• Journal of computational fluids engineering
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• v.17 no.2
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• pp.58-64
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• 2012

Turbulent flows in a intake part of vacuum cleaner are studied by RANS simulations. The governing equations are solved by the SIMPLE algorithm based on the finite volume method of the unstructured grid system. The predicted results show that the suction performance is closely related to the variation of flow structure in the intake part. In order to investigate for the cleaning of bedclothes and carpet without sticking, several design changes are applied. The introduction of a solid cylinder in the intake part changes vortical structures significantly. Based on this result, a new design with spiral brushes is proposed. The design shows a good behavior for the suction performance and the flow control.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1211-1217
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• 2007

In this paper, a 10kVA PV inverter applying Incremental Conductance(IncCond) method for maximum power point tracking WIS developed and its performance tests were carried out. Modeling and simulation of PV array and system controller was performed using PSCAD/EMTDC, an electromagnetic transient analysis program. After comparison and analysis of Perturbation & Observation (P & O) and IncCond method, a PV inverter based on IncCond method was designed and manufactured. Grid interface transient characteristics including start-up, normal operation, and fault operation were tested, which verified the usefulness of the proposed system. In the near future, commercialization process will proceed through additional extensive tests of transients.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.935-940
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• 2005

We are faced with water pollutions on a population explosion. Considering the importance, we research European rivers based on OECD reports. Observations in the reports have defects that make evaluation of environmental situations be difficult. By using interpolations in the compensation quantitative structure-activity relation ships (CQSAR), we complement the defects in the water quality of rivers through big cities. Thus, we get complete data set for dissolved oxygen, biochemical oxygen demand, and total phosphorus. Using the data set, we examine re-naturalization of the Rhein and the Donau in Germany. We investigate the effect of dams between Slovakia and Hungary, by using reconstructions of neural networks in CQSAR. The reconstructions have functions to extract a principal relation. On the investigation, we examine assertions of conservation groups. As the result, we confirm the re-naturalization is effective, and find a negative effect of the dam construction on changes of dissolved oxygens in the Hungary Donau. We investigate the Seine and the Thames, too.

• Smart Structures and Systems
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• v.20 no.1
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• pp.61-74
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• 2017

One of the most reliable and simplest tools for structural vibration control in civil engineering is Tuned Mass Damper, TMD. Provided that the frequency and damping parameters of these dampers are tuned appropriately, they can reduce the vibrations of the structure through their generated inertia forces, as they vibrate continuously. To achieve the optimal parameters of TMD, many different methods have been provided so far. In old approaches, some formulas have been offered based on simplifying models and their applied loadings while novel procedures need to model structures completely in order to obtain TMD parameters. In this paper, with regard to the nonlinear decision-making of fuzzy systems and their enough ability to cope with different unreliability, a method is proposed. Furthermore, by taking advantage of both old and new methods a fuzzy system is designed to be operational and reduce uncertainties related to models and applied loads. To design fuzzy system, it is required to gain data on structures and optimum parameters of TMDs corresponding to these structures. This information is obtained through modeling MDOF systems with various numbers of stories subjected to far and near field earthquakes. The design of the fuzzy systems is performed by three methods: look-up table, the data space grid-partitioning, and clustering. After that, rule weights of Mamdani fuzzy system using the look-up table are optimized through genetic algorithm and rule weights of Sugeno fuzzy system designed based on grid-partitioning methods and clustering data are optimized through ANFIS (Adaptive Neuro-Fuzzy Inference System). By comparing these methods, it is observed that the fuzzy system technique based on data clustering has an efficient function to predict the optimal parameters of TMDs. In this method, average of errors in estimating frequency and damping ratio is close to zero. Also, standard deviation of frequency errors and damping ratio errors decrease by 78% and 4.1% respectively in comparison with the look-up table method. While, this reductions compared to the grid partitioning method are 2.2% and 1.8% respectively. In this research, TMD parameters are estimated for a 15-degree of freedom structure based on designed fuzzy system and are compared to parameters obtained from the genetic algorithm and empirical relations. The progress up to 1.9% and 2% under far-field earthquakes and 0.4% and 2.2% under near-field earthquakes is obtained in decreasing respectively roof maximum displacement and its RMS ratio through fuzzy system method compared to those obtained by empirical relations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.233-238
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• 2018

This paper proposes a seamless MPPT operation mode transfer method of photovoltaic system. The photovoltaic system consists of a DC-DC boost converter, a DC-Link, and a 3-level neutral point clamp (NPC) type inverter. The PV voltage fluctuates due to the output characteristics of the solar pane1 depending on the irradiation amount and the temperature. The photovoltaic system requires seamless MPPT mode transfer method that the discontinuity does not occur in order to supply the stable power to system without affecting the fluctuation of the PV voltage. MPPT operation is divided into two modes by the voltage reference. Under the condition that the PV voltage is below 650V, the DC-DC boost converter performs MPPT through duty control based on perturb & observe (P&O) method, and the inverter conducts DC-link voltage and grid current controls in synchronous reference frame. On the other hand, when the PV voltage exceeds above 650V, inverter performs MPPT in accordance with the variation of DC-link voltage control while the converter stops operating. Two MPPT operation modes is smoothly transferred through the proposed method that DC-link voltage or grid current commands are appropriately adjusted from the certain criteria. The feasibility of the MPPT operation mode transfer method is verified using a 10kW solar photovoltaic system, experimental results have good performances that the fluctuation of PV current is reduced to 100%.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.6
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• pp.451-459
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• 2010

This paper describes the performance comparison results for a hybrid back-to-back converter, which is composed of a 3-level 24-pulse converter and a 3-level PWM converter, in order to interconnect a large scale wind farm with the power grid. Also it describes the performance comparison results when the 24-pulse converter operates in only firing-angle control, and both firing-angle and the zero-voltage control. For the purpose of systematic performance comparison, computer simulations with PSCAD/EMTDC software were carried out, and based on simulation results a scaled hardware model with 2 kVA rating was built and tested.

• Convergence Security Journal
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• v.22 no.2
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• pp.105-114
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• 2022

This paper is presents an intelligent control system that visualizes the direction of arrival for hearing impaired using personal mobility, and aims to recognize and prevent dangerous situations caused by sound such as alarm sounds and crack sounds on roads. The position estimation method of sound source uses a machine learning classification model characterized by generalized correlated phase transformation based on time difference of arrival. In the experimental environment reproducing the road situations, four classification models learned after extracting learning data according to wind speeds 0km/h, 5.8km/h, 14.2km/h, and 26.4km/h were compared with grid search cross validation, and the Muti-Layer Perceptron(MLP) model with the best performance was applied as the optimal algorithm. When wind occurred, the proposed algorithm showed an average performance improvement of 7.6-11.5% compared to the previous studies.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.704-712
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• 2016

Recently, the installation of photovoltaic (PV) systems has been increasing due to the worldwide interest in eco-friendly and infinitely abundant solar energy. However, the output power of PV systems is highly influenced by the surrounding environment. For instance, a string of PV systems composed of modules in series may become inoperable under cloudy conditions or when in the shade of a building. In other words, under these conditions, the existing control method of PV systems does not allow the string to be operated in the normal way, because its output voltage is lower than the operating range of the grid connected inverter. In order to overcome this problem, we propose a new control method using a DC-DC voltage regulator which can compensate for the voltage of each string in the PV system. Also, based on the PSIM S/W, we model the DC-DC voltage regulator with constant voltage control & MPPT (Maximum Power Point Tracking) control functions and 3-Phase grid connected inverter with PLL (Phase-Locked Loop) control function. From the simulation results, it is confirmed that the present control method can improve the operating efficiency of PV systems by compensating for the fluctuation of the voltage of the strings caused by the surrounding conditions.

• Smart Structures and Systems
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• v.14 no.2
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• pp.145-158
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• 2014

The objective of this study is to improve the survivability and reliability of the FBG sensor network in the structural health monitoring (SHM) system. Therefore, a model reconstruction soft computing recognition algorithm based on support vector regression (SVR) is proposed to achieve the high reliability of the FBG sensor network, and the grid search algorithm is used to optimize the parameters of SVR model. Furthermore, in order to demonstrate the effectiveness of the proposed model reconstruction algorithm, a SHM system based on an eight-point fiber Bragg grating (FBG) sensor network is designed to monitor the foreign-object low velocity impact of a CFRP composite plate. Simultaneously, some sensors data are neglected to simulate different kinds of FBG sensor network failure modes, the predicting results are compared with non-reconstruction for the same failure mode. The comparative results indicate that the performance of the model reconstruction recognition algorithm based on SVR has more excellence than that of non-reconstruction, and the model reconstruction algorithm almost keeps the consistent predicting accuracy when no sensor, one sensor and two sensors are invalid in the FBG sensor network, thus the reliability is improved when there are FBG sensors are invalid in the structural health monitoring system.