An algorithm for hidden line ruled surface display using microprocessor

A simple algorithm for hidden line Ruled Surface graphics, typically used in Radio Astronomy is discussed using low cost microcomputer system built around Motorola 6800 processor and Matrox 256**2 graphics module of raster size 256 × 256 points. Resolution of the plot can be improved by displaying on a bigger raster like 1024 × 1024 points.     

A hidden line elimination method for curved surfaces

An algorithm for displaying curved surfaces with hidden lines eliminated is described. The surfaces are displayed as rectangular grids of straight segments which approximate those surfaces. The algorith is essentially one of brute force. However, it divides the screen space into small 3D boxes to reduce the number of subpatches to be checked to determine the visibility of each grid point. It turned out that this division process made this algorithm about 10 times faster than the algorithm which had no use of the division.Expanding the algorithm for making contour drawings is very easy. Some methods for drawing silhouette lines are considered.     

A simple efficient hidden line algorithm

A simple efficient hidden line algorithm for finite element models is described. The algorithm is general purpose, requires minimal computer storage, and can easily be added to existing plot programs. The routine does not require angle determination, trigonometric functions, or elaborate bookkeeping schemes. Detailed flowcharts and a listing of the computer code are presented.     

An approach to a calculation-minimized hidden line algorithm

Hidden line algorithms based only on picture primitives, cannot recognize the areas of a picture which are completely visible or invisible. By testing each primitive of these areas they produce an overhead of calculation. Moreover, they waste time by first calculating the hidden lines explicitely and then displaying the remaining edges. This paper describes a method, which rather computes the visible parts of a picture, than excludes the invisible ones. Furthermore an approach to recognize complete visible areas instead of single visible edges is made. A comparison with other techniques will show the qualitatively better calculation rate of the new method.     

A modified conjugate gradient algorithm with backtracking line search technique for large-scale nonlinear equations

Conjugate gradient methods are widely used for solving unconstrained optimization and nonlinear equations, specially in large-scale cases. Since they own the attractive practical factors of simple computation and low memory requirement, interesting theoretical features of curvature information and strong global convergence. In this paper, we present a modified conjugate gradient algorithm by line search method with acceleration scheme for nonlinear symmetric equations. Furthermore, the proposed method not only possess descent property but also owns global convergence in mild conditions. Numerical results also indicate that the presented method is much more effective than the other methods for the test problems.     

The fuzzy c spherical shells algorithm: A new approach

The fuzzy c spherical shells (FCSS) algorithm is specially designed to search for clusters that can be described by circular arcs or, generally, by shells of hyperspheres. A new approach to the FCSS algorithm is presented. This algorithm is computationally and implementationally simpler than other clustering algorithms that have been suggested for this purpose. An unsupervised algorithm which automatically finds the optimum number of clusters is not known. It uses a cluster validity measure to identify good clusters, merges all compatible clusters, and eliminates spurious clusters to achieve the final results. Experimental results on several data sets are presented.     

A new algorithm for clustering search results

We develop a new algorithm for clustering search results. Differently from many other clustering systems that have been recently proposed as a post-processing step for Web search engines, our system is not based on phrase analysis inside snippets, but instead uses latent semantic indexing on the whole document content. A main contribution of the paper is a novel strategy – called dynamic SVD clustering – to discover the optimal number of singular values to be used for clustering purposes. Moreover, the algorithm is such that the SVD computation step has in practice good performance, which makes it feasible to perform clustering when term vectors are available. We show that the algorithm has very good classification performance, and that it can be effectively used to cluster results of a search engine to make them easier to browse by users. The algorithm has being integrated into the Noodles search engine, a tool for searching and clustering Web and desktop documents.     

A new optimization algorithm for single hidden layer feedforward neural networks

Feedforward neural networks are the most commonly used function approximation techniques in neural networks. By the universal approximation theorem, it is clear that a single-hidden layer feedforward neural network (FNN) is sufficient to approximate the corresponding desired outputs arbitrarily close. Some researchers use genetic algorithms (GAs) to explore the global optimal solution of the FNN structure. However, it is rather time consuming to use GA for the training of FNN. In this paper, we propose a new optimization algorithm for a single-hidden layer FNN. The method is based on the convex combination algorithm for massaging information in the hidden layer. In fact, this technique explores a continuum idea which combines the classic mutation and crossover strategies in GA together. The proposed method has the advantage over GA which requires a lot of preprocessing works in breaking down the data into a sequence of binary codes before learning or mutation can apply. Also, we set up a new error function to measure the performance of the FNN and obtain the optimal choice of the connection weights and thus the nonlinear optimization problem can be solved directly. Several computational experiments are used to illustrate the proposed algorithm, which has good exploration and exploitation capabilities in search of the optimal weight for single hidden layer FNNs.     

UnitWalk: A new SAT solver that uses local search guided by unit clause elimination

In this paper we present a new randomized algorithm for SAT, i.e., the satisfiability problem for Boolean formulas in conjunctive normal form. Despite its simplicity, this algorithm performs well on many common benchmarks ranging from graph coloring problems to microprocessor verification. Our algorithm is inspired by two randomized algorithms having the best current worst-case upper bounds ([27,28] and [30,31]). We combine the main ideas of these algorithms in one algorithm. The two approaches we use are local search (which is used in many SAT algorithms, e.g., in GSAT [34] and WalkSAT [33]) and unit clause elimination (which is rarely used in local search algorithms). In this paper we do not prove any theoretical bounds. However, we present encouraging results of computational experiments comparing several implementations of our algorithm with other SAT solvers. We also prove that our algorithm is probabilistically approximately complete (PAC).     

UnitWalk: A new SAT solver that uses local search guided by unit clause elimination

In this paper we present a new randomized algorithm for SAT, i.e., the satisfiability problem for Boolean formulas in conjunctive normal form. Despite its simplicity, this algorithm performs well on many common benchmarks ranging from graph coloring problems to microprocessor verification. Our algorithm is inspired by two randomized algorithms having the best current worst-case upper bounds ([27,28] and [30,31]). We combine the main ideas of these algorithms in one algorithm. The two approaches we use are local search (which is used in many SAT algorithms, e.g., in GSAT [34] and WalkSAT [33]) and unit clause elimination (which is rarely used in local search algorithms). In this paper we do not prove any theoretical bounds. However, we present encouraging results of computational experiments comparing several implementations of our algorithm with other SAT solvers. We also prove that our algorithm is probabilistically approximately complete (PAC).     

A trust-region algorithm combining line search filter technique for nonlinear constrained optimization

In this paper, we propose a trust-region algorithm in association with line search filter technique for solving nonlinear equality constrained programming. At current iteration, a trial step is formed as the sum of a normal step and a tangential step which is generated by trust-region subproblem and the step size is decided by interior backtracking line search together with filter methods. Then, the next iteration is determined. This is different from general trust-region methods in which the next iteration is determined by the ratio of the actual reduction to the predicted reduction. The global convergence analysis for this algorithm is presented under some reasonable assumptions and the preliminary numerical results are reported.     

A note to: A multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem

Journal of Intelligent Manufacturing - Some of the solutions reported in a recent paper (Akyol and Baykasoglu in J Intell Manuf 30(2):557–573, 2019. https://doi.org/10.1007/s10845-016-1262-6...     

Snap-drift cuckoo search: A novel cuckoo search optimization algorithm

Cuckoo search (CS) is one of the well-known evolutionary techniques in global optimization. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploration and exploitation. To address these issues, a new CS extension namely snap-drift cuckoo search (SDCS) is proposed in this study. The proposed algorithm first employs a learning strategy and then considers improved search operators. The learning strategy provides an online trade-off between local and global search via two snap and drift modes. In snap mode, SDCS tends to increase global search to prevent algorithm of being trapped in a local minima; and in drift mode, it reinforces the local search to enhance the convergence rate. Thereafter, SDCS improves search capability by employing new crossover and mutation search operators. The accuracy and performance of the proposed approach are evaluated by well-known benchmark functions. Statistical comparisons of experimental results show that SDCS is superior to CS, modified CS (MCS), and state-of-the-art optimization algorithms in terms of convergence speed and robustness.     

基于视频压缩的快速自适应菱形搜索算法

针对视频压缩标准H.264中运动估计算法的特点,在兼顾视频图像质量和算法运行时间的基础上,提出了基于视频压缩的快速自适应菱形搜索算法。实验结果表明,该算法与菱形搜索算法相比,搜索时间平均减少了1.5ms,信噪比平均提高了1.3dB。     

A new shooting algorithm for the search of periodic orbits

The periodic orbit theory gives the basic framework to study a quantum and classical correspondence. In this paper, we firstly report that we have found the existence of a certain surface, which we call the devil’s staircase surface. Secondly, taking the advantage of some intriguing properties of this surface, we propose a new method to exhaustively search for periodic orbits in the anisotropic Kepler problem. Our method fully takes into account of an intriguing property of the initial value problem of the anisotropic Kepler problem, and it reduces the two-dimensional search into the one-dimensional search. Using this method, all of the periodic orbits up to the length \(2N=20\) (altogether 19284 distinct periodic orbits) have been successfully obtained, which exceeds the world record of 76 periodic orbits up to \(2N=10\) .     

一种十字形运动搜索算法

近几年,虽然运动估计算法有了很多种的快速算法,但是,运动搜索巨大的计算量依然是视频压缩速率的瓶颈,本文针对运动矢量的分布特点,提出了一种新的运动搜索算法,算法不仅结构简单,而且测试结果表明,该算法比原有DS9(dia-mondsearch)算法在搜索点数和图像质量方面有较大的提高,最好时的搜索点数只有DS算法的3/4。     

一种新的矢量传感器线列阵波束形成算法

为了提高基于矢量线列阵的目标方位估计能力,将基于时域解析信号实现最小方差无畸变响应（ MVDR）的方法——TAMVDR算法引入到了矢量线列阵信号处理中,提出了实现矢量线列阵波束形成的VTAMVDR算法。理论分析了矢量线列阵VTAMVDR算法的原理,该算法通过Hilbert变换对时域宽带信号引入复权向量,不需要进行子带分割,且不需要对数据进行分块处理,获得稳定优化权向量估计所需要的数据长度远小于频域MVDR方法,数据长度合适时,单次快拍即可实现波束形成,大大降低了运算量。仿真和海上试验数据分析结果表明：VTMVDR算法相比于频域MVDR算法具有较好的性能,具有更高的分辨率和更窄的波束角,有更好的探测性能。     

A new local search based hybrid genetic algorithm for feature selection

This paper presents a new hybrid genetic algorithm (HGA) for feature selection (FS), called as HGAFS. The vital aspect of this algorithm is the selection of salient feature subset within a reduced size. HGAFS incorporates a new local search operation that is devised and embedded in HGA to fine-tune the search in FS process. The local search technique works on basis of the distinct and informative nature of input features that is computed by their correlation information. The aim is to guide the search process so that the newly generated offsprings can be adjusted by the less correlated (distinct) features consisting of general and special characteristics of a given dataset. Thus, the proposed HGAFS receives the reduced redundancy of information among the selected features. On the other hand, HGAFS emphasizes on selecting a subset of salient features with reduced number using a subset size determination scheme. We have tested our HGAFS on 11 real-world classification datasets having dimensions varying from 8 to 7129. The performances of HGAFS have been compared with the results of other existing ten well-known FS algorithms. It is found that, HGAFS produces consistently better performances on selecting the subsets of salient features with resulting better classification accuracies.     

A new algorithm for numerical solution of ordinary differential equations

A numerical integration scheme which is particularly well suited to initial value problems having oscillatory or exponential solutions is proposed. The derivation of the algorithm is based on a representation of problems (that is problems having oscillatory or exponential solutions), the complex parameters have the real plane. The interpolating function has two complex parameters whose numerical estimates are obtained by using Newton-like scheme to solve three simultaneous nonlinear equations. For the above class of paoblems (that is problems having oscillatory or exponential solutions), the complex parameters have constant values throughout the interval of integration. Hence, the parameters are obtainable at the first integration step. As the approach is applicable to systems of equations, then for an initial value problem of order m, m sets of simultaneous equations have to be solved for the complex parameters.