• The Journal of Korean Society for Radiation Therapy
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• v.23 no.2
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• pp.109-117
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• 2011

Purpose: To analyze the accuracy of tumor volume dose following field width change, to check the difference of dose change by using self-made moving car, and to evaluate practical delivery tumor dose when tomotherapy in the treatment of organ influenced by breathing. Materials and Methods: By using self-made moving car, the difference of longitudinal movement (0.0 cm, 1.0 cm, 1.5 cm, 2.0 cm) was applied and compared calculated dose with measured dose according to change of field width (1.05 cm, 2.50 cm, 5.02 cm) and apprehended margin of error. Then done comparative analysis in degree of photosensitivity of DQA film measured by using Gafchromic EBT film. Dose profile and Gamma histogram were used to measure degree of photosensitivity of DQA film. Results: When field width were 1.05 cm, 2.50 cm, 5.02 cm, margin of error of dose delivery coefficient was -2.00%, -0.39%, -2.55%. In dose profile of Gafchromic EBT film's analysis, the movement of moving car had greater motion toward longitudinal direction and as field width was narrower, big error increased considerably at high dose part compared to calculated dose. The more field width was narrowed, gamma index had a large considerable influence of moving at gamma histogram. Conclusion: We could check the difference of longitudinal dose of moving organ. In order to small field width and minimize organ moving due to breathing, it is thought to be needed to develop breathing control unit and fixation tool.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.113-129
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• 2015

Location branding is a very important income making activity, by giving special meanings to a specific location while producing identity and communal value which are based around the understanding of a place's location branding concept methodology. Many other areas, such as marketing, architecture, and city construction, exert an influence creating an impressive brand image. A place brand which shows great recognition to both native people of S. Korea and foreigners creates significant economic effects. There has been research on creating a strategically and detailed place brand image, and the representative research has been carried out by Anholt who surveyed two million people from 50 different countries. However, the investigation, including survey research, required a great deal of effort from the workforce and required significant expense. As a result, there is a need to make more affordable, objective and effective research methods. The purpose of this paper is to find a way to measure the intensity of the image of the brand objective and at a low cost through text mining purposes. The proposed method extracts the keyword and the factors constructing the location brand image from the related web documents. In this way, we can measure the brand image intensity of the specific location. The performance of the proposed methodology was verified through comparison with Anholt's 50 city image consistency index ranking around the world. Four methods are applied to the test. First, RNADOM method artificially ranks the cities included in the experiment. HUMAN method firstly makes a questionnaire and selects 9 volunteers who are well acquainted with brand management and at the same time cities to evaluate. Then they are requested to rank the cities and compared with the Anholt's evaluation results. TM method applies the proposed method to evaluate the cities with all evaluation criteria. TM-LEARN, which is the extended method of TM, selects significant evaluation items from the items in every criterion. Then the method evaluates the cities with all selected evaluation criteria. RMSE is used to as a metric to compare the evaluation results. Experimental results suggested by this paper's methodology are as follows: Firstly, compared to the evaluation method that targets ordinary people, this method appeared to be more accurate. Secondly, compared to the traditional survey method, the time and the cost are much less because in this research we used automated means. Thirdly, this proposed methodology is very timely because it can be evaluated from time to time. Fourthly, compared to Anholt's method which evaluated only for an already specified city, this proposed methodology is applicable to any location. Finally, this proposed methodology has a relatively high objectivity because our research was conducted based on open source data. As a result, our city image evaluation text mining approach has found validity in terms of accuracy, cost-effectiveness, timeliness, scalability, and reliability. The proposed method provides managers with clear guidelines regarding brand management in public and private sectors. As public sectors such as local officers, the proposed method could be used to formulate strategies and enhance the image of their places in an efficient manner. Rather than conducting heavy questionnaires, the local officers could monitor the current place image very shortly a priori, than may make decisions to go over the formal place image test only if the evaluation results from the proposed method are not ordinary no matter what the results indicate opportunity or threat to the place. Moreover, with co-using the morphological analysis, extracting meaningful facets of place brand from text, sentiment analysis and more with the proposed method, marketing strategy planners or civil engineering professionals may obtain deeper and more abundant insights for better place rand images. In the future, a prototype system will be implemented to show the feasibility of the idea proposed in this paper.

• Korean Journal of Acupuncture
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• v.30 no.4
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• pp.252-263
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• 2013

Objectives : Skin color deformation by cupping has been widely used as a diagnostic parameter in Traditional Korean Medicine(TKM). Skin color deformation such as ecchymoses and purpura is induced by local vacuum in a suction cup. Since existing studies have relied on a visual diagnostic method, there is a need to use the quantitative measurement method. Methods : We conducted an analysis of cross-polarization photographic images to assess the changes in skin color deformation. The skin color variation was analyzed using $L^*a^*b^*$ space and the skin erythema index(E.I.). The meridian theory in TKM indicates that the condition of primary internal organs is closely related to the skin color deformation at special acupoints. Before conducting these studies, it is necessary to evaluate whether or not skin color deformation is influenced by muscle condition. Hence, we applied cupping at BL13, BL15, BL18, BL20 and BL23 at Bladder Meridian(BL) and measured blood lactate at every acupoint. Results : We confirmed the high system measurement accuracy, and observed the diverse skin color deformations. Moreover, we confirmed that the $L^*$, $a^*$ and E.I. had not changed after 40 minutes(p>0.05). The distribution of blood lactate levels at each part was observed differently. Blood lactate level and skin color deformation at each part was independent of each other. Conclusions : The negative pressure produced by the suction cup induces a reduction in the volumetric fraction of melanosomes and subsequent reduction in epidermal thickness. The relationship between variations of tissue and skin properties and skin color deformation degree must be investigated prior to considering the relationship between internal organ dysfunction and skin color deformation.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Radiation Oncology Journal
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• v.27 no.3
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• pp.140-144
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• 2009

Purpose: This study was performed to retrospectively analyze patient survival by weighting according to the primary tumor oncotype in 160 patients with brain metastasis and who underwent whole brain radiotherapy. Materials and Methods: A total of 160 metastatic brain cancer patients who were treated with whole brain radiotherapy of 30 Gy between 2002 and 2008 were retrospectively analyzed. The primary tumor oncotype of 20 patients was breast cancer, and that of 103 patients was lung cancer. Except for 18 patients with leptomeningeal seeding, a total of 142 patients were analyzed according to the prognostic factors and the Recursive Partitioning Analysis (RPA) class. Weighted Partitioning Analysis (WPA), with the weighting being done according to the primary tumor oncotype, was performed and the results were correlated with survival and then compared with the RPA Class. Results: The median survival of the patients in RPA Class I (8 patients) was 20.0 months, that for Class II (76 patients) was 10.0 months and that for Class III (58 patients) was 3.0 months (p<0.003). The median survival of patients in WPA Class I (3 patients) was 36 months, that for the patients in Class II (9 patients) was 23.7 months, that for the patients in Class III (70 patients) was 10.9 months and that for the patients in Class IV (60 patients) was 8.6 months (p<0.001). The WPA Class might have more accuracy in assessing survival, and it may be superior to the RPA Class for assessing survival. Conclusion: A new prognostic index, the WPA Class, has more prognostic value than the RPA Class for the treatment of patients with metastatic brain cancer. This WPA Class may be useful to guide the appropriate treatment of metastatic brain lesions.

• Journal of radiological science and technology
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• v.39 no.4
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• pp.577-585
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• 2016

The purpose of this study is to evaluate the accuracy of beam delivery QA software using the MLC dynalog file, about the VMAT plan with AAPM TG-119 protocol. The Clinac iX with a built-in 120 MLC was used to acquire the MLC dynalog file be imported in MobiusFx(MFX). To establish VMAT plan, Oncentra RTP system was used target and organ structures were contoured in Im'RT phantom. For evaluation of dose distribution was evaluated by using gamma index, and the point dose was evaluated by using the CC13 ion chamber in Im'RT phantom. For the evaluation of point dose, the mean of relative error between measured and calculated value was $1.41{\pm}0.92%$(Target) and $0.89{\pm}0.86%$(OAR), the confidence limit were 3.21(96.79%, Target) and 2.58(97.42%, OAR). For the evaluation of dose distribution, in case of $Delta^{4PT}$, the average percentage of passing rate were $99.78{\pm}0.2%$(3%/3 mm), $96.86{\pm}1.76%$(2%/2 mm). In case of MFX, the average percentage of passing rate were $99.90{\pm}0.14%$(3%/3 mm), $97.98{\pm}1.97%$(2%/2 mm), the confidence limits(CL) were in case of $Delta^{4PT}$ 0.62(99.38%, 3%/3 mm), 6.6(93.4%, 2%/2 mm), in case of MFX, 0.38(99.62%, 3%/3 mm), 5.88(94.12%, 2%/2 mm). In this study, we performed VMAT QA method using dynamic MLC log file compare to binary diode array chamber. All analyzed results were satisfied with acceptance criteria based on TG-119 protocol.

• Sleep Medicine and Psychophysiology
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• v.18 no.2
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• pp.82-86
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• 2011

Objectives: The Berlin Questionnaire (BQ) has been used to help identify patients at high risk of having sleep apnea in primary care. But it has not been validated in a sleep clinic for Korean patients. The aim of this study is to evaluate the usefulness of the BQ as a screening tool for obstructive sleep apnea (OSA) for Korean patients in a sleep clinic. Methods: The BQ was prospectively applied to 121 subjects with OSA suspicion who visited to our sleep clinic. All subjects performed overnight polysomnography. OSA was defined as an apnea-hypopnea index (AHI) ${\geq}5$. We investigated the sensitivity, specificity, positive and negative predictive values of the BQ according to severity by AHI. Results: In 121 subjects, 73.6% were males, with a mean age of $48.8{\pm}13.0$ years. Twenty-five (20.6%) patients did not have OSA (AHI<5), 30 (25%) patients had mild OSA ($AHI{\geq}5$ and <15), 26 (21.4%) had moderate ($AHI{\geq}15$ and <30), and 40 (33%) had severe OSA ($AHI{\geq}30$). The BQ identified 69.4% of the patients as being at high risk for having OSA. The sensitivity and specificity of the BQ were 71.9% and 40%, for $AHI{\geq}5$, 75.8% and 38.2% for $AHI{\geq}15$, 77.5% and 34.6% for $AHI{\geq}30$, respectively. The positive and negative predictive values of the BQ were 82.1% and 27.0% for $AHI{\geq}5$, respectively. Positive and negative likelihood ratios were 1.2 and 0.7, and the overall diagnostic accuracy of the BQ was 65.3%, using an AHI cut-off of 5. Conclusion: Due to modest sensitivity and low specificity, the BQ does not seem to be an appropriate tool for identifying patients with obstructive sleep apnea in a sleep clinic population.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.2
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• pp.191-197
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• 2019

Although auto-adjusting positive airway pressure (APAP) titration at home has several advantages over a CPAP titration in terms of convenience and time saving, there are still concerns as to whether it will show corresponding accuracy when compared to laboratory-based polysomnography (PSG) and CPAP titration. To obtain more evidence supporting home-based auto-titration, APAP titration was performed at home for patients who were presented with OSA on laboratory-based diagnostic PSG followed by CPAP titration. A total of 79 patients were included in the study. They all underwent split-night PSG with CPAP titration, and APAP titration for more than 7 days. The patients with successful titration at both situations were selected. The optimal pressure and apnea-hypopnea index (AHI) of CPAP and APAP titration were compared. The optimal pressure for CPAP and APAP titration were $7.0{\pm}1.8cmH_2O$ and $7.6{\pm}1.6cmH_2O$ (P<0.001), whereas the corresponding AHI were $1.3{\pm}1.5/h$ and $3.0{\pm}1.7/h$ (P<0.001). As a result, the achievement rates of optimal pressure for CPAP and APAP titration were 96.2% and 94.9% (r=-0.045, P=0.688), respectively. The results of this study did not differ with regard to the optimal pressure between CPAP and APAP titration. Overall, CPAP and APAP titrations should be chosen depending on a required situation.

• Journal of agriculture & life science
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• v.51 no.2
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• pp.117-126
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• 2017

Hanwoo young bulls are selected based on performance test using the weight at 12 months and pedigree index comprising marbling score. Pedigree index was not based on the progeny tested data but the breeding value of the proven bulls; resulting a lower accuracy. The progeny testing of the young bulls was categorized into testing at farm and at the test station. The farm tested data was difficult to compare with those from test station data. Farm tested bulls had different slaughter ages than those for test station bulls. Therefore, this study had considered a different age at slaughter for respective records on ultrasound traits. Records on body weight at 12 months, ultrasound measures at 12 and 24 months(uIMF, uEMA, uBFT, and uRFT), and carcass traits(CWT, EMA, BFT, and MS) were collected from steers and bulls of Hanwoo national improvement scheme between 2008 and 2013. Fixed effects of batch, test date, test station, personnel for measurement, personnel for judging, and a linear covariate of weight at measurement were fitted in the animal models for ultrasound traits. The ranges of heritability estimates of the ultrasound traits at 12 and 24 months were 0.21-0.43 and 0.32-0.47, respectively. Ultrasound traits at 12 and 24 months between similar carcass traits was genetically correlated at 0.52-0.75 and 0.86-0.89, respectively.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.