• Korean Institute of Interior Design Journal
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• v.27 no.2
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• pp.175-185
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• 2018

The purpose of this study is to analyze the concept of 'Gradual Differentiation' in parametric design in terms of pure model logic and thus describe the distinctive feature from the previous design method. To meet the purpose, it explores external cases like gradual factor identified in natural phenomenon and artworks and define the inherent model principles into "Self-similarity', "Correlation', and 'Temporality' by examining these features in terms of algorithm. Meanwhile, it identified the principle of gradual model representation in parametric design within a single system called 'Attractor System' by applying these three concepts into specific methods of parametric design, and by interpreting the logical structure through the association among 'Attractor', 'Field', and 'Differentiation'. The creative utilization of parameter shows that gradual model process in parametric design does not mean a passive "conversion process" merely replacing natural parameter with algorithm; rather, it refers to an active "generating process" creating new meanings and value. By continuing this process of conceptual understanding and insight, creative perspective and practical ability to interpret parameter can be improved.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1344-1352
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• 2018

In this paper, we propose a two-port S-parameter data to diagnose the fault conditions of a single-phase transformer. Using the S-parameters we can measure the reflection and transmission characteristics of signal power at the port of a transformer, which can also be converted into ABCD parameters and Z parameters through a well-known conversion formulas. Transformer fault diagnoses can be performed based on the intuitive and qualitative/quantitative characteristics of the these parameters. In addition, we can obtain wide frequency characteristics at the primary and secondary sides of the transformer, which can be used to get time domain responses using the inverse Fourier transformation with some specific input waveform. In order to verify the effectiveness of the proposed method, the fault conditions were analyzed in simulation and experiment for 3 kVA single phase transformer with 15: 5 turns ratio, and the validity of the proposed method was verified.

• Journal of Power Electronics
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• v.7 no.2
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• pp.159-173
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• 2007

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.

• Journal of Power Electronics
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• v.13 no.4
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1103-1111
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• 2014

The results of control law design for a tilt-rotor unmanned aerial vehicle that has a nacelle mounted wing extension (WE) are presented in this paper. It consists of a control surface mixer, stability and control augmentation system (SCAS), hold mode for altitude / speed / heading, and a guidance mode for preprogram and point navigation which includes automatic take-off and landing. The conversion corridor and the control moments derivatives between the original tilt-rotor and its variant of the nacelle mounted WE were compared to show the effectiveness of the WE. The nacelle conversion of the original tilt-rotor starts when the airspeed is greater than 30 km/h but its WE variant starts at 0 km/h in order to reduce the drag caused by the high incidence angle of the WE. The stability margins of the inner loop are presented with the optimization approach. The outer loops for the hold mode are designed with trial and error methods with linear and nonlinear simulation. The main control parameter for altitude control of the helicopter mode is thrust command and it is transferred to the pitch attitude command in airplane mode. Otherwise, the control parameter for the speed of the helicopter mode is the pitch attitude command and it is transferred to the thrust command in airplane mode. Therefore the speed and altitude hold mode are coupled to each other and are engaged at the same time when an internal pilot engages any of the altitude or speed hold modes. The nonlinear simulation results of the guidance control for the preprogrammed mode and point navigation are also presented including automatic take-off and landing in order to prove the full control law.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.10
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• pp.787-793
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• 2015

In response to the growing interest in supercritical carbon dioxide ($S-CO_2$) power cycle technology because of its potential enhancement in compactness and efficiency, the $S-CO_2$ cycles have been studied intensively in the fields of nuclear power, concentrated solar power (CSP), and fossil fuel power generation. Despite this interest, there are relatively few studies on waste heat recovery applications. In this study, the $S-CO_2$ cycle that has a split flow with preheating was modeled and simulated. The variation in the power was investigated with respect to the changes in the value of a design parameter. Under the simulation conditions considered in this study, it was confirmed that the design parameter has an optimal value that can maximize the power in the $S-CO_2$ power cycle that has a split flow with preheating.

• Journal of IKEEE
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• v.24 no.3
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• pp.735-742
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• 2020

The pick & place module is used as a core module in the process equipment for producing and inspecting semiconductor components. The conventional pick & place module has the disadvantage that the precision and durability of the system are reduced and the size and weight of the module are increased by using a conversion device that converts rotary motion into linear motion. In this study, we proposed a pick & place module that implements up-and-down linear motion without a conversion device by improving such disadvantage and employs a linear motor with no limit on average thrust and travel distance. Design parameter values, that can reduce cogging force while maintaining average thrust by selecting parameters for designing a core type linear motor with a large thrust to volume ratio and analyzing the effect of cogging force according to design parameter changes through magnetic analysis, was selected. Average thrust and cogging force were measured for the pick & place module composed of the manufactured linear motor and compared with the design values.

• Applied Chemistry for Engineering
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• v.10 no.8
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• pp.1200-1209
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• 1999

Prediction model was composed by optimal parameter estimation from best fitting on reactant temperature profile, inlet and outlet temperature of coolant and yield of dual fixed-bed catalytic reactor(FBCR) which was measured in the industrial field. In order to design the FBCR which could obtain maximum conversion and yield, we investigated the effect of catalyst bed length and reactor radius changes. An uniform activity FBCR showed the best performance at z = 2.8 m of total catalysst bed length in case of reactor radius r = 0.01241 m and z =2.80 m(upper layer: 1.88 m, lower layer: 0.92 m) under reactor radius r = 0.01254 m for a dual activities FCBR. In case of reactor radius changes, the axial temperature profile and maximum radial temperature was rapidly risen for radius increase. The reactor radius decrease showed the opposite result.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.47-53
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• 2007

True motion estimation is necessary for deinterlacing, frame-rate conversion, and film judder compensation. There have been several block-based approaches to find true motion vectors by tracing minimum sum-of-absolute-difference (SAD) values by considering spatial and temporal consistency. However, the algorithms cannot find robust motion vectors when the texture of objects is changed. To find the robust motion vectors in the region, a recursive vector selection scheme and an adaptive weighting parameter are proposed. Previous frame vectors are recursively averaged to be utilized for motion error region. The weighting parameter controls fidelity to input vectors and the recursively averaged ones, where the input vectors come from the conventional estimators. If the input vectors are not reliable, then the mean vectors of the previous frame are used for temporal consistency. Experimental results show more robust motion vectors than those of the conventional methods in time-varying texture objects.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.