Two-stage Iterative Algorithm Research Articles

Day-ahead Economic Dispatch of Wind Integrated Power System Considering Optimal Scheduling of Reserve Capacity

This paper proposes a novel day-ahead dynamic economic dispatch model for the wind integrated power system, aiming to minimize the total cost of generation and reserve capacity of all units. The proposed model is formulated as a chance-constrained stochastic nonlinear programming (CSNLP) problem, in which the constraints of regulating characteristics of units are taken into account. A two-stage iterative algorithm, based on sequential quadratic programming (SQP) method and chance-constraint checking, is developed to solve the CSNLP efficiently. Finally, the proposed dispatch strategy is illustrated in an IEEE30 power system with six generators and a wind farm.

Semi‐blind parallel factor based receiver for joint symbol and channel estimation in amplify‐and‐forward multiple‐input multiple‐output relay systems

In this study, the authors consider a new space-time coding scheme for two-hop amplify-and-forward (AF) relay multiple-input multiple-output (MIMO) relay communication systems. This scheme, called multiple Khatri–Rao space-time coding, which combines symbol stream blocking, spatial precoding and time-domain spreading. The authors show that the received signals at the destination node can be formulated as two parallel factor (PARAFAC) models, and then a novel semi-blind receiver is derived using a two-stage iterative fitting algorithm for joint symbol and channel estimation. Under the assumption that channel state information (CSI) is not available neither at the relay nodes nor at the destination node, the proposed semi-blind receiver operates close to the zero forcing receiver with perfect CSI. Moreover, without any channel training sequence, the proposed semi-blind receiver provides the destination node with full knowledge of all channel matrices involved in the transmission. Simulation results illustrate the performance of the proposed semi-blind receiver in comparison with alternative approaches.

Cluster-enhanced sparse approximation of overlapping ultrasonic echoes.

Ultrasonic pulse-echo methods have been used extensively in non-destructive testing of layered structures. In acoustic measurements on thin layers, the resulting echoes from two successive interfaces overlap in time, making it difficult to assess the individual echo parameters. Over the last decade sparse approximation methods have been extensively used to address this issue. These methods employ a large dictionary of elementary functions (atoms) and attempt to select the smallest subset of atoms (sparsest approximation) that represent the ultrasonic signal accurately. In this paper we propose the cluster-enhanced sparse approximation (CESA) method for estimating overlapping ultrasonic echoes. CESA is specifically adapted to deal with a large number of signals acquired during an ultrasonic scan. It incorporates two principal algorithms. The first is a clustering algorithm, which divides a set of signals comprising an ultrasonic scan into groups of signals that can be approximated by the same set of atoms. The second is a two-stage iterative algorithm, which alternates between update of the atoms associated with each cluster, and re-clustering of the signals according to the updated atoms. Because CESA operates on clusters of signals, it achieves improved results in terms of approximation error and computation time compared with conventional sparse methods, which operate on each signal separately. The superior ability of CESA to approximate highly overlapping ultrasonic echoes is demonstrated through simulation and experiments on adhesively bonded structures.

Spatially Weighted Principal Component Analysis for Imaging Classification

The aim of this article is to develop a supervised dimension-reduction framework, called spatially weighted principal component analysis (SWPCA), for high-dimensional imaging classification. Two main challenges in imaging classification are the high dimensionality of the feature space and the complex spatial structure of imaging data. In SWPCA, we introduce two sets of novel weights, including global and local spatial weights, which enable a selective treatment of individual features and incorporation of the spatial structure of imaging data and class label information. We develop an efficient two-stage iterative SWPCA algorithm and its penalized version along with the associated weight determination. We use both simulation studies and real data analysis to evaluate the finite-sample performance of our SWPCA. The results show that SWPCA outperforms several competing principal component analysis (PCA) methods, such as supervised PCA (SPCA), and other competing methods, such as sparse discriminant analysis (SDA).

Least Squares Based and Two-Stage Least Squares Based Iterative Estimation Algorithms for H-FIR-MA Systems

This paper studies the identification of Hammerstein finite impulse response moving average (H-FIR-MA for short) systems. A new two-stage least squares iterative algorithm is developed to identify the parameters of the H-FIR-MA systems. The simulation cases indicate the efficiency of the proposed algorithms.

Determining shape and motion from monocular camera: A direct approach using normal flows

Determining the spatial motion of a moving camera from a video is a classical problem in computer vision. The difficulty of this problem is that the flow pattern directly observable in the video is generally not the complete flow pattern induced by the motion, but only the partial information of it, which is known as the normal flow. In this paper, we present a direct method which neither requires the establishment of feature correspondences nor the recovery of optical flow between two image frames, but we directly utilize all observable normal flow data to recover the camera motion. We propose a two-stage iterative algorithm to search the solution in the motion space in a coarse-to-fine framework. The first stage involves the use of the direction part of the normal flow. Each of these normal flow data can provide a constrained solution space to the direction of motion. The intersection of the motion solutions from all the available normal flow data can reduce the motion ambiguity to a certain extent. We then use the globality of the rotational magnitude to all image positions to constrain the motion parameters further. Once the camera motion is determined, the depth map of the imaged scene (up to an arbitrary scale) can be recovered. Experimental results on synthetic data and real images are provided to reveal the performance of the proposed method.

Real-time decentralized cooperative robust trajectory planning for multiple UCAVs air-to-ground target attack

Planning cooperative trajectories in real time is of great importance for multiple unmanned combat aerial vehicles (multi-UCAV) in performing autonomous time-critical cooperative air-to-ground target attack missions. The models of the vehicle, constraints, and a multi-criteria objective function are set up, and then the problem is formulated as a decentralized cooperative receding horizon optimal control problem. An elaborate framework for provable effective decentralized cooperative trajectory planning in two-degree-of-freedom decentralized receding horizon control is presented to provide the robustness and adaptivity for trajectory planning in dynamic and uncertain environments. Furthermore, a two-stage iterative optimization algorithm is developed to solve the problem. The novel algorithm integrates the differential flatness theory to generate trajectories, and the coordination variable-based strategy to coordinate the arrival time of multi-UCAV online, which can improve the computational efficiency and achieve the cooperative objective. The results of numerical simulations show that the proposed approach is feasible and well suited for the real-time implementation in dynamic and uncertain environment.

基于共面特征点的单目视觉位姿测量误差分析

In order to overcome the lack of depth information in monocular vision measurement,an improved two-stage iterative algorithm was proposed to optimize the posture and attitude measurement models.The characteristics of the target layout optimization were given.The relationships between the calculating accuracy with the target size,baseline length and characteristics of the target dot size were analyzed theoretically.Experimental results show that optimizing the layout of the target characteristic, the larger the length of the baseline and smaller characteristics of the target dots can improve the measurement target solver accuracy.The actual research has a certain guiding theoretical significance to improve the practical measurement system calculation accuracy.

Efficient Stopping Criterion for Hybrid Weighted Symbol-Flipping Decoding of Nonbinary LDPC Codes

A two-stage hybrid iterative decoding algorithm with an efficient stopping criterion for nonbinary low-density parity-check (LDPC) codes is proposed, which combines weighted symbol-flipping (WSF) algorithm and fast Fourier transform q-ary sum-product algorithm (FFT-QSPA). The first WSF decoding would be stopped in advance by analyzing the trend of the number of unsatisfied checks. If the first stage decoding is stopped or failed, the second powerful FFT-QSPA is activated. The proposed decoding with the efficient stopping achieves error performance as good as that of FFT-QSPA with a low complexity, and converges faster than hybrid WSF (HWSF) algorithm.

A data-driven framework for neural field modeling

This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch–Tung–Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.

Monocular vision-based iterative pose estimation algorithm from corresponding feature points

To determine the relative pose between an object and a single camera using 2D-to-3D point correspondences, a kind of iterative methods based on inverse projection ray approach is proposed. An iterative algorithm which is divided into depth recovery stage and absolute orientation stage is also proposed. In the first stage, the optimal translation vector is first computed in terms of rotation matrix via least square method, then the depths of the observed points are estimated by projecting the estimated point orthogonally to the inverse projection ray defined by the image point, and finally 3D points are reconstructed using the estimated depths from previous step. In the second stage, the optimal rotation matrix is estimated by applying Umeyama algorithm to fitting of the 3D model points and 3D estimated points. The above two stages are repeated until the result converges. The global convergence of the two-stage iterative algorithm is proven based on the global convergence theorem. Finally, a spacecraft docking application is implemented to test the effectiveness and convergence of the proposed algorithm by mathematical simulation and physical simulation.

Monocular Vision-based Two-stage Iterative Algorithm for Relative Position and Attitude Estimation of Docking Spacecraft

Visual sensors are used to measure the relative state of the chaser spacecraft to the target spacecraft during close range rendezvous phases. This article proposes a two-stage iterative algorithm based on an inverse projection ray approach to address the relative position and attitude estimation by using feature points and monocular vision. It consists of two stages: absolute orientation and depth recovery. In the first stage, Umeyama's algorithm is used to fit the three-dimensional (3D) model set and estimate the 3D point set while in the second stage, the depths of the observed feature points are estimated. This procedure is repeated until the result converges. Moreover, the effectiveness and convergence of the proposed algorithm are verified through theoretical analysis and mathematical simulation.

RSSI-based Localization without a Prior Knowledge of Channel Model Parameters

In target node localization problem, conventional methods based on received signal strength indicator (RSSI) assume a prior knowledge of a channel model and values of its parameters specific for an environment. This limits the conventional localization system to be set up quickly and effectively due to a necessary pre-measurement step to determine both the channel model and the values of its parameters. To address the limitation, a two-stage iterative algorithm which allows to localize a target node without any prior knowledge of the parameter values has been propose. Each stage of the algorithm can be implemented using different estimation methods, such as maximum likelihood (ML) and least square (LS) estimation which provides four different combinations. To determine the best combination, the location estimation performance for all four combinations is evaluated using experimental data collected in measurement campaigns on various indoor locations. The results reveal that the combination of ML estimation method implemented in both stages provides the best location estimation accuracy and the fastest convergence rate.

Hybrid Iterative Decoding for Low-Density Parity-Check Codes Based on Finite Geometries

In this letter, a two-stage hybrid iterative decoding algorithm which combines two iterative decoding algorithms is proposed to reduce the computational complexity of finite geometry low-density parity-check (FG-LDPC) codes. We introduce a fast weighted bit-flipping (WBF) decoding algorithm for the first stage decoding. If the first stage decoding fails, the decoding is continued by the powerful belief propagation (BP) algorithm. The proposed hybrid decoding algorithm greatly reduces the computational complexity while maintains the same performance compared to that of using the BP algorithm only.

Feedforward Carrier Recovery for Coherent Optical Communications

We study a carrier-synchronization scheme for coherent optical communications that uses a feedforward architecture that can be implemented in digital hardware without a phase-locked loop. We derive the equations for maximum a posteriori joint detection of the transmitted symbols and the carrier phase. The result is a multidimensional optimization problem that we approximate with a two-stage iterative algorithm: The first stage is a symbol-by-symbol soft detector of the carrier phase, and the second stage is a hard-decision phase estimator that uses prior and subsequent soft-phase decisions to obtain a minimum mean-square-error phase estimate by exploiting the temporal correlation in the phase-noise process. The received symbols are then derotated by the hard-decision phase estimates, and maximum- likelihood sequence detection of the symbols follows. As each component in the carrier-recovery unit can be separately optimized, the resulting system is highly flexible. We show that the optimum hard-decision phase estimator is a linear filter whose impulse response consists of a causal and an anticausal exponential sequence, which we can truncate and implement as an finite-impulse- response filter. We derive equations for the phase-error variance and the system bit-error ratio (BER). Our results show that 4, 8, and 16 quadrature-amplitude-modulation (QAM) transmissions at 1 dB above sensitivity for BER = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> is possible with laser beat linewidths of DeltanuT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> = 1.3 X 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , 1.3 X 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , and 1.5 x 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> when a decision-directed soft-decision phase estimator is employed.

Texture Segmentation using LBP embedded Region Competition

In this paper, we modify the region competition method to segment textures. First, local Binary pattern (LBP) histogram is adopted to capture the texture information. Then, considering the specific goal of texture segmentation, we propose new assumption about region competition and rewrite the energy function based on LBP histograms. We also develop the two-stage iterative algorithm to make our energy converge to a local minimum. Because of the fast LBP operator and nonparametric histogram model, we can simplify the step of parameter estimating, which is always the most time-consuming. Besides, LBP` s high performance for texture characterization helps to make our method more suitable for texture segmentation problem. Experiments show that the performance of our proposed method is promising, and a robust and fast segmentation of texture images is obtained.

ELLIPTICAL ANISOTROPY IN PRACTICE — A STUDY OF AIR MONITORING DATA

In the paper a method of modelling the spatial variogram structure of an anisotropic space-time process is described. The method uses linear transformation of the space in which the process is observed. A proper optimization problem is formulated. The numerical solution is based on a two-stage iterative algorithm. Application of the method for a set of air monitoring data is presented.

Parameter estimation for hysteretic systems

The differential system characterization of hysteretic system is well known. The problem of estimating the parameters of this system on the basis of input-output data, possibly noise corrupted, is considered. It turns out that the estimation problem is a non-linear optimization problem. The Gauss Newton method is used in setting up a two-stage iterative least squares algorithm. The usefulness of the algorithm is validated through its application to various simulated time histories from the hysteretic model.

The condensed nearest neighbor rule using the concept of mutual nearest neighborhood (Corresp.)

A two-stage iterative algorithm for selecting a subset of a training set of samples for use in a condensed nearest neighbor (CNN) decision rule is introduced. The proposed method uses the concept of mutual nearest neighborhood for selecting samples close to the decision line. The efficacy of the algorithm is brought out by means of an example.