• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.699-705
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• 2015

Thousands of glyphs are necessary for Hangul font generation. It is mandatory to generate the required glyphs before producing Hangul font. This paper, entitled "Multiple Master Glyph Algorithm", presents an process that generates a target number of glyphs automatically from a very small number of glyphs by using a combination rule setting and a glyph interpolation method. A font editor, which is able to generate Hangul glyphs or fonts, is developed based on this algorithm. The editor generates a target number of fundamental glyphs automatically by using a combination rule setting and four master glyphs, which can be set up by a user. The automatically generated glyphs can be used to generate a target font by combining KSX1001 standard Hangul 2350 characters or Unicode standard Hangul 11172 characters automatically. The efficiency of the proposed Hangul editor is analyzed quantitatively in this paper through application to several commercial typefaces.

• Composites Research
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• v.19 no.5
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• pp.12-19
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• 2006

In this paper, conceptual structural design of the main wing for a small scale WIG(Wing in Ground Effect) among high speed ship projects, which will be a high speed maritime transportation system for the next generation in Rep. of Korea, was performed. The Carbon/Epoxy material was selected for the major structure, and the skin-spar with a foam sandwich structural type was adopted for improvement of lightness and structural stability. As a design procedure for the present study, firstly the design load was estimated through the critical flight load case study, and then flanges of the front and rear spars from major bending loads and the skin and the spar webs from shear loads were preliminarily sized using the netting rule and the rule of mixture. Stress analysis was performed by a commercial FEA code, NASTRAN. From the stress analysis results for the first designed wing structure, it was confirmed that the upper skin between the front spar and the rear spar was unstable fer the buckling. Therefore in order to solve this problem, a middle spar and the foam sandwich type structure at the skin and the web were added. After design modification, the structural safety and stability for the final design feature was confirmed. In addition to this, the insert bolt type structure with eight high strength bolts to fix the wing structure to the fuselage was adopted for easy assembly and removal as well as in consideration of more than 20 years fatigue life.

• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.1
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• pp.7-17
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• 2016

The present study experimentally considers dynamic performance of large floating wave-offshore hybrid power generation platform in extreme conditions. In order to evaluate the motion performance of the large floating hybrid power generation platform, 1/50 scaled model was manufactured. A mooring line was also manufactured, and free-decay and static pull-out tests were carried out to check the mooring model. A mooring line table was introduced to satisfy the water depth, and environmental conditions were checked. Motion responses in regular waves were measured and complicated environmental conditions including wave, wind, and current were applied to see the dynamic performance in extreme/survival conditions. Maximum motion and acceleration were judged following the design criteria, and maximum offset and mooring tension were also checked based on the rule. The characteristics of hybrid power generation platform are discussed based on these data.

• Journal of The Korean Association For Science Education
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• v.23 no.3
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• pp.215-228
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• 2003

The present study is to analyze the arguments about the generation of declarative scientific-knowledge in the philosophy of science and invent a structured model of the process of scientific-knowledge generation with the types of the generated scientific-knowledge. The invented model shows that scientific-knowledge generation is a distinctive process with the processes of inductive, abductive, and deductive thinking. Furthermore, inductive process is included with observation, which is consisted of simple observation and operative observation, and rule-discovery which is involved with the processes of commonness discovery, classification, pattern discovery, and hierarchical relationship. Also, abductive process has two components. One component generates question and second component generates hypothesis in which the process consists of representing question situation, identifying experienced situation, identifying causal explicans, and generating hypothetical explicans. Finally, deductive process is involved with logical inventing test method and evaluation criteria, concrete inventing test method and evaluation criteria, evaluating hypothesis, and making conclusion.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.183-187
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• 2017

Smart phone users prefer fast reading and texting. Hence, users frequently use abbreviated sequences of words and phrases. Nowadays, abbreviations are widely used from chat terms to technical terms. Therefore, gathering abbreviations would be helpful to many services, including information retrieval, recommendation system, and so on. However, manually gathering abbreviations needs to much effort and cost. This is because new abbreviations are continuously generated whenever a new material such as a TV program or a phenomenon is made. Thus it is required to generate of abbreviations automatically. To generate Korean abbreviations, the existing methods use the rule-based approach. The rule-based approach has limitations, in that it is unable to generate irregular abbreviations. Another problem is to decide the correct abbreviation among candidate abbreviations generated rules. To address the limitations, we propose a method of generating Korean abbreviations automatically using sequence-to-sequence learning in this paper. The sequence-to-sequence learning can generate irregular abbreviation and does not lead to the problem of deciding correct abbreviation among candidate abbreviations. Accordingly, it is suitable for generating Korean abbreviations. To evaluate the proposed method, we use dataset of two type. As experimental results, we prove that our method is effective for irregular abbreviations.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.32-40
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• 2008

Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.12
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• pp.85-98
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• 1998

This paper proposes a new design method of neuro-FLC by the Lamarckian co-adaptation scheme that incorporates the backpropagation learning into the GA evolution in an attempt to find optimal design parameters (fuzzy rule base and membership functions) of application-specific FLC. The design parameters are determined by evolution and learning in a way that the evolution performs the global search and makes inter-FLC parameter adjustments in order to obtain both the optimal rule base having high covering value and small number of useful fuzzy rules and the optimal membership functions having small approximation error and good control performance while the learning performs the local search and makes intra-FLC parameter adjustments by interacting each FLC with its environment. The proposed co-adaptive design method produces better approximation ability because it includes the backpropagation learning in every generation of GA evolution, shows better control performance because the used COG defuzzifier computes the crisp value accurately, and requires small workspace because the optimization procedure of fuzzy rule base and membership functions is performed concurrently by an integrated fitness function on the same fuzzy partition. Simulation results show that the Lamarckian co-adapted FLC produces the most superior one among the differently generated FLCs in all aspects such as the number of fuzzy rules, the approximation ability, and the control performance.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.531-543
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• 2022

Integrated water resources management (IWRM) has focused on efficient response to various water related disasters by climate change. In particular, more flexible usage of conventional water resources infrastructures is expected to provide an eco-friendly water management. Multi-purpose dams and water supply dams are well known as water management facilities for securing and supplying water in drought season. Recently, based on the report '2021 multi-purpose use of hydropower dams in Han river', contribution of hydropower dams on water resources management is becoming more significant beyond the traditional role of hydropower generation. In drought conditions, the dams control water supply depending on the pre-defined drought stages. In the case of multi-purpose dams, an operation standard during drought has been already prepared and applied; however, for the hydropower dams, specific standards are not fully prepared yet in South Korea. In this study, a method for calculation of standard water storage and discharge reduction of hydropower dams according to drought stage is newly proposed reflecting the characteristics of hydropower dams. The proposed method was applied to the hydropower dams in Han river, where six hydropower dams are located. A case study of the historical droughts occurred in 2014-2017 demonstrated that the proposed hydropower dam operation rule could improve the water supply stability under severe drought conditions compared to the conventional operations. In the future, the role of hydropower dams for water resources management is expected to become more important, and this study can be widely used for water supply planning such as drought response using hydropower dams.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.