• Tunnel and Underground Space
• /
• v.21 no.6
• /
• pp.439-449
• /
• 2011

The characteristics of rock fractures for engineering rock classification are investigated by analyzing three dimensional point cloud generated from adjusted digital images of a tunnel face during construction and the tunnel is reinforced based on the supporting pattern suggested by the RMR and the Q system using parameters extracted from those images. As results, it is possible saving time required from face mapping to tunnel reinforcing work, enhancing safety during face mapping work in tunnels and reliability of both the mapping information and selecting supporting pattern by storing the files of digital images and related information which can be checked again, if necessary sometime in the future.

• (The)Study of the Eastern Classic
• /
• no.59
• /
• pp.233-261
• /
• 2015

This paper describes the system and check the characteristics of Korean classic literature in English. Examine the name of the classic novel, classification. More specifically would described the Purpose of writing of Korean classic novel(soseolsa). Name of the classic novel, How classic novel genre classification, Classic novels are classified into several types, Are some types of literature are applicable, How to determine the type of name, how did the name written work, a description of the work is to encompass any attitude looks. This paper is significant to the Korea Arts and Technology of Korea as an classic novel in English.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.12
• /
• pp.1-7
• /
• 2021

The deep learning image processing technique was used to prevent accidents in lathe work caused by worker negligence. During lathe operation, when the chuck is rotated, it is very dangerous if the operator's hand is near the chuck. However, if the chuck is stopped during operation, it is not dangerous for the operator's hand to be in close proximity to the chuck for workpiece measurement, chip removal or tool change. We used YOLO (You Only Look Once), a deep learning image processing program for object detection and classification. Lathe work images such as hand, chuck rotation and chuck stop are used for learning, object detection and classification. As a result of the experiment, object detection and class classification were performed with a success probability of over 80% at a confidence score 0.5. Thus, we conclude that the artificial intelligence deep learning image processing technique can be effective in preventing incidents resulting from worker negligence in future manufacturing systems.

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.2
• /
• pp.313-325
• /
• 2005

In Korea, there exists a large corpus of handwritten historical documents that serve as a valuable resource. Most of them are hand-written by the King's chroniclers and secretaries. Recently, the historical archives of Lee dynasty have been digitalized. Since it is extremely difficult to utilize conventional OCR system, most of the processes have been performed manually. In this paper, we propose OCR-based digitalization system using Mahalanobis distance-based rejection and interface for eye inspection about historical Hanja documents. Compared with our previous work, experimental results show that the proposed system can help enhancing the overall efficiency of the process.

• The Journal of Internal Korean Medicine
• /
• v.31 no.1
• /
• pp.1-10
• /
• 2010

Objectives : It is necessary that the international classification of diseases (ICD) be examined in order to comprise the third revision of the Korean Classification of Disease on Oriental Medicine (KCD-OM) and disease classification in the oriental internal medicine field. It is essential that the selection, classification and definition of disease and pattern names of oriental concepts in internal medicine be clear. Since 2008, the fifth revision of the Korean Classification of Disease (KCD-5) has been used in Korea. It was required to use the reference classification from the Oriental medicine area based on the ICD-10. Methods : In this review, the necessity for, meaning of and content of the third revision are briefly described. The ICD system was reviewed and KCD-OM was reconstructed. How diagnosis in the oriental internal medicine area had changed is discussed. Review and Results : In 1973, the disease classification of oriental medicine was established the basis on the contents of Dongeuibogam. It was irrespective of the ICD. As to the classification system in the Oriental internal medicine field, systemic disease was comprised of wind, cold, warm, wet, dryness, heat, spirit, ki, blood, phlegm and retained fluid, consumptive disease, etc. Diseases of internal medicine comprised a system according to the five viscera and the six internal organs and followed the classification system of Dongeuibogam. The first and second revisions were of the classification system based on the curriculum in 1979 and 1995. In 1979, in the first revision, geriatric disease and idiopathic types of disease were deleted, and skin disease was included among surgery diseases. This classification was expanded to 792 small classification items and 1,535 detailed classification items to the dozen disease classes. In 1995, in the second revision, it was adjusted to 644 small classes and 1,784 detailed classification items in the dozen disease classes. KCD-OM3 did KCD from this basis. It added and comprised the oriental medical doctor's concept names of diseases considering the special conditions in Korea. KCD-OM3 examined the KCD-OMsecond revised edition (1994). It improved the duplex classification, improper classifications, etc. It is difficult for us to separate the disease names and pattern names in oriental medicine. We added to the U code and made one classification system. By considering the special conditions in Korea, 169 codes (83 disease name codes, 86 pattern name codes) became the pre-existence classification and links among 306 U codes of KCD-OM3. 137 codes were newly added in the third revision. U code added 3 domains. These are composed of the disease name (U20-U33, 97 codes), the disease pattern name (U50-U79, 191 codes) and the constitution pattern name of each disease (U95-U98, 18 codes). Conclusion : The introduction of KCD-OM3 conforms to the diagnostic system by which oriental medical doctors examine classes used with the basic structure of the reference classification of WHO and raises the clinical study and academic activity of the Korean oriental medicine and makes the production of all kinds of nation statistical indices possible. The introduction of KCD-OM3 promotes the diagnostic system by which doctors of Oriental medicine examine classes using the association with KCD-5. It will raise the smoothness and efficiency of oriental medical treatment payments in the health insurance, automobile insurance, industrial accident compensation insurance, etc. In addition, internationally, the eleventh revision work of the ICD has been initiated. It needs to consider incorporating into the International Classification of Diseases some of every country's traditional medicine.

• Journal of KIISE:Software and Applications
• /
• v.35 no.12
• /
• pp.774-779
• /
• 2008

In this work, we describe comments classification system using topic signature. Topic signature is widely used for selecting feature in document classification and summarization. Comments are short and have so many word spacing errors, special characters. We firstly convert comments into 7-gram. We consider the 7-gram as sentence. We convert the 7-gram into 3-gram. We consider the 3-gram as word. We select key feature using topic signature and classify new inputs by the Naive Bayesian method. From the result of experiments, we can see that the proposed method is outstanding over the previous methods.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.7C
• /
• pp.712-718
• /
• 2003

C.elegans are often used to study of function of gene, but it is difficult for human observation to distinguish the mutants of C.elegans. To solve this problem, the system, which can classify the mutant types automatically using the computer vision, is now studying. Tn previous work［1］, we described the preprocessing method for automated-classification system. In this paper, we introduce shape features, which can be extracted from an acquisition image. We divide the feature into two categories, which are related to size and posture of the worm, and each feature is described mathematically We validate the shape information experimentally. And we use hierarchical clustering algorithm for classification. It reveals that 4 mutants of the worm, which are used in experiment, can be classified with over 90% of success rate.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.05a
• /
• pp.231-234
• /
• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.9
• /
• pp.359-366
• /
• 2021

A number of construction companies in Korea invest considerable human and financial resources to construct a system for managing apartment defect data and for categorizing repair tasks. Thus, this study proposes machine learning models to automatically classify defect complaint text-data into one of the sub categories of 'finishing work' (i.e., one of the defect repair tasks). In the proposed models, we employed two word representation methods (Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF)) and two machine learning classifiers (Support Vector Machine, Random Forest). In particular, we conducted both binary- and multi- classification tasks to classify 9 sub categories of finishing work: home appliance installation work, paperwork, painting work, plastering work, interior masonry work, plaster finishing work, indoor furniture installation work, kitchen facility installation work, and tiling work. The machine learning classifiers using the TF-IDF representation method and Random Forest classification achieved more than 90% accuracy, precision, recall, and F1 score. We shed light on the possibility of constructing automated defect classification systems based on the proposed machine learning models.

• The Journal of the Acoustical Society of Korea
• /
• v.32 no.2
• /
• pp.167-173
• /
• 2013

This work examines classification of diphthongs, as part of a distinctive feature-based speech recognition system. Acoustic measurements related to the vocal tract and the voice source are examined, and analysis of variance (ANOVA) results show that vowel duration, energy trajectory, and formant variation are significant. A balanced error rate of 17.8% is obtained for 2-way diphthong classification on the TIMIT database, and error rates of 32.9%, 29.9%, and 20.2% are obtained for /aw/, /ay/, and /oy/, for 4-way classification, respectively. Adding the acoustic features to widely used Mel-frequency cepstral coefficients also improves classification.