• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.31-36
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• 2017

This study aims to develop an BCI speller utilizing eye-closed and double-blinking EEG based on asynchronous mechanism. The proposed system comprised a signal processing module and a graphical user interface (virtual keyboard-VK) with 26 English characters plus a special symbol. A detected "eye-closed" event induces the "select" command, whereas a "double-blinking" (DB) event functions the "undo" command. A three-class support vector machine (SVM) classifier involving EEG signal analysis of three groups of events ("eye-open"-idle state, "eye-closed", and "double -blinking") is proposed. The results showed that the proposed BCI could achieve an overall accuracy of 92.6% and a spelling rate of 5 letters/min on average. Overall, this study showed an improvement of accuracy and the spelling rate resulting from in the feasibility and reliability of implementing a real-world BCI speller.

• Journal of Sensor Science and Technology
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• v.25 no.1
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• pp.20-26
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• 2016

The bioelectrical impedance (BI) for the young and the elderly was measured using bioelectrical impedance spectroscopy (BIS). First, while applying a current of $600{\mu}A$ to the foot and hand, BI was measured at 50 frequencies ranging from 5 to 1000 kHz. The BI for young subjects was considerably lower than that for old subjects since young subjects have more lean mass (hydration). The prediction marker was 0.74 for young subjects and 0.78 for old subjects. Second, a Cole-Cole diagram was obtained for young subjects and old subjects, indicating the different characteristic frequencies. At 50 kHz, the average phase angle was $7.8^{\circ}$ for young subjects whereas that was $6.1^{\circ}$ for old subjects. Third, BIVA was analyzed for young subjects and old subjects. The vector length was 210.89 [${\Omega}/m$] for young subjects and 326.12 [${\Omega}/m$] for old subjects. At 50 kHz, the resistance (R/H) and the reactance ($X_C/H$) divided by height were 208.94 [${\Omega}/m$] and 28.68 [${\Omega}/m$] for young subject, and 324.33 [${\Omega}/m$] and 34.09 [${\Omega}/m$] for old subjects.

• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.4
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• pp.291-299
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• 2007

To operate a permanent magnet synchronous motor (PMSM) at a maximum torque per ampere (MTPA) operation point, the exact values of machine parameters such as inductances and back-EMF constant, which are sensitive to motor phase currents and temperature respectively, should be blown. An adaptive estimation method for on-line estimation of the machine parameters is not suitable for practical applications since it has difficulties in estimating exact values and requires complex mathematical calculations. The purpose of this paper is to present a simple MTPA operation point tracking control strategy for vector controlled PMSM drives with slow dynamic loads. The proposed method searches MTPA operation points by modulating current phase angle and observing the variation in command power. The current angle modulation strategy is designed to sense the effect of load variations in the command power. Therefore, the proposed method can track the MTPA operation points of the PMSM regardless of load variations. Computer simulation and experimental study is also presented to show the effectiveness of the proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.55-62
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• 2019

In this paper, we consider K-user multiple-input multiple-output (MIMO) interference channel and present a transceiver design for simultaneous wireless information and power transfer (SWIPT) systems. In addition, we consider a SWIPT system where an information decoding receiver and an energy harvesting receiver are co-located at the same receiver. In the proposed scheme, signal-to-leakage plus noise ratio (SLNR) is used as a cost function and a transceiver is designed to satisfy the threshold of the harvested energy. More specifically, transmitter precoding vector, receiver filter vector, and power spitting factor are simultaneously designed to maximize SLNR with a constraint on the harvested energy. Through computer simulation, we compare the signal-to-interference plus noise ratio (SINR) performance of the proposed and conventional schemes. When a special condition among the number of transmit antennas, receive antennas, and users is satisfied, the proposed scheme showed better SINR performance than the conventional scheme at low signal-to-noise ratio (SNR) range. Also, when the condition is not satisfied, the proposed scheme showed better performance than the conventional scheme at all SNR range.

• Advances in Computational Design
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• v.4 no.2
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• pp.155-177
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• 2019

Operation and Maintenance (O&M) phase is the main contributor to the total lifecycle cost of a building. Previous studies have described that Building Information Models (BIM), if available with detailed asset information and their properties, can enable rapid troubleshooting and execution of O&M tasks by providing the required information of the facility. Despite the potential benefits, there is still rarely BIM with Mechanical, Electrical and Plumbing (MEP) assets and properties that are available for O&M. BIM is usually not in possession for existing buildings and generating BIM manually is a time-consuming process. Hence, there is a need for an automated approach that can reconstruct the MEP systems in BIM. Previous studies investigated automatic reconstruction of BIM using architectural drawings, structural drawings, or the combination with photos. But most of the previous studies are limited to reconstruct the architectural and structural components. Note that mechanical components in the building typically require more frequent maintenance than architectural or structural components. However, the building mechanical drawings are relatively more complex due to various type of symbols that are used to represent the mechanical systems. In order to address this challenge, this paper proposed a symbol recognition framework that can automatically recognize the different type of symbols in the building mechanical drawings. This study applied vector-based computer vision techniques to recognize the symbols and their properties (e.g., location, type, etc.) in two vector-based input documents: 2D drawings and the symbol description document. The framework not only enables recognizing and locating the mechanical component of interest for BIM reconstruction purpose but opens the possibility of merging the updated information into the current BIM in the future reducing the time of repeated manual creation of BIM after every renovation project.

• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.28-36
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• 2009

In this paper, we propose a frame-rate up-conversion method for temporal quality enhancement. The proposed method adaptively changes search range during hierarchical motion estimation and reconstructs hole regions using the proposed bi-direction prediction and linear interpolation. In order to alleviate errors due to inaccurate motion vector estimation, search range is adaptively changed based on reliability and for more accurate, motion estimation is performed in descending order of block variance. After segmentation of background and object regions, for filling hole regions, the pixel values of background regions are reconstructed using linear interpolation and those of object regions are compensated based on the proposed hi-directional prediction. The proposed algorithm is evaluated in terms of PSNR with original uncompressed sequences. Experimental results show that the proposed algorithm is better than conventional methods by around 2dB, and blocky artifacts and blur artifacts are significantly diminished.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.33-38
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• 2014

The FCM based hybrid RBF network is a heterogeneous learning network model that applies FCM algorithm between input and middle layer and applies Max_Min algorithm between middle layer and output. The Max-Min neural network uses winner nodes of the middle layer as input but shows inefficient learning in performance when the input vector consists of too many patterns. To overcome this problem, we propose a dynamic learning rate control based on fuzzy logic. The proposed method first classifies accurate/inaccurate class with respect to the difference between target value and output value with threshold and then fuzzy membership function and fuzzy decision logic is designed to control the learning rate dynamically. We apply this proposed RBF network to the character recognition problem and the efficacy of the proposed method is verified in the experiment.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.47-56
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• 2015

Automated detection of interesting web pages could be used in many different application domains. Determining a user's interesting web pages can be performed implicitly by observing the user's behavior. The task of distinguishing interesting web pages belongs to a classification problem, and we choose white box learning methods (fixed effect logit regression and support vector machine) to test empirically. The result indicated that (1) fixed effect logit regression, fixed effect SVMs with both polynomial and radial basis kernels showed higher performance than the linear kernel model, (2) a personalization is a critical issue for improving the performance of a model, (3) when asking a user explicit grading of web pages, the scale could be as simple as yes/no answer, (4) every second the duration in a web page increases, the ratio of the probability to be interesting increased 1.004 times, but the number of scrollbar clicks (p=0.56) and the number of mouse clicks (p=0.36) did not have statistically significant relations with the interest.