• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• ETRI Journal
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• v.42 no.3
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• pp.333-340
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• 2020

We consider a hybrid combiner design for downlink massive multiple-input multiple-output systems when there is residual inter-user interference and each user is equipped with a limited number of radio frequency (RF) chains (less than the number of receive antennas). We propose a hybrid combiner that minimizes the mean-squared error (MSE) between the information symbols and the ones estimated with a constant amplitude constraint on the RF combiner. In the proposed scheme, an iterative alternating optimization method is utilized. At each iteration, one of the analog RF and digital baseband combining matrices is updated to minimize the MSE by fixing the other matrix without considering the constant amplitude constraint. Then, the other matrix is updated by changing the roles of the two matrices. Each element in the RF combining matrix is obtained from the phase component of the solution matrix of the optimization problem for the RF combining matrix. Simulation results show that the proposed scheme performs better than conventional matrix-decomposition schemes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.97-105
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• 2015

Multiresponse optimization (MRO) seeks to find the setting of input variables, which optimizes the multiple responses simultaneously. The approach of weighted mean squared error (WMSE) minimization for MRO imposes a different weight on the squared bias and variance, which are the two components of the mean squared error (MSE). To date, a weighted sum-based method has been proposed for WMSE minimization. On the other hand, this method has a limitation in that it cannot find the most preferred solution located in a nonconvex region in objective function space. This paper proposes a Tchebycheff metric-based method to overcome the limitations of the weighted sum-based method.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.285-292
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• 1999

In this paper we propose the several estimators of the Lorenz curve in the Pareto distribution and obtain the bias and the mean squared error for each estimator. We compare the proposed estimators with the uniformly minimum variance unbiased estimator (UMVUE) and the maximum likelihood estimator (MLE) in terms of the mean squared error (MSE) through Monte Carlo methods and discuss the results.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.209-217
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• 2001

We obtain the approximate maximum likelihood estimators (AMLEs) for the scale and location parameters $\theta$ and $\mu$ in the three-parameter Weibull distribution based on Type-II censored samples. We also compare the AMLEs with the modified maximum likelihood estimators (MMLEs) in the sense of the mean squared error (MSE) based on complete sample.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.895-904
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• 1998

Two stage shrinkage testimator is a kind of adaptive estimators based on a test on an initial estimate of parameter. Since weighing factor plays an important roll in assessing the properties of testimator, its choice is extremely crucial in two stage testimation. Adke, Waikar and Schuurmann(1987) proposed a testimator for the mean of an exponential distribution defined with their own weighing factor. Two alternative testimators obtained using changed weighing factors are presented, and their Mean squared error(MSE) formulae are provided in this paper. Their properties are compared with those of existing one by means of MSE.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.623-625
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• 2012

The restoration of an image corrupted by Gaussian noise is an important task in image processing. There are many kinds of filters are proposed to remove Gaussian noise such as Gaussian filter, mean filter, weighted filter, etc. However, they perform not good enough for denoising and edge preservation. Hence, in this paper we proposed an adaptive weighted filter which considers spatial distance and the estimated variance of noise. We also compared the proposed method with existing methods through the simulation and used MSE(mean squared error) as the standard of judgement of improvement effect.

• Journal of electromagnetic engineering and science
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• v.4 no.1
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• pp.1-7
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• 2004

In this paper the Individual tap - Least Mean Square(IT-LMS) algorithm is applied to the adaptive multipath channel equalization using hybrid-type Genetic Algorithm(GA) for achieving lower minimum Mean Squared Error(MSE). Owing to the global search performance of GA, LMS-type equalizers combined with it have shown preferable performance in both global and local search but those still have unsatisfying minimum MSE performance. In order to lower the minimum MSE we investigated excess MSE of IT-LMS algorithm and applied it to the hybrid GA equalizer. The high convergence rate and lower minimum MSE of the proposed system give us reason to expect that it will perform well in practical multi-path channel equalization systems.

• Journal of the Korean Society of Radiology
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• v.9 no.5
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• pp.263-267
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• 2015

In this paper, a method was proposed for objective and scientific judgment on disease conditions that is currently relied on subjective judgment of ultrasound practitioners. In the proposed method, mean squared errors (MSE) in ultrasound images for follow-up of HCC patients were obtained and the Peak Signal to Noise Ratio(PSNR) of the ultrasound images was analyzed. According to the results of analysis, MSE and PSNR values changed over time. This is attributable to changes in ultrasound images resulting from increases in utrasonic echoes following the progression of HCC. The results of the present study can be used as a method for scientific and objective judgment in ultrasonic scan instead of current subjective judgment by practitioners.