• Journal of Information Processing Systems
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• v.17 no.4
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• pp.818-833
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• 2021

To address the problems of low precision rate, insufficient feature extraction, and poor contextual ability in existing text sentiment analysis methods, a mixed model account of a CNN-BiLSTM-TE (convolutional neural network, bidirectional long short-term memory, and topic extraction) model was proposed. First, Chinese text data was converted into vectors through the method of transfer learning by Word2Vec. Second, local features were extracted by the CNN model. Then, contextual information was extracted by the BiLSTM neural network and the emotional tendency was obtained using softmax. Finally, topics were extracted by the term frequency-inverse document frequency and K-means. Compared with the CNN, BiLSTM, and gate recurrent unit (GRU) models, the CNN-BiLSTM-TE model's F1-score was higher than other models by 0.0147, 0.006, and 0.0052, respectively. Then compared with CNN-LSTM, LSTM-CNN, and BiLSTM-CNN models, the F1-score was higher by 0.0071, 0.0038, and 0.0049, respectively. Experimental results showed that the CNN-BiLSTM-TE model can effectively improve various indicators in application. Lastly, performed scalability verification through a takeaway dataset, which has great value in practical applications.

• Coupled systems mechanics
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• v.11 no.1
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• pp.33-41
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• 2022

Quite often we have a lot of measurement data and would like to find some relation between them. One common task is to see whether some measured data or a curve of known shape fit into the cumulative measured data. The problem can be visualized since data could generally be presented as curves or planes in Cartesian coordinates where each curve could be represented as a vector. In most cases we have measured the cumulative 'curve', we know shapes of other 'curves' and would like to determine unknown coefficients that multiply the known shapes in order to match the measured cumulative 'curve'. This problem could be presented in more complex variants, e.g., a constant could be added, some missing (unknown) data vector could be added to the measured summary vector, and instead of constant factors we could have polynomials, etc. All of them could be solved with slightly extended version of the procedure presented in the sequel. Solution procedure could be devised by reformulating the problem as a measurement problem and applying the generalized inverse of the measurement matrix. Measurement problem often has some errors involved in the measurement data but the least squares method that is comprised in the formulation quite successfully addresses the problem. Numerical examples illustrate the solution procedure.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.37-48
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• 1995

This paper presents an sstimation of ARMA coefficients of noisy ARMA system using generalized total least square (GTLS) method. GTLS problem for ARMA system is defined as minimizing the errors between the noisy output vectors and estimated noisy-free output. The GTLS problem is solved in closed form by eigen-problem and the perturbation analysis of GTLS is presented. Also its recursive solution (recursive GTLS) is proposed using the power method and the covariance formula of the projected output error vector into the input vector space. The simulation results show that GTLS ARMA coefficients estimator is an unbiased estimator and that recursive GTLS achieves fast convergence.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.443-452
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• 2006

Today, satellite remote sensing (RS) and geographic information systems (GIS) plays an important role as an advanced science and technology. This study was developed a Line Density Algorithm which was clarify and describe the thermal front by using NOAA SST (sea surface temperature) and GIS spatial analysis for systemic and effective management of fish raising industry and sea environmental pollution by land reclamation program. Before this, a study about a interpolation method was carry out which was very important for estimate the hidden value between a special point. For this study Inverse Distance Weighted interpolation, Spline interpolation, Kriging interpolation methods were choose and SST data from 2001 to 2004 in spring (March, April, May) were analyzed. According to the study Kriging interpolation method was the very adaptive method from a practical point of view and excellent in description and precision then others. Finally, the result of this study will be use for develope the Line Density Index Algorism.

• Journal of Magnetics
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• v.18 no.2
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• pp.135-141
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• 2013

This paper proposes an efficient fault detection method for onboard degaussing coils which are installed to minimize underwater magnetic fields due to the ferromagnetic hull. To achieve this, the method basically uses field signals measured at specific magnetic treatment facilities instead of time-consuming numerical field solutions in a three-dimensional analysis space. In addition, an analytical design sensitivity formula and the linear property of degaussing coil fields is being exploited for detecting fault coil positions and assessing individual degaussing coil currents. Such peculiar features make it possible to yield fast and accurate results on the fault detection of degaussing coils. For foreseeable fault conditions, the proposed method is tested with a model ship equipped with 20 degaussing coils.

• Journal of the Optical Society of Korea
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• v.18 no.5
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• pp.507-516
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• 2014

We present a hybrid method for spectral reflectivity recovery, using 3D extrapolation as a supplemental method for 3D interpolation. The proposed 3D extrapolation is an extended version of 3D interpolation based on the barycentric algorithm. It is faster and more accurate than the conventional spectral-recovery techniques of principal-component analysis and nonnegative matrix transformation. Four different extrapolation techniques (based on nearest neighbors, circumcenters, in-centers, and centroids) are formulated and applied to recover spectral reflectivity. Under the standard conditions of a D65 illuminant and 1964 $10^{\circ}$ observer, all reflectivity data from 1269 Munsell color chips are successfully reconstructed. The superiority of the proposed method is demonstrated using statistical data to compare coefficients of correlation and determination. The proposed hybrid method can be applied for fast and accurate spectral reflectivity recovery in image processing.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.96-101
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• 2009

This paper presents a method for identifying the parameter set of inelastic constitutive equations, which is based on an Evolutionary Algorithm. The advantage of the method is that appropriate parameters can be identified even when the measured data are subject to considerable errors and the model equations are inaccurate. The design of experiments suited for the parameter identification of a material model by Chaboche under the uniaxial loading and stationary temperature conditions was first considered. Then the parameter set of the model was identified by the proposed method from a set of experimental data. In comparison to those by other methods, the resultant stress-strain curves by the proposed method correlated better to the actual material behaviors.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.437-446
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• 2023

We propose a transient evaluation scheme using a pressure measurement in a complicate pipeline systems. Conservation of mass and momentum equations in time domain can be transformed into a pressure head and flowrate relationship between upstream and downstream point in frequency domain. The impedance formulations were derived to address measured pressure at downstream to evaluate of flowrate or pressure head at any point of system. Both branched pipeline element and looped pipeline element can be generally addressed in the platform of the basic reservoir pipeline valve system. The convolution of time domain response function with measured pressure head from a downstream point provides flowrate or pressure head response in any point of the designated pipeline system. The proposed method was validated through comparison between traditional method of characteristics and the proposed method in several hypothetical systems.

• Journal of Korean Society of Disaster and Security
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• v.15 no.3
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• pp.101-113
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• 2022

This study is planned with the aim of developing a systematic construction process based on the scientific and engineering theory of the water-sealing grouting construction applied to the tunnel excavation process during the construction of the downtown underground traffic network, so that the construction quality of the relatively backward domestic tunnel water-sealing grouting construction is improved and continuously maintained no matter who constructs it. The main contents of the improved tunnel water-sealing grouting can be largely examined in the classification of tunnel water-sealing grouting application and the definition of grouting materials, the correlation analysis of groundwater pressure conditions with groundwater inflow, the study of the characteristic factors of bedrock, and the element technologies and injection management techniques required for grouting construction. Looking at the trends in global research, research in the field of theoretical-based science and engineering grouting is actively progressing in Nordic countries (Sweden, Finland, Norway, etc.), Japan, Germany, and the United States. Therefore, in this study, the algorithm is established through theoretical analysis of the elements of tunnel water-sealing grouting construction techniques to provide an integrated solution including a construction process that can effectively construct tunnel water-sealing grouting construction.

• Journal of the Korean Geotechnical Society
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• v.22 no.8
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• pp.107-118
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• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels, however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results is clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the first part are used to prepare a set of data for learning process. Tunnel behavior, especially the displacements of the lining, has been exclusively investigated for the back analysis.