• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.155-156
• /
• 2019

Traditional machine learning based emotion analysis methods such as Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) are less accurate. In this paper, we propose an improved kNN classification method. Improved methods and data normalization achieve the goal of improving accuracy. Then, three classification algorithms and an improved algorithm were compared based on experimental data.

• Smart Structures and Systems
• /
• v.23 no.1
• /
• pp.71-79
• /
• 2019

In general, it may be advantageous to measure the pressure pulsation near a valve to detect a valve defect in a linear compressor. However, the acceleration signals are more advantageous for rapid classification in a mass-production line. This paper deals with the performance improvement of fault classification using only the compressor-shell acceleration signal based on the relation between the refrigerant pressure pulsation and the shell acceleration of the compressor. A transfer function was estimated experimentally to take into account the signal noise ratio between the pressure pulsation of the refrigerant in the suction pipe and the shell acceleration. The shell acceleration signal of the compressor was modified using this transfer function to improve the defect classification performance. The defect classification of the modified signal was evaluated in the acceleration signal in the frequency domain using Fisher's discriminant ratio (FDR). The defect classification method was validated by experimental data. By using the method presented, the classification of valve defects can be performed rapidly and efficiently during mass production.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.2
• /
• pp.109-114
• /
• 2021

When we inspect scaffold defect using sight, inspecting performance is decrease and inspecting time is increase. We need for automatically scaffold defect detection method to increase detection accuracy and reduce detection times. In this paper. We produced scaffold defect classification models using densenet, alexnet, vggnet algorithms based on CNN. We photographed scaffold using multi dimension camera. We learned scaffold defect classification model using photographed scaffold images. We evaluated the scaffold defect classification accuracy of each models. As result of evaluation, the defect classification performance using densenet algorithm was at 99.1%. The defect classification performance using VGGnet algorithm was at 98.3%. The defect classification performance using Alexnet algorithm was at 96.8%. We were able to quantitatively compare defect classification performance of three type algorithms based on CNN.

• ETRI Journal
• /
• v.44 no.5
• /
• pp.826-836
• /
• 2022

Signal decomposition is a computational technique that dissects a signal into its constituent components, providing supplementary information. In this study, the capability of two common signal decomposition techniques, including wavelet-based and empirical mode decomposition, on preterm birth classification was investigated. Ten time-domain features were extracted from the constituent components of electrohysterogram (EHG) signals, including EHG subbands and EHG intrinsic mode functions, and employed for preterm birth classification. Preterm birth classification and anticipation are crucial tasks that can help reduce preterm birth complications. The computational results show that the preterm birth classification obtained using wavelet-based decomposition is superior. This, therefore, implies that EHG subbands decomposed through wavelet-based decomposition provide more applicable information for preterm birth classification. Furthermore, an accuracy of 0.9776 and a specificity of 0.9978, the best performance on preterm birth classification among state-of-the-art signal processing techniques, were obtained using the time-domain features of EHG subbands.

• International Journal of Computer Science & Network Security
• /
• v.23 no.1
• /
• pp.33-40
• /
• 2023

Machine-learning techniques are discovering effective performance on data analytics. Classification and regression are supported for prediction on different kinds of data. There are various breeds of classification techniques are using based on nature of data. Threshold determination is essential to making better model for unlabelled data. In this paper, threshold value applied as range, based on min-max normalization technique for creating labels and multiclass classification performed on rainfall data. Binary classification is applied on autism data and classification techniques applied on child abuse data. Performance of each technique analysed with the evaluation metrics.

• Nuclear Engineering and Technology
• /
• v.55 no.2
• /
• pp.792-799
• /
• 2023

Non-reactor nuclear facilities are increasing remarkably in Korea combined with advanced technologies such as life and space engineering, and the diversification of the nuclear industry. However, the absence of a basic classification guideline related to the design of non-reactor nuclear facilities has created confusion whenever related projects are carried out. In this paper, related domestic and international technical guidelines are reviewed to present the classification criteria of non-reactor nuclear facilities in Korea. Based on these criteria, the classification of structures, systems and components (SSCs) for safety controls is presented. Using the presented classification criteria, classification of a hot cell facility, a representative non-reactor nuclear facility, was performed. As a result of the classification, the hot cell facility is classified as the hazard category 3, accordingly, the safety class was classified as non-nuclear safety, the seismic category as non-seismic (RW-IIb), and the quality class as manufacturers' standards (S).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.420-437
• /
• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• Journal of Acupuncture Research
• /
• v.26 no.6
• /
• pp.225-239
• /
• 2009

Objectives : To establish a well organized and systematic oriental medicine classification of headache, the western and oriental medicine diagnosis and treatment systems of headache were reviewed. Methods : The history and development process of western medicine classification of headache were studied. A literature review of oriental medicine classification of headache was done. The characters of each classification systems were assessed. Results : In western medicine, many international societies concerning headache have been established. Through these societies, a classification of headache which can be used by both researchers and practitioners has been suggested. And the suggested classification system is highly recommended to be used in studies in order to increase utilization. As data is accumulated, new versions of the classification system were updated. But in the case of oriental medicine, various classification systems of headache are presented in numerous literatures. But the effort to unify and systemize the oriental medicine headache classification has been in lack. Conclusions : Establishment and utilization of a standardized oriental medicine headache classification system, based on various classifications and detailed descriptions is needed.

• Journal of Korean Biological Nursing Science
• /
• v.16 no.4
• /
• pp.342-348
• /
• 2014

Purpose: The purpose of this study was to examine the effects of pressure ulcer classification system education on hospital nurses' knowledge and visual discrimination ability of pressure ulcer classification system and incontinence-associated dermatitis. Methods: One group pre- and post-test was used. A convenience sample of 96 nurses participating in pressure ulcer classification system education, were enrolled in single institute. The education program was composed of a 50-minute lecture on pressure ulcer classification system and case-studies. The pressure ulcer classification system and incontinence-associated dermatitis knowledge test and visual discrimination tool, consisting of 21 photographs including clinical information were used. Paired t-test was performed using SPSS/WIN 18.0. Results: The overall mean difference of pressure ulcer classification system knowledge (t=4.67, p<.001) and visual discrimination ability (t=10.58, p<.001) were statistically and significantly increased after pressure ulcer classification system education. Conclusion: Overall understanding of pressure ulcer classification system and incontinence-associated dermatitis after pressure ulcer classification system education was increased, but tended to have lack of visual discrimination ability regarding stage III, suspected deep tissue injury. Differentiated continuing education based on clinical practice is needed to improve knowledge and visual discrimination ability for pressure ulcer classification system, and comparison experiment research is required to evaluate its effects.

• Journal of Korean Foot and Ankle Society
• /
• v.11 no.2
• /
• pp.130-134
• /
• 2007

Purpose: To investigate the relationship between classification based on simple radiographic findings and arthroscopic findings of the cartilage lesions in medial degenerative arthritis of the ankle joint. Materials and Methods: We studied 41 ankles of 36 patients with asymmetrical narrowing of the medial joint space. Degenerative arthritis following fracture and those with generalized arthritic disease were excluded, but those with a history of ankle sprain were included. Standing radiographs of all patients were graded according to the Takakura classification and the Kellgren-Lawrence (K/L) classification. Arthroscopic findings were classified according to the depth, width, and anteroposterior dimension of articular cartilage damage. Results: According to the Takakura classification, 29 ankles were classified as stage II, 7 cases as stage IIIA and 2 cases as stage IIIB. According to our classification of arthroscopic findings of 29 ankles in stage II, 1 ankle was graded as Grade I, 3 ankles as grade II, 10 ankles as grade III, and 15 ankles as grade IV. Spearman correlation coefficient between Takakura classification and arthroscopic classification was 0.342 (P=0.028), and coefficient between K/L classification and arthroscopic classification was 0.480 (P=0.001). Conclusion: Degenerative changes of the articular cartilage are more advanced than radiographic findings in many patients with ankle degenerative arthritis with asymmetrical narrowing of medial joint space. Therefore, we conclude that more aggressive effort should be made for correct diagnosis and treatment of degenerative arthritis.