• Journal of IKEEE
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• v.25 no.3
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• pp.445-450
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• 2021

Bio-impedance measurement is a measurement device that can be used to obtain biometric information and diagnose skin diseases using convenience, low cost, and low cost devices. In this study, the bio-impedance was measured using a direct dry gold electrode and a simulation study through animal bio modeling to obtain biometric information in a biometric form. Impedance was measured by inserting electrodes into subcutaneous areas of animal tissue and applying frequencies of 100 uA, 1-100 kHz using a two-electrode method. As a result of the measurement, the resistance of the electrodes is measured high at 5 mm electrodes compared to 7.5 mm and 10 mm electrodes based on 5 mm electrodes. Based on the 5 mm electrode, an average difference of 1.49% was found for the 7.5 mm electrode in the total frequency range, and the impedance difference was confirmed to be 2.624% for the 10 mm electrode. In the future, the research results are expected to be valuable in designing and manufacturing electrodes for bio-inserted electrocardiogram sensors.

• Journal of IKEEE
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• v.23 no.4
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• pp.1122-1127
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• 2019

In this study, the bio-impedance of the human body is able to obtain a lot of information by monitoring the pathological and physiological conditions of clinical and biological tissues. The four electrode method system for biometrics measured the potential difference between two electrodes and the other two electrodes were used as electrodes for current flow. The newly developed dry gold electrode measured impedance from 1 Hz to 50 kHz and produced reproducible results. To verify the impedance measurement of the dry electrode, the pitting was performed using an equivalent circuit model of the bioelectrode skin, and the effectiveness was demonstrated through modeling. Fixed electrode types have a constant position of the electrodes attached during the measurement, so that a stable measurement can be obtained, thereby minimizing the error.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.1
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• pp.55-64
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• 2015

Wind turbine market is showed height growth rate of about 30% for year, and is increasingly growing. Total rate of domestic wind turbine installation is showing share of 0.2% of the global market that is 380MW. However, wind turbine installed in domestic are foreign product more than 90%. Similarly, Datalogger of pretest system for ocean wind turbine plant installation has been leaked huge cost to abroad by mostly abroad company product. In this paper, we proposed pretest weather resource measurement system for efficiency and investment cost cutting of wind turbine construction work. Preset weather resource measurement system is ocean weather resource measurement datalogger based on wireless communication(wifi) that have consist of hardware and software(wind rose) that is able to monitoring as datalogger of wireless bridge and battery state, wind direction, wind speed, temperature, humidity, radiation around weather tower and is able to analysis of measured data.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.6 s.336
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• pp.53-60
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• 2005

Considerable efforts has been recently devoted to the determination of accurate channel models for the power line environment, both for the indoor and outdoor cases. The common limitation of the known and previously published models is the particular type of approach followed. This paper is concerned with a power line channel characteristic measurement for the more fast and efficiently power line communication experiment. The need arises from the fact that indoor power cables consist of conductors and inductors. A capacitive load simulator is a essential equipment in the power line modem development for indoor power line network. We accomplished a channel data base by the frequency response method about the total 224 capacitor load cases. On the basis of this measurement modeling it is here revealed that the power line communication channel is a more deterministic media.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.475-487
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• 2024

Many uncertainties affect the stability assessment of rock structures. Some of these factors significantly influence technology decisions. Some of these factors belong to the geological domain, and spatial uncertainty measurements are useful for structural stability analysis. This paper presents an integrated approach to study the stability of rock structures, including spatial factors. This study models two main components: discrete structures (fault zones) and well known geotechnical parameters (rock quality indicators). The geostatistical modeling criterion are used to quantify geographic uncertainty by producing simulated maps and RQD values for multiple equally likely error regions. Slope stability theorem would be demonstrated by modeling local failure zones and RQDs. The approach proided is validated and finally, the slope stability analysis method and fuzzy Laypunov criterion are applied to mining projects with limited measurement data. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and fuzzy theory.

• Smart Structures and Systems
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• v.30 no.4
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• pp.421-432
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• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1855-1866
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• 2003

This paper discusses an entire procedure for a robust controller design and its implementation of an AMB (active magnetic bearing) spindle, which is part II of the papers presenting details of system modeling and robust control of an AMB spindle. Since there are various uncertainties in an AMB system and reliability is the most important factor for applications, robust control naturally gains attentions in this field. However, tight evaluations of various uncertainties based on experimental data and appropriate performance weightings for an AMB spindle are still ongoing research topics. In addition, there are few publications on experimental justification of a designed robust controller. In this paper, uncertainties for the AMB spindle are classified and described based on the measurement and identification results of part I, and an appropriate performance weighting scheme for the AMB spindle is developed. Then, a robust control is designed through the mixed ${\mu}$ synthesis based on the validated accurate nominal model of part I, and the robust controller is reduced considering its closed loop performance. The reduced robust controller is implemented and confirmed with measurements of closed-loop responses. The AMB spindle is operated up to 57,600 rpm and performance of the designed controller is compared with a benchmark PID controller through experiments. Experiments show that the robust controller offers higher stiffness and more efficient control of rigid modes than the benchmark PID controller.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.717-730
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• 2004

Recently. many researches on the personalization of a web-site have been actively made. The web personalization predicts the sets of the most interesting URLs for each user through data mining approaches such as clustering techniques. Most existing methods using clustering techniques represented the web transactions as bit vectors that represent whether users visit a certain WRL or not to cluster web transactions. The similarity of the web transactions was decided according to the match degree of bit vectors. However, since the existing methods consider only whether users visit a certain URL or not, users' interestingness on the URL is excluded from clustering web transactions. That is, it is possible that the web transactions with different visit proposes or inclinations are classified into the same group. In this paper. we propose an enhanced transaction modeling with interestingness weight to solve such problems and a new similarity measuring method that exploits the proposed transaction modeling. It is shown through performance evaluation that our similarity measuring method improves the accuracy of the web transaction clustering over the existing method.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.531-538
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• 2011

This paper describes a novel sensor system for 3D world modeling of an autonomous vehicle in large-scale outdoor environments. When an autonomous vehicle performs path planning and path following, well-constructed 3D world model of target environment is very important for analyze the environment and track the determined path. To generate well-construct 3D world model, we develop a novel sensor system. The proposed novel sensor system consists of two 2D laser scanners, two single cameras, a DGPS (Differential Global Positioning System) and an IMU (Inertial Measurement System). We verify the effectiveness of the proposed sensor system through experiment in large-scale outdoor environment.

• Elastomers and Composites
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• v.51 no.4
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• pp.301-307
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• 2016

Additive manufacturing (AM), so called 3D Printing is a new manufacturing process and is getting attraction from many industries. There are several methods of 3D printing. Among them fused deposition modeling (FDM) type is most widely used by reason of cheap maintenance, easy operation and variety of polymeric materials. Articles manufactured by 3D printing have weak deposition strength compared with conventionally manufactured products. Deposition strength of FDM type 3D printed article is highly dependent of deposition temperature. Subsequently the nozzle temperature in the FDM type 3D printing is very important and it is controlled by heat source in the 3D printer. Nozzle is connected with heat block and barrel, and heat block contains heat source. Nozzle becomes hot through heat conduction from heat source. Nozzle temperature has been predicted for various thermal boundary conditions by computer simulation and compared with experimental measurement. Nozzle temperature highly depends upon thermal conductivities of heat block and nozzle. Simulation results are good agreement with experiment.