• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.675-682
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• 2013

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.727-734
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• 2021

The study aims to examine and analyze the factors that affect the return on assets (ROA) by placing net interest margin (NIM) as a moderating variable in influencing ROA. This research was conducted on 27 banks listed on the Indonesia Stock Exchange (IDX) for the period 2015 to 2018 with a total sample data of 91. The data used is a combination of time series data and cross-section data. The sampling technique used was the purposive sampling method. The data analysis technique used was path analysis with multiple regression analysis technique. The results of the analysis showed that the capital adequacy ratio (CAR) and loan to deposit ratio (LDR) have a positive but insignificant effect on ROA. NIM as a moderating variable does not influence the impact of CAR on ROA. However, NIM as a moderating variable is able to influence the impact of LDR on ROA. From the results of this study, it is evident that the LDR will increase the ROA at banks that generate high NIM.

• Journal of Mechanical Science and Technology
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• v.18 no.11
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• pp.1996-2008
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• 2004

A seamless analysis of material behavior incorporating complex geometry and crack- tip modeling is one of greatly interesting topics in engineering and computational fracture mechanics fields. However, there are still large gaps between the industrial applications and fundamental academic studies due to a time consuming detailed modeling. In order to resolve this problem, a numerical method to calculate an energy release rate by virtual crack closure technique was proposed in this paper. Both free mesh method and finite element method have been utilized and, thereafter, robust local and global elements for various geometries and boundary conditions were generated. A validity of the proposed method has been demonstrated through a series of fracture mechanics analyses without tedious crack-tip meshing.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.6
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• pp.726-734
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• 2008

While the structural safety assessment of Cargo Containment System(CCS) in membrane type LNG carriers has to be carried out in consideration of sloshing impact pressure, it is very difficult to figure out its dynamic response behaviors due to its very complex structural arrangements/materials and complicated phenomena of sloshing impact loading. For the development of its original technique, it is necessary to understand the characteristics of dynamic response behavior of CCS structure under sloshing impact pressure. In this study, for the exact understanding of dynamic response behavior of CCS structure in membrane Mark III type LNG carriers under sloshing impact pressure, its wet drop impact response analyses were carried out by using Fluid-Structure Interaction(FSI) analysis technique of LS-DYNA code, and were also validated through a series of wet drop experiments for the enhancement of more accurate shock response analysis technique. It might be thought that the structural response behaviors of impact response analysis, such as impact pressure impulses and resulted strain time histories, generally showed very good agreement with experimental ones with very appropriate use of FSI analysis technique of LS-DYNA code, finite element modeling and material properties of CCS structure, finite element modeling and equation of state(EOS) of fluid domain.

• New & Renewable Energy
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• v.4 no.2
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• pp.74-80
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• 2008

To improve the blue responses of screen-printed single crystalline silicon solar cells, we investigated an emitter etch-back technique to obtain high emitter sheet resistances, where the defective dead layer on the emitter surface was etched and became thinner as the etch-back time increased, resulting in the monotonous increase of short circuit current and open circuit voltage. We found that an optimal etch-back time should be determined to achieve the maximal performance enhancement because of fill factor decrease due to a series resistance increment mainly affected by contact and lateral resistance in this case. To elucidate the reason for the fill factor decrease, we studied the resistance analysis by potential mapping to determine the contact and the lateral series resistance. As a result, we found that the fill factor decrease was attributed to the relatively fast increase of contact resistance due to the dead layer thinning down with the lowest contact resistivity when the emitter was contacted with screen-printed silver electrode.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.390-393
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• 2008

To improve the blue responses of screen-printed single crystalline silicon solar cells, we investigated an emitter etch-back technique to obtain high emitter sheet resistances, where the defective dead layer on the emitter surface was etched and became thinner as the etch-back time increased, resulting in the monotonous increase of short circuit current and open circuit voltage. We found that an optimal etch-back time should be determined to achieve the maximal performance enhancement because of fill factor decrease due to a series resistance increment mainly affected by contact and lateral resistance in this case. To elucidate the reason for the fill factor decrease, we studied the resistance analysis by potential mapping to determine the contact and the lateral series resistance. As a result, we found that the fill factor decrease was attributed to the relatively fast increase of contact resistance due to the dead layer thinning down with the lowest contact resistivity when the emitter was contacted with screen-printed silver electrode.

• Journal of Distribution Science
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• v.11 no.4
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• pp.5-11
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• 2013

Purpose - The aim of this work is to study the reorientation that the export industrial sectors in Costa Rica have experienced during the last 20 years. Research design, data, methodology - The study employs the Cluster Analysis with the export data (20 years of cut-off period) from Costa Rica to the U.S-market. To make the predictions, the technique of the time series was used, with official data (from 2001 to 2010) from the U.S. Department of Commerce and the U.S. International Trade Commission. Results - The Cluster Analysis, show how the economic sectors of traditional products exports of Costa Rica have progressively become in exporters of non-traditional products, meanwhile,the time series confirms that this trend will continue, at least during the next five years. Conclusions - The industry of traditional products exports of Costa Rica (dressmaking, vegetables, coffee, mate, species, etc.) will progressively become in exporters of non- traditional products with a high-tech component (i.e., mechanical equipment and devices, electronic devices and medical equipment),as a consequence of the Chinese (Costa Rica's main competitor) economy's presence in the Organization for Economic Co-operation and Development (OCDE). This fact has enabled the potential improvement of Costa Rica's international competitiveness in the U.S. market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• Steel and Composite Structures
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• v.17 no.5
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• pp.561-572
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• 2014

In this paper, we present the scattering characteristics of infinite and finite array using method of moment (MoM) with Floquet harmonics and asymptotic waveform evaluation (AWE) technique. First, infinite cylindrical dipole array is analyzed using the MoM with entire domain basis function and cylindrical Floquet harmonics. To provide the validity of results, we fabricated the cylindrical dipole array and measured the transmission characteristics. The results show good agreements. Second, we analyzed the scattering characteristics of finite array. A large simulation time is needed to obtain the scattering characteristics of finite array over wide frequency range because Floquet harmonics can't be applied. So, we used the MoM with AWE technique using Taylor series and Pade approximation to overcome the shortcomings of conventional MoM. We calculated the radar cross section (RCS) as scattering characteristics using the proposed method in this paper and the conventional MoM for finite planar slot array, finite spherical slot array, and finite cylindrical dipole array, respectively. The compared results agree well and show that the proposed method in this paper is good for electromagnetic analysis of finite FSS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.267-268
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• 2022

Recently, big data analysis can use various techniques according to the development of machine learning. Big data collected in reality lacks an automated refining technique for the same or similar terms based on semantic analysis of the relationship between words. Big data is usually in the form of sentences, and morphological analysis or understanding of the sentences is required. Accordingly, NLP, a technique for analyzing natural language, can understand the relationship of words and sentences. In this paper, we study the advantages and disadvantages of Transformers and Reformers, which are techniques that complement the disadvantages of RNN, which is a time series approach to big data.