• Smart Structures and Systems
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• v.20 no.4
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• pp.499-508
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• 2017

The ionic polymer-metal composite (IPMC) is an electroactive polymer material and has a promising potential as actuators for propulsion and locomotion in underwater systems. In this paper a physics based model is used to analyse the actuation dynamics of the IPMC propulsor. Moreover, proportional-integral (PI) controller is used for position control of the tip displacement of IPMC propulsor. PI parameter tuning is performed using particle swarm optimization (PSO) algorithm. Several performance indices have been used as an objective function to optimize the error of the system. Finally, the best tuning method is found out by comparing the results under various performance indices.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.6
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• pp.1211-1219
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• 2001

The electro-rheological(ER) fluids for smart hydraulic system are a class of colloidal dispersion which exhibit large reversible Changes in their rheological behavior when they are subjected to external electrical fields. This paper presents experimental results on material properties of an ER fluids subjected to electrical fatigues. As a first step, ER fluid is made of methyl cellulose(MC) choosing 25% of particle weight-concentration. Following the construction of test mechanism for durability estimation, the dynamic yield shear stress and the current density for the ER fluids of MC component are experimentally distilled as a function of electric field. In addition, the surface roughness of the employed electrode are evaluated as a function of the number of the electric-field cycles.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.7 s.94
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• pp.1649-1657
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• 1993

This paper reports on a proof-of-concept experimental investigation focused on evaluating the vibration characteristics and control of smart hollow cantilever beams filled with an electro-rheological(ER) fluid. The beams are considered to be of uniform viscoelastic materials and modelled as a viscously-damped harmonic oscillator. Electric field-dependent natural frequencies, loss factors and complex moduli are evaluated and compared among three different beams : two types of different volume fraction of ER fluid and one type of different particle concentration of ER fluid by weight. Modal characteristics of the beams are observed in both the absence and the presence of electric potentials. It is also shown that by constructing active control algorithm the removal of structural resonances and the suppression of tip deflection are obtained. This result provides the feasiblility of ER fluids as an active vibration control element.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.177-185
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• 2015

In this paper we introduce Kernel Fisher Discriminant Analysis (KFDA) to transform our database of received signal strength (RSS) measurements into a smaller dimension space to maximize the difference between reference points (RP) as possible. By KFDA, we can efficiently utilize RSS data than other method so that we can achieve a better performance.

• Journal of Powder Materials
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• v.29 no.6
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• pp.459-467
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• 2022

Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Journal of Korea Water Resources Association
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• v.53 no.spc1
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• pp.813-826
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• 2020

This study analyzes the effects of the Smart Water City (SWC) project that was introduced from 2014 to 2016 in Paju City, Gyeonggi Province, Korea, focusing on the achievement of the business goals. The SWC is referred to as a city that embraces a healthy water supply system based on Smart Water Management (SWM) that promotes the efficiency of water management by combining Information and Communication Technologies (ICTs) with water and sewerage facilities. In order to evaluate the effectiveness of the SWC project, this study deploys evaluation criteria corresponding to the project objectives, and analyzes the outputs before and after the project. The results show that the SWC has contributed to enhancing water supply services and the reliability and drinking rate of tap water. Specific improvement areas include the rise of average water flow rate and water leakage reduction, the diffusion of water quality monitoring system, and the reduction of floating particle concentration and turbidity in drainage pipes was achieved. These were possible because of specific implementation plans for clear goal setting and achievement and active services for citizens. The data related to water quantity and quality showed improved performance compared to before the introduction of SWMS, which is a positive effect. However, a quantitative analysis of the outputs has limitations in identifying other external factors that have led to the changes. In the future, guidelines for spreading SWC and more comprehensive and specific evaluation indicators for SWC should be prepared, and SWMS should be developed in consideration of the needs of users.

• Bulletin of the Korean Chemical Society
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• v.35 no.11
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• pp.3188-3194
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• 2014

Compound K (CK) was formulated as polymeric micelles (PM) using Pluronic$^{(R)}$ F-127 to enhance the oral absorption of CK, an intestinal bacterial metabolite of ginseng protopanaxadiol saponin. The physicochemical properties of Ck-loaded PM were characterized and an in vitro transport study using the Caco-2 cell system as well as an in vivo pharmacokinetic study using SD rats was carried out. The hydrodynamic mean particle size of CK-loaded PM (CK-PM) was $254{\pm}23.45nm$ after rehydration and the drug loading efficiency was ca. 99.9%. The FT-IR spectroscopy, X-ray diffraction, differential scanning calorimetry and scanning electron microscopy data supported the presence of a new solid phase in the PM. The $P_{app}$ value of in vitro Caco-2 cell permeation of CK-PM and the oral absorption of CK was enhanced about 1.2-fold and 2.6-fold compared to CK suspension, respectively, showing that the present PM formulation enabled an enhancement of oral CK absorption.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.601-611
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• 2017

This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective model-based prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.297-304
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• 2015

A multilayer gaseous detector has been developed for fast dose-verification measurements of raster-scan-mode therapeutic beams in particle therapy. The detector, which was constructed with eight thin parallel-plate ionization chambers (PPICs) and polymethyl methacrylate (PMMA) absorber plates, is closely tissue-equivalent in a beam's eye view. The gas-electron signals, collected on the strips and pad arrays of each PPIC, were amplified and processed with a continuous charge.integration mode. The detector was tested with 190-MeV raster-scan-mode beams that were provided by the Proton Therapy Facility at Samsung Medical Center, Seoul, South Korea. The detector responses of the PPICs for a 190-MeV raster-scan-mode proton beam agreed well with the dose data, measured using a 2D ionization chamber array (Octavius model, PTW). Furthermore, in this study it was confirmed that the detector simultaneously tracked the doses induced at the PPICs by the fast-oscillating beam, with a scanning speed of 2 m s-1. Thus, it is anticipated that the present detector, composed of thin PPICs and operating in charge.integration mode, will allow medical scientists to perform reliable fast dose-verification measurements for typical dynamic mode therapeutic beams.