• Imaging Science in Dentistry
• /
• v.37 no.1
• /
• pp.1-7
• /
• 2007

Fibro-osseous lesions are composed of connective tissue and varying amount of mineralized substances, which may be bony or cementum-like structures. It is necessary for oral radiologist to differentiate due to the tendency of these fibro-osseous lesions to show similar histopathologic appearances, while the management of each lesion is different. However we often encounter a little difficulty in judgement because there are some overlaps between concept of each lesions. So recently I suggest, we face a need to review basic concept and classification of several fibro-osseous jaw lesions. In this article, several fibre-osseous lesions, such as fibrous dysplasia, cemento-ossifying fibroma and cemento-osseous dysplasia, will be discussed basing on the review of literature. particular emphasis will be made on the nomenclature revision of WHO's classification in 1992.

• Journal of Information Processing Systems
• /
• v.15 no.5
• /
• pp.1119-1130
• /
• 2019

With the wide spread of Social Network Services (SNS), fake news-which is a way of disguising false information as legitimate media-has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and "Fasttext" which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.

• International Journal of Computer Science & Network Security
• /
• v.21 no.6
• /
• pp.207-212
• /
• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

• Journal of the korean Society of Automotive Engineers
• /
• v.26 no.1
• /
• pp.76-82
• /
• 2004

In this paper, the trend of quality management system that suppliers in the automotive industry are adopting, the classification system of suppliers and car manufacturers requirements for quality are described. Due to the complexity of the requirements for quality, the introduction of an active quality management system which can meet all conditions is a difficult task. Thus, to cope with this difficulty, this paper shows the optimal requirements that suppliers have to consider when they are introducing quality management system and the discriminated strategies to assure parts quality by supplier model.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.6 no.1
• /
• pp.34-42
• /
• 2011

In an effort to the practical realization of phonetic typewriter, we concentrate on the classification of consonants in this paper. Since many of consonants do not show periodic behavior in time domain and thus the validity for Fourier analysis of them are not convincing, vector quantization (VQ) via LBG clustering is first performed to check if the feature vectors of MFCC and LPCC are ever meaningful for consonants. Experimental results of VQ showed that it's not easy to draw a clear-cut conclusion as to the validity of Fourier analysis for consonants. For classification purpose, two kinds of neural networks are employed in our study: self organizing map (SOM) and learning vector quantization (LVQ). Results from SOM revealed that some pairs of phonemes are not resolved. Though LVQ is free from this difficulty inherently, the classification accuracy was found to be low. This suggests that, as long as consonant classification by LVQ is concerned, other types of feature vectors than MFCC should be deployed in parallel. However, the combination of MFCC/LVQ was not found to be inferior to the classification of phonemes by language-moded based approach. In all of our work, LPCC worked worse than MFCC.

• The Journal of Information Technology and Database
• /
• v.8 no.1
• /
• pp.117-128
• /
• 2001

Recently there are many researches of text mining to find interesting patterns or association rules from mass textual documents. However, the words extracted from informal documents are tend to be irregular and there are too many general words, so if we use pre-exist method, we would have difficulty in retrieving knowledge information effectively. In this paper, we propose a new feature extraction method to classify mass documents using association rule based on unsupervised learning technique. In experiment, we show the efficiency of suggested method by extracting features and classifying of documents.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.5
• /
• pp.2171-2185
• /
• 2020

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

• Archives of Craniofacial Surgery
• /
• v.18 no.2
• /
• pp.97-104
• /
• 2017

Background: Nasal bone fracture is one of the most common facial bone fracture types, and the surgical results exert a strong influence on the facial contour and patient satisfaction. Preventing secondary deformity and restoring the original bone state are the major goals of surgeons managing nasal bone fracture patients. In this study, a treatment algorithm was established by applying the modified open reduction technique and postoperative care for several years. Methods: This article is a retrospective chart review of 417 patients who had been received surgical treatment from 2014 to 2015. Using prepared questionnaires and visual analogue scale, several components (postoperative nasal contour; degree of pain; minor complications like dry mouth, sleep disturbance, swallowing difficulty, conversation difficulty, and headache; and degree of patient satisfaction) were evaluated. Results: The average scores for the postoperative nasal contour given by three experts, and the degree of patient satisfaction, were within the "satisfied" (4) to "very satisfied" (5) range (4.5, 4.6, 4.5, and 4.2, respectively). The postoperative degree of pain was sufficiently low that the patients needed only the minimum dose of painkiller. The scores for the minor complications (dry mouth, sleep disturbance, swallowing difficulty, conversation difficulty, headache) were relatively low (36.4, 40.8, 65.2, 32.3, and 34 out of the maximum score of 100, respectively). Conclusion: Satisfactory results were obtained through the algorithm-oriented management of nasal bone fracture. The degree of postoperative pain and minor complications were considerably low, and the degree of satisfaction with the nasal contour was high.

• Korean Journal of Remote Sensing
• /
• v.20 no.2
• /
• pp.139-152
• /
• 2004

This paper proposes to improve the results of image classification with spatial region growing segmentation by using an MRF-based classifier. The proposed approach is to re-classify the pixels in the boundary area, which have high probability of having classification error. The MRF-based classifier performs iteratively classification using the class parameters estimated from the region growing segmentation scheme. The proposed method has been evaluated using simulated data, and the experiment shows that it improve the classification results. But, conventional MRF-based techniques may yield incorrect results of classification for remotely-sensed images acquired over the ground area where has complicated types of land-use. A multistage MRF-based iterative class-modification in boundary is proposed to alleviate difficulty in classifying intricate land-cover. It has applied to remotely-sensed images collected on the Korean peninsula. The results show that the multistage scheme can produce a spatially smooth class-map with a more distinctive configuration of the classes and also preserve detailed features in the map.

• Nuclear Engineering and Technology
• /
• v.55 no.10
• /
• pp.3716-3724
• /
• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.