• Structural Engineering and Mechanics
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• v.89 no.1
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• pp.77-91
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• 2024

During strong seismic events, inelastic shear deformation occurs in beam-column joints. To capture inelastic shear deformation, an analytical model for beam-column joint in reinforced concrete (RC) frame structures has been proposed in this study. The proposed model has been developed using a rotational spring and rigid links. The stiffness properties of the rotational spring element have been assigned in terms of a moment rotation curve developed from the shear stress-strain backbone curve. The inelastic rotation behavior of joint has been categorized in three stages viz. cracking, yielding and ultimate. The joint shear stress and strain values at these stages have been estimated using analytical models and experimental database respectively. The stiffness properties of joint rotational spring have been modified by incorporating a geometry factor based on dimensions of adjoining beam and column members. The hysteretic response of the joint rotational spring has been defined by a pivot hysteresis model. The response of the proposed analytical model has been verified initially at the component level and later at the structural level with the two actually tested RC frame structures. The proposed joint model effectively emulates the inelastic behavior precisely with the experimental results at component as well as at structural levels.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.191-197
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• 2009

Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fatal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input-output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.

• Journal of the Korean Society for information Management
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• v.21 no.2
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• pp.23-44
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• 2004

This study proposed a theoretical framework that could explain the service use of a community informatics. The proposed community informatics use model was developed based on three theoretical models: (1) Ajzen's Theory of Planned Behavior (TPB) from social psychology: (2) Uses and gratifications approaches from media use research; and (3) Technology Acceptance Model(TAM) from information technology use research. The proposed model consists of three basic components: expectations of the outcomes from use, approvals from important others, and perceivied controllability over using the service. The initially proposed model was assessed using structural equation modeling, and then re-sepcified in order to propose a better fitting model. The initially proposed and revised community informatics use models were discussed with their theoretical and practical implications.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.1-17
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• 2015

In this paper, we propose a portfolio selection model utilizing a Markov chain for investing in the foreign exchange market based on market forecasts and exchange rate movement predictions. The proposed model is utilized to compute optimum investment portfolio weights for investing in margin-based markets such as the FX margin market. We further present an objective investment algorithm for applying the proposed model in real-life investments. Empirical performance of the proposed model and investment algorithm is evaluated by conducting an experiment in the FX market consisting of the 7 most traded currency pairs, for a period of 9 years, from the beginning of 2005 to the end of 2013. We compare performance with 1) the Dollar Index, 2) a 1/N Portfolio that invests the equal amount in the N target assets, and 3) the Barclay BTOP FX Index. Performance is compared in terms of cumulated returns and Sharpe ratios. The results suggest that the proposed model outperforms all benchmarks during the period of our experiment, for both performance measures. Even when compared in terms of pre- and post-financial crisis, the proposed model outperformed all other benchmarks, showing that the model based on objective data and mathematical optimization achieves superior performance empirically.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.145-152
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• 2007

In this paper, we propose an automatic extraction model for unknown translations and implement an unknown translation extraction system using the proposed model. The proposed model as a phrase-alignment model is incorporated with three models: a phrase-boundary model, a language model, and a translation model. Using the proposed model we implement the system for extracting unknown translations, which consists of three parts: construction of parallel corpora, alignment of Korean and English words, extraction of unknown translations. To evaluate the performance of the proposed system, we have established the reference corpus for extracting unknown translation, which comprises of 2,220 parallel sentences including about 1,500 unknown translations. Through several experiments, we have observed that the proposed model is very useful for extracting unknown translations. In the future, researches on objective evaluation and establishment of parallel corpora with good quality should be performed and studies on improving the performance of unknown translation extraction should be kept up.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1413-1432
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• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.53-62
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• 1997

In this study, a controller design method is proposed for controlling the discrete-time chaotic systems efficiently. The proposed control method is based on Generalized Predictive Control and uses NARMAX models as controlled models. In order to evaluate the performance of the proposed method, a proposed controller is applied to discrete-time chaotic systems, and then the control performance and initial sensitivity of the proposed controller are compared with those of the conventional model-based controler through computer simulations. Through simulations results, it is shown that the control performance of the proposed controller is superior to that of the conventional model-based controller and shown that the peorposed controller is less sensitive to initial values of discrete-time chaotic systems in comparison with the conventional model-based controller.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.9
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• pp.692-699
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• 2003

A novel rotor speed estimation method using Model Reference Adaptive Control(MRAC) is proposed to improve the performance of a sensorless vector controller. In the proposed mettled, the stator current is used as the model variable for estimating the speed. In conventional MRAC methods, the relation between the two model errors and the speed estmation error is unclear. Yet, in the proposed method, the stator current error is represented as a function of the first degree for the error value in the speed estimation. Therefore, the proposed method can produce a fast speed estimation and is robust to the parameters error In addition, the proposed method of offers a considerable improvement in the performance of a sensorless vector controller at a low speed. The superiority of the proposed method is verified by simulation and experiment in a low speed region and at a zero-speed.