• Radiation Oncology Journal
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• v.23 no.4
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• pp.223-229
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• 2005

Purpose: In this work we designed and made MPBP(Multi Purpose Brachytherapy Phantom). The MPBP enables one to reproduce the same patient set-up in MPBP as the treatment of the patient and we tried to get an exact analysis of rectal doses in the phantom without need of in-vivo dosimetry. Materials and Methods: Dose measurements were tried at a point of rectum 1, the reference point of rectum, with a diode detector for 4 patients treated with tandem and ovoid for a brachytherapy of a cervix cancer. Total 20 times of rectal dose measurements were made with 5 times a patient. The set-up variation of the diode detector was analyzed. The same patient set-ups were reproduced in self-made MPBP and then rectal doses were measured with TLD. Results: The measurement results of the diode detector showed that the set-up variation of the diode detector was the maximum $11.25{\pm}0.95mm$ in the y-direction for Patient 1 and the maximum $9.90{\pm}4.50mm,\;20.85{\pm}4.50mm,\;and\;19.15{\pm}3.33mm$ in the z-direction for Patient 2, 3, and 4, respectively. Un analyzing the degree of variation in 3 directions the more variation was showed in the z-direction than x- and y-direction except Patient 1. The results of TLD measurements in MPBP showed the relative maximum error of 8.6% and 7.7% at a point of rectum 1 for Patient 1 and 4, respectively and 1.7% and 1.2% for Patient 2 and 3, respectively. The doses measured at R1 and R2 were higher than those calculated except R point of Patient 2. this can be thought to related to the algorithm of dose calculation, whcih corrects for air and water but is guessed not to consider the correction for the scattered rays, but by considering the self-error (${\pm}5%$) TLD has the relative error of values measured and calculated was analyzed to be in a good agreement within 15%. Conclusion: The reproducibility of dose measurements under the same condition as the treatment could be achieved owing to the self-made MPMP and the dose at the point of interest could be analyzed accurately. If a treatment is peformed after achieving dose optimization using the data obtained in the phantom, dose will be able to be minimized to important organs.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• 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.

• Investigative Magnetic Resonance Imaging
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• v.22 no.1
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• pp.1-9
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• 2018

Purpose: This study was designed to optimize the flip angle (FA) and scan timing of the hepatobiliary phase (HBP) using the 3D T1-weighted, gradient-echo (GRE) imaging with controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) technique on gadoxetic acid-enhanced 3T liver MR imaging. Materials and Methods: Sixty-two patients who underwent gadoxetic acid-enhanced 3T liver MR imaging were included in this study. Four 3D T1-weighted GRE imaging studies using the CAIPIRINHA technique and FAs of $9^{\circ}$ and $13^{\circ}$ were acquired during HBP at 15 and 20 min after intravenous injection of gadoxetic acid. Two abdominal radiologists, who were blinded to the FA and the timing of image acquisition, assessed the sharpness of liver edge, hepatic vessel clarity, lesion conspicuity, artifact severity, and overall image quality using a five-point scale. Quantitative analysis was performed by another radiologist to estimate the relative liver enhancement (RLE) and the signal-to-noise ratio (SNR). Statistical analyses were performed using the Wilcoxon signed rank test and one-way analysis of variance. Results: The scores of the HBP with an FA of $13^{\circ}$ during the same delayed time were significantly higher than those of the HBP with an FA of $9^{\circ}$ in all the assessment items (P < 0.01). In terms of the delay time, images at the same FA obtained with a 20-min-HBP showed better quality than those obtained with a 15-min-HBP. There was no significant difference in qualitative scores between the 20-min-HBP and the 15-min-HBP images in the non-liver cirrhosis (LC) group except for the hepatic vessel clarity score with $9^{\circ}$ FA. In the quantitative analysis, a statistically significant difference was found in the degree of RLE in the four HBP images (P = 0.012). However, in the subgroup analysis, no significant difference in RLE was found in the four HBP images in either the LC or the non-LC groups. The SNR did not differ significantly in the four HBP images. In the subgroup analysis, 20-min-HBP imaging with a $13^{\circ}$ FA showed the highest SNR value in the LC-group, whereas 15-min-HBP imaging with a $13^{\circ}$ FA showed the best value of SNR in the non-LC group. Conclusion: The use of a moderately high FA improves the image quality and lesion conspicuity on 3D, T1-weighted GRE imaging using the CAIPIRINHA technique on gadoxetic acid, 3T liver MR imaging. In patients with normal liver function, the 15-min-HBP with a $13^{\circ}$ FA represents a feasible option without a significant decrease in image quality.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.75-85
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• 2000

In existing models in optimization, the crisp data improve has been used in the objective or constraints to derive the optimal solution, Besides, the subjective environments are eliminated because the complex and uncertain circumstances were regarded as Probable ambiguity, In other words those optimal solutions in the existing models could be the complete satisfactory solutions to the objective functions in the Process of application for industrial engineering methods to minimize risks of decision-making. As a result of those, decision-makers in location Problems couldn't face appropriately with the variation of demand as well as other variables and couldn't Provide the chance of wide selection because of the insufficient information. So under the circumstance. it has been to develop the model for the location and size decision problems of logistics facility in the use of the fuzzy theory in the intention of making the most reasonable decision in the Point of subjective view under ambiguous circumstances, in the foundation of the existing decision-making problems which must satisfy the constraints to optimize the objective function in strictly given conditions in this study. Introducing the Process used in this study after the establishment of a general mixed integer Programming(MIP) model based upon the result of existing studies to decide the location and size simultaneously, a fuzzy mixed integer Programming(FMIP) model has been developed in the use of fuzzy theory. And the general linear Programming software, LINDO 6.01 has been used to simulate, to evaluate the developed model with the examples and to judge of the appropriateness and adaptability of the model(FMIP) in the real world.

• Journal of Korean Society of Forest Science
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• v.90 no.2
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• pp.197-209
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• 2001

TOPMODEL, semi-distributed hydrological model, is frequently applied to predict the amount of discharge, main flow pathways and water quality in a forested catchment, especially in a spatial dimension. TOPMODEL is a kind of conceptual model, not physical one. The main concept of TOPMODEL is constituted by the topographic index and soil transmissivity. Two components can be used for predicting the surface and subsurface contributing area. This study is conducted for the validation of applicability of TOPMODEL at small forested catchments in Korea. The experimental area is located at Gwangneung forest operated by Korea Forest Research Institute, Gyeonggi-do near Seoul metropolitan. Two study catchments in this area have been working since 1979 ; one is the natural mature deciduous forest(22.0 ha) about 80 years old and the other is the planted young coniferous forest(13.6 ha) about 22 years old. The data collected during the two events in July 1995 and June 2000 at the mature deciduous forest and the three events in July 1995 and 1999, August 2000 at the young coniferous forest were used as the observed data set, respectively. The topographic index was calculated using $10m{\times}10m$ resolution raster digital elevation map(DEM). The distribution of the topographic index ranged from 2.6 to 11.1 at the deciduous and 2.7 to 16.0 at the coniferous catchment. The result of the optimization using the forecasting efficiency as the objective function showed that the model parameter, m and the mean catchment value of surface saturated transmissivity, $lnT_0$ had a high sensitivity. The values of the optimized parameters for m and InT_0 were 0.034 and 0.038; 8.672 and 9.475 at the deciduous and 0.031, 0.032 and 0.033; 5.969, 7.129 and 7.575 at the coniferous catchment, respectively. The forecasting efficiencies resulted from the simulation using the optimized parameter were comparatively high ; 0.958 and 0.909 at the deciduous and 0.825, 0.922 and 0.961 at the coniferous catchment. The observed and simulated hyeto-hydrograph shoed that the time of lag to peak coincided well. Though the total runoff and peakflow of some events showed a discrepancy between the observed and simulated output, TOPMODEL could overall predict a hydrologic output at the estimation error less than 10 %. Therefore, TOPMODEL is useful tool for the prediction of runoff at an ungaged forested catchment in Korea.

• Korean Security Journal
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• no.10
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• pp.189-213
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• 2005

Risk management should be controlled systematically by effectively evaluating and suggesting countermeasures against the various risks which are followed by the change of the society and environment. These days, enterprise risk management became a new trend in the field. The first step in risk analysis is to recognize the risk factors, that is to verify the vulnerabilities of loss in the security facilities. The second step is to consider the probability of loss in assessing the risk factors. And the third step is to evaluate the criticality of loss. The security manager will determine the assessment grades and then the risk levels of each risk factor, on the basis of the result of risk analysis which includes the assessment of vulnerability, the provability of loss and the criticality. It is of great importance to put the result of risk analysis in mathematical statement for a scientific approach to risk management. Using the risk levels gained from the risk analysis, the security manager can develop a comprehensive and supplementary security plan. In planning the risk management measures to prepare against and minimize the loss, insurance is one of the best loss-prevention programs. However, insurance in and of itself is no longer able to meet the security challenges faced by major corporations. The security manager have to consider the cost-effectiveness, to suggest the productive risk management alternatives by using the security files which contains every information about the security matters. Also he/she have to reinforce the company regulations on security and safety, and to execute education repeatedly on security and risk management. Risk management makes the most efficient before-the-loss arrangement for and after-the-loss continuation of a business. So it is very much important to suggest a best cost-effective and realistic alternatives for optimizing risk management above all, and this function should by maintained and developed continuously and repeatedly.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.417-428
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• 2005

Today, IT organizations perform projects with vision related to marketing and financial profit. The objective of realizing the vision is to improve the project performing ability in terms of QCD. Organizations have made a lot of efforts to achieve this objective through process improvement. Large companies such as IBM, Ford, and GE have made over $80\%$ of success through business process re-engineering using information technology instead of business improvement effect by computers. It is important to collect, analyze and manage the data on performed projects to achieve the objective, but quantitative measurement is difficult as software is invisible and the effect and efficiency caused by process change are not visibly identified. Therefore, it is not easy to extract the strategy of improvement. This paper measures and analyzes the project performance, focusing on organizations' external effectiveness and internal efficiency (Qualify, Delivery, Cycle time, and Waste). Based on the measured project performance scores, an OT (Opportunity Tree) model was designed for optimizing the project performance. The process of design is as follows. First, meta data are derived from projects and analyzed by quantitative GQM(Goal-Question-Metric) questionnaire. Then, the project performance model is designed with the data obtained from the quantitative GQM questionnaire and organization's performance score for each area is calculated. The value is revised by integrating the measured scores by area vision weights from all stakeholders (CEO, middle-class managers, developer, investor, and custom). Through this, routes for improvement are presented and an optimized improvement method is suggested. Existing methods to improve software process have been highly effective in division of processes' but somewhat unsatisfactory in structural function to develop and systemically manage strategies by applying the processes to Projects. The proposed OT model provides a solution to this problem. The OT model is useful to provide an optimal improvement method in line with organization's goals and can reduce risks which may occur in the course of improving process if it is applied with proposed methods. In addition, satisfaction about the improvement strategy can be improved by obtaining input about vision weight from all stakeholders through the qualitative questionnaire and by reflecting it to the calculation. The OT is also useful to optimize the expansion of market and financial performance by controlling the ability of Quality, Delivery, Cycle time, and Waste.

• Progress in Medical Physics
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• v.23 no.1
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• pp.8-14
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• 2012

In order to develop a Patient respiratory management system includinga biofeedback function for4-dimentional radiation therapy, this study investigated anoptimal tracking algorithmfor moving target using IR (Infra-red) camera as well as commercial camera. A tracking system was developed by LabVIEW 2010. Motion phantom images were acquired using a camera (IR or commercial). After image process were conducted to convert acquired image to binary image by applying a threshold values, several edge enhance methods such as Sobel, Prewitt, Differentiation, Sigma, Gradient, Roberts, were applied. The targetpattern was defined in the images, and acquired image from a moving targetwas tracked by matching pre-defined tracking pattern. During the matching of imagee, thecoordinateof tracking point was recorded. In order to assess the performance of tracking algorithm, the value of score which represents theaccuracy of pattern matching was defined. To compare the algorithm objectively, we repeat experiments 3 times for 5 minuts for each algorithm. Average valueand standard deviations (SD) of score were automatically calculatedsaved as ASCII format. Score of threshold only was 706, and standard deviation was 84. The value of average and SD for other algorithms which combined edge detection method and thresholdwere 794, 64 in Sobel, 770, 101 in Differentiation, 754, 85 in Gradient, 763, 75 in Prewitt, 777, 93 in Roberts, and 822, 62 in Sigma, respectively. According to score analysis, the most efficient tracking algorithm is the Sigma method. Therefore, 4-dimentional radiation threapy is expected tobemore efficient if threshold and Sigma edge detection method are used together in target tracking.

• Nuclear Medicine and Molecular Imaging
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• v.40 no.4
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• pp.228-232
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• 2006

Purpose: electrophilic $^{18}F(T_{1/2}=110\;min)$ radionuclide in the form of $[^{18}F]F_2$ gas is of great significance for labeling radiopharmaceuticals for positron omission tomography (PET). However, its production In high yield and with high specific radioactivity is still a challenge to overcome several problems on targetry. The aim of the present study was to develop a method suitable for the routine production of $[^{18}F]F_2$ for the electrophilic substitution reaction. Materials and Methods: The target was designed water-cooled aluminum target chamber system with a conical bore shape. Production of the elemental fluorine was carried out via the $^{18}O(p,n)^{18}F$ reaction using a two-step irradiation protocol. In the first irradiation, the target filled with highly enriched $^{18}O_2$ was irradiated with protons for $^{18}F$ production, which were adsorbed on the inner surface of target body. In the second irradiation, the mixed gas ($1%[^{19}F]F_2/Ar$) was leaded into the target chamber, fellowing a short irradiation of proton for isotopic exchange between the carrier-fluorine and the radiofluorine absorbed in the target chamber. Optimization of production was performed as the function of irradiation time, the beam current and $^{18}O_2$ loading pressure. Results: Production runs was performed under the following optimum conditions: The 1st irradiation for the nuclear reaction (15.0 bar of 97% enriched $^{18}O_2$, 13.2 MeV protons, 30 ${\mu}A$, 60-90 min irradiation), the recovery of enriched oxygen via cryogenic pumping; The 2nd irradiation for the recovery of absorbed radiofluorine (12.0 bar of 1% $[^{19}F]fluorine/argon$ gas, 13.2 MeV protons, 30 ${\mu}A$, 20-30 min irradiation) the recovery of $[^{18}F]fluorine$ for synthesis. The yield of $[^{18}F]fluorine$ at EOB (end of bombardment) was achieved around $34{\pm}6.0$ GBq (n>10). Conclusion: The production of $^{18}F$ electrophilic agent via $^{18}O(p,n)^{18}F$ reaction was much under investigation. Especially, an aluminum gas target was very advantageous for routine production of $[^{18}F]fluorine$. These results suggest the possibility to use $[^{18}F]F_2$ gas as a electrophilic substitution agent.