• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.351-360
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• 2016

Sensors equipped in Internet-of-Thing (IoT) devices are used to measure the surrounding contexts, and IoT applications analyze the contexts to infer situations and provide situation-specific smart services. There are different context acquisition schemes including pulling, pushing, and broadcasting. Most IoT devices support only one of the schemes. Hence, there can be an incompatible issue on data acquisition schemes between applications and devices, and consequently it could result in an increased development cost and inefficiency on application maintenance. This paper presents design patterns which can effectively remedy the incompatibility problem. By applying the patterns, IoT applications with incompatibility can be systematically and effectively developed. And, also its maintainability is expected to increase.

• International Commerce and Information Review
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• v.15 no.2
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• pp.71-82
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• 2013

The automotive supply chain is increasingly complex as automakers seek more profitable solutions with global out-sourcing and manufacturing strategies. In the automotive industry, using returnable plastic containers (RPCs) is very common for domestic operations, but for internationally, it has not been considered by many companies because of issues such as overall distance and difficulty of control. The results of this simulation can help to analyze the interactive and coherent behavior of packaging and supply chain systems. The data obtained from the model can be applied to make substantial decisions for choosing the most profitable packaging types, at the same time as it can lead to designing an optimum supply chain for RPCs used in international supply chains.

• Journal of IKEEE
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• v.23 no.4
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• pp.1302-1308
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• 2019

This paper presents an approach for classifying image made by acquired position data from a 2D Lidar sensor with a convolutional neural network (CNN). Lidar sensor has been widely used for unmanned devices owing to advantages in term of data accuracy, robustness against geometry distortion and light variations. A CNN algorithm consists of one or more convolutional and pooling layers and has shown a satisfactory performance for image classification. In this paper, different types of CNN architectures based on training methods, Gradient Descent(GD) and Levenberg-arquardt(LM), are implemented. The LM method has two types based on the frequency of approximating Hessian matrix, one of the factors to update training parameters. Simulation results of the LM algorithms show better classification performance of the image data than that of the GD algorithm. In addition, the LM algorithm with more frequent Hessian matrix approximation shows a smaller error than the other type of LM algorithm.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.211-223
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• 2013

In Korea, the location of yard tractors is figured out in real time by using RFID system in container terminals. However, even though the location recognition of RFID system works fine when transfer crane is in yard operation, there are some problems when container crane is in ship operation. That is because yard tractors come one by one to each transfer crane in an order, but yard tractors come in 4 lanes to the container crane, which makes the system impossible to recognize each yard tractor separately. Therefore, we developed the infrared-ray communication system which can recognize yard tractors accurately in not only in the yard operation of transfer crane but also in the ship operation of container crane in same way in this study. The result in this study showed constant number of recognition, and the range of recognition measures 5.7m in 25m distance. The range of recognition shown in this study is enough to recognize each yard tractor passing under container crane separately.

• Korean Journal of Materials Research
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• v.11 no.6
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• pp.492-497
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• 2001

We report the characteristics and the dependence of sputter-deposited Ta films on the process parameters. The properties of as-deposited Ta films such as deposition rate, resistivity, Rs uniformity, reflectivity, and stress were investigated and analyzed as a function of process parameter using a statistical experimental method. The functional relationships between the independent and dependent variables were predicted by surface response. The optimal deposition condition of DC magnetron sputtered Ta films was obtained at the chamber pressure of 2 mTorr, power density of 8 W/$\textrm{cm}^2$, and substrate temperature of 2$0^{\circ}C$ by means of resistivity and Rs uniformity. The fitness value for quadratic model as evaluated by the R- square was 0.85~ 0.9 without pooling. The as-deposited Ta films exhibited the resistivity of ~180$\mu$$\Omega$cm with Rs uniformity of ~2%. The transmission electron microscopy and x-ray diffractometry identified that the phase of as-deposited film was $\beta$-Ta having the grain size of 100~200.

• Environmental and Resource Economics Review
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• v.27 no.3
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• pp.545-567
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• 2018

This study applied and compared Poisson model, negative binomial model, zero inflated Poisson model, and zero inflated negative binomial model to estimate determinants of employed labour quantity. To estimate each of models, this study used fisheries census data which were obtained at microdata integrated service running by Statistics Korea. The study selected zero inflated negative binomial model according to the Vuong test and Likelihood-ratio test. In addition, the study estimated fishing village's practical changes on employed labour quantity as analyzing changes from 2010 to 2015. The results showed that the household with fishing vessels and high selling price had a significant effect on decrease of the labour quantities. Meanwhile, the longer work experience of the household, the more significant the increase in the labour quantities. In conclusion, this study presented that capitalized fishing household and the acceleration of aging had a significant impact on the change in the labour quantities.

• Review of Culture and Economy
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• v.21 no.2
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• pp.3-28
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• 2018

This study focuses on analyzing the glass ceiling effect in arts and culture professionals through the quintile decomposition applied to the RIF unconditional quantile regression and Oaxaca-Blinder decomposition technique. From the industrial viewpoint, we divide arts and culture professionals into cultural contents professionals(large category J industry) and arts professionals(large category R industry). For our analysis, we employ the pooling data of 'Wage Structure Survey' from 2009 to 2016. Our results are summarized as follows. First, as OLS wage decomposition showed that the gender wage gap among the arts professionals was lower than cultural contents professionals, but the discrimination portion of total gender wage gap was larger. Second, from quintile regression decompositions, the glass ceiling effects of two types of professionals showed different results. Cultural contents sector was observed with the "steady glass ceiling effect" as the portion of the discrimination was continuously increased, while the arts sector was observed with the "limited glass ceiling effect" as the discrimination had drastically increased in the 80s and 90s.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.561-568
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• 2019

Recently, Korea has suffered from severe droughts due to climate change. Therefore, we need to pay attention to the change of drought risk to develop appropriate drought mitigation measures. In this study, we investigated the changes of hydrologic risk of extreme drought using the current observed data and the projected data according to the RCP 4.5 and 8.5 climate change scenarios. The bivariate frequency analysis was performed for the paired data of drought duration and severity extracted by the threshold level method and by eliminating pooling and minor droughts. Based on the hydrologic risk of extreme drought events Jeonbuk showed the highest risk and increased by 51 % than the past for the RCP 4.5 scenario, while Gangwon showed the highest risk and increased by 47 % than the past for the RCP 8.5 scenario.

• Journal of Convergence for Information Technology
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• v.7 no.2
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• pp.31-36
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• 2017

In this paper, we try to search and reproduce the image part of a specific cast from a large number of images. The conventional method must manually set the offset value when searching for a scene or viewing a corner. However, in this paper, the proposed method learns the main character 's face, then finds the main character in the image recognition and moves to the scene where the main character appears to reproduce the image. Data for specific performers is extracted and collected using crawl techniques. Based on the collected data, we learn using convolutional neural network algorithm and perform performance evaluation using it. The performance evaluation measures the accuracy by extracting and judging a specific performer learned in the extracted key frame while playing the drama. The performance confirmation of how quickly and accurately the learned scene is searched has obtained about 93% accuracy. Based on the derived performance, it is applied to the image service such as viewing, searching for person and detailed information retrieval per corner

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.141-147
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• 2021

In this paper, in order to improve the utilization of hyperspectral large-capacity data feature information by reducing complex computations by dimension reduction of neural network inputs in embedded systems, the band selection algorithm is applied in each subset. Among feature extraction and feature selection techniques, the feature selection aim to improve the optimal number of bands suitable for datasets, regardless of wavelength range, and the time and performance, more than others algorithms. Through this experiment, although the time required was reduced by 1/3 to 1/9 times compared to the others band selection technique, meaningful results were improved by more than 4% in terms of performance through the K-neighbor classifier. Although it is difficult to utilize real-time hyperspectral data analysis now, it has confirmed the possibility of improvement.