• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.7-17
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• 2005

The determination of seismic velocities in refractors for near-surface seismic refraction investigations is an ill-posed problem. Small variations in the computed time parameters can result in quite large lateral variations in the derived velocities, which are often artefacts of the inversion algorithms. Such artefacts are usually not recognized or corrected with forward modelling. Therefore, if detailed refractor models are sought with model based inversion, then detailed starting models are required. The usual source of artefacts in seismic velocities is irregular refractors. Under most circumstances, the variable migration of the generalized reciprocal method (GRM) is able to accommodate irregular interfaces and generate detailed starting models of the refractor. However, where the very-near-surface environment of the Earth is also irregular, the efficacy of the GRM is reduced, and weathering corrections can be necessary. Standard methods for correcting for surface irregularities are usually not practical where the very-near-surface irregularities are of limited lateral extent. In such circumstances, the GRM smoothing statics method (SSM) is a simple and robust approach, which can facilitate more-accurate estimates of refractor velocities. The GRM SSM generates a smoothing 'statics' correction by subtracting an average of the time-depths computed with a range of XY values from the time-depths computed with a zero XY value (where the XY value is the separation between the receivers used to compute the time-depth). The time-depths to the deeper target refractors do not vary greatly with varying XY values, and therefore an average is much the same as the optimum value. However, the time-depths for the very-near-surface irregularities migrate laterally with increasing XY values and they are substantially reduced with the averaging process. As a result, the time-depth profile averaged over a range of XY values is effectively corrected for the near-surface irregularities. In addition, the time-depths computed with a Bero XY value are the sum of both the near-surface effects and the time-depths to the target refractor. Therefore, their subtraction generates an approximate 'statics' correction, which in turn, is subtracted from the traveltimes The GRM SSM is essentially a smoothing procedure, rather than a deterministic weathering correction approach, and it is most effective with near-surface irregularities of quite limited lateral extent. Model and case studies demonstrate that the GRM SSM substantially improves the reliability in determining detailed seismic velocities in irregular refractors.

• Journal of Dental Rehabilitation and Applied Science
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• v.37 no.3
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• pp.130-137
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• 2021

Purpose: The various suture techniques can be utilized in order to maximize the keratinized tissue healing around dental implants. The aim of this study is to compare the soft tissue healing pattern between two different suture techniques after implant placement. Materials and Methods: 15 patients with 18 implants were enrolled in this study. Simple implant placement without any additional bone graft was performed. Two different suture techniques were used to tug in the mobilized flap near the healing abutment after paramarginal flap design. Digital intraoral scan was performed at baseline, post-operation, stitch out, and 3 months after operation. The scan data were aligned using multiple points such as cusp, fossa of adjacent teeth, and/or healing abutment. After subtracting scan data at baseline with other time-point results, closed space indicating volume increment of peri-implant mucosa was selected. The volume of the close space was measured in mm³. The volume between two suture techniques at three time-points was compared using nonparametric rank-based analysis. Results: Healing was uneventful in both groups. Both suture technique groups showed increased soft tissue volume immediately after surgery. The amount of volume increment significantly decreased after 3 months (P < 0.001). Flap folding suture group showed higher median of volume increment than interrupted suture group after 3 months without any statistical significance (P > 0.05). Conclusion: After paramarginal flap reflection, the raised flaps stabilized by flap folding suture showed relatively higher volume maintenance after 3-month healing period. However, further studies are warranted.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.613-621
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• 2019

The purpose of this study is to evaluate the effectiveness of supportive devices which are for minimizing the patient's movement during lower extremity angiography and to verify image quality of phantom by analyzing of Mask image, DSA image and Roadmap image into SNR and CNR. As a result of comparing SNR with CNR of mask image obtained by DSA technique using the phantom alone and phantom placed on the supportive devices, there was no significant difference between about 0~0.06 for SNR and about 0~0.003 for CNR. The study showed about 0.11~0.35 for SNR and 0.016~0.031 for CNR of DSA imaging by DSA technique about only water phantom of the blood vessel model and the water phantom placed on the device. Analyzing SNR and CNR of Roadmap technique about water phantom on the auxiliary device (hardboard paper, pomax, polycarbonate, acrylic) and water phantom alone, there was no significant difference between 0.02~0.05 for SNR and 0.002~0.004 for CNR. In conclusion, there was no significant difference on image quality by using supportive devices made by hardboard paper, pomax, polycarbonate or acryl regardless of whether using supportive devices or not. Supportive devices to minimize of the patient's movement may reduce the total amount of contrast, exam-time, radiation exposure and eliminate risk factors during angiogram. Supportive devices made by hardboard paper can be applied easily during angiogram due to advantages of reasonable price and simple processing. It is considered that will be useful to consider cost efficiency and types of materials and their properties in accordance with purpose and method of the study when the operator makes and uses supportive devices.

• (The)Study of the Eastern Classic
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• no.55
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• pp.147-172
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• 2014

Kang Jae Eung translated "Sang-bong-lok" of Kang Ho Boo from Korean into Chinese. There is Yeonhaengnok(燕行錄) written in with Korean and Chinese among 500 Yeonhaengnok(燕行錄). Especially it is very rare that is translated from Korean into Chinese. Because of there aspect, it is valuable and worth studying. "Sang-bong-lok" was 3 series by origin, but now we have only 2 series. That is the original text written in Chinese by Kang Ho Boo, a Korean version by Kang Ho Boo, and Chinese translated of Korean version by his descindants Kang Jae Eung. Original text dosen't exists now undiscovered yet. Chinese version "Sang-bong-lok" is distinguished from the other Yeonhaengnok(燕行錄) and classical novel in form and contents. In formal aspect, Chinese version "Sang-bong-lok" describes an industry remarks in prologue. This industry remark describes standard and form of writing. Looking industry remark, you can find that Kang Jae Eung didn't add or subject the sentence of original text and distinguished between his own sentence and original text. This compiling system distinguishing compiler from original writter is rare enough to so that you cannot find it in other Yeonhaengnok(燕行錄). In contents, Kang Jae Eung almost transcribed Korean Yeonhaengnok(燕行錄) without subtraction and added special information to promote the view of Kang Ho Boo. After discription, Kang Jae Eung covered all information and reviewed it and added opinion to it. Kang Jae Eung's conclusion is sometimes same or different from Kang Ho Boo's. Anyway it is worthy of noticing that Kang Jae Eung wrote his opinion after Kang Ho Boo's sentence.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.57-61
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• 2012

Purpose: The multi-gated cardiac blood pool scan is to evaluate the function of left ventricle (LV) and usefully observe a value of ejection fraction (EF) for a patient who is receiving chemotherapy. To calculate LVEF, we should adjust an angle of left anterior oblique (LAO) view to separate both ventricles. And by overlapped ventricles, it is possible to affect LVEF. The purpose of this study is to investigate and compare quantitative indices by changing an angle of LAO view. Materials and methods: We analyzed the 49 patients who were examined by multi-gated cardiac blood pool scan in department of nuclear medicine at Asan Medical Center from June to September 2011. Firstly, we acquired "Best septal" view. And then, we got images by addition and subtraction of angle for LAO view to anterior and lateral. We compared three LAO views for 20 people by 5 degrees and 39 people by 10 degrees. And we analyzed quantitative indices, EF, end diastole and end systole counts, by automated and manual region of interest (ROI) modes. Results: Firstly, we analyzed quantitative indices by automated ROI mode. In case of 5 degrees, the averages of EF are $61.0{\pm}7.5$, $62.1{\pm}7.1$, $60.9{\pm}6.7%$ ($p$=0.841) in LAO, LAO $-5^{\circ}$ and LAO $+5^{\circ}$ respectively. And there is no difference in end diastole and end systole counts ($p$<0.05). In case of 10 degrees, the averages of EF are $62.4{\pm}9.5$, $62.3{\pm}10.8$, $61.6{\pm}.9.3%$ ($p$=0.938) in LAO, LAO $-10^{\circ}$ and LAO $+10^{\circ}$ respectively. And there is no difference in end diastole and end systole counts ($p$<0.05). Secondly, we analyzed quantitative indices by manual ROI mode. In case of 5 degrees, the averages of EF are $62.8{\pm}7.1$, $63.6{\pm}7.5$, $62.7{\pm}7.3%$ ($p$=0.903) in LAO, LAO $-5^{\circ}$ and LAO $+5^{\circ}$ respectively. And there is no difference in end diastole and end systole counts ($p$<0.05). In case of 10 degrees, the averages of EF are $65.5{\pm}9.0$, $66.3{\pm}8.7$, $63.5{\pm}.9.3%$ (p=0.473) in LAO, LAO $-10^{\circ}$ and LAO $+10^{\circ}$ respectively. And there is no difference in end diastole and end systole counts ($p$<0.05). Conclusion: When an image is nearly "Best septal" view, the difference of LAO angle would not affect to change LVEF. Although there was no difference in quantitative analysis, deviations could happen when to interpret wall motion qualitatively by reading physicians.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.