• ETRI Journal
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• v.38 no.5
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• pp.807-817
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• 2016

A novel fast algorithm is suggested for a coding unit (CU) mode decision using pseudo rate-distortion based on a separated encoding structure in High Efficiency Video Coding (HEVC). A conventional HEVC encoder requires a large computational time for a CU mode prediction because prediction and transformation procedures are applied to obtain a rate-distortion cost. Hence, for the practical application of HEVC encoding, it is necessary to significantly reduce the computational time of CU mode prediction. As described in this paper, under the proposed separated encoder structure, it is possible to decide the CU prediction mode without a full processing of the prediction and transformation to obtain a rate-distortion cost based on a suitable condition. Furthermore, to construct a suitable condition to improve the encoding speed, we employ a pseudo rate-distortion estimation based on a Hadamard transformation and a simple quantization. The experimental results show that the proposed method achieves a 38.68% reduction in the total encoding time with a similar coding performance to that of the HEVC reference model.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.6
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• pp.609-615
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• 2000

The energy conservation is one of the most important issues in recent years. Especially, the energy conservation through improved control strategies is one of the most highly possible area to be implemented in the near future. The energy conservation of the ice storage system can be accomplished through the improved control strategies. A real time building load prediction algorithm was developed. The expected highest and the lowest outdoor temperature of the next day were used to estimate the next day outdoor temperature profile. The measured dry bulb temperature and the measured building load were used to estimate system parameters by using the on-line weighted recursive least square method. The estimated hourly outdoor temperatures and the estimated hourly system parameters were used to predict the next day hourly building loads. In order to see the effectiveness of the building load prediction algorithm, two different types of building models were selected and analysed. The simulation results show less than 1% in error for the prediction of the next day building loads. Therefore, this algorithm may successfully be used for the development of improved control algorithms of the ice storage system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• Journal of Applied Reliability
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• v.11 no.4
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• pp.433-445
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• 2011

Duty cycle is determined as the ratio of operating time to total time. Duty cycle in reliability prediction is one of the significant factors to be considered. In duty cycle application, non-operating time failure rate has been easily ignored even though the failure rate in non-operating period has not been proved to be small enough. Ignorance of non-operating time failure rate can result in over-estimated system reliability calculation. Furthermore, utilization of duty cycle in reliability prediction has not been evaluated in its effectiveness. In order to address these problems, two reliability models, such as MIL-HDBK-217F and RIAC-HDBK-217Plus, were used to analyze non-operating time failure rate. This research has proved that applying duty cycle in 217F model is not reasonable by the quantitative comparison and analysis.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.6
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• pp.135-142
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• 2001

The effect of tensile hold time on the creep-fatigue interaction in AISI 316 stainless steel was investigated. To study the fatigue characteristics of the material, strain controlled low cycle fatigue(LCF) tests were carried out under the continuous triangular waveshape with three different total strain ranges of 1.0%, 1.5% and 2.0%. To study the creep-fatigue interaction, 5min., 10min., and 30min. of tensile hold times were applied to the continuous triangular waveshape with the same three total strain ranges. The creep-fatigue life was found to be the longest when the 5min. tensile hold time was applied and was the shortest when the 30min. tensile hold time was applied. The cause fur the shortest creep-fatigue life under the 30min. tensile hold time is believed to be the effect of the increased creep damage per cycle as the hold time increases. The creep-fatigue life prediction using artificial neural network(ANN) showed closer prediction values to the experimental values than by the modified Coffin-Manson method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.1
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• pp.71-82
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• 2022

Most of the predictions using machine learning are neutral predictions considering the symmetrical situation where the predicted value is not smaller or larger than the actual value. However, in some situations, asymmetric prediction such as over-prediction or under-prediction may be better than neutral prediction, and it can induce better judgment by providing various predictions to decision makers. A method called Asymmetric Twin Support Vector Regression (ATSVR) using TSVR(Twin Support Vector Regression), which has a fast calculation time, was proposed by controlling the asymmetry of the upper and lower widths of the ε-tube and the asymmetry of the penalty with two parameters. In addition, by applying the existing GSVQR and the proposed ATSVR, prediction using the prediction propensities of over-prediction, under-prediction, and neutral prediction was performed. When two parameters were used for both GSVQR and ATSVR, it was possible to predict according to the prediction propensity, and ATSVR was found to be more than twice as fast in terms of calculation time. On the other hand, in terms of accuracy, there was no significant difference between ATSVR and GSVQR, but it was found that GSVQR reflected the prediction propensity better than ATSVR when checking the figures. The accuracy of under-prediction or over-prediction was lower than that of neutral prediction. It seems that using both parameters rather than using one of the two parameters (p_1,p_2) increases the change in the prediction tendency. However, depending on the situation, it may be better to use only one of the two parameters.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.E2
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• pp.63-73
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• 2008

In this study, neural network models (NNMs) were examined as alternatives to dispersion models in predicting the short-term $SO_2$ concentrations in a coastal area because the performances of dispersion models in coastal areas have been found to be unsatisfactory. The NNMs were constructed for various combinations of averaging time and prediction time in advance by using the historical data of meteorological parameters and $SO_2$ concentrations in 2002 in the coastal area of Boryeung, Korea. The NNMs were able to make much more accurate predictions of 1 hr $SO_2$ concentrations at ground level in the morning in coastal area than the atmospheric dispersion models such as fumigation models, ADMS3 and ISCST3 for identical conditions of atmospheric stability, area, and weather. Even when predictions of 24-h $SO_2$ concentrations were made 24 hours in advance, the predictions and measurements were in good accordance(correlation coefficient=0.65 for n=216). This accordance level could be improved by appropriate expansion of training parameters. Thus it may be concluded that the NNMs can be successfully used to predict short-term ground level concentrations averaged over time less than 24 hours even in complex terrain. The prediction performance of ANN models tends to improve as the prediction lagging time approaches the concentration averaging time, but to become worse as the lagging time departs from the averaging time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• Journal of Drive and Control
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• v.18 no.2
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• pp.32-37
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• 2021

The piston reservoir is mainly used in hydraulic blow-down system for aerospace engineering. The reservoir is heavy due to both hydraulic cylinder and piston in pressurization. The positive expulsion tank with rubber diaphragm has been mostly applied propellant and fuel tank at low pressure to satellites. To reduce weight, the reservoir that can be used at high pressure with rubber diaphragm was developed. In this research, the prediction of life-time for the rubber diaphragm was implemented through an accelerated life test, as a part of development of new reservoir. Also, the diaphragm was stored in an temperature chamber at the same condition as and operation with hydraulic oil. As a result, the life-time for a rubber diaphragm was successfully evaluated via Arrhenius law and Time-Temperature Superposition based on failure times over temperatures in the accelerated test.

• 전기의세계
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• v.31 no.2
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• pp.126-131
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• 1982

Induction motors are known to cause voltage dip, oscillating torque and inrush current on the dynamic period. To compensate for these undesirable effects, the prediction of dynamic performances is required. The dynamic performances are determinated by circuit time constants. From this point of view, in this paper, the dynamic equivalent circuit included only three time-constant parameters are presented. To predict more simply dynamic performances, the new characteristics time constant parameters are analyzed, and now these parameters are described as the function of circuit time constants. This paper reviews and analyzes the use of series capacitance compensations, and the use of this analysis can make simply a prediction about oscillating conditions.