• Title/Summary/Keyword: classification of class

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Soft Independent Modeling of Class Analogy for Classifying Lumber Species Using Their Near-infrared Spectra

  • Yang, Sang-Yun;Park, Yonggun;Chung, Hyunwoo;Kim, Hyunbin;Park, Se-Yeong;Choi, In-Gyu;Kwon, Ohkyung;Yeo, Hwanmyeong
    • Journal of the Korean Wood Science and Technology
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    • v.47 no.1
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    • pp.101-109
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    • 2019
  • This paper examines the classification of five coniferous species, including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa), using near-infrared (NIR) spectra. Fifty lumber samples were collected for each species. After air-drying the lumber, the NIR spectra (wavelength = 780-2500 nm) were acquired on the wide face of the lumber samples. Soft independent modeling of class analogy (SIMCA) was performed to classify the five species using their NIR spectra. Three types of spectra (raw, standard normal variated, and Savitzky-Golay $2^{nd}$ derivative) were used to compare the classification reliability of the SIMCA models. The SIMCA model based on Savitzky-Golay $2^{nd}$ derivatives preprocessing was determined as the best classification model in this study. The accuracy, minimum precision, and minimum recall of the best model (PCA models using Savitzky-Golay $2^{nd}$ derivative preprocessed spectra) were evaluated as 73.00%, 98.54% (Korean pine), and 67.50% (Korean pine), respectively.

Semi-Supervised Recursive Learning of Discriminative Mixture Models for Time-Series Classification

  • Kim, Minyoung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.13 no.3
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    • pp.186-199
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    • 2013
  • We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.

The Performance Improvement of Face Recognition Using Multi-Class SVMs (다중 클래스 SVMs를 이용한 얼굴 인식의 성능 개선)

  • 박성욱;박종욱
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.6
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    • pp.43-49
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    • 2004
  • The classification time required by conventional multi-class SVMs(Support Vector Machines) greatly increases as the number of pattern classes increases. This is due to the fact that the needed set of binary class SVMs gets quite large. In this paper, we propose a method to reduce the number of classes by using nearest neighbor rule (NNR) in the principle component analysis and linear discriminant analysis (PCA+LDA) feature subspace. The proposed method reduces the number of face classes by selecting a few classes closest to the test data projected in the PCA+LDA feature subspace. Results of experiment show that our proposed method has a lower error rate than nearest neighbor classification (NNC) method. Though our error rate is comparable to the conventional multi-class SVMs, the classification process of our method is much faster.

A Study on Dynamic Positioning System IMO class upgrade requirements (Dynamic Positioning System의 IMO Class 변경 요건에 관한 연구)

  • Chae, Chong-Ju
    • Journal of Navigation and Port Research
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    • v.39 no.3
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    • pp.165-172
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    • 2015
  • The class of Dynamic Positioning System is divided in 3 classes depending on its redundancy and reliability according to IMO and classification society. There are 3 DP classes such as DP Class 1, 2 and 3 according to IMO MSC/Circ. 645. Higher DP class vessel has higher reliability, since redundancy concept is applied to the DP vessel depending on its DP class and can operate more safely. There are not enough information about DP class notation, which are needed when a company builds a new or buys second hand DP vessel or modifies DP classes, even the Korean shipyard is building a lot of DP vessels now. Also, the practical case of DP vessel modification, which had been done in Korea, to meet DP notation of IMO and classification society, will be helpful for DP vessel modification and sales industry development in Korea as a new business. As such this research identified what kind of requirements need to be taken into account to be from DP class 1 to DP class 2. The real DP class modification case is used to identify the requirements of DP class upgrade. Through the FMEA the redundancy concept on power system, thruster system and DP control system need to apply for DP class upgrade. The power system have to keep its DP function even if just a single fault happens on the generator or switchboard. Also, the PMS is required to monitor and control power system. Ship's Surge, Sway and Yaw movements can be controlled by the remaining thruster system after a single thruster fails. Lastly, multiple installation of PRS, sensors and DP control system are required to keep DP ability after a single fault on the DP control systems.

Five-year investigation of a large orthodontic patient population at a dental hospital in South Korea

  • Piao, Yongxu;Kim, Sung-Jin;Yu, Hyung-Seog;Cha, Jung-Yul;Baik, Hyoung-Seon
    • The korean journal of orthodontics
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    • v.46 no.3
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    • pp.137-145
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    • 2016
  • Objective: The purpose of this study was to investigate the characteristics of orthodontic patients at Yonsei Dental Hospital from 2008 to 2012. Methods: We evaluated Angle's classification from molar relationships, classification of skeletal malocclusion from the A point-nasion-B point angle, facial asymmetry, and temporomandibular joint disorders (TMDs) from the records of 7,476 patients who received an orthodontic diagnosis. The orthognathic surgery rate, extraction rate, and extraction sites were determined from the records of 4,861 treated patients. Results: The patient number increased until 2010 and gradually decreased thereafter. Most patients were aged 19-39 years, with a gradual increase in patients aged ${\geq}40years$. Angle's Class I, Class II divisions 1 and 2, and Class III malocclusions were observed in 27.7%, 25.6%, 10.6%, and 36.1% patients, respectively, with a gradual decrease in the frequency of Class I malocclusion. The proportion of patients with skeletal Class I, Class II, and Class III malocclusions was 34.3%, 34.3%, and 31.4%, respectively, while the prevalence of facial asymmetry and TMDs was 11.0% and 24.9%, respectively. The orthognathic surgery rate was 18.5%, with 70% surgical patients exhibiting skeletal Class III malocclusion. The overall extraction rate among nonsurgical patients was 35.4%, and the maxillary and mandibular first premolars were the most commonly extracted teeth. Conclusions: The most noticeable changes over time included a decrease in the patient number after 2010, an increase in the average patient age, and a decrease in the frequency of Angle's Class I malocclusion. Our results suggest that periodic characterization is necessary to meet the changing demands of orthodontic patients.

Classification of ratings in online reviews (온라인 리뷰에서 평점의 분류)

  • Choi, Dongjun;Choi, Hosik;Park, Changyi
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.4
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    • pp.845-854
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    • 2016
  • Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.

A Design of Classification System for Military Information Resources on the Internet (군사학 분야 웹 문서 분류체계의 설계)

  • 오동근;황재영;배영활
    • Journal of Korean Library and Information Science Society
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    • v.32 no.2
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    • pp.323-347
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    • 2001
  • This study is to suggest a classification system to classify the military information resources on the internet. In the first part, it compares LCC\`s Class U(military Science) and Class V(Naval Science) with Yahoo! Korea\`s web site classification system(Home/Government/Military). The second part compares the classes of Yahoo! Korea with those of Simmani search engine and Yahoo! US. Based on this comparative analysis, it proposes a classificatory system for the military information resources on the internet.

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Determination of Cost and Measurement of nursing Care Hours for Hospice Patients Hospitalized in one University Hospital (일 대학병원 호스피스 병동 입원 환자의 간호활동시간 측정과 원가산정)

  • Kim, Kyeong-Uoon
    • Journal of Korean Academy of Nursing Administration
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    • v.6 no.3
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    • pp.389-404
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    • 2000
  • This study was designed to determine the cost and measurement of nursing care hours for hospice patients hostpitalized in one university hospital. 314 inpatients in the hospice unit 11 nursing manpower were enrolled. Study was taken place in C University Hospital from 8th to 28th, Nov, 1999. Researcher and investigator did pilot study for selecting compatible hospice patient classification indicators. After modifying patient classification indicators and nursing care details for general ward, approved of content validity by specialist. Using hospice patient classification indicators and per 5 min continuing observation method, researcher and investigator recorded direct nursing care hours, indirect nursing care hours, and personnel time on hospice nursing care hours, and personnel time on hospice nursing care activities sheet. All of the patients were classified into Class I(mildly ill), Class II (moderately ill), Class III (acutely ill), and Class IV (critically ill) by patient classification system (PCS) which had been carefully developed to be suitable for the Korean hospice ward. And then the elements of the nursing care cost was investigated. Based on the data from an accounting section (Riccolo, 1988), nursing care hours per patient per day in each class and nursing care cost per patient per hour were multiplied. And then the mean of the nursing care cost per patient per day in each class was calculated. Using SAS, The number of patients in class and nursing activities in duty for nursing care hours were calculated the percent, the mean, the standard deviation respectively. According to the ANOVA and the $Scheff{\'{e}$ test, direct nursing care hours per patient per day for the each class were analyzed. The results of this study were summarized as follows : 1. Distribution of patient class : class IN(33.5%) was the largest class the rest were class II(26.1%) class III(22.6%), class I(17.8%). Nursing care requirements of the inpatients in hospice ward were greater than that of the inpatients in general ward. 2. Direct nursing care activities : Measurement ${\cdot}$ observation 41.7%, medication 16.6%, exercise ${\cdot}$ safety 12.5%, education ${\cdot}$ communication 7.2% etc. The mean hours of direct nursing care per patient per day per duty were needed ; 69.3 min for day duty, 64.7 min for evening duty, 88.2 min for night duty, 38.7 min for shift duty. The mean hours of direct nursing care of night duty was longer than that of the other duty. Direct nursing care hours per patient per day in each class were needed ; 3.1 hrs for class I, 3.9 hrs for class II, 4.7 hrs for class III, and 5.2 hrs for class IV. The mean hours of direct nursing care per patient per day without the PCS was 4.1 hours. The mean hours of direct nursing care per patient per day in class was increased significantly according to increasing nursing care requirements of the inpatients(F=49.04, p=.0001). The each class was significantly different(p<0.05). The mean hours of direct nursing care of several direct nursing care activities in each class were increased according to increasing nursing care requirements of the inpatients(p<0.05) ; class III and class IV for medication and education ${\cdot}$ communication, class I, class III and class IV for measurement ${\cdot}$ observation, class I, class II and class IV for elimination ${\cdot}$ irrigation, all of class for exercise ${\cdot}$ safety. 3. Indirect nursing care activities and personnel time : Recognization 24.2%, house keeping activity 22.7%, charting 17.2%, personnel time 11.8% etc. The mean hours of indirect nursing care and personnel time per nursing manpower was 4.7 hrs. The mean hours of indirect nursing care and personnel time per duty were 294.8 min for day duty, 212.3 min for evening duty, 387.9 min for night duty, 143.3 min for shift duty. The mean of indirect nursing care hours and personnel time of night duty was longer than that of the other duty. 4. The mean hours of indirect nursing care and personnel time per patient per day was 2.5 hrs. 5. The mean hours of nursing care per patient per day in each class were class I 5.6 hrs, class II 6.4 hrs, class III 7.2 hrs, class IV 7.7 hrs. 6. The elements of the nursing care cost were composed of 2,212 won for direct nursing care cost, 267 won for direct material cost and 307 won for indirect cost. Sum of the elements of the nursing care cost was 2,786 won. 7. The mean cost of the nursing care per patient per day in each class were 15,601.6 won for class I, 17,830.4 won for class II, 20,259.2 won for class III, 21,452.2 won for class IV. As above, using modified hospice patient classification indicators and nursing care activity details, many critical ill patients were hospitalized in the hospice unit and it reflected that the more nursing care requirements of the patients, the more direct nursing care hours. Emotional ${\cdot}$ spiritual care, pain ${\cdot}$ symptom control, terminal care, education ${\cdot}$ communication, narcotics management and delivery, attending funeral ceremony, the major nursing care activities, were also the independent hospice service. But it is not compensated by the present medical insurance system. Exercise ${\cdot}$ safety, elimination ${\cdot}$ irrigation needed more nursing care hours as equal to that of intensive care units. The present nursing management fee in the medical insurance system compensated only a part of nursing car service in hospice unit, which rewarded lower cost that that of nursing care.

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Various Quality Fingerprint Classification Using the Optimal Stochastic Models (최적화된 확률 모델을 이용한 다양한 품질의 지문분류)

  • Jung, Hye-Wuk;Lee, Jee-Hyong
    • Journal of the Korea Society for Simulation
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    • v.19 no.1
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    • pp.143-151
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    • 2010
  • Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.

CLASSIFICATION OF SOLVABLE LIE GROUPS WHOSE NON-TRIVIAL COADJOINT ORBITS ARE OF CODIMENSION 1

  • Ha, Hieu Van;Hoa, Duong Quang;Le, Vu Anh
    • Communications of the Korean Mathematical Society
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    • v.37 no.4
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    • pp.1181-1197
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    • 2022
  • We give a complete classification of simply connected and solvable real Lie groups whose nontrivial coadjoint orbits are of codimension 1. This classification of the Lie groups is one to one corresponding to the classification of their Lie algebras. Such a Lie group belongs to a class, called the class of MD-groups. The Lie algebra of an MD-group is called an MD-algebra. Some interest properties of MD-algebras will be investigated as well.