• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.133-138
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• 2003

The prediction of protein secondary structure has been an important bioinformatics tool that is an essential component of the template-based protein tertiary structure prediction process. It has been known that the predicted secondary structure information improves both the fold recognition performance and the alignment accuracy. In this paper, we describe several novel ideas that may improve the prediction accuracy. The main idea is motivated by an observation that the protein's structural information, especially when it is combined with the evolutionary information, significantly improves the accuracy of the predicted tertiary structure. From the non-redundant set of protein structures, we derive the 'potential' parameters for the protein secondary structure prediction that contains the structural information of proteins, by following the procedure similar to the way to derive the directional information table of GOR method. Those potential parameters are combined with the frequency matrices obtained by running PSI-BLAST to construct the feature vectors that are used to train the support vector machines (SVM) to build the secondary structure classifiers. Moreover, the problem of huge model file size, which is one of the known shortcomings of SVM, is partially overcome by reducing the size of training data by filtering out the redundancy not only at the protein level but also at the feature vector level. A preliminary result measured by the average three-state prediction accuracy is encouraging.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.273-279
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• 2016

In this paper, a high-performance control of the induction motor for electric car was implemented to escape dependence of the rare earth magnet. Proposed high-efficiency control algorithm is a Direct Rotor Field-Oriented Control method that is insensitive to the fluctuation of motor parameters. In the DRFOC method, we need to compensate fluctuation of stator transient inductance and magnetizing inductance caused by the magnetic saturation of induction motor in high-speed area. This paper proposes Back-EMF Observer based on stator current estimator of Luenberger style. Motor control system applied the Voltage Feedback Flux Weakening Control method for high-speed operation. The proposed algorithm was verified through tests by the power train of Extended Range Electric Vehicle consists of induction motor and differential gear.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5427-5445
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• 2019

While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.

• Computers and Concrete
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• v.10 no.6
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• pp.557-573
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• 2012

This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1139-1158
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• 2015

This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.98-107
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• 2015

Hierarchical constraints and constraint satisfaction were analyzed in order to solve the problem of conflict detection in collaborative design. The constraints were divided into two sets: one set consisted of known constraints and the other of unknown constraints. The constraints of the two sets were detected with corresponding methods. The set of the known constraints was detected using an interval propagation algorithm, a back propagation (BP) neural network was proposed to detect the set with the unknown constraints. An immune algorithm (IA) was utilized to optimize the weights and the thresholds of the BP neural network, and the steps were designed for the optimization process. The results of the simulation indicated that the BP neural network that was optimized by IA has a better performance in terms of convergent speed and global searching ability than a genetic algorithm. The constraints were described using the eXtensible Markup Language (XML) for computers to be able to automatically recognize and establish the constraint network. The implementation of the conflict detection system was designed based on constraint satisfaction. A wind planetary gear train is taken as an example of collaborative design with a conflict detection system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.6
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• pp.177-182
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• 2010

As the technology is developing, the power system is also developing. Nowaday the power system operation has been complicated because of new technology development, new electric equipments and new operation method of the power system. As the electric equipments performance developing, the fault ratio of power system has been decreased. Therefore, new operation method is needed. More complicated the power system operation method, we need more the power system simulator to train and educate the novice power system operators. In this paper, we made real-time power system simulator using LabVIEW through the TCP/IP communication. With this simulator the instructor can simulate the trainee within various unpredicted cases that real power system could happen.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.225-234
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• 2019

Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.68-71
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• 2018

In this paper, we propose a synchronization protocol for underwater acoustic networks which aims to minimize the effects of long propagation delay and uncertain delay variations and employs packet train scheme with considering low data rate. The proposed protocol uses an one-way delay measurement method by transmitting consecutive packets and acquires synchronization only considering propagation delay variations by calculating packet arrival time differences. We perform simulations under various network conditions, such as node mobility, time interval for packet transmission, network range, and elapsed time after synchronizing. The simulation results shows the superiority of our protocol, compared with a previously proposed protocol.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.218-219
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• 2006

This paper is about modeling on 1500V DC electric railroad system. Electric railroad systems have peculiar characteristics against other electric system. The characteristics arc that the railroad systems have electric vehicle loads which are power-varying and location-varying with time. Because of this load characteristic, the electric railroad system modeling which reflects its own characteristics on EMTDC simulation could not be achieved. However, to reflect load characteristic on EMTDC, this paper suggests electric railroad system modeling by using TPS (Train Performance Simulator) that was developed in Korea Railroad Research Institute. A TPS program has various kinds of input data, such as operation condition, vehicle condition, and power system condition. By these data, TPS calculates mechanical power consumption and location, especially it decide electric power consumption on the basis of the fact that consumed electric and mechanical power are equal. Moreover, on this paper, movement of vehicle is reflected on EMTDC simulation as variation of feeder impedance. Also, an electric vehicle load is modeled as time-varying constant power load model.