• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.4
• /
• pp.587-595
• /
• 2002

Recent trend changes a criterion for evaluation of humors that environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the NO/sub x/ and CO regulation. Consequently, 'good burner'means one whose thermal efficiency is high under the constraint of NO/sub x/ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of NO/sub x/ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro-Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro-Fuzzy loaming algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of NO/sub x/ and CO of the combustion gas was successfully inferred.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.265-270
• /
• 2005

This paper presents a new attitude stabilization and control of an unmanned helicopter based on neural network compensation. A systematic derivation on the dynamics of an unmanned small-scale helicopter is performed. Combined rotor-fuselage-tail dynamics is derived in body-fixed reference frame with its origin at the C.G. of the helicopter. And the resulting nonlinear equation of motion consists of 6-DOF air vehicle dynamics as well as the rotor flapping and engine torque equations. A simulation model was modified using the existing simulator for an unmanned helicopter dynamic model, which reflects the unmanned test helicopter(CNUHELI). The dynamic response of the refined model was compared with the flight test data. It can be shown that a good coincidence was accomplished between the real unmanned helicopter system and the mathematical model. This dynamic model was linearized for classical controller design using small perturbation method. A Neuro-PD control system was designed for both longitudinal and lateral flight modes, and the results were compared with the PD-only control response. Simulation results show that the proposed Neuro-PD control system demonstrates better performance.

• Journal of the Korean housing association
• /
• v.22 no.3
• /
• pp.113-122
• /
• 2011

This study aimed to develop AI- (Artificial Intelligence) based thermal control logics and test their performance for identifying the optimal thermal control method in buildings. For this objective, a conventional Two-Position On/Off logic and two AI-based variable logics, which applied ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System), have developed. Performance of each logic was tested in a typical two-story residential building in U.S.A. using the computer simulation incorporating MATLAB and IBPT (International Building Physics Toolbox). In the analysis of the test results, AI-based control logic presented the advanced thermal comfort with stability compared to the conventional logic while they did not show significant energy saving effects. In conclusion, the predictive and adaptive AI-based control logics have a potential to maintain interior air temperature more comfortably, and the findings in this study could be a solid foundation for identifying the optimal thermal control method in buildings.

• ETRI Journal
• /
• v.31 no.2
• /
• pp.182-192
• /
• 2009

Rain attenuation and intercell interference are two crucial factors in the performance of broadband wireless access networks such as local multipoint distribution systems (LMDS) operating at frequencies above 20 GHz. Power control can enhance the performance of downlink CDMA-based LMDS systems by reducing intercell interference under clear sky conditions; however, it may damage system performance under rainy conditions. To ensure robust operation under both clear sky and rainy conditions, we propose a novel power-control scheme which applies an adaptive neuro-fuzzy inference system (ANFIS) for downlink CDMA-based LMDS systems. In the proposed system, the rain rate and the number of users are two inputs of the fuzzy inference system, and output is defined as channel quality, which is applied in the power control scheme to adjust the power control region. Moreover, ITU-R P.530 is employed to estimate the rain attenuation. The influence of the rain rate and the number of users on the distance-based power control (DBPC) scheme is included in the simulation model as the training database. Simulation results indicate that the proposed scheme improves the throughput of the DBPC scheme.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.23 no.3
• /
• pp.134-142
• /
• 2012

Objectives : Childhood depressive disorder one of the most prevalent chronic health conditions affecting school aged children. The objective of this study was to examine the prevalence and epidemiological characteristics of childhood depressive disorder in Korea. Methods : In this study, a survey was conducted of elementary school children in the Cheonan area from September 2009 to August 2010. A total of 12,084 children were included in the analyses. The primary measure of depression was the 27-item Kovacs' Children's Depression Inventory (CDI), the Korean Dupaul attention-deficit hyperactivity disorder Rating Scales (K-ARS), Autism Spectrum Screening Questionnaire (ASSQ) and Restless Legs Syndrome Questionnaire (RLSQ) were also administered. Results : The prevalence rate of childhood depressive disorder was 3.07%. The mean age was $9.21{\pm}1.81$ years for the control group (5,969 male, 6,054 female) and $9.91{\pm}1.83$ years for the depression group (CDI score${\geq}$22 ; 159 male, 210 female). Age, height, weight, and economic status were similar for the two groups. However, there were statistically significant differences in the distribution of sex. The distribution of school grade also differed between the two groups. Scores for CDI, K-ARS, and ASSQ in the depressive group were higher than those of the control group. Conclusions : This is the first large-scale population-based study to report on the prevalence of childhood depressive disorder in South Korea. Increased rates of childhood depressive disorder, as reported by primary caretakers, might reflect the increasing negative impact of environmental risk factors on neurobehavioral health. Longitudinal study of the prevalence of childhood depressive disorder should be considered for further evaluation.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.4
• /
• pp.436-443
• /
• 2007

In this paper, an intelligent method called BELBIC (Brain Emotional Learning Based Intelligent Controller) is used to control of Locally Linear Neuro-Fuzzy Model (LOLIMOT) of Washing Machine. The Locally Linear Neuro-Fuzzy Model of Washing Machine is obtained based on previously extracted data. One of the important issues in using BELBIC is its parameters setting. On the other hand, the controller design for Washing Machine is a multi objective problem. Indeed, the two objectives, energy consumption and effectiveness of washing process, are main issues in this problem, and these two objectives are in contrast. Due to these challenges, a Multi Objective Genetic Algorithm is used for tuning the BELBIC parameters. The algorithm provides a set of non-dominated set points rather than a single point, so the designer has the advantage of selecting the desired set point. With considering the proper parameters after using additional assumptions, the simulation results show that this controller with optimal parameters has very good performance and considerable saving in energy consumption.

• Proceedings of the KIEE Conference
• /
• 2008.04a
• /
• pp.219-220
• /
• 2008

The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neuro inference system(AFNIS) and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled AFNIS and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the AFNIS and ANN controller.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.1
• /
• pp.119-125
• /
• 1997

This paper deals with an experimental investigation on an active vibration control of ahybrid smart structure(HSS) via an electro-rheological fluid actuator(ERFA) and a piezoelectric film actuator(PFA). Firstly, an HSS is constructed by inserting a silicone oil-based electro-rheological fluid into a hollow can- tilevered beam and perfectly bonding piezoelectric films ofn the upper and lower surfaces of the beam as an actuator and a sensor, respectively. The control scheme of the ERFA tuning stiffness and damping charac- teristics of the HSS with imposed electric fields is formulated as a function of excitation frequencies on the basis of field-dependent respnses. On the other hand, as for the control scheme of the PFA permitting control voltages to generate axial forces or bending moments for suppressing deflections of the HSS, a neuro sliding mode controller(NSC) is employed. Furthermore, an experimental implementation activating the ERFA and the PFA independently is established to carry out an active vibration control in both the transient and forced vibrations. The experimental results exhibit a superior ability of the gtbrid actuation system to tailor elastodynamic response characteristics of the HSS rather than a single class of actuator system alone.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.5
• /
• pp.830-836
• /
• 2006

In this paper described an auto tuning neuro-fuzzy PID controller based neural network. The PID type controller has been widely used in industrial application due to its simply control structure, easy of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. In this paper will design to take advantage of neural network fuzzy theory and pid controller auto toning technique. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. This controller simple structure and computational complexity are less, and also application is easy and performance is excellent in system that is strong and has nonlinearity to system dynamic behaviour change or disturbance. Finally, the proposed auto tuning neuro-fuzzy controller is applied to magnetic levitation. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1792-1795
• /
• 1997

It is difficulte to control in the flow system because there are many disturbance. So it is impossible to control delicately sometimes by PI or PID. In this paper, we study on the application of intellignet control algorithms such as 2DOF PID control, neural network, Fuzzy contro, Relay feedback to the flow control system. the resultings are 2DOF-PID control is more good response.