Algorithm Vector Research Articles

Hedge-Algebras-Based Controller for Mechanisms of Relative Manipulation

Mechanisms of relative manipulation (MRM robot) has become an interesting topic in recent years which aims to enhance the flexibility and the accuracy in mechanical manufacturing processes. However, the identification of exactly dynamical equations of MRM robot is tough and time-consuming; and the modeling error is inevitable. It is difficult to control MRM robot via vector algorithms since these methods require exactly dynamical equations of control systems. To this end, advanced controllers based on the inference mechanism of fuzzy logic are presented to overcome above problems. Unfortunately, the order relationships between the linguistic terms must be utilized by human experts when building the fuzzy-rule-base of fuzzy controller because there is no formalized liaison of the fuzzy sets with the natural linguistic term semantics. This paper proposes an alternative solution for controlling MRM robot that based on a bilinear-hedge-algebra approach. To our best knowledge, a controller based on such algebraic approach is firstly considered for application in robotics. The proposed method discovers order-based semantic relationships of terms and term-domains. Besides, the normalization, denormalization and composition operators of the fuzzy logic are eliminated. The proposed controller is evaluated by conducting a welding task using a two-component mechanism.

Analysis of Drawbacks and Constraints of Classification Algorithms for Three-Phase Voltage Dips

Voltage events are one of the most common and harmful disturbances of power electric systems. Voltage dips, swells and interruptions are included under this heading. Given the economic cost that these disturbances represent for electrical power transmission and distribution companies and the industry, it becomes imperative to detect and classify them properly. Several classification criteria and algorithms have been proposed in the literature as analysis tools to differentiate voltage events by their characteristics and, if possible, to determine their causes and consequences. Even though some of these approaches make a correct classification of the voltage events, there are certain operation conditions that are common in real electrical grids, in which the classification criteria, and their corresponding algorithms, make a wrong classification. These particular conditions, together with the lack of a fair comparison in a common scenario, have not been addressed in the specific field literature. This work explores in detail all these aspects by evaluating the symmetrical components criterion and ABC classification criterion, and rigorously analyzes three specific algorithms: the Symmetrical Components Algorithm, the Six Phases Algorithm and the Space Vector Algorithm. Drawbacks arise from both classification criteria and algorithms. The causes of the classification errors are described and discussed in detail in order to better understand the problem, and evidence the constraints of these classification methods.

A novel rotational matrix and translation vector algorithm: geometric accuracy for augmented reality in oral and maxillofacial surgeries.

Augmented reality-based surgeries have not been successfully implemented in oral and maxillofacial areas due to limitations in geometric accuracy and image registration. This paper aims to improve the accuracy and depth perception of the augmented video. The proposed system consists of a rotational matrix and translation vector algorithm to reduce the geometric error and improve the depth perception by including 2 stereo cameras and a translucent mirror in the operating room. The results on the mandible/maxilla area show that the new algorithm improves the video accuracy by 0.30-0.40mm (in terms of overlay error) and the processing rate to 10-13 frames/s compared to 7-10 frames/s in existing systems. The depth perception increased by 90-100mm. The proposed system concentrates on reducing the geometric error. Thus, this study provides an acceptable range of accuracy with a shorter operating time, which provides surgeons with a smooth surgical flow.

Reversible watermarking algorithm of vector maps based on ECC

Robustness of current watermarking schemes of vector maps is mainly dependent on algorithms of watermark embedding and extraction, while few works have been done focusing on error correction algorithms after watermark extraction. In this paper, a reversible watermarking scheme is proposed for vector maps based on error correction codes (ECC). Original copyright information is firstly expressed by a binary array (copyright watermark data), for which ECC are then generated exploiting XOR operation. Then, the copyright watermark data and its corresponding ECC are merged to compose the final watermark data. To enhance the robustness of this scheme under geometric transformation, watermarks are embedded into polar coordinates of map vertices. After watermark extraction, this scheme can not only obtain the original copyright information, but also recover the original map content accurately. Both theoretical analysis and comprehensive experimental results validate the reversibility, invisibility, capacity and robustness of the proposed watermarking scheme.

Orthogonal vector algorithm to obtain the solar vector using the single-scattering Rayleigh model.

Information obtained from a polarization pattern in the sky provides many animals like insects and birds with vital long-distance navigation cues. The solar vector can be derived from the polarization pattern using the single-scattering Rayleigh model. In this paper, an orthogonal vector algorithm, which utilizes the redundancy of the single-scattering Rayleigh model, is proposed. We use the intersection angles between the polarization vectors as the main criteria in our algorithm. The assumption that all polarization vectors can be considered coplanar is used to simplify the three-dimensional (3D) problem with respect to the polarization vectors in our simulation. The surface-normal vector of the plane, which is determined by the polarization vectors after translation, represents the solar vector. Unfortunately, the two-directionality of the polarization vectors makes the resulting solar vector ambiguous. One important result of this study is, however, that this apparent disadvantage has no effect on the complexity of the algorithm. Furthermore, two other universal least-squares algorithms were investigated and compared. A device was then constructed, which consists of five polarized-light sensors as well as a 3D attitude sensor. Both the simulation and experimental data indicate that the orthogonal vector algorithms, if used with a suitable threshold, perform equally well or better than the other two algorithms. Our experimental data reveal that if the intersection angles between the polarization vectors are close to 90°, the solar-vector angle deviations are small. The data also support the assumption of coplanarity. During the 51min experiment, the mean of the measured solar-vector angle deviations was about 0.242°, as predicted by our theoretical model.

Low Complexity Syndrome-Based Decoding Algorithm Applied to Block Turbo Codes

This paper presents a technique for reducing the decoding complexity of block turbo code with an extended Hamming code as a component code. In conventional decoding algorithms, when an input vector has a zero syndrome, complexity can be reduced by using the hard-input soft-output (HISO) algorithm. Although sufficient error correction can be achieved using hard decision decoding (HDD) of a component code, conventional methods have used the soft-input soft-output (SISO) algorithm for input vectors with a single error. However, when HDD is applied to all input vectors in which the syndrome is detected as a single error, performance loss occurs owing to the occasional presence of input vectors with triple errors. To solve this problem, we used two criteria for distinguishing between instances of single and triple errors. We maximized the applied rates of the HDD-based HISO algorithm depending on whether the criteria were satisfied. The SISO algorithm was applied when the two criteria were not met. In this case, the number of HDD usages can be reduced to half by removing duplicates or unnecessary candidate codewords. Simulation results show that the proposed algorithm can considerably reduce decoding complexity without performance loss compared with conventional algorithms.

An improvement on parametric $$\nu $$ ν -support vector algorithm for classification

One effective technique that has recently been considered for solving classification problems is parametric $$\nu $$ -support vector regression. This method obtains a concurrent learning framework for both margin determination and function approximation and leads to a convex quadratic programming problem. In this paper we introduce a new idea that converts this problem into an unconstrained convex problem. Moreover, we propose an extension of Newton’s method for solving the unconstrained convex problem. We compare the accuracy and efficiency of our method with support vector machines and parametric $$\nu $$ -support vector regression methods. Experimental results on several UCI benchmark data sets indicate the high efficiency and accuracy of this method.

Parallelized implementation of an explicit finite element method in many integrated core (MIC) architecture

Hardware accelerators are becoming increasingly important in boosting high performance computing systems. In this study, we develop a parallel explicit finite element (FE) analysis system based on a many integrated core (MIC) architecture for fast simulation of nonlinear dynamic problems of plate and shell structures. To minimize data transfer between heterogeneous architectures, parallel computation of the all explicit FE calculation is realized by developing a vectorized thread-level parallelism algorithm. The parallelism includes a novel dependency relationship link based method for efficiently solving parallel explicit shell element equations. A heterogeneous model is established to overlap data transfer and offloaded computation, and thus reduce the time required for large intermediate data storage in the actual engineering nonlinear problem simulation. Finally, a high performance nonlinear dynamic simulation system is developed. The simulations of benchmarks and engineering problems show that the parallel computing method proposed in this paper can give full play to the hardware performance of MIC architecture and effectively improve the computation efficiency of an explicit FE solution. For a bus body model containing approximately 3.8 million degrees of freedom, the computational speed is improved 17 times over CPU sequential computation, and the relative speedup grows with the increasing number of threads, the highest relative speedup exceeds 80.

Object based classification using multisensor data fusion and support vector algorithm

ABSTRACTThe present work aims at classifying the natural and man-made objects by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classified results comprise of five classes namely, road, trees, building, vegetation and soil. The methodology includes extraction of spatial and spectral features to obtain the knowledge base for various classes. Besides vegetation index and morphological building index, the features extracted also include the textural features to obtain the database on spatial values for all the different classes. After extracting these features, bounding boxes have been generated to have appreciable information on the edges of different classes. Finally, connected component analysis has been used for segmentation of classes. The training and testing samples are generated through the knowledge base of connected components which is uniquely fed to Support Vector Machine (SVM) classifier for classification purpose. The classified results indicate definite improvement in object-based classification using multisensor data.

Learning Based Route Management in Mobile Ad-Hoc Networks

Ad hoc networks are mobile wireless networks where each node is acting as a router. The existing routing protocols such as Destination sequences distance vector, Optimized list state routing protocols, Ad hoc on demand routing protocol, dynamic source routing are optimized versions of distance vector or link state routing protocols. Reinforcement Learning is new method evolved recently which is learning from interaction with an environment. Q Learning which is based on Reinforcement learning that learns from the delayed reinforcements and becomes more popular in areas of networking. Q Learning is applied to the routing algorithms where the routing tables in the distance vector algorithms are replaced by the estimation tables called as Q values. These Q values are based on the link delay. In this paper, various optimization techniques over Q routing are described in detail with their algorithms.

Modifications of the standard vector Monte Carlo estimate for characteristics analysis of scattered polarized radiation

There are two versions of weighted vector algorithms for the statistical modeling of polarized radiative transfer: a “standard” one, which is convenient for parametric analysis of results, and an “adaptive” one, which ensures finite variances of estimates. The application of the adaptive algorithm is complicated by the necessity of modeling the previously unknown transition density. An optimal version of the elimination algorithm used in this case is presented in this paper. A new combined algorithm with a finite variance and an algorithm with a mixed transition density are constructed. The comparative efficiency of the latter is numerically studied as applied to radiative transfer with a molecular scattering matrix.

A Bayesian Sparse-Plus-Low-Rank Matrix Decomposition Method for Direction-of-Arrival Tracking

The sparse Bayesian learning algorithm for multiple measurement vectors (also known as the MSBL algorithm) is an automatic method to estimate the stationary directions-of-arrival (DOAs) of multiple signals using an array of sensors. If there are time-varying DOAs along with stationary DOAs, then the DOAs of the signals can be tracked by using the sparse Bayesian learning algorithm for a single measurement vector (also known as the SSBL algorithm) by formulating the DOA estimation problem as a snapshot-by-snapshot estimation problem. When both stationary and time-varying DOAs are present, a novel approach is proposed in which we can estimate the DOAs of multiple signals by considering all snapshots together rather than estimating the DOAs snapshot-by-snapshot. This method is motivated by the fact that if the signals are sparse in the spatial domain, the stationary DOAs form the low-rank component, and the time-varying DOAs form the sparse component when all snapshots are considered together. All the parameters in the formulation are automatically estimated by using the sparse Bayesian learning principle. Simulation study using a uniform-linear-array has been carried out to compare the performance of the Bayesian sparse-plus-low-rank (BSPLR) matrix decomposition method and the SSBL method in terms of the root-mean-squared-error of the DOA estimates. We show that the BSPLR method has the potential to outperform the SSBL algorithm, which estimates the DOAs snapshot-by-snapshot. The methods are also applied on passive sonar data from the SWellEx- $\mathbf {96}$ ocean acoustic experiment to demonstrate their robustness to the modeling assumptions made.

A scalable interface-resolved simulation of particle-laden flow using the lattice Boltzmann method

Abstract We examine the scalable implementation of the lattice Boltzmann method (LBM) in the context of interface-resolved simulation of wall-bounded particle-laden flows. Three distinct aspects relevant to performance optimization of our lattice Boltzmann simulation are studied. First, we optimize the core sub-steps of LBM, the collision and the propagation (or streaming) sub-steps, by reviewing and implementing five different published algorithms to reduce memory loading and storing requirements to boost performance. For each, two different array storage formats are benchmarked to test effective cache utilization. Second, the vectorization of the multiple-relaxation-time collision model is discussed and our vectorized collision and propagation algorithm is presented. We find that careful use of Intel’s Advance Vector Extensions and appropriate array storage formats can significantly enhance performance. Third, in the presence of many finite-size, moving solid particles within the flow field, three different communication schemes are proposed and compared in order to optimize the treatment of fluid-solid interactions. These efforts together lead to a very efficient LBM simulation code for interface-resolved simulation of particle-laden flows. Overall, the optimized scalable code of particle-laden flow is a factor of 4.0-to-8.5 times faster than our previous implementation.

Effective Higher-Order Time Integration Algorithms for the Analysis of Linear Structural Dynamics

For the development of a new family of higher-order time integration algorithms for structural dynamics problems, the displacement vector is approximated over a typical time interval using the pth-degree Hermite interpolation functions in time. The residual vector is defined by substituting the approximated displacement vector into the equation of structural dynamics. The modified weighted-residual method is applied to the residual vector. The weight parameters are used to restate the integral forms of the weighted-residual statements in algebraic forms, and then, these parameters are optimized by using the single-degree-of-freedom problem and its exact solution to achieve improved accuracy and unconditional stability. As a result of the pth-degree Hermite approximation of the displacement vector, pth-order (for dissipative cases) and (p + 1)st-order (for the nondissipative case) accurate algorithms with dissipation control capabilities are obtained. Numerical examples are used to illustrate performances of the newly developed algorithms.

Field Oriented Control of Space Vector Modulated Multilevel Inverter fed PMSM Drive

In this article, field orient control (FOC) of a space vector modulated asymmetrical Multilevel inverter (MLI) fed Permanent Magnet Synchronous Motor (PMSM) is presented. The MLI considered is a reduced switch inverter which produces seven level output with six power switches and two input sources. A general low computational space vector algorithm is used to modulate the MLI. The PMSM is controlled by Field oriented control with speed regulated PI. The implementation of the FOC applied to a PMSM and validated with simulation results. Simulation of the whole system is carried out in MATLAB/simulink tool.

Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve.

The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve but in terms of the partial area under the ROC curve between two false-positive rates. In this letter, we develop support vector algorithms for directly optimizing the partial AUC between any two false-positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. In the case of the full AUC, one can readily construct and optimize convex surrogates by expressing the performance measure as a summation of pairwise terms. The partial AUC, on the other hand, does not admit such a simple decomposable structure, making it more challenging to design and optimize (tight) convex surrogates for this measure. Our approach builds on the structural SVM framework of Joachims ( 2005 ) to design convex surrogates for partial AUC and solves the resulting optimization problem using a cutting plane solver. Unlike the full AUC, where the combinatorial optimization needed in each iteration of the cutting plane solver can be decomposed and solved efficiently, the corresponding problem for the partial AUC is harder to decompose. One of our main contributions is a polynomial time algorithm for solving the combinatorial optimization problem associated with partial AUC. We also develop an approach for optimizing a tighter nonconvex hinge loss-based surrogate for the partial AUC using difference-of-convex programming. Our experiments on a variety of real-world and benchmark tasks confirm the efficacy of the proposed methods.

A Review of Color Image Vectorization Techniques

Digital image processing of gray scale or color images has become an important research and investigation tool in many areas of science and engineering. As the resolution of output devices has risen over the past 20 decades, the demand of high resolution contents has also increased. However, most images are stored in raster format. This format represents images on the computer screen using a rectangular grid of pixels, arranged in rows and columns and the images have a fixed resolution; once the image has been defined at a specific resolution, it cannot be scaled further. Therefore, the image is degraded when enlarged or scaled down which is a major problem in computer graphics, vision, and imaging. Since images with higher pixel density are enviable in many applications, there is an increasing demand to acquire high resolution raster images from low resolution. Raster images are resolution independent therefore; there is a need to convert raster images to vector. Vector graphics use geometrical primitives to express a raster image. These primitives are more compact, editable, scalable, resolution independent and also smaller in file size. Several vectorization algorithms have been developed for the last three decades. This paper presents a review of three categories of raster to vector algorithms, triangulation, mesh-based and parametric patches and algorithms developed using these techniques by various academic authors. Our findings reveal that important algorithms for converting raster images into vector have been developed. However, some of these algorithms perform better than others. Some deficiencies in the algorithms are caused by many possible outlines that can be obtained from the same bitmap image and the technique used by authors. The techniques are powerful but they also have some limitations when representing pixels. Most of the methods produce acceptable results for non-photorealistic images but some are worse or the same as the problem since they blur the image as well especially in photorealistic images. Further research should focus on how to use techniques discussed in this paper to develop a raster to vector algorithm that can convert photorealistic images to vector without compromising image quality.

A new approach for image detection based on refined Bag of Words algorithm

This paper presents an efficient image retrieval approach based on enhanced Bag of Words (BOW). By keeping eyes on low classification efficiency and accuracy of existing image retrieval system based on traditional BOW, a new classification method combined features of scale invariant feature transform (SIFT) and GIST principle (CGSF) is proposed. This method use SIFT algorithm to extract the local feature vector and GIST algorithm to extract the global feature vector for images. The feature fusion scheme is applied to combine local with global features through weight which can enhance the classification accuracy with efficiency. At the end, VLFeat linear support vector machine (SVM) classifier is used to classify the visual dictionary and Osirix medical computed tomography (CT) image dataset is selected for experimental verification. Three experiments are carried out based on factors that contribute to performance and accuracy (dictionary size and λ co-efficient). The experimental results prove that our proposed technique is much robust and accurate than traditional algorithms in image retrieval. Furthermore, our method is matched with literature in classification accuracy and the outcomes indicate the noticeable benefits in medical, internet of things and many other domains.

A multiple-input multiple-output orthogonal frequency division multiplexing underwater communication system using vector transducers

Vector sensors and transducers are compact multichannel devices that can be used for underwater communication via acoustic particle velocity channels (A. Abdi and H. Guo, “A new compact multichannel receiver for underwater wireless communication networks,” IEEE Transactions on Wireless Communications, vol. 8, pp. 3326-3329, 2009). In this paper, a multiple-input multiple-output (MIMO) underwater acoustic communication system is presented using orthogonal frequency division multiplexing (OFDM) modulation. Upon transmitting multiple independent data streams simultaneously over several channels, this MIMO system can increase the transmission rate, whereas the OFDM modulation mitigates the highly frequency selective underwater channels. Various components of the system including vector transducers and algorithms for synchronization, channel estimation, MIMO detection, channel coding, etc., are designed and implemented. Using this system, experiments are conducted to measure and study acoustic particle velocity channels in the MIMO setup. Additionally, system performance parameters such as bit error rate and spectral efficiency are measured and discussed for various conditions and configurations, to understand the performance of the developed vector MIMO-OFDM system. [The work was supported in part by the National Science Foundation (NSF), Grant IIP-1500123.]

Efficient algorithms for solving the non-linear vibrational coupled-cluster equations using full and decomposed tensors.

Vibrational coupled-cluster (VCC) theory provides an accurate method for calculating vibrational spectra and properties of small to medium-sized molecules. Obtaining these properties requires the solution of the non-linear VCC equations which can in some cases be hard to converge depending on the molecule, the basis set, and the vibrational state in question. We present and compare a range of different algorithms for solving the VCC equations ranging from a full Newton-Raphson method to approximate quasi-Newton models using an array of different convergence-acceleration schemes. The convergence properties and computational cost of the algorithms are compared for the optimization of VCC states. This includes both simple ground-state problems and difficult excited states with strong non-linearities. Furthermore, the effects of using tensor-decomposed solution vectors and residuals are investigated and discussed. The results show that for standard ground-state calculations, the conjugate residual with optimal trial vectors algorithm has the shortest time-to-solution although the full Newton-Raphson method converges in fewer macro-iterations. Using decomposed tensors does not affect the observed convergence rates in our test calculations as long as the tensors are decomposed to sufficient accuracy.