• The Journal of Korean Society for Radiation Therapy
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• v.15 no.1
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• pp.35-40
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• 2003

I. Purpose Confirming an error to be able to break out in a method to move couch manually while operator sees the skin marks on patient in case of curing head who got 2 targets adjoined, so we analyze coordinates price of couch, evaluate reproducibility and precision of change movements between targets. II. Materials and Methods In radiotherapy, for confirming errors in manual movements by operators by exchanging between two targets to treat patient head, we read coordinates price(vertical, longitudinal, lateral three directions of couch) shown on a monitor of LINAC( CL 2100, Varian, USA) in order to evaluate accuracy about the length that moved in time for moving couch manually. After reading movement length of coordinates recorded in three directions of all treatment, we compared distance between targets recorded in RTP(Pinnacle, ADAC, USA) with reading coordinates price of couch, setting actually done the same patient for ten times, coordinates were recorded, treated for evaluating averages and degrees of errors and standard deviations. III. Results In method to confirm skin marks of patient by operators' view and to move couch manually, average standard deviations of movements between two targets are vertical 1.4mm, longitudinal 0.9mm, lateral 2.2mm in each direction. As for the error in straight dimension, it is about 3.6mm averages and 5.1mm maximum. The average of errors in each directions was vertical 1mm, longitudinal 0.7mm, lateral 2.7mm. The greatest error broke out in lateral direction with $25\%$ of all cases ; to exceed an error average. IV. Conclusions If operators moved manually couch for changing target points, errors about 3.6mm average degrees occur. It is important that operators confirm the errors prices of actual couch coordinates for asking a correct movement between the targets adjoined each other ; in case of treatment demanding high precision like 3D conformal therapy or IMRT. Therefore, if we apply couch coordinates confirmation to reproducibility and to precision evaluation of treatment, it's expected that we can execute high-quality radiotherapy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.55-63
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• 2000

This paper investigated the sub-region compression effect of the three dimensional DCT(3D-DCT) using the difference component(DC) of inter-frame in images. The proposed algorithm are the method that obtain compression effect to divide the information into subband after 3D-DCT, the data appear the type of cubic block(8${\times}$8${\times}$8) in eight difference components per unit. In the frequence domain that transform the eight differential component frames into eight DCT frames with components of both spatial and temporal frequencies of inter-frame, the image data are divided into frame component(8${\times}$8 block) of time-axis direction into 4${\times}$4 sub block in order to effectively obtain compression data because image components are concentrate in corner region with low-frequency of cubic block. Here, using the weight of sub block, we progressed compression ratio as consider to adaptive sub-region of low frequency part. In simulation, we estimated compression ratio, reconstructed image resolution(PSNR) with the simpler image and the complex image contained the higher frequency component. In the result, we could obtain the high compression effect of 30.36dB(average value in the complex-image) and 34.75dB(average value in the simple-image) in compression range of 0.04~0.05bpp.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.2
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• pp.227-242
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• 2019

This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.45-52
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• 2024

In this study, an AI-based algorithm was developed to prevent image quality deterioration and reading errors due to patient movement in PET/CT examinations that use radioisotopes in medical institutions to test cancer and other diseases. Using the Mothion Free software developed using, we checked the degree of correction of movement due to breathing, evaluated its usefulness, and conducted a study for clinical application. The experimental method was to use an RPM Phantom to inject the radioisotope 18F-FDG into a vacuum vial and a sphere of a NEMA IEC body Phantom of different sizes, and to produce images by directing the movement of the radioisotope into a moving lesion during respiration. The vacuum vial had different degrees of movement at different positions, and the spheres of the NEMA IEC body Phantom of different sizes produced different sizes of lesions. Through the acquired images, the lesion volume, maximum SUV, and average SUV were each measured to quantitatively evaluate the degree of motion correction by Motion Free. The average SUV of vacuum vial A, with a large degree of movement, was reduced by 23.36 %, and the error rate of vacuum vial B, with a small degree of movement, was reduced by 29.3 %. The average SUV error rate at the sphere 37mm and 22mm of the NEMA IEC body Phantom was reduced by 29.3 % and 26.51 %, respectively. The average error rate of the four measurements from which the error rate was calculated decreased by 30.03 %, indicating a more accurate average SUV value. In this study, only two-dimensional movements could be produced, so in order to obtain more accurate data, a Phantom that can embody the actual breathing movement of the human body was used, and if the diversity of the range of movement was configured, a more accurate evaluation of usability could be made.

• Journal of the Earthquake Engineering Society of Korea
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• v.1 no.2
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• pp.1-15
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• 1997

This paper presents substructural identification methods for the assessment of local damages in complex and large structural systems. For this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for a substructure to process the measurement data impaired by noises. Using the substructural methods, the number of unknown parameters for each identification can be significantly reduced, hence the convergence and accuracy of estimation can be improved. Secondly, the damage index is defined as the ratio of the current stiffness to the baseline value at each element for the damage assessment. The indirect estimation method was performed using the estimated results from the identification of the system matrices from the substructural identification. To demonstrate the proposed techniques, several simulation and experimental example analyses are carried out for structural models of a 2-span truss structure, a 3-span continuous beam model and 3-story building model. The results indicate that the present substructural identification method and damage estimation methods are effective and efficient for local damage estimation of complex structures.

• Journal of Gastric Cancer
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• v.13 no.1
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• pp.26-33
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• 2013

Purpose: The aims are to: (i) display the multidimensional learning curve of totally laparoscopic distal gastrectomy, and (ii) verify the feasibility of totally laparoscopic distal gastrectomy after learning curve completion by comparing it with laparoscopy-assisted distal gastrectomy. Materials and Methods: From January 2005 to June 2012, 247 patients who underwent laparoscopy-assisted distal gastrectomy (n=136) and totally laparoscopic distal gastrectomy (n=111) for early gastric cancer were enrolled. Their clinicopathological characteristics and early surgical outcomes were analyzed. Analysis of the totally laparoscopic distal gastrectomy learning curve was conducted using the moving average method and the cumulative sum method on 180 patients who underwent totally laparoscopic distal gastrectomy. Results: Our study indicated that experience with 40 and 20 totally laparoscopic distal gastrectomy cases, is required in order to achieve optimum proficiency by two surgeons. There were no remarkable differences in the clinicopathological characteristics between laparoscopy-assisted distal gastrectomy and totally laparoscopic distal gastrectomy groups. The two groups were comparable in terms of open conversion, combined resection, morbidities, reoperation rate, hospital stay and time to first flatus (P>0.05). However, totally laparoscopic distal gastrectomy had a significantly shorter mean operation time than laparoscopy-assisted distal gastrectomy (P<0.01). We also found that intra-abdominal abscess and overall complication rates were significantly higher before the learning curve than after the learning curve (P<0.05). Conclusions: Experience with 20~40 cases of totally laparoscopic distal gastrectomy is required to complete the learning curve. The use of totally laparoscopic distal gastrectomy after learning curve completion is a feasible and timesaving method compared to laparoscopy-assisted distal gastrectomy.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.53-61
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• 2012

This paper proposes a real-time distance measurement system of high temperature and high speed target using infrared stereo camera. We construct an infrared stereo camera system that measure the difference between target and background temperatures for automatic target measurement. First, the proposed method detects target region based on target motion and intensity variation of local region using difference between target and background temperatures. Second, stereo matching by left and right target information is used to estimate disparity about real-time distance of target. In the proposed method using infrared stereo camera system, we compare distances in three dimension trajectory measuring instrument and in infrared stereo camera measurement. In this experiment from three video data, the result shows an average 9.68% distance error rate. The proposed method is suitable for distance and position measurement of varied targets using infrared stereo system.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.139-152
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• 2015

Along with climate change, it is reported that the scale and frequency of extreme climate events show unstable tendency of increase. Thus, to comprehend the change characteristics of precipitation data, it is needed to consider non-stationary. The main objectives of this study were to estimate future design floods for Wonpyeongcheon watershed based on RCP (Representative Concentration Pathways) scenario. Wonpyeongcheon located in the Keum River watershed was selected as the study area. Historical precipitation data of the past 35 years (1976~2010) were collected from the Jeonju meteorological station. Future precipitation data based on RCP4.5 were also obtained for the period of 2011~2100. Systematic bias between observed and simulated data were corrected using the quantile mapping (QM) method. The parameters for the bias-correction were estimated by non-parametric method. A non-stationary frequency analysis was conducted with moving average method which derives change characteristics of generalized extreme value (GEV) distribution parameters. Design floods for different durations and frequencies were estimated using rational formula. As the result, the GEV parameters (location and scale) showed an upward tendency indicating the increase of quantity and fluctuation of an extreme precipitation in the future. The probable rainfall and design flood based on non-stationarity showed higher values than those of stationarity assumption by 1.2%~54.9% and 3.6%~54.9%, respectively, thus empathizing the necessity of non-stationary frequency analysis. The study findings are expected to be used as a basis to analyze the impacts of climate change and to reconsider the future design criteria of Wonpyeongcheon watershed.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.1
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• pp.98-106
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• 2001

This paper presents external load disturbance compensation that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a dead beat observer that is well-known method. However it has disadvantage such as a noise amplification effect. To reduce of the effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. Although RLSM estimator is one of the most effective methods for online parameter identification, it is difficult to obtain unbiased result in this application. It is caused by disturbed dynamic model with external torque. The proposed RLSM estimator is combined with a high performance torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation and experiment, are shown in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.