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

Gas detection system for dry and wet cupping process

Cupping therapy is an alternative medical approach that adopts the suction mechanism of cups to withdraw blood towards the surface of the skin. The therapy is hereby differentiated between dry cupping therapy (DCT) and wet cupping therapy (WCT). While both techniques involve releasing gas from the human body, the former merely undertakes suctions, with the latter deliberately includes the process of medicinal bleeding. Upon executions of the cupping process, the released gas can potentially affect involved practitioners in form of diseases. Seeing limited studies conducted within the area of actual gas release detection, mentioned issue, thus, demonstrates value in the study of the gas detection system in dry and wet cupping practices. Hence, the current paper set out to develop a gas detection system that investigates and measures the gas existed release in dry and wet cupping practices. To satisfy this objective, the system used several general sensors comprising a natural gas sensor, carbon monoxides gas sensor, hydrogen gas sensor, and LPG gas sensor to investigate the pattern of type gas occurred. Several experiments were further operationalized on both dry and wet cupping therapies under several conditions and time frames to analyze the contents of the released gas. The operated comparison then uncovered the robustness of the gas detection systems in identifying the gas compositions based on sensor detection for both DCT and WCT processes

A data-driven neuroendocrine-PID controller for underactuated systems based on safe experimentation dynamics

This paper presents a data-driven neuroendocrine-PID controller for underactuated systems. Safe Experimentation Dynamics (SED) is employed to find the optimum neuroendocrine-PID parameters such that the control tracking performance and input energy are minimized. The advantage of the proposed approach is that it can generate fast neuroendocrine-PID parameter tuning by measuring the input and output data of the system without using the plant mathematical model. Moreover, the combination of neuroendocrine structure with PID has a great potential in improving the control performance as compared to the PID controller. An underactuated container crane model is considered to validate the proposed data-driven design. In addition, the performance of the proposed method is investigated in terms of the trolley position, hoist rope length and sway angle trajectory tracking. The simulation results show that the data-driven neuroendocrine-PID approach provides better control performance as compared to the PID controller

A multiobjective simulated Kalman filter optimization algorithm

This paper presents a new multiobjective type optimization algorithm known as a Multiobjective Optimization Simulated Kalman Filter (MOSKF). It is a further enhancement of a single-objective Simulated Kalman Filter (SKF) optimization algorithm. A synergy between SKF and Non-dominated Solution (NS) approach is introduced to formulate the multiobjective type algorithm. SKF is a random based optimization algorithm inspired from Kalman Filter theory. A Kalman gain is formulated following the prediction, measurement and estimation steps of the Kalman filter design. The Kalman gain is utilized to introduce a dynamic step size of a search agent in the SKF algorithm. A Non-dominated Solution (NS) approach is utilized in the formulation of the multiobjective strategy. Cost function value and diversity spacing parameters are taken into consideration in the strategy. Every single agent carries those two parameters in which will be used to compare with other solutions from other agents in order to determine its domination. A solution that has a lower cost function value and higher diversity spacing is considered as a solution that dominates other solutions and thus is ranked in a higher ranking. The algorithm is tested with various multiobjective benchmark functions and compared with Non-Dominated Sorting Genetic Algorithm 2 (NSGA2) multiobjective algorithm. Result of the analysis on the accuracy tested on the benchmark functions is tabulated in a table form and shows that the proposed algorithm outperforms NSGA2 significantly. The result also is presented in a graphical form to compare the generated Pareto solution based on proposed MOSKF and original NSGA2 with the theoretical Pareto solution

Spiral-sooty tern optimization algorithm for dynamic modelling of a twin rotor system

This paper presents a hybrid Spiral - Sooty Tern Algorithm (SSTA) which is an improved version of the original STOA. A spiral model is incorporated into the Sooty-Tern Optimization Algorithm (STOA) structure. A random switching is utilized to change from random-based to deterministic-based searching operations and vice versa. This is to balance between the exploration and exploitation of all searching agents throughout a feasible search area. For solving a real-world problem, the proposed SSTA algorithm in comparison to STOA is applied to optimize parameters of a linear Autoregressive-Exogenous (ARX) dynamic model for a twin rotor system. The dynamic modelling of the system is challenging in the presence of cross coupling effect between the main and tail rotors. 3000 pairs of captured input-output data from the system are used for the identification and optimization purpose. Result of the test has shown that the SSTA has achieved a better accuracy performance compared to the competing algorithm. For dynamic modelling of the nonlinear system, both SSTA and STOA have acquired a sufficiently good model for the twin rotor system

A simplify fuzzy logic controller design based safe experimentation dynamics for pantograph-catenary system

Contact force between catenary and pantograph of high speed train is a crucial system to deliver power to the train. The inconsistence force between them can cause the contact wire oscillate a lot and it can damage the mechanical structure of system and produce electric arc that can reduce the performance of system. This project proposes a single-input fuzzy logic controller (SIFLC) to control the contact force between the pantograph-catenary by implement Safe Experimentation Dynamics (SED) method to tune the SIFLC parameters. The essential feature of SIFLC is that it is model-free type controller design with less pre-defined variables as compared to other existing model-based controllers. The performance of the SIFLC is analyzed in terms of input tracking of contact force of pantograph-catenary and time response specifications. A simplified model of three degree of freedom (3-DOF) pantograph-catenary system is considered. In this study, the simulation result shows that the SIFLC successfully track the given contact force with less overshoot with percentage different of peak to peak response from actual force 2% and fast response within 5.27s

Identification of the Thermoelectric Cooler using hybrid multi-verse optimizer and Sine Cosine Algorithm based continuous-Time Hammerstein Model

This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. These modifications are mainly for escaping from local minima and for making the balance between exploration and exploitation. In the Hammerstein model identification a continuous-time linear system is used and the hMVOSCA based method is used to tune the coefficients of both the Hammerstein model subsystems (linear and nonlinear) such that the error between the estimated output and the actual output is reduced. The efficiency of the proposed method is evaluated based on the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon's rank test. The experimental findings show that the hMVOSCA can produce a Hammerstein system that generates an estimated output like the actual TEC output. Moreover, the identified outputs also show that the hMVOSCA outperforms other popular metaheuristic algorithms

Opposition-sooty tern algorithm for fuzzy control optimization of an inverted pendulum system

This paper presents a novel Opposition-Sooty Tern Algorithm (OSTA) which is an improved version of the original Sooty- Tern Optimization Algorithm (STOA). An opposition scheme is incorporated into the STOA structure. This is to enhance the exploration and exploitation of all searching agents throughout a feasible search area. In solving a real-world problem, the algorithm is applied to optimize parameters of a fuzzy logic model for controlling cart's position and pendulum's angle of an inverted pendulum system. Result of the optimization test shows the OSTA has a better accuracy performance compared to its predecessor algorithm. For controlling the inverted pendulum, both OSTA and STOA acquired sufficiently good control performance for the system. However, the fuzzy control scheme optimized by OSTA has resulted in a better tracking and control performance for both cart's position and pendulum's angle

Opposition based Spiral Dynamic Algorithm with an Application to a PID Control of a Flexible Manipulator

This paper presents an improved version of a Spiral Dynamic Algorithm (SDA). The original SDA is a relatively simple optimization algorithm. It uses a spiral strategy to move search agents within the feasible search space. However, SDA suffers from a premature convergence due to an unbalanced diversification and intensification throughout its search operation. Hence, the algorithm unable to acquire an optimal accuracy solution. An Opposition learning is adopted into SDA to improve the searching strategy of the SDA agents. Therefore in the proposed strategy, a random and a deterministic approaches are synergized and complement each other. The algorithm is tested on several benchmark functions in comparison to the original SDA. A statistical nonparametric Wilcoxon sign rank test is conducted to analyze the accuracy achievement of both algorithms. For solving a real world application, the algorithms are applied to optimize a PID controller for a flexible manipulator system. Result of the test on the benchmark functions shows that the Opposition based SDA outperformed the SDA significantly. For solving the PID control design, both algorithms acquire PID parameters and hence can control the flexible manipulator very well. However, the proposed algorithm shows a better control response

Manta ray foraging optimization with quasi-reflected opposition strategy for global optimization

This paper proposes an extension of Manta Ray Foraging Optimization (MRFO) using Oppositional-based Learning (OBL) technique called Quasi Reflected Opposition (QRO). MRFO is a new algorithm that developed based on the nature of a species in cartilaginous fish called Manta Ray. Manta ray employs three foraging strategies which are chain, cyclone and somersault foraging. Nonetheless, MRFO is tends to getting trap into local optima due to the redundant of intensification of the search agents in the search space. On the other side, OBL is a prominent technique in reducing chance of local optimum while increasing the convergence speed. Thus, QRO is synergized into MRFO to form QR-MRFO, in objective to improve MRFO in term of finding better accuracy of solution and faster convergence rate. Latter, QR-MRFO was performed on a series of benchmark functions and analyzed using statistical non-parametric test of Wilcoxon to measure the significant level of improvement. Results from the test shows that MRFO is undoubtedly defeated by QR-MRFO in term of accuracy