• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.3
• /
• pp.115-121
• /
• 2024

As the radar mounted on a moving platform moves and rotates, the state of the radar's coordinate system also changes. At this time, in order to track target, the target's coordinates should be converted using the platform state measured from the sensor, and tracking performance may deteriorate due to causes such as sensor noise, communication delay, and sensor update cycle. In this paper, to minimize the degradation of tracking performance because of sensor error, we designed a navigation filter to estimate the state of the moving platform and analyzed the effect of improving tracking performance by applying the navigation filter through a simulation test. To design this navigation filter, three filter algorithms were applied and analyzed to confirm the effect of improving platform position and attitude performance for each filter, and the navigation filter designed by applying the highest performance filter algorithm was applied to a tracking simulation test. Finally we confirmed Improvement in tracking performance before and after applying navigation filters.

• Progress in Medical Physics
• /
• v.25 no.2
• /
• pp.72-78
• /
• 2014

The aim of the study is to test a hypothesis that quasi-breath-hold (QBH) biofeedback improves the residual respiratory motion management in gated 3D thoracic MR imaging, reducing respiratory motion artifacts with insignificant acquisition time alteration. To test the hypothesis five healthy human subjects underwent two gated MR imaging studies based on a T2 weighted SPACE MR pulse sequence using a respiratory navigator of a 3T Siemens MRI: one under free breathing and the other under QBH biofeedback breathing. The QBH biofeedback system utilized the external marker position on the abdomen obtained with an RPM system (Real-time Position Management, Varian) to audio-visually guide a human subject for 2s breath-hold at 90% exhalation position in each respiratory cycle. The improvement in the upper liver breath-hold motion reproducibility within the gating window using the QBH biofeedback system has been assessed for a group of volunteers. We assessed the residual respiratory motion management within the gating window and respiratory motion artifacts in 3D thoracic MRI both with/without QBH biofeedback. In addition, the RMSE (root mean square error) of abdominal displacement has been investigated. The QBH biofeedback reduced the residual upper liver motion within the gating window during MR acquisitions (~6 minutes) compared to that for free breathing, resulting in the reduction of respiratory motion artifacts in lung and liver of gated 3D thoracic MR images. The abdominal motion reduction in the gated window was consistent with the residual motion reduction of the diaphragm with QBH biofeedback. Consequently, average RMSE (root mean square error) of abdominal displacement obtained from the RPM has been also reduced from 2.0 mm of free breathing to 0.7 mm of QBH biofeedback breathing over the entire cycle (67% reduction, p-value=0.02) and from 1.7 mm of free breathing to 0.7 mm of QBH biofeedback breathing in the gated window (58% reduction, p-value=0.14). The average baseline drift obtained using a linear fit was reduced from 5.5 mm/min with free breathing to 0.6 mm/min (89% reduction, p-value=0.017) with QBH biofeedback. The study demonstrated that the QBH biofeedback improved the upper liver breath-hold motion reproducibility during the gated 3D thoracic MR imaging. This system can provide clinically applicable motion management of the internal anatomy for gated medical imaging as well as gated radiotherapy.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.32 no.1
• /
• pp.65-73
• /
• 2019

In order to increase the production efficiency of the ship and shorten the production cycle, it is important to evaluate the accuracy of the ship components efficiently during the drying cycle. The accuracy control of the block is important for shortening the ship process, reducing the cost, and improving the accuracy of the ship. Some systems have been developed and used mainly in large shipyards, but in some cases, they are measured and managed using conventional measuring instruments such as tape measure and beam, optical instruments as optical equipment, In order to perform accuracy control, these tools and equipment as well as equipment for recording measurement data and paper drawings for measuring the measurement position are inevitably combined. The measured results are managed by the accuracy control system through manual input or recording device. In this case, the measurement result is influenced by the work environment and the skill level of the worker. Also, in the measurement result management side, there are a human error about the lack of the measurement result creation, the lack of the management sheet management, And costs are lost in terms of efficiency due to consumption. The purpose of this study is to improve the working environment in the existing accuracy management process by using the augmented reality technology to visualize the measurement information on the actual block and to obtain the measurement information And a smart management system based on augmented reality that can effectively manage the accuracy management data through interworking with measurement equipment. We confirmed the applicability of the proposed system to the accuracy control through the prototype implementation.

• Journal of the Korean Society of Radiology
• /
• v.7 no.2
• /
• pp.157-163
• /
• 2013

This study was to analyze quantitatively movement of planning target volume (PTV) and change of PTV volume through movement of diaphragm according to breathing phase. The purpose of present study was to investigate optimized respiration phase for radiation therapy of lung cancer. Simulated breathing training was performed in order to minimize systematic errors which is caused non-specific or irregular breathing. We performed 4-dimensional computed tomography (4DCTi) in accordance with each respiratory phase in the normalized respiratory gated radiation therapy procedures, then not only defined PTVi in 0 ~ 90%, 30 ~ 70% and 40 ~ 60% in the reconstructed 4DCTi images but analyzed quantitatively movement and changes of volume in PTVi. As a results, average respiratory cycle was $3.4{\pm}0.5$ seconds by simulated breathing training. R2-value which is expressed as concordance between clinically induced expected value and actual measured value, was almost 1. There was a statistically significant. And also movement of PTVi according to each respiration phase 0 ~ 90%, 30 ~ 70% and 40 ~ 60% were $13.4{\pm}6.4mm$, $6.1{\pm}2.9mm$ and $4.0{\pm}2.1mm$ respectively. Change of volume in PTVi of respiration phase 30 ~ 70% was decreased by $32.6{\pm}8.7%$ and 40 ~ 60% was decreased by $41.6{\pm}6.2%$. In conclusion, PTVi movement and volume change was reduced, when we apply a short breathing phase (40 ~ 60%: 30% duty cycle) range. Furthermore, PTVi margin considered respiration was not only within 4mm but able to get uniformity of dose.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.27 no.4
• /
• pp.413-433
• /
• 1994

Jinhae Bay once was a productive area of fisheries. It is, however, now notorious for its red tides; and oxygen deficient water-masses extensively develop at present in summer. Therefore the shellfish production of the bay has been decreasing and mass mortality often occurs. Under these circumstances, the three-dimensional numerical hydrodynamic and the material cycle models, which were developed by the Institute for Resources and Environment of Japan, were applied to analyze the processes affecting the oxygen depletion and also to evaluate the environment capacity for the reception of pollutant loads without dissolved oxygen depletion. In field surveys, oxygen deficient water-masses were formed with concentrations of below 2.0mg/l at the bottom layer in Masan Bay and the western part of Jinhae Bay during the summer. Current directions, computed by the $M_2$ constituent, were mainly toward the western part of Jinhae Bay during flood flows and in opposite directions during ebb flows. Tidal currents velocities during the ebb tide were stronger than that of the flood tide. The comparision between the simulated and observed tidal ellipses showed fairly good agreement. The residual currents, which were obtained by averaging the simulated tidal currents over 1 tidal cycle, showed the presence of counterclockwise eddies in the central part of Jinhae Bay. Density driven currents were generated southward at surface and northward at the bottom in Masan Bay and Jindong Bay, where the fresh water of rivers entered. The material cycle model was calibrated with the data surveyed in the field of the study area from June to July, 1992. The calibrated results are in fairly good agreement with measured values within relative error of $28\%$. The simulated dissolved oxygen distributions of bottom layer were relatively high with the concentration of $6.0{\sim}8.0mg/l$ at the boundaries, but an oxygen deficient water-masses were formed within the concentration of 2.0mg/l at the inner part of Masan Bay and the western part of Jinhae Bay. The results of sensitivity analyses showed that sediment oxygen demand(SOD) was one of the most important influence on the formation of oxygen depletion. Therefore, to control the oxygen deficient water-masses and to conserve the coastal environment, it is an effective method to reduce the SOD by improving the polluted sediment. As the results of simulations, in Masan Bay, oxygen deficient water-masses recovered to 5.0mg/l when the $50\%$ reduction in input COD loads from Masan basin and $70\%$ reduction in SOD was conducted. In the western part of Jinhae Bay, oxygen deficient water-masses recovered to 5.0mg/l when the $95\%$ reduction in SOD and $90\%$ reduction in culturing ground fecal loads was conducted.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.12C
• /
• pp.1173-1183
• /
• 2006

In this paper, we propose an efficient method for improving visual quality of AR-FGS (Adaptive Reference FGS) which is adopted as a key scheme for SVC (Scalable Video Coding) or H.264 scalable extension. The standard FGS (Fine Granularity Scalability) adopts AR-FGS that introduces temporal prediction into FGS layer by using a high quality reference signal which is constructed by the weighted average between the base layer reconstructed imageand enhancement reference to improve the coding efficiency in the FGS layer. However, when the enhancement stream is truncated at certain bitstream position in transmission, the rest of the data of the FGS layer will not be available at the FGS decoder. Thus the most noticeable problem of using the enhancement layer in prediction is the degraded visual quality caused by drifting because of the mismatch between the reference frame used by the FGS encoder and that by the decoder. To solve this problem, we exploit the principle of cyclical block coding that is used to encode quantized transform coefficients in a cyclical manner in the FGS layer. Encoding block coefficients in a cyclical manner places 'higher-value' bits earlier in the bitstream. The quantized transform coefficients included in the ealry coding cycle of cyclical block coding have higher probability to be correctly received and decoded than the others included in the later cycle of the cyclical block coding. Therefore, we can minimize visual quality degradation caused by bitstream truncation by adjusting weighting factor to control the contribution of the bitstream produced in each coding cycle of cyclical block coding when constructing the enhancement layer reference frame. It is shown by simulations that the improved AR-FGS scheme outperforms the standard AR-FGS by about 1 dB in maximum in the reconstructed visual quality.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_2
• /
• pp.721-732
• /
• 2017

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.

• Journal of the Korean earth science society
• /
• v.41 no.5
• /
• pp.469-477
• /
• 2020

Salinity is not only an important variable that determines the density of the ocean but also one of the main parameters representing the global water cycle. Ocean salinity observations have been mainly conducted using ships, Argo floats, and buoys. Since the first satellite salinity was launched in 2009, it is also possible to observe sea surface salinity in the global ocean using satellite salinity data. However, the satellite salinity data contain various errors, it is necessary to validate its accuracy before applying it as research data. In this study, the salinity accuracy between the Soil Moisture Active Passive (SMAP) satellite salinity data and the in-situ salinity data provided by the Ieodo ocean research station was evaluated, and the error characteristics were analyzed from April 2015 to August 2020. As a result, a total of 314 match-up points were produced, and the root mean square error (RMSE) and mean bias of salinity were 1.79 and 0.91 psu, respectively. Overall, the satellite salinity was overestimated compare to the in-situ salinity. Satellite salinity is dependent on various marine environmental factors such as season, sea surface temperature (SST), and wind speed. In summer, the difference between the satellite salinity and the in-situ salinity was less than 0.18 psu. This means that the accuracy of satellite salinity increases at high SST rather than at low SST. This accuracy was affected by the sensitivity of the sensor. Likewise, the error was reduced at wind speeds greater than 5 m s^-1. This study suggests that satellite-derived salinity data should be used in coastal areas for limited use by checking if they are suitable for specific research purposes.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.127-142
• /
• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.11
• /
• pp.5196-5205
• /
• 2012

Purpose: The Purpose of this study were to compare the level of perception and to identify factors associated with perception on patient safety culture by nurses and hygienists. Method: The data were collected from september to December, 2010 using Hospital survey on patient safety culture questionnaires. The subjects were 399 Nurses, hygienists, recruited from the hospital in Busan & Kyungnam. The collected data were analyzed using SPSS descriptive statistics, mean and standard deviation, t-test and ANOVA, Spearman rank coefficient. Result: The perception level of nurses on patient safety culture was 3.48. In case of hygienists, the level was 3.51. Compared to nurses, hygienists showed a significantly difference on the items "Staff arrangement"(t=2.841, p<.01) and "Administator attitude"(t=-2.471, p<.05), "Feedback and communication in accident"(t=-3.356, p<.01). Nurses and hygienists' age and career, working hour per week were identified as factor associated with patient safety culture. Conclusion: The perception level of hospital health providers on patient safety culture was moderate. and identified factors associated with patient safety culture were age and career, working hour per week.