• Reproductive and Developmental Biology
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• v.28 no.1
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• pp.37-43
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• 2004

This study was carried out to investigate PSS (Porcine Stress Syndrome) with the PSE (Pale, Soft, Exudative) in 319 different pigs(Yorkshire 150; Landrace 89 and Duroc 80). The PCR-RFLP method was adapted to detect the ryanodine receptor (RYR 1) gene mutation and to estimate the genotype frequency of the RYR1 gene in breeding pig population. The DNA samples were collected from hair follicles of pigs of Yorkshire, Landrace and Duroc. After DNA amplification by PCR, the PCR products were digested by restriction enzyme, Cfo I. Primary PCR products of ryanodine receptor gene were length of 659 bp in hair follicle and their second PCR products were length of 522 bp in hair follicle. The exon region (522 bp) including point mutation ($C \arrow T; Arg \arrow Cys$) in the porcine ryanodine receptor gene, which is a causal mutation for PSS, was digested with Cfo I restriction enzyme. The RYR1 gene was classifed into three genotypes by agarose gel electrophoresis. The normal homozygous (NN) individuals showed two DNA fragments consisted of 439 and 83 bp. The mutant homozygous (nn) individuals showed only one DNA fragment 522 bp. In addition, all three fragments (522, 439 and 83 bp) were showed in heterozygous (Nn) carrier animals. The normal homozygous (NN), heterozygous (Nn) and mutant homozygous (nn) were 98.00, 2.00 and 0.00% in Yorkshire pigs, 87.64, 11.24 and 1.12% in Landrace, 100.00, 0.00 and 0.00% in Duroc, respectively. The gene frequencies of N and n were 0.990 and 0.010 in Yorkshire pigs, 0.933 and 0.067 in Landrace, 1.000 and 0.000 in Duroc, respectively.

• Journal of the Korean Wood Science and Technology
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• v.47 no.2
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• pp.229-238
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• 2019

Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.18 no.4
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• pp.570-593
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• 1996

This study was intended to perform the influence of condyle positional change after surgical correction of skeletal Class III malocclusion after BSSRO in 20 patients(males 9, females 11) using computed tomogram that were taken in centric occlusion before, immediate, and long term after surgery and lateral cephalogram that were taken in centric occlusion before, 7 days within the period intermaxillary fixation, 24hour after removing intermaxillary fixation and long term after surgery. 1. Mean intercondylar distance was $84.45{\pm}4.01mm$ and horizontal long axis of condylar angle was $11.89{\pm}5.19^{\circ}$on right, $11.65{\pm}2.09^{\circ}$on left side and condylar lateral poles were located about 12mm and medial poles about 7mm from reference line(AA') on the axial tomograph. Mean intercondylar distance was $84.43{\pm}3.96mm$ and vertical axis angle of condylar angle was $78.72{\pm}3.43^{\circ}$on right, $78.09{\pm}6.12^{\circ}$on left. 2. No statistical significance was found on the condylar change(T2C-T1C) but it had definitive increasing tendency. There was significant decreasing of the distance between both condylar pole and the AA'(p<0.05) during the long term(TLC-T2C). 3. On the lateral cephalogram, no statistical significance was found between immediate after surgery and 24 hours after the removing of intermaxillary fixation but only the lower incisor tip moved forward about 0.33mm(p<0.05). Considering individual relapse rate, mean relapse rate was 1.2% on L1, 5.0% on B, 2.0% on Pog, 9.1% on Gn, 10.3% on Me(p<0.05). 4. There was statistical significance on the influence of the mandibular set-back to the total mandibular relapse(p<0.05). 5. There was no statistical significance on the influence of the mandibular set-back(T2-T1) to the condylar change(T2C-T1C), the condylar change(T2C-T1C, TLC-T2C) to the mandibular total relapse, the pre-operative condylar position to the condylar change(T2C-T1C, TLC-T2C), the pre-operative mandibular posture to the condylar change(T2C-T1C, TLC-T2C)(p>0.05). 6. The result of multiple regression analysis on the influence of the pre-operative condylar position to the total mandibular relapse revealed that the more increasing of intercondylar distance and condylar vertical axis angle and decreasing of condyalr head long axis angle, the more increasing of mandibular horizontal relapse(L1,B,Pog,Gn,Me) on the right side condyle. The same result was founded in the case of horizontal relapse(L1,Me) on the left side condyle.(p<0.05). 7. The result of multiple regression analysis on the influence of the pre-operative condylar position to the pre-operative mandibular posture revealed that the more increasing of intercondylar distance and condylar vertical axis angle and decreasing of condylar head long axis angle, the more increasing of mandibular vertical length on the right side condyle. and increasing of vertical lengh & prognathism on the left side condyle(p<0.05). 8. The result of simple regression analysis on the influence of the pre-operative mandibular posture to the mandibular total relapse revealed that the more increasing of prognathism, the more increasing of mandibular total relapse in B and the more increasing of over-jet the more increasing of mandibular total relapse(p<0.05). Consequently, surgical mandibular repositioning was not significantly influenced to the change of condylar position with condylar reposition method.

• KSBB Journal
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• v.16 no.5
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• pp.452-458
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• 2001

Concentrations of ammonia in biological processes were controlled by PID controllers and also neural network based controllers (NN controllers). A flow injection analysis system has been to on-line monitor the concentrations of ammonia in a bioreactor. The effect of the analysis error and the residence time of samples on the control performance were studied. The optimal neural network structure was investigated by using computer simulation and found to be a 3(input layer)-2(hidden layer)-1(output layer). The NN controller is often time consuming, but it has advantage over the PID controller in sensitivity. The 3-2-1 NN controller has been applied to control the ammonia concentrations in a simulated bioprocess and also a real cultivation process of yeast. The good control performance showed that the 3-2-1 NN controller based on the FIA system can be used to control the concentration of substrates in biological processes very well.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1204-1217
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• 2004

Numerous statistical and machine learning techniques have been studied for automatic text classification. However, because they train the classifiers using only feature vectors of documents, ambiguity between two possible categories significantly degrades precision of classification. To remedy the drawback, we propose a new method which incorporates relationship information of categories into extant classifiers. In this paper, we first perform the document classification using the k-NN classifier which is generally known for relatively good performance in spite of its simplicity. We employ the relationship information from an object-based thesaurus to reduce the ambiguity. By referencing various relationships in the thesaurus corresponding to the structured categories, the precision of k-NN classification is drastically improved, removing the ambiguity. Experiment result shows that this method achieves the precision up to 13.86% over the k-NN classification, preserving its recall.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.39-48
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• 2011

Global warming causes the climate change and makes severe damage to ecosystem and civilization Carbon dioxide greatly contributes to global warming, thus many studies have been conducted to estimate the forest biomass carbon stock as an important carbon storage. However, more studies are required for the selection and use of technique and remotely sensed data suitable for the carbon stock estimation in Korea In this study, the aboveground forest biomass carbon stocks of Danyang-Gun in South Korea was estimated using $k$NN($k$-Nearest Neighbor) algorithm and regression model, then the results were compared. The Landsat TM and 5th NFI(National Forest Inventory) data were prepared, and ratio images, which are effective in topographic effect correction and distinction of forest biomass, were also used. Consequently, it was found that $k$NN algorithm was better than regression model to estimate the forest carbon stocks in Danyang-Gun, and there was no significant improvement in terms of accuracy for the use of ratio images.

• Journal of Life Science
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• v.20 no.8
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• pp.1241-1248
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• 2010

The solvent extracts of Nelumbo nucifera G. were investigated for antioxidant activities, whitening and anti-wrinkle effects to apply as a functional ingredient in cosmetic products. For their industrial application, the cosmetic products were also prepared with advanced formulation techniques such as W/O/W multiple emulsion. Total phenolic and flavonoids contents increased in Nelumbo nucifera G.-Leaf (NN-L). The electron donating ability of Nelumbo nucifera G.-Flower (NN-F) or Nelumbo nucifera G.-Leaf (NN-L) extracts were above 85% at a concentration of 500 ppm. The superoxide dismutase (SOD)-like activity of Nelumbo nucifera G. (NN-L) extracts was about 60% at a concentration of 1,000 ppm. The xanthine oxidase inhibitory effect of NN-L extract was higher than that of NN-F and NN-S extracts. The tyrosinase inhibitory effect, which is related to skin-whitening, was 36% in NN-F at 1,000 ppm. For anti-wrinkle effect, the elastase inhibition activity of NN-L was about 30% at 1,000 ppm. The results of stability test showed that W/O/W multiple emulsion (ME) containing Nelumbo nucifera G. extracts. The electron donating ability of the ME containing NN-F and NN-L were about 60% at a concentration of 100 ppm. The superoxide dismutase (SOD)-like activity of the ME containing NN-L was 30% at 1,000 ppm. The tyrosinase inhibitory effect, which is related to skin-whitening, was 34% in the ME containing NN-F at 1,000 ppm. In anti-wrinkle effect, the elastase inhibition activity of the ME containing NN-L was about 55% at 1,000 ppm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.1-8
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• 2016

In this study, an WiFi fingerprinting method based on the k-nn algorithm was applied to improve the accuracy of location tracking of a moving train on a platform and evaluate the performance to minimize the estimation error of location tracking. The data related to the position of the moving train are monitored by the control center for trains and used widely for the safety and comfort of passengers. The train location tracking methods based on WiFi installed by telecom companies were evaluated. In this study, a simulator was developed to consider the environments of two cases; in already installed WiFi devices and new installed WiFi devices. The developed simulator can simulate the localized estimation of the position under a variety of conditions, such as the number of WiFi devices, the area of platform and entry velocity of train. To apply location tracking algorithms, a k-nn algorithm and fuzzy k-nn algorithm were applied selectively according to the underlying condition and also four distance measurement algorithms were applied to compare the error of location tracking. In conclusion, the best method to estimate train location tracking is a combination of the k-nn algorithm and Minkoski distance measurement at a 0.5m grid unit and 8 WiFi AP installed.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.187-192
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• 2008

A saturation and deadzone compensator is designed for robot systems using fuzzy logic (FL) and neural network (NN). The classification property of FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and deadzone. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the saturation and deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The NN saturation and FL deadzone compensator is simulated on a robot system to show its efficacy.