• Journal of the Korean Society for information Management
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• v.18 no.3
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• pp.159-178
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• 2001

This paper presents an automatic text summarization model which selects representative sentences from sentence clusters to create a summary. Summary generation experiments were performed on two sets of test documents after learning the optimum environment from a training set. Centroid clustering method turned out to be the most effective in clustering sentences, and sentence weight was found more effective than the similarity value between sentence and cluster centroid vectors in selecting a representative sentence from each cluster. The result of experiments also proves that inverse sentence weight as well as title word weight for terms and location weight for sentences are effective in improving the performance of summarization.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.361-366
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• 2018

For each character constituting learning data, different fonts are added in pixel unit to create MASK, and then pixel values belonging to the MASK are divided into three groups. The experimental data are modified into skeletal forms, and then regional boundary operation is used to create a boundary that distinguishes the background region adjacent to the skeleton of the character from the background of the modified experimental data. Discordance values between the modified experimental data and the MASKs are calculated, and then the MASK with the minimum value is found. This MASK is selected as a finally recognized result for the given experiment data. The recognition algorithm using skeleton of the character and the regional boundary operation can easily extend the learning data set by adding new fonts to the given learning data, and also it is simple to implement, and high character recognition rate can be obtained.

• Convergence Security Journal
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• v.23 no.5
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• pp.3-8
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• 2023

The Fourth Industrial Revolution is bringing about great changes in many industrial fields, and among them, active research is being conducted on convergence technology using artificial intelligence. Among them, the demand is increasing day by day in the field of object recognition using artificial intelligence and digital transformation using recognition results. In this paper, we proposed an optimal learning model construction method to accurately recognize letters, symbols, and lines in images and save the recognition results as files in a standardized format so that they can be used in simulations. In order to recognize letters, symbols, and lines in images, the characteristics of each recognition target were analyzed and the optimal recognition technique was selected. Next, a method to build an optimal learning model was proposed to improve the recognition rate for each recognition target. The recognition results were confirmed by setting different order and weights for character, symbol, and line recognition, and a plan for recognition post-processing was also prepared. The final recognition results were saved in a standardized format that can be used for various processing such as simulation. The excellent performance of building the optimal learning model proposed in this paper was confirmed through experiments.

• Proceedings of the Korea Database Society Conference
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• 2002.10a
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• pp.403-416
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• 2002

워크플로우 관리 시스템은 정부나 기업과 같은 조직의 작업을 처리하기 위한 비즈니스 프로세스를 컴퓨터를 기반으로 자동화함으로서 작업의 효율을 높이고 비용을 절감한다. 현재에 이르러 이런 워크플로우 시스템을 사용하는 조직들이 점차 거대화되어 가고 네트워크의 발달과 인터넷의 출현으로 인하여 워크플로우 시스템이 처리하여야 하는 작업의 수와 고객과 작업자 수 등이 빠른 속도로 증가하는 추세이다. 이런 추세에서 워크플로우 시스템은 거대 조직 환경에 적합한 워크플로우 시스템 아키텍쳐를 필요하게 된다. 이에 본 논문은 거대 조직 환경을 관리할 수 있는 워크플로우 관리 시스템으로 워크케이스 기반의 초대형 워크플로우 시스템의 아키텍쳐를 설계 및 구현 하고자 한다. 그리고 워크플로우 시스템 아키텍쳐를 분류, 분석하여 장단점을 가려내어 이를 기반으로 워크플로우 시스템 아키텍쳐의 성능을 예측하여 워크케이스 기반 워크플로우 시스템 아키텍쳐가 본 논문에서 제안하는 초대형 워크플로우 시스템의 아키텍쳐라는 것을 예측하여 본다. 또한 초대형 워크플로우 시스템을 위하하부 구조로 EJB(Enterprise Java Beans)를 사용하고 사용 이유를 기술한다. 본 논문에서는 이런 워크케이스 기반의 초대형 워크플로우 시스템 아키텍쳐를 위하여 개념적인 단계와 설계 단계, 구현 단계로 나누어 설계 및 구현을 하며 개념적인 단계에서는 워크케이스 기반 워크플로우 시스템 아키텍쳐에 대하여 상세히 기술하고 설계단계에서는 전체적인 기능 정의와 초대형 워크플로우 시스템의 구조를 설계한다. 그리고 구현 단계에서는 워크케이스 기반의 초대형 워크플로우 시스템 아키텍쳐를 실제 구현하기 위한 환경을 선택하고 구현 단계의 문제점들과 해결책을 기술한다. 다 솔레노이드방식 감압건조장치로 건조한 표고버섯으로 품위에 대한 유의성 검증결과, 표고버섯의 경우 온도별로는 색택과 복원률, 건조실 내부 압력별로는 수축률, 복원률에서 유의차가 있는 것으로 나타났다. 라. 본 연구에서 구명된 감압건조특성을 기초로 하여 배치식 감압건조기를 설계 제작에 활용하고자 한다.ational banks. Several financial interchange standards which are involved in B2B business of e-procurement, e-placement, e-payment are also investigated.. monocytogenes, E. coli 및 S. enteritidis에 대한 키토산의 최소저해농도는 각각 0.1461 mg/mL, 0.2419 mg/mL, 0.0980 mg/mL 및 0.0490 mg/mL로 측정되었다. 또한 2%(v/v) 초산 자체의 최소저해농도를 측정한 결과, B. cereus, L. mosocytogenes, E. eoli에 대해서는 control과 비교시 유의적인 항균효과는 나타나지 않았다. 반면에 S. enteritidis의 경우는 배양시간 4시간까지는 항균활성을 나타내었지만, 8시간 이후부터는 S. enteritidis의 성장이 control 보다 높아져 배양시간 20시간에서는 control 보다 약 2배 이상 균주의 성장을 촉진시켰다.차에 따른 개별화 학습을 가능하게 할 뿐만 아니라 능동적인 참여를 유도하여 학습효율을 높일 수 있을 것으로 기대된다.향은 패션마케팅의 정의와 적용범위를 축소시킬 수 있는 위험을 내재한 것으로 보여진다. 그런가 하면, 많이 다루어진 주제라 할지라도 개념이나 용어가 통일되지 않고

• Journal of The Korean Association For Science Education
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• v.26 no.2
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• pp.200-211
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• 2006

The purpose of this study was to analyze Korean middle school student achievement in environmental science based on the TIMSS 2003 (Trends in International Mathematics and Science Study), a student comparison of 46 participating nations. Korea ranked the fourth with a mean score of 554 in environmental science. However, all 3 environment science topics assessed in TIMSS are not included in the Korean science curriculum through 8th grade, even though they are included in most other participating nations' curricula. The average percent correct of items was analyzed according to the main topic, the item type and the cognitive domain. Items that showed differences between the average percent correct of Korea and the international average as well as differences between the average percent correct of boys and girls were further analyzed. Results revealed that Korean students performed better than the international average, especially in 'use and conservation of natural resources', multiple-choice items, and items requiring 'factual knowledge'. Also, male students demonstrated significantly higher achievement than female students. On the other hand, Korean students showed relatively lower achievement in constructed-response items, items that contained content they had not learned in science lessons and items requiring descriptions of the uses and effect of science and technology. Moreover, Korean student lacked understanding about acid rain, global warming, and ozone layer destruction. Korean female students showed relatively lower environmental conceptions and lower performance on items requiring data analysis than Korean male students. On the basis of these results, this study suggested that topics of environmental science be included in the science curriculum and taught in the science classroom to help middle school students more fully comprehend environmental issues.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1062-1071
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• 2003

A genre-based classification means classifying documents by the purpose for which they were written, not by the semantics or subject areas. Most genre classifying methods in the past were based on the existing documents categorization algorithms and ineffective for feature selections, resulting in low quality classification results. In this research, we propose a new method for automatic classification of digital documents by genre. The genre classifier we developed uses the deviation statistic between the genre-revealing term frequencies and between the subject-revealing term frequencies within a genre. We collected Web documents to evaluate the proposed genre classification method. The experimental results show that the proposed method outperforms a direct application of a kai-square feature selection and bayesian classifier often used for subject classification by proving an excellent accuracy of about 30 percent.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.41-55
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• 1995

In this paper, an optimal idenfication method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together wlth optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzzy-neural networks(FNNs) are tuned automatically using improved modified complex method and modified learning algorithm. For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activateti sluge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.9-15
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• 2023

Existing simple statistics-based recommendation systems rely solely on students' course enrollment history data, making it difficult to identify classes that match students' preferences. To address this issue, this study proposes a personalized major subject recommendation system based on deep reinforcement learning (DRL). This system gauges the similarity between students based on structured data, such as the student's department, grade level, and course history. Based on this information, it recommends the most suitable major subjects by comprehensively considering information about each available major subject and evaluations of the student's courses. We confirmed that this DRL-based recommendation system provides useful insights for university students while selecting their major subjects, and our simulation results indicate that it outperforms conventional statistics-based recommendation systems by approximately 20%. In light of these results, we propose a new system that offers personalized subject recommendations by incorporating students' course evaluations. This system is expected to assist students significantly in finding major subjects that align with their preferences and academic goals.

• Korean Journal of School Psychology
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• v.17 no.3
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• pp.287-306
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• 2020

The purpose of this study was to investigate the effect of motivation promotion on task performance. Unlike most previous studies that have used survey methods, this study examined the effects of motivation promotion on arithmetic and linguistic task performances under experimental conditions. Effort-inducing instructions and positive feedbacks were used to promote participants' motivation. Experiments 1 and 2 examined the effect of effort-inducing instructions and positive feedback on participants' autonomous selection of task difficulty when performing arithmetic and linguistic tasks. The results of the both experiments showed that the experimental group which received effort-inducing instructions and positive feedback chose more difficult task than the control group did. Experiment 3 examined whether motivation promotion enhances task performance and task persistence. The experimental group was more accurate and persistent than the control group. The results of the current study offer experimental evidence suggesting that activating intrinsic motivation through motivation promotion improves attitudes toward tasks and task performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.200-208
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• 2003

This paper proposes a separation and feature extraction of image signals using the independent component analysis(ICA) based on neural networks of efficient learning rule. The proposed learning rule is a hybrid fixed-point(FP) algorithm based on secant method and momentum. Secant method is applied to improve the performance by simplifying the 1st-order derivative computation for optimizing the objective function, which is to minimize the mutual informations of the independent components. The momentum is applied for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution. The proposed algorithm has been applied to the composite images generated by random mixing matrix from the 10 images of $512\times512$-pixel. The simulation results show that the proposed algorithm has better performances of the separation speed and rate than those using the FP algorithm based on Newton and secant method. The proposed algorithm has been also applied to extract the features using a 3 set of 10,000 image patches from the 10 fingerprints of $256\times256$-pixel and the front and the rear paper money of $480\times225$-pixel, respectively, The simulation results show that the proposed algorithm has also better extraction speed than those using the another methods. Especially, the 160 basis vectors(features) of $16\times16$-pixel show the local features which have the characteristics of spatial frequency and oriented edges in the images.