Linear Iterative Method Research Articles

Optimization Techniques and Multi-Objective Analysis in Hybrid Solar-Wind Power Systems for Grid-Connected Supply

Renewable energy has become a vital source for generating electricity, mitigating greenhouse gas emissions, and eliminating the reliance on non-renewable forms of energy. This work highlights on the urgent need for clean, efficient, and resilient energy sources through the development of advanced optimization methods. The hybrid solar-wind systems are meant to maximize power generation. Complexity arises when determining the optimum design, size, functionality, materials, distribution, and mechanism of each system as they are combined to ensure system reliability and optimal outcomes. The development of the new optimization methods (such as genetic algorithms, heuristic approaches, and particle swarm techniques) was generated to overcome the encountered barriers in traditional or classical approaches; such as in iterative techniques and linear programming methods. Additionally, the multi-objective analysis has been involved in various studies where vectors, criteria, and alternatives are applied with respect to the main objectives. However, further research efforts are encouraged to innovatively optimize hybrid solar-wind systems; including the optimization of hybrid solar-wind sizing (HSWS), design, capacity, battery storage, energy distribution systems and techno-economical considerations.

Linear iterative method for closed-loop control of quasiperiodic flows

This work proposes a feedback-loop strategy to suppress intrinsic oscillations of resonating flows in the fully nonlinear regime. The frequency response of the flow is obtained from the resolvent operator about the mean flow, extending the framework initially introduced by McKeon & Sharma (J. Fluid Mech., vol. 658, 2010, pp. 336–382) to study receptivity mechanisms in turbulent flows. Using this linear time-invariant model of the nonlinear flow, modern control methods such as structured ${\mathcal{H}}_{\infty }$-synthesis can be used to design a controller. The approach is successful in damping self-sustained oscillations associated with specific eigenmodes of the mean-flow spectrum. Despite excellent performance, the linear controller is however unable to completely suppress flow oscillations, and the controlled flow is effectively attracted towards a new dynamical equilibrium. This new attractor is characterized by a different mean flow, which can in turn be used to design a second controller. The method can then be iterated on subsequent mean flows, until the coupled system eventually converges to the base flow. An intuitive parallel can be drawn with Newton’s iteration: at each step, a linearized model of the flow response to a perturbation of the input is sought, and a new linear controller is designed, aiming at further reducing the fluctuations. The method is illustrated on the well-known case of two-dimensional incompressible open-cavity flow at Reynolds number $Re=7500$, where the fully developed flow is initially quasiperiodic (2-torus state). The base flow is reached after five iterations. The present work demonstrates that nonlinear control problems may be solved without resorting to nonlinear reduced-order models. It also shows that physically relevant linear models can be systematically derived for nonlinear flows, without resorting to black-box identification from input–output data; the key ingredient being frequency-domain models based on the linearized Navier–Stokes equations about the mean flow. Applicability to amplifier flows and turbulent dynamics has, however, yet to be investigated.

Modified Finite Difference and Linear Iterative PDE Methods in Digital Image Inpainting

Modified Finite Difference and Linear Iterative PDE Methods in Digital Image Inpainting

A novel method for breast mass segmentation: from superpixel to subpixel segmentation

In this paper, an effective method is proposed for breast mass segmentation using a superpixel generation and curve evolution method. The simple linear iterative clustering method and density-based spatial clustering of applications with noise method are applied to generate superpixels in mammograms at first. Thereafter, a region of interesting (ROI) that contains the breast mass is built on the superpixel generation results. Finally, the image patch and the position of the manual labeled seed are used to build the prior knowledge for the level set method driven by the local Gaussian distribution fitting energy and evolve the curve to capture the edge of breast mass in ROI. Experimental results on mammogram data set demonstrate that the proposed method shows superior performance in contrast to some well-known methods in breast mass segmentation.

Three‐dimensional‐based landmark tracker employing a superpixels method for neuroscience, biomechanics, and biology studies

AbstractExamining locomotion has improved our basic understanding of motor control and aided in treating motor impairment. Mice and rats are premier models of human disease and increasingly the model systems of choice for basic neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics of these running rodents. Manual tracking, especially for multiple markers, becomes time‐consuming and impossible for large sample sizes. Therefore, the need for automatic segmentation of these markers has grown in recent years. Here, we address this need by presenting a method to segment the markers using the simple linear iterative clustering superpixel method. The two‐dimensional coordinates on the image plane are projected to a three‐dimensional (3D) domain using direct linear transform and a 3D Kalman filter has been used to predict the position of markers based on the speed and position of markers from the previous frames. A probabilistic function is used to find the best match from among superpixels. The method is evaluated for different difficulties for tracking of the markers, and it achieves 95% correct labeling of markers. Finally, the method was used to analyze data from an ongoing study seeking to improve recovery from spinal cord injury in rats

An Effective Subsuperpixel-Based Approach for Background Subtraction

How to achieve competitive accuracy and less computation time simultaneously for background estimation is still an intractable task. In this paper, an effective background subtraction approach for video sequences is proposed based on a subsuperpixel model. In our algorithm, the superpixels of the first frame are constructed using a simple linear iterative clustering method. After transforming the frame from a color format to gray level, the initial superpixels are divided into K smaller units, i.e., subsuperpixels, via the k -means clustering algorithm. The background model is then initialized by representing each subsuperpixel as a multidimensional feature vector. For the subsequent frames, moving objects are detected by the subsuperpixel representation and a weighting measure. In order to deal with ghost artifacts, a background model updating strategy is devised, based on the number of pixels represented by each cluster center. As each superpixel is refined via the subsuperpixel representation, the proposed method is more efficient and achieves a competitive accuracy for background subtraction. Experimental results demonstrate the effectiveness of the proposed method.

Linear Physical Programming Iteration Method of Multi-Player Multi-Objective Decision Making Supply Chain

In real situations of supply chain, the companies have different environments, but they deal with common kinds of products. So, they may conflict each other in order to continue businesses and an optimal solution with respect to one company may be unacceptable with respect to another company. In car design, several teams take charge of several kinds of performances and have common elements among them, and one of the methods of multi-player multi-objective decision making by iterations of the steps of negotiations among the teams and reviews of their requirements is used. This method is called “Suriawase process.” In Yatsuka et al. (2018), in order to reproduce the step of negotiation of Suriawase process, Linear Physical Programming (LPP) is used as one of multi-objective optimization methods with single decision maker and, by extending LPP to multi-player with Robust Optimization (RO), the mathematical model of multi-player multi-objective decision making is reproduced in the simple problem of production planning in supply chain. However, in Suriawase process, if the solution is not acceptable to some of decision makers after the step of negotiation, the steps of reviews of requirements are carried out to make an acceptable solution to all decision makers, but they are not reproduced in Yatsuka et al. (2018). Therefore, the purpose of this research is to reproduce the iteration method of LPP with the steps of reviews of requirements.

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM.

The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature “Color Coherence Vectors,” the texture feature “Gray Level Co-Occurrence Matrix,” and a special image feature “Edge Histogram Descriptor” are then exacted from these patches to generate a high-dimensional matrix called the “feature matrix.” Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79–0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings.

Development of a Computational Fluid Dynamics Model to Simulate Three-Dimensional Gap Resonance Driven by Surface Waves

This paper expounds the process of successfully establishing a computational fluid dynamics (CFD) model to accurately reproduce experimental results of three-dimensional (3D) gap resonance between two fixed ship-shaped boxes. The ship-shaped boxes with round bilges were arranged in a side-by-side configuration to represent a floating liquefied natural gas offloading scenario and were subjected to NewWave-type transient wave groups. We employ the open-source CFD package openfoam to develop the numerical model. Three-dimensional gap resonance differs from its two-dimensional (2D) counterpart in allowing spatial structure along the gap and hence multiple modes can easily be excited in the gap by waves of moderate spectral bandwidth. In terms of numerical setup and computational cost, a 3D simulation is much more challenging than a 2D simulation and requires careful selection of relevant parameters. In this respect, the mesh topology and size, domain size and boundary conditions are systematically optimized. It is shown that to accurately reproduce the experimental results in this case, the cell size must be adequate to resolve both the undisturbed incident waves and near-wall boundary layer. By using a linear iterative method, the NewWave-type transient wave group used in the experiment is accurately recreated in the numerical wave tank (NWT). Numerical results including time series of gap responses, resonant amplitudes and frequencies, and mode shapes show excellent agreement with experimental data.

Comparative analysis of application efficiency of two iterative multi objective linear programming methods (MP method and STEM method)

In this paper we consider a production plan optimization problem for a company that produces textile products. The problem is solved using two iterative methods: a new method based on the cooperative game theory (MP method) and the well-known STEM method. Their application efficiency and the solutions obtained are compared. For this purpose we use four groups of criteria: (1) the general characteristics of the method (2) the criteria from the standpoint of the decision makers, (3) the criteria from the perspective of the analysts, and (4) the ‘economic’ criteria. The analysis indicates that both methods are highly efficient for solving this kind of production plan optimization problems. However, the decision-makers preferred the MP method.

Semiautomatic right-angle building extraction from very high-resolution aerial images using graph cuts with star shape constraint and regularization

This paper proposes a method of semiautomatic right-angle building extraction from very high resolution (VHR) aerial images, based on graph cuts with star shape constraint and regularization. In the proposed method, first, the image block containing the target building is obtained by a seed line from humans. Next, the image block is preprocessed by bilateral filtering and then the simple linear iterative clustering oversegmentation method was used to segment the image into small regions. Then regions that contain the pixels belonging to the seed line are merged as the foreground area, and the background area is automatically composed by annular regions near the imagery boundary. With the obtained foreground and background areas, the graph cuts model with the star shape prior is used to acquire the building object. Finally, we defined a method of detecting the building orientation and proposed a regularizing building method based on the corners and the building orientation. The experiments performed on two VHR aerial images demonstrate the accuracy and stability of the proposed method.

Learning Tomography Assessed Using Mie Theory

In Optical diffraction tomography, the multiply scattered field is a nonlinear function of the refractive index of the object. The Rytov method is a linear approximation of the forward model, and is commonly used to reconstruct images. Recently, we introduced a reconstruction method based on the Beam Propagation Method (BPM) that takes the nonlinearity into account. We refer to this method as Learning Tomography (LT). In this paper, we carry out simulations in order to assess the performance of LT over the linear iterative method. Each algorithm has been rigorously assessed for spherical objects, with synthetic data generated using the Mie theory. By varying the RI contrast and the size of the objects, we show that the LT reconstruction is more accurate and robust than the reconstruction based on the linear model. In addition, we show that LT is able to correct distortion that is evident in Rytov approximation due to limitations in phase unwrapping. More importantly, the capacity of LT in handling multiple scattering problem are demonstrated by simulations of multiple cylinders using the Mie theory and confirmed by experimental results of two spheres.

How to avoid eutrophication in coastal seas? A new approach to derive river-specific combined nitrate and phosphate maximum concentrations

Since 1950, increase in nitrogen (N) and phosphorus (P) river loadings in the North-East Atlantic (NEA) continental seas has induced a deep change in the marine coastal ecosystems, leading to eutrophication symptoms in some areas. In order to recover a Good Ecological Status (GES) in the NEA, as required by European Water Framework Directive (WFD) and Marine Strategy Framework Directive (MSFD), reductions in N- and P-river loadings are necessary but they need to be minimal due to their economic impact on the farming industry. In the frame of the “EMoSEM” European project, we used two marine 3D ecological models (ECO-MARS3D, MIRO&CO) covering the Bay of Biscay, the English Channel and the southern North Sea to estimate the contributions of various sources (riverine, oceanic and atmospheric) to the winter nitrate and phosphate marine concentrations. The various distributed descriptors provided by the simulations allowed also to find a log-linear relationship between the 90th percentile of satellite-derived chlorophyll concentrations and the “fully bioavailable” nutrients, i.e. simulated nutrient concentrations weighted by light and stoichiometric limitation factors. Any GES threshold on the 90th percentile of marine chlorophyll concentration can then be translated in maximum admissible ‘fully bioavailable’ DIN and DIP concentrations, from which an iterative linear optimization method can compute river-specific minimal abatements of N and P loadings. The method has been applied to four major river groups, assuming either a conservative (8μgChlL−1) or a more socially acceptable (15μgChlL−1) GES chlorophyll concentration threshold. In the conservative case, maximum admissible winter concentrations for nutrients correspond to marine background values, whereas in the lenient case, they are close to values recommended by the WFD/MSFD. Both models suggest that to reach chlorophyll GES, strong reductions of DIN and DIP are required in the Eastern French and Belgian-Dutch river groups.

Optimization and Numerical Simulation of Outlet of Twin Screw Extruder

In view of the unreasonable design of non-intermeshing counter-rotating twin screw extruder die, the problem of productivity reduction was discussed. Firstly, the mathematical model of extruder productivity was established. The extruder die model was improved. Secondly, the force analysis of twin screw extruder physical model was carried out. Meanwhile, A combination of mechanical analysis and numerical simulation was adopted. The velocity field, pressure field and viscosity field were calculated by Mini-Element interpolation method, linear interpolation method and Picard iterative convergence method respectively. The influence of die model on the quantity of each field before and after improvement was analyzed. The results show that the improved model had increased the rheological parameters of the flow field, the leakage and reverse flow decreased. Through post-processing calculation, the productivity of the third dies extruder was 10% higher than before. The research results provide a theoretical basis for the design and optimization of die model of non intermeshing counter-rotating twin screw extruder.

Background Subtraction Based on Random Superpixels Under Multiple Scales for Video Analytics

Background subtraction is a fundamental problem of computer vision, which is usually the first step of video analytics to extract the interesting region. Most previously available region-based background subtraction methods ignore the similarity between the pixels, meaning that the information gained from the pixels that do not contribute, or even contribute negatively to understanding an image, is taken into account. A new background subtraction model based on random superpixel segmentation under multiple scales is proposed. A custom region segmentation area is replaced with a superpixel segmentation area that uses similarity characteristics for pixels in the superpixel area. The compactness of the pixels in the same superpixel area means that the pixels positively contribute to understanding an image compared with when using custom region pixels. Superpixel segmentation is performed using the random simple linear iterative cluster method. Taking random samples during the superpixel segmentation process produces the Matthew effect, thus improving the robustness and efficiency of the model. Multi-scale superpixel segmentation is therefore guaranteed to give more accurate results. Standard benchmark experiments using the proposed approach produced encouraging results compared with the results given by previously available algorithms.

Multimorphological Superpixel Model for Hyperspectral Image Classification

With the development of hyperspectral sensors, nowadays, we can easily acquire large amount of hyperspectral images (HSIs) with very high spatial resolution, which has led to a better identification of relatively small structures. Owing to the high spatial resolution, there are much less mixed pixels in the HSIs, and the boundaries between these categories are much clearer. However, the high spatial resolution also leads to complex and fine geometrical structures and high inner-class variability, which make the classification results very “noisy.” In this paper, we propose a multimorphological superpixel (MMSP) method to extract the spectral and spatial features and address the aforementioned problems. To reduce the difference within the same class and obtain multilevel spatial information, morphological features (multistructuring element extended morphological profile or multiattribute filter extended multi-attribute profiles) are first obtained from the original HSI. After that, simple linear iterative clustering segmentation method is performed on each morphological feature to acquire the MMSPs. Then, uniformity constraint is used to merge the MMSPs belonging to the same class which can avoid introducing the information from different classes and acquire spatial structures at object level. Subsequently, mean filtering is utilized to extract the spatial features within and among MMSPs. At last, base kernels are obtained from the spatial features and original HSI, and several multiple kernel learning methods are used to obtain the optimal kernel to incorporate into the support vector machine. Experiments conducted on three widely used real HSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model.

DECREASING RADIUS ITERATIVE METHOD FOR REGIONAL‐RESIDUAL SEPARATION OF POTENTIAL FIELD DATA

AbstractA decreasing radius iterative method in spatial domain is presented for regional‐residual separation of potential field data. A new eight‐point circumference average formula is derived by arithmetical average of potential field values at eight points along the circumference of a circle of given radius, which can be seen as a filter for calculating regional anomaly from gravity or magnetic data. The transfer function of the filter has a main lobe and multiple side lobes. When the radius becomes large, the number of the side lobes increases, and the filter characteristics become bad. The product of the transfer functions for various values of the radius from large to small is constructed, which is defined as decreasing radius iterative transfer function herein, with the largest radius as its parameter. The decreasing radius iterative transfer function is similar to the low‐pass filter, and the cut‐off wave number is inversely proportional to the largest radius. Based on the decreasing radius iterative transfer function, the decreasing radius linear iterative method in spatial domain is presented for separating regional anomaly, and the residual anomaly is obtained by subtracting the regional anomaly from the gravity or magnetic data. Furthermore, by constructing the nonlinear correction coefficient, the linear iterative formula of the decreasing radius linear iterative method is transformed into the nonlinear iterative formula, and the decreasing radius nonlinear iterative method in spatial domain is proposed. The decreasing radius nonlinear iterative method is tested with synthetic data from model and a field data set from the Nihe iron deposit in Anhui Province. The results show that the proposed method effectively suppresses false anomaly and high frequency interference, reduces anomaly distortion, and separates regional anomaly and residual anomaly from the gravity and magnetic data.

GLOBAL PATCH MATCHING

Abstract. This paper introduces a novel global patch matching method that focuses on how to remove fronto-parallel bias and obtain continuous smooth surfaces with assuming that the scenes covered by stereos are piecewise continuous. Firstly, simple linear iterative cluster method (SLIC) is used to segment the base image into a series of patches. Then, a global energy function, which consists of a data term and a smoothness term, is built on the patches. The data term is the second-order Taylor expansion of correlation coefficients, and the smoothness term is built by combing connectivity constraints and the coplanarity constraints are combined to construct the smoothness term. Finally, the global energy function can be built by combining the data term and the smoothness term. We rewrite the global energy function in a quadratic matrix function, and use least square methods to obtain the optimal solution. Experiments on Adirondack stereo and Motorcycle stereo of Middlebury benchmark show that the proposed method can remove fronto-parallel bias effectively, and produce continuous smooth surfaces.

Constraint data‐driven optimal terminal ILC of end product quality for a class of I/O constrained batch processes

AbstractIn order to deal with the I/O constraints in a practical plant, a soft limiter is often added into the control design procedure directly; however, the performance of the soft limiter based control approach will be degraded greatly due to the use of the soft constraints. This paper proposes a data‐driven optimal terminal iterative learning control (constraint‐DDOTILC) approach for the end product quality control of batch processes with I/O hard constraints. To deal with nonlinearities, a novel iterative dynamic linearization method without omitting any information of the original plant is introduced such that the derived linearized data‐driven model is completely equivalent to the original nonlinear system. By transferring all the constraints on the system output, control input, and the change rate of input signals into a linear inequality, a novel constraint‐DDOTILC is developed by minimizing an objective function under the derived linear matrix inequality constraint. The optimal learning gain of the constraint‐DDOTILC can be updated iteratively according to the input and output measurements to enhance the flexibility for modifications and expansions of the controlled plant. Both theoretical analysis and simulation results confirm the effectiveness of the proposed constraint‐DDOTILC.

Theory of wavelet-based coarse-graining hierarchies for molecular dynamics.

We present a multiresolution approach to compressing the degrees of freedom and potentials associated with molecular dynamics, such as the bond potentials. The approach suggests a systematic way to accelerate large-scale molecular simulations with more than two levels of coarse graining, particularly applications of polymeric materials. In particular, we derive explicit models for (arbitrarily large) linear (homo)polymers and iterative methods to compute large-scale wavelet decompositions from fragment solutions. This approach does not require explicit preparation of atomistic-to-coarse-grained mappings, but instead uses the theory of diffusion wavelets for graph Laplacians to develop system-specific mappings. Our methodology leads to a hierarchy of system-specific coarse-grained degrees of freedom that provides a conceptually clear and mathematically rigorous framework for modeling chemical systems at relevant model scales. The approach is capable of automatically generating as many coarse-grained model scales as necessary, that is, to go beyond the two scales in conventional coarse-grained strategies; furthermore, the wavelet-based coarse-grained models explicitly link time and length scales. Furthermore, a straightforward method for the reintroduction of omitted degrees of freedom is presented, which plays a major role in maintaining model fidelity in long-time simulations and in capturing emergent behaviors.