• Asian-Australasian Journal of Animal Sciences
• /
• v.32 no.7
• /
• pp.1015-1026
• /
• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.3
• /
• pp.160-174
• /
• 2023

LiDAR plays a key role in autonomous vehicles, and to improve its visibility, it is necessary to improve its performance and the detection objects. Accordingly, this study proposes a shape for traffic safety signs that is advantageous for self-driving vehicles to recognize. Improvement plans are also proposed using a shape-recognition algorithm based on point cloud data collected through LiDAR sensors. For the experiment, a DBSCAN-based road-sign recognition and classification algorithm, which is commonly used in point cloud research, was developed, and a 32ch LiDAR was used in an actual road environment to conduct recognition performance tests for 5 types of road signs. As a result of the study, it was possible to detect a smaller number of point clouds with a regular triangle or rectangular shape that has vertical asymmetry than a square or circle. The results showed a high classification accuracy of 83% or more. In addition, when the size of the square mark was enlarged by 1.5 times, it was possible to classify it as a square despite an increase in the measurement distance. These results are expected to be used to improve dedicated roads and traffic safety facilities for sensors in the future autonomous driving era and to develop new facilities.

• Korean Journal of Radiology
• /
• v.24 no.10
• /
• pp.1028-1037
• /
• 2023

Objective: To evaluate the computed tomography (CT) features for diagnosing metastatic cervical lymph nodes (LNs) in patients with differentiated thyroid cancer (DTC) and validate the CT-based risk stratification system suggested by the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) guidelines. Materials and Methods: A total of 463 LNs from 399 patients with DTC who underwent preoperative CT staging and ultrasound-guided fine-needle aspiration were included. The following CT features for each LN were evaluated: absence of hilum, cystic changes, calcification, strong enhancement, and heterogeneous enhancement. Multivariable logistic regression analysis was performed to identify independent CT features associated with metastatic LNs, and their diagnostic performances were evaluated. LNs were classified into probably benign, indeterminate, and suspicious categories according to the K-TIRADS and the modified LN classification proposed in our study. The diagnostic performance of both classification systems was compared using the exact McNemar and Kosinski tests. Results: The absence of hilum (odds ratio [OR], 4.859; 95% confidence interval [CI], 1.593-14.823; P = 0.005), strong enhancement (OR, 28.755; 95% CI, 12.719-65.007; P < 0.001), and cystic changes (OR, 46.157; 95% CI, 5.07-420.234; P = 0.001) were independently associated with metastatic LNs. All LNs showing calcification were diagnosed as metastases. Heterogeneous enhancement did not show a significant independent association with metastatic LNs. Strong enhancement, calcification, and cystic changes showed moderate to high specificity (70.1%-100%) and positive predictive value (PPV) (91.8%-100%). The absence of the hilum showed high sensitivity (97.8%) but low specificity (34.0%). The modified LN classification, which excluded heterogeneous enhancement from the K-TIRADS, demonstrated higher specificity (70.1% vs. 62.9%, P = 0.016) and PPV (92.5% vs. 90.9%, P = 0.011) than the K-TIRADS. Conclusion: Excluding heterogeneous enhancement as a suspicious feature resulted in a higher specificity and PPV for diagnosing metastatic LNs than the K-TIRADS. Our research results may provide a basis for revising the LN classification in future guidelines.

• Journal of the Korean GEO-environmental Society
• /
• v.13 no.4
• /
• pp.13-26
• /
• 2012

The deformation modulus of rock masses is a very important design factor for the computation of stability of tunnels and their support systems. Several empirical formulas to estimate the deformation modulus using simple rock classification methods such as RQD or RMR are widely used because field tests to evaluate the deformation modulus are very expensive and time consuming work. However, these formulas can be depended on experiences from the characteristics of local sites in each country. So it is possible that there might be limitations to estimate appropriate deformation modulus in South Korea using the empirical formulas. Therefore, in this study, the applicability of empirical formulas was analyzed by comparing estimated value with the measured value from eight sites in South Korea. The results show that the estimated value based on the empirical formulas partially have tendency to overestimate. Especially, in case of sedimentary rocks, it was too difficult to apply to the empirical formulas because there was no relation ship between estimated value and measured value. For these reasons, additional data from many tests and accurate analyses are necessary to evaluate the estimation method for the deformation modulus considering the local characteristics of rock masses.

• Korean Journal of Agricultural Science
• /
• v.39 no.3
• /
• pp.407-412
• /
• 2012

Soil texture has an important influence on agriculture such as crop selection, movement of nutrient and water, soil electrical conductivity, and crop growth. Conventionally, soil texture has been determined in the laboratory using pipette and hydrometer methods requiring significant amount of time, labor, and cost. Recently, in-situ soil texture classification systems using optical diffuse reflectometry or mechanical resistance have been reported, especially for precision agriculture that needs more data than conventional agriculture. This paper is a part of overall research to develop an in-situ soil texture classification system using image processing. Issues investigated in this study were effects of sensor travel speed and light source and intensity on image quality. When travel speed of image sensor increased from 0 to 10 mm/s, travel distance and number of pixel were increased to 3.30 mm and 9.4, respectively. This travel distances were not negligible even at a speed of 2 mm/s (i.e., 0.66 mm and 1.4), and image degradation was significant. Tests for effects of illumination intensity showed that 7 to 11 Lux seemed a good condition minimizing shade and reflection. When soil water content increased, illumination intensity should be greater to compensate decrease in brightness. Results of the paper would be useful for construction, test, and application of the sensor.

• Clinical and Experimental Pediatrics
• /
• v.63 no.3
• /
• pp.79-87
• /
• 2020

Inherited platelet disorders (IPDs), which manifest as primary hemostasis defects, often underlie abnormal bleeding and a family history of thrombocytopenia, bone marrow failure, hematologic malignancies, undefined mucocutaneous bleeding disorder, or congenital bony defects. Wide heterogeneity in IPD types with regard to the presence or absence of thrombocytopenia, platelet dysfunction, bone marrow failure, and dysmegakaryopoiesis is observed in patients. The individual processes involved in platelet production and hemostasis are genetically controlled; to date, mutations of more than 50 genes involved in various platelet biogenesis steps have been implicated in IPDs. Representative IPDs resulting from defects in specific pathways, such as thrombopoietin/MPL signaling; transcriptional regulation; granule formation, trafficking, and secretion; proplatelet formation; cytoskeleton regulation; and transmembrane glycoprotein signaling are reviewed, and the underlying gene mutations are discussed based on the National Center for Biotechnology Information database and Online Mendelian Inheritance in Man accession number. Further, the status and prevalence of genetically confirmed IPDs in Korea are explored based on searches of the PubMed and KoreaMed databases. IPDs are congenital bleeding disorders that can be dangerous due to unexpected bleeding and require genetic counseling for family members and descendants. Therefore, the pediatrician should be suspicious and aware of IPDs and perform the appropriate tests if the patient has unexpected bleeding. However, all IPDs are extremely rare; thus, the domestic incidences of IPDs are unclear and their diagnosis is difficult. Diagnostic confirmation or differential diagnoses of IPDs are challenging, time-consuming, and expensive, and patients are frequently misdiagnosed. Comprehensive molecular characterization and classification of these disorders should enable accurate and precise diagnosis and facilitate improved patient management.

• The Journal of Engineering Geology
• /
• v.26 no.4
• /
• pp.593-600
• /
• 2016

To understand the mechanical properties of rock masses and intact rock in Korea, data from 4,280 in situ and laboratory tests from 107 tunnels on general national roads were analyzed. The mechanical properties (unit weight, cohesion, friction angle, modulus of deformation, Young's modulus, Poisson's ratio, uniaxial compressive strength, tensile strength, coefficient of permeability, and specific gravity) were analyzed by rock types and strength of rock in each rock type. The results of analysis, the mean specific gravity was highest in gneiss. The coefficient of permeability and Poisson's ratio show the highest mean values in granite and metamorphic rock, respectively. In addition, the unit weight, cohesion and friction angle in sedimentary rock, modulus of deformation, Young's modulus, uniaxial compressive strength and tensile strength in volcanic rock have the highest mean values. The values for each mechanical property showed wide ranges by the heterogeneity and anisotropy of rock masses in spite of detailed analysis by rock type and classification of rocks according to the strength.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.3
• /
• pp.229-234
• /
• 2018

Texture is one of the most useful features in classifying and segmenting images. The LBP-based approach previously presented in the literature has been successful in many applications. However, it's theoretical foundation is based only on the difference of pixel values, and consequently it has a number of drawbacks like it performs poorly for the images corrupted with noise, and especially it cannot be used as a multiscale texture descriptor due to the exploding increase of feature vector dimension with increase of the number of neighbor pixels. In this paper, we present a method to address these drawbacks of LBP-based approach. More specifically, our approach quantizes the range of pixels values and construct a 3D histogram which captures the correlative information of pixels. This histogram is used as a texture feature. Several tests with texture images show that the proposed method outperforms the LBP-based approach in the problem of texture classification.

• Proceedings of the KAIS Fall Conference
• /
• 2012.05a
• /
• pp.22-24
• /
• 2012

The tools that classify the severity of patients based on the prediction of mortality include APACHE, SAPS, and MPM. Theses tools rely crucially on the evaluation of patients' general clinical status on the first date of their admission to ICU. Nursing activities are one of the most crucial factors influencing on the quality of treatment that patients receive and one of the contributing factors for their prognosis and safety. The purpose of this study was to identify the goodness-of-fit of CPSCS of critical patient severity classification system(CPSCS) and Glasgow coma scale(GCS) and the clinical usefulness of its death rate prediction. Data were collected from the medical records of 187 neurological patients who were admitted to the ICU of C University Hospital. The data were analyzed through $x^2$ test, t-test, Mann-Whitney, Kruskal-Wallis, goodness-of-fit test, and ROC curve. In accordance with patients' general and clinical characteristics, patient mortality turned out to be statistically different depending on ICU stay, endotracheal intubation, central venous catheter, and severity by CPSCS. Homer-Lemeshow goodness-of-fit tests were CPSCS and GCS and the results of the discrimination test using the ROC curve were $CPSCS_0$, .734, $GCS_0$,.583, $CPSCS_{24}$,.734, $GCS_{24}$, .612, $CPSCS_{48}$,.591, $GCS_{48}$,.646, $CPSCS_{72}$,.622, and $GCS_{72}$,.623. Logistic regression analysis showed that each point on the CPSCS score signifies1.034 higher likelihood of dying. Applied to neurologically ill patients, early CPSCS scores can be regarded as a useful tool.

• Spatial Information Research
• /
• v.8 no.1
• /
• pp.171-182
• /
• 2000

Random sampling or systematic sampling method is commonly used to assess the accuracy of classification results. In remote sensing, with these sampling method, much time and tedious works are required to acquire sufficient ground truth data. So , a more effective sampling method that can retain the characteristics of the population is required. In this study, fractal analysis is adopted as an index for reference sampling . The fractal dimensions of the whole study area and the sub-regions are calculated to choose sub-regions that have the most similar dimensionality to that of whole-area. Then the whole -area s classification accuracy is compared to those of sub-regions, respectively, and it is verified that the accuracies of selected sub regions are similar to that of full-area . Using the above procedure, a new kind of reference sampling method is proposed. The result shows that it is possible to reduced sampling area and sample size keeping up the same results as existing methods in accuracy tests. Thus, the proposed method is proved cost-effective for reference data sampling.