock indices were the most influential to other assets in the global financial markets network, while in terms of the betweenness centrality, Gold, Silver and AUD. In the practical perspective, we propose the methods such as MST length and network visualization to monitor the change of the correlation risk for the risk management of the multi-asset portfolio.

• Computers and Concrete
• /
• v.8 no.5
• /
• pp.583-595
• /
• 2011

This paper studied the cement efficiency factor (k factor) of high calcium Class F fly ash. This k factor represents a unit of fly ash with efficiency equivalent to k unit of cement. The high calcium Class F fly ash was used to replace cement in concrete. The modified Bolomey's law with linear relationship was used for the analysis of the result of compressive strength, cement to water ratio (c/w) and fly ash to water ratio (f/w) by using the multi-linear regression to determine the k factor and other constants in the equations. The results of analysis were compared with the results from other researcher and showed that the k factor of high calcium Class F fly ash depends on the fineness of fly ash, replacement level and curing age. While the amount of CaO content in Class F fly ash not evident. Furthermore, necessary criteria and variables for the determination of the k factor including the use of the k factor in concrete mix design containing fly ash were proposed.

• Journal of Information Technology Services
• /
• v.16 no.4
• /
• pp.149-160
• /
• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.6
• /
• pp.33-43
• /
• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.4
• /
• pp.845-854
• /
• 2016

Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.7
• /
• pp.1424-1434
• /
• 2009

DNA computing-inspired pattern classification based on the hypernetwork model is a novel approach to pattern classification problems. The hypernetwork model has been shown to be a powerful tool for multi-class data analysis. However, the ordinary hypernetwork model has limitations, such as operating sequentially only. In this paper, we propose a efficient implementing method of DNA computing-inspired pattern classifier using GPU. We show simulation results of multi-class pattern classification from hand-written digit data, DNA microarray data and 8 category scene data for performance evaluation. and we also compare of operation time of the proposed DNA computing-inspired pattern classifier on each operating environments such as CPU and GPU. Experiment results show competitive diagnosis results over other conventional machine learning algorithms. We could confirm the proposed DNA computing-inspired pattern classifier, designed on GPU using CUDA platform, which is suitable for multi-class data classification. And its operating speed is fast enough to comply point-of-care diagnostic purpose and real-time scene categorization and hand-written digit data classification.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.985-988
• /
• 1993

An alternative approach to the design of application-driven fuzzy systems is proposed. A broad class of fuzzy systems applications requires a certain fuzzy partition of the input space while it demands for simple numerical quantities. For this class, a dedicated fuzzy system archictecture is presented and a design strategy is proposed. Both the single-input/single-output and multi-input/multi-output cases are considered. Numerical analysis are complete illustrating several aspects of the proposed framework.

• The Journal of the Korea Contents Association
• /
• v.22 no.4
• /
• pp.365-378
• /
• 2022

This study attempted to understand the effect of subjective class consciousness on the quality of life of female elderly people and to verify the multi-mediated effect of expectation of living standards and depression. To this end, an analysis was conducted using the SPSS Process Macro for 8,070 female elderly people aged 55 or older in the data of the "2018 Aging Research Panel Survey 7" of the Korea Employment Information Service. As a result of the analysis, first, it was found that the higher the subjective class consciousness of the elderly women, the higher the quality of life. Second, the higher the subjective class consciousness of the elderly women, the higher the expectation of living standards and the lower the depression. Third, in the relationship between the subjective class consciousness and quality of life of female elderly, when expectations and depression of living standards were simultaneously put into the regression equation, both variables were found to have significant mediating effects. Based on these research results, practical and policy implications for improving the quality of life of the elderly women were discussed together.

• Strategy21
• /
• s.34
• /
• pp.178-206
• /
• 2014

The battlefield environment in the maritime has been changed by advanced IT technology, variation of naval warfare condition, and developed military science and technology. In addition, state-of-the-art surface combatants has become to multi-purpose battleship that is heavily armed in order to meet actively in composed future sea battlefield condition and perform multi-purpose missions as well as having capability of strategic strike. To maximize the combat strength and survivability of ship, it is not only possible for Zumwalt(DDG-1000) class combatant to conduct multi-purpose mission with advanced weapon system installation, innovative hull form and upper structure such as deckhouse, shipboard high-powered sensor, total ship computing environment, and integrated power control but it was designed so that can be installed with energy based weapon systems in immediate future. Zumwalt class combatant has been set a high value with enormous threatening surface battleship in the present, it seems to be expected that this ship will be restraint means during operation in the littoral. The advent of Zumwalt class battleship in the US Navy can be constructed as a powerful intention of naval strength building for preparing future warfare. It is required surface ship that can be perform multi-purpose mission when the trend of constructed surface combatants was analyzed. In addition, shipboard system has been continuously modernized to keep the optimized ship and maximize the survivability with high-powered detection and surveillance sensor as well as modularity of combat system to efficient operation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.2
• /
• pp.391-411
• /
• 2023

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.