• Journal of the Korean Society for Precision Engineering
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• v.27 no.1
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• pp.125-133
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• 2010

A versatile micro-robotic platform for micro/nano scale assembly has been demanded in a variety of application areas such as micro-biology and nanotechnology. In the near future, a flexible and compact platform could be effectively used in a scanning electron microscope chamber. We are developing a platform that consists of miniature mobile robots and a compact positioning stage with multi degree-of-freedom. This paper presents the design and the implementation of a low-cost and compact multi degree of freedom position sensor that is capable of measuring absolute translational and rotational displacement. The proposed sensor is implemented by using a CMOS type image sensor and a target with specific hole patterns. Experimental design based on statistics was applied to finding optimal design of the target. Efficient algorithms for image processing and absolute position decoding are discussed. Simple calibration to eliminate the influence of inaccuracy of the fabricated target on the measuring performance also presented. The developed sensor was characterized by using a laser interferometer. It can be concluded that the sensor system has submicron resolution and accuracy of ${\pm}4{\mu}m$ over full travel range. The proposed vision-based sensor is cost-effective and used as a compact feedback device for implementation of a micro robotic platform.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.457-459
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• 2018

Solar power generation system is a energy generation technology that produces electricity from solar power, and it is growing fastest among renewable energy technologies. It is of utmost importance that the solar power system supply energy to the load stably. However, due to unstable energy production due to weather and weather conditions, accurate prediction of energy production is needed. In this paper, an Artificial Neural Network(ANN) that predicts solar energy using 15 kinds of meteorological data such as precipitation, long and short wave radiation averages and temperature is implemented and its performance is evaluated. The ANN is constructed by adjusting hidden parameters and parameters such as penalty for preventing overfitting. In order to verify the accuracy and validity of the prediction model, we use Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) as performance indices. The experimental results show that MAPE = 19.54 and MAE = 2155345.10776 when Hidden Layer $Sizes=^{\prime}16{\times}10^{\prime}$.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1273-1281
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• 2023

Absolute radiometric calibration is a crucial process in converting the electromagnetic signals obtained from satellite sensors into physical quantities. It is performed to enhance the accuracy of satellite data, facilitate comparison and integration with other satellite datasets, and address changes in sensor characteristics over time or due to environmental conditions. In this study, field campaigns were conducted to perform vicarious calibration for the multispectral channels of the CAS500-1. Two valid field observations were obtained under clear-sky conditions, and the top-of-atmosphere (TOA) radiance was simulated using the MODerate resolution atmospheric TRANsmission 6 (MODTRAN 6) radiative transfer model. While a linear relationship was observed between the simulated TOA radiance of tarps and CAS500-1 digital numbers(DN), challenges such as a wide field of view and saturation in CAS500-1 imagery suggest the need for future refinement of the calibration coefficients. Nevertheless, this study represents the first attempt at absolute radiometric calibration for CAS500-1. Despite the challenges, it provides valuable insights for future research aiming to determine reliable coefficients for enhanced accuracy in CAS500-1's absolute radiometric calibration.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.443-451
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• 2015

In photogrammetry, GCPs (Ground Control Points) have traditionally been used to determine EOPs (Exterior Orientation Parameters) and to produce DEM (Digital Elevation Model). The existing DEM can be used as GCPs, where the observer’s approach is a difficult area, because it is very restrictive to survey in the field. For this, DEM matching should be performed. This study proposed the fusion method using ICP (Iterative Closest Point) and RT (proposed method by Rosenholm and Torlegard, 1988) in order to improve accuracy of the DEM matching. The proposed method was compared to the ICP method to evaluate its usefulness. Pseudo reference DEM with resolution 10m, and modified DEM (random-numbers are added from 0 to 2 at height; scale is 0.9; translation is 100 meters in 3-D axes; rotation is from 10° to 50° from the reference DEM) were used in the experiment. The results proposed accuracy was highest in the matching and absolute orientation. In the case of ICP, according to rotation of the modified DEM being increased, absolute orientation error is increased, while the proposed method generally showed consistent results without increasing the error. The proposed method would be applied to matching when the DEM is modified up to 30° rotation, compared to the reference DEM, based on the results of experiments. In addition when we use Drone, this method can be utilized to identify EOPs or detect 3-D surface deformation from the existing DEM of the inaccessible area.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8553-8557
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• 2016

Background: Accurate dose assessment and correct identification of irradiated from non-irradiated people are goals of biological dosimetry in radiation accidents. Objectives: Changes in the FDXR and the RAD51 gene expression (GE) levels were here analyzed in response to total body exposure (TBE) to a 6 MV x-ray beam in rats. We determined the accuracy for absolute quantification of GE to predict the dose at 24 hours. Materials and Methods: For this in vivo experimental study, using simple randomized sampling, peripheral blood samples were collected from a total of 20 Wistar rats at 24 hours following exposure of total body to 6 MV X-ray beam energy with doses (0.2, 0.5, 2 and 4 Gy) for TBE in Linac Varian 2100C/D (Varian, USA) in Golestan Hospital, in Ahvaz, Iran. Also, 9 rats was irradiated with a 6MV X-ray beam at doses of 1, 2, 3 Gy in 6MV energy as a validation group. A sham group was also included. After RNA extraction and DNA synthesis, GE changes were measured by the QRT-PCR technique and an absolute quantification strategy by taqman methodology in peripheral blood from rats. ROC analysis was used to distinguish irradiated from non-irradiated samples (qualitative dose assessment) at a dose of 2 Gy. Results: The best fits for mean of responses were polynomial equations with a R2 of 0.98 and 0.90 (for FDXR and RAD51 dose response curves, respectively). Dose response of the FDXR gene produced a better mean dose estimation of irradiated "validation" samples compared to the RAD51 gene at doses of 1, 2 and 3 Gy. FDXR gene expression separated the irradiated rats from controls with a sensitivity, specificity and accuracy of 87.5%, 83.5% and 81.3%, respectively, 24 hours after dose of 2 Gy. These values were significantly (p<0.05) higher than the 75%, 75% and 75%, respectively, obtained using gene expression of RAD51 analysis at a dose of 2 Gy. Conclusions: Collectively, these data suggest that absolute quantification by gel purified quantitative RT-PCR can be used to measure the mRNA copies for GE biodosimetry studies at comparable accuracy to similar methods. In the case of TBE with 6MV energy, FDXR gene expression analysis is more precise than that with RAD51 for quantitative and qualitative dose assessment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.11 no.4
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• pp.121-131
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• 1991

This study is about the analysis of the application of non-metric camera to accident sites and aims to present an efficient, an economical and an accurate method of processing accident sites. This was accomplished by observation and accuracy analysis of an experimental model. It can be concluded that by applying the 3-D coordinate system and the bundle adjustment with additional parameters to non-metric cameras, it is possible to achieve an accuracy level of positional values which is similar to that achieved by conventional control surveying and by metric cameras. It was also found that the accuracy of absolute coordinates approached towards the accuracy of metric cameras with the increase of the film size and with the increase of the focal length of the non-metric camera.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.95-102
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• 2007

Recommender System based on the Collaborative Filtering has a problem of trust of the prediction accuracy because of its problem of sparsity. If the sparsity of a preference value is large, it causes a problem on a process of a choice of neighbors and also lowers the prediction accuracy. In this article, a change of MAE based on the sparsity is studied, groups are classified by sparsity and then, the significant difference among MAEs of classified groups is analyzed. To improve the accuracy of prediction among groups by the problem of sparsity, We studied the improvement of an accurate prediction for recommending system through reducing sparsity by sorting sparsity items, and replacing the average preference among them that has a lot of respondents with the preference evaluation value.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Structural Engineering and Mechanics
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• v.82 no.2
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• pp.245-258
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• 2022

Many Structural Health Monitoring (SHM) methods have been proposed for structural damage diagnosis and prognosis. However, SHM for pinched hysteretic structures can be problematic due to the high level of nonlinearity. The model-free hysteresis loop analysis (HLA) has displayed notable robustness and accuracy in identifying damage for full-scaled and scaled test buildings. In this paper, the performance of HLA is compared with seven other SHM methods in identifying lateral elastic stiffness for a six-story numerical building with highly nonlinear pinching behavior. Two successive earthquakes are employed to compare the accuracy and consistency of methods within and between events. Robustness is assessed across sampling rates 50-1000 Hz in noise-free condition and then assessed with 10% root mean square (RMS) noise added to responses at 250 Hz sampling rate. Results confirm HLA is the most robust method to sampling rate and noise. HLA preserves high accuracy even when the sampling rate drops to 50 Hz, where the performance of other methods deteriorates considerably. In noisy conditions, the maximum absolute estimation error is less than 4% for HLA. The overall results show HLA has high robustness and accuracy for an extremely nonlinear, but realistic case compared to a range of leading and recent model-based and model-free methods.

• Journal of Chest Surgery
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• v.25 no.11
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• pp.1218-1220
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• 1992

Pneumonectomy on a patient with documented poor pulmonary function indicating a contraindication to surgery can be associated with a high risk of serious postoperative morbidity or mortality. However the usual criterias, on the performance of a pneumonectomy on a high risk patient based on the preoperative assessment of the pulmonary function may not sometimes predict with accuracy the operative outcome in the postoperative period. We recently performed pneumonectomy with good results on a patient with poor pulmonary function that would otherwise have been an absolute contraindication to surgery by usual criteria.