path planning algorithms based on physical models have been in development to allow navigating an environment in a way that mimics nature. Contrary to combinatorial and sampling-based algorithms, which represent the f...
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path planning algorithms based on physical models have been in development to allow navigating an environment in a way that mimics nature. Contrary to combinatorial and sampling-based algorithms, which represent the field by a set of nodes then use searching techniques to find a path, the artificial potential field method solves this problem by attracting the robot to the target and repelling it from the obstacles While the Stream Field Navigation (SFN) algorithm solves the problem by simulating a fluid field. This research proposes a dynamic path search and selection schema to allow the SFN algorithm to deal with moving obstacles. This research shows how the algorithm deals with a kinematic (variable) environment using the discretized path calculations. The dynamic search and selection algorithm is also capable of updating the robot's path when the goal is relocated. The developed approach is shown to greatly reduce the computational time required to obtain a path compared to re -simulating the field when a change is introduced to the environment.
It is a problem to build Inter Satellite Links so that Region Navigation System has optimal performance. Therefore, the article puts forward an algorithm, which synthesizes the time of satellites accessed and the dist...
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ISBN:
(纸本)9783037856932
It is a problem to build Inter Satellite Links so that Region Navigation System has optimal performance. Therefore, the article puts forward an algorithm, which synthesizes the time of satellites accessed and the distance of satellites. And then it carries out a simulation in navigation system to certify the superiority of the algorithm. The experimental result shows that the ISLs can effectively improve the performance of navigation system. And comparing with the traditional Minimum Distance algorithm, the proposed algorithm has the same effect on improving the performance of navigation system and needs fewer times of changing links.
This paper describes a solution to automate the 3D model of an arbitrary object using an embedded UAV system through its movement and an image sensor to take pictures of an arbitrary static target, which is located wi...
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ISBN:
(数字)9781665497671
ISBN:
(纸本)9781665497671
This paper describes a solution to automate the 3D model of an arbitrary object using an embedded UAV system through its movement and an image sensor to take pictures of an arbitrary static target, which is located with a trajectory algorithm that allows the quadcopter to travel, photograph and come back. Then, the images are sent to a server, stored in a computer to run the photogrammetry process, and finally create the three-dimensional object without human intervention. It's important to mention that the algorithm parameters are set through an application that obtains the object's height, coordinates, size, and other parameters. Also, this kind of UAV device costs around 200 dollars, and an application could control it, or if it's open-source, It could be configured by some programming language. This UAV system can be beneficial in different disciplinary fields, such as agriculture, construction, surveying, inventory control, surveillance, geology, biology, and other applications.
UHV power transmission lines are playing very important role in the UHV power system, and the design of transmission line selection is the leading part of the power transmission line project, which is playing a most i...
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ISBN:
(纸本)9781728157122
UHV power transmission lines are playing very important role in the UHV power system, and the design of transmission line selection is the leading part of the power transmission line project, which is playing a most important role. The rationality of transmission line design impacts cost, difficulty and security of the whole UHV project construction, also impacts overall benefit of power transmission project. Furthermore, it also impacts the whole planning of grid system and country's security and stability. Along with the development of data gathering technology and hardware of computer, 3-D Aided UHV transmission Line Selection Design Platform (based on 3-D topographic data and 3-D device modeling data,) is already available from both theoretical and technology prospect. And its most key point is the algorithm of 3-D visible Aided transmission Line Selection Design Platform, this paper investigates the algorithm related researches, also includes the technical selection of the algorithm.
Based on the rich experience of teaching practice and theoretical analysis, it scientifically explained the cognitive semantic theory in vocabulary teaching methods, and apply them to the practice, in order to test th...
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ISBN:
(纸本)9781912407408
Based on the rich experience of teaching practice and theoretical analysis, it scientifically explained the cognitive semantic theory in vocabulary teaching methods, and apply them to the practice, in order to test the effectiveness of new vocabulary teaching method. This paper has conducted the empirical research combined vocabulary teaching empirical analysis with questionnaire survey. The results show that, English vocabulary teaching in higher vocational colleges under the guidance of the cognitive semantics theory helps to strengthen the students' vocabulary cognitive thinking, to construct complete and systematic vocabulary semantic network, greatly stimulate students' vocabulary learning interest and initiative, improve the students' vocabulary learning efficiency, to improve students' vocabulary level in a maximum degree.
This paper reports on our study of a regularization path algorithm for a regression spline estimator based on B-splines and total variation penalty. The total variation-penalized regression spline estimation method en...
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This paper reports on our study of a regularization path algorithm for a regression spline estimator based on B-splines and total variation penalty. The total variation-penalized regression spline estimation method enjoys a spatial adaptation property, since the penalty function enables data-adaptive knot selection. Although the theoretical properties of the related methods have been well established in the literature, the existing implementation algorithms are usually based on techniques with a high computational cost. In particular, the selection of the optimal complexity parameter, required to strike a balance in bias-variance tradeoff, is based on the computationally intensive grid search method. In this study, we propose an efficient path algorithm by formulating an equivalent l1 penalized optimization problem. The least angle regression algorithm is applied to the reformulated problem to obtain an entire path of the jump size of B-spline derivatives that represents the knot removal condition. Numerical studies based on real and simulated data are provided to illustrate the advantages of the proposed algorithm. The simulation result shows that the proposed algorithm is significantly faster than an existing algorithm. The proposed algorithm has an additional benefit of obtaining an entire regularization path, which can provide additional statistical insights into the given problem.
We consider the problem of approximating a sequence of data points with a "nearly-isotonic," or nearly-monotone function. This is formulated as a convex optimization problem that yields a family of solutions...
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We consider the problem of approximating a sequence of data points with a "nearly-isotonic," or nearly-monotone function. This is formulated as a convex optimization problem that yields a family of solutions, with one extreme member being the standard isotonic regression fit. We devise a simple algorithm to solve for the path of solutions, which can be viewed as a modified version of the well-known pool adjacent violators algorithm, and computes the entire path in O(n log n) operations (n being the number of data points). In practice, the intermediate fits can be used to examine the assumption of monotonicity. Nearly-isotonic regression admits a nice property in terms of its degrees of freedom: at any point along the path, the number of joined pieces in the solution is an unbiased estimate of its degrees of freedom. We also extend the ideas to provide "nearly-convex" approximations.
Significantly more immigrant students are served by US public schools. To provide more efficient support to those immigrant students, we need to investigate the current situation of those immigrant students and gradua...
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ISBN:
(纸本)9781450388368
Significantly more immigrant students are served by US public schools. To provide more efficient support to those immigrant students, we need to investigate the current situation of those immigrant students and graduation rate is used as a reference of their situation. In this article, fused lasso model is used as a shrinkage method for categorical explanatory variables. Data from graduation rate of New York state are considered. The result is given that students with better English skills and from Asian/Pacific Islander are more likely to have a higher graduation rate, which may suggest language support can be an efficient way to help those immigrant students.
We study the problem of high-dimensional regression when there may be interacting variables. Approaches using sparsity-inducing penalty functions such as the Lasso can be useful for producing interpretable models. How...
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We study the problem of high-dimensional regression when there may be interacting variables. Approaches using sparsity-inducing penalty functions such as the Lasso can be useful for producing interpretable models. However, when the number variables runs into the thousands, and so even two-way interactions number in the millions, these methods may become computationally infeasible. Typically variable screening based on model fits using only main effects must be performed first. One problem with screening is that important variables may be missed if they are only useful for prediction when certain interaction terms are also present in the *** tackle this issue, we introduce a new method we call Backtracking. It can be incorporated into many existing high-dimensional methods based on penalty functions, and works by building increasing sets of candidate interactions iteratively. Models fitted on the main effects and interactions selected early on in this process guide the selection of future interactions. By also making use of previous fits for computation, as well as performing calculations is parallel, the overall run-time of the algorithm can be greatly *** effectiveness of our method when applied to regression and classification problems is demonstrated on simulated and real data sets. In the case of using Backtracking with the Lasso, we also give some theoretical support for our procedure.
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