• Title/Summary/Keyword: particle swarm optimization

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Feature Selection for Abnormal Driving Behavior Recognition Based on Variance Distribution of Power Spectral Density

  • Nassuna, Hellen;Kim, Jaehoon;Eyobu, Odongo Steven;Lee, Dongik
    • IEMEK Journal of Embedded Systems and Applications
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    • v.15 no.3
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    • pp.119-127
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    • 2020
  • The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

Active Control of On-board Jitter Isolation for Spacecraft (인공위성의 내부 진동 분리를 위한 능동 제어 연구)

  • Oh, Se-Boung;Bang, Hyo-Choong;Tahk, Min-Jea
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.32 no.9
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    • pp.80-87
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    • 2004
  • Active control of on-orbit spacecraft jitter is a significant problem for future spacecraft mission requiring stringent pointing performance. Jitter is major disturbance source degrading payload pointing performance. Both passive and active jitter isolation techniques have been studied during the last decade. We present active jitter isolation for a model device in this work. The device provides active control capability by 3 degree-of-freedom control of payload in feedback control strategy. Mathematical modeling of the device is pursued which is naturally used for a baseline controller design. Simulation results are used to validate the designed control law.

Decentralized Neural Network-based Excitation Control of Large-scale Power Systems

  • Liu, Wenxin;Sarangapani, Jagannathan;Venayagamoorthy, Ganesh K.;Liu, Li;Wunsch II, Donald C.;Crow, Mariesa L.;Cartes, David A.
    • International Journal of Control, Automation, and Systems
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    • v.5 no.5
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    • pp.526-538
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    • 2007
  • This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activities are guaranteed through rigorous stability analysis. Neural networks in the controller design are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded. To evaluate its performance, the proposed controller design is compared with conventional controllers optimized using particle swarm optimization. Simulations with a three-machine power system under different disturbances demonstrate the effectiveness of the proposed controller design.

Runoff estimation using modified adaptive neuro-fuzzy inference system

  • Nath, Amitabha;Mthethwa, Fisokuhle;Saha, Goutam
    • Environmental Engineering Research
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    • v.25 no.4
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    • pp.545-553
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    • 2020
  • Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

Design of pRBFNN Based on Interval Type-2 Fuzzy Set (Interval Type-2 퍼지 집합 기반의 pRBFNN 설계)

  • Kim, In-Jae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
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    • pp.1871_1872
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    • 2009
  • 본 논문 에서는 Type-2 퍼지 논리 시스템을 설계하고, 불확실한 정보를 갖는 입력 데이터에 대하여 Type-1 퍼지 논리 시스템과 성능을 비교한다. Type-1 퍼지 논리 시스템은 외부 잡음에 민감한 단점을 가지고 있는 반면, Type-2 퍼지 논리 시스템은 불확실한 정보를 잘 표현 할 수 있다. 따라서 Type-2 퍼지 논리 시스템을 이용하여 이러한 단점을 극복하고자 2가지의 모델을 설계한다. 첫 번째 모델은 규칙의 전 후반부가 Type-1 퍼지 집합으로 구성된 Type-1 퍼지 논리 시스템을 설계 한다. 두 번째는 규칙 전 후반부에 Type-2 퍼지 집합으로 구성된 Type-2 퍼지 논리 시스템을 설계한다. 여기서 규칙 전반부의 입력 공간 분할 및 FOU(Footprint Of Uncertainty)형성에는 FCM(Fuzzy C_Means) clustering 방법을 사용하고, 입자 군집 최적화(Particle Swarm Optimization) 알고리즘을 사용하여 최적의 파라미터를 설계한다. 본 논문 에서는 또한 입력 데이터에 인위적으로 가하는 노이즈에 따른 각각 모델의 성능을 비교한다. 마지막으로 비선형 모델 평가에 주로 사용되는 NOx 데이터를 제안된 모델에 적용하고, 실험을 통하여 노이즈가 첨가되고, 불확실한 정보를 다루기에 Type-1 퍼지 논리 시스템 보다 Type-2 퍼지 논리 시스템이 효율적이라는 것을 보인다.

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Strategy based PSO for Dynamic Control of UPFC to Enhance Power System Security

  • Mahdad, Belkacem;Bouktir, T.;Srairi, K.
    • Journal of Electrical Engineering and Technology
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    • v.4 no.3
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    • pp.315-322
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    • 2009
  • Penetration and installation of a new dynamic technology known as Flexible AC Transmission Systems (FACTS) in a practical and dynamic network requires and force expert engineer to develop robust and flexible strategy for planning and control. Unified Power Flow Controller (UPFC) is one of the recent and effective FACTS devices designed for multi control operation to enhance the power system security. This paper presents a dynamic strategy based on Particle Swarm Optimization (PSO) for optimal parameters setting of UPFC to enhance the system loadability. Firstly, we perform a multi power flow analysis with load incrementation to construct a global database to determine the initial efficient bounds associated to active power and reactive power target vector. Secondly a PSO technique applied to search the new parameters setting of the UPFC within the initial new active power and reactive power target bounds. The proposed approach is implemented with Matlab program and verified with IEEE 30-Bus test network. The results show that the proposed approach can converge to the near optimum solution with accuracy, and confirm that flexible multi-control of this device coordinated with efficient location enhance the system security of power system by eliminating the overloaded lines and the bus voltage violation.

Design of Nonlinear Model by Means of Interval Type-2 Fuzzy Logic System (Interval Type-2 퍼지 논리 시스템 기반의 비선형 모델 설계)

  • Kim, In-Jae;O, Seong-Gwon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.317-320
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    • 2008
  • 본 논문에서는 Type-1 퍼지 논리 시스템과 Type-2 퍼지 논리 시스템을 설계하고, 불확실한 정보를 갖는 입력 데이터에 대하여 각각의 성능을 비교한다. Type-1 퍼지 논리 시스템은 외부잡음에 민감한 단점을 가지고 있는 반면, Type-2 퍼지 논리 시스템은 불확실한 정보를 잘 표현할 수 있으며 효율적으로 취급한다. 따라서 Type-2 퍼지 논리 시스템을 이용하여 이러한 단점을 극복하고자 2가지의 모델을 설계한다. 첫 번째 모델은 규칙의 전 ${\cdot}$ 후반부가 불확실성을 표현 할 수 없는 Type-1 퍼지 집합으로 구성된 Type-1 퍼지 논리 시스템을 설계한다. 두 번째는 규칙 후반부만 Type-2 퍼지 집합으로 구성한 두가지의 Type-2 퍼지 논리 시스템을 설계한다. 여기서 규칙 전반부의 입력 공간 분할에는 Min-Max 방법의 균등분할을 사용하고, 규칙 후반부 멤버쉽 함수의 중심 결정에는 입자 군집 최적화(Particle Swarm Optimization) 알고리즘을 사용하여 동정한다. 또한 입력 데이터에 인위적으로 가하는 노이즈의 정도에 따른 각각 모델의 성능을 비교한다. 마지막으로 비선형 모델 평가에 주로 사용되는 가스로 시계열 데이터를 제안된 모델에 적용하고, 실험을 통하여 불확실한 정보를 다루기에 Type-1 퍼지 논리 시스템 보다 Type-2 퍼지 논리 시스템이 효율적이라는 것을 보인다.

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A semi-analytical study on the nonlinear pull-in instability of FGM nanoactuators

  • Attia, Mohamed A.;Abo-Bakr, Rasha M.
    • Structural Engineering and Mechanics
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    • v.76 no.4
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    • pp.451-463
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    • 2020
  • In this paper, a new semi-analytical solution for estimating the pull-in parameters of electrically actuated functionally graded (FG) nanobeams is proposed. All the bulk and surface material properties of the FG nanoactuator vary continuously in thickness direction according to power law distribution. Here, the modified couple stress theory (MCST) and Gurtin-Murdoch surface elasticity theory (SET) are jointly employed to capture the size effects of the nanoscale beam in the context of Euler-Bernoulli beam theory. According to the MCST and SET and accounting for the mid-plane stretching, axial residual stress, electrostatic actuation, fringing field, and dispersion (Casimir or/and van der Waals) forces, the nonlinear nonclassical equation of motion and boundary conditions are obtained derived using Hamilton principle. The proposed semi-analytical solution is derived by employing Galerkin method in conjunction with the Particle Swarm Optimization (PSO) method. The proposed solution approach is validated with the available literature. The freestanding behavior of nanoactuators is also investigated. A parametric study is conducted to illustrate the effects of different material and geometrical parameters on the pull-in response of cantilever and doubly-clamped FG nanoactuators. This model and proposed solution are helpful especially in mechanical design of micro/nanoactuators made of FGMs.

PSO-Based PID Controller for AVR Systems Concerned with Design Specification (설계사양을 고려한 AVR 시스템의 PSO 기반 PID 제어기)

  • Lee, Yun-Hyung
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.10
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    • pp.330-338
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    • 2018
  • The proportional-integral-derivative(PID) controller has been widely used in the industry because of its robust performance and simple structure in a wide range of operating conditions. However, the AVR(Automatic Voltage Regulator) as a control system is not robust to variations of the power system parameters. Therefore, it is necessary to use PID controller to increase the stability and performance of the AVR system. In this paper, a novel design method for determining the optimal PID controller parameters of an AVR system using the particle swarm optimization(PSO) algorithm is presented. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. In order to assist estimating the performance of the proposed PSO-PID controller, a new performance criterion function is also defined. This evaluation function is intended to reflect when the maximum percentage overshoot, the settling time are given as design specifications. The ITAE evaluation function should impose a penalty if the design specifications are violated, so that the PSO algorithm satisfies the specifications when searching for the PID controller parameter. Finally, through the computer simulations, the proposed PSO-PID controller not only satisfies the given design specifications for the terminal voltage step response, but also shows better control performance than other similar recent studies.

A Study on Heavy Rainfall Guidance Realized with the Aid of Neuro-Fuzzy and SVR Algorithm Using AWS Data (AWS자료 기반 SVR과 뉴로-퍼지 알고리즘 구현 호우주의보 가이던스 연구)

  • Kim, Hyun-Myung;Oh, Sung-Kwun;Kim, Yong-Hyuk;Lee, Yong-Hee
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.4
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    • pp.526-533
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    • 2014
  • In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.