• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.575-594
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• 2010

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

• Atmosphere
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• v.28 no.3
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• pp.233-245
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• 2018

This study explores the Subseasonal-to-Seasonal (S2S) prediction skills of the Northern Hemisphere mid-latitude geopotential height in the Global Seasonal forecasting model version 5 (GloSea5) hindcast experiment. The prediction skills are quantitatively verified for the period of 1991~2010 by computing the Anomaly Correlation Coefficient (ACC) and Mean Square Skill Score (MSSS). GloSea5 model shows a higher prediction skill in winter than in summer at most levels regardless of verification methods. Quantitatively, the prediction limit diagnosed with ACC skill of 500 hPa geopotential height, averaged over $30^{\circ}N{\sim}90^{\circ}N$, is 11.0 days in winter, but only 9.1 days in summer. These prediction limits are primarily set by the planetary-scale eddy phase errors. The stratospheric prediction skills are typically higher than the tropospheric skills except in the summer upper-stratosphere where prediction skills are substantially lower than upper-troposphere. The lack of the summer upper-stratospheric prediction skill is caused by zonal mean error, perhaps strongly related to model mean bias in the stratosphere.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.23-33
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• 1996

In this paper we point out that if the classical principal domain for bispectra is utilized to determine a second-order Volterra model's output, such and output will be incomplete. This deficiency is associated with the periodic nature of the DFT. For this reason, the objective of this paper is to present an "extended" principal domain for Volterra kernels which leads to an improved estimate of the nonlinear system's response. In order to define the extended principal domain, we derive a new discrete frequency-domain Volterra model from a discrete time-domain Volterra model utilizing 2-dimensional DFT and the relationship between the quadratic component of the Volterra model and a square filter. The effect of the extended domain on the model output is interpreted in terms of the periodicity of DFT. Through computer simulations, we demonstrate the effects of the extended principal domain on the Volterra modeling. The simulation results indicate that the extended principal domain plays and important role in computing Volterra model outputs and estimating Volterra model coefficients.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.1
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• pp.91-97
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• 2013

The input parameters of scattering models for computing the backscattering coefficients of earth terrains are mainly soil moisture and surface roughness. The backscattering coefficients of soil surfaces are more sensitive to surface roughness than soil moisture. In this study, we propose a precise measurement method for roughness parameters and analyze measurement errors. We measured surface roughness using a pin-board profiler(1 m, 0.5 cm interval) and a laser profiler(1 m, 0.25 cm interval). The measurement differences between two profilers in an average sense are 0.097 cm for root-mean-square (RMS) height and 1.828 cm for correlation length. The analysis of the correlation functions and relative errors shows that the laser measurements are more stable than the pin-board measurements. The differences of the calculated backscattering coefficients using a surface scattering model between pin-board and laser profiler measurements are less than 1 dB.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.6
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• pp.939-946
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• 2018

In this work, we present simple schematics for a three-terminal graphene-based nanoelectromechanical switch with the vertical electrode, and we investigated their operational dynamics via classical molecular dynamics simulations. The main structure is both the vertical pin electrode grown in the center of the square hole and the graphene covering on the hole. The potential difference between the bottom gate of the hole and the graphene of the top cover is applied to deflect the graphene. By performing classical molecular dynamic simulations, we investigate the nanoelectromechanical properties of a three-terminal graphene-based nanoelectromechanical switch with vertical pin electrode, which can be switched by the externally applied force. The elastostatic energy of the deflected graphene is also very important factor to analyze the three-terminal graphene-based nanoelectromechanical switch. This simulation work explicitly demonstrated that such devices are applicable to nanoscale sensors and quantum computing, as well as ultra-fast-response switching devices.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• Journal of Information Science Theory and Practice
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• v.12 no.1
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• pp.1-20
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• 2024

This research aims to study and confirm enabling factors affecting the knowledge transfer and business process of community enterprise groups in Pattani province, Thailand. Key informants were community enterprise entrepreneurs; 30 people were selected purposively with criteria. This study used a mixed-methods approach and conducted semi-structured interviews to collect data. Qualitative data were analyzed using content analysis and classification, while quantitative data were analyzed using descriptive statistics with frequency, percentage, mean, and standard deviation. Moreover, inferential statistics chi-square value, Phi Cramer's V, and multiple regression analysis with the R program for statistical computing were employed to analyze the relationship between the variables, test the research hypothesis, and create forecasting equations. The research results revealed that the overview of enabling factors had a very high relationship (Cramer's V=0.965). Regarding community enterprise, it was found that enabling factors related to the knowledge transfer and business process consisted of four factors: regulations and administrative guidelines, business plan, reinforcement, and brainstorming. Reinforcement was the factor with the highest degree of correlation (Cramer's V=0.873) and predictor of influence on the knowledge transfer and business process (R₂=0.670, p<0.05). This study's findings can lead to the developing of guidelines for promoting community enterprises properly and timely. These guidelines are expected to be used to develop knowledge about business models for community enterprises, which will help to improve their competency and competitiveness.

• Atmosphere
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• v.34 no.2
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• pp.83-95
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• 2024

Atmospheric rivers (ARs) are closely related to local precipitation which can be both beneficial and destructive. Although several studies have evaluated their predictability, there is a lack of studies on East Asian ARs. This study evaluates the prediction skill of East Asian ARs in the Korean Integrated Model (KIM) for 2020~2022 summer. The spatial distribution of AR frequency in KIM is qualitatively similar to the observation but overestimated. In particular, the model errors greatly increase along the boundary of the western North Pacific subtropical high as the forecast lead time increases. When the prediction skills are quantitatively verified by computing the Anomaly Correlation Coefficient and Mean Square Skill Score, the useful prediction skill of daily AR around the Korean Peninsula is found up to 5 days. Such prediction limit is primarily set by the wind field errors with a minor contribution of moisture distribution errors. This result suggests that the improved prediction of atmospheric circulation field can improve the prediction of East Asian summer ARs and the associated precipitation.