• Biomolecules & Therapeutics
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• 제29권5호
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• pp.483-491
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• 2021

Parkinson's disease (PD) is a neurodegenerative disorder that involves the loss of dopaminergic neurons in the substantia nigra (SN). Matrix metalloproteinases-8 (MMP-8), neutrophil collagenase, is a functional player in the progressive pathology of various inflammatory disorders. In this study, we administered an MMP-8 inhibitor (MMP-8i) in Leucine-rich repeat kinase 2 (LRRK2) G2019S transgenic mice, to determine the effects of MMP-8i on PD pathology. We observed a significant increase of ionized calcium-binding adapter molecule 1 (Iba1)-positive activated microglia in the striatum of LRRK2 G2019S mice compared to normal control mice, indicating enhanced neuro-inflammatory responses. The increased number of Iba1-positive activated microglia in LRRK2 G2019S PD mice was down-regulated by systemic administration of MMP-8i. Interestingly, this LRRK2 G2019S PD mice showed significantly reduced size of cell body area of tyrosine hydroxylase (TH) positive neurons in SN region and MMP-8i significantly recovered cellular atrophy shown in PD model indicating distinct neuro-protective effects of MMP-8i. Furthermore, MMP-8i administration markedly improved behavioral abnormalities of motor balancing coordination in rota-rod test in LRRK2 G2019S mice. These data suggest that MMP-8i attenuates the pathological symptoms of PD through anti-inflammatory processes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1561-1564
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• 2003

Intelligent Wearable Module is intelligent system that arises when a human is part of the feedback loop of a computational process like a certain control system. Applied system is mobile robot. This paper represents the mobile robot control system remote controlled by Intelligent Wearable Module. So far, owing to the development of 802.l1b technologies, lots of remote control methods through internet have been proposed. To control a mobile robot through internet and guide it under unknown environment. The information about the direction and velocity of the mobile robot feedbacks to the PDA and the PDA send new control method produced from the combination of Neuro and Hierarchical Fuzzy Algorithm

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 춘계학술대회 논문집
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• pp.62-67
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• 1997

This paper reprdsents a new scheme of neural network control system analysis the robustues of robot manipulator using digital signal processors. Digtal signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In additions, DSPs are a s fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. Durng past decade it was proposed the well-established theorys for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. The proposed neuro network control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.41-44
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• 1997

본 논문에서는 뉴로퍼지제어기를 이용한 유도가열기의 시변부하에 대한 적응 정전력 제어를 하고자 한다. 유도가열기의 정전력 조절을 위해 IGBT를 사용한 위상전이형 펄스폭변조(PWM)와 PLL에 의한 부하공진주파수 추종형 펄스 주파수변수(PFM)가 조절되는 공진 고주파 인버터를 유용한 유도가열기를 설명하고, 실험 제작된 유도가열기에서의 부하에 대한 규정 전력 추종이 잘되고 있음이 실제적으로 논증되어졌다.

• 한국정밀공학회지
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• 제14권6호
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• pp.90-98
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• 1997

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilzation of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the systems with constrained inputs and/or states. A neural identifier or a genetic algorithm identifier could be utilized for modeling the partially known systems and the unknown systems. The nature of neural networks as a parallel processor would circumvent the problem of "curwe of dimensionality". The control law has been demonstrated for both a torque input motor and a velocity input motor identified by a genetic algorithm called GENOCOPed GENOCOP.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2003년도 춘계학술발표대회 논문집
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• pp.14-17
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• 2003

Vibration control of a composite plate with a surface-bonded fiber Bragg grating (FBG) sensor and piezoceramic actuators has been performed using a neural network based adaptive predictive control algorithm. For the detection of Bragg wavelength changes, two cavity lengths in Fabry-Perot read-out interferometers are used in order to produce two quadrature phase shifted signals. The FBG sensor system and real-time neuro-adaptive control algorithm could be applicable to diverse dynamic systems.

• Structural Engineering and Mechanics
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• 제31권2호
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• pp.153-162
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• 2009

The reduction of the dynamic response of an offshore structure subjected to wind-generated random ocean waves is of extreme significance in the aspects of serviceability, fatigue life and safety of the structure. In this study, a new neuro-control scheme is applied to the vibration control of a fixed offshore platform under random wave loads to examine the applicability of the proposed method. It is called the Lattice Probabilistic Neural Network (LPNN), as it utilizes lattice pattern of state vectors as the training data of PNN. When control results of the LPNN are compared with those of the NN and PNN, LPNN showed better performance in effectively suppressing the structural responses in a shorter computational time.

• 전자공학회논문지CI
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• 제38권5호
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• pp.77-85
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• 2001

본 논문에서는 주시각과 초점을 동시에 제어할 수 있는 수평이동방식 입체카메라를 설계/제작하고, 이를 자동으로 제어하기 위한 시차정보 추출 알고리즘을 제안하였다. 먼저 수평이동방식 입체카메라의 기하학적 구조를 분석하여 주시각과 초점 제어량 사이의 선형관계를 도출하였으며, 이를 근거로 주시각과 초점이 동시 제어되는 입체카메라를 설계/제작하였다. 그리고, 1차원 Cepstrum 필터를 이용한 시차정보 추출 알고리즘을 적용하여, 주시각과 초점이 동시에 자동 제어되는 입체카메라 시스템을 구현하였다. 제안한 알고리즘은 기존의 알고리즘에 비해 시차 추출시간 및 에러율에서 우수한 성능을 보임을 확인하였다. 본 논문에서 제안한 입체카메라 시스템은 제어시간 및 에러율을 크게 줄여 자연스럽고 선명한 입체영상을 획득할 수 있게 하였으며, 입체카메라 조작을 단순화시킴으로서 사용자의 편리성을 추구하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.