A neural-network-based controller for the cost-effective operation of a hybrid compensator for nonactive power

The need to eliminate distortion from power networks has led to the development of various compensator topologies. The increasing cost of electrical energy requires the choice of the most cost-effective compensator operation. An investigation of a neural-network-based controller that chooses the most cost-effective compensator mode of operation on the basis of a continuous analysis of load conditions and the operational losses of the elements in the compensator structure are reported. The modeling of operational losses of each subtopology and the required control strategy are discussed. The results show that the operational loss savings due to the neural-network-controlled hybrid compensator were 30%-70% as compared to the conventionally controlled hybrid compensator, while also conforming to other control strategy requirements.     

An input-delay neural-network-based approach for piecewise ECG signal compression

We propose an input delay neural network (IDNN) based time series prediction algorithm for compressing electrocardiogram (ECG) signals. Our algorithm has been tested and successfully compared vis-à-vis other popular techniques for its compression efficiency and reconstruction capability.     

Robust controller for a full-bridge rectifier using the IDA approach and GSSA modeling

An interconnection-and-damping assignment passivity-based control (IDA-PBC) for a full-bridge rectifier is presented in this paper. The closed-loop system performance fulfils unity power factor in the ac mains and output dc voltage regulation. The controller design takes advantage of the generalized state space averaging (GSSA) modeling technique to convert the quoted nonstandard problem (in actual variables) into a standard regulation one (in GSSA variables). In this approach, the output current is the measured signal instead of the line current; therefore, the number of sensors does not increase in comparison with traditional approaches. The whole system is robust with respect to load variations.     

Computer-aided diagnosis: a neural-network-based approach to lung nodule detection

In this work, we have developed a computer-aided diagnosis system, based on a two-level artificial neural network (ANN) architecture. This was trained, tested, and evaluated specifically on the problem of detecting lung cancer nodules found on digitized chest radiographs. The first ANN performs the detection of suspicious regions in a low-resolution image. The input to the second ANN are the curvature peaks computed for all pixels in each suspicious region. This comes from the fact that small tumors possess and identifiable signature in curvature-peak feature space, where curvature is the local curvature of the image data when viewed as a relief map. The output of this network is thresholded at a chosen level of significance to give a positive detection. Tests are performed using 60 radiographs taken from routine clinic with 90 real nodules and 288 simulated nodules. We employed free-response receiver operating characteristics method with the mean number of false positives (FP's) and the sensitivity as performance indexes to evaluate all the simulation results. The combination of the two networks provide results of 89%-96% sensitivity and 5-7 FP's/image, depending on the size of the nodules.     

A recurrent neural-network-based real-time learning controlstrategy applying to nonlinear systems with unknown dynamics

In this paper, the authors present a real-time learning control scheme for unknown nonlinear dynamical systems using recurrent neural networks (RNNs). Two RNNs, based on the same network architecture, are utilized in the learning control system. One is used to approximate the nonlinear system, and the other is used to mimic the desired system response output. The learning rule is achieved by combining the two RNNs to form the neural network control system. A generalized real-time iterative learning algorithm is developed and used to train the RNNs. The algorithm is derived by means of two-dimensional (2-D) system theory that is different from the conventional algorithms that employ the steepest optimization to minimize a cost function. This paper shows that an RNN using the real-time iterative learning algorithm can approximate any trajectory tracking to a very high degree of accuracy. The proposed learning control scheme is applied to numerical problems, and simulation results are included. The results are very promising, and this paper suggests that the 2-D system theory-based RNN learning algorithm provides a new dimension in real-time neural control systems     

A nonrecursive Newton-Euler formulation for the parallelcomputation of manipulator inverse dynamics

The paper focuses on the inverse dynamics formulation of manipulators that is suitable for parallel computation, and a corresponding nonrecursive Newton-Euler formulation is presented. In order to illustrate its potential parallelism, a simple parallel scheduling scheme is proposed, and the parallel computational efficiency for the inverse dynamics of the basic three links of a PUMA 560 robot is analyzed. Compared with other algorithms, the theoretical computation cost of this parallel algorithm, in which factors such as communications overhead are ignored, is smaller     

Current controller design for an electromagnetic actuator using anonline parameter optimization approach

The design is presented of a digital proportional-plus-integral current controller for a nonlinear electromagnetic actuator using an online parameter optimization approach. The minimization algorithm of J.A. Nelder and R. Mead (1965) is used to calculate the proportional and integral controller gain such that a design objective function is minimized. At each iteration of the minimization procedure, the objective function is calculated by sampling the actual current and voltage signals of the actuator     

An approach to inverse nonlinear control using neural networks

In this paper, we present a strategy for controlling a class of nonlinear dynamical systems using techniques based on neural networks. The proposed strategy essentially exploits the property of neural networks in being able to approximate arbitrary nonlinear maps when suitable learning strategies are applied. For the closed-loop control, such a network is used in conjunction with a technique of inverse nonlinear control to form what we call an inverse nonlinear controller using neural networks, abbreviated as the INC/NN controller. Properties of the controller are discussed, and it is shown that the proposed INC/NN controller allows the closed-loop error dynamics to be specified directly through a set of controller gains. Extensions of the basic INC/NN controller to incorporate integral control action, to higher order systems, and to a class of nonlinear multi-input multi-output dynamical systems are also indicated. Finally, results of some real-time experiments in applying the INC/NN controller to a position control system which has inherent nonlinearities are presented.     

A game theoretic approach to controller design for hybrid systems

We present a method to design controllers for safety specifications in hybrid systems. The hybrid system combines discrete event dynamics with nonlinear continuous dynamics: the discrete event dynamics model linguistic and qualitative information and naturally accommodate mode switching logic, and the continuous dynamics model the physical processes themselves, such as the continuous response of an aircraft to the forces of aileron and throttle. Input variables model both continuous and discrete control and disturbance parameters. We translate safety specifications into restrictions on the system's reachable sets of states. Then, using analysis based on optimal control and game theory for automata and continuous dynamical systems, we derive Hamilton-Jacobi equations whose solutions describe the boundaries of reachable sets. These equations are the heart of our general controller synthesis technique for hybrid systems, in which we calculate feedback control laws for the continuous and discrete variables, which guarantee that the hybrid system remains in the “safe subset” of the reachable set. We discuss issues related to computing solutions to Hamilton-Jacobi equations. Throughout, we demonstrate out techniques on examples of hybrid automata modeling aircraft conflict resolution, autopilot flight mode switching, and vehicle collision avoidance     

A fully neural-network-based planning scheme for torqueminimization of redundant manipulators

The aim of this paper is to develop a new method for minimizing joint torques of redundant manipulators in the Chebyshev sense and to present a fully neural-network-based computational scheme for its implementation. Minimax techniques are used to determine the null space acceleration vector which can guarantee to minimize the maximum joint torque. For real-time implementation, we transform the proposed method into a computation of a recurrent neural network. At each time step, the neural network is adopted for both the solution of the least-norm joint acceleration and the determination of the optimum null space acceleration vector. Compared with previous torque minimization schemes, the proposed method enables more direct monitoring and control of the magnitudes of the individual joint torques than does the minimization of the sum of squares of the components. Simulation results demonstrate that the proposed method is effective for the torque minimization control of redundant manipulators     

A generalized eigensystem approach to the inverse problem ofelectrocardiography

The authors develop a new approach to the ill-conditioned inverse problem of electrocardiography which employs finite element techniques to generate a truncated eigenvector expansion to stabilize the inversion. Ordinary three-dimensional isoparametric finite elements are used to generate the conductivity matrix for the body. The authors introduce a related eigenproblem, for which a special two-dimensional isoparametric area matrix is used, and solve for the lowest eigenvalues and eigenvectors. The body surface potentials are expanded in terms, of the eigenvectors, and a least squares fit to the measured body surface potentials is used to determine the coefficients of the expansion. This expansion is then used directly to determine the potentials on the surface of the heart. The number of measurement points on the surface of the body can be less than the number of finite element nodes on the body surface, and the number of modes employed in the expansion can be adjusted to reduce errors due to noise     

Measurement of lung hyperelastic properties using inverse finite element approach

Hyperelastic properties of deflated lung tissue have been characterized via an inverse finite element approach. Such properties are useful in many medical diagnosis and treatment applications where tissue deformation can be modeled to account for during the procedure. Several indentation experiments were conducted on various porcine lungs' tissue specimens resected immediately from different regions and lobes after the animals were sacrificed. Three different strain energy models, namely Ogden, Yeoh, and Polynomial, were used and respective hyperelastic parameters were obtained. The parameters for each model were estimated through an optimization process where the experimental force-displacement profiles of indentation were fitted to those obtained from finite element simulations performed specifically for the samples' geometries. Results obtained in this investigation for all the three models indicate convergence with reasonably low average fitting errors ranging from 2.3% to 6.2%. Independent tests were also performed to assess the effects of samples' heterogeneities on the obtained parameters. The outcome of these tests was encouraging and confirmed small impact of tissue inhomogeneities on the estimated parameters. The reported hyperelastic properties can, accordingly, pave the way for more accurate biomechanical modeling of the lung's soft tissue in the emerging applications of minimally invasive medical intervention for lung cancer diagnosis and treatment.     

Computing the single-link performance measurement for elastic optical OFDM

Elastic optical networks based on orthogonal frequency-division multiplexing (OFDM) have emerged as the preferred technology for future optical networks because of their good spectral efficiency and flexibility. In OFDM optical networks, multiple subcarriers can be allocated to accommodate both subwavelength and superwavelength traffic. In this work, we developed an algorithm based on the superposition concept in electrical networks using the Kaufman/Delbrouck recursion model to accurately compute the revenue loss in the OFDM-based single link. The algorithm is applicable when there are many call types requesting diverse numbers of contiguous subcarriers offered to a link with numerous subcarriers.     

A regularized inverse approach to ultrasonic pulse-echo imaging

An inverse-theoretic approach to ultrasonic pulse-echo imaging based on nonquadratic regularization is presented, and its effectiveness is investigated computationally by: 1) evaluating the quality of the reconstruction of speckle-based images as a function of the transmit-receive bandwidth and focal number of the transducer; 2) comparing the reconstructed images with those obtained by using conventional B-mode imaging. The L-curve and the generalized cross-validation methods were evaluated as automatic regularization parameter selection techniques. The inversion using regularization produced better results than conventional B-mode imaging for high signal-to-noise ratios (SNRs). A lower bound of 30 dB for the SNR was found for this study, below which several of the image features were lost during the reconstruction process in order to control the distortion due to the noise.     

A power-factor-corrected AC-AC inverter topology using a unified controller for high-frequency power distribution architecture

High-frequency power distribution architecture (HFPDA) has gained more and more attention from both academics and industry in recent years. It is not only applicable in space systems, but also found attractive in power system design for emerging telecommunication and computer systems. As the technology has matured, HFPDA even seems to be attractive for powering the desktop computers employing the latest generation fast microprocessors. This paper presents an ac-ac inverter for HFPDA. The inverter includes a high-frequency resonant inverter and a buck-boost converter for power-factor correction (PFC). A unified controller controls both the resonant inverter and the PFC stage. Unlike other single-stage power-factor-corrected inverter topologies, the proposed inverter system has reduced dc-bus voltage stress for the universal input line voltage. The proposed inverter is found attractive in low-power applications.     

一款实用的继电器步进式音量控制器

１引言在常见的双声道音频功率放大器中,其音量控制器一般均采用同轴双联电位器,普通的双联碳膜电位器,两联阻值的一致性较差,会使两声道的输入阻抗不平衡,两声道的平衡度无法保证,声像定位变差,尤其是在小信号区这种偏差将会十分严重。另外,这种电位器的耐磨性差、寿命短,滑动臂的金属刷子会磨损碳膜层,造成接触不良、产生难以容忍的噪声。在实际使用中发现,一台功放使用一段时间后,最先出问题的地方往往就是音量控制电位器损坏。要想改善听音效果,提高放大器的使用寿命,解决音量控制电位器存在的问题是十分重要的。解决的方…     

A hybrid inverse approach applied to the design of lumped-element RF coils

A combination of inverse procedures is employed in the design of radio-frequency (RF) coils with specific examples in, but not restricted to, magnetic resonance imaging. The first inverse procedure is the use of functional methods for the optimization of coil characteristics subject to restrictions on the field behavior. Continuous current distributions are derived from analysis of the fields they are required to produce. To make use of these distributions at a desired frequency, the method of moments is applied as a second inverse procedure to a discretized version of the current distribution. The advantage of this hybrid technique is that it provides a computational algorithm for optimization of feeding, tuning, impedance matching and other aspects of RF coil design. A prototype RF coil has been built using the engineering values predicted by the theory. Experimental results including images acquired from the prototype coil are presented.     

Regularized total least squares approach for nonconvolutionallinear inverse problems

In this correspondence, a solution is developed for the regularized total least squares (RTLS) estimate in linear inverse problems where the linear operator is nonconvolutional. Our approach is based on a Rayleigh quotient (RQ) formulation of the TLS problem, and we accomplish regularization by modifying the RQ function to enforce a smooth solution. A conjugate gradient algorithm is used to minimize the modified RQ function. As an example, the proposed approach has been applied to the perturbation equation encountered in optical tomography. Simulation results show that this method provides more stable and accurate solutions than the regularized least squares and a previously reported total least squares approach, also based on the RQ formulation     

A pressure controller for estimating parameters for a nonlinear CSFmodel

Pressure feedback control of cerebrospinal fluid (CSF) infusion rate was used to estimate the parameters of a nonlinear model of the CSF system. The steady-state pressure and infusion rate were used to estimate the parameters of CSF formation and CSF absorption using the nonlinear least-squares method. The CSF compliance was then estimated using the transient portion of the pressure/infusion rate responses     

A DSP-based adaptive controller for a smooth positioning system

The implementation of a self-tuning regulator for the positioning of a direct-drive servomotor is described. The servo motor is a permanent magnet DC motor in which no speed reducer is used. The auto-tuning regulator consists of two major loops. The inner loop contains a feedback (PD or PID) regulator with additional feedforward terms. The parameters of the feedforward compensation are adjusted by the outer loop, which contains an online parameter estimator. The estimator is based on a recursive least-squares equation, and the estimated parameters are the load inertia and viscous friction. This self-tuning regulator has been simulated with PC.MATLAB, and the results demonstrate the high performance of the scheme. Experimental results obtained with a small DC motor (Electrocraft E-576) are presented, and these results show good agreement with the digital simulation results. There are two innovative aspects to this work. First, parameter estimation is used to adapt the feedforward compensation terms instead of the gains of the feedback controller, as usually is the case in conventional indirect self-tuning regulators. Secondly, the complete adaptive controller has been implemented using a single-chip digital signal processor (DSP), which results in the reduction of system hardware and cost