Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach

"(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time

A Real-time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

This paper proposes a real-time nonlinear model predictive control (NMPC) strategy for direct yaw moment control (DYC) of distributed drive electric vehicles (DDEVs). The NMPC strategy is based on a control-oriented model built by integrating a single track vehicle model with the Magic Formula (MF) tire model. To mitigate the NMPC computational cost, the continuation/generalized minimal residual (C/GMRES) algorithm is employed and modified for real-time optimization. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the external penalty method is introduced to transform inequality constraints into an equivalently unconstrained optimization problem. Based on the Pontryagin’s minimum principle (PMP), the existence and uniqueness for solution of the proposed C/GMRES algorithm are proven. Additionally, to achieve fast initialization in C/GMRES algorithm, the varying predictive duration is adopted so that the analytic expressions of optimally initial solutions in C/GMRES algorithm can be derived and gained. A Karush-Kuhn-Tucker (KKT) condition based control allocation method distributes the desired traction and yaw moment among four independent motors. Numerical simulations are carried out by combining CarSim and Matlab/Simulink to evaluate the effectiveness of the proposed strategy. Results demonstrate that the real-time NMPC strategy can achieve superior vehicle stability performance, guarantee the given safety constraints, and significantly reduce the computational efforts

Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation

In the UK, in 2014 almost fifty thousand motorists made claims about vehicle damages caused by potholes. Pothole damage mitigation has become so important that a number of car manufacturers have officially designated it as one of their priorities. The objective is to improve suspension shock performance without degrading road holding and ride comfort. In this study, it is shown that significant improvement in performance is achieved if a clipped quadratic parameter varying suspension is employed. Optimal design of the proposed system is challenging because of the multiple local minima causing global optimisation algorithms to get trapped at local minima, located far from the optimum solution. To this end an enhanced Fruit Fly Optimisation Algorithm − based on a recent study on how well a fruit fly’s tiny brain finds food − was developed. The new algorithm is first evaluated using standard and nonstandard benchmark tests and then applied to the computationally expensive suspension design problem. The proposed algorithm is simple to use, robust and well suited for the solution of highly nonlinear problems. For the suspension design problem new insight is gained, leading to optimum damping profiles as a function of excitation level and rattle space velocity

Development of Nonlinear Adaptive PI Controller For Improved Pneumatic Actuator System

The wide application of pneumatic actuator in electrical and electronics sectors are undeniable hence ask for a good control environment. PID controller is always known with easy implementation and good control performance. But the limitation of the PID static gains to effectively control the complex nonlinear system is unavoidable. This suggests the enhancement of the PI controller with a nonlinear adaptive interaction algorithm (AIA). The modification is introduced by integrating a nonlinear gain function that adaptively tunes the AIA parameter, hence resulting the best tuning of the PI control gains. The uncertainties and nonlinearities inherent in the system parameters are believed to be handled by the integration, therefore improving the controller performances while maintaining the pneumatic actuator at the desired position. It was proved that improved error performance criteria’s, settling time and overshoot were resulted by the nonlinear AIA PI compared to fix AIA PI. Besides, the nonlinear AIA PI has successfully reduced the overshoot to 5.35% and 6.70% compared to optimal AIA PI and optimal PI controller, respectively. To conclude, the development of the proposed controller is demonstrated to function well and offers an alternative tuning strategy in other electronical and electronic engineering applications

PSO-backstepping controller of a grid connected DFIG based wind turbine

The paper demonstrates the feasibility of an optimal backstepping controller for doubly fed induction generator based wind turbine (DFIG). The main purpose is the extract of maximum energy and the control of active and reactive power exchanged between the generator and electrical grid in presence of uncertainty. The maximum energy is obtained by applying an algorithm based on artificial bee colony approach. Particle swarm optimization is used to select optimal value of backstepping’s parameters. The simulation is carried out on 2.4 MW DFIG based wind turbine system. The optimized performance of the proposed control technique under uncertainty parameters is established by simulation results

Efficiency Analysis of Swarm Intelligence and Randomization Techniques

Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks. Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark. The outstanding performance and efficiency of swarm-based algorithms inspired many new developments, though mathematical understanding of metaheuristics remains partly a mystery. In contrast to the classic deterministic algorithms, metaheuristics such as PSO always use some form of randomness, and such randomization now employs various techniques. This paper intends to review and analyze some of the convergence and efficiency associated with metaheuristics such as firefly algorithm, random walks, and L\'evy flights. We will discuss how these techniques are used and their implications for further research.Comment: 10 pages. arXiv admin note: substantial text overlap with arXiv:1212.0220, arXiv:1208.0527, arXiv:1003.146

Development of Nonlinear Adaptive PI Controller For Improved Pneumatic Actuator System

The wide application of pneumatic actuator in electrical and electronics sectors are undeniable hence ask for a good control environment. PID controller is always known with easy implementation and good control performance. But the limitation of the PID static gains to effectively control the complex nonlinear system is unavoidable. This suggests the enhancement of the PI controller with a nonlinear adaptive interaction algorithm (AIA). The modification is introduced by integrating a nonlinear gain function that adaptively tunes the AIA parameter, hence resulting the best tuning of the PI control gains. The uncertainties and nonlinearities inherent in the system parameters are believed to be handled by the integration, therefore improving the controller performances while maintaining the pneumatic actuator at the desired position. It was proved that improved error performance criteria’s, settling time and overshoot were resulted by the nonlinear AIA PI compared to fix AIA PI. Besides, the nonlinear AIA PI has successfully reduced the overshoot to 5.35% and 6.70% compared to optimal AIA PI and optimal PI controller, respectively. To conclude, the development of the proposed controller is demonstrated to function well and offers an alternative tuning strategy in other electronical and electronic engineering applications

Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO

A global parameter estimation method for a PMSM drive system is proposed, where the electrical parameters, mechanical parameters and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters and VSI of PMSM drive by using dynamic self learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multi-scale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning (OBL) strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously