• The Journal of the Korean bone and joint tumor society
• /
• v.14 no.1
• /
• pp.1-9
• /
• 2008

Soft tissue tumor classifications should be an important part of radiology, oncology and, for clinicians and pathologists, they provide diagnostic instruction and prognostic guidelines. In soft tissue tumor classification systems, the World Health Organization (WHO) classifications have become dominant, enabled by the timely publication of new 'blue books' which included detailed text and numerous good illustrations. The new WHO classification of soft tissue tumors was introduced in 2002. Because the classification represents a broad consensus concept, it has gained widespread acceptance around the globe. This article reviews the changes which were introduced the adipose tumors, fibroblastic/myofibroblastic tumors, so-called fibrohistiocytic tumors, smooth muscle tumors, pericytic tumors and skeletal muscle tumors which have been first recognized or properly classified during the past decade.

• The Journal of the Korean bone and joint tumor society
• /
• v.14 no.2
• /
• pp.79-85
• /
• 2008

Soft tissue tumor classifications should be an important part of radiology, oncology and, for orthopedic clinicians and pathologists, they provide diagnostic instruction and prognostic guidelines. In soft tissue tumor classification systems, the World Health Organization (WHO) classifications have become dominant, enabled by the timely publication of new blue books which included detailed text and numerous good illustrations. The new WHO classification of soft tissue tumors was introduced in 2002. Because the classification represents a broad consensus concept, it has gained widespread acceptance around the globe. This article reviews the changes which were introduced the vascular tumors, chondroid-osseous tumors and tumors of uncertain differentiation which have been first recognized or properly classified during the past decade.

• The korean journal of orthodontics
• /
• v.43 no.6
• /
• pp.279-287
• /
• 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
• /
• v.47 no.1
• /
• pp.101-109
• /
• 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.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.23 no.3
• /
• pp.420-428
• /
• 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.

• The korean journal of orthodontics
• /
• v.21 no.1 s.33
• /
• pp.113-130
• /
• 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.

• Korean Journal of Remote Sensing
• /
• v.21 no.3
• /
• pp.189-211
• /
• 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
• /
• v.24 no.2
• /
• pp.158-168
• /
• 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%.

• Resources Recycling
• /
• v.27 no.1
• /
• pp.84-91
• /
• 2018

In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.

• Journal of Korea Multimedia Society
• /
• v.20 no.8
• /
• pp.1175-1186
• /
• 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.