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Support vector machine (SVM) is one of the most powerful technologies of machine learning, which has been widely concerned because of its remarkable performance. However, when dealing with the classification problem of large-scale datasets, the high complexity of SVM model leads to low efficiency and become impractical. Due to the sparsity of SVM in the sample space, this paper presents a new parallel data geometry analysis (PDGA) algorithm to reduce the training set of SVM, which helps to improve the efficiency of SVM training. The PDGA introduce Mahalanobis distance to measure the distance from each sample to its centroid. And based on this, proposes a method that can identify non support vectors and outliers at the same time to help remove redundant data. When the training set is further reduced, cosine angle distance analysis method is proposed to determine whether the samples are redundant data, ensure that the valuable data are not removed. Different from the previous data geometry analysis methods, the PDGA algorithm is implemented in parallel, which greatly saving the computational cost. Experimental results on artificial dataset and 6 real datasets show that the algorithm can adapt to different sample distributions. Which significantly reduce the training time and memory requirements without sacrificing the classification accuracy, and its performance is obviously better than the other five competitive algorithms.

Clustering is one of the most important unsupervised machine learning tasks. It is widely used to solve problems of intrusion detection, text analysis, image segmentation etc. Subspace clustering is the most important method for high-dimensional data clustering. In order to solve the problem of parallel subspace clustering for high-dimensional big data, this paper proposes a parallel subspace clustering algorithm based on spark named PSubCLUS which is inspired by SubCLU, a classical subspace clustering algorithm. While Spark is the most popular big data parallel processing platform currently, PSubCLUS uses the Resilient Distributed Datasets (RDD) provided by Spark to store data points in a distributed way. The two main performing stages of this algorithm, one-dimensional subspace clustering and iterative clustering, can be executed in parallel on each worker node of cluster. PSubCLUS also uses a repartition method based on the number of data points to achieve load balancing. Experimental results show that PSubCLUS has good parallel speedup and ideal load balancing effect, which is suitable for solving the parallel subspace clustering of high-dimensional big data.

Usually, the sensitivity matrix-based algorithms are used for image reconstruction in electrical tomography. These algorithms assume that the inverse problem is like that in the well-established medical CT and find successful applications. In the medical CT, rather than using the matrix-based algorithms, the inverse Radon transform and its derivatives dominate. However, these inverse transforms have not been introduced to electrical tomography due to a lack of the link between the parallel projections and electrical distributions. This article presents a map from electrical field lines to parallel lines in the inverse Radon transform and a novel image reconstruction algorithm by using the transform for dual-modality electrical tomography. The transform is realized through mapping the coordinates in electric field to the assumed parallel projections. The points of intersections between electrical field lines from different exciting electrodes are mapped to those of parallel lines from the related projections in a typical inverse Radon transform. Unlike the medical CT, different excitation schemes are related to distinct equivalent projections, and iterative scanning is used to apply all available projected data. Simulated and experimental data are implemented to validate the feasibility and effectiveness of the proposed algorithm, and performance comparisons are made with three algorithms, such as Calderon’s method, iterative method based on electrical field lines, and typical Landweber method. The proposed method yields good quality images in the least time and dynamic flames are monitored. Dual-modality images of material distributions are obtained to capture the ignition and blowout of the flame evolution in a Bunsen burner.

Purpose To develop a method for fast chemical exchange saturation transfer (CEST) imaging. Methods The periodically rotated overlapping parallel lines enhanced reconstruction (PROPELLER) sampling scheme was introduced to shorten the acquisition time. Deep neural network was employed to reconstruct CEST contrast images. Numerical simulation and experiments on a creatine phantom, hen egg, and in vivo tumor rat brain were performed to test the feasibility of this method. Results The results from numerical simulation and experiments show that there is no significant difference between reference images and CEST-PROPELLER reconstructed images under an acceleration factor of 8. Conclusion Although the deep neural network is trained entirely on synthesized data, it works well on reconstructing experimental data. The proof of concept study demonstrates that the combination of the PROPELLER sampling scheme and the deep neural network enables considerable acceleration of saturated image acquisition and may find applications in CEST MRI.

Structured search strategies have been well studied, but consequences of frequently made assumptions can lead to “optimum” recommendations that are far from optimum in that they ignore risks of failure. Common assumptions are many targets that have average sizes and shapes, and are randomly orientated. The effects of common assumptions are examined for parallel and grid line searches. Equations are provided for estimating probabilities of hitting elliptical targets with parallel and square grid lines along with new simplified equations when orientations are random. In a large portion of searches, the probability of missing the largest, most valuable target, dangerous ordnance, or key target is critical to the decision-maker. The risk of missing a target is frequently central to the decision. Where the target is elliptical in shape and has unknown orientation, square grid line search should be considered over line search. Square grid line search has a slightly lower expected probability of detecting an elliptical target with one or more hits than an equal-cost parallel line search, but the probability of missing regardless of orientation in square grid search is significantly lower. Where two or more hits are required, the square grid has a significantly higher probability of hitting and a slightly lower standard deviation than an equal-cost line search. For elliptical targets, the use of a square grid significantly reduces the chances of search failure.

This study proposes a set of new robust parallel hybrid metaheuristic algorithms based on artificial bee colony (ABC) and teaching learning-based optimization (TLBO) for the multi-dimensional numerical problems. The best practices of ABC and TLBO are implemented to provide robust algorithms on a distributed memory computation environment using MPI libraries. Island parallel versions of the proposed hybrid algorithm are observed to obtain much better results than those of sequential versions. Parallel pseudorandom number generators are used to provide diverse solution candidates to prevent stagnation into local optima. The performances of the proposed hybrid algorithms are compared with eight different metaheuristics algorithms of particle swarm optimization, differential evolution variants, ABC variants and evolutionary algorithm. The empirical results show that the new hybrid parallel algorithms are scalable and the best performing algorithms when compared to the state-of-the-art metaheuristics.

In the domain of computer vision, camera calibration is a key step in recovering the two-dimensional Euclidean structure. Circles are considered important image features similar to points, lines, and conics. In this paper, a novel linear calibration method is proposed using two separate same-radius (SSR) circles as the calibration pattern. We show that the distinct pair of dual circles encodes three lines, two of which are parallel to each other and perpendicular to the remaining line. When any two coplanar or parallel circles degenerate to SSR circles, a solution can be found to recover another pair of parallel lines based on the geometric properties of the SSR circles. Using the vanishing points obtained as the key helper for determining the imaged circular points and the orthogonal vanishing points, we deduce the constraints on the image of the absolute conic (IAC) and then employ it for complete camera calibration. Furthermore, a closed-form solution for the extrinsic parameters can be obtained based on the projective invariance of the conic dual to the circular points. Evaluations based on simulated and real data confirmed the effectiveness and feasibility of the proposed algorithms.

Drone swarms are employed for multiple applications nowadays, such as early detection of forest fires, disaster response missions etc., area coverage being the main hurdle these applications face. In this work, an algorithm is proposed for drone swarms that achieves full area coverage and Point of Interest (PoI) detection with no decomposition for areas with simple polygon shapes and no obstacles. It employs small pieces of information that are exchanged among the swarm drones in real-time. Parallel lines and spiral coverage paths are examined in terms of the number of slowdowns in the swarms speed to fully collect information from a detected PoI, the scanned PoIs, and the formation switches that are executed when the swarm has to alter its flying direction. Simulations are developed for a square area shape. The results showcase that the parallel coverage path is more efficient in terms of cover time and travel distance compared to the spiral. The parallel coverage requires less slowdowns for the same percentage of scanned PoIs.

In this paper, we propose a boosted 3-D PCA algorithm based on an efficient analysis method. The proposed method involves three steps that improve image detection. In the first step, the proposed method designs a new analysis method to solve the performance problem caused by data imbalance. In the second step, a parallel cross-validation structure is used to enhance the new analysis method further. We also design a modified AdaBoost algorithm to improve the detector accuracy performance of the new analysis method. In order to verify the performance of this system, we experimented with a benchmark dataset. The results show that the new analysis method is more efficient than other image detection methods.

Aiming at the problems of over stacking, warping deformation and rapid adjustment of layer thickness in electron beam additive manufacturing, the 3D printing slicing algorithm and partition scanning strategy for numerical control systems are studied. The GPU (graphics processing unit) is used to slice the 3D model, and the STL (stereolithography) file is calculated in parallel according to the normal vector and the vertex coordinates. The voxel information of the specified layer is dynamically obtained by adjusting the projection matrix to the slice height. The MS (marching squares) algorithm is used to extract the coordinate sequence of the binary image, and the ordered contour coordinates are output. In order to avoid shaking of the electron gun when the numerical control system is forming the microsegment straight line, and reduce metal overcrowding in the continuous curve C0, the NURBS (non-uniform rational b-splines) basis function is used to perform curve interpolation on the contour data. Aiming at the deformation problem of large block components in the forming process, a hexagonal partition and parallel line variable angle scanning technology is adopted, and an effective temperature and deformation control strategy is formed according to the European-distance planning scan order of each partition. The results show that the NURBS segmentation fits closer to the original polysurface cut line, and the error is reduced by 34.2% compared with the STL file slice data. As the number of triangular patches increases, the algorithm exhibits higher efficiency, STL files with 1,483,132 facets can be cut into 4488 layers in 89 s. The slicing algorithm involved in this research can be used as a general data processing algorithm for additive manufacturing technology to reduce the waiting time of the contour extraction process. Combined with the partition strategy, it can provide new ideas for the dynamic adjustment of layer thickness and deformation control in the forming process of large parts.

Tic-Tac-Toe is one of the board games. It is played by filling the columns on the board with X or O in such a way as to form parallel lines vertically, horizontally and diagonally. This study aims to design Non-Player Characters (NPC) in the tic-tac-toe game with the minimax algorithm. The Tic-tac-toe game will be designed with two game modes: easy and minimum random modes. While in minimax NPC mode will determine the best step. The game development process of the tic-tac-toe application is based on a linear sequence process model. In the analysis phase, the NPC will be designed based on the concept of minimax. Software modeling was designed using Unified Modeling Language (UML), and coded with Visual Basic programming. Our tests show that NPCs with the Minimax algorithm can work well.

Model predictive control (MPC) is one of the most widely spread advanced control schemes in industry today. In MPC, a constrained finite-time optimal control (CFTOC) problem is solved at each iteration in the control loop. The CFTOC problem can be solved using, for example, second-order methods, such as interior-point or active-set methods, where the computationally most demanding part often consists of computing the sequence of second-order search directions. Each search direction can be computed by solving a set of linear equations that corresponds to solving an unconstrained finite-time optimal control (UFTOC) problem. In this paper, different direct (noniterative) parallel algorithms for solving UFTOC problems are presented. The parallel algorithms are all based on a recursive variable elimination and solution propagation technique. Numerical evaluations of one of the parallel algorithms indicate that a significant boost in performance can be obtained, which can facilitate high-performance second-order MPC solvers. Funding Agencies|Swedish Research Council (VR); Center for Industrial Information Technology (CENIIT); Excellence Center at Linkoping-Lund on Information Technology (ELLIIT)

Given a bipartite graph, the problem we address, called the bipartite graph crossing minimization problem (BGCMP), is to find an embedding the parts of the graph along two parallel lines so that the number of edge crossings is minimized. It is assumed that each edge is drawn as a straight line segment and edges sharing an end vertex do not cross. We propose an approach for the BGCMP, which combines a simulated annealing (SA) method with a variable neighborhood search (VNS) scheme. These two algorithms are executed iteratively. At each iteration, the solution produced by SA is submitted as input to the VNS component of the approach. Our VNS algorithm uses a local search technique which is based on a fast insertion neighborhood exploration procedure. We show that the time complexity of this procedure is O(n2), where n is the order of the graph. Another fast procedure is proposed for computing the gain in the objective function value obtained by swapping positions of two vertices. We experimentally compare our algorithm (called SA-VNS) against the tabu search algorithm as well as GRASP approach from the literature. Computational results are reported on four sets of bipartite graphs. The results demonstrate the superiority of SA-VNS over the state-of-the-art methods. The source code implementing SA-VNS is made publicly available as a benchmark for future comparisons.

In this paper has been proposed a geometric model for forming problem of contour-parallel lines (equidistant lines) for a flat contour with an island, and has been obtained the problem’s analytical solution, which is relevant for computer-aided design of cutting tools processing pocket surfaces on CNC machines. The proposed geometric model is based on cyclograph mapping of space on a plane. Beyond the analytical solution the geometric model differs from the known algebraic models and their solutions for considered forming problem also by the fact that it allows obtain a more complete and evident representation on the relationship and interaction for all its geometric components at the stages of 3D computer visualization. A 3D geometric model based on a cyclograph mapping of space has been proposed for obtaining the families of equidistant lines for connected and multiply connected regions with closed contours taken as a basis for pocket surfaces modeling. An algorithm for the analytical solution of the problem related to equidistant families generation is getting from the geometric model. All stages of the analytical solution are accompanied by a figurative representation of geometric objects and their relations in the geometric model’s virtual electronic space. The proposed in this paper algorithm for the case of a doubly connected polygonal region can be used as a basis for generation of equidistant families for multiply connected polygonal regions. The presence of the analytical solution for the problem related to equidistant families generation simplifies greatly the automated calculation of the tool path and preparation of control programs for pocket surfaces manufacturing on CNC machines. Have been presented an example and algorithm providing support for working capacity of the proposed geometric model for considered forming problem.

Serial robot manipulators have their servo motors with reduction gears on the link joints. When it comes to hyper-redundant robots, this kind of joint actuation mechanism cannot be implemented since this makes hyper-redundant robots too heavy. Instead, cable driven mechanisms are preferred. However, the positioning accuracy is negatively affected by the cables. This paper addresses the positioning accuracy problem of cable driven hyper-redundant robots by employing a 2-DOF robotic arm whose modules are counter-balanced. While the actuators connected to the base actively do most of the work using cables and springs, light and compact actuators connected to the links produce precise motion. The method will result in compact, light and precise hyper-redundant robotic arms. The above-mentioned procedure governed by a control software including a 2D simulator developed is experimentally proved to be a feasible method to compensate the gravitational torque successfully.

Research on merging lines is expanding as their use grows significantly in the contexts of remanufacturing, reverse logistics and developing economies. This article is the first to study the behavior of unpaced, reliable, and unreliable merging assembly lines that are deliberately unbalanced with respect to their coefficients of variation (CV). Conducting a series of simulation runs with varying line lengths, buffer storage capacities and unbalanced CV patterns delivers intriguing results. For both reliable and unreliable lines, the best pattern for generating higher throughput is found to be a balanced configuration (equal CVs along both parallel lines), except for unreliable lines with a station buffer capacity of six. In that case, the highest throughput results from the descending configuration, i.e. concentrating the variable stations close to the beginning of both parallel lines and the steady stations towards the end of the line. Ordering from the least to most steady station also provides the best average buffer level. By exploring the experimental Pareto Frontier, this study shows the combined performance of unbalanced CV patterns for throughput and average buffer level. Study results suggest that caution should be exercised when assuming equivalent behavior from reliable and unreliable lines, or single serial lines and merging lines, since the relative throughput performance of some CV patterns changed between the different configurations.

Fastest-path queries between two points in a very large road map is an increasingly important primitive in modern transportation and navigation systems, thus very efficient computation of these paths is critical for system performance and throughput. We present a novel method to compute an effective admissible heuristic for the fastest-path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths. Our basic method - called the Hierarchical Separator Heuristic (HSH) - is based on a hierarchical set of linear separators of the map represented by a binary tree, where all the separators are parallel lines in a specific direction. A preprocessing step computes a short vector of values per road junction based on the separator tree, which is then stored with the map and used to efficiently compute the heuristic at the online query stage. We demonstrate experimentally that this method scales well to any map size, providing a high-quality heuristic, thus very efficient A* search, for fastest-path queries between points at all distances - especially small and medium range. We show how to significantly improve the basic HSH method by combining separator hierarchies in multiple directions and by partitioning the linear separators. Experimental results on real-world road maps show that HSH achieves accuracy above 95% in estimating the true travel time between two junctions at the price of storing approximately 25 values per junction.

In this paper, an improved calibration method based on vanishing constraints is proposed for calculating the extrinsic parameters of cameras. First, we come up with a improved target based on the conventional target with two groups of orthogonal parallel lines. The novel target is composed of two groups of parallel lines with a certain angle range from 80° to 90°, which can reduce the difficulty of target production and the manufacturing cost. Furthermore, in the optimization process, we design a new function with a more robust penalty factor instead of using the experienced values to get the extrinsic parameters for the binocular vision sensors. Finally, on account of using the improved target and the novel optimiazation function, the proposed method is more flexible and robust compared with Zhang’s method.

Unit hydrographs (UHs) remain widely used in hydrologic modeling to predict the stormflow that is produced at a basin outlet in response to runoff generated throughout the basin. Numerous studies have demonstrated that a basin&rsquo, s UH depends on its geomorphic properties, and several methods estimate synthetic UHs using such properties. However, previous studies have not examined whether the channel network type (such as dendritic, parallel, pinnate, rectangular, and trellis) impacts the UH shape. The objectives of this study were to determine: (1) whether those five network types exhibit distinct UHs, and (2) whether knowledge of the network type is sufficient to replace the actual flow path structure in UH estimation. To achieve these objectives, a UH framework is developed based on kinematic wave theory. The framework allows the impacts of the network structure on the UH to be isolated into two random variables, which facilitates comparisons between network types. The framework is applied to 10 basins of each type. The results show that the five network types exhibit distinct instantaneous UHs, but the type allows accurate UH determination only for pinnate basins.

No separate or additional fees are collected for access to or distribution of the work. In this paper we present a novel algorithm for adaptive mesh refinement in computational physics meshes in a distributed memory parallel setting. The proposed method is developed for nodally based parallel domain partitions where the nodes of the mesh belong to a single processor, whereas the elements can belong to multiple processors. Some of the main features of the algorithm presented in this paper are its capability of handling multiple types of elements in two and three dimensions (triangular, quadrilateral, tetrahedral, and hexahedral), the small amount of memory required per processor, and the parallel scalability up to thousands of processors. The presented algorithm is also capable of dealing with nonbalanced hierarchical refinement, where multirefinement level jumps are possible between neighbor elements. An algorithm for dealing with load rebalancing is also presented, which allows us to move the hierarchical data structure between processors so that load unbalancing is kept below an acceptable level at all times during the simulation. A particular feature of the proposed algorithm is that arbitrary renumbering algorithms can be used in the load rebalancing step, including both graph partitioning and space-filling renumbering algorithms. The presented algorithm is packed in the Fortran 2003 object oriented library \textttRefficientLib, whose interface calls which allow it to be used from any computational physics code are summarized. Finally, numerical experiments illustrating the performance and scalability of the algorithm are presented.

This paper presents a robust method for rotation estimation of planar point sets using the Angular Radon Spectrum (ARS). Given a Gaussian Mixture Model (GMM) representing the point distribution, the ARS is a continuous function derived from the Radon Transform of such distribution. The ARS characterizes the orientation of a point distribution by measuring its alignment w.r.t. a pencil of parallel lines. By exploting its translation and angular-shift invariance, the rotation angle between two point sets can be estimated through the correlation of the corresponding spectra. Beside its definition, the novel contributions of this paper include the efficient computation of the ARS and of the correlation function through their Fourier expansion, and a new algorithm for assessing the rotation between two point sets. Moreover, experiments with standard benchmark datasets assess the performance of the proposed algorithm and other state-of-the-art methods in presence of noisy and incomplete data.

A novel numerical algorithm for the analysis of single line to ground faults on long overhead transmission lines is presented in this paper. The algorithm is an extension of previous work by the authors and takes into consideration the shunt capacitance of the transmission line, making it applicable to long overhead transmission lines. The algorithm utilizes synchronized data sampling from both line terminals and a non-recursive parameter estimation method. The algorithm was developed using an accurate fault model including arcing phenomena and tower footing resistance, and a dynamic arc model was included in the fault model used to test the algorithm. The algorithm was derived in the time domain and simultaneously estimates the arc voltage amplitude, tower footing resistance, and the fault location. The proposed algorithm was thoroughly tested using simulated fault cases. In particular, the sensitivity of the algorithm to fault resistance, harmonics, untransposed line models, and parallel lines was investigated.

Restoring pixel intensities corrupted by impulse noise has a great impact on the quality of decision-based filters. In this study, the authors' focus is on intensity restoration of noisy pixels. Their assumption is that noisy pixels are already established by the noise-detection unit being considered as missing data in the image. When the interpolation methods are adopted in the noise-restoration unit of the decision-based filters for the purpose of restoring the intensities of the noisy pixels, two unexpected problems emerge - jagged edges and blurred details. These drawbacks can be ameliorated by using extra information obtained from structures in the images. Their structure-based interpolation method comprises two steps: pre-interpolation and post-interpolation. In the first step (pre-interpolation), the Sibson natural neighbour interpolation is adopted for the initial estimation of the intensities of all noisy pixels. In the second step (post-interpolation, modifying-phase), for each noisy pixel in pre-interpolated image, the intensity variations of the pixels on two adjacent parallel lines, in different directions in their corresponding local windows, are analysed. Based on the obtained structural information, the intensity of the centred noisy pixel is restored more effectively. Since the structures in the images are far more noticeable at low-density impulse noise, the proposed method works more efficiently in this case; however, a gradual improvement is achieved for high-density impulse noise.

This paper proposes k-nearest neighbour (k-NN)-based method for fault location estimation of all types of fault in parallel lines using one-terminal measurement. Discrete Fourier Transform (DFT) is used for pre-processing the signals and then the standard deviation of one cycle of pre-fault and one cycle of post-fault samples are used as inputs to k-NN algorithm. The results obtained under various fault conditions demonstrate the high accuracy of the proposed scheme to estimate the fault location. The accuracy of the k-NN-based fault location scheme is not affected by alteration in fault type including inter-circuit faults, fault location, fault inception angle, fault resistance, and pre-fault load angle.

This study presents a new fault location algorithm for multiple-circuit shared tower transmission lines with different voltages utilising synchronised measurements. The formulation of the proposed algorithm is based on overhead transmission lines theory and Taylor series expansion of distributed-parameter line model. The untransposition of the lines and the mutual coupling between the parallel lines are considered to achieve precise fault location. Kirchhoff's current law and fault location equation are used to identify the faulty section and obtain the location of all normal shunt faults and inter-circuit faults. The power system is simulated with DIgSILENT Power Factory software and all calculations are performed by MATLAB. Numerous fault cases are conducted to demonstrate the efficacy of the proposed algorithm considering different values of fault resistances, different fault locations and all fault types. In addition, the effects of synchrophasors errors, measurement errors and the soil resistivity variations are investigated. The results of simulation prove the efficacy of the proposed algorithm for all fault cases.

The present study explores the applicability and effectiveness of an optimization technique applied to electrical resistivity tomography data. The procedure is based on the Jacobian matrix, where the most sensitive measurements are selected from a comprehensive data set to enhance the least resolvable parameters of the reconstructed model. Two existing inversion programs in two and three dimensions are modified to incorporate this new approach. Both of them are selecting the optimum data from an initial comprehensive data set which is comprised of merged conventional arrays. With the two-dimensional (2-D) optimization approach, the most sensitive measurements are selected from a 2-D survey profile and then a clone of the resulting optimum profile reproduces a three-dimensional (3-D) optimum data set composed of equally spaced parallel lines. In a different approach, with the 3-D optimization technique, the optimum data are selected from a 3-D data set of equally spaced individual parallel lines. Both approaches are compared with Stummer’s optimization technique which is based on the resolution matrix. The Jacobian optimization approach has the advantage of selecting the optimum data set without the need for the solution of the inversion problem since the Jacobian matrix is calculated as part of the forward resistivity problem, thus being faster from previous published approached based on the calculation of the sensitivity matrix. Synthetic 3-D data based on the extension of previous published works for the 2-D optimization case and field data from two case studies in Greece are tested, thus verifying the validity of the present study, where fewer measurements from the initial data set (about 15–50%) are able to reconstruct a model similar with the one produced from the original comprehensive data set.

In this paper, a robust lane detection method based on vanishing point estimation is proposed. Estimating a vanishing point can be helpful in detecting lanes, because parallel lines converge on the vanishing point in a projected 2-D image. However, it is not easy to estimate the vanishing point correctly in an image with a complex background. Thus, a robust vanishing point estimation method is proposed that uses a probabilistic voting procedure based on intersection points of line segments extracted from an input image. The proposed voting function is defined with line segment strength that represents relevance of the extracted line segments. Next, candidate line segments for lanes are selected by considering geometric constraints. Finally, the host lane is detected by using the proposed score function, which is designed to remove outliers in the candidate line segments. Also, the detected host lane is refined by using inter-frame similarity that considers location consistency of the detected host lane and the estimated vanishing point in consecutive frames. Furthermore, in order to reduce computational costs in the vanishing point estimation process, a method using a lookup table is proposed. Experimental results show that the proposed method efficiently estimates the vanishing point and detects lanes in various environments.

This paper presents a novel non-iterative first-zone distance relaying algorithm for single-line-to-ground fault detection in long parallel transmission lines. The conventional distance protection scheme is based on the series impedance line model, in which the shunt capacitance and propagation effects are neglected. Such a simplification, despite being adequate for short lines, can lead to significant errors in the apparent impedance estimation for long-distance faults. The proposed algorithm is derived from a distributed parameter line model applicable to long parallel lines, in which the shunt capacitance and mutual impedance and admittance are all fully considered. Sliding faults simulations were carried out in MATLAB using symmetrical components and graph theory assuming two long parallel transmission lines. It is shown that the proposed algorithm leads to a very good agreement between the apparent and positive-sequence impedances to the faults for line lengths up to approximately 800 km, without using iterative methods. This indicates that the proposed algorithm prevents the distance relay from underreaching for long-distance faults in parallel lines, without additional computational cost.

A new fault location algorithm for two-end series-compensated double-circuit transmission lines utilizing unsynchronized two-terminal current phasors and local voltage phasors is presented in this paper. The distributed parameter line model is adopted to take into account the shunt capacitance of the lines. The mutual coupling between the parallel lines in the zero-sequence network is also considered. The boundary conditions under different fault types are used to derive the fault location formulation. The developed algorithm directly uses the local voltage phasors on the line side of series compensation (SC) and metal oxide varistor (MOV). However, when potential transformers are not installed on the line side of SC and MOVs for the local terminal, these measurements can be calculated from the local terminal bus voltage and currents by estimating the voltages across the SC and MOVs. MATLAB SimPowerSystems is used to generate cases under diverse fault conditions to evaluating accuracy. The simulat...

Many power system optimization problems constrain line flows with limits specified in terms of the magnitudes of apparent power or current flows. The set of line-flow constraints may have redundancies (i.e., the feasible space may be unchanged upon removal of a subset of the line-flow constraints), which unnecessarily complicate optimization problems. This letter describes an algorithm for identifying redundant line-flow constraints corresponding to certain parallel lines. After formulating the constraints as ellipsoids in the voltage variables, redundancies are detected from the absence of intersections between pairs of ellipsoids corresponding to parallel lines. This algorithm is demonstrated using several large test cases.

This paper describes a novel technique for generating vehicle trajectories motivated by the need for fast and robust path planning in hazardous traffic situations. We use the well–known approach of assembling trajectories from circular arcs, line segments, and clothoids to construct curves and s- bends aligned to a given target line. To this end, we use a special geometric technique based on discrete shape patterns built from elementary blocks. This enables us to derive complex maneuvers from simple design specifications. We can use it to create curves that enclose a random angle and build s-bends that are aligned with a parallel line at any distance. If these two methods are combined, curves and s-bends can be generated which are aligned to lines of arbitrary orientation. Simulations prove the correct operation of the proposed algorithm. The applicability of the approach was demonstrated in real driving tests using a collision evasion scenario.

Graphical abstract, We provide more technical details about the HLIBCov package, which is using parallel hierarchical (H-) matrices to: • Approximate large dense inhomogeneous covariance matrices with a log-linear computational cost and storage requirement. • Compute matrix-vector product, Cholesky factorization and inverse with a log-linear complexity. • Identify unknown parameters of the covariance function (variance, smoothness, and covariance length). These unknown parameters are estimated by maximizing the joint Gaussian log-likelihood function. To demonstrate the numerical performance, we identify three unknown parameters in an example with 2,000,000 locations on a PC-desktop.