• Speech Sciences
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• v.7 no.3
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• pp.167-183
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• 2000

In this paper, we propose two kinds of chaos dimensions, the fuzzy correlation and fuzzy Lyapunov dimensions, for speaker recognition. The proposal is based on the point that chaos enables us to analyze the non-linear information contained in individual's speech signal and to obtain superior discrimination capability. We confirm that the proposed fuzzy chaos dimensions play an important role in enhancing speaker recognition ratio, by absorbing the variations of the reference and test pattern attractors. In order to evaluate the proposed fuzzy chaos dimensions, we suggest speaker recognition using the proposed dimensions. In other words, we investigate the validity of the speaker recognition parameters, by estimating the recognition error according to the discrimination error of an individual speaker from the reference pattern.

• Proceedings of the SAREK Conference
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• 2005.11a
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• pp.235-240
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• 2005

Recently urban utility plants in urban areas of Korea, such as energy supply systems, municipal waste incineration systems, sewage treatment systems and so on, have caused some critical troubles, for instance the insensitive response to the seasonal or daily variation of loads, the low system efficiency and inefficient use of energy because of the large-scale system located a great distance. Therefor the design method of optimal integrated system model of various urban utility plants proposed in this study suitably to the present situation of Korea. Also, the effect analysis for the introduction of compound utility plants was studied for a new town model on a 60,000 persons scale. As the results we found that the complex plant was superior to individual urban utility plant in side of the initial investment expenses, the operating cost and other reasons.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.8
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• pp.92-103
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• 1997

This paper proposes a new gray-level image segmentation method using GA(genetic algorithm) and an ACA(adaptive clustering algorithm). The solution in the general GA can be moving because of stochastic reinsertion, and suffer from the premature convergence problem owing to deficiency of individuals before finding the optimal solution. To cope with these problems and to reduce processing time, we propose the new GBR algorithm and the technique that resolves the premature convergence problem. GBR selects the individual in the child pool that has the fitness value superior to that of the individual in the parents pool. We resolvethe premature convergence problem with producing the mutation in the parents population, and propose the new method that removes the small regions in the segmented results. The experimental results show that the proposed segmentation algorithm gives better perfodrmance than the ACA ones in Gaussian noise environments.

• Journal of the Korea Safety Management & Science
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• v.7 no.3
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• pp.17-27
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• 2005

With Balling, Loughlin and Kelloway's(2002) research, occupational safety and health literatures begin to emphasize the influence of superior and organizational context. Based on this research trend, this paper tried to review the relationship between safety-specific transformational leadership and employee safety compliance, and the moderating effect of A-type personality on such relationship. Based on the responses from 643 manufacturing workers, the results of statistical analysis showed that perceived charisma and individual consideration have affirmative effects on the employee safety compliance. The extent individual consideration impacts on employee compliance is proved to be positively influenced by A-type personality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• The Korean Journal of Financial Studies
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• v.2 no.1
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• pp.159-187
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• 1995

A two-country overlapping generations model with fiat monies is used to study international coordination of monetary policies under the flexible exchange rate system. The optimal monetary policy and the welfare of individual countries are investigated for: coordination and non-coordination cases. It is shown that the coordination is Pareto superior to the non-coordination. The countries choose more inflationary policies in the non-coordination case; the world output decreases, which depends on the degree of risk aversion.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.211-218
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• 2005

This study presents an integrated data mining model for the credit evaluation of the customers of a capital company. Based on customer information and financing processes in capital market, we derived individual models from multi-layered perceptrons(MLP), multivariate discrimination analysis(MDA), and decision tree. Further, the results from the existing models were compared with the results from the integrated model using genetic algorithm. The integrated model presented by this study turned out to be superior to the existing models. This study contributes not only to verifying the existing individual models but also to overcoming the limitations of the existing approaches.

• The Journal of Information Systems
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• v.26 no.3
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• pp.91-118
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• 2017

Purpose The purpose of this research is to investigate the effect of social capitals on organizational knowledge sharing characteristics and individual innovation activities in community of practice (CoP) of manufacturing company. Design/methodology/approach For this purpose, we divide social capitals as three dimensions, i.e. structural, relational, and cognitive dimension. Structural dimension also consists of closure and Brokerage. Relational social capital is defined as trust about colleagues, superior authorities, and organization. Then, cognitive social capital is defined as a shared understanding among individuals, such as a shared language and codes within CoP. Knowledge Sharing is defined as quantity and quality of shared knowledge. We also defines the cause and effect relationships among social capitals, organizational knowledge sharing characteristics, and individual innovation activities in CoP of manufacturing company as follows. The social capitals will have positive effects on quality of shared knowledge. Then the quality of shared knowledge will have positive effects on the individual innovation activities. This paper tested the validity of these hypothesized casual effects and the sub-hypothesized causal relationships. For the purpose, we used the Partial Least Squares (PLS) for analyzing the causal relationships. Findings Our empirical results show that social capitals of CoP mostly have effects on organizational knowledge sharing characteristics (quantity and quality of shared knowledge) and knowledge sharing activities also have effects on individual innovative activities in the workplace. In this study, these result have a significant implication that a private company will be able to gain organizational innovative performance much better by strengthening CoP supporting activities.

• The Journal of Fisheries Business Administration
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• v.50 no.2
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• pp.1-21
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• 2019

This study is to excavate superior fishing communities by applying the Ostrom (1990)'s principles of sustainable and successful use of common goods. Ostrom(1990)'s principles are (1) clearly defined boundaries (2) congruence between appropriation and provision rules and local conditions, (3) collective choice arrangements (4) monitoring (5) graduated sanctions (6) conflict-resolution mechanism (7) recognition of rights to organize by external government authorities (8) nested enterprises. The survey was carried out under the individual interview method of 15 fishing village members in 32 fishing communities with the government's fishery environment improvement and fishery creation projects. The total effective samples are 477. These data were analyzed. The analysis result shows that 24 fishing villages are selected among the 32 fishing communities in the samples, including Nanji, Sanghwang, Songseok, Sinshido, Jukyo, Jinsan, Changli, Pado, Beopsan, Rahyang, Palbong, Woongdo, Daehwang, Sapsi, Chido, Jinri, Daeri, Songgak, Joongwang, Ojii, Doripo, Doseong, Mongsan 1ri and Songnim as superior fishing villages. The results of this study have limitation that may vary depending on the rigor of the criteria in the process of deriving good fishing communities. Despite this limitation, this study has expanded existing research focused on validating the theoretical applicability of the framework through case analysis of specific fishing communities to objectively and quantitatively to many fishing communities. The results of this study are expected to contribute to the creation of conditions in which fishermen can continue to manage their fishing grounds and stand on their own feet by presenting the framework and principles for developing desirable fishing village models for the continued use of mudflat shells grounds as the common goods.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.23-32
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• 2023

E-sports, a burgeoning facet of modern sports culture, has achieved global prominence. Particularly, Aeon of Strife (AOS) games, emblematic of E-sports, blend individual player prowess with team dynamics to significantly influence outcomes. This study aggregates and analyzes real user gameplay data using statistical techniques. Furthermore, it develops and tests win-loss prediction models through machine learning, leveraging a substantial dataset of 1,149,950 individual data points and 230,234 team data points. These models, employing five machine learning algorithms, demonstrate an average accuracy of 80% for individual and 95% for team predictions. The findings not only provide insights beneficial to game developers for enhancing game operations but also offer strategic guidance to general users. Notably, the team-based model outperformed the individual-based model, suggesting its superior predictive capability.