• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.4 no.2
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• pp.43-58
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• 1986

In a mapping, stereo-restitution of an aerial photogrammetric process, of which is a major factor for map-base preparation dominates the accuracy and the reliability of a topographical map. The majority of a map-base preparation has nowadays been carried out by an analogue method ie, by the stereo-plotter. In consequence, it is evident that the skilled, the level of technique and personal attitude of operator have influence upon observational error which relates the accuracy and the quality of a map. This research aims at detection and analysis of operator's carrier and types of stereoplotter. The test is also examined that the level of details and features of terrain would have influence on the accuracy of map. With the results. it is also considered that the field check has impact on map accuracy ; whether the field check prior to restitution or after restitution.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.2
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• pp.112-122
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• 2012

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.536-564
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• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.13-30
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• 2019

This paper presents the recent improvements in the DeCART code for HTGR analysis. A new 190-group DeCART cross-section library based on ENDF/B-VII.0 was generated using the KAERI library processing system for HTGR. Two methods for the eigen-mode adjoint flux calculation were implemented. An azimuthal angle discretization method based on the Gaussian quadrature was implemented to reduce the error from the azimuthal angle discretization. A two-level parallelization using MPI and OpenMP was adopted for massive parallel computations. A quadratic depletion solver was implemented to reduce the error involved in the Gd depletion. A module to generate equivalent group constants was implemented for the nodal codes. The capabilities of the DeCART code were improved for geometry handling including an approximate treatment of a cylindrical outer boundary, an explicit border model, the R-G-B checker-board model, and a super-cell model for a hexagonal geometry. The newly improved and implemented functionalities were verified against various numerical benchmarks such as OECD/MHTGR-350 benchmark phase III problems, two-dimensional high temperature gas cooled reactor benchmark problems derived from the MHTGR-350 reference design, and numerical benchmark problems based on the compact nuclear power source experiment by comparing the DeCART solutions with the Monte-Carlo reference solutions obtained using the McCARD code.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.839-849
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• 2023

The development of automation technology to reduce human error by minimizing human intervention is accelerating with artificial intelligence and big data processing technology, even in the nuclear field. Among nuclear power plant operation modes, the startup and shutdown operations are still performed manually and thus have the potential for human error. As part of the development of an autonomous operation system for startup operation, this paper proposes an action coordinating strategy to obtain the optimal actions. The lower level of the system consists of operating blocks that are created by analyzing the operation tasks to achieve local goals through soft actor-critic algorithms. However, when multiple agents try to perform conflicting actions, a method is needed to coordinate them, and for this, an action coordination strategy was developed in this work as the upper level of the system. Three quantification methods were compared and evaluated based on the future plant state predicted by plant parameter prediction models using long short-term memory networks. Results confirmed that the optimal action to satisfy the limiting conditions for operation can be selected by coordinating the action sets. It is expected that this methodology can be generalized through future research.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.182-188
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• 2023

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.103-114
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• 2024

The escalating demands for high-resolution satellite imagery necessitate the dissemination of geospatial data with superior accuracy.Achieving precise positioning is imperative for mitigating geometric distortions inherent in high-resolution satellite imagery. However, maintaining sub-pixel level accuracy poses significant challenges within the current technological landscape. This research introduces an approach wherein upsampling is employed on both the satellite image and ground control points (GCPs) chip, facilitating the establishment of a high-resolution satellite image precision sensor orientation. The ensuing analysis entails a comprehensive comparison of matching performance. To evaluate the proposed methodology, the Compact Advanced Satellite 500-1 (CAS500-1), boasting a resolution of 0.5 m, serves as the high-resolution satellite image. Correspondingly, GCP chips with resolutions of 0.25 m and 0.5 m are utilized for the South Korean and North Korean regions, respectively. Results from the experiment reveal that concurrent upsampling of satellite imagery and GCP chips enhances matching performance by up to 50% in comparison to the original resolution. Furthermore, the position error only improved with 2x upsampling. However,with 3x upsampling, the position error tended to increase. This study affirms that meticulous upsampling of high-resolution satellite imagery and GCP chips can yield sub-pixel-level positioning accuracy, thereby advancing the state-of-the-art in the field.

• The Journal of Korean Society for Radiation Therapy
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• v.16 no.2
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• pp.81-89
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• 2004

Introduction : The setup error due to the patient and the staff from radiation treatment as the reason which is important the treatment record could be decided is a possibility of effect. The SET-UP ERROR of the patient analyzes the effect of dose distribution and DVH from radiation treatment of the patient. Material & Methode : This test uses human phantom and when C-T scan doing, It rotated the Left direction of the human phantom and it made SET-UP ERROR , Standard plan and 3mm, 5mm, 7mm, 10mm, 15mm, 20mm with to distinguish, it made the C-T scan error. With the result, The SET-UP ERROR got each C-T image Using RTP equipment It used the plan which is used generally from clinical - Box plan, 3Dimension plan( identical angle 5beam plan) Also, ( CTV+1cm margin, CTV+0.5cm margin, CTV+0.3,cm margin = PTV) it distinguished the standard plan and each set-up error plan and The plan used a dose distribution and the DVH and it analyzed Result : The Box4 the plan and 3Dimension plan which it bites it got similar an dose distribution and DVH in 3mm, 5mm From rotation error and Rectilinear movement( $0\%{\sim}2\%$ ). Rotation error and rectilinear error 7mm, 10mm, 15mm, 20mm appeared effect it will go mad to a enough change in treatment ( $2\%{\sim}^11\%$ ) Conclusion : The diminishes the effect of the SET-UP ERROR must reduce move with tension of the patient Also, we are important accessory development and the supply that it reducing of reproducibility and the move

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.1-6
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• 2020

To make an automated system for a salt field, it is necessary to measure the salinity and water level of the evaporation site. In this paper, a method to simultaneously measure the salinity and water level by measuring the buoyancy forces of two fixed buoyancy bodies is proposed. The proposed measurement method measures the buoyancy of the main part and reference part when the measuring device is immersed in the salty water, and simultaneously measures the salinity and water level through the sum and difference of the two buoyancy forces. Since there is no mechanical movement in the measurement of buoyancy, measurement errors and maintenance needs can be reduced in the mudy environment of salt field. By applying the proposed method, we developed a system that can simultaneously measure salinity and water level remotely at the evaporation site of a salt field. Through a measurement experiment using a reference salty water having various levels of salinity, the results of a salinity error of 0% and a water level error of 2mm were obtained, and the effectiveness of the proposed salinity and water level measuring device was verified. When an automated system is constructed using the developed salinity and water level measuring device, labor reduction, work environment improvement, and productivity improvement are expected.