• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.593-598
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• 2003

Machine learning organizes knowledge for efficient and accurate reuse. This paper is concerned with methods of concept learning from examples, which glean knowledge from a training set of preclassified ‘objects’. Ideally, training facilitates classification of novel, previously unseen objects. However, every learning system relies on processing and representation assumptions that may be detrimental under certain circumstances. We explore the biases of a well-known learning system, ID3, review improvements, and introduce some improvements of our own, each designed to yield accurate and pedagogically sound classification.

• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.1-8
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• 2003

Among many dimensional or dimensionless amplitude parameters, kurtosis and ID Factor are said to be sensitive good parameters for machine diagnosis. In this paper, a simplified calculation method for both parameters is introduced when impact vibration arise in the observed data. Compared with the past papers' results, this new method shows a good result which fit well. This calculation method is simple enough to be executed even on a pocketsize calculator and is very practical at the factory of maintenance field. This can be installed in microcomputer chips and utilized as a tool for early stage detection of the failure.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.1
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• pp.35-45
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• 2004

In explosively increasing internet environments, information security is one of the most important consideration. Nowadays, various security solutions are used as such problems countermeasure; IDS, Firewall and VPN. However, basically internet has much vulnerability of protocol itself. Specially, it is possible to establish a covert channel using TCP/IP header fields such as identification, sequence number, acknowledge number, timestamp and so on. In this Paper, we focus cm the covert channels using identification field of IP header and the sequence number field of TCP header. To detect such covert channels, we used Support Vector Machine which has excellent performance in pattern classification problems. Our experiments showed that proposed method could discern the abnormal cases(including covert channels) from normal TCP/IP traffic using Support Vector Machine.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1172-1174
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• 2002

An adaptive fuzzy P + ID controller for variable speed operation of BLDC motor drives is presented in this paper. Generally, a conventional PID controller is most widely used in industry due to its simple control structure and ease of design. However, the PID controller suffers from the electrical machine parameter variations and disturbances. To improve the tracking performance for parameter and load variations, the controller proposed in this paper is constructed by using an adaptive fuzzy logic controller in place of the proportional term in a conventional PID controller. For implementing this controller, only one additional parameter has to be adjusted in comparison with the PID controller. An adaptive fuzzy controller applied to proportional term to achieve robustness against parameter variations has simple structure and computational simplicity. The controller based on optimal fuzzy logic controller has an self-tuning characteristics with clustering. Computer simulation results show the usefulness of the proposed controller.

• Journal of Ocean Engineering and Technology
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• v.2 no.1
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• pp.150-162
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• 1988

The dynamic fracture toughness, $K_{Id}$,is measured in the heat-treated and ion-nitrided Cr-Mo, Ni-Cr-Mo steel using standard and Precracked Charpy specimens an imstrumented impact machine. The value of $K_{Id}$and both the energy of initiate fracture, and the total energy of fracture. Since the $K_{Id}$values of the precraked impact specimens are in accord with their theoretical ones, this testing method is sufficently practical. The effect of ion-nitriding are found to be larger than the heat-treaded specimen.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.139-147
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• 1993

The rapid industrial growth, the consequent shortage of farm labour and increase in their wage level have facilitated more capitalized agricultural mechanization pattern in Korea. The efficiency of capital intensive machine is highly dependent on farm land structure. This paper describes a model explaining the relationship between farmland structure and required inputs for machine operation and to estimate required inputs for machine operation on the national basis for Korea for its paddy production system. The machine cost is closely related to operation area, but the required labour-hours are more related to machine type adopted . From the technology introduction point of view, if capital intensive machine is introduced, less labour-hours are required but machine kW-hours increase rapidly. From the plot geometry point of view, on good geometry plots, machine kW-hour and labour-hour required are less than that on the poor geometry plots. The kW-jhour per hectare of mechani al energy input id better indicator of mechanization level than kW per hectare or number of machine. If the adopted technology is more capital intensive and plot geometry is good, the cost reduction effect is highly significant.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.309-311
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• 2017

With the recent development of the Internet (IoT) technology, we have come to a very connected society. This paper focuses on the security aspects that can occur within the LPWA Network environment of the Internet of things, and proposes a new machine learning method considering next generation IPS / IDS that can detect and block unexpected and unusual behavior of devices.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.667-674
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• 2007

Facial expressions provide significant clues about one's emotional state; however, it always has been a great challenge for machine to recognize facial expressions effectively and reliably. In this paper, we report a method of feature-based adaptive motion energy analysis for recognizing facial expression. Our method optimizes the information gain heuristics of ID3 tree and introduces new approaches on (1) facial feature representation, (2) facial feature extraction, and (3) facial feature classification. We use minimal reasonable facial features, suggested by the information gain heuristics of ID3 tree, to represent the geometric face model. For the feature extraction, our method proceeds as follows. Features are first detected and then carefully "selected." Feature "selection" is finding the features with high variability for differentiating features with high variability from the ones with low variability, to effectively estimate the feature's motion pattern. For each facial feature, motion analysis is performed adaptively. That is, each facial feature's motion pattern (from the neutral face to the expressed face) is estimated based on its variability. After the feature extraction is done, the facial expression is classified using the ID3 tree (which is built from the 1728 possible facial expressions) and the test images from the JAFFE database. The proposed method excels and overcomes the problems aroused by previous methods. First of all, it is simple but effective. Our method effectively and reliably estimates the expressive facial features by differentiating features with high variability from the ones with low variability. Second, it is fast by avoiding complicated or time-consuming computations. Rather, it exploits few selected expressive features' motion energy values (acquired from intensity-based threshold). Lastly, our method gives reliable recognition rates with overall recognition rate of 77%. The effectiveness of the proposed method will be demonstrated from the experimental results.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.345-349
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• 2009

Social bookmarking systems are a typical web 2.0 service based on folksonomy, providing the platform for storing and sharing bookmarking information. Spammers in social bookmarking systems denote the users who abuse the system for their own interests in an improper way. They can make the entire resources in social bookmarking systems useless by posting lots of wrong information. Hence, it is important to detect spammers as early as possible and protect social bookmarking systems from their attack. In this paper, we applied a diverse set of machine learning approaches, i.e., decision tables, decision trees (ID3), $na{\ddot{i}}ve$ Bayes classifiers, TAN (tree-augment $na{\ddot{i}}ve$ Bayes) classifiers, and artificial neural networks to this task. In our experiments, $na{\ddot{i}}ve$ Bayes classifiers performed significantly better than other methods with respect to the AUC (area under the ROC curve) score as veil as the model building time. Plausible explanations for this result are as follows. First, $na{\ddot{i}}ve$> Bayes classifiers art known to usually perform better than decision trees in terms of the AUC score. Second, the spammer detection problem in our experiments is likely to be linearly separable.