• Journal of Navigation and Port Research
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• v.33 no.10
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• pp.671-678
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• 2009

Recently, there have been some researches in the equipment fault detection based on shock and vibration information. Most research of them is based on shock and vibration monitoring to determine the equipment fault or not. Different with engine fault detection based on shock and vibration information we focus on detection of engine for boat and system control. First, it use the duplicated-checking method for shock and vibration information to determine the engine fault or not. If there is a fault happened, we use the integral to determine the error engine shock wave width and detect the fault area. On the other hand, we use the engine trend analysis and standard of safety engine to implement the shock and vibration information database. Our simulation results show that the probability of engine fault determination is 98% and the probability of engine fault detection is 72%

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.717-725
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• 2000

Within the last several years, there has been a large number of algorithms developed for face recognition. The majority of these algorithms have been view- and projection-based algorithms. Our definition of projection is not restricted to projecting the image onto an orthogonal basis the definition is expansive and includes a general class of linear transformation of the image pixel values. The class includes correlation, principal component analysis, clustering, gray scale projection, and matching pursuit filters. In this paper, we perform a detailed analysis of this class of algorithms by evaluating them on the FERET database of facial images. In our experiments, a projection-based algorithms consists of three steps. The first step is done off-line and determines the new basis for the images. The bases is either set by the algorithm designer or is learned from a training set. The last two steps are on-line and perform the recognition. The second step projects an image onto the new basis and the third step recognizes a face in an with a nearest neighbor classifier. The classification is performed in the projection space. Most evaluation methods report algorithm performance on a single gallery. This does not fully capture algorithm performance. In our study, we construct set of independent galleries. This allows us to see how individual algorithm performance varies over different galleries. In addition, we report on the relative performance of the algorithms over the different galleries.

• Korean Journal of Head & Neck Oncology
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• v.28 no.1
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• pp.3-7
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• 2012

Background and Objectives : Nasopharyngeal carcinoma(NPC) shows a distinct geographic and demographic distribution with high incidences in Chinese and Southeast Asians. Current WHO classification divides NPC into nonkeratinizing carcinoma(NKC)(differentiated and undifferentiated subtypes), keratinizing squamous cell carcinoma(KSCC), and basaloid squamous cell carcinoma(BSCC). Relative frequency of histologic subtypes of NPC is known to vary according to the incidence of NPC. Korea is one of the low-incidence countries according to the GLOBOCAN 2008 database by IARC. The aim of this study is to assess the histopathologic types and EBV status of NPC of Koreans. Materials and Methods : We reviewed and reclassified 168 cases of NPC(132 males and 36 females) diagnosed from January 1996 through July 2006. In situ hybridization for EBV-encoded early RNA(EBER) was performed on 146 cases and the results were compared among different histologic types, genders, and age and stage groups. Results : NKC, undifferentiated subtype(NKC-U) was identified in 106 cases(63.1%) and differentiated subtype(NKC-D) in 49 cases(29.2%). Remaining 13 cases(7.7%) were classified as KSCC. NKC and NKC-U were more common in females than in males. EBV prevalence was higher in NKC than in KSCC(NKC-U, 90% ;, NKC-D, 84.1% ; KSCC, 7.7%) and more common in younger age(${\leq}40$) than older age(>40) group. Conclusion : Histologic type distribution and EBV prevalence of NPC in Korean patients corresponded to that of intermediate incidence area. Pathogenesis of nasopharyngeal KSCC is assumed to be different from that of NKC.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.1-8
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• 2016

In this study, an WiFi fingerprinting method based on the k-nn algorithm was applied to improve the accuracy of location tracking of a moving train on a platform and evaluate the performance to minimize the estimation error of location tracking. The data related to the position of the moving train are monitored by the control center for trains and used widely for the safety and comfort of passengers. The train location tracking methods based on WiFi installed by telecom companies were evaluated. In this study, a simulator was developed to consider the environments of two cases; in already installed WiFi devices and new installed WiFi devices. The developed simulator can simulate the localized estimation of the position under a variety of conditions, such as the number of WiFi devices, the area of platform and entry velocity of train. To apply location tracking algorithms, a k-nn algorithm and fuzzy k-nn algorithm were applied selectively according to the underlying condition and also four distance measurement algorithms were applied to compare the error of location tracking. In conclusion, the best method to estimate train location tracking is a combination of the k-nn algorithm and Minkoski distance measurement at a 0.5m grid unit and 8 WiFi AP installed.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1397-1404
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• 2018

Due to the technical difficulties and high cost for obtaining 3-dimensional structure data, sequence-based approaches in proteins have not been widely acknowledged. A motif can be defined as any segments in protein or gene sequences. With this simplicity, motifs have been actively and widely used in various areas. However, the motif itself has not been studied comprehensively. The value of this study can be categorized in three fields in order to analyze the human proteins using artificial intelligence method: (1) Based on our best knowledge, this research is the first comprehensive motif analysis by analyzing motifs with all human proteins in Protein Data Bank (PDB) associated with the database of Enzyme Commission (EC) number and Structural Classification of Proteins (SCOP). (2) We deeply analyze the motif in three different categories: pattern, statistical, and functional analysis of clusters. (3) At the last and most importantly, we proposed random motif frequency(RMF) matric that can efficiently distinct the characteristics of proteins by identifying interface residues from non-interface residues and clustering protein functions based on big data while varying the size of random motif.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.249-260
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• 2021

This study is a meta-analysis study conducted to integrate and analyze the results of flip-learning studies for Korean nursing students. We searched PubMed, EMBASE, Cochrane, CINAHL, and Korean databases. Randomized controlled trials (RCTs) and Non-Randomized controlled trials (Non-RCTs) evaluating the effects of flipped learning for Korean nursing students were included. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using a random-effects meta-analysis. The entire effect size in flipped learning was big in effect size (SMD = 1.21; 95% CI = 0.84 to 1.63; I2 = 93.9; n = 23) compared to the control groups. The analysis results of subgroups according to the classification of Bloom showed that flipped learning was found to have a significant effect on psychomotor domain, cognitive domain, and affective domain. A total of 10 literature analyses, this meta-analysis showed that flipped learning on Korean nursing students is effective in psychomotor, cognitive, and affective domain than the traditional teaching method. The flip learning can be integrated into theoretical and practical nursing education to improve the academic performance of nursing students.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.55-67
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• 2023

This study aims to develop a model that can automatically identify the rooftop shape of apartment buildings using GIS and machine learning algorithms, and apply it to analyze the relationship between rooftop shape and characteristics of apartment complexes. A database of rooftop data for each building in an apartment complex was constructed using geospatial data, and individual buildings within each complex were classified into flat type, tower type, and mixed types using the random forest algorithm. In addition, the relationship between the proportion of rooftop shapes, development density, height, and other characteristics of apartment complexes was analyzed to propose the potential application of geospatial information in the real estate field. This study is expected to serve as a basic research on AI-based building type classification and to be utilized in various spatial and real estate analyses.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.157-163
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• 2013

Data mining is a useful technology that can support new discoveries based on the pattern analysis and a variety of linkages between data, and currently is utilized in various fields such as finance, marketing, medical. In this paper, we propose an effective utilization method of data based on analysis of spatial data mining. We make use of basic data of foreigners living in Seoul. However, the data has some features distinguished from other areas of data, classification as sensitive information and legal problem such as personal information protection. So, we use the basic statistical data that does not contain personal information. The main features and contributions of the proposed method are as follows. First, we can use Big Data as information through a variety of ways and can classify and cluster Big Data through refinement. Second. we can use these kinds of information for decision-making of future and new patterns. In the performance evaluation, we will use visual approach through graph of themes. The results of performance evaluation show that the analysis using data mining technology can support new discoveries of patterns and results.

• Journal of Korean Society of Forest Science
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• v.105 no.4
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• pp.391-400
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• 2016

This study was conducted to set up ecological database for effective forest management and conservation of KNU Research Forest in Mt. Myeonbong with the characteristic analysis of stand structures. Following the results of clustering analysis, they were classified under 6 communities (Quercus mongolica-Pinus densiflora, Pinus densiflora, Carpinus cordata, Fraxinus rhynchophylla-Acer pseudosieboldianum-Acer pictum subsp. Mono, Quercus mongolica-Quercus variabilis, Quercus mongolica). Importance value tests were estimated that on ridge; Pinus densiflora, valley; Carpinus cordata, Fraxinus rhynchophylla-Acer pseudosieboldianum-Acer pictum subsp. Mono were recorded dominant species. Carpinus cordata and Fraxinus rhynchophylla-Acer pseudosieboldianum-Acer pictum subsp. Mono community, north aspect and valley were investigated high species richness value. It was showed decreasing tendency as altitude and degree of slope were high. Results of NMS, upper & middle layers and shrub & herbal layers were influenced by species richness and the case of species association.