• Journal of Information Processing Systems
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• v.17 no.2
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.1158-1164
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• 2005

This study suggests how to classify owner's standard specifications in organizations such as local governments which place an order for constructing general and various types of facilities. And the principal conclusions of this study can be summarized as follows; first, the Standard Specifications for Seoul Metropolitan Government of for all facilities could be integrated by seven individual works. Second, it is advisable from a viewpoint of long term to draw up integrally owner's standard specifications by trade for facilities, rather than draw up by group of facilities. Third, editing integrated standard specifications for all trades to meet the unified work classification structure should be backed up by high technology, therefore owner's standard specifications by trades would be favorable to our situation.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.159-166
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• 2023

The COI gene is a sequence of approximately 650 bp at the 5' terminal of the mitochondrial Cytochrome c Oxidase subunit I (COI) gene. As an effective DeoxyriboNucleic Acid (DNA) barcode, it is widely used for the taxonomic identification and evolutionary analysis of species. We created a CNN-LSTM hybrid model by combining the gene features partially extracted by the Long Short-Term Memory ( LSTM ) network with the feature maps obtained by the CNN. Compared to K-Means Clustering, Support Vector Machines (SVM), and a single CNN classification model, after training 278 samples in a training set that included 15 genera from two orders, the CNN-LSTM hybrid model achieved 94% accuracy in the test set, which contained 118 samples. We augmented the training set samples and four genera into four orders, and the classification accuracy of the test set reached 100%. This study also proposes calculating the cosine similarity between the training and test sets to initially assess the reliability of the predicted results and discover new species.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.173-176
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• 1997

In this paper, the classification performances of inductive learning methods are investigated using the credit rating data. The adopted classifiers are Multiple Discriminant Analysis (MDA), C4.5 of Quilan, Multi-Layer Perceptron (MLP) and Cascade Correlation Network (CCN). The data used in this analysis is obtained using the publicly announced rating reports from the three korean rating agencies. The performances of 4 classifiers are analyzed in term of prediction accuracy. The results show that no classifier is dominated by the other classifiers.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.125-126
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• 1996

Beijing-Arizona-Taipei-Connecticut (BATC) survey is a long term project to map the spectral energy distribution of various objects using 15 intermediate band filters and aims to cover about 450 sq degrees of northern sky. The SED information, combined with image structure information, is used to classify objects into several stellar and galaxy categories as well as QSO candidates. In this paper, we present a preliminary setup of robust data reduction procedure recently developed at NCU and also briefly discuss general classification scheme: redshift estimate, and automatic detection of variable objects.

• Bulletin of the Korean Mathematical Society
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• v.59 no.3
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• pp.567-608
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• 2022

We show that certain extensions of classifiable C^*-algebras are strongly classified by the associated six-term exact sequence in K-theory together with the positive cone of K₀-groups of the ideal and quotient. We use our results to completely classify all unital graph C^*-algebras with exactly one non-trivial ideal.

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.551-556
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• 2011

This research presents an implementation strategy of Process Capability Index (PCI) according to the types of process characteristics. The types of process feature are classified as four perspectives of variation range, time period, error position, and process stage. The paper examines short-term or long-term PCI, within or between variation, position of precision or accuracy, and inclusion of measurement or calibration stage. Moreover, the study proposes normality test of unilateral PCI.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.1
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• pp.115-136
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• 2020

It is necessary to find a genre classification by reflecting the needs of users since a subject that makes up the highest proportion of books in the school library is fictions in literature and KDC cannot accept user's need to access fiction in school libraries. This study suggested the genre classification for fictions in school libraries through surveying classification of fictions in domestic and foreign libraries, and comparing between classification systems of online/offline bookstores, KDC and DDC. For developing the genre classification system, it is to collect genre terms for fictions, to extract 14 genre headings among them, and to assign the acronym of English genre terms as classification notation. For applying the newly developed genre classification, KDC number of one middle school library was converted as the 3 methods such as combination of KDC, genre term before 800 and only genre terms. This study could contribute to suggest the genre classification of fiction to reflect user needs and to overcome the limitation of hierachical classification in KDC.

• Journal of Korean Medical classics
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• v.23 no.1
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• pp.163-186
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• 2010

Objective and Background : Attempt to express knowledge by IT is the current of the times, knowledge of the oriental medicine have to meet the needs of the times. It takes 'classification system of the oriental medicine terms' and 'system of the predicate' for explaining the relation between concepts to express knowledge by IT technique. Researches for 'classification system of the oriental medicine terms' are in progress already, researches for 'system of the predicate' are insufficient. Subject of study : We proceeded to study of the predicate in Idongwon(李東垣)'s "Biwiron(脾胃論)" has clear theory system and considerable influence upon knowledge of the oriental medicine for studying 'system of the predicate' which expresses knowledge of the oriental medicine in early stage. Method : Acquire Chinese play a predicate part in "Biwiron(脾胃論)", translate the Chinese to answer the context, group the similar predicate, decide representative predicate of group. And attempt to make classification system of the representative predicate with Term management system based on SQL Server 2005. Results and Considerations : I classify the predicate which predicate diagnosis, treatment, symptoms and knowledge of the oriental medicine into existence, condition, cognition and will. This classification seems to be useful to explain factors which have an effect on demonstration and treatment.

• 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.