• Title/Summary/Keyword: discriminant function analysis

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Recognizing asymmetric moire patterns for human spinal deformity detection

  • Kim, Hyoung-Seop;Hiroshi UENO;Seiji ISHIKAWA;Yoshinori Otsuka
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.568-571
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    • 1997
  • Recently, the number of techniques for analyzing medical images has been increasing in computer vision, employing X-ray CT images, ultrasound images, MR images, moire topographic images, etc. Spinal deformity is a serious problem especially for teenagers and medical doctors inspect moire topographic images of their backs visually for the primary screening. If a subject is normal, the moire image is almost symmetric with respect to the middle line of the subject's back, otherwise it shows asymmetric shape. In this paper, an image analysis technique is described for discriminating suspicious cases from normal in human spinal deformity by recognizing asymmetric moire images of human backs. The principal axes which are sensitive to asymmetry of the moire image are extracted at two parts on a subject's back and their angles are evaluated with respect to the detected middle line of the back. The two angles compose a 2-D feature space and inspected cases are divided into two clusters in the space by a linear discriminant function based on the Mahalanobis distance. Given 120 cases, 60 normal and 60 abnormal, the leave-out method was applied for the recognition and 75% recognition rate was achieved.

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A Study on Classification and Localization of Structural Damage through Wavelet Analysis

  • Koh, Bong-Hwan;Jung, Uk
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2007.11a
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    • pp.754-759
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    • 2007
  • This study exploits the data discriminating capability of silhouette statistics, which combines wavelet-based vertical energy threshold technique for the purpose of extracting damage-sensitive features and clustering signals of the same class. This threshold technique allows to first obtain a suitable subset of the extracted or modified features of our data, i.e., good predictor sets should contain features that are strongly correlated to the characteristics of the data without considering the classification method used, although each of these features should be as uncorrelated with each other as possible. The silhouette statistics have been used to assess the quality of clustering by measuring how well an object is assigned to its corresponding cluster. We use this concept for the discriminant power function used in this paper. The simulation results of damage detection in a truss structure show that the approach proposed in this study can be successfully applied for locating both open- and breathing-type damage even in the presence of a considerable amount of process and measurement noise.

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A case of corporate failure prediction

  • Shin, Kyung-Shik;Jo, Hongkyu;Han, Ingoo
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1996.10a
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    • pp.199-202
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    • 1996
  • Although numerous studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the prediction performance. This paper proposes the post-model integration method, which means integration is performed after individual techniques produce their own outputs, by finding the best combination of the results of each method. To get the optimal or near optimal combination of different prediction techniques. Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. This study applied three individual classification techniques (Discriminant analysis, Logit and Neural Networks) as base models to the corporate failure prediction context. Results of composite prediction were compared to the individual models. Preliminary results suggests that the use of integrated methods will offer improved performance in business classification problems.

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The Hybrid Systems for Credit Rating

  • Goo, Han-In;Jo, Hong-Kyuo;Shin, Kyung-Shik
    • Journal of the Korean Operations Research and Management Science Society
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    • v.22 no.3
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    • pp.163-173
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    • 1997
  • Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.

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Analysis and Implementation of Speech/Music Classification for 3GPP2 SMV Codec Employing SVM Based on Discriminative Weight Training (SMV코덱의 음성/음악 분류 성능 향상을 위한 최적화된 가중치를 적용한 입력벡터 기반의 SVM 구현)

  • Kim, Sang-Kyun;Chang, Joon-Hyuk;Cho, Ki-Ho;Kim, Nam-Soo
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.5
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    • pp.471-476
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    • 2009
  • In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.

Flavor Analysis of Commercial Korean Distilled Spirits using an Electronic Nose and Electronic Tongue (전자코와 전자혀를 이용한 시판 증류주의 향미특성 분석)

  • Kim, Jung Sun;Jung, Hyo Yeon;Park, Eun Young;Noh, Bong Soo
    • Korean Journal of Food Science and Technology
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    • v.48 no.2
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    • pp.117-121
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    • 2016
  • The objective of this study was to analyze the flavor patterns of commercial Korean distilled spirits using a mass spectrometry-based electronic nose and an electronic tongue. Thirteen types of domestic distilled spirits were used for analysis. The flavor patterns of the distilled spirits were clearly identified through discriminant function analysis of the raw materials. The obtained data of the electronic nose analysis showed that the flavor pattern of the distilled spirits aged in oak barrels was not significantly different from that of the general distilled spirits. According to the results of the electronic tongue, the distilled spirits made with rice had a stronger flavor than those made with foxtail millet. Compared to the sensitivity value of each taste sensor, the distilled spirits showed various intensities of sourness and umami. Therefore, an electronic nose and electronic tongue could be successfully used for quality evaluation and new product development of distilled spirits.

Analysis of obese adult men body size and shape - Focus on 50s and 60s - (성인 비만남성 신체 치수 및 체형 분석- 50~60대를 중심으로 -)

  • Yejin Kim;Dong-Eun Kim
    • The Research Journal of the Costume Culture
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    • v.31 no.2
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    • pp.193-212
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    • 2023
  • The purpose of this study is to classify the body types of obese men in their 50-60s and compare them with those of obese middle-aged men in their 30-40s. The 3D anthropometric data of obese men aged 50 to 60 years from the 6th Size Korea. The data are analyzed using SPSS 25.0 for Windows, and descriptive statistics, χ2 test, correlation analysis, and cluster analysis are used to classify obese body types. As a result of the study, five factors are extracted to determine body types, which are classified into three obese body types through cluster analysis. 1) a large physique and consequently large circumference and height; 2) A short upper body length, short height, and thick belly; 3) the lowest rate of obesity and relatively flat abdominal curve. For the 30-40s group, Type1 showed the highest rate at 55.6%, whereas for the 50s group, Type3 showed the highest rate at 49.3%, and for 60s group, Type2 showed the highest rate at 41.2%. The classification accuracy of the discriminant function for each type is 94.7%, indicating relatively high accuracy. Furthemore, the recently changed obese body type are analyzed by comparing it with the 3D anthropometric data of 8th Size Korea, which will contribute to the utilization of basic data for manufacturing apparel for obese men.

A Taxonomy of Geriatric Hospitals Using National Health Insurance Claim Data (건강보험청구자료로 본 요양병원의 기능 유형)

  • Min Kyoung Lim;Sun-Jea Kim;Jeong-Yeon Seon
    • Korea Journal of Hospital Management
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    • v.28 no.2
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    • pp.9-20
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    • 2023
  • Purpose: This study classified the actual functions of geriatric hospitals and examined the differences in their characteristics, in order to provide a basis for discussions on defining the functions of geriatric hospitals and how to pay for care. Methodology: This study used various administrative data such as health insurance data and long-term care insurance data. Cluster analysis was used to categorize geriatric hospitals. To examine the validity of the cluster analysis results, we conducted a discriminant analysis to calculate the accuracy of the classification. To examine cluster characteristics, we examined structure, process, and outcome indicators for each cluster. Findings: The cluster analysis identified five clusters. They were geriatric hospitals with relatively short stays for cancer patients(cluster 1; cancer patient-centered), geriatric hospitals with relatively large numbers of patients using rehabilitation services(cluster 2; rehabilitation patient-centered), geriatric hospitals with a high proportion of relatively severe elderly patients(cluster 3; severe elderly patient-centered), geriatric hospitals with a high proportion of mildly ill elderly patients with various conditions(cluster 4; mildly ill elderly patient-centered), and geriatric hospitals with a significantly higher proportion of dementia patients(cluster 5; dementia patient-centered). The largest number of geriatric hospitals were categorized in clusters 4 and 5, and the structure and process indicators for these clusters were generally lower than for the other clusters. Practical Implications: We have confirmed the existence of geriatric hospitals where the medical function, which is the original purpose of a geriatric hospital, has been weakened. It has been observed that the quality level of these geriatric hospitals is likely to be lower compared to hospitals that prioritize enhanced medical functions. Therefore, it is suggested to consider the conversion of these geriatric hospitals into long-term care facilities, and careful consideration should be given to the review of care-giver payment coverage.

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Morphometric and genetic diversity of Rasbora several species from farmed and wild stocks

  • Bambang Retnoaji;Boby Muslimin;Arif Wibowo;Ike Trismawanti
    • Fisheries and Aquatic Sciences
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    • v.26 no.9
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    • pp.569-581
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    • 2023
  • The morphology and genetic identification of Rasbora lateristriata and Rasbora argyrotaenia between cultivated and wild populations has never been reported. This study compares morphology and cytochrome c oxidase (COI) genes between farmed and wild stock Rasbora spp. in Java and Sumatra island, Indonesia. We analyzed the truss network measurement (TNM) characters of 80 fish using discriminant function analysis statistical tests. DNA was extracted from muscle tissue of 24 fish specimens, which was then followed by polymerase chain reaction, sequencing, phylogenetic analysis, fixation index analysis, and statistical analysis of haplotype networks. Basic Local Alignment Search Tool analysis validated the following species: R. lateristriata and R. argyrotaenia from farming (Jogjakarta); Rasbora agryotaenia (Purworejo), R. lateristriata (Purworejo and Malang), Rasbora dusonensis (Palembang), and Rasbora einthovenii (Riau) from natural resources. Based on TNM characters, Rasbora spp. were divided into four groups, referring to four distinct characters in the middle of the body. The phylogenetic tree is divided into five clades. The genetic distance between R. argyrotaenia (Jogjakarta) and R. lateristriata (Malang) populations (0.66) was significantly different (p < 0.05). R. lateristriata (Purworejo) has the highest nucleotide diversity (0.43). R. argyrotaenia from Jogjakarta and Purworejo shared the same haplotype. The pattern of gene flow among them results from the two populations' close geographic proximity and environmental effects. R. argyrotaenia had low genetic diversity, therefore, increasing heterozygosity in cultivated populations is necessary to avoid inbreeding. Otherwise, R. lateristriata (Purworejo) had a greater gene variety that could be used to develop breeding. In conclusion, the middle body parts are a distinguishing morphometric character of Rasbora spp., and the COI gene is more heterozygous in the wild population than in farmed fish, therefore, enrichment of genetic variation is required for sustainable Rasbora fish farming.

Seabed Sediment Feature Extraction Algorithm using Attenuation Coefficient Variation According to Frequency (주파수에 따른 감쇠계수 변화량을 이용한 해저 퇴적물 특징 추출 알고리즘)

  • Lee, Kibae;Kim, Juho;Lee, Chong Hyun;Bae, Jinho;Lee, Jaeil;Cho, Jung Hong
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.1
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    • pp.111-120
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    • 2017
  • In this paper, we propose novel feature extraction algorithm for classification of seabed sediment. In previous researches, acoustic reflection coefficient has been used to classify seabed sediments, which is constant in terms of frequency. However, attenuation of seabed sediment is a function of frequency and is highly influenced by sediment types in general. Hence, we developed a feature vector by using attenuation variation with respect to frequency. The attenuation variation is obtained by using reflected signal from the second sediment layer, which is generated by broadband chirp. The proposed feature vector has advantage in number of dimensions to classify the seabed sediment over the classical scalar feature (reflection coefficient). To compare the proposed feature with the classical scalar feature, dimension of proposed feature vector is reduced by using linear discriminant analysis (LDA). Synthesised acoustic amplitudes reflected by seabed sediments are generated by using Biot model and the performance of proposed feature is evaluated by using Fisher scoring and classification accuracy computed by maximum likelihood decision (MLD). As a result, the proposed feature shows higher discrimination performance and more robustness against measurement errors than that of classical feature.