• Progress in Medical Physics
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• v.29 no.3
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• pp.92-100
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• 2018

The manufacturer of a linear accelerator (LINAC) has reported that the target melting phenomenon could be caused by a non-recommended output setting and the excessive use of monitor unit (MU) with intensity-modulated radiation therapy (IMRT). Due to these reasons, we observed an unexpected beam interruption during the treatment of a patient in our institution. The target status was inspected and a replacement of the target was determined. After the target replacement, the beam profile was adjusted to the machine commissioning beam data, and the absolute doses-to-water for 6 MV and 10 MV photon beams were calibrated according to American Association of Physicists in Medicine (AAPM) Task Group (TG)-51 protocol. To verify the beam data after target replacement, the beam flatness, symmetry, output factor, and percent depth dose (PDD) were measured and compared with the commissioning data. The difference between the referenced and measured data for flatness and symmetry exhibited a coincidence within 0.3% for both 6 MV and 10 MV, and the difference of the PDD at 10 cm depth ($PDD_{10}$) was also within 0.3% for both photon energies. Also, patient-specific quality assurances (QAs) were performed with gamma analysis using a 2-D diode and ion chamber array detector for eight patients. The average gamma passing rates for all patients for the relative dose distribution was $99.1%{\pm}1.0%$, and those for absolute dose distribution was $97.2%{\pm}2.7%$, which means the gamma analysis results were all clinically acceptable. In this study, we recommend that the beam characteristics, such as beam profile, depth dose, and output factors, should be examined. Further, patient-specific QAs should be performed to verify the changes in the overall beam delivery system when a target replacement is inevitable; although it is more important to check the beam output in a daily routine.

• Journal of Korea Technology Innovation Society
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• v.13 no.4
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• pp.772-793
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• 2010

In this paper, author estimated economic scale of radiation usage in Korea using Input-Output table 2005 and other micro data published. This estimation focused all kind of radiation usage in whole economic activity. Estimation of economic scale is quantitative analysis for how much radiation usage increase productivity and welfare. Economic scale estimation of radiation usage in Korea 2005 is 6,297 Billion Won and it occupies 0.74% of GDP. It is smaller level compared with that of US and Japan. It is 1.5% of GDP in US (1997) and 1.2% of GDP in Japan (2005). Radiation usage in industrial sector is 5,775 Billion Won and it is 0.68% of GDP. Radiation usage in agriculture sector is 171 Billion Won and it is 0.02% of GDP. Radiation usage in medical sector is 351 Billion Won and it is 0.04% of GDP. This implied that radiation usage in industrial sector is larger than other sector. Use of medical radiology may be enlarge in the future due to population structure. The result that radiation usage occupied 0.74% of GDP arouse contribution of radiation usage in daily life. It helps people to have more understanding and public acceptance for radiation.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.252-260
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• 2019

Smart devices have penetrated deeply into our daily lives. They have not only increased user convenience, but also changed the overall lifestyle of society. The objective of this study was to examine the change process of user experience through device classification and technology by generation. In order to achieve the objective, this study analyzed the purpose and pattern of using a device, which is a digital platform, and the input and output, which are the most important digital components for personal exclusiveness and interaction. The analysis results of this study showed that, in the past, the purpose of using a device was clear, a device was used in common, and a separate device was used for input and output. However, as devices evolved, users began to emphasize the fun aspect than the purpose of a device. As a result, personal exclusiveness has increased. Moreover, unlike devices in the past depending on separate input or output methods, devices are evolving to employ a method performing input and output using the five senses of people such as the touchscreen using a body part of a user, voice, and motion. This study evaluated how the overall experience of users, which was obtained through technology, has changed for each generation. Furthermore, this study proposed the future direction of device development by considering the user experience. It is believed that the results of this study will be useful for future studies on the overall experience of users who will use a range of smart devices, which will be released in the future.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.7-14
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• 2017

A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accelerometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) were calculated using MATLAB. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ${\omega}_{res}$ was larger than 1.75 rad/s (TH2), and ${\theta}_{res}$ was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied, the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problem in distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were applied to the sequential processing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the specificity was determined to be 100% for the eleven ADL action sequences.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.39-44
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• 2018

The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.

• Journal of the Ergonomics Society of Korea
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• v.35 no.5
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• pp.319-341
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• 2016

Objective:This research analyzed the lower-limb motion in kinetic and kinematic way while walking on various terrains to develop Foot-Ground Contact Detection (FGCD) algorithm using the Inertial Measurement Unit (IMU). Background: To estimate the location of human in GPS-denied environments, it is well known that the lower-limb kinematics based on IMU sensors, and pressure insoles are very useful. IMU is mainly used to solve the lower-limb kinematics, and pressure insole are mainly used to detect the foot-ground contacts in stance phase. However, the use of multiple sensors are not desirable in most cases. Therefore, only IMU based FGCD can be an efficient method. Method: Orientation and acceleration of lower-limb of 10 participants were measured using IMU while walking on flat ground, ascending and descending slope and stairs. And the inertial information showing significant changes at the Heel strike (HS), Full contact (FC), Heel off (HO) and Toe off (TO) was analyzed. Results: The results confirm that pitch angle, rate of pitch angle of foot and shank, and acceleration in x, z directions of the foot are useful in detecting the four different contacts in five different walking terrain. Conclusion: IMU based FGCD Algorithm considering all walking terrain possible in daily life was successfully developed based on all IMU output signals showing significant changes at the four steps of stance phase. Application: The information of the contact between foot and ground can be used for solving lower-limb kinematics to estimating an individual's location and walking speed.

• Advances in pediatric surgery
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• v.4 no.1
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• pp.16-26
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• 1998

Parenteral nutrition has been an essential part of postoperative care of neonates requiring major surgery who are unable to tolerate enteral feeding for long periods during the postoperative period. However, TPN via central venous catheters(central TPN), used in increasing trend, still presents significant morbidity. To find out whether TPN via peripheral veins(peripheral TPN) could be used as a viable alternative for postoperative parenteral nutrition in neonates, a clinical study was carried out by a retrospective analysis of 53 neonates subjected to peripheral TPN for more than 7 days after surgery. Operations consisted of procedures for esophageal atresia with tracheoesophageal fistula, gastroschisis and omphalocele. Surgery was performed at the Division of Pediatric Surgery, Department of Surgery, Hanyang University Hospitall, from 1983 to 1994. The mean total duration of TPN was 13.3 days (range; 7-58 days), the average daily total fluid intake was 117.6 ml/kg during TPN and 158.6 ml/kg during subsequent oral feeding. The average daily total calorie intake was 57.7 kcal/kg during full strength TPN and 101.3 kcal/kg during subsequent oral feeding. The mean urine output was maintained at 3.5 ml/kg/ hour during TPN and at 3.6 ml/kg/hour during subsequent oral feeding. The increment of body weight observed during TPN was 132 g in TEF, 53 g in gastroschisis and 3 g in omphalocele patients, while loss of body weight was not observed. The mortality rate was 5.7 %(3/53) and was related to the underlying congenital anomalies, not the TPN. The most common complication of peripheral TPN observed was laboratory findings suggestive of liver dysfunction in 23 cases(43.4 %) with no significant clinical symptom or signs in any case, transient pulmonary edema in one case, and generalized edema in one case. None of the major complications usually expected associated with central TPN were observed. The result of this study suggest that peripheral TPN can be used for adeguate postoperative nutritional support in neonates requiring 2 to 3 weeks of TPN.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.141-146
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• 2014

This paper suggests a way of generating one-day load curves for performance improvement of peak shaving in a power system. This Peak Shaving algorithm is a long-term scheduling algorithm of PMS (Power Management System) for BESS (Battery Energy Storage System). The main purpose of a PMS is to manage the input and output power from battery modules placed in a power system. Generally, when a Peak Shaving algorithm is used, a difference occurs between predict load curves and real load curves. This paper suggests a way of minimizing the difference by making predict load curves that consider weekly normalization and seasonal load characteristics for smooth energy charging and discharging.

• The Plant Pathology Journal
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• v.36 no.1
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.