• Journal of Korea Foundry Society
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• v.41 no.5
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• pp.445-453
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• 2021

Computational thermodynamics for various alloy systems is well known as the CALPHAD technique. Gibbs energy model parameters for each phase are obtained from experimentally measured thermodynamic properties and are mainly used to predict areas not experimentally measured and to analyze experimental results thermodynamically. In this study, the thermodynamic modeling of the Ni-Cr-Nb-C quaternary system is conducted for a thermodynamic analysis of the phenomena by which heat-resistant cast alloys (IN-657) are destroyed in certain areas after long-term use. The stable phases in the system according to the Cr content, phase fraction depending on the temperature, and long-range ordering parameters for the Ni₂Cr phase are calculated and compared to results obtained experimentally. The calculated thermodynamic properties suitably explain the experimentally reported fracture temperature range and the results of stable phases formed in the fractured areas. Thermodynamic modeling through the CALPHD method is expected to be useful for analyzing and predicting the thermodynamic behaviors of various cast alloys.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.33-40
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• 2019

In this paper, a microstrip-fed triple-band antenna for WLAN system with microstrip lines was designed, fabricated and measured. The proposed antenna is composed of two strip lines and slit in the ground plane and then designed in order to get triple band characteristics. We carried out simulation on $L_3$, $L_{10}$, and slit parameters, and adjusted the parameters of the proposed antenna to satisfy the required frequency band and bandwidth. The proposed antenna is made of $32.0{\times}44.0{\times}1.0mm$ and is fabricated on the permittivity 4.4 FR-4 substrate. The experiment results shows that the proposed antenna obtained the -10 dB impedance bandwidth 120 MHz (890 MHz~1.01 MHz), 440 MHz (2.35~2.79 GHz), and 1,280 MHz (5.07~6.35 GHz) covering the triple WLAN bands. Also, the measured gain and radiation patterns characteristics of the proposed antenna are presented at required frequency band, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.161-167
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• 2019

Recently, demand forecasting techniques have been actively studied due to interest in stable power supply with surging power demand, and increase in spread of smart meters that enable real-time power measurement. In this study, we proceeded the deep learning prediction model experiments which learns actual measured power usage data of home and outputs the forecasting result. And we proceeded pre-processing with moving average method. The predicted value made by the model is evaluated with the actual measured data. Through this forecasting, it is possible to lower the power supply reserve ratio and reduce the waste of the unused power. In this paper, we conducted experiments on three types of networks: Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) and we evaluate the results of each scheme. Evaluation is conducted with following method: MSE(Mean Squared Error) method and MAE(Mean Absolute Error).

• Journal of IKEEE
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• v.27 no.4
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• pp.657-667
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• 2023

This paper proposes two-stage optimization framework to re-estimate the photovoltaic (PV) hosting capacity (HC) by improving parameters for voltage controls of the smart inverter. In the first stage, PV HC is estimated considering Volt-Var (VV) and Volt-Watt (VW) controls, aligning with IEEE Std 1547-2018 guidelines. In the second stage, adjust parameters of VV and VW to improve HC. To investigate the performance of the proposed algorithm, simulations conducted using OpenDSS on an IEEE 37-bus system. The results demonstrate that effectively increases PV HC.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.333-339
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• 2016

Technology to optimize and utilize the use and supply of the electric power between consumer and supplier has been on the rise among the smart grid power market network in electric power demand management based on the Internet. Open Automated Demand Response system protocol, which can deliver Demand Response needed in electric power demand management to electricity supplier, system supplier and even the user is openADR 2.0b. This paper used the most credible, cosmopolitanly proliferated EPRI open source and analysed the variety of security vulnerability that developed VEN and VTN system may have. Using the simulator for attacking openADR protocol, the VEN/VTN system that has been implemented as EPRI open source was conducted to attack in a variety of ways. As a result of the analysis, we were able to get the results that the VEN/VTN system has security vulnerabilities to the parameter tampering attacks and service flow falsification attack. In conclusion, if you want to implement the openADR2.0b protocol system in the open or two-way communication environment smart grid network, considering a variety of security vulnerability should be sure to seek security technology and services.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.65-74
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• 2021

With recent innovations in the ICT industry, the demand for wearable sensing devices to recognize and respond to biological signals has increased. In this study, a three-dimensional (3D) spacer fabric was embedded in a single-wall carbon nanotube (SWCNT) dispersive solution through a simple penetration process to develop a monolayer piezoresistive pressure sensor. To induce electrical conductivity in the 3D spacer fabric, samples were immersed in the SWCNT dispersive solution and dried. To determine the electrical properties of the impregnated specimen, a universal testing machine and multimeter were used to measure the resistance of the pressure change. Moreover, to examine the changes in the electrical properties of the sensor, its performance was evaluated by varying the concentration, number of penetrations, and thickness of the specimen. Samples that penetrated twice in the SWCNT distributed solution of 0.1 wt% showed the best performance as sensors. The 7-mm thick sensors showed the highest GF, and the 13-mm thick sensors showed the widest operating range. This study confirms the effectiveness of the simple process of fabricating smart textile sensors comprising 3D spacer fabrics and the excellent performance of the sensors.

• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.79-86
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• 2023

This study aims to create a highly conductive E-textile made by recycling PET with a Dip-coating process. PET fiber with hydrophobic properties is characterized by the difficulty in imparting great conductivity when both Virgin and Recycled are made of electronic fibers through a Dip-coating process. To advance the effectiveness of the Dip-coating process, a sample made of recycled PET was surface modified for 50 w 5 minutes and 10 minutes employing a Covance-2mprfq model from FEMTO SCIENCE. After that, the sample was immersed in an SWCNT dispersion (.1 wt%, Carbon Co., Ltd.) for 5 minutes, and then dip coating was conducted to allow the solution to permeate well into the sample through a padder (DAELIM lab). After the procedure was completed, the resistance measurement was measured with a multimeter at both ends and then accurately remeasured with a wider electrode. As a result of this contemplation, it was affirmed that great conductivity might be given through an impregnation process through the plasma surface modification. When the surface modification was performed for 10 minutes, the resistance was reduced by up to 2.880 times. Dependent on the results of this research, E-fibers employed in the smart wearable sector can also be made of recycled materials, improving smart wearable products that can save oil resources and reduce carbon emissions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.817-827
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• 2015

In this modern society, people are having a close relationship with smartphone. This makes easier for hackers to gain the user's information by installing the malware in the user's smartphone without the user's authority. This kind of action are threats to the user's privacy. The malware characteristics are different to the general applications. It requires the user's authority. In this paper, we proposed a new classification method of user requirements method by each application using the Principle Component Analysis(PCA) and Probabilistic K-Nearest Neighbor(PKNN) methods. The combination of those method outputs the improved result to classify between malware and general applications. By using the K-fold Cross Validation, the measurement precision of PKNN is improved compare to the previous K-Nearest Neighbor(KNN). The classification which difficult to solve by KNN also can be solve by PKNN with optimizing the discovering the parameter k and ${\beta}$. Also the sample that has being use in this experiment is based on the Contagio.

• Journal of Korean Society of Steel Construction
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• v.27 no.1
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• pp.99-108
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• 2015

The partially restrained smart CFT (concrete filled tube) column-to-beam connections with top-seat split T connections show various behavior characteristics according to the changes in the diameter and tightening force of the fastener, the geometric shape of T-stub, and material properties. This paper presents results from a systematic three-dimensional nonlinear finite element study on the structural behavior of the top-seat split T connections subjected to cyclic loadings. This connection includes super-elastic shape memory alloy (SMA) T-stub and rods to obtain the re-centering capabilities as well as great energy dissipation properties of the CFT composite frame. A wide scope of additional structural behaviors explain the influences of the top-seat split T connections parameters, such as the different thickness and gage distances of split T-stub.

• Journal of IKEEE
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• v.24 no.2
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• pp.632-637
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• 2020

The non-intrusive load monitoring (NILM) is an important way to cost-effective real-time monitoring the energy consumption and time of use for each appliance in a home or business using aggregated energy from a single recording meter. In this paper, we collect from the smart farm's power consumption data acquisition system to the server via an LTE modem, converted the total power consumption, and the power of individual electric devices into HDF5 format and performed NILM analysis. We perform NILM analysis using open source denoising autoencoder (DAE), long short-term memory (LSTM), gated recurrent unit (GRU), and sequence-to-point (seq2point) learning methods.