• Journal of Veterinary Clinics
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• v.12 no.1
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• pp.861-876
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• 1995

This study was carried out to get fundamental information about the normal ultrasonogram of the liver and heart in Korean native cattle and calves. The interventricular septum, left ventricular internal diameter, left ventricular free wall thickness, aortic diameter, left atrial diameter, and right ventricular internal diameter of hear in 10 Korean native calves were determined at 4-5 right intercostal spare by use of ultrasonography. The caudal vena cava, portal vein, gallbladder, liver of 9 Korean native cattle and 10 calves were determined at 12, 11 and 10th intercostal spares by use of ultrasonography. Cursor-directed M-mode and gray-scale, B-mode ultrasonograms were obtained with electronic scanning ultrasound equipment with a 3.5 or 5.0-MHz convex transducer. The results obtained through the experiments were summarized as follows: 1. The result of ultrasonographic examination of the korean native calves' heart 1) Interventricular septum in systole and diastole was 1.23 and 0.81 cm, respectively(vc=28.84, 17.4). 2) Ventricular internal diameter in systole and diastole was 2.50 and 4.91 cm, respectively(vc=17.44, 12.73). 3) Left ventricular free was thickness in systole and diastole was 1.44 and 0.92 cm, respectively(vc=26.85, 23.54). 4) Aortic diameter was 2.69.m, .rspectevely(vc=11.29). 5) Left atrial diameter was 1.82 cm(vc=15.31). 6) Right ventricular internal diameter in systole and diastole was 1.12 and 1.9 cm, respectively(vc=33.71, 24.43). 3. Ultrasonographic measurments of caudal vena cava, portal vein, gallbladder of Korean native calves 1) Dorsal margin of caudal vena cava at the 12, 11 and 10th intercostal space was 13.5, 15.3 and 18.1 cm, respectively(p<0.01). 3) Depth of caudal vena cava at the 12, 11 and 10th intercostal space was 4.4, 4.5 and 4.6 cm, respectively. 3) Diameter of caudal vena cava at the 12, 11 and 10th intercostal space was 11.6, 1.7 and 1.6 cm, respectively. 4) Dorsal margin of portal vein at the 12, 11 and 10th intercostal space was 16.2, 18.6 and 21.4 cm, respectively(p<0.01) 5) Depth of portal vein at the 12, 11 and 10th intercostal spare was 4.5, 4.4 and 3.9 cm respectively. 6) Diameter of portal vein at the 13, 11 and 10th intercostal space was 2.1, 2.2 and 1.9 cm respectively. 7) Dorsal margin of gallbladder at the 11 and 10th intercostal space was 23.6 and 23.9 cm, respectively(p<0.01), 8) Longitudinal diameter of gallbladder at the 11 and 10th intercostal space was 7.1 and 5.9 cm, respectively(p<0.05). 9) Transverse diameter of gallbladder at the 11 and 10th intercostal space was 2.4 and 2.1 cm respectively(p<0.01). 3. Ultrasonographic measurments of caudal vena cava, portal vein, gallbladder of Korean native cattle 1) Dorsal margin of caudal vena cava at the 12 and 11th intercostal space was 22.2, and 25.4 cm, respectively(p<0.01). 2) Depth of caudal vena cava at the 12 and 11th intercostal space was 103 and 11.1 cm, respectively(p<0.01). 3) Diameter of caudal vena cava at the 12 and 11th intercostal space was 3.1 and 3.0 cm, respectively. 4) Dorsal margin of portal vein at the 12 and 11th intercostal space was 29.3 and 32.9 cm, respectively(p<0.01). 5) Depth of portal vein at the 12 and 11th intercostal space was 9.6, and 9.2 cm, respectively. 6) Diameter of portal vein at the 12 and lith intercostal space was 3.4 and 3.3 cm, respectively. 7) Dorsal margin of gallbladder at the 11 and 10th intercostal space was 43.1 and 45.5 cm, respectively(p<0.01). 8) Longitudinal diameter of gallbladder at the 11 and 10th intercostal space was 10.1 and 9.4 cm, respectively. 9) Transverse diameter of gallbladder at the 11 and 10th intercostal space was 4.0 and 3.7 cm, respectively. 4, Ultrasonogaphic measurments of dorsal margin, ventral margin, size and angles of the Korean native calves' liver. 1) Dorsal margin of liver at the 12, 11 and 10th intercostal space was 11.0, 9.6, and 12.4 cm, respectively(p<0.01). 2) Ventral margin of liver at the 12, 11 and 10th intercostal spate was 20, 24 and 26.1 cm, respectively(p<0.01). 3) Size of the liver at the 12, 11 and 10th intercostal space was 9.0, 14.6 and 13.8 cm, respectively(p<0.01). 4) Angle of liver at the 12, 11 and 10th intercostal space was 40, 46 and 37, respectively(p<0.01). 5. Ultrasonographic measurmants of dorsal margin, ventral margin, size and anglses of the korean native cattle's liver 1) Dorsal margin of the liver at the 12, 11 and 10th intercostal space was 14.4, 18.2 and 26, 3 cm, respectively. 2) Ventral margin of liver at the 12, 11 and 10th intercostal space was 41.1, 46.4 and 49.3 cm, respectively(p<0.01). 3) Size of the liver at the 12, 11 and 10th intercostal space was 26.8, 28.2 and 23.2 cm, respectively(p<0.01). 4) Angel of liverat the 15, 11 and 10 intercostal space was 41, 40.6 and 35.7, respectively(p<0.05). It was concluded that the ultrasonographic values oletermined in this study can be used as references for the diagnosis of morphologic changes in the hear and liver in korean native calves, and in the liver in korean native rattle.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.25-32
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• 2019

Purpose: The size, shape, and volume of prosthetic appliance depend on the metal artifacts resulting from dental implant during head and neck treatment with radiation. This reduced the accuracy of contouring targets and surrounding normal tissues in radiation treatment plan. Therefore, the purpose of this study is to obtain the images of metal representing the size of tooth through MVCT, SMART-MAR CT and KVCT, evaluate the volumes, apply them into the proton therapy plan, and analyze the difference of dose distribution. Materials and Methods : Metal A ($0.5{\times}0.5{\times}0.5cm$), Metal B ($1{\times}1{\times}1cm$), and Metal C ($1{\times}2{\times}1cm$) similar in size to inlay, crown, and bridge taking the treatments used at the dentist's into account were made with Cerrobend ($9.64g/cm^3$). Metal was placed into the In House Head & Neck Phantom and by using CT Simulator (Discovery CT 590RT, GE, USA) the images of KVCT and SMART-MAR were obtained with slice thickness 1.25 mm. The images of MVCT were obtained in the same way with $RADIXACT^{(R)}$ Series (Accuracy $Precision^{(R)}$, USA). The images of metal obtained through MVCT, SMART-MAR CT, and KVCT were compared in both size of axis X, Y, and Z and volume based on the Autocontour Thresholds Raw Values from the computerized treatment planning equipment Pinnacle (Ver 9.10, Philips, Palo Alto, USA). The proton treatment plan (Ray station 5.1, RaySearch, USA) was set by fusing the contour of metal B ($1{\times}1{\times}1cm$) obtained from the above experiment by each CT into KVCT in order to compare the difference of dose distribution. Result: Referencing the actual sizes, it was appeared: Metal A (MVCT: 1.0 times, SMART-MAR CT: 1.84 times, and KVCT: 1.92 times), Metal B (MVCT: 1.02 times, SMART-MAR CT: 1.47 times, and KVCT: 1.82 times), and Metal C (MVCT: 1.0 times, SMART-MAR CT: 1.46 times, and KVCT: 1.66 times). MVCT was measured most similarly to the actual metal volume. As a result of measurement by applying the volume of metal B into proton treatment plan, the dose of $D_{99%}$ volume was measured as: MVCT: 3094 CcGE, SMART-MAR CT: 2902 CcGE, and KVCT: 2880 CcGE, against the reference 3082 CcGE Conclusion: Overall volume and axes X and Z were most identical to the actual sizes in MVCT and axis Y, which is in the superior-Inferior direction, was regular in length without differences in CT. The best dose distribution was shown in MVCT having similar size, shape, and volume of metal when treating head and neck protons. Thus it is thought that it would be very useful if the contour of prosthetic appliance using MVCT is applied into KVCT for proton treatment plan.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.107-116
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• 2018

Purpose : Intensity-modulated radiation therapy(IMRT) has been widely used for radiation therapy of Prostate Cancer because it can reduce radiation adverse effects on normal tissues and deliver more dose to the Prostate than 3D radiation therapy. Volumetric modulated arc therapy(VMAT) has been widely used due to recent advances in equipment and treatment techniques. VMAT can reduce treatment time by up to 55 % compared to IMRT, minimizing motion error during treatment. Materials and Methods : In this study, compared the MU and DVH values of 10 patients with prostate cancer by classifying them into 4 groups with 5 LN-Prostate groups and 5 Only-Prostate. And DQA measurements were performed using ArcCHECK and MapCHECK. Results : The results of Target and OAR dose distribution of Prostate patients are as follows. $D_{max}$ was in the range of 100~110 % in 4 groups, and more than 110 % of hot spot was not seen. Only-Prostate ($P_1$, $P_2$) without LN had a satisfactory dose distribution for the target dose, but slightly better for 2 arc plan($P_2$) than 1 arc plan($P_1$). The target dose $D_{98%}$ distribution in the LN-Prostate ($P_{L1}$, $P_{L2}$) group showed better 2 arc plan($P_{L2}$) than 1 arc plan($P_{L1}$), But in the case of 1 arc plan($P_{L1}$), the target dose $D_{98%}$ value was not enough. In OAR, the dose distribution of 1 Arc($P_1$) Plan and 2 Arc($P_2$) Plan in the Only-Prostate ($P_1$, $P_2$) Group satisfied the prescribed dose value. But, The dose distribution of 1 arc($P_1$) was slightly higher. In LN-Prostate OAR, 1 Arc($P_{L1}$) Plan showed higher dose than the prescribed dose. The Gamma evaluation pass rate of ArcCHECK and MapCHECK calculated from the DQA measurements was slightly higher than 99 % and the mean error range of the point dose measurements using the CC04 ion chamber was less than 1 %. Conclusion : In this study, Only-Prostate ($P_1$, $P_2$) group, the dose of 2 Arc plan was better. However, considering the treatment time and MU value, 1 Arc treatment method was more suitable. In the LN-Prostate ($P_{L1}$, $P_{L2}$) group, 2 Arc($P_{L2}$) treatment method showed better results and satisfied with Target $D_{98%}$ and OAR prescription dose.