Structure optimisation of input layer for feed-forward NARX neural network

This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. Applications of vehicle handling and ride model identification are presented in this paper to demonstrate the optimization technique. The optimal input layer structure and the optimal number of neurons for the NN models are investigated and the results show that the optimised NN model requires significantly less coefficients and training time while maintains high simulation accuracy compared with that of the unoptimised model

Model structure selection in powertrain calibration and control

This thesis develops and investigates the application of novel identification and structure identification techniques for I.C. engine systems. The legislated demand for reduced vehicle fuel consumption and emissions indicates that improved model-based dynamical engine calibration and control methods are required in place of the existing static set-point based mapping methods currently used in industry. The choice of structure of any dynamical engine model has significant consequences for the accuracy and the calibration/optimization time of engine systems. This thesis primarily addresses the issue of this structure selection. Linear models are well understood and relatively easy to implement however the modern I.C. engine is a highly nonlinear system which restricts the use of linear structures. Further the newer technologies required to achieve demanding fuel consumption and emission targets are increasingly more complex and nonlinear. The selection of appropriate nonlinear model regressor terms presents a combinatorial explosion problem which must be solved for accurate engine system modelling. In this thesis, two systematic nonlinear model structure selection techniques, namely stepwise regression with F-statistics and orthogonal least squares method with error reduction ratio, are accordingly investigated. SISO algebraic NARMAX engine models are then established in simulation studies with these methods and demonstrate the effectiveness of the approach. The thesis also investigates the development and application of multi-modelling techniques and the expansion of the model structure selection techniques to the identification of the local models terms within the multi-model structures for the engine. Based on the en- gine operating regions, novel multi-model networks can be established and several alternative multi-modelling techniques, such as LOLIMOT, Neural Network, Gaussian and log-sigmoid function weighted multi-models, for the multi-model engine system identification are explored and compared. An experimental validation of the methods is given by a black box identification of SISO engine models which are developed purely from the experimental engine test data sets. The results demonstrate that the multi-model structure selection techniques can be successfully applied on the engine systems, and that the multi-modelling techniques give good model accuracy and that good modelling efficiency can also be achieved. The outcome is a set of techniques for the efficient development of accurate nonlinear black-box models which can be acquired from experimental dynamometer test-bed data which should assist in the dynamic control of future advanced technology engine systems

An Improved Global Harmony Search Algorithm for the Identification of Nonlinear Discrete-Time Systems Based on Volterra Filter Modeling

This paper describes an improved global harmony search (IGHS) algorithm for identifying the nonlinear discrete-time systems based on second-order Volterra model. The IGHS is an improved version of the novel global harmony search (NGHS) algorithm, and it makes two significant improvements on the NGHS. First, the genetic mutation operation is modified by combining normal distribution and Cauchy distribution, which enables the IGHS to fully explore and exploit the solution space. Second, an opposition-based learning (OBL) is introduced and modified to improve the quality of harmony vectors. The IGHS algorithm is implemented on two numerical examples, and they are nonlinear discrete-time rational system and the real heat exchanger, respectively. The results of the IGHS are compared with those of the other three methods, and it has been verified to be more effective than the other three methods on solving the above two problems with different input signals and system memory sizes

Protection Evaluation of a Five-Gene-Deleted African Swine Fever Virus Vaccine Candidate Against Homologous Challenge

African swine fever virus (ASFV) represents a serious threat to the global swine industry, and there are no safe or commercially available vaccines. Previous studies have demonstrated that inactivated vaccines do not provide sufficient protection against ASFV and that attenuated vaccines are effective, but raise safety concerns. Here, we first constructed a deletion mutant in which EP153R and EP402R gene clusters were knocked out. Based on the deletion mutant, a further deletion from the MGF_360-12L, MGF_360-13L to MGF_360-14L genes was obtained. The five-genes knockout virus was designated as ASFV-ΔECM3. To investigate the efficacy and safety of the ASFV-ΔECM3 virus as a vaccine candidate, the evaluation of the virus was subsequently carried out in pigs. The results showed that the ASFV-ΔECM3 virus could induce homologous protection against the parental isolate, and no significant clinical signs or viremia were observed. These results show that the contiguous deletion mutant, ASFV-ΔECM3 encompassing the EP153R/EP402R and MGF_360-12L/13L/14L genes, could be a potential live-attenuated vaccine candidate for the prevention of ASFV infection

Highly Sensitive Fluorescence Probe Based on Functional SBA-15 for Selective Detection of Hg2+

An inorganic–organic hybrid fluorescence chemosensor (DA/SBA-15) was prepared by covalent immobilization of a dansylamide derivative into the channels of mesoporous silica material SBA-15 via (3-aminopropyl)triethoxysilane (APTES) groups. The primary hexagonally ordered mesoporous structure of SBA-15 was preserved after the grafting procedure. Fluorescence characterization shows that the obtained inorganic–organic hybrid composite is highly selective and sensitive to Hg2+ detection, suggesting the possibility for real-time qualitative or quantitative detection of Hg2+ and the convenience for potential application in toxicology and environmental science

The duck hepatitis virus 5'-UTR possesses HCV-like IRES activity that is independent of eIF4F complex and modulated by downstream coding sequences

Duck hepatitis virus (DHV-1) is a worldwide distributed picornavirus that causes acute and fatal disease in young ducklings. Recently, the complete genome of DHV-1 has been determined and comparative sequence analysis has shown that possesses the typical picornavirus organization but exhibits several unique features. For the first time, we provide evidence that the 626-nucleotide-long 5'-UTR of the DHV-1 genome contains an internal ribosome entry site (IRES) element that functions efficiently both in vitro and in mammalian cells. The prediction of the secondary structure of the DHV-1 IRES shows significant similarity to the hepatitis C virus (HCV) IRES. Moreover, similarly to HCV IRES, DHV-1 IRES can direct translation initiation in the absence of a functional eIF4F complex. We also demonstrate that the activity of the DHV-1 IRES is modulated by a viral coding sequence located downstream of the DHV-1 5'-UTR, which enhances DHV-1 IRES activity both in vitro and in vivo. Furthermore, mutational analysis of the predicted pseudo-knot structures at the 3'-end of the putative DHV-1 IRES supported the presence of conserved domains II and III and, as it has been previously described for other picornaviruses, these structures are essential for keeping the normal internal initiation of translation of DHV-1

Structure optimisation of input layer for feed-forward NARX neural network

This paper was accepted for publication in the journal International Journal of Modelling and the definitive published version is available at http://dx.doi.org/10.1504/IJMIC.2016.075814This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. Applications of vehicle handling and ride model identification are presented in this paper to demonstrate the optimization technique. The optimal input layer structure and the optimal number of neurons for the NN models are investigated and the results show that the optimised NN model requires significantly less coefficients and training time while maintains high simulation accuracy compared with that of the unoptimised model

Optimization of the input layer structure for feed-forward NARX neural network

This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated

A stereo matching algorithm based on SIFT feature and homography matrix

Aiming at the low speed of traditional scale-invariant feature transform (SIFT) matching algorithm, an improved matching algorithm is proposed in this paper. Firstly, feature points are detected and the speed of feature points matching is improved by adding epipolar constraint; then according to the matching feature points, the homography matrix is obtained by the least square method; finally, according to the homography matrix, the points in the left image can be mapped into the right image, and if the distance between the mapping point and the matching point in the right image is smaller than the threshold value, the pair of matching points is retained, otherwise discarded. Experimental results show that with the improved matching algorithm, the matching time is reduced by 73.3% and the matching points are entirely correct. In addition, the improved method is robust to rotation and translation