• Steel and Composite Structures
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• v.10 no.6
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• pp.501-516
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• 2010

This paper is aiming to study the performances of reinforced high-strength concrete (HSC) short columns confined with aramid fibre-reinforced polymer (AFRP) sheets. An experimental program, which involved 45 confined columns and nine unconfined columns, was carried out in this study. All the columns were circular in cross section and tested under axial compressive load. The considered parameters included the concrete strength, amount of AFRP layers, and ratio of hoop reinforcements. Based on the experimental results, a prediction model for the axial stress-strain curves of the confined columns was proposed. It was observed from the experiment that there was a great increment in the compressive strength of the columns when the amount of AFRP layers increases, similar as the ultimate strain. However, these increments were reduced as the concrete strength increasing. Comparisons with other existing prediction models present that the proposed model can provide more accurate predictions.

• Food Science and Preservation
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• v.4 no.2
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• pp.123-130
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• 1997

To develop the prediction program for quality change of Citrus unshiu during marketing, we examined the quality characteristics of Citrus unshiu stored at experimental refrigerator set to 4, 8, 12 and 16$^{\circ}C$ for 2 months. According to the storage temperature the changes of quality characteristics were different respectively, but it was most severe during 16$^{\circ}C$ storage. Activation energy and Q10 value were 6683.16 cal/mol K and 1.53 respectively. The determination coefficient of regression equation of pH, acidity and vitamin C by surface response analysis were over 0.85. Using these regression equation, we developed the prediction program for the change of pH, acidity and vitamin C contents. The calculated values and experimental values of pH, acidity and vitamin C contents for short-term storage of Citrus unshiu were coincided well.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.157-165
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• 2022

As the stock market is affected by various circumstances including economic and political variables, predicting the stock market is considered a still open problem. When combined with corporate financial statement data analysis, which is used as fundamental analysis, and technical analysis with a short data generation cycle, there is a problem that the time domain does not match. Our proposed method, LSTE the operating profit and market outlook of a petrochemical company and estimates the sales and operating profit of the company, it was possible to solve the above-mentioned problems and improve the accuracy of stock price prediction. Extensive experiments on real-world stock data show that our method outperforms the 8.58% relative improvements on average w.r.t. accuracy.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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• pp.318-324
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• 2005

Theoretical efforts are performed to extend the formulation of NSLT(New Shear Lag Theory) for the prediction of the elastic modulus in short fiber composite. The formulation is based on the elastic stress transfer considering the stress concentration effects influenced by elastic modulus ratio between fiber and matrix. The composite modulus, thus far, is calculated by changing the fiber aspect ratio and volume fraction. It is found that the comparison with FEA(Finite Element Analysis) results gives a good agreement with the present theory (NSLT). It is also found that the NSLT is more accurate than the SLT(Shear Lag Theory) in short fiber regime when compared by FEA results. However, The modulus predicted by NSLT becomes similar values that of SLT when the fiber aspect ratio increases. Finally, It is shown that the present model has the capability to predict the composite modulus correctly in elastic regime.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.11a
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• pp.445-448
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• 2003

Polymer concrete using wastes PET recycled resin that is, in general, more excellent mechanical properties than portland cement concrete. A lot of works are carried out about short-term properties of polymer concrete, however, little work has done to define their long-term properties, that is, sustain load such as creep. In this study will show the data that can long-term behavior of polymer concrete by short term creep test of polymer concrete that was affect to the temperature and the time to predict to long-term creep behavior. Then prediction equation was similar tendency that was comparing to short-term creep test and long-term creep test.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.151-162
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• 2002

The object of this study is to develop an operating time prediction model for expressways using toll collection data. A Prediction model based on modular neural network model was developed and tested using real data. Two toll collection system(TCS) data set. Seoul-Suwon section for short range and Seoul-Daejeon section for long range, in Kyongbu expressway line were collected and analyzed. A time series analysis on TCS data indicated that operating times on both ranges are in reasonable prediction ranges. It was also found that prediction for the long section was more complex than that for the short section. However, a long term prediction for the short section turned out to be more difficult than that for the long section because of the higher sensitivity to initial condition. An application of the suggested model produced accurate prediction time. The features of suggested prediction model are in the requirement of minimum (3) input layers and in the ability of stable operating time prediction.