A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models

This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant 2009I0016

A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay

In this paper, the parameter identification problem of the lateral flow immunoassay (LFIA) devices is investigated via a new switching delayed particle swarm optimization (SDPSO) algorithm. By evaluating an evolutionary factor in each generation, the velocity of the particle can adaptively adjust the model according to a Markov chain in the proposed SDPSO method. During the iteration process, the SDPSO can adaptively select the inertia weight, acceleration coefficients, locally best particle pbest and globally best particle gbest in the swarm. It is worth highlighting that the pbest and the gbest can be randomly selected from the corresponding values in the previous iteration. That is, the delayed information of the pbest and the gbest can be exploited to update the particle’s velocity in current iteration according to the evolutionary states. The strategy can not only improve the global search but also enhance the possibility of eventually reaching the gbest. The superiority of the proposed SDPSO is evaluated on a series of unimodal and multimodal benchmark functions. Results demonstrate that the novel SDPSO algorithm outperforms some well-known PSO algorithms in aspects of global search and efficiency of convergence. Finally, the novel SDPSO is successfully exploited to estimate the unknown time-delay parameters of a class of nonlinear state-space LFIA model.This work was supported in part by the Royal Society of the U.K., the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of China under Grant 61403319, the Fujian Natural Science Foundation under Grant 2015J05131, and the Fujian Provincial Key Laboratory of Eco-Industrial Green Technology

Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter

This paper is concerned with the problem of learning structure of the lateral flow immunoassay (LFIA) devices via short but available time series of the experiment measurement. The model for the LFIA is considered as a nonlinear state-space model that includes equations describing both the biochemical reaction process of LFIA system and the observation output. Especially, the time-delays occurring among the biochemical reactions are considered in the established model. Furthermore, we utilize the unscented Kalman filter (UKF) algorithm to simultaneously identify not only the states but also the parameters of the improved state-space model by using short but high-dimensional experiment data in terms of images. It is shown via experiment results that the UKF approach is particularly suitable for modelling the LFIA devices. The identified model with time-delay is of great significance for the quantitative analysis of LFIA in both the accurate prediction of the dynamic process of the concentration distribution of the antigens/antibodies and the performance optimization of the LFIA devices.This work was supported in part by National Natural Science Foundation of China (Grant No. 61403319), Fujian Natural Science Foundation (Grant No. 2015J05131), Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, and Fundamental Research Funds for the Central Universities

Recent Advances on Modeling the Lateral Flow Immunoassay

The rapid immunochromatographic test strip, also called lateral flow immunoassay (LFIA), has recently attracted considerable research attention in the past decade because of its advantages when applied to a wide variety of point-of-care (POC) tests. This paper reviewed recent advances on modeling the LFIA and summarized their advantages and limitations. It is worth mentioning that there is a growing research interest on the general modeling issue for the LFIA system. In order to optimize LFIA performance for the purpose of quantification, it is of great importance to develop a mathematical model that allows us to simulate dynamic characteristics and also find out the effects of various design parameters in a both rapid and inexpensive way

A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer

European Union’s Horizon 2020 Research and Innovation Programme (INTEGRADDE); U.K.–China Industry Academia Partnership Programme; 10.13039/501100000266-Engineering and Physical Sciences Research Council of the U.K.; 10.13039/501100000288-Royal Society of the U.K.; 10.13039/100005156-Alexander von Humboldt Foundation of Germany

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown

Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach

Background:Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagnosis. Methods:This study aims to fill this void by utilizing a Bayesian network (BN) modelling approach. A K2 learning algorithm and statistical computation methods are used to construct BN structure and assess the obtained BN model. The data used in this study were collected from a clinical ultrasound dataset derived from a Chinese local hospital and a fine-needle aspiration cytology (FNAC) dataset from UCI machine learning repository. Results: Our study suggested that, in terms of ultrasound data, cell shape is the most significant feature for breast cancer diagnosis, and the resistance index presents a strong probabilistic dependency on blood signals. With respect to FNAC data, bare nuclei are the most important discriminating feature of malignant and benign breast tumours, and uniformity of both cell size and cell shape are tightly interdependent. Contributions: The BN modelling approach can support clinicians in making diagnostic decisions based on the significant features identified by the model, especially when some other features are missing for specific patients. The approach is also applicable to other healthcare data analytics and data modelling for disease diagnosis

Improved Wavelet Threshold for Image De-noising

With the development of communication technology and network technology, as well as the rising popularity of digital electronic products, an image has become an important carrier of access to outside information. However, images are vulnerable to noise interference during collection, transmission and storage, thereby decreasing image quality. Therefore, image noise reduction processing is necessary to obtain higher-quality images. For the characteristics of its multi-analysis, relativity removal, low entropy, and flexible bases, the wavelet transform has become a powerful tool in the field of image de-noising. The wavelet transform in application mathematics has a rapid development. De-noising methods based on wavelet transform is proposed and achieved with good results, but shortcomings still remain. Traditional threshold functions have some deficiencies in image de-noising. A hard threshold function is discontinuous, whereas a soft threshold function causes constant deviation. To address these shortcomings, a method for removing image noise is proposed in this paper. First, the method decomposes the noise image to determine the wavelet coefficients. Second, the wavelet coefficient is applied on the high-frequency part of the threshold processing by using the improved threshold function. Finally, the de-noised images are obtained to rebuild the images in accordance with the estimation in the wavelet-based conditions. Experiment results show that this method, discussed in this paper, is better than traditional hard threshold de-noising and soft threshold de-noising methods, in terms of objective effects and subjective visual effects

Novel particle swarm optimization algorithms with applications in power systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonOptimization problems are vital in physical sciences, commercial and finance matters. In a nutshell, almost everyone is the stake-holder in certain optimization problems aiming at minimizing the cost of production and losses of system, and also maximizing the profit. In control systems, the optimal configuration problems are essential that have been solved by various newly developed methods. The literature is exhaustively explored for an appropriate optimization method to solve such kind of problems. Particle Swarm Optimization is found to be one of the best among several optimization methods by analysing the experimental results. Two novel PSO variants are introduced in this thesis. The first one is named as N State Markov Jumping Particle Swarm Optimization, which is based on the stochastic technique and Markov chain in updating the particle velocity. We have named the second variant as N State Switching Particle Swarm Optimization, which is based on the evolutionary factor information for updating the velocity. The proposed algorithms are then applied to some widely used mathematical benchmark functions. The statistical results of 30 independent trails illustrate the robustness and accuracy of the proposed algorithms for most of the benchmark functions. The better results in terms of mean minimum evaluation errors and the shortest computation time are illustrated. In order to verify the satisfactory performance and robustness of the proposed algorithms, we have further formulated some basic applications in power system operations. The first application is about the static Economic Load Dispatch and the second application is on the Dynamic Economic Load Dispatch. These are highly complex and non-linear problems of power system operations consisting of various systems and generator constraints. Basically, in the static Economic Load Dispatch, a single load is considered for calculating the cost function. In contrast, the Dynamic Economic Load Dispatch changes the load demand for the cost function dynamically with time. In such a challenging and complex environment the proposed algorithms can be applied. The empirical results obtained by applying both of the proposed methods have substantiated their adaptability and robustness into the real-world environment. It is shown in the numerical results that the proposed algorithms are robust and accurate as compared to the other algorithms. The proposed algorithms have produced consistent best values for their objectives, where satisfying all constraints with zero penalty

Optimizing the coefficients of the particle swarm optimization algorithm to solve the problem of economic dispatching to reduce the emission of environmental pollutants

Environmental issues due to the emission of pollutants produced by fossil fuel power plants have recently become an important issue. In this study, the coefficients of particle swarm optimization (PSO) algorithm to solve the problem of economic dispatching to reduce the emission of environmental pollutants were obtained. According to Clerk method, personal learning coefficient was equal to 1.4962, global learning coefficient was equal to 1.4962 and inertia coefficient was equal to 0.73. Also, the penalty coefficient according to the Co evolution particle swarm (CPSO) optimization algorithm was 15.8. As a result, optimization of coefficients by Taguchi method, it showed that the optimal value of personal learning coefficient is equal to 1.5, global learning coefficient is equal to 1.5, inertia coefficient is equal to 0.70 and penalty coefficient is equal to 15, in this case the amount emission of environmental pollutants were reduced by 6.5% compared to the coefficients determined by Clerk and 1.2% compared to the coefficients determined by the Co evolution particle swarm (CPSO) optimization algorithm