Kanokvate Tungpimolrut

A framework for automatic heart sound analysis without segmentation

BackgroundA new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs.MethodEqual number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors.ResultThe proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration.ConclusionThe proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.

Adaptive Integral Sliding-Mode Position Control of a Coupled-Phase Linear Variable Reluctance Motor for High-Precision Applications

Key factors limiting the greater use of linear motors are motor cost and complexity of controls. This paper develops an adaptive sliding-mode position control of a coupled-phase linear variable reluctance (LVR) motor for high-precision applications. With several distinct features, the LVR motor can be considered a strong candidate for high-performance linear motion applications due to its simple structure, compactness, and low cost with no permanent magnet. The adaptive position controller based on sliding-mode control is considered because of its simple structure and robustness against uncertain perturbations and external disturbances. The designed controller consists of the following: (1) an inner force control loop based on the sinusoidal flux model for simplicity and computational efficiency and (2) an outer position control based on the adaptive sliding-mode control to enhance the system robustness and to achieve high accuracy for highprecision applications. The LVR motor prototype was constructed for laboratory test, and the controller is implemented on a realtime DSP-based controller card. The comparative experimental results clearly show that the proposed controller is suitable for controlling the LVR motor system for high-accuracy applications and effective for reducing the chattering.

Cardiac auscultation analysis system with Neural Network and SVM technique

The system of this study is aimed to support doctors with an analyzed Cardiac auscultation. Extract the information from the heart sound signal obtained from stethoscope mobbed on the robot arm control is used in the further signal processing. Due to time consuming and accuracy, Support Vector Machine is introduced to replace Neural Network for better performance.With the proposed technique, the high classification performances were achieved 96.4% accuaracy for classifying normal and abnormal heart sound while shortening the training time almost 300%. As the result, multi-classifier was introduced for advance development. This paper shows the comparison work between Neural Network and Support Vector Machine.

Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network

An inverse controller is proposed for a magnetorheological (MR) damper that consists of a hysteresis model and a voltage controller. The force characteristics of the MR damper caused by excitation signals are represented by a feedforward neural network (FNN) with an elementary hysteresis model (EHM). The voltage controller is constructed using another FNN to calculate a suitable input signal that will allow the MR damper to produce the desired damping force. The performance of the proposed EHM-based FNN controller is experimentally compared to existing control methodologies, such as clipped-optimal control, signum function control, conventional FNN, and recurrent neural network with displacement or velocity inputs. The results show that the proposed controller, which does not require force feedback to implement, provides excellent accuracy, fast response time, and lower energy consumption.

An analysis and a solution for inrush current elimination for three-phase PWM voltage source inverters

This paper analyzes the cause of the inrush current at the starting time of the three-phase PWM voltage source inverters (VSIs) based on d-q synchronous reference frame. Then the simple method for solving the problem is proposed. The proposed technique can be applied to either active power applications such as Battery Energy Storage Systems (BESSs) or reactive power applications such as STATCOMs (Static Synchronous Compensators). The computer simulation and experimental results well agree with each other and verify that the proposed solution can completely eliminate the inrush current of the inverters. In addition, the proposed technique does not affect the overall system performance and does not require any additional control or power circuitry. The experimental system is DSP (Digital Signal Processor) based and has a power rating of 2 kW, as a BESS, and 2 kVAR, as a STATCOM.

Modular learning laboratory of AC servo drive

This paper presents an implementation of learning laboratory of AC servo drive using the eZdsptrade F2812 digital signal processor (DSP) for undergraduate senior students. Since the electric motor drive is an interdisciplinary course, the students need to sufficiently know several backgrounds such as machines, digital control system, programming, and power electronics. Therefore, the learning laboratory is intended to design both hardware and software as modules. So students would easily learn the hardware implementation of overall system circuit by circuit. Similarly, codes of each functional block are well defined and written by using object oriented programming (OOP) concept. As a result, students can clearly understand the complex control of permanent magnet synchronous motor (PMSM) using quadrature encoder pulse (QEP), known as AC servo drive. In this paper, the details of modular hardware and software aspects will be presented along with the experimental results.

An automatic screening method for primary open-angle glaucoma assessment using binary and multi-class support vector machines

Glaucoma is a chronic progressive eye condition leading to permanent visual loss. An automatic screening system is necessary to detect primary open-angle glaucoma because it is an insidious disease appearing without symptoms or early warning signs. This work introduces an automatic screening technique to diagnose glaucoma using a support vector machine (SVM). Two case studies are investigated: binary-stage and multi-stage classification of glaucoma. First, there is a comparison of the performance of the hard threshold-based approach to the supervised learning approach using an SVM. Image segmentation techniques are performed to detect important features: the actual sizes of the optic cup and optic disc in vertical and horizontal directions. SVMs with a linear kernel function are used to generate the classifier model, and the results show that using threshold-based classification is inadequate to screen glaucoma. In a second case study, an SVM is applied to develop the classification algorithm focused more on the detection of the glaucoma suspect stage, which is an intermediate stage between the healthy and glaucoma stages. A polynomial kernel function is used to implement the classification model. The unbalanced decision tree (UDT) and one-versus-the-rest (OVR) techniques are combined in the models in order to overcome the limitations of an SVM. Finally, the combination of an SVM with both UDT and OVR techniques yields a reliable result with respect to belonging classes at 99.4%.

Variable walking speed controller of MR damper prosthetic knee using neural network predictive control

Semi-active prosthetic knees utilizing magnetorheological (MR) damper have been introduced to help transfemoral amputees to improve their quality of life. However, they lack the ability to generate power required to achieve the normal gait of healthy people. The aim of this paper is to propose a novel control algorithm for MR damper prosthetic knee, focusing on swing phase of the gait cycle. The proposed controller is developed by using a neural network predictive control optimized using constrained nonlinear optimization. It provides the ability to predict future knee angle trajectory when a certain command voltage is applied in order to determine the optimal command voltage. Moreover, the proposed algorithm is designed to support amputees at walking speeds. Performance of the proposed controller has been evaluated in an offline simulation compared to the normal gait of healthy people. The results show that the proposed controller can generate the knee trajectory with minimal errors.

A control of MR damper using feed-forward neural network without force sensor

This paper proposes an inverse model feed-forward neural network (FNN) that does not require any force sensor to control magneto-rheological (MR) dampers. The system is designed by using time-histories of displacement and velocity in combination with desired force to predict voltage input to control MR damper. Unlike conventional MR damper controller, the proposed system does not require force inputs, providing more economical control system. Additional dead zone filter is also introduced here to reduce errors at near zero state of velocity. Using training and validation data sets generated by a modified Bouc-Wen model, the results of the proposed system with and without dead zone filter are also presented.