Non-zero Error Research Articles

Uncertainty in Measured Raindrop Size Distributions from Four Types of Collocated Instruments

Four types (2D-video disdrometer: 2DVD; precipitation occurrence sensor system: POSS; micro-rain radar: MRR; and Joss–Waldvogel disdrometer: JWD) of sixteen instruments were collocated within a square area of 400 m2 from 16 April to 8 May 2008 for intercomparison of drop size distribution (DSD) of rain. This unique dataset was used to study the inherent measurement uncertainty due to the diversity of the measuring principles and sampling sizes of the four types of instruments. The DSD intercomparison shows generally good agreement among them, except that the POSS and MRR had higher concentrations of small raindrops (<1.0 mm) and offered a better chance to observe big raindrops (>5.2 mm). The measurement uncertainty ( σ ) was obtained quantitatively after considering the zero or non-zero measurement error covariance between two instruments of the same type. The results indicate the measurement uncertainties were found to be neither independent nor identical among the same type of instruments. The MRR is relatively accurate (lower σ ) due to large sampling volumes and accurate measurement of the Doppler power spectrum. The JWD is the least accurate due to the small sampling volumes. The σ decreases rapidly with increasing time-averaging window. The 2DVD shows the best accuracy of R in longer averaging time, but this is not true for Z due to the small sampling volume. The MRR outperformed other instruments for Z for entire averaging time due to its measuring principle.

$\mathcal {L}_1$ Adaptive Output Feedback for Nonsquare Systems With Arbitrary Relative Degree

This article considers adaptive output-feedback control problem for nonsquare multi-input-multi-output systems (MIMO) with arbitrary relative degree. The proposed controller, based on the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> adaptive control architecture, is designed using the right interactor matrix and a suitably defined projection matrix. A state-output predictor, a low-pass filter, and adaptive laws are introduced that achieve output tracking of a desired reference signal. It is shown that the proposed control strategy guarantees closed-loop stability with arbitrarily small steady-state errors. The transient performance in the presence of nonzero initialization errors is quantified in terms of decreasing functions. Rigorous mathematical analysis and illustrative examples are provided to validate the theoretical claims.

A sliding mode controller design for a missile autopilot system

A description is given of an application of sliding mode control (SMC) for stabilizing the static and dynamic characteristics of an anti-aircraft missile. The solution provides effective separation of the control process from the dynamics of the missile airframe. In the equivalent part of the stabilization system a linear-quadratic regulator (LQR) is considered, and an analytical method of selecting the weighting elements of the amplification matrix is proposed. This eliminates the need for a cyclic solution of the Riccati equation; however, due to the simplifications adopted in the model, there is the undesired consequence of a non-zero steady-state error. Therefore, a nonlinear switching component is introduced into the control signal, to reduce the error to zero. In simulation tests, the proposed solution was evaluated against selected quantity indices. The paper ends with conclusions and observations.

Adaptive Iterative Learning Control for Tank Gun Servo Systems With Input Deadzone

In this paper, an adaptive iterative learning control scheme is proposed to solve the trajectory-tracking problem for tank gun servo systems with input deadzone and arbitrary initial states. A time-varying boundary layer is constructed to deal with the nonzero initial error during the iterative learning controller design. Neural network control and robust control are jointly used to compensate uncertainties and deadzone nonlinearity. The ideal weight of neural network and the upper bound of noncontinuous uncertainties are estimated by using difference learning method. As the iteration number increases, the filtering error can converge to the time-varying boundary layer. All signal are guaranteed to be bounded. A simulation example is presented to verify the effectiveness of the proposed scheme.

Neural Network-Based Error-Tracking Iterative Learning Control for Tank Gun Control Systems With Arbitrary Initial States

In this paper, a novel neural network-based error-track iterative learning control scheme is proposed to tackle trajectory tracking problem for tank gun control systems. Firstly, the system modeling for tank gun control systems is introduced as a preparation of controller design. Then, the reference error trajectory is constructed to deal with the nonzero initial error of iterative learning control. The adaptive iterative learning controller for tank gun control systems is designed by using Lyapunov approach. Adaptive learning neural network is adopted to approximate nonlinear uncertainties, with robust control technique being used compensate the approximation error and external disturbances. As the iteration number increases, the system error can follow the desired error trajectory over the whole time interval, which makes the system state accurately track the reference error trajectory during the predetermined part time interval. Numerical simulations demonstrate the effectiveness of the proposed iterative learning control scheme.

A Method to Eliminate Steady-State Error of Model Predictive Control in Power Electronics

Finite control set model predictive control (FCS-MPC) allows dealing with nonlinearities of the system and obtains a fast dynamic response. Therefore, FCS-MPC is a good alternative to govern complex power converters or when fast transient operation is required. The main drawback of FCS-MPC is the performance obtained during steady state, one of the problems is the presence of nonzero error in steady state. This paper presents a new way to implement FCS-MPC to correct the mismatch in the model parameters and eliminate the error in steady state. This is achieved through the incorporation of the past error in the action of the control, adding an additional term in the cost function with a variable weighting factor.

Experimental test of error-tradeoff uncertainty relation using a continuous-variable entangled state

Heisenberg’s original uncertainty relation is related to measurement effect, which is different from the preparation uncertainty relation. However, it has been shown that Heisenberg’s error disturbance uncertainty relation is not valid in some cases. We experimentally test the error-tradeoff uncertainty relation by using a continuous-variable Gaussian Einstein–Podolsky–Rosen (EPR)-entangled state. Based on the quantum correlation between the two entangled optical beams, the errors on amplitude and phase quadratures of one EPR optical beam coming from joint measurement are estimated, respectively, which are used to verify the error–tradeoff relation. Especially, the error–tradeoff relation for error-free measurement of one observable is verified in our experiment. We also verify the error–tradeoff relations for nonzero errors and mixed state by introducing loss on one EPR beam. Our experimental results demonstrate that Heisenberg’s error–tradeoff relation is violated in some cases for a continuous-variable system, while the Ozawa’s and Branciard’s relations are valid.

Generalized framework for PIV-based pressure gradient error field determination and correction

A framework leveraging the governing equations of incompressible flow for the reconstruction and correction of pressure gradient estimation errors from experimental data is extended to incorporate non-zero errors on domain boundaries and Lagrangian pseudo-tracking methods for material acceleration estimation. A second-order system derived from the first-order divergence-curl system governing the pressure gradient error field facilitates handling a variety of error boundary conditions, and the solution can be split into independent Poisson equations, making it straightforward to implement. For known boundary errors, a precise determination of the pressure gradient field is possible, up to the limitations of the numerical method. In practice, the error equations cannot be solved exactly; however, a number of terms may be computed exactly and approximations may be applied on the remaining terms. For the selected test case of a cylinder wake flow in turbulent shedding regime, the analysis of simulated three-dimensional, three-component velocity measurements demonstrates that the errors in pressure estimates can be reduced by up to 50% using a basic finite difference implementation.

A 16-nm Always-On DNN Processor With Adaptive Clocking and Multi-Cycle Banked SRAMs

Always-on subsystems in mobile/Internet of Things (IoT) SoCs process a variety of real-time sensor data deep neural network (DNN) classification workloads in a heavily constrained energy budget. This can be achieved with robust, low-voltage circuits, and specialized hardware accelerators. We present a 16-nm always-on DNN processor, which consists primarily of a microcontroller and a DNN accelerator with on-chip SRAM for the model weights. The design operates robustly from 0.4 to 1-V, with calibration-free automatic voltage/frequency tuning provided by tracking small non-zero razor timing error rates. A novel timing error-driven synchronization-free adaptive clocking scheme significantly reduces the adaptation latency to provide resilience to fast on-chip supply noise and reduce margins. To accommodate the tight energy constraints of always-on IoT workloads, we implement a multi-cycle SRAM read scheme that allows the memory voltage to scale at iso-throughput, improving energy efficiency across the entire operating range. The wide operating range allows for high performance at 1.36 GHz, low-power consumption downs to 750 μW, and stateof-the-art raw efficiency at 16-bit precision of 750 GOPS/W dense or 1.81 TOPS/W sparse.

A Low Loss and Area Efficient RC Passive Poly Phase Filter for Monolithic GHz Vector-Sum Circuits

In this brief, type-II RC poly phase filters (PPFs) are investigated as the I/Q network of a vector-sum phase shifter. In a system analysis, it has been shown that it is possible to improve the phase shifter performance by simultaneously accepting some gain error and decreasing phase error. Moreover, the degree of freedom resulting from the non-zero gain error allows to reduce the loss of poly-phase circuits. Accordingly, a new structure of PPF has been proposed to improve the multi-stage PPF circuits for use in the vector-sum phase shifter, which reduced the loss and area without significantly changing the phase shift performance. Finally, a two-stage PPF has been designed that has a 30% smaller area than the conventional type-II PPF, while the loss has decreased more than 0.6 dB.

Conservative Sensor Error Modeling Using a Modified Paired Overbound Method and its Application in Satellite-Based Augmentation Systems.

Conservative sensor error modeling is of great significance in the field of safety-of-life. At present, the overbound method has been widely used in areas such as satellite-based augmentation systems (SBASs) and ground-based augmentation systems (GBASs) that provide integrity service. It can effectively solve the difficulties of non-Gaussian and non-zero mean error modeling and confidence interval estimation of user position error. However, there is still a problem in that the model is too conservative and leads to the lack of availability. In order to further improve the availability of SBASs, an improved paired overbound method is proposed in this paper. Compared with the traditional method, the improved algorithm no longer requires the overbound function to conform to the characteristics of the probability distribution function, so that under the premise of ensuring the integrity of the system, the real error characteristics can be more accurately modeled and measured. The experimental results show that the modified paired overbound method can improve the availability of the system with a probability of about 99%. In view of the fact that conservative error modeling is more sensitive to large deviations, this paper analyzes the robustness of the improved algorithm in the case of abnormal data loss. The maximum deviation under a certain integrity risk is used to illustrate the effectiveness of the improved paired overbound method compared with the original method.

Occlusion-Aware Depth Map Coding Optimization Using Allowable Depth Map Distortions

In depth map coding, rate-distortion optimization for those pixels that will cause occlusion in view synthesis is a rather challenging task, since the synthesis distortion estimation is complicated by the warping competition and the occlusion order can be easily changed by the adopted optimization strategy. In this paper, an efficient depth map coding approach using allowable depth map distortions is proposed for occlusion-inducing pixels. First, we derive the range of allowable depth level change for both the zero disparity error case and non-zero disparity error case with theoretic and geometrical proofs. Then, we formulate the problem of optimally selecting the depth distortion within allowable depth distortion range with the objective to minimize the overall synthesis distortion involved in the occlusion. The unicity and occlusion order invariance properties of allowable depth distortion range is demonstrated. Finally, we propose a dynamic programming based algorithm to locate the optimal depth distortion for each pixel. Simulation results illustrate the performance improvement of the proposed algorithm over the other state-of-the-art depth map coding optimization schemes.

Efficient Direct-Model Predictive Control With Discrete-Time Integral Action for PMSGs

This paper presents an efficient direct-model predictive control (EDMPC) technique for surface-mounted permanent-magnet synchronous generators (PMSGs) in variable-speed wind energy conversion systems. The proposed control technique is based on directly computing the reference voltage vector (VV) from the demanded currents using a deadbeat (DB) function. Then, a discrete-time integral action (DTIA) is added to this DB function to enhance the robustness of the proposed EDMPC scheme against variations of the machine parameters and to achieve a good steady-state response. The proposed DTIA is simple and easy to implement. Finally, according to the location of this reference VV, two evaluations of the quality function are only required. Accordingly, the proposed EDMPC technique with DTIA overcomes the following drawbacks of the conventional direct-model predictive control (DMPC): high calculation burden, sensitivity to parameters mismatches, and non-zero steady-state error. The performance of the proposed EDMPC technique with DTIA has been experimentally investigated and compared with that of the EDMPC with time delay control approach and with the performance of the convention DMPC using a $\text{14.5}\,\text{kW}$ PMSG.

A Stationary-Frame Current Vector Control Strategy for Single-Phase PWM Rectifier

Traditional current control methods for a single-phase rectifier in an electric traction drive system suffer from either long response time or nonzero steady-state error. A novel current vector control strategy in stationary-frame is therefore proposed, in this paper, to improve the control performance of a rectifier. The mathematic model of the single-phase two-level pulse rectifier was first analyzed by complex space vector. Then, the principle of the proposed current control method was described. Based on this, the implementation and parameter tuning method of the current control algorithm based on a space complex vector in a two-phase stationary coordinate system are presented. Moreover, two kinds of general current control algorithms are analyzed and compared with the proposed algorithm to demonstrate the advantages of the proposed algorithm. The proposed control scheme absorbs advantages of the conventional control schemes, including fast response, zero steady-state error, and active and reactive power independent control, but overcomes their shortcomings. Finally, the theoretical analysis is verified by means of simulation as well as a hardware-in-loop experimental test on an experimental laboratory setup and some comparisons with conventional current control algorithms prove the advantages of the utilized method.

Modeling Error Estimation and Response Prediction of a 10-Story Building Model Through a Hierarchical Bayesian Model Updating Framework

In this paper a hierarchical Bayesian model updating approach is proposed for calibration of model parameters, estimation of modeling error, and response prediction of dynamic structural systems. The approach is especially suitable for civil structural systems where modeling errors are usually significant. The proposed framework is demonstrated through a numerical case study, namely a 10-story building model. The ‘measured data’ include the numerically simulated modal parameters of a frame model which represents the true structure. A simplified shear building model with significant modeling errors is then considered for model updating with stiffness of different structural components (substructures) chosen as updating parameters. In the proposed hierarchical Bayesian framework, updating parameters are assumed to follow a known distribution model (normal distribution is considered here) and are characterized by the distribution parameters (mean vector and covariance matrix). The error function, which is defined as the misfit between model-predicted and identified modal parameters, is also assumed to follow a normal distribution with unknown parameters. The hierarchical Bayesian approach is applied to estimate the stiffness parameter distributions with mean and covariance matrix referred to as hyperparameters, as well as the modeling error which is quantified by the mean and covariance of error function. Joint posterior probability distribution of all updating parameters is derived from the likelihood function and the prior distributions. A Metropolis-Hastings within Gibbs sampler is implemented to evaluate the joint posterior distribution numerically. Two cases of model updating are studied with Case 1 assuming a zero mean for the error function, and Case 2 considering a non-zero error mean. The response time history of the building to a ground motion is predicted using the calibrated shear building model for both cases and compared with the exact response (simulated). Good agreements between predictions and measurements are observed for both cases with better accuracy in Case 2. This verifies the proposed hierarchical Bayesian approach for model calibration and response prediction and underlines the importance of considering and propagating the uncertainties of structural parameters and more importantly modeling errors.

The Role of Factor Strength and Pricing Errors for Estimation and Inference in Asset Pricing Models

In this paper we are concerned with the role of factor strength and pricing errors in asset pricing models, and their implications for identification and estimation of risk premia. We establish an explicit relationship between the pricing errors and the presence of weak factors that are correlated with stochastic discount factor. We introduce a measure of factor strength, and distinguish between observed factors and unobserved factors. We show that unobserved factors matter for pricing if they are correlated with the discount factor, and relate the strength of the weak factors to the strength (pervasiveness) of non-zero pricing errors. We then show, that even when the factor loadings are known, the risk premia of a factor can be consistently estimated only if it is strong and if the pricing errors are weak. Similar results hold when factor loadings are estimated, irrespective of whether individual returns or portfolio returns are used. We derive distributional results for two pass estimators of risk premia, allowing for non-zero pricing errors. We show that for inference on risk premia the pricing errors must be sufficiently weak. We consider both when n (the number of securities) is large and T (the number of time periods) is short, and the case of large n and T. Large n is required for consistent estimation of risk premia, whereas the choice of short T is intended to reduce the possibility of time variations in the factor loadings. We provide monthly rolling estimates of the factor strengths for the three Fama-French factors over the period 1989-2018.

A reversible database watermarking method with low distortion

In this paper, a low distortion reversible database watermarking method based on histogram gap is proposed in view of the large gap in histogram of database integer data. By using the method, the tolerance of the attribute column containing all integer data is firstly calculated and the prediction error is obtained according to the tolerance. Then according to the watermark bits to be embedded, the database tuples will be randomly grouped and the histogram can be constructed by using the prediction error. Finally, the histogram correction rule is used to find the histogram peak bin, the number of consecutive non-zero prediction errors on the left and right sides of the peak is obtained, and the histogram shift is performed on the side with a smaller number of non-zero prediction errors, and then the watermark embedding will be realized. The results of the experiments based on the published dataset of FCTD (Forest Cover Type Dataset) show that compared with the existing GAHSW which also considers distortion, the proposed method significantly reduces the number of histogram column shift while embedding the watermarks, greatly reduces the changes to the carrier data, and effectively reduces the database's data distortion caused by watermark embedding.

Moment-Based Approach for Statistical and Simulative Analysis of Turbulent Atmospheric Channels in FSO Communication

The free space optical (FSO) or wireless optical communication networks involve atmosphere as the channel, which at times can be turbulent in nature, thus degrading the system performance. These turbulent channels can be analyzed by introducing various channel models and modulation techniques. Various researchers have analyzed the performance by presenting different approaches for each type of channel model. In this paper, we present a unified approach for the performance analysis of the Log-normal, Nakagami-n (Rician), and Rayleigh statistical models by deriving the exact closed-form expressions for average bit error probability and the outage probability of each model. It is achieved by using the alternative form of Q-function and Gauss-Hermite Quadrature approximation to the moment-generating function of the output signal-to-noise ratio (SNR). Furthermore, definite expressions for each type of modulation technique are also derived to describe the channel fading in an FSO communication system. The system of interest includes the M-ary phase shift keying and M-ary quadrature amplitude modulation techniques. Moreover, the average output SNR, the average received irradiance, and the amount of fading (fading strength) are also expressed in simple closed forms for above-mentioned turbulent channels. In addition to this, the performance of the FSO system in the presence of atmospheric turbulence and pointing errors is also investigated. The statistical model is examined for zero and nonzero boresight point errors modeled by Rayleigh and Rician distributions in a Log-normal fading channel. The composite Log-normal PDFs along with asymptotic error rate and outage probability are also presented for such system. The effect of Tikhonov distributed phase noise arise due to BPSK modulation on BER performance is evaluated on three turbulent channels by deriving its closed-form expression using a moment-based approach. The mathematical analysis is verified by comparing the numerical results with Monte-Carlo simulated values and graphs.

Neuroadaptive quantized PID sliding‐mode control for heterogeneous vehicular platoon with unknown actuator deadzone

SummaryThis paper focuses on the problem of neuroadaptive quantized control for heterogeneous vehicular platoon when the follower vehicles suffer from external disturbances, mismatch input quantization, and unknown actuator deadzone. The PID‐based sliding‐mode (PIDSM) control technique is used due to its superior capability to reduce spacing errors and to eliminate the steady‐state spacing errors. Then, a neuroadaptive quantized PIDSM control scheme with minimal learning parameters is designed not only to guarantee the string stability of the whole vehicular platoon and ultimate uniform boundedness of all adaptive law signals but also to attenuate the negative effects caused by external disturbance, mismatch input quantization, and unknown actuator deadzone. Furthermore, optimizing the interspacing between consecutive vehicles is very important to reduce traffic congestion on highways, and a new modified constant time headway policy is proposed to not only increase traffic density but also address the negative effect of nonzero initial spacing, velocity, and acceleration errors. Compared with most existing methods, the proposed method does not linearize the system model and neither requires precise knowledge of the system model. Finally, the effectiveness and advantage of the proposed method are demonstrated by comparative simulation studies.

Starting Point Independent Iterative Learning Algorithm on LTI System with Non-zero Initial Errors

In this paper, we propose a starting point independent iterative learning algorithm. This algorithm does not need to train iterative learning control (ILC) algorithm each time when initial condition changes. If the plant is sufficiently stable, the output trajectory converges to the desired trajectory exponentially fast. Regardless of learning rule type, this algorithm can be used and has a same convergence rate. So in case of the multiple starting and goal points application problems, it can be used with significant reduced computational cost. In fact, if there are M starting points and N goal points, the conventional ILC algorithm needs MN training for generating whole trajectories, however proposed algorithm needs only N training. In order to expand our algorithm to the multi-dimensional point-to-point (PTP) problem, we propose a learning rule for PTP problem, then shows its convergence.