• Journal of Navigation and Port Research
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• v.38 no.4
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• pp.391-397
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• 2014

Wide Area Differential GNSS(WA-DGNSS) was developed in order to improve the accuracy and integrity performance of GNSS. In this paper, overall structure of Pseudolite-Based Augmentation System(PBAS) and experimental methods which enables the post-processing test with commercial receiver will be described. For generating augmenting message, GPS measurement collected from five NDGPS reference stations were processed by reference station S/W and master station S/W. The accuracy of augmenting message was tested by comparing SP3, IONEX data. In the test, RF signal of user was collected and correction data were generated. After that, RF signal was broadcasted with pseudolite signal. Test was conducted using three commercial receiver and the performance was compared with MSAS and standalone user. From the position output of each receiver, it was shown that improved position was obtained by applying augmenting message.

• Journal of KIISE:Information Networking
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• v.34 no.3
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• pp.203-217
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• 2007

In this paper, a performance evaluation model of Networks with the multiple-buffered crossbar switches is proposed and examined. Buffered switch technique is well known to solve the data collision problem of the switch networks. The characteristic of a network with crossbar switches is determined by both the connection pattern of the switches and the limitation of data flow in a each switch. In this thesis, the evaluation models of three different networks : Multistage interconnection network, Fat-tree network, and other ordinary communication network are developed. The proposed evaluation model is developed by investigating the transfer patterns of data packets in a switch with output-buffers. Two important parameters of the network performance, throughput and delay, are evaluated. The proposed model takes simple and primitive switch networks, i.e., no flow control and drop packet, to demonstrate analysis procedures clearly. It, however, can not only be applied to any other complicate modern switch networks that have intelligent flow control but also estimate the performance of any size networks with multiple-buffered switches. To validate the proposed analysis model, the simulation is carried out on the various sizes of networks that uses the multiple buffered crossbar switches. It is shown that both the analysis and the simulation results match closely. It is also observed that the increasing rate of Normalized Throughput is reduced and the Network Delay is getting bigger as the buffer size increased.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.107-114
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• 2017

In this paper, a novel power factor compensation system based on voltage-controlled method is proposed for 3-phase 4-wire power system. The proposed voltage-controlled power factor compensation system generates a reactive power required for compensation by applying a variable output voltage by a slidac to a capacitor. In conventional power factor compensation system using the capacitor bank method, the power factor compensation error occurs depending on the load condition due to the limited capacity of the capacitors. However, the proposed system compensates the power factor up to 100% without error. In this paper, we have developed a voltage-controlled power factor compensation system and a control algorithm for 3-phase 4-wire power system, and verify its performance through simulation and experiments. If the proposed power factor compensation system is applied to an industrial field, a power factor compensation performance can be maximized. As a result, it is possible to reduce of electricity prices, reduce of line loss, increase of load capacity, ensure the transmission margin capacity, and reduce the amount of power generation.

• Radiation Oncology Journal
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• v.8 no.1
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• pp.115-124
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• 1990

The characteristics of 23 MV photon beam have been presented with respect to clinical parameters of central axis depth dose, tissue-maxi mum ratios, scatter-maximum ratios, surface dose and scatter correction factors. The nominal accelerating potential was found to be $18.5\pm0.5$ MV on the central axis. The half-value layer (HVL) of this photon beam was measured with narrow beam geometry from central axis, and it has been showed the thickness of $24.5\;g/cm^2$. The tissue-maximum ratio values have been determined from measured percentage depth dose data. In our experimental dosimetry, the surface dose of maximum showed only $9.6\%$ of maximum dose at $10\times10\;cm^2$, 100 cm SSD, without blocking tray in. The TMR'S of $0\times0$ field size have been determined to get average $2.3\%$ uncertainties from three different methodis; are zero effective attenuation coefficient, non-ilnear least square fit of TMR's data and effective linear attenuation coefficient from the HVL of 23 MV photon beams of dual energy linear accelerator.

• The Korean Journal of Nuclear Medicine Technology
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• v.24 no.1
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• pp.15-19
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• 2020

Purpose Generative Adversarial Network(GAN) is one of deep learning technologies. This is a way to create a real fake image after learning the real image. In this study, after acquiring artificial intelligence images through GAN, We were compared and evaluated with real scan time images. We want to see if these technologies are potentially useful. Materials and Methods 30 patients who underwent ¹⁸F-FDG Brain PET/CT scanning at Severance Hospital, were acquired in 15-minute List mode and reconstructed into 1,2,3,4,5 and 15minute images, respectively. 25 out of 30 patients were used as learning images for learning of GAN and 5 patients used as verification images for confirming the learning model. The program was implemented using the Python and Tensorflow frameworks. After learning using the Pix2Pix model of GAN technology, this learning model generated artificial intelligence images. The artificial intelligence image generated in this way were evaluated as Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR), and Structural Similarity Index(SSIM) with real scan time image. Results The trained model was evaluated with the verification image. As a result, The 15-minute image created by the 5-minute image rather than 1-minute after the start of the scan showed a smaller MSE, and the PSNR and SSIM increased. Conclusion Through this study, it was confirmed that AI imaging technology is applicable. In the future, if these artificial intelligence imaging technologies are applied to nuclear medicine imaging, it will be possible to acquire images even with a short scan time, which can be expected to reduce artifacts caused by patient movement and increase the efficiency of the scanning room.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.13-25
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• 1998

It is necessary to develop methodologies for the application of artificial neural network into hydrologic rainfall-runoff process, although there is so much applicability by using the functions of associative memory based on recognition for the relationships between causes and effects and the excellent fitting capacity for the nonlinear phenomenon. In this study, some problems are presented in the application procedures of artificial neural networks and the simulation of runoff hydrograph experiences are reviewed with nonlinear functional approximator by artificial neural network for rainfall-runoff relationships in a watershed. which is regarded as hydrdologic black box model. The neural network models are constructed by organizing input and output patterns with the deserved rainfall and runoff data in Pyoungchang river basin under the assumption that the rainfall data is the input pattern and runoff hydrograph is the output patterns. Analyzed with the results. it is possible to simulate the runoff hydrograph with processing element of artificial neural network with any hydrologic concepts and the weight among processing elements are well-adapted as model parameters with the assumed model structure during learning process. Based upon these results. it is expected that neural network theory can be utilized as an efficient approach to simulate runoff hydrograph and identify the relationship between rainfall and runoff as hydrosystems which is necessary to develop and manage water resources.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.167-174
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• 2015

Purpose : The pre-treatment QA using Portal dosimetry for Volumetric Arc Therapy To analyze whether maintaining the reproducibility depending on various factors. Materials and Methods : Test was used for TrueBeam STx$^{TM}$ (Ver.1.5, Varian, USA). Varian Eclipse Treatment planning system(TPS) was used for planning with total of seven patients include head and neck cancer, lung cancer, prostate cancer, and cervical cancer was established for a Portal dosimetry QA plan. In order to measure these plans, Portal Dosimetry application (Ver.10) (Varian) and Portal Vision aS1000 Imager was used. Each Points of QA was determined by dividing, before and after morning treatment, and the after afternoon treatment ended (after 4 hours). Calibration of EPID(Dark field correction, Flood field correction, Dose normalization) was implemented before Every QA measure points. MLC initialize was implemented after each QA points and QA was retried. Also before QA measurements, Beam Ouput at the each of QA points was measured using the Water Phantom and Ionization chamber(IBA dosimetry, Germany). Results : The mean values of the Gamma pass rate(GPR, 3%, 3mm) for every patients between morning, afternoon and evening was 97.3%, 96.1%, 95.4% and the patient's showing maximum difference was 95.7%, 94.2% 93.7%. The mean value of GPR before and after EPID calibration were 95.94%, 96.01%. The mean value of Beam Output were 100.45%, 100.46%, 100.59% at each QA points. The mean value of GPR before and after MLC initialization were 95.83%, 96.40%. Conclusion : Maintain the reproducibility of the Portal Dosimetry as a VMAT QA tool required management of the various factors that can affect the dosimetry.

• Radiation Oncology Journal
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• v.18 no.2
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• pp.150-156
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• 2000

Purpose : Stereotactic radiation therapy (SRT) can deliver highly focused radiation to a small and spherical target lesion with very high degree of mechanical accuracy. For non-spherical and large lesions, however, inclusion of the neighboring normal structures within the high dose radiation volume is inevitable in SRT This is to report the beam shaping using the partial closure of the independent jaw in SRT and the verification of dose calculation and the dose display using a home-made soft ware. Materials and Methods : Authors adopted the idea to partially close one or more independent collimator jaw(5) in addition to the circular collimator cones to shield the neighboring normal structures while keeping the target lesion within the radiation beam field at all angles along the arc trajectory. The output factors (OF's) and the tissue-maximum ratios (TMR's) were measured using the micro ion chamber in the water phantom dosimetry system, and were compared with the theoretical calculations. A film dosimetry procedure was peformed to obtain the depth dose profiles at 5 cm, and they were also compared with the theoretical calculations, where the radiation dose would depend on the actual area of irradiation. Authors incorporated this algorithm into the home-made SRT software for the isodose calculation and display, and was tried on an example case with single brain metastasis. The dose-volume histograms (DVH's) of the planning target volume (PTV) and the normal brain derived by the control plan were reciprocally compared with those derived by the plan using the same arc arrangement plus the independent collimator jaw closure. Results : When using 5.0 cm diameter collimator, the measurements of the OF's and the TMR's with one independent jaw set at 30 mm (unblocked), 15.5 mm, 8.6 mm, and 0 mm from th central beam axis showed good correlation to the theoretical calculation within 0.5% and 0.3% error range. The dose profiles at 5 cm depth obtained by the film dosimetry also showed very good correlation to the theoretical calculations. The isodose profiles obtained on the home-made software demonstrated a slightly more conformal dose distribution around the target lesion by using the independent jaw closure, where the DVH's of the PTV were almost equivalent on the two plans, while the DVH's for the normal brain showed that less volume of the normal brain receiving high radiation dose by using this modification than the control plan employing the circular collimator cone only. Conclusions : With the beam shaping modification using the independent jaw closure, authors have realized wider clinical application of SRT with more conformal dose planning. Authors believe that SRT, with beam shaping ideas and efforts, should no longer be limited to the small spherical lesions, but be more widely applied to rather irregularly shaped tumors in the intracranial and the head and neck regions.

• The Journal of Engineering Geology
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• v.15 no.1
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• pp.67-76
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• 2005

This paper presents the application of a neural network for prediction of the unconfined compressive strength from physical properties and schmidt hardness number on rock samples. To investigate the suitability of this approach, the results of analysis using a neural network are compared to predictions obtained by statistical relations. The data sets containing 55 rock sample records which are composed of sandstone and shale were assembled in Daegu area. They were used to learn the neural network model with the back-propagation teaming algorithm. The rock characteristics as the teaming input of the neural network are: schmidt hardness number, specific gravity, absorption, porosity, p-wave velocity and S-wave velocity, while the corresponding unconfined compressive strength value functions as the teaming output of the neural network. A data set containing 45 test results was used to train the networks with the back-propagation teaming algorithm. Another data set of 10 test results was used to validate the generalization and prediction capabilities of the neural network.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.577-587
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• 2023

The utilization of crowdsourced spatial data has been actively researched; however, issues stemming from the uncertainty of data quality have been raised. In particular, when low-quality data is mixed into drone imagery datasets, it can degrade the quality of spatial information output. In order to address these problems, the study presents a methodology for automatically validating the geometric quality of crowdsourced imagery. Key quality factors such as spatial resolution, resolution variation, matching point reprojection error, and bundle adjustment results are utilized. To classify imagery suitable for spatial information generation, training and validation datasets are constructed, and machine learning is conducted using a radial basis function (RBF)-based support vector machine (SVM) model. The trained SVM model achieved a classification accuracy of 99.1%. To evaluate the effectiveness of the quality validation model, imagery sets before and after applying the model to drone imagery not used in training and validation are compared by generating orthoimages. The results confirm that the application of the quality validation model reduces various distortions that can be included in orthoimages and enhances object identifiability. The proposed quality validation methodology is expected to increase the utility of crowdsourced data in spatial information generation by automatically selecting high-quality data from the multitude of crowdsourced data with varying qualities.