Design of Predictive Controllers by Dynamic Programming and Neural Networks

This paper proposes a method for the design of predictive controllers for nonlinear systems. The method consists of two phases, a solution phase and a learning phase. In the solution phase, dynamic programming is applied to obtain a closed-loop control law. In the learning phase, neural networks are used to simulate the control law. This phase overcomes the curse of dimensionality problem that has often hindered the implementation of control laws generated by dynamic programming. Experimental results demonstrate the effectiveness of the metho

Insights into the pulmonary vascular complications of heart failure with preserved ejection fraction

Pulmonary hypertension in the setting of heart failure with preserved ejection fraction (PH-HFpEF) is a growing public health problem that is increasing in prevalence. While PH-HFpEF is defined by a high mean pulmonary artery pressure, high left ventricular end-diastolic pressure and a normal ejection fraction, some HFpEF patients develop PH in the presence of pulmonary vascular remodelling with a high transpulmonary pressure gradient or pulmonary vascular resistance. Ageing, increased left atrial pressure and stiffness, mitral regurgitation, as well as features of metabolic syndrome, which include obesity, diabetes and hypertension, are recognized as risk factors for PH-HFpEF. Qualitative studies have documented that patients with PH-HFpEF develop more severe symptoms than those with HFpEF and are associated with more significant exercise intolerance, frequent hospitalizations, right heart failure and reduced survival. Currently, there are no effective therapies for PH-HFpEF, although a number of candidate drugs are being evaluated, including soluble guanylate cyclase stimulators, phosphodiesterase type 5 inhibitors, sodium nitrite and endothelin receptor antagonists. In this review we attempt to provide an updated overview of recent findings pertaining to the pulmonary vascular complications in HFpEF in terms of clinical definitions, epidemiology and pathophysiology. Mechanisms leading to pulmonary vascular remodelling in HFpEF, a summary of pre-clinical models of HFpEF and PH-HFpEF, and new candidate therapeutic strategies for the treatment of PH-HFpEF are summarized

A False Acceptance Error Controlling Method for Hyperspherical Classifiers

Controlling false acceptance errors is of critical importance in many pattern recognition applications, including signature and speaker verification problems. Toward this goal, this paper presents two post-processing methods to improve the performance of hyperspherical classifiers in rejecting patterns from unknown classes. The first method uses a self-organizational approach to design minimum radius hyperspheres, reducing the redundancy of the class region defined by the hyperspherical classifiers. The second method removes additional redundant class regions from the hyperspheres by using a clustering technique to generate a number of smaller hyperspheres. Simulation and experimental results demonstrate that by removing redundant regions these two post-processing methods can reduce the false acceptance error without significantly increasing the false rejection error

A Training Sample Sequence Planning Method for Pattern Recognition Problems

In solving pattern recognition problems, many classification methods, such as the nearest-neighbor (NN) rule, need to determine prototypes from a training set. To improve the performance of these classifiers in finding an efficient set of prototypes, this paper introduces a training sample sequence planning method. In particular, by estimating the relative nearness of the training samples to the decision boundary, the approach proposed here incrementally increases the number of prototypes until the desired classification accuracy has been reached. This approach has been tested with a NN classification method and a neural network training approach. Studies based on both artificial and real data demonstrate that higher classification accuracy can be achieved with fewer prototypes

One-Class-at-a-Time Removal Sequence Planning Method for Multiclass Classification Problems

Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. To resolve this difficulty, a partial decomposition technique is introduced that reduces the computational cost by generating a suboptimal solution. Experimental results demonstrate that the proposed approach consistently outperforms two conventional decomposition methods

Chemical freezeout in relativistic A+A collisions: is it close to the QGP?

Preliminary experimental data for particle number ratios in the collisions of Au+Au at the BNL AGS (11A GeV/c) and Pb+Pb at the CERN SPS (160A GeV/c) are analyzed in a thermodynamically consistent hadron gas model with excluded volume. Large values of temperature, T = 140 185 MeV, and baryonic chemical potential, µb = 590 270 MeV, close to the boundary of the quark-gluon plasma phase are found from fitting the data. This seems to indicate that the energy density at the chemical freezeout is tremendous which would be indeed the case for the point-like hadrons. However, a self-consistent treatment of the van der Waals excluded volume reveals much smaller energy densities which are very far below a lowest limit estimate of the quark-gluon plasma energy density. PACS number(s): 25.75.-q, 24.10.P

Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

Gait Verification using Knee Acceleration Signals

A novel gait recognition method for biometric applications is proposed. The approach has the following distinct features. First, gait patterns are determined via knee acceleration signals, circumventing difficulties associated with conventional vision-based gait recognition methods. Second, an automatic procedure to extract gait features from acceleration signals is developed that employs a multiple-template classification method. Consequently, the proposed approach can adjust the sensitivity and specificity of the gait recognition system with great flexibility. Experimental results from 35 subjects demonstrate the potential of the approach for successful recognition. By setting sensitivity to be 0.95 and 0.90, the resulting specificity ranges from 1 to 0.783 and 1.00 to 0.945, respectively