• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.517-524
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• 2010

Recently, the size of online texts and textual information is increasing explosively, and the automated classification has a great potential for handling data such as news materials and images. Text classification system is based on supervised learning which needs laborous work by human expert. The main goal of this paper is to reduce the manual intervention, required for the task. The other goal is to increase accuracy to be high. Most of the documents have high complexity in contents and the high similarities in their described style. So, the classification results are not satisfactory. This paper shows the implementation of classification system based on ETOM+RPost algorithm and classification progress using SPAM data. In experiments, we verified our system with right-training documents and wrong-training documents. The experimental results show that our system has high accuracy and stability in all situation as 16% improvement in accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.63-70
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• 2006

This study aims to development of web-based process management system for spatial data construction. For developing this system work classification of basic surveying was standardized and quality management method for spatial data was established. Production process and work classification system for basic surveying such as control point surveying using GPS, leveling, aerial photographing, digital mapping, topographic mapping, digital elevation modeling, aerial photographic DB construction and digital orthophotomap was standardized. The status of the output and quality inspection for basic surveying project were analyzed, and the elements of quality inspection and data format for the type of outputs were analyzed. Based on standardized and analyzed contents, web-based process management system was developed after database and process was designed. The process management system consisted of process management, quality control, metadata management, and system management.

• Journal of Korean Society of Archives and Records Management
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• v.18 no.1
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• pp.155-175
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• 2018

The purpose of this study is to establish a classification system for the records of the Association for the Bereaved Families of the Hampyeong Massacre Victims. The content of the records is accordingly implemented through a functional source principle, and a classification table is presented in such a way that it reflects the characteristics by type and by production period so that the records can be used effectively. DIRKS, a methodology for the development of the functional classification system, is used to conduct a functional analysis of Hampyeong massacre victims' families to derive a task classification table that leads to task function-work activity-handling actions. The category is determined by taking into consideration the type and nature of the time of the production of the records of the Hampyeong massacre victims' families. The records are mapped according to the function classification system, which corresponds to the task classification table, and the multicategory system that drafts the type and period, which is used to classify the functions. The medical institution introduces a system for classifying records into task subjects, task activities, handling actions, types, and period.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.671-674
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• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.716-721
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• 2010

In this work we address the use of support vector machine (SVM) in the multi-class gas classification system. The objective is to classify single gases and their mixture with a semiconductor-type electronic nose. The SVM has some typical multi-class classification models; One vs. One (OVO) and One vs. All (OVA). However, studies on those models show weaknesses on calculation time, decision time and the reject region. We propose a hierarchical clustering method (HCM) based on the SVM for real-time gas mixture classification. Experimental results show that the proposed method has better performance than the typical multi-class systems based on the SVM, and that the proposed method can classify single gases and their mixture easily and fast in the embedded system compared with BP-MLP and Fuzzy ARTMAP.

• Korean Journal of Construction Engineering and Management
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• v.3 no.2 s.10
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• pp.99-106
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• 2002

It was few to the integrated information classification system In domestic construction. Furthermore, it became considerable obstacles to hamper the increase of national competitiveness as well the hoarding of information and overlapped investment. The standardization of information classification is also necessary prior to the Computer-Integrated Construction, CALS(Continuous Acquisition & Life-cycle Support/Electronic Commerce) in construction, EVMS(Earned Value Management System) and the various projects which manipulate construction information. Therefore, this report suggests the development direction and application proposal of Integrated Construction Information Classification.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.1053-1061
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• 2017

The study is concerned with providing the improvement method on making a reasonable and scientific decision on the damages accrued from the bid rigging in the construction work. According to the review on the precedent studies and decision cases on the damages caused by bid rigging, the representative problems include the insufficiency of the classification system on the damage calculation method and the omission of the necessary stage in the damage determination process. First, the improved classification system on calculating the damages caused by bid rigging is presented with the application to the bid rigging in the construction work by adding the ratio factor in addition to the damage calculation parameters such as price and cost. Second, the standard procedures organized with six stages is presented as the process required for determining the damages if the indemnification for bid rigging is claimed. The study becomes the foundation for resolving the problem with the undue burden on a party and for preventing the opportunity loss by resolving a dispute early through the improvement classification system and standard procedures presented in the study.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.109-116
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• 2003

In this paper, a CORBA-based collaborative medical image analysis and visualization system, which provides high accessibility and usability of the system for the users on distributed environment is introduced. The system allows us to manage datasets and manipulates medical images such as segmentation and volume visualization of computed geometry from biomedical images in distributed environments. Using Bayesian classification technique and an active contour model the system provides classification results of medical images or boundary information of specific tissue. Based on such information, the system can create real time 3D volume model from medical imagery. Moreover, the developed system supports collaborative work among multiple users using broadcasting and synchronization mechanisms. Since the system is developed using Java and CORBA, which provide distributed programming, the remote clients can access server objects via method invocation, without knowing where the distributed objects reside or what operating system it executes on.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.1-8
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• 2019

Dialog system is becoming a new dominant interaction way between human and computer. It allows people to be provided with various services through natural language. The dialog system has a common structure of a pipeline consisting of several modules (e.g., speech recognition, natural language understanding, and dialog management). In this paper, we tackle a task of domain classification for the natural language understanding module by employing machine learning models such as convolutional neural network and random forest. For our dataset of seven service domains, we showed that the random forest model achieved the best performance (F1 score 0.97). As a future work, we will keep finding a better approach for domain classification by investigating other machine learning models.