Pendekatan Baharu Penapis Median Pensuisan Untuk Pengurangan Hingar Impuls Tahap Rendah Pada Imej Digital

Penggunaan informasi visual berasaskan imej digital telah mendapat perhatian yang begitu meluas kerana ciri-cirinya yang fleksibel dan mudah untuk dimanipulasi. Namun, imej digital yang menjadi input asas kepada sesuatu sistem aplikasi sering dicemari oleh hingar. Antara jenis hingar yang lazimnya terdapat pada imej digital ialah hingar impuls. Oleh itu, dua jenis penapis baharu berasaskan skim pensuisan untuk penyingkiran hingar impuls pada imej digital telah diperkenalkan. Kedua-dua penapis ini yang dinamakan sebagai penapis Median Pensuisan Statistik Dwigelongsor (Dual Sliding Statistics Swi/ching Median filter (MPSDG)) dan penapis Median Pensuisan Hibrid Mahir (Adroit Hybrid Swi/ching Median filter (MPHM) adalah penapis dua-peringkat yang terbahagi kepada peringkat pengesanan hingar serta penapisan hingar. Dalam kaedah MPSDG, pengesanan hingar dilaksanakan terlebih dahulu dengan memproses statistik tetingkap pengesan setempat dalam susunan teratur dan tidak teratur secara serentak. Kemudian, median perbezaan mutlak yang diperolehi daripada statistik kedua-dua tetingkap akan digunakan bagi mengklasifikasikan piksel hingar yang wujud

Optimal tuning of sigmoid PID controller using nonlinear sine cosine algorithm for the automatic voltage regulator system --- KIV (status in press)

Automatic Voltage Regulator (AVR) is fabricated to sustain the voltage level of a synchronous generator spontaneously. Several control strategies have been introduced into the AVR system with the aim of gaining a better dynamic response. One of the most universally utilized controllers is the Proportional-Integral-Derivative (PID) controller. Despite the PID controller having a relatively high dynamic response, there are still further possibilities to improve in order to obtain more appropriate responses. This paper designed a sigmoid-based PID (SPID) controller for the AVR system in order to allow for an accelerated settling to rated voltage, as well as increasing the control accuracy. In addition, the parameters of the proposed SPID controller are obtained using an enhanced self-tuning heuristic optimization method called Nonlinear Sine Cosine Algorithm (NSCA), for achieving a better dynamic response, particularly with regards to the steady-state errors and overshoot of the system. A time-response specifications index is used to validate the proposed SPID controller. The obtained simulation results revealed that the proposed method was not only highly effective but also greatly improved the AVR system transient response in comparison to those with the modern heuristic optimization based PID controllers

A modified grey wolf optimizer for improving wind plant energy production

The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods

A Modified Grey Wolf Optimizer For Improving Wind Plant Energy Production

The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods

A modified sine cosine algorithm for improving wind plant energy production

This paper presents a Modified Sine Cosine Algorithm (M-SCA) to improve the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The two modifications employed to the original SCA are in terms of the updated step size gain and the updated design variable equation. Those modifications are expected to enhance the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-SCA is applied to maximize energy production of a row of ten turbines. The statistical performance analysis shows that the M-SCA provides the highest total energy production as compared to other existing methods

Simple Pole Placement Controller for Elastic Joint Manipulator

This paper presents investigations into the development of simple pole placement controller for tip angular position tracking and deflection reduction of an elastic joint manipulator system. A Quanser elastic joint manipulator is considered and the dynamic model of the system is derived using the Euler-Lagrange formulation. The pole placement controller is designed based on integral state feedback structure and the feedback gain is computed based on the desired time response specifications of tip angular position. The proposed control scheme is also compared with a hybrid Linear Quadratic Regulator (LQR) with input shaper control scheme. The performances of the control schemes are assessed in terms of tip angular tracking capability, level of deflection angle reduction and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed

GGrey Wolf Optimizer For Identification Of Liquid Slosh Behavior Using Continuous-Time Hammerstein Model

This paper presents the identification of liquid slosh plant using the Hammerstein model based on Grey Wolf Optimizer (GWO) method. A remote car that carrying a container of liquid is considered as the liquid slosh experimental rig. In contrast to other research works, this paper consider a piece-wise affine function in the nonlinear function of the Hammerstein model, which is more generalized function. Moreover, a continuous-time transfer function is utilized in the Hammerstein model, which is more suitable to represent a real system. The GWO method is used to tune both coefficients in the nonlinear function and transfer function of the Hammerstein model such that the error between the identified output and the real experimental output is minimized. The effectiveness of the proposed framework is assessed in terms of the convergence curve response, output response, and the stability of the identified model through the bode plot and pole zero map. The results show that the GWO based method is able to produce a Hammerstein model that yields identified output response close to the real experimental slosh output

Sliding Statistics Switching Median Filter for the Removalof Low Level Mix Impulse Noise

A new nonlinearfiltering algorithm for effectively removing mix impulse noise in digital images, called twin sliding statistics switching median (TSSSM) filter is presented in this paper. The proposed TSSSM filter is made up of two subunits; i.e.impulse noise detection and noise filtering.At first,the impulse noise detection stage ofTSSSMalgorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order,concurrently. Next, the median of absolute difference (MAD) obtained from both statistics(i.e. sorted and non-sorted) will be further processed in order to classify any possible noise pixels.In addition, histogram based noise detector also used at this stage in order to increase the filter’s robustness. Subsequently, the filtering stage will replace the detected noise pixels with the estimatedmedian value of the surrounding pixels. Extensive simulations results conducted on grayscale images indicate that the TSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation

Implementation of safe experimentation spiral dynamics algorithm for self-tuning of PID controller in elastic joint manipulator

This paper exclusively endorses the optimization of self-tuned PID using Safe Experimentation Spiral Dynamic Algorithm (SESDA) for elastic joint handling. SESDA is hereby devised by adoption of spiral function to a standard Safe Experimentation Dynamics Algorithm (SEDA). Such modification is implemented to exploit the ability of spiral function in enhancing both the algorithm's exploration competency and convergence accuracy. Rotating angle tracking and vibration were then commanded by employing a pair of self-tuned PID controllers to the elastic joint system in appraising the optimization efficacy of SESDA. Performance of the updated self-tuned PID controller was further assessed in accordance to the recorded outputs on angular motion trajectory tracking, vibration suppression and statistical evaluations centering its pre-established control fitness function. The proposed SESDA produced 6.51 %, 5.54 % and 8.51 % improvement of fitness function, tracking error and control input energy, respectively, as compared with the standard SEDA. Acquired results ultimately confirmed the excellence of SESDA towards self-tuned PID's superior regulatory precision against the standard SEDA as well as its variants

Model order reduction method based on improved sine cosine algorithm

This paper presents an improved sine cosine algorithm (iSCA) for the reduction of high-order single-input single-output (SISO) systems. The proposed iSCA is adopted to solve the imbalance portion of the exploration and exploitation stages in the standard sine cosine algorithm (SCA). Specifically, a nonlinear decreasing updated gain is adopted to provide a proper balance of exploration and exploitation stages. The proposed iSCA is expected to yield a most accurate reduced-order model for a particular original high-order system by minimizing the integral square error (ISE) between their system output responses. The effectiveness of the proposed technique is evaluated by reducing a 6 th order double pendulum overhead crane model. The obtained simulation results revealed that the proposed iSCA is highly effective and remarkably consistent in obtaining an ideal reduced-order model compared to its original version