• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.6
• /
• pp.922-934
• /
• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.

• Journal of Power Electronics
• /
• v.19 no.4
• /
• pp.881-893
• /
• 2019

This paper proposes a practical sliding-mode controller design for shaping the impedances of cascaded boost-converter power decoupling circuits for reducing the second order harmonic ripple in photovoltaic (PV) current. The cascaded double-boost converter, when used as power decoupling circuit, has some advantages in terms of a high step-up voltage-ratio, a small number of switches and a better efficiency when compared to conventional topologies. From these features, it can be seen that this topology is suitable for residential (PV) rooftop systems. However, a robust controller design capable of rejecting double frequency inverter ripple from passing to the (PV) source is a challenge. The design constraints are related to the principle of the impedance-shaping technique to maximize the output impedance of the input-side boost converter, to block the double frequency PV current ripple component, and to prevent it from passing to the source without degrading the system dynamic responses. The design has a small recovery time in the presence of transients with a low overshoot or undershoot. Moreover, the proposed controller ensures that the ripple component swings freely within a voltage-gap between the (PV) and the DC-link voltages by the small capacitance of the auxiliary DC-link for electrolytic-capacitor elimination. The second boost controls the main DC-link voltage tightly within a satisfactory ripple range. The inverter controller performs maximum power point tracking (MPPT) for the input voltage source using ripple correlation control (RCC). The robustness of the proposed control was verified by varying system parameters under different load conditions. Finally, the proposed controller was verified by simulation and experimental results.

• Korean Journal of Computational Design and Engineering
• /
• v.12 no.2
• /
• pp.137-145
• /
• 2007

This paper concerns about residential environment analysis program implementation for design and analysis on public housing complexes such that view and daylight analysis processes are automated and integrated into existing design routine to achieve better design efficiency. Considering the architectural design trends this paper chooses ArchiCAD as a platform for a CAD system, which contains the concepts such as integrated object-oriented CAD, virtual building and BIM. Residential environment analysis system consists of three components. The first component is the 3D modeling part defining 3D form information for external geographic contour models, site models and interior/exterior of apartment buildings. The second is the parametric library part handling the design parameters for view and daylight analysis. The last is the user interface for the input/output and integration of data for the environment analysis. Daylight analysis shows rendered images as well as results of daylight reports and grades per time and performs the calculations for floor shadow. It separates the site-only analysis from the analysis of site and exterior environmental parameters. View analysis considers horizontal and vertical view angles to produce view image from each unit and uses the bitmap analysis method to determine opening ratio, scenery ratio and void ratio. We could expect better performance and precision from this residential environment analysis system than the existing 2D drawing based view and daylight analysis methods and overcome the existing one-way flow of design information from 3D form to analysis reports so that site design modifications are automatically reflected on analysis results. Each part is developed in a module so that further integration and extension into other related estimation and construction management systems are made possible.

• Journal of the Optical Society of Korea
• /
• v.18 no.3
• /
• pp.261-273
• /
• 2014

In this paper, metal insulator metal (MIM) plasmonic slot cavity narrow band-pass filters (NBPFs) are studied. The metal and dielectric of the structures are silver (Ag) and air, respectively. To improve the quality factor and attenuation range, two novel NBPFs based on tapered structures and double cavity systems are proposed and numerically analyzed by using the two-dimensional (2-D) finite difference time domain (FDTD) method. The impact of different parameters on the transmission spectrum is scrutinized. We have shown that increasing the cavities' lengths increases the resonance wavelength in a linear relationship, and also increases the quality factor, and simultaneously the attenuation of the wave transmitted through the cavities. Furthermore, increasing the slope of tapers of the input and output waveguides decreases attenuation of the wave transmitted through the waveguide, but simultaneously decreases the quality factor, hence there should be a trade-off between loss and quality factor. However, the idea of adding tapers to the waveguides' discontinuities of the simple structure helps us to improve the device total performance, such as quality factor for the single cavity and attenuation range for the double cavity. According to the proposed NBPFs, two, three, and four-port power splitters functioning at 1320 nm and novel ultra-compact two-wavelength and triple-wavelength demultiplexers in the range of 1300-1550 nm are proposed and the impacts of different parameters on their performances are numerically investigated. The idea of using tapered waveguides at the structure discontinuities facilitates the design of ultra-compact demultiplexers and splitters.

• Nuclear Engineering and Technology
• /
• v.53 no.10
• /
• pp.3327-3334
• /
• 2021

Present electric grids are advanced to integrate smart grids, distributed resources, high-speed sensing and control, and other advanced metering technologies. Cybersecurity is one of the challenges of the smart grid and nuclear plant digital system. It affects the advanced metering infrastructure (AMI), for grid data communication and controls the information in real-time. The research article is emphasized solving the nuclear and smart grid hardware security issues with the integration of field programmable gate array (FPGA), and implementing the latest Time Authenticated Cryptographic Identity Transmission (TACIT) cryptographic algorithm in the chip. The cryptographic-based encryption and decryption approach can be used for a smart grid distribution system embedding with FPGA hardware. The chip design is carried in Xilinx ISE 14.7 and synthesized on Virtex-5 FPGA hardware. The state of the art of work is that the algorithm is implemented on FPGA hardware that provides the scalable design with different key sizes, and its integration enhances the grid hardware security and switching. It has been reported by similar state-of-the-art approaches, that the algorithm was limited in software, not implemented in a hardware chip. The main finding of the research work is that the design predicts the utilization of hardware parameters such as slices, LUTs, flip-flops, memory, input/output blocks, and timing information for Virtex-5 FPGA synthesis before the chip fabrication. The information is extracted for 8-bit to 128-bit key and grid data with initial parameters. TACIT security chip supports 400 MHz frequency for 128-bit key. The research work is an effort to provide the solution for the industries working towards embedded hardware security for the smart grid, power plants, and nuclear applications.

• Advances in concrete construction
• /
• v.10 no.1
• /
• pp.81-92
• /
• 2020

Flexural and splitting strength behavior of conventional concrete can significantly be improved by incorporating the fibers in it. A significant number of research studies have been conducted on various types of fibers and their influence on the tensile capacity of concrete. However, as an important property, tensile capacity of fiber reinforced concrete (FRC) is not modelled properly. Therefore, this paper intends to formulate a model based on experiments that show the relationship between the fiber properties such as the aspect ratio (length/diameter), fiber content, compressive strength, flexural strength and splitting strength of FRC. For the purpose of modeling, various FRC mixes only with steel fiber are adopted from the existing research papers. Automated neural network search (ANS) is then developed and used to investigate the effect of input parameters such as fiber content, aspect ratio and compressive strength to the output parameters of flexural and splitting strength of FRC. It is found that the ANS model can be used to predict the flexural and splitting strength of FRC in a sensible precision.

• Proceedings of the Korea Association of Information Systems Conference
• /
• 2000.11a
• /
• pp.1.2-24
• /
• 2000

The field of neural networks has been a recent surge in activity as a result of progress in developments of efficient training algorithms. For this reason, and coupled with the widespread availability of powerful personal computer hardware for running simulations of networks, there is increasing focus on the potential benefits this field can offer. The neural network may be viewed as an advanced pattern recognition technique and can be applied in many areas such as financial time series forecasting, medical diagnostic expert system and etc.. The intention of this study is to build and implement one simple artificial neural networks hereinafter called ANN. For this purpose, some literature survey was undertaken to understand the structures and algorithms of ANN theoretically. Based on the review of theories about ANN, the system adopted 3-layer back propagation algorithms as its learning algorithm to simulate one case of medical diagnostic model. The adopted ANN algorithm was performed in PC by using turbo PASCAL and many input parameters such as the numbers of layers, the numbers of nodes, the number of cycles for learning, learning rate and momentum term. The system output more or less successful results which nearly agree with goals we assumed. However, the system has some limitations such as the simplicity of the programming structure and the range of parameters it can dealing with. But, this study is useful for understanding general algorithms and applications of ANN system and can be expanded for further refinement for more complex ANN algorithms.

• Journal of Biomedical Engineering Research
• /
• v.40 no.2
• /
• pp.62-67
• /
• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• Journal of Advanced Marine Engineering and Technology
• /
• v.38 no.6
• /
• pp.716-722
• /
• 2014

In order to design the speed controller of the DC motor system, firstly, parameters estimation of the system must be preceded. In this paper, we proposed the application of genetic algorithm(GA) optimization in estimating the parameters of DC motor. Estimated models are considered both first and second order models, and each estimated model is optimized by minimizing three different types of the evaluation function of GA. Also, GA is imported in comparison with estimation result of numerical analysis method because of its power in searching entire solution space with more probability of finding the global optimum. Data for parameter estimation is acquired from input and output signals of the actual experiment device and the butterworth filter also designs for removing noise in the signals. Finally comparison between real data of the actual device and estimated models is presented to indicate effectiveness and resolution of proposed identification method.

• Journal of Korean Society for Geospatial Information Science
• /
• v.17 no.2
• /
• pp.71-79
• /
• 2009

In this study, a distributed rainfall-runoff model, K-DRUM, based on physical kinematic wave was developed to simulate temporal and spatial distribution of flood discharge considering grid rainfall and grid based GIS hydrological parameters. The developed model can simulate temporal and spatial distribution of surface flow and sub-surface flow during flood period, and input parameters of ASCII format as pre-process can be extracted using ArcView. Output results of ASCII format as post-process can be created to express distribution of discharge in the watershed using GIS and express discharge as animation using TecPlot. an auto calibration method for initial soil moisture conditions that have an effect on discharge in the physics based K-DRUM was additionally developed. The baseflow for Namgang Dam Watershed was analysed to review the applicability of the developed auto calibration method. The accuracy of discharge analysis for application of the method was evaluated using RMSE and NRMSE. Problems in running time and inaccuracy setting using the existing trial and error method were solved by applying an auto calibration method in setting initial soil moisture conditions of K-DRUM.