• 대한골관절종양학회지
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• 제14권1호
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• pp.1-9
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• 2008

연조직종양의 이해는 과거 10년 동안에 걸쳐 주요 변화와 더불어 실질적인 진보가 있었고, 이를 바탕으로 연조직종양의 새로운 분류가 WHO에 의해 2002년에 이루어졌다. 이 개정은 이전에 발표와 상당히 다른 내용의 접근을 하였고, 이 작업에 유전학과 분자생물학 그리고 임상분야의 전문가들이 참여하였다. 여기에서는 과거에 알고 있었거나 특성이 알려진 많은 종양을 포함하여 새로운 큰 변화나 작은 변화가 일어난 부분에 대해서 정리를 하였다. 이러한 내용을 연조직종양의 새로운 WHO 분류를 중심으로 지방세포종, 섬유모세포성/근육섬유모세포 성종과 소위섬유조직구종, 평활근종, 혈관주위종과 근골격종을 중심으로, 큰 변화와 작은 변화로 나누어서 설명하고 새롭게 소개되는 병명을 소개하고 정리하였다. 이 새로운 WHO의 연조직종양의 분류를 이해하여, 종양의 진단과 예후의 재현을 용이하게 하는 필수적인 지침으로 사용할 수 있을 것으로 생각된다.

• 대한골관절종양학회지
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• 제14권2호
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• pp.79-85
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• 2008

연조직종양의 분류는 종양학에서 영상의학과의사와 임상을 담당하는 정형외과의사, 종양학자 그리고 병리학자의 진단과 예후의 재현을 용이하게 하는 필수적인 지침이다. 연조직종양의 이해는 과거 10년 동안에 걸쳐 주요 변화와 더불어 진보가 있었고, 이를 바탕으로 연조직종양의 새로운 분류가 WHO에 의해 2002년에 이루어졌다. 이 개정은 이전에 발표된 분류와 많은 부분에서 다른 내용의 접근을 하였고, 이 작업에는 유전학과 분자생물학 그리고 임상분야의 전문가들이 참여하였다. 여기에서는 과거에 알고 있었거나 특성이 알려진 종양을 포함하여 새로운 큰 변화나 작은 변화가 일어난 부분에 대해서 정리를 하였다. 이러한 내용의 연조직종양의 새로운 WHO 분류를 혈관종, 연골-골종 그리고 불확실한분화종을 중심으로, 큰 변화와 작은 변화로 나누어서 설명하고 새롭게 소개되는 병명을 정리하였다. 이 새로운 WHO의 연조직 종양의 분류를 이해하여, 종양의 진단과 예후의 재현을 용이하게 하는 필수적인 지침으로 사용할 수 있을 것으로 생각된다.

• 대한치과교정학회지
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• 제43권6호
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• pp.279-287
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• 2013

Objective: To investigate the dentoskeletal factors which may predict soft-tissue chin strain during lip closure. Methods: The pretreatment frontal and lateral facial photographs and lateral cephalograms of 209 women (aged 18-30 years) with Angle's Class I or II malocclusion were examined. The subjects were categorized by three examiners into the no-strain and strain groups according to the soft-tissue chin tension or deformation during lip closure. Relationships of the cephalometric measurements with the group classification were analyzed by logistic regression analysis, and a classification and regression tree (CART) model was used to define the predictive variables for the group classification. Results: The lower the value of the overbite depth indicator (ODI) and the higher the values of upper incisor to Nasion-Pogonion (U1-NPog, mm), overjet, and upper incisor to upper lip (U1-upper lip, mm), the more likely was the subject to be classified into the strain group. The CART showed that U1-NPog was the most prominent predictor of soft-tissue chin strain (cut-off value of 14.2 mm), followed by overjet. Conclusions: To minimize strain of the soft-tissue chin, orthodontic treatment should be oriented toward increasing the ODI value while decreasing the U1-NPog, overjet, and U1 upper lip values.

• Journal of the Korean Wood Science and Technology
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• 제47권1호
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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.

• 한국식품영양과학회지
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• 제23권3호
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• pp.420-428
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• 1994

The purpose of this study was to determine relationship among the observed frequencies of 12 beverages selected by college men and women according to sex, age, race and academic classification and to estimate consumption of milk according to sex, age, race and academic classification. The instrument consisted of a check list and four questions. The sample of 282 subjects, 149 college men and 133 college women, was made by the accidental choice method. Observations occurred in the university center cafeteria at the dinner meal. The significant relationship s were sex and race in association with beverage selections by all subjects. The proportion of men in the distribution who selected regular , carbonated soft drinks and the proportion of white students who selected any of the carobnated soft drinks were the influencies. The result of the study indicated that carbonated soft drinks were the most preferred items followed by milk, water, iced tea, fruit juices, coffee, cocoa, and tea.

• 대한치과교정학회지
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• 제21권1호
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• pp.113-130
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• 1991

This study was undertaken to investigate soft tissue profile changes by orthodontic treatment in female patients. Traditional cephalometric appraisal yields data of dubious scientific value, the soft tissue profile forms were evaluated by finite element method. The subject was divided into three groups according to Angle's classification and each group was composed of 25 female patients averaged aged 12-14 years at the start of treatment. The changes in soft tissue form were evaluated by computing the degree of distortion in each triangle after treatment compared with the triangle before treatment. The conclusions were as follows; 1. The soft tissue profile forms were evaluated by finite element method and independent evaluation of each element by local changes was possible. 2. Maximum and minimum principal strains showed marked variability depending on the particular finite element and each group and Class II, III sample was greater than Class I sample. 3. Soft tissue size changes as a result of orthodontic treatment was not related to those of shape. 4. Soft tissue changes by orthodontic treatment were variable in individual patient, and were not related to Angle's classification.

• 대한원격탐사학회지
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• 제21권3호
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.158-168
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• 2024

There is hardly any person in modern times who has not taken soft drinks instead of drinking water. The rate of people taking soft drinks being surprisingly high, researchers around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases and so on. Therefore, in this work an image-based tool is developed to monitor the nutritional information of soft drinks by using deep convolutional neural network with transfer learning. At first, visual saliency, mean shift segmentation, thresholding and noise reduction technique, collectively known as 'pre-processing' are adopted to extract the location of drinks region. After removing backgrounds and segment out only the desired area from image, we impose Discrete Wavelength Transform (DWT) based resolution enhancement technique is applied to improve the quality of image. After that, transfer learning model is employed for the classification of drinks. Finally, nutrition value of each drink is estimated using Bag-of-Feature (BoF) based classification and Euclidean distance-based ratio calculation technique. To achieve this, a dataset is built with ten most consumed soft drinks in Bangladesh. These images were collected from imageNet dataset as well as internet and proposed method confirms that it has the ability to detect and recognize different types of drinks with an accuracy of 98.51%.

• 자원리싸이클링
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• 제27권1호
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• pp.84-91
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• 2018

본 연구에서는 레이저유도 플라즈마 분광법(Laser induced breakdown spectroscopy, LIBS) 기반의 금속 종류별 스펙트럼 데이터를 이용하여 연성정보(soft information)를 추정하고 최빈 클래스로 분류하는(most probable classification) 금속 분류 방법을 제안한다. 폐금속 자원과 같이 사전 정보가 없는 금속을 분류하는 경우 몇 가지 핵심 구성성분에 대한 정량 분석을 통해서 클래스를 추정하는 방법이 효율적이다. 이에 따라 부분 집합 기반의 부분최소제곱회귀법(Partial Least Square Regression, PLSR)을 이용하여 LIBS 검출 스펙트럼으로부터 각 성분의 농도를 독립적으로 신뢰성 있게 추정하고, 인증 표준물질(CRM) 등 알려진 모집합의 농도정보에 기반하여 최고 확률을 갖도록 분류하는 기술을 제안한다. 샘플 스펙트럼들의 다변량 분석을 통해서 여러 성분의 추정 농도를 다변량 정규 분포를 갖는 것으로 가정하고 통합(Joint) 추정 연성정보를 구할 수 있으며, 이를 활용한 최빈 확률 검출이나 추가적인 사전 정보의 결합 등을 통해서 분류 성능을 향상시킬 수 있다. 제안된 기술의 평가를 위해서 9가지 종류의 CRM 금속시료의 LIBS 스펙트럼 데이터를 사용하며, 부분 집합 기반의 PLSR 농도 추정 기술을 기반으로 단변량 혹은 다변량 정규 분포 연성 정보추정을 통해 미지 금속의 검출과 연성 정보의 검출 등을 테스트 하였다. 또한 방사형 차트(Radar chart)를 이용하여 추정된 농도와 획득한 연성정보를 효과적으로 시각화함으로써 기존 라이브러리에 포함된 부분 집합의 금속과 비교하여 해당 금속과의 유사성을 그래프를 통해 추정할 수 있다.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1175-1186
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• 2017

Human emotion recognition is one of the promising applications in the era of artificial super intelligence. Thus far, facial expression traits are considered to be the most widely used information cues for realizing automated emotion recognition. This paper proposes a novel facial expression recognition (FER) method that works well for recognizing emotion from image sequences. To this end, we develop the so-called weighted soft voting classification (WSVC) algorithm. In the proposed WSVC, a number of classifiers are first constructed using different and multiple feature representations. In next, multiple classifiers are used for generating the recognition result (namely, soft voting) of each face image within a face sequence, yielding multiple soft voting outputs. Finally, these soft voting outputs are combined through using a weighted combination to decide the emotion class (e.g., anger) of a given face sequence. The weights for combination are effectively determined by measuring the quality of each face image, namely "peak expression intensity" and "frontal-pose degree". To test the proposed WSVC, CK+ FER database was used to perform extensive and comparative experimentations. The feasibility of our WSVC algorithm has been successfully demonstrated by comparing recently developed FER algorithms.