• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• The Korean Journal of Archival Studies
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• no.5
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• pp.209-242
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• 2002

Bibliographical Description for Revolutionary Historical Archives is created to describe records at the item level. It defines descriptive elements, punctuations, formats and methods. Descriptive elements are composed of 20 elements, each of which is either mandatory or optional. Mandatory elements are: repositories codes, documents codes, dates, creators, title, classification codes, and subject vocabularies. Abstracts were previously included in card cataloging and are removed in the computerized system. New elements, such as "uncontrolled vocabularies," "name of places," "personal names," "organizational structures" and "meetings," are added to allow keyword search. Considering that subject vocabulary searches are the most important in computerized systems, however, Guidelines for the Subject Indexing for Revolutionary Historical Archives as well as Subject Headings, as a result from the Guidelines, are created. The most extraordinary features in Chinese archival description are said to be the Guidelines for the Classification Indexing for Revolutionary Historical Archives and Materials as well as the Classification Scheme, both of which are created to allow subject search of records content. It is because Chinese practice of records management distinguishes the classification for arrangement from that for retrieval. Chinese archival description is, therefore, composed of bibliographic description rules, subject headings, and the classification scheme for retrieval.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.160-166
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• 2007

When a mail was given to users, each user's response could be different according to his or her preference. This paper presents a solution for this situation by constructing a user preferred ontology for anti-spam systems. To define an ontology for describing user behaviors, we applied associative classification mining to study preference information of users and their responses to emails. Generated classification rules can be represented in a formal ontology language. A user preferred ontology can explain why mail is decided to be spam or ron-spam in a meaningful way. We also suggest a new rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.20-26
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• 2001

The research into the conditions of the active rules is a very important element in the enhancing of the performance of the active database, and the processing time of the calculation generated from the conditions must be minimized in order to improve performance. In this paper, we propose the conditions processing system with the preprocessor which determines the delta tree structure and constructs the classification tree. Due to the characteristics of the active database through which the active rules can be comprehended beforehand. the preprocessor can be introduced. In this paper, the delta tree and classification tree which can effectively Process the join and selection operations instead of the delta relation is proposed. enhancing the condition evaluation performance.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• The Transactions of the Korea Information Processing Society
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• v.5 no.1
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• pp.103-110
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• 1998

In case of traditional Rule-Based Reasoning(RBR) and Case-Based Reasoning(CBR), although knowledge is reasoned either by one of them or by the integration of RBR and CBR, there is a problem that much time should be consumed by numerous rules and cases. In order to improve this time-consuming problem, in this paper, a new type of reasoning technique, which is a kind of integration of reduced RB and CB, is to be introduced. Such a new type of reasoning uses Rough Set, by which we can represent multi-meaning and/or random knowledge easily. In Rough Set, solution is to be obtained by its own complementary rules, using the process of RB and CB into equivalence class by the classification and approximation of Rough Set. and then using reduced RB and CB through the integrated reasoning.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.310-315
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• 2007

When a mail was given to users, each user's response could be different according to his or her preference. This paper presents a solution for this situation by constructing a u!;or preferred ontology for anti-spam systems. To define an ontology for describing user behaviors, we applied associative classification mining to study preference information of users and their responses to emails. Generated classification rules can be represented in a formal ontology language. A user preferred ontology can explain why mail is decided to be spam or non-spam in a meaningful way. We also suggest a nor rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules.

• Journal of Ocean Engineering and Technology
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• v.35 no.2
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• pp.161-171
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• 2021

For the fatigue design of offshore structures, it is essential to understand and use the S-N curves specified in various industry standards and codes. This study compared the characteristics of the S-N curves for five major codes. The codes reviewed in this paper were DNV Classification Rules (DNV GL, 2016), ABS Classification Rules (ABS, 2003), British Standards (BSI, 2015), International Welding Association Standards (IIW, 2008), and European Standards (BSI, 2005). Types of stress, such as nominal stress, hot-spot stress, and effective notch stress, were analyzed according to the code. The basic shape of the S-N curve for each code was analyzed. A review of the survival probability of the basic design S-N curve for each code was performed. Finally, the impact on the conservatism of the design was analyzed by comparing the S-N curves of three grades D, E, and F by the five codes. The results presented in this paper are considered to be a good guideline for the fatigue design of offshore structures because the S-N curves of the five most-used codes were analyzed in depth.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.