• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.472-476
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• 2007

Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.5
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• pp.425-432
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• 2009

In this study, the ability of neural network in modeling and predicting of the unsteady aerodynamic force coefficients of 2D airfoil with the data obtained from Euler CFD code has been confirmed. Neural network models are constructed based on supervised training process using Levenberg-Marquardt algorithm, combining this into genetic algorithm, hybrid genetic algorithm and the efficiency of the two cases are analyzed and compared. It is shown that hybrid-genetic algorithm is more efficient for neural network of complex system and the predicted properties of the unsteady aerodynamic force coefficients of 2D airfoil by the neural network models are confirmed to be similar to that of the numerical results and verified as suitable representing reduced models.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.181-188
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• 2024

Traditional profanity detection methods have limitations in identifying intentionally altered profanities. This paper introduces a new method based on Named Entity Recognition, a subfield of Natural Language Processing. We developed a profanity detection technique using sequence labeling, for which we constructed a dataset by labeling some profanities in Korean malicious comments and conducted experiments. Additionally, to enhance the model's performance, we augmented the dataset by labeling parts of a Korean hate speech dataset using one of the large language models, ChatGPT, and conducted training. During this process, we confirmed that filtering the dataset created by the large language model by humans alone could improve performance. This suggests that human oversight is still necessary in the dataset augmentation process.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.693-703
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• 2005

Given the local traders with pattern-based multi-predictors of stock prices, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset allocated to each recommendation of the predictors, we design an asset allocation strategy called meta policy in the reinforcement teaming framework. We utilize both the information of each predictor's recommendations and the ratio of the stock fund over the total asset to efficiently describe the state space. The experimental results on Korean stock market show that the trading system with the proposed meta policy outperforms other systems with fixed asset allocation methods. This means that reinforcement learning can bring synergy effects to the decision making problem through exploiting supervised-learned predictors.

• (The) Research of the performance art and culture
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• no.18
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• pp.481-512
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• 2009

This thesis is to investigate the effectiveness of Cinema Therapy through the aspect of vicarious learning, that is, Nayeoug's modelling after Yeonsun, and Yeonsun's modelling after The Little Mermaid, by making My Mother, the Mermaid directed by Park Heungsik. After immature Nayeoung returns from the travel into past time, she is reborn as an mature woman, and finally she can manage a happy marriage life. In Nayeoung's initiation, there is Yeonsun's initiation, in that again, there is a initiation of The Little Mermaid by Hans Christian Andersen. This work is a proper text in explaining the effectiveness of Cinema Therapy which aims at self-awareness and self-improvement because it contains some aspects of the vicarious learning through the movie. Nayeon's mother(Yeonsun), Nayeoung's father(Jinguk) have trouble, and sometimes have friendly relations. The major characters' wish and need are examined out through the analysis of the five basic needs based on reality therapy. As a result, it is found out that when their needs are fulfilled, friendly relation is formed, and when their needs are not fulfilled, unfriendly relation is formed. If their own basic desires are examined, their desires can be fulfilled through the inner control by themselves.

• Journal of Radiation Protection and Research
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• v.39 no.4
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• pp.218-221
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• 2014

Nuclear Safety Act had described the three types of licenses on radioisotope handling, such as a general license, a special license and a supervisory license. Applicants should be qualified by careers and qualifications for the education and training to acquire the licenses. In particular, the information on the estimation for the career is notified by Nuclear Safety and Security Commission(NSSC). In this paper, we suggest an improvement by analyzing survey data at the end of the education course on a license for the supervisor of radiation handling. We applied the learning evaluation to improve the education course. The level of satisfaction with the improved curriculum was compared with the existing curriculum. The improved curriculum with the learning evaluation has shown high grades of performance, i.e. above 4.0 points (full mark: 5.0 points) on the level of satisfaction and field application. The learning evaluation should be applied to the basic education course on a general license for radioisotope handling.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.349-356
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• 2006

A cartpole system is controlled by a control system consisting of two fuzzy controllers cooperating by a CMAC. Each controller uses 2 different input variables and yields the control force provided to the CMAC. The cooperation is due to training of the CMAC supervised by a judge which selects training information for the CMAC between two fuzzy controllers. The control scheme could be appreciated in terms of the tight structure of the controller, simple cooperating scheme due to the CMAC training, and accomplishing control goal that could not be attained by individual controllers.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.23-36
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• 2009

Network intrusion detection have been continuously improved by using data mining techniques. There are two kinds of methods in intrusion detection using data mining-supervised learning with class label and unsupervised learning without class label. In this paper we have studied the way of improving network intrusion detection accuracy by using LBG clustering algorithm which is one of unsupervised learning methods. The K-means method, that starts with random initial centroids and performs clustering based on the Euclidean distance, is vulnerable to noisy data and outliers. The nonuniform binary split algorithm uses binary decomposition without assigning initial values, and it is relatively fast. In this paper we applied the EM(Expectation Maximization) based LBG algorithm that incorporates the strength of two algorithms to intrusion detection. The experimental results using the KDD cup dataset showed that the accuracy of detection can be improved by using the LBG algorithm.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.93-103
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• 2021

Despite the efforts of financial authorities in conducting the direct management and supervision of collection agents and bond-collecting guideline, the illegal and unfair collection of debts still exist. To effectively prevent such illegal and unfair debt collection activities, we need a method for strengthening the monitoring of illegal collection activities even with little manpower using technologies such as unstructured data machine learning. In this study, we propose a classification model for illegal debt collection that combine machine learning such as Support Vector Machine (SVM) with a rule-based technique that obtains the collection transcript of loan companies and converts them into text data to identify illegal activities. Moreover, the study also compares how accurate identification was made in accordance with the machine learning algorithm. The study shows that a case of using the combination of the rule-based illegal rules and machine learning for classification has higher accuracy than the classification model of the previous study that applied only machine learning. This study is the first attempt to classify illegalities by combining rule-based illegal detection rules with machine learning. If further research will be conducted to improve the model's completeness, it will greatly contribute in preventing consumer damage from illegal debt collection activities.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.287-294
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• 2007

Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.