• The Journal of Korean Academy of Prosthodontics
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• v.46 no.2
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• pp.148-156
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• 2008

Statement of problems: Self-etch adhesives exhibit some clinical benefits such as ease of manipulation and reduced technique-sensitivity. Nevertheless, some concern remains regarding the bonding effectiveness of self-etch adhesives to enamel, in particular when so-called 'mild' self-etch adhesives are employed. This study compared the microtensile bond strengths to ground enamel of the two-step self-etch adhesive Clearfil SE Bond (Kuraray) to the three-step etch-and- rinse adhesive Scotchbond Multi-Purpose (3M ESPE) and the one-step self-etch adhesive iBond (Heraeus Kulzer). Purpose: The purpose of this study was to determine the effect of a preceding phosphoric acid conditioning step on the bonding effectiveness of a two-step self-etch adhesive to ground enamel. Material and methods: The two-step self-etch adhesive Clearfil SE Bond non-etch group, Clearfil SE Bond etch group with prior 35% phosphoric acid etching, and the one-step self-etch adhesive iBond group were used as experimental groups. The three-step etch-and-rinse adhesive Scotchbond Multi-Purpose was used as a control group. The facial surfaces of bovine incisors were divided in four equal parts cruciformly, and randomly distributed into each group. The facial surface of each incisor was ground with 800-grit silicon carbide paper. Each adhesive group was applied according to the manufacturer's instructions to ground enamel, after which the surface was built up using Light-Core (Bisco). After storage in distilled water at $37^{\circ}C$ for 1 week, the restored teeth were sectioned into enamel beams approximately 0.8*0.8mm in cross section using a low speed precision diamond saw (TOPMET Metsaw-LS). After storage in distilled water at $37^{\circ}C$ for 1 month, 3 months, microtensile bond strength evaluations were performed using microspecimens. The microtensile bond strength (MPa) was derived by dividing the imposed force (N) at time of fracture by the bond area ($mm^2$). The mode of failure at the interface was determined with a microscope (Microscope-B nocular, Nikon). The data of microtensile bond strength were statistically analyzed using a one-way ANOVA, followed by Least Significant Difference Post Hoc Test at a significance level of 5%. Results: The mean microtensile bond strength after 1 month of storage showed no statistically significant difference between all adhesive groups (P>0.05). After 3 months of storage, adhesion to ground enamel of iBond was not significantly different from Clearfil SE Bond etch (P>>0.05), while Clearfil SE Bond non-etch and Scotchbond Multi-Purpose demonstrated significantly lower bond strengths (P<0.05), with no significant differences between the two adhesives. Conclusion: In this study the microtensile bond strength to ground enamel of two-step self-etch adhesive Clearfil SE Bond was not significantly different from three-step etch-and-rinse adhesive Scotchbond Multi-Purpose, and prior etching with 35% phosphoric acid significantly increased the bonding effectiveness of Clearfil SE Bond to enamel at 3 months.

• Journal of the korean academy of Pediatric Dentistry
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• v.28 no.4
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• pp.709-727
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• 2001

The pattern of programmed cell death(PCD) has been examined during the early developmental period of development in mouse embryos, from embryonic day 4.5(E4.5) to E11.5 Embryos from Balb/c breedings were harvested at various embryonic stages between E4.5 and El1.5. Cell death was analysed by in situ terminal deoxynucleotidyl transferase mediated dUTP nick end labeling(TUNEL) staining in tissue sections and whole embryos. At the blastocyst stage(E4.5), a very few apoptotic cells were found in the inner cell mass of the blastocyst. In the early egg cylinder stage(35.0-5.5), a few apoptotic cells were detected in the embryonic ectoderm, the embryonic endoerm and the proamniotic cavity. In the advanced egg cylinder stage(E5.5-6.5), TUNEL-posifive cells were observed in the extra-embryonic ectoderm and extra-embryonic endoderm as well as in the embryonic ectoderm, embryonic visceral endoderm and proamniotic cavity. In the streak stage(E6.75-7.75), many TUNEL-positive cells were found in the ectoplacental cone. In contrast, only very few apoptotic cells were found in the chorion and extra-embryonic endoderm in extra-embryonic regions. In intra-embryonic region, a few apoptotic cells were randomly found in the embryonic ectoderm, mesoderm and visceral endoderm. At the early somitogenesis stage(E8.0-8.5), most apoptotic cells were observed in the most cranial portion of neural fold (neural ectoderm and adjacent ectoderm). At the mid somitogenesis stage(39.0-9.5), the otic placode first showed TUNEL-positive at this stage. Small number of TUNEL-positive cells were also first seen around optic placode and branchial arches. Three streams of TUNEL-positive cells were clearly seen in the cranial region at 59.5-9.75. At E10.5, apoptotic cells were localized in the developing eye, the junctional portion of medial nasal, lateral nasal and maxillary processes, the lateral portion of branchial arches, the junction of bilateral mandibular processes, and apical ectodermal ridges of limb buds. At E11.5, apoptotic cells were noticeably decreased in most area, except the developing limbs and several somites in the tail region. In this study, the global temporospatial pattern of PCD throughout early development of mouse embryos was discussed. It may provide the basis for further studies on its role in the morphogenesis of the embryo.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Korean Journal of Poultry Science
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• v.7 no.2
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• pp.54-76
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• 1980

A survey was conducted to determine the status of health and productivity of poultry farms in Korea. Area included Was Kyunggido where exist nearly 50% of national poultry population. From this area, 41 layer and 34 broiler farms covering 21 Countries were selected randomly for the survey. When farms were divided in the operation size, 95.1% of layer and 82.3% of broiler farms were classified as business or industrial level while the rest were managed in a small scale as part time job. Generally layer farms had been established much earlier than broiler farms. Geographically 10.7% of layer farms were sited near the housing area such as field foreast and rice field. No farms were located near the seashore. The distance from one farm from the other was very close, being 80% of the farms within the distance of 1km and as many as 28% of the farms within loom. This concentrated poultry farming in a certain area created serious problems for the sanitation and preventive measures, especially in case of outbreak of infectious diseases. Average farm size was 5,016${\times}$3.3㎡ for layers and 1,037${\times}$3.3㎡ for broilers. 89.5% of layer ana 70.6% of broiler farms owned the land for farming while the rest were on lease. In 60% of layer farms welters were employed for farming while in the rest their own labour was used. Majority of farms were equipped poorly for taking necessary practice of hygiene and sanitation. The amount of disinfectant used by farms was considerably low. As many as 97.6% of lave. farms were practised with Newcastle(ND) and fowl pox(F$.$pox) vaccine, whereas only 43.6% and 5.1% of broiler farms were practised with ND and F$.$pox vaccine, respectively. In 17-32.7% of farms ND vaccine was used less than twice until 60 days of age and in only 14.6% of farms adult birds were vaccinated every 4months. Monthly expense for preventive measures was over 200,000W in 32% of farms. Only 4.9-2.7% of vaccine users were soaking advice from veterinarians before practising vaccination, 85% of the users trusted the efficacy of the vaccines. Selection of medicine was generally determined by the farm owner rather than by veterinarans on whom 33.3% of farms were dependant. When diseases outbroke, 49.3% of farms called for veterinary hospital and the rest were handled by their own veterinarians, salesmen or professionals. Approximately 70% of farms were satisfied with the diagnosis made by the veterinarians. Frequency of disease outbreaks varied according to the age and type of birds. The livabilities of layers during the period of brooding, rearing ana adultwere 90.5, 98.9 and 75.2%, respectively while the livalibility of broilers until marketing was 92.2%. In layers, average culling age, was 533.3 day and hen housed eggs were 232.7. Average feed conversion rates of layers and broilers were 3.30 and 2.48, respectively. Those figures were considerably higher than anticipated but still far lower than those in developed countries.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.4
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• pp.307-313
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• 2008

Purpose: Vascular endothelial growth factor (VEGF) and its receptor, fetal liver kinase 1 (Flk-1), play an important role in vascular permeability and tumor angiogenesis. The aim of this study is to evaluate the therapeutic efficacy of $^{131}I$ labeled anti-Flk-1 monoclonal antibody (DC101) on the growth of melanoma tumor, which is known to be very aggressive in vivo. Materials and Methods: Balb/c nude mice were injected subcutaneously with melanoma cells in the right flank. Tumors were allowed to grow up to $200-250\;mm^3$ in volume. Gamma camera imaging and biodistribution studies were performed to identify an uptake of $^{131}I$-DC101 in various organs. Mice with tumor were randomly divided into five groups (10 mice per group) and injected intravenously; control PBS (group 1), $^{131}I$-DC101 $50\;{\mu}g/mouse$ (group 2), non-labeled DC101 $50\;{\mu}g/mouse$ (group 3), $^{131}I$-DC101 $30\;{\mu}g/mouse$ (group 4) and $15\;{\mu}g/mouse$ (group 5) every 3 or 4 days for 20 days. Tumor volume was measured with caliper twice a week. Results: In gamma camera images, the uptake of $^{131}I$-DC101 into tumor and thyroid was increased with time. Biodistribution results showed that the radioactivity of blood and other major organ was gradually decreased with time whereas tumor uptake was increased up to 48 hr and then decreased. After 4th injection of $^{131}I$-DC101, tumor volume of group 2 and 4 was significantly smaller than that group 1. After 5th injection, the tumor volume of group 5 also significantly reduced. Conclusion: These results indicated that delivery of $^{131}I$ to tumor using FlK-1 antibody, DC101, effectively blocks tumor growth in aggressive melanoma xenograft model.

• Journal of Nutrition and Health
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• v.50 no.3
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• pp.270-283
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• 2017

Purpose: This study was conducted to examine coffee consumption behaviors, dietary habits, and nutrient intakes by coffee intake amount among university students. Methods: Questionnaires were distributed to 300 university students randomly selected in Gongju. Dietary survey was administered during two weekdays by the food record method. Results: Subjects were divided into three groups: NCG (non-coffee group), LCG (low coffee group, 1~2 cups/d), and HCG (high coffee group, 3 cups/d) by coffee intake amount and subjects' distribution. Coffee intake frequency was significantly greater in the HCG compared to the LCG (p < 0.001). The HCG was more likely to intake dripped coffee with or without milk and/or sugar than the LCG (p < 0.05). More than 80% of coffee drinkers chose their favorite coffee or accompanying snacks regardless of energy content. More than 75% of coffee takers did not eat accompanying snacks instead of meals, and the HCG ate them more frequently than LCG (p < 0.05). Breakfast skipping rate was high while vegetable and fruit intakes were very low in most subjects. Subjects who drank carbonated drinks, sweet beverages, or alcohol were significantly greater in number in the LCG and HCG than in the NCG (p < 0.01). Energy intakes from coffee were $0.88{\pm}5.62kcal/d$ and $7.07{\pm}16.93kcal/d$ for the LCG and HCG. For total subjects, daily mean dietary energy intake was low at less than 72% of estimated energy requirement. Levels of vitamin C and calcium were lower than the estimated average requirements while that of vitamin D was low (24~34% of adequate intake). There was no difference in nutrient intakes by coffee intake amount, except protein, vitamin A, and niacin. Conclusion: Coffee intake amount did not affect dietary nutrient intakes. Dietary habits were poor,and most nutrient intakes were lower than recommend levels. High intakes of coffee seemed to be related with high consumption of sweet beverages and alcohol. Therefore, it is necessary to improve nutritional intakes and encourage proper water intake habits, including coffee intake, for improved nutritional status of subjects.

• 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.

• 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.

• The Journal of Korean Society for Radiation Therapy
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• v.34
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• pp.21-30
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• 2022

Purpose: The purpose of this study is to evaluate the effectiveness of oral positioning stent, the BinkieRT^TM in radiation treatment for head and neck cancer patients in terms of tongue positions reproducibility, tongue doses and material properties. Materials and Methods: 24 cases using BinkieRT^TM during radiation treatments were enrolled. The tongue was contoured on planning CT and CBCT images taken every 3 days during treatment, and then the DSC and center of tongue shift values were analyzed to evaluate the reproducibility of the tongue. The tongue dose was compared in terms of dose distribution when using BinkieRT^TM and different type of oral stents (mouthpiece, paraffin wax). Randomly selected respective 10 patients were measured tongue doses of initial treatment plan for nasal cavity and unilateral parotid cancer. Finally, In terms of material evaluation, HU and relative electron density were identified in RTPS. Results: As a result of DSC analysis, it was 0.8 ± 0.07, skewness -0.8, kurtosis 0.61, and 95% CI was 0.79~0.82. To analyze the deviation of the central tongue shift during the treatment period, a 95% confidence interval for shift in the LR, SI, and AP directions were indicated, and a one-sample t-test for 0, which is an ideal value in the deviation(n=144). As a result of the t-test, the mean and SD in the LR and SI directions were 0.01 ± 0.14 cm (p→.05), 0.03 ± 0.25 cm (p→.05), and -0.08 ± 0.25 cm (p ←.05) in the AP direction. In the case of unilateral parotid cancer patients, the Dmean to the tongue of patients using BinkieRT^TM was 16.92% ± 3.58% compared to the prescribed dose, and 23.99% ± 10.86% of patients with Paraffin Wax, indicating that the tongue dose was relatively lower when using BinkieRT^TM (p←.05). On the other hand, among nasal cavity cancer patients, the Dmean of tongue dose for patients who used BinkieRT^TM was 4.4% ± 5.6%, and for those who used mouthpiece, 5.9% ± 6.8%, but it was not statistically significant (p→.05). The relative electron density of Paraffin Wax, BinkieRT^TM and Putty is 0.94, 0.99, 1.26 and the mass density is 0.95, 0.99 and 1.32 (g/cc), Transmission Factor is 0.99, 0.98, 0.96 respectively. Conclusion: The result of the tongue DSC analysis over the treatment period was about 0.8 and Deviation of the center of tongue shifts were within 0.2 cm, the reproducibility was more likely excellent. In the case of unilateral head and neck cancer patients, it was found that the use of BinkieRT^TM rather than Paraffin Wax or Putty can reduce the unnecessary dose irradiated to the tongue. This study might be useful to understand of BinkieRT^TM's properties and advantages. And also it could be another considered option as oral stent to keep the reproducibility of tongue and reducing dose during head and neck radiation treatments.