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Abstract Layout optimization of large-scale oil–gas gathering system is a kind of NP-hard problem in the field of system optimization. It involves a large number of network nodes, coupled optimization variables, complex network structures and hydraulic constraints, which cause the great difficulty in constructing optimization models and solution methods. In this paper, a high-dimensional mixed integer nonlinear layout optimization mathematical model involving the pipeline network structure parameters and pipeline design parameters is established, which can be applied to large-scale oil gathering system and gas gathering system universally. A modified particle swarm optimization (MPSO) algorithm with global search ability is proposed. The convergence theorem of the stochastic optimization algorithm is established based on the Poincare cycle theory. Global convergence of MPSO algorithm is proved, and the performance of MPSO algorithm is analyzed by numerical experiments. Based on dimension reduction planning and modularization thought, the grid dissection set partition method is proposed, and the theoretical foundation and complexity of the grid dissection method are discussed. In order to reduce the dimension of the layout optimization problem, the concept of the fuzzy set of adjacent position, and a novel approach for the well-station connection mode optimization are put forward. Based on the MPSO algorithm, grid dissection set partition method and solution method by fuzzy set of adjacent position, a combined optimization strategy for layout optimization model is proposed. The reliability and practicality of the proposed layout optimization model and combined optimization strategy are verified by the successful application of a real-world large-scale oil field with 661 wells. [ABSTRACT FROM AUTHOR]

We consider a large-scale minimization problem (not necessarily convex) with non-smooth separable convex penalty. Problems in this form widely arise in many modern large-scale machine learning and signal processing applications. In this paper, we present a new perspective towards the parallel Block Coordinate Descent (BCD) methods. Specifically we explicitly give a concept of so-called two-layered block variable updating loop for parallel BCD methods in modern computing environment comprised of multiple distributed computing nodes. The outer loop refers to the block variable updating assigned to distributed nodes, and the inner loop involves the updating step inside each node. Each loop allows to adopt either Jacobi or Gauss–Seidel update rule. In particular, we give detailed theoretical convergence analysis to two practical schemes: Jacobi/Gauss–Seidel and Gauss–Seidel/Jacobi that embodies two algorithms respectively. Our new perspective and behind theoretical results help devise parallel BCD algorithms in a principled fashion, which in turn lend them a flexible implementation for BCD methods suited to the parallel computing environment. The effectiveness of the algorithm framework is verified on the benchmark tasks of large-scale ℓ 1 regularized sparse logistic regression and non-negative matrix factorization. [ABSTRACT FROM AUTHOR]

With rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named four-variable SMO (FV-SMO), for credit risk classification model. At each iteration, it jointly selects four variables into the working set and an theorem is proposed to guarantee the analytical solution of sub-problem. The assessment is made on China credit dataset and two benchmark credit datasets from UCI database and CD-ROM database. Experimental results demonstrate FV-SMO is competitive in saving the computational cost and outperforms other five state-of-the-art classification methods in credit risk assessment accuracy. [ABSTRACT FROM AUTHOR]

In this paper, the sampled-data control is applied to synchronize chaotic neural networks subject to actuator saturation. By employing a time-dependent Lyapunov functional that captures the characteristic information of actual sampling pattern, we derive a local stability condition for the synchronization error systems. By this condition, we design a sampled-data controller to regionally synchronize the drive neural networks and response neural networks subject to actuator saturation. Moreover, an optimization method is given to design the desired sampled-data controller such that the set of admissible initial conditions is maximized. A numerical example is given to demonstrate the effectiveness and merits of the proposed design technique. [ABSTRACT FROM AUTHOR]

Automated parameter search methods are commonly used to optimize neuron models. A more challenging task is to fit models of neural systems since the model response is determined by both intrinsic properties of neurons and the neural wiring and architecture of the network. Neural records of cells in the visual system are often analyzed in terms of the cell’s receptive field and its temporal response. This type of data requires a finer point-by-point comparison of response traces between the simulated output and the recorded data. To address these issues, we applied a genetic algorithm optimization in conjunction with a multiobjective fitness function and a population-based error metric. Two different models of the early stages in the visual system were fitted to electrophysiological recordings and results from a modeling study, respectively. The first one is a model of cone photoreceptors and horizontal cells that reproduces adaptation to the mean light intensity in the retina. A multiobjective fitness function based on the normalized root-mean-square error (NRMSE) and a shape error descriptor captures high-frequency oscillations in the impulse response to uniform white flashes. The second one is a large-scale model of the thalamocortical system that accounts for the slow rhythms observed during sleep. An error metric of the population neural activity is used in this case. We argue that the optimization framework proposed in this paper could serve as a useful tool for parameter fitting of neuron models and large-scale models in the visual system pathway. [ABSTRACT FROM AUTHOR]

Sparse representation based nonlocal self-similarity methods have been proved to be effective for single image super-resolution. However, as the noise level increases, these methods always lead to the aggravated blurring of image small scale structures, which means the failure to preserve the edge structures. In this paper, we propose a new single image super-resolution method by combining edge difference with nonlocal self-similarity constraints. In the proposed method, firstly, we extract the image texture feature in the main direction for dictionary learning with Principal Components Analysis (PCA) to ensure the learned subdictionaries contain the image texture structures. Then, we explore the one dimensional edge difference between LR image and degraded version (e.g., blurred, noisy, and down-sampled) of the image reconstructed by the sparse representation based nonlocal self-similarity method with the leaned PCA subdictionaries and utilize it as the edge difference constraint. Thirdly, we incorporate the edge difference constraint into the sparse representation model based nonlocal self-similarity to preserve the edge structures and nonlocal self-similarity structures simultaneously. Moreover, we propose a nonlocal structure tensor optimization model to further improve image quality, which can effectively mitigate the loss of image high-frequency texture and edge information. Experiments on natural images validate that our method outperforms other state-of-the-art methods, especially for the noise image. [ABSTRACT FROM AUTHOR]

Constructing a visual appearance model is essential for visual tracking. However, relying only on the visual model during appearance changes is insufficient and may even interfere with achieving good results. Although several visual tracking algorithms emphasize motional tracking that estimates the motion state of the object center between consecutive frames, they suffer from accumulated error during runtime. As neither visual nor motional trackers are capable of performing well separately, several groups have recently proposed simultaneous visual and motional tracking algorithms. However, because tracking problems are often NP-hard, these algorithms cannot provide good solutions for the reason that they are driven top-down with low flexibility and often encounter drift problems. This paper proposes a spiral visual and motional tracking (SVMT) algorithm which, unlike existing algorithms, builds a strong tracker by cyclically combining weak trackers from both the visual and motional layers. In the spiral-like framework, an iteration model is used to search for the optimum until convergence, with the potential for achieving optimization. Three learned procedures including visual classification, motional estimation, and risk analysis are integrated into the generalized framework and implement corresponding modifications with regard to their performances. The experimental results demonstrate that SVMT performs well in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Visual tracking is highly challenged by factors such as occlusion, background clutter, an abrupt target motion, illumination variation, and changes in scale and orientation. In this paper, an integrated framework for online learning of a fused temporal appearance and spatial constraint models for robust and accurate visual target tracking is proposed. The temporal appearance model aims to encapsulate historical appearance information of the target in order to cope with variations due to illumination changes and motion dynamics. On the other hand, the spatial constraint model exploits the relationships between the target and its neighbors to handle occlusion and deal with a cluttered background. For the purposes of reducing the computational complexity of the state estimation algorithm and in order to emphasize the importance of the different basis vectors, a K -nearest Local Smooth Algorithm (KLSA) is used to describe the spatial state model. Further, a customized Accelerated Proximal Gradient (APG) method is implemented for iteratively obtaining an optimal solution using KLSA. Finally, the optimal state estimate is obtained by using weighted samples within a particle filtering framework. Experimental results on large-scale benchmark sequences show that the proposed tracker achieves favorable performance compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Facial landmark localization or facial alignment is a crucial initial step in face analysis. The paper proposes a novel discriminative regression framework called Robust Discriminative Regression (RDR) for facial landmark localization. RDR framework consists of multiple partial feature regressors and a regression tree combination strategy. The proposed method copes with the partial facial landmarks invisible problem together with the optimization problem of multiple outputs combination. The RDR framework can be applied to both raw shape regression and model-based shape parameters regression. In model-based shape parameters regression we propose a two-level regression strategy, the first level is for rigid motion parameter regression and the second one is for non-rigid deformation parameter regression. Experiments on three widely used “face in-the-wild” databases (LFPW, COFW and IBUG) show that the proposed RDR outperforms other state-of-the-art facial landmark localization strategies in raw shape regression especially under partial occlusions or large pose variations. It also shows that the two-level regression strategy within RDR framework could achieve better performance than one-level parameters regression. [ABSTRACT FROM AUTHOR]

This paper is discussed with the problem of passivity based synchronization for a class of complex dynamical networks (CDNs) consisting uncertain inner coupling matrix together with successive time-varying delays via a state feedback delayed controller. Due to occurrence of uncertainties in coupling strengths, the considered CDNs take account of an uncertain inner coupling strength which is more general than the previously existing inner coupling strengths. Specifically, the uncertainties encountering in coupling terms are characterized with the aid of interval matrix approach. Also, by introducing a simple linear transformation, the corresponding error system is formulated. Then, based on the information about control delay term, two cases are considered namely, differentiable and non-differentiable. More precisely, by constructing an appropriate Lyapunov–Krasovskii functional (LKF) containing triple integral terms in respect of Kronecker product, for both the cases, some sufficient criteria are established in terms of linear matrix inequalities (LMIs) to guarantee the robust synchronization of the addressed CDNs based on passivity property. And the established criteria optimistically reduce the L 2 gain level from the disturbance to the output vector. Subsequently, the desired state feedback gain matrix is designed in terms of the solution to a convex optimization problem. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we consider the quantized distributed optimization problem over general directed digital multi-agent networks, where the communication channels have limited data transmission rates. To solve the optimization problem, a distributed quantized subgradient algorithm is presented among agents. Based on an encoder–decoder scheme and a zoom-in technique, we can achieve not only a consensus, but also an optimal solution. In particular, we study two cases of the quantization levels of each connected directed digital communication channel. One is under the case that the quantization levels are time-varying at each time step, and the other is under the case of fixed quantization level. Two rigorous theoretical analyses are performed and the optimal solutions can be obtained asymptotically. Moreover, the upper bound of the quantization levels at each time step and the convergence rate are analytically characterized. The effectiveness of proposed algorithm is demonstrated by two illustrative examples. [ABSTRACT FROM AUTHOR]

In this paper, a defect detection model using optimized Gabor filters, which is suitable for real-time operation, is proposed to tackle the woven fabric inspection problem in fashion industry. Based on the analysis of the particular characteristics of fabric defects, the proposed model utilizes composite differential evolution (CoDE) to optimize the parameters of Gabor filters, which can achieve the optimal feature extraction of fabric defects. Together with thresholding and fusion operations, the optimal Gabor filters can successfully segment the defects from the original image background. By using optimal Gabor filters instead of a Gabor filter bank, the computational cost of the detection model can be significantly reduced. The performance of the proposed defect detection model is evaluated off-line through extensive experiments based on various types of fabric. Experimental results reveal that the proposed detection model is effective and robust, and is superior than four existing models in terms of the high detection rate and low false alarm rate. [ABSTRACT FROM AUTHOR]

In this paper, we propose a visual saliency detection algorithm which incorporates both generative and discriminative saliency models into a unified framework. First, we develop a generative model by defining image saliency as the sparse coding residual based on a learned background dictionary. Second, we introduce a discriminative model by solving an optimization problem that exploits the intrinsic relevance of similar regions for regressing region-based saliency to the smooth state. Third, a weighted sum of multi-scale region-level saliency is computed as the pixel-level saliency, which generates a more continuous and smooth result. Furthermore, object location is also utilized to suppress background noise, which acts as a vital prior for saliency detection. Experimental results show that the proposed algorithm generates more accurate saliency maps and performs favorably against the state-of-the-art saliency detection methods on three publicly available datasets. [ABSTRACT FROM AUTHOR]

Stock market forecasting is an important and challenging task. Conventional single objective optimization based adaptive prediction models reported in the literature do not satisfy many cost functions simultaneously. Very few reported materials are available on the development of multiobjective optimization based stock market prediction models. In this paper multiobjective particle swarm optimization (MOPSO) and nondominated sorting genetic algorithm version-II (NSGA-II) have been introduced to effectively train the adaptive stock market prediction models which simultaneously optimize four performance measures. The model developed is an adaptive one with nonlinearity introduced at the input end by Legendre polynomial expansion scheme. The stepwise algorithms are provided to develop the model and simulation study is carried to evaluate the performance. To arrive at the best possible solutions from these models, fuzzy logic based decision making strategy is suggested. Close examination of simulation results reveals that in terms of directional accuracy (DA) and computation time MOPSO based model is better where as in terms of average relative variance (ARV) and I-metric the NSGA-II model is superior. However, with regard to mean average percentage of error (MAPE) and Theli’s U, MOPSO is better above one month ahead prediction. But for below one month ahead prediction, the NSGA-II model is preferable. To facilitate comparison two single objective optimization based models (PSO and GA based) are also developed and the performance has been obtained through simulation study. Comparison of the results demonstrate that in terms of MAPE and DA the performance of multiobjective is better where as the single objective optimization model exhibit superior performance in terms of Theli’s U and the ARV. [ABSTRACT FROM AUTHOR]