• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.1B
• /
• pp.128-140
• /
• 2000

In this paper, we propose a fast block matching algorithm using the mean absolute error (MAE) of neighbor search point and search region reduction. The proposed algorithm is composed of two stages. At the first stage,the search region is divided into nonoverlapped 3$\times$3 areas and MAE of the center point of each area iscalculated. The minimum MAE value of all the calculated MAE's is determined as reference MAE. At thesecond stage, because the possibility that final motion vector exist near the position of reference MAE is veryhigh, we use smaller search region than first stage, And, using the MAE of center point of each area, the lowerbound of rest search point of each area is calculated and block matching process is performed only at the searchpoints that the lower bound is smaller than reference MAE. By doing so, we can significantly reduce thecomputational complexity while keep the increasement of motion estimation error small.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.9A
• /
• pp.1392-1400
• /
• 1999

In this paper, we propose a two-stage fast full search algorithm for block motion estimation that produces the same performance to that of full search algorithm (FSA) but with remarkable computation reduction. The proposed algorithm uses the search region subsampling and the difference of adjacent pixels in the current block. In the first stage, we subsample the search region by a factor of 9, and then calculate mean absolute error (MAE) at the subsampled search points. And in the second stage, we reduce the search points that need block matching process by using the lower bound of MAE value at each search Point. We Set the lower bound of MAE value for each search point from the MAE values which are calculated at the first stage and the difference of adjacent pixels in the current block. The experimental results show that we can reduce the computational complexity considerably without any degradation of picture quality.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.4
• /
• pp.83-90
• /
• 2012

In e-commerce systems that deal with a large number of items, the function of personalized recommendation is essential. Collaborative filtering that is a successful recommendation algorithm, suffers from the sparsity, cold-start, and scalability restrictions. Additionally, this work raises a new flaw of the algorithm, inconsistent performance of recommendation. This is also not measurable by the current MAE-based evaluation that does not consider the deviation of prediction error, and furthermore is performed independently of precision and recall measurement. To evaluate the collaborative filtering comprehensively, this work proposes an extended evaluation model that includes the current criteria such as MAE, Precision, Recall, deviation, and applies it to cluster-based combined collaborative filtering.

• Food Science and Preservation
• /
• v.9 no.3
• /
• pp.332-337
• /
• 2002

This study was undertaken in order to compare reflux extraction(RE) and microwave-assisted extraction(MAE) in extraction efficiency and establish optimum microwave extraction conditions in obtaining Ligularia fischeri extracts. A considerable reduction in extraction time was accomplished by MAE. When 70% methanol 50% methanol 70% ethanol, or 50% ethanol was used, MAE extract contained equal levels of soluble solid and total polyphenol as obtained by RE. The optimum microwave-assisted extraction conditions for Ligularia fischeri were achieved by 120∼150 watts of microwave energy and 4∼8 minutes of extraction time. No significant changes were found in antioxidant activity with DPPH scavenging method over the variation of microwave energy or extraction time. The use of diluted methanol or ethanol improved soluble solid content(30%), total polyphenol content(2.7%) and antioxidant activity(68%).

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.249-255
• /
• 2000

HRIV(Hybrid Rule-Interval Variation) method is presented to stabilize a class of nonlinear systems, where SMC(Sliding Mode Control) and ADC (ADaptive Control) schemes are incorporated to overcome the unstable characteristics of a conventional FLC(Fuzzy Logic Control). HRIV method consists of two modes: I-mode (Integral Sliding Mode PLC) and R-mode(RIV method). In I-mode, SMC is used to compensate for MAE(Minimum Approximation Error) caused by the heuristic characteristics of FLC. In R-mode, RIV method reduces interval lengths of rules as states converge to an equilibrium point, which makes the defined Lyapunov function candidate negative semi-definite without considering MAE, and the new uncertain parameters generated in R-mode are compensated by SMC. In RIV method, the overcontraction problem that the states are out of a rule-table can happen by the excessive reduction of rule intervals, which is solved with a dynamic modification of rule-intervals and a transition to I-mode. Especially, HRIV method has advantages to use the analytic upper bound of MAE and to reduce Its effect in the control input, compared with the previous researches. Finally, the proposed method is applied to stabilize a simple nonlinear system and a modified inverted pendulum system in simulation experiments.

• Korean Journal of Food Science and Technology
• /
• v.32 no.5
• /
• pp.1022-1028
• /
• 2000

Aster glehni was extracted by microwave-assisted extraction(MAE) and reflux extraction(RE) methods and their extraction efficiencies were compared. A considerable reduction in extraction time was achieved by MAE. When 70% methanol, 50% methanol, 70% ethanol, or 50% ethanol was used, MAE extract contained nearly same amounts of soluble solid and total polyphenol contents as obtained by RE. The optimum MAE conditions for the extraction of Aster glehni were $120{\sim}150$ watts of microwave energy and $4{\sim}8$ minutes of extraction time. No significant changes were found in electron donating ability(EDA) over the variation of microwave energy or extraction time. The use of diluted methanol or ethanol resulted in improving extraction yield(24%), total polyphenol content(2.6%) and EDA(60%).

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.6
• /
• pp.987-994
• /
• 2020

In this paper, we studied the effect of kernel size of CNN layer on performance in acoustic noise attenuators. This system uses a deep learning algorithm using a neural network adaptive prediction filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using a 100-neuron, 16-filter CNN filter and an error back propagation algorithm. This is to use the quasi-periodic property in the voiced sound section of the voice signal. In this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed to verify the performance of the noise attenuator for the kernel size. As a result of the simulation, when the kernel size is about 16, the MSE and MAE values are the smallest, and when the size is smaller or larger than 16, the MSE and MAE values increase. It can be seen that in the case of an speech signal, the features can be best captured when the kernel size is about 16.

• The Journal of the Korea Contents Association
• /
• v.15 no.10
• /
• pp.362-373
• /
• 2015

This study analysed the effectiveness of aluminium(Al) filter in the added compound filtration for the removal of characteristic radiation from high atomic number material by DQE and image evaluation. 1mm Al was applied to each 0.1, 0.2, 0.3 mm copper and befere and after use were evaluated. Beam quality and DQE were tested by IEC regulations and image quality was evaluated by PSNR, MAE, MSE, CNR, SNR and qualitative analysis was performed by 7 items for resolution and contrast from chest x-ray criteria of national cancer checkup. MTF 10 and 50% were the same by 4.6, 2.54 cycle/mm and NPS, DQE, PSNR MAE, MSE, CNR, SNR and qualitative analysis were all the same or slightly better when Al was not used. PSNR is over 30dB and all significant and at the qualitative analysis, the p-value of t-test was over 0.05. The DQE and image quality evaluation have little difference between before and after use of Al filter and it is effective to use the Al filter for the reduction of skin dose by removal of characteristic radiation.

• Journal of Industrial Convergence
• /
• v.21 no.7
• /
• pp.19-27
• /
• 2023

As the secondary battery market expands, the process of producing laterite ore using the rotary kiln and electric furnace method is expanding worldwide. As ESG management expands, the management of air pollutants such as nitrogen oxides in exhaust gases is strengthened. The rotary kiln, one of the main facilities of the pyrometallurgy process, is a facility for drying and preliminary reduction of ore, and it generate nitrogen oxides, thus prediction of nitrogen oxide is important. In this study, LSTM for regression prediction and LightGBM for classification prediction were used to predict and then model optimization was performed using AutoML. When applying LSTM, the predicted value after 5 minutes was 0.86, MAE 5.13ppm, and after 40 minutes, the predicted value was 0.38 and MAE 10.84ppm. As a result of applying LightGBM for classification prediction, the test accuracy rose from 0.75 after 5 minutes to 0.61 after 40 minutes, to a level that can be used for actual operation, and as a result of model optimization through AutoML, the accuracy of the prediction after 5 minutes improved from 0.75 to 0.80 and from 0.61 to 0.70. Through this study, nitrogen oxide prediction values can be applied to actual operations to contribute to compliance with air pollutant emission regulations and ESG management.

• Journal of the Korean Society of Visualization
• /
• v.19 no.1
• /
• pp.62-67
• /
• 2021

Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.