• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.395-407
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• 2023

Super-resolution (SR) has great significance in image processing because it enables downstream vision tasks with high spatial resolution. Recently, SR studies have adopted deep learning networks and achieved remarkable SR performance compared to conventional example-based methods. Deep-learning-based SR models generally require low-resolution (LR) images and the corresponding high-resolution (HR) images as training dataset. Due to the difficulties in obtaining real-world LR-HR datasets, most SR models have used only HR images and generated LR images with predefined degradation such as bicubic downsampling. However, SR models trained on simple image degradation do not reflect the properties of the images and often result in deteriorated SR qualities when applied to real-world images. In this study, we propose an image degradation model for HR satellite images based on the modulation transfer function (MTF) of an imaging sensor. Because the proposed method determines the image degradation based on the sensor properties, it is more suitable for training SR models on remote sensing images. Experimental results on HR satellite image datasets demonstrated the effectiveness of applying MTF-based filters to construct a more realistic LR-HR training dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2314-2333
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• 2023

It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.4
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• pp.75-86
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• 2019

Recently, natural disasters due to abnormal climates are frequently outbreaking, and there is rapid increase of damage to aged agricultural infrastructure. As agricultural infrastructure facilities are in contact with water throughout the year and the number of them is significant, it is important to build a maintenance management system. Especially, the current maintenance management system of pumping and drainage stations among the agricultural facilities has the limit of lack of objectivity and management personnel. The purpose of this study is to develop a performance evaluation model using the factors related to performance degradation of pumping and drainage facilities and to predict the performance of the facilities in response to climate change. In this study, we focused on the pumping and drainage stations belonging to each climatic zone separated by the Korea geographical climatic classification system. The performance evaluation model was developed using three different statistical models of POLS, RE, and LASSO. As the result of analysis of statistical models, LASSO was selected for the performance evaluation model as it solved the multicollinearity problem between variables, and showed the smallest MSE. To predict the performance degradation due to climate change, the climate change response variables were classified into three categories: climate exposure, sensitivity, and adaptive capacity. The performance degradation prediction was performed at each facility using the developed performance evaluation model and the climate change response variables.

• Steel and Composite Structures
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• v.15 no.4
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• pp.357-377
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• 2013

In order to study the degradation and damage behaviors of steel frame welded connections, two series of tests in references with different connection constructions were carried out subjected to various cyclic loading patterns. Hysteretic curves, degradation and damage behaviours and fatigue properties of specimens were firstly studied. Typical failure modes and probable damage reasons were discussed. Then, various damage index models with variables of dissipative energy, cumulative displacement and combined energy and displacement were summarized and applied for all experimental specimens. The damage developing curves of ten damage index models for each connection were obtained. Finally, the predicted and evaluated capacities of damage index models were compared in order to describe the degraded performance and failure modes. The characteristics of each damage index model were discussed in depth, and then their distributive laws were summarized. The tests and analysis results showed that the loading histories significantly affected the distributive shapes of damage index models. Different models had their own ranges of application. The selected parameters of damage index models had great effect on the developing trends of damage curves. The model with only displacement variable was recommended because of a more simple form and no integral calculation, which was easier to be formulated and embedded in application programs.

• Journal of Applied Reliability
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• v.7 no.4
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• pp.149-162
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• 2007

Field and accelerated tests are performed to assess the service life of construction sealants. Mathematical degradation models for tensile strength and elongation, that are the two major performance characteristics of sealants, are derived from the test results. Accelerated degradation test methods for assessing service life of construction sealants are developed based on the degrading performance and a numerical example is provided.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.316-324
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• 2022

This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.

• Journal of the Korean Operations Research and Management Science Society
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• v.10 no.2
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• pp.75-82
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• 1985

Reorganization is essential part of data base maintenanc work and the reasonable reorganization points can be determined from the trade-off between reorganization cost and performance degradation. There has been many reorganization models so far, but none of these models have assumed nonlinear search cost function. This paper presents the existensions of two existing linear reorganization models for the case where the search cost function is quadratic. The higher performance of these extended models was shown in quadratic search cost function case.

• Steel and Composite Structures
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• v.19 no.5
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• pp.1055-1075
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• 2015

In order to investigate the accuracy and applicability of steel equivalent constitutive model, the calculated results were compared with typical tests of steel frames under static and dynamic loading patterns firstly. Secondly, four widely used models for time history analysis of steel frames were compared to discuss the applicability and efficiency of different methods, including shell element model, multi-scale model, equivalent constitutive model (ECM) and traditional beam element model (especially bilinear model). Four-story steel frame models of above-mentioned finite element methods were established. The structural deformation, failure modes and the computational efficiency of different models were compared. Finally, the equivalent constitutive model was applied in seismic incremental dynamic analysis of a ten-floor steel frame and compared with the cyclic hardening model without considering damage and degradation. Meanwhile, the effects of damage and degradation on the seismic performance of steel frame were discussed in depth. The analysis results showed that: damages would lead to larger deformations. Therefore, when the calculated results of steel structures subjected to rare earthquake without considering damage were close to the collapse limit, the actual story drift of structure might already exceed the limit, leading to a certain security risk. ECM could simulate the damage and degradation behaviors of steel structures more accurately, and improve the calculation accuracy of traditional beam element model with acceptable computational efficiency.

• IE interfaces
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• v.12 no.3
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• pp.448-457
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• 1999

Accelerated degradation is concerned with models and data analyses for degradation of product performance over time at overstress and design conditions. Although there have been numerous studies with accelerated degradation theory in reliability, very few actually apply to parametric statistical analyses. This paper shows how to analyze degradation data, provides tests for how well the assumptions hold. Reel sensors, a sort of photointerrupters in home VCR, hive been tested, and least-square analyses are used to illustrate our approach. Tests for linearity of the performance-time relationship, dependence of the lognormal distribution, and the standard deviation on time are performed. The mean life of tested sensors is assessed at about 414,000 hours, and the Arrhenius activation energy of this reaction is concluded to be 0.39 eV as results.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.1-12
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• 2006

For advanced civil engineering structures a service life of hundred up to hundred fifty and even two hundred years is sometimes required. The prediction of the performance of concrete structures over such a long period requires accurate and reliable predictive models. Most of the presently used, mostly experience based models don't have the quality and reliability that is required for reliable long-term predictions. The models designers are searching for should be based on an accurate description of the relevant degradation mechanisms. The starting point of such models is a realistic description of the microstructure of the concrete. In this presentation the need and the role of fundamental microstructural models for predicting the performance of concrete structures will be presented. An example will be given of a microstructural model with a proven potential for long-term predictions. Besides this also the role of models in general, i.e. in the whole design and execution process of concrete structures, will be dealt with. Finally recent trends in concrete research will be presented, like the research on self-healing cement-bases systems.