• Korean Journal of Remote Sensing
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• v.15 no.1
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• pp.39-50
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• 1999

It is complicate and time-consuming process to classify a multi-band satellite imagery according to the application. In addition, classification rate sensitively depends on the selection of training data set and features in a supervised classification process. This paper introduced a classification network adopting a fuzzy-based $\gamma$-model in order to select a training data set and to extract feature which highly contribute to an actual classification. The features used in the classification were gray-level histogram, textures, and NDVI(Normalized Difference Vegetation Index) of target imagery. Moreover, in order to minimize the errors in the classification network, the Gradient Descent method was used in the training process for the $\gamma$-parameters at each code used. The trained parameters made it possible to know the connectivity of each node and to delete the void features from all the possible input features.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.85-95
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• 2016

In National Defense field, the importance of M&S and T&E has been increased due to complexity of modern Weapon System. And research reducing time and cost is being conducted continually on using limited resources efficiently. In the existing research, Weapon System's Performance Evaluation System using the Process-based method has been in progress. But, Objective basis or scientific method is insufficient in selecting appropriate function of a target to performance evaluation. Due to this, it's difficult to select functions suitable to the situation in same type. Also, Requirements of user and interrelation of evaluation factors can't be reflected systematically. In this research, it proposes the method to reflecting requirements of user, interrelation of elements in realistic situation for selecting evaluation object in Performance Evaluation Simulation. First, Evaluation Objects is selected using ANP which is multi-criterion decision making method. Second, decision support system is constructed using Programming Language(C#) based on the research result.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.87-96
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• 2006

Most conventional database systems support specific queries that are concerned only with data that match a query qualification precisely. A cooperative query answering supports query analysis, query relaxation and provides approximate answers as well as exact answers. The key problem in the cooperative answering is how to provide an approximate functionality for alphanumeric as well as categorical queries. In this paper, we propose a metricized knowledge abstraction hierarchy that supports multi-level data abstraction hierarchy and distance metric among data values. In order to facilitate the query relaxation, a knowledge representation framework has been adopted, which accommodates semantic relationships or distance metrics to represent similarities among data values. The numeric domains also compatibly incorporated in the knowledge abstraction hierarchy by calculating the distance between target record and neighbor records.

• Journal of Soil and Groundwater Environment
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• v.19 no.4
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• pp.31-44
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• 2014

In the present study, a few of recently developed geostatistical models are comparatively studied. The models are two-point statistics based sequential indicator simulation (SISIM) and generalized coupled Markov chain (GCMC), multi-point statistics single normal equation simulation (SNESIM), and object based model of FLUVSIM (fluvial simulation) that predicts structures of target object from the provided geometric information. Out of the models, SNESIM and FLUVSIM require additional information other than conditioning data such as training map and geometry, respectively, which generally claim demanding additional resources. For the comparative studies, three-dimensional fluvial reservoir model is developed considering the genetic information and the samples, as input data for the models, are acquired by mimicking realistic sampling (i.e. random sampling). For SNESIM and FLUVSIM, additional training map and the geometry data are synthesized based on the same information used for the objective model. For the comparisons of the predictabilities of the models, two different measures are employed. In the first measure, the ensemble probability maps of the models are developed from multiple realizations, which are compared in depth to the objective model. In the second measure, the developed realizations are converted to hydrogeologic properties and the groundwater flow simulation results are compared to that of the objective model. From the comparisons, it is found that the predictability of GCMC outperforms the other models in terms of the first measure. On the other hand, in terms of the second measure, the both predictabilities of GCMC and SNESIM are outstanding out of the considered models. The excellences of GCMC model in the comparisons may attribute to the incorporations of directional non-stationarity and the non-linear prediction structure. From the results, it is concluded that the various geostatistical models need to be comprehensively considered and comparatively analyzed for appropriate characterizations.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.5
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• pp.155-162
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• 2021

n-step TD learning is a combination of Monte Carlo method and one-step TD learning. If appropriate n is selected, n-step TD learning is known as an algorithm that performs better than Monte Carlo method and 1-step TD learning, but it is difficult to select the best values of n. In order to solve the difficulty of selecting the values of n in n-step TD learning, in this paper, using the characteristic that overestimation of Q can improve the performance of initial learning and that all n-step returns have similar values for Q ≈ Q^*, we propose a new learning target, which is composed of the maximum and the mean of all k-step returns for 1 ≤ k ≤ n. Finally, in OpenAI Gym's Atari game environment, we compare the proposed algorithm with n-step TD learning and proved that the proposed algorithm is superior to n-step TD learning algorithm.

• Journal of the Korean Applied Science and Technology
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• v.35 no.4
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• pp.1386-1392
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• 2018

Cispatin has become one of the most widely used anticancer drugs for decades. One of the drawback of cisplatin (II) complex is that it not only targets cancerous cells but also normal cells causing several serious side effects in patients. We have synthesized Pt(IV) complex that are needed to have the ability to kill target cells selectively in a short time before drug resistance develops. By introducing PDK inhibitor, butyric acid and valproic acid, on Pt complex, two fusion anti-cancer agents 3 and 4 have been synthesized and characterized their structures by nmr and mass spectrometer. MTT assay was performed with $Pt(IV)-Bu_2$ 3 and $Pt(IV)-Val_2$ 4 against MCF-7 cell line. As a result, cisplatin, Pt(IV) complexes 3 and 4 were treated, cell viabilities at $50{\mu}M$ cencentration were decreased to 39%, 54% and 84% respectively.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.135-144
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• 2019

The importance of logistics of warehouse stores has increased as their prices are cheaper and more convenient than those of large supermarkets. However, few studies on integrated depot location selection of warehouse stores have been conducted. In this regard, this study aims to derive factors for integrated depot location selection and calculate weights and select the location priority of target candidates by introducing an analytic hierarchy process (AHP). The analysis results exhibited that the most important selection factor was the cost reduction in transportation and delivery (0.198) followed by distance reduction in transportation and delivery (0.168), and time reduction in transportation. This study quantified the reduction in cost and increase in efficiency if depots were integrated and operated thereby presenting more realistic foundational data to hands-on workers. For the future study, the analysis model will be needed to be advanced through additional investigation on the factors in the analysis.

• Korean Chemical Engineering Research
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• v.56 no.6
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• pp.826-831
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• 2018

This study presents fabrication of bi-compartmental particles labeled by multiple fluorescence. To compartmentalize fluorescent expression at the particle, two fluorescent dyes with less overlap of the excitation and emission spectra are selected. To ensure the fluorescence stability, the fluorescent dyes contain acrylate functional groups in the molecules so that they can be cross-linked together with monomers constituting the particle. Strong fluorescent expression and compartmentalization were observed at the particle fabricated using the selected fluorescent dyes through confocal microscopy. Furthermore, long-term fluorescence stability was verified by measuring fluorescent expression and intensity for 4 weeks. We anticipate that the bi-compartmental particles labeled by multiple fluorescence can be widely used for multi-target drug delivery system, analysis of 3 dimensional Brownian motion, and investigation of 3 dimensional complex self-assembled morphologies.

• Progress in Medical Physics
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• v.30 no.1
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• pp.1-6
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• 2019

Purpose: This study conducts a comparative evaluation of the skin dose in CyberKnife (CK) and Helical Tomotherapy (HT) to predict the accurate dose of radiation and minimize skin burns in head-and-neck stereotactic body radiotherapy. Materials and Methods: Arbitrarily-defined planning target volume (PTV) close to the skin was drawn on the planning computed tomography acquired from a head-and-neck phantom with 19 optically stimulated luminescent dosimeters (OSLDs) attached to the surface (3 OSLDs were positioned at the skin close to PTV and 16 OSLDs were near sideburns and forehead, away from PTV). The calculation doses were obtained from the MultiPlan 5.1.2 treatment planning system using raytracing (RT), finite size pencil beam (FSPB), and Monte Carlo (MC) algorithms for CK. For HT, the skin dose was estimated via convolution superposition (CS) algorithm from the Tomotherapy planning station 5.0.2.5. The prescribed dose was 8 Gy for 95% coverage of the PTV. Results and Conclusions: The mean differences between calculation and measurement values were $-1.2{\pm}3.1%$, $2.5{\pm}7.9%$, $-2.8{\pm}3.8%$, $-6.6{\pm}8.8%$, and $-1.4{\pm}1.8%$ in CS, RT, RT with contour correction (CC), FSPB, and MC, respectively. FSPB showed a dose error comparable to RT. CS and RT with CC led to a small error as compared to FSPB and RT. Considering OSLDs close to PTV, MC minimized the uncertainty of skin dose as compared to other algorithms.