• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.45-53
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• 2023

This study constructs various scenarios required for landscape lighting; furthermore, a large-capacity general-purpose multifunctional controller is designed and implemented to validate the operation of the various scenarios. The multi-functional controller is a large-capacity general-purpose controller composed of a drive and control unit that controls the scenarios and colors of LED modules and an LED display unit. In addition, we conduct a computer simulation by designing a control system to represent the most appropriate color according to the input values of the temperature, illuminance, and humidity, using the neuro-control system. Consequently, when examining the result and output color according to neuro-control, unlike existing crisp logic, neuro-control does not require the storage of many data inputs because of the characteristics of artificial intelligence; the desired value can be controlled by learning with learning data.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.4
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• pp.349-358
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• 2013

In this paper, the sensorless MPPT algorithm is proposed where the performance of varied duty ratio change has been improved using multi-layer neuro-fuzzy that aligns with neuro-fuzzy based optimized membership function. Since the change of duty ratio of sensorless MPPT is varied by using the neuro-fuzzy, the MPPT response speed is faster than the convectional method and is able to reduce the steady-state ripple. The neuro fuzzy controller has the response characteristics which is superior to the existing fuzzy controller, because of the usage of the optimal width of the fuzzy membership function. The effectiveness of the proposed method has been verified by simulations and experimental results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.390-393
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• 2007

In this paper, applications of self recurrent neuro-fuzzy controller to stabilization of nonlinear system are considered. The architecture of self recurrent neuro-fuzzy controller is fix layer, and the hidden layer is comprised of self recurrent architecture. Also, generalized dynamic error-backpropagation algorithm is used for the learning of the self recurrent neuro-fuzzy controller. To demonstrate the efficiency of the self recurrent neuro-fuzzy control algorithm presented in this study, a self recurrent neuro-fuzzy controller was designed and then a comparative analysis was made with LQR controller through an simulation.

• Journal of Trauma and Injury
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• v.32 no.4
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• pp.195-201
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• 2019

Purpose: This study was conducted to investigate the usefulness of a polyester urethane dural substitute (Neuro-Patch^®, B. Braun, Boulogne, France) as an anti-adhesion agent in subsequent cranioplasty by analyzing the use of Neuro-Patch^® during decompressive craniectomy in traumatic brain injury patients. Methods: We retrospectively analyzed patients with traumatic brain injury who underwent decompressive craniectomy followed by cranioplasty from January 2015 to December 2018. Patients were analyzed according to whether they received treatment with Neuro-Patch^® or not (Neuro-Patch^® group, n=71; control group, n=55). Patients' baseline characteristics were analyzed to identify factors that could affect cranioplasty results, including age, sex, hypertension, diabetes mellitus, use of antiplatelet agents or anticoagulant medication, the interval between craniectomy and cranioplasty, and the type of bone used in cranioplasty. The cranioplasty results were analyzed according to the following factors: operation time, blood loss, postoperative hospitalization period, surgical site infection, and revision surgery due to extra-axial hematoma. Results: No significant difference was found between the two groups regarding patients' baseline characteristics. For the cranioplasty procedures, the operation time (155 vs. 190 minutes, p=0.003), intraoperative blood loss (350 vs. 450 mL, p=0.012), and number of surgical site infections (4 vs. 11 cases, p=0.024) were significantly lower in the Neuro-Patch^® group than in the control group. Conclusions: The use of Neuro-Patch^® was associated with a shorter operation time, less blood loss, and a lower number of surgical site infections in subsequent cranioplasties. These results may provide a rationale for prospective studies investigating the efficacy of Neuro-Patch^®.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.170-173
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• 2005

This paper presents an Fuzzy-Neuro Observer system for an ac servo motor dirve to track periodic commands using a neuro-fuzzy observer. AC servo motor drive system is rather similar to a linear system. However, the uncertainties, such as machanical parametric variation, external disturbance, uncertainty due to nonideal in transient state. therefore an intelligent control system that isan on-line trained neural network controller with adaptive learning rates.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.48-54
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• 2002

In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules are created by an expert. To adapt the more precise model is implemented by error back-propagation learning algorithm to adjust the link-weight of fuzzy membership function in the neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of the proposed algorithm designed above, an operating characteristic of a DC servo motor with variable load is investigated.

• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.5
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• pp.484-493
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• 2011

The MPPT algorithm using neuro-fuzzy controller is proposed to improve the performance of fuzzy controller in this paper. The width of membership function and fuzzy rule have an effect on the performance of fuzzy controller. The neuro-fuzzy controller has the response characteristic which is superior to the existing fuzzy controller, because of using the optimal width of the fuzzy membership function through the neural learning. The superior control characteristic of a proposed algorithm is confirmed through simulation and experiment results.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.161.4-161
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• 2001

This paper presents adaptive control of robot manipulator using neuro-fuzzy controller Fuzzy logic is control incorrect system without correct mathematical modeling. And, neural network has learning ability, error interpolation ability of information distributed data processing, robustness for distortion and adaptive ability. To reduce the number of fuzzy rules of the FLS(fuzzy logic system), we consider the properties of robot dynamic. In fuzzy logic, speciality and optimization of rule-base creation using learning ability of neural network. This paper presents control of robot manipulator using neuro-fuzzy controller. In proposed controller, fuzzy input is trajectory following error and trajectory following error differential ...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.384-384
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• 2000

In adaptive neuro-control, neural networks are used to approximate the unknown plant nonlinearities. Until now, most of the papers in the field of controller design fur nonlinear system using neural networks considers the affine system with fixed number of neurons. This paper considers nonaffne nonlinear systems and dynamic variation of the number of neurons. Control laws and adaptive laws for weights are established so that the whole system is stable in the sense of Lyapunov.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.20 no.1
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• pp.39-45
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• 2014

Background: The purpose of this study is to identify effects of neuro-muscular control training on vastus medialis oblique (VMO) after menisectomy of the knee. Methods: The subjects of this study are women aged 42 and 39 each who did menisectomy. Case 1 was applied quadriceps setting exercise and neuro-muscular contrlol training and case 2 was applied quadriecps setting. Intervention was done 5 times a week for 2 weeks. Measurement of muscle activity on VMO and vastus lateralis (VL) was standardized signals of each muscle to %RVC using surface EMG. Results: On comparison of exercise before and after on VMO and VL, VL activation of case 2 was increased more than case 1. Conclusion: Quadriecps-setting exercise and selective neuro-muscular control training of VMO is effective intervention on VMO activity and muscle activity ratio of VMO to VL.