• Journal of Internet Computing and Services
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• v.5 no.2
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• pp.97-105
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• 2004

The reason that an intelligent agent cannot understand the representation of its own perception or activity is caused by the traditional syntactic approach that translates a semantic feature into a simulated string, To implement an autonomously learning intelligent agent, Cohen introduces a experimentally semantic approach that the system learns a contentful representation of physical schema from physically interacting with environment using its own sensors and effectors. We propose that negation is a meta-level schema that enables an agent to recognize its own physical schema, To improve the planner's efficiency, Graphplan introduces the control rule that manipulates the inconsistency between planning operators, but it cannot cognitively understand negation and suffers from redundancy problem. By introducing a negative function not, IPP solves the problem, but its approach is still syntactic and is inefficient in terms of time and space. In this paper, we propose that, to represent a negative fact, a positive atom, which is called opposite concept, is a very efficient technique for implementing an cognitive agent, and demonstrate some empirical results supporting the hypothesis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.342-345
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• 2014

Network intrusion detection system based on machine learning methods is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features from generally used features to detect network intrusion requires extensive computing resources. For instance, the number of possible feature combinations from given n features is $2^n-1$. In this paper, to tackle this problem we propose a optimal feature selection algorithm. Proposed algorithm is based on the local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In addition, the accuracy of clusters which obtained using selected feature components and k-means clustering algorithm is adopted to evaluate a feature assembly. In order to estimate the performance of our proposed algorithm, comparing with a method where all features are used on NSL-KDD data set and multi-layer perceptron.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.285-290
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• 2022

The 21st century of the 4th industrial revolution demands 'competency' as a new educational concept that is different from that required in the industrialization era. In accordance with these changes and demands of society, universities are being called for key competency-based education and innovation in education through this by breaking away from the existing methods in liberal arts and major education. At this point, universities are presenting a comprehensive reform of liberal arts education for various financial support projects. This study presents the case of University C regarding the reform of the liberal arts curriculum for the innovation of liberal arts education in universities. According to the research results, each university is reorganizing the liberal arts curriculum in a way that can suggest various key competencies according to the university's founding philosophy and induce key competencies in the aspect of liberal arts education. For the key competency-based liberal arts curriculum of University C, we proposed various subjects for insufficient key competency subjects, various micro-degrees were proposed to specialize liberal arts education, and meta-learning-related liberal arts subjects were presented to improve basic learning ability of the students.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.20 no.4
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• pp.85-100
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• 2009

This study suggested the expansion strategies of school library catalog considering educational context which should be used teaching and learning process. To achieve the purpose of research, this study derived educational context categories by comparing and analyzing teaching and learning related factors, information resource related factors. Also, this study analysed case system considering educational context. Based on the results, this study designed the catalog data elements as an element to be added to an existing school libraries system(DLS). The derived data element is end user(teacher, students), instructional situations (teaching method, instructional object, curriculum, evaluation type), resource type(feature, discipline, format), reading situation(contextual reading, literature topic), related materials(teacher representation, student representation).

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Journal of Gifted/Talented Education
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• v.18 no.3
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• pp.591-612
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• 2008

In order to investigate the composition and characteristics of instructional objectives in a teaching-learning material for gifted elementary students in science, 217 instructional objectives across 13 themes in 4 areas of 'energy','materials', 'life' and 'earth' were analyzed by Bloom's revised taxonomy of educational objectives. Four types of factual, conceptual, procedural and meta-cognitive knowledge in knowledge dimension were all comprised in the objectives. Conceptual knowledge was primary constituent of the objectives and the proportion of factual knowledge was the least. On the other hand, all 6 categories of 'remember', 'understand', 'apply', 'analyze', 'evaluate' and 'create' in cognitive process dimension were also comprised in the objectives. The category of 'understand' was primary constituent and that of 'remember' was the least one. While conceptual knowledge in knowledge dimension was primary constituent of the objectives in 'energy', 'materials' and 'earth' areas, procedural knowledge was the most objectives in 'life' area. The least type of knowledge was factual knowledge in all 4 areas. In cognitive process dimension, the category of 'understand' was primary constituent and that of 'remember' was the least one in all 4 areas. In conclusion, it was showed that the instructional objectives in the teaching-learning material reflected the characteristics of educational objectives for gifted students in science.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.33-42
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• 2021

Despite the importance of science education in an informal environment, the reality is that there is a lack of trend analysis research on 'Informal Science Learning (ISL)' and its effects. Therefore, the purpose of this paper is to find out the educational effects of ISL and how to use it, and to provide guidelines for future ISL research directions. This study classifies specific ISL-related papers published from 2010 to 2019 and compares them with each element of GLO used to measure the effectiveness of informal education. The fit of the analyzed data was checked for each part through SPSS and Chi-Square. In conclusion, it was found that researchers are using 'ISL' to pursue 'Knowledge and Understanding' and 'Attitudes and Values' among the five performance indicators of 'GLO'. On the other hand, 'Skills' and 'Enjoyment, Inspiration and Creativity' appear to have the least expectations, so supplementation is required in these areas in the future. In addition, this study intends to suggest a direction for informal science education-related program development and future research to various education workers.

• Korean Institute of Interior Design Journal
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• no.16
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• pp.29-35
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• 1998

The purpose of this study is to analyze the elements forming interior design and to examine dimensional relationship among the elements which form space through the comparison of the spatial language and semiotics of space for the component of interior design. In addition to that it indtends to derive the principle of design which dominate interior design and the inherent diversified meaning by comparing those elements with the square of semiotic used in semiotics. Through this comparsion the meaning of constituent forming space which can be observed through the comparsion of square of semiotic has redefined flexbility among relational system of elements and this flexible concept make the scope of environment including human being broad and enriched. This study fist of all analyzes various phenomena of social culture review semiotics meta-learning and examines back theoretical ground of semiotics which is needed for space analysis. Second of all in the area of presenting an analysis tool for meaningful analysis this report introduces the square of semiotics which was invented,. A. J. Greimas in order to analyze the meaning of literary work and defind three categories of the progressive research method for the analysis of interior design and research itself. Finally as for the analysis of meaning for interior design this report sets the space and analyzed the space in accordance with the method and research procedure. being