Kinematic-mapping-model-guided analysis and optimization of 2-PSS&1-RR circular-rail parallel mechanism for fully steerable phased array antennas

This paper presents a systematic methodology for analyzing and optimizing an innovative antenna mount designed for phased array antennas, implemented through a novel 2-PSS&1-RR circular-rail parallel mechanism. Initially, a comparative motion analysis between the 3D model of the mount and its full-scale prototype is conducted to validate effectiveness. Given the inherent complexity, a kinematic mapping model is established between the mount and the crank-slider linkage, providing a guiding framework for subsequent analysis and optimization. Guided by this model, feasible inverse and forward solutions are derived, enabling precise identification of stiffness singularities. The concept of singularity distance is thus introduced to reflect the structural stiffness of the mount. Subsequently, also guided by the mapping model, a heuristic algorithm incorporating two backtracking procedures is developed to reduce the mount's mass. Additionally, a parametric finite-element model is employed to explore the relation between singularity distance and structural stiffness. The results indicate a significant reduction(about 16%) in the antenna mount's mass through the developed algorithm, while highlighting the singularity distance as an effective stiffness indicator for this type of antenna mount.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.

中太平洋海山群漂移史及其来源

中太平洋海山群的海山呈簇状排列且近EW向展布于太平洋海山密集区,其四周分布着一系列NW走向的线性列岛及规模巨大的水下海山链.它位于明显属于海山链构造的夏威夷群岛和马绍尔群岛之间,处于莱恩群岛的北西向延伸方向上,与热点作用的板内火山作用关系密切.结合前人对中太平洋海山群形成和漂移的认识,从板决运动和几何学角度出发,采用Backtracking和Hot-spotting海山追踪方法对该区海山进行了追踪,发现该区海山的热点源主要位于现今法属玻利尼西亚群岛区(法属玻利尼西亚热点群),这与前人从古地磁和钻探资料出发得到的结果相接近.研究表明,中太平洋海山群是多热点成因的板内火山作用和板块构造运动的共同产物,它在130～90 Ma期间起源于法属玻利尼西亚热点群,曾随着太平洋板块发生过向南和向北的运动,既有水平方向的漂移又有垂直方向上的升降,在漂移和升降过程中还受到了当时大规模多期的热点活动和断裂活动的改造作用,经过一个漫长的过程后才到达今天的位置,最后形成了现今呈簇状分布的海山群.

麦哲伦海山链漂移史及可能的来源

麦哲伦海山链主要由十多座相对独立的平顶海山组成,呈北西向展布于太平洋海山密集区。结合前人对麦哲伦海山链形成和漂移的认识,从板块运动和几何学角度出发采用backtracking和hot-spotting海山追踪方法对该区海山的起源与形成进行了研究,结果表明,麦哲伦海山链是多热点成因的板内火山和板块构造运动共同作用的产物,最初于白垩纪期间由法属玻利尼西亚热点群西侧呈北北西向排列的热点活动产生,继而受到板块运动与区内近南北向和北北东向两组断裂及其热活化等的改造作用,最后形成了现今看到的平顶海山。

基于数据格式支持机制的自动化渗透测试框架

Backtrack4是功能最全面的一款测试平台,但由于数据交换处理机制的缺失使得它难以胜任高效的测试需求.设计了相应的数据格式支持机制,并依此开发了一个渗透测试框架(PTF).该框架会自动使用有关的渗透测试工具进行信息探测、漏洞评估、报告生成.真实网络环境中的实验验证了PTF能高效完成自动化渗透测试,进而大幅提升了使用Backtrack4进行渗透测试的有效性.

改进的迭代卡尔曼滤波及其在全球导航星系统/惯导紧组合中的应用

为改善GNSS/INS(Global Navigation Satellite System/Inertial Navigation System)紧组合中非线性滤波器的稳定性和精度,基于Backtracking线搜索(BLS)方法提出一种新型的迭代容积卡尔曼滤波方法(BLS-ICKF)。首先建立Sigma点滤波方法的迭代框架,采用阻尼Newton迭代改善状态初始误差较大时的滤波更新精度,同时,为提高组合系统状态预测过程的鲁棒性定义了一种简化的迭代更新结构。实验和仿真结果表明,BLS-ICKF牺牲较小的时间复杂度较CKF的航向角误差减少约59%。

Research on Guaranteed Scheduling Algorithms

Guaranteed scheduling is necessary for hard real time systems, because each process of them must meet their deadline, or a serious consequence will result.In this paper we discuss two guaranteed schedule algorithms:backtracking and branch and bound,introduce the process to find the optimal solution by both methods,propose the concept of logical node and physical node.Through some experiments for different process sets,we have made comparisons between the two algorithms in branch nodes,comparing time,topology of the search tree,memory size needed,etc.

A heuristic method for solving triangle packing problem

Given a set of triangles and a rectangle container, the triangle packing problem is to determine if these triangles can be placed into the container without overlapping. Triangle packing problem is a special case of polygon packing problem and also NP-hard, so it is unlikely that an efficient and exact algorithm can be developed to solve this problem. In this paper, a new concept of rigid placement is proposed, based on which a discrete solution space called rigid solution space is constructed. Each solution in the rigid solution space can be built by continuously applying legal rigid placements one by one until all the triangles are placed into the rectangle container without overlapping. The proposed Least-Destruction-First (LDF) strategy determines which rigid placement has the privilege to go into the rectangle container. Based on this, a heuristic algorithm is proposed to solve the problem. Combining Least-Destruction-First strategy with backtracking, the corresponding backtracking algorithm is proposed. Computa- tional results show that our proposed algorithms are efficient and robust. With slight modification, these techniques can be con- veniently used for solving polygon packing problem.

Adaptive backtracking search optimization algorithm with pattern search for numerical optimization

The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.

GPU acceleration of subgraph isomorphism search in large scale graph

A novel framework for parallel subgraph isomorphism on GPUs is proposed, named GPUSI, which consists of GPU region exploration and GPU subgraph matching. The GPUSI iteratively enumerates subgraph instances and solves the subgraph isomorphism in a divide-and-conquer fashion. The framework completely relies on the graph traversal, and avoids the explicit join operation. Moreover, in order to improve its performance, a task-queue based method and the virtual-CSR graph structure are used to balance the workload among warps, and warp-centric programming model is used to balance the workload among threads in a warp. The prototype of GPUSI is implemented, and comprehensive experiments of various graph isomorphism operations are carried on diverse large graphs. The experiments clearly demonstrate that GPUSI has good scalability and can achieve speed-up of 1.4–2.6 compared to the state-of-the-art solutions.

Improved CoSaMP Reconstruction Algorithm Based on Residual Update

A large number of sparse signal reconstruction algorithms have been continuously proposed, but almost all greedy algorithms add a fixed number of indices to the support set in each iteration. Although the mechanism of selecting the fixed number of indexes improves the reconstruction efficiency, it also brings the problem of low index selection accuracy. Based on the full study of the theory of compressed sensing, we propose a dynamic indexes selection strategy based on residual update to improve the performance of the compressed sampling matching pursuit algorithm (CoSaMP). As an extension of CoSaMP algorithm, the proposed algorithm adopts a residual comparison strategy to improve the accuracy of backtracking selected indexes. This backtracking strategy can efficiently select backtracking indexes. And without increasing the computational complexity, the proposed improvement algorithm has a higher exact reconstruction rate and peak signal to noise ratio (PSNR). Simulation results demonstrate the proposed algorithm significantly outperforms the CoSaMP for image recovery and one-dimensional signal.

Applying the Method for Solving Traveling Salesman Problem Based on Backtracking Algorithm to Order Picking

In the distribution center, the way of order picking personnel to pick goods has two kinds: single picking and batch picking. Based on the way of the single picking and assumed warehouse model, in order to reduce the walking path of order picking, the order picking problem is transformed into the traveling salesman problem in this paper. Based on backtracking algorithm, the order picking path gets optimized. Finally verifing the optimization method under the environment of VC++6.0, order picking path in the warehouse model get optimized, and compared with the traditional order picking walking paths. The results show that in small and medium-sized warehouse, the optimization method proposed in this paper can reduce order picking walking path and improve the work efficiency as well as reduce the time cost.

AN EFFICIENT AND STABLE STRUCTURE PRESERVING ALGORITHM FOR COMPUTING THE EIGENVALUES OF A HAMILTONIAN MATRIX

An efficient and stable structure preserving algorithm, which is a variant of the QR like (SR) algorithm due to Bunse-Gerstner and Mehrmann, is presented for computing the eigenvalues and stable invariant subspaces of a Hamiltonian matrix. In the algorithm two strategies are employed, one of which is called dis-unstabilization technique and the other is preprocessing technique. Together with them, a so-called ratio-reduction equation and a backtrack technique are introduced to avoid the instability and breakdown in the original algorithm. It is shown that the new algorithm can overcome the instability and breakdown at low cost. Numerical results have demonstrated that the algorithm is stable and can compute the eigenvalues to very high accuracy.

A new backtracking-based sparsity adaptive algorithm for distributed compressed sensing

A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing(DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but the numbers of non-zero(significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing strong DCS algorithms.

THE NEW METHOD OF GENERATION OF BINARY TREES FROM ZER0(0) AND ONE(1) CODES

A binary tree can be represented by a code reflecting the traversal of the corresponding regular binary tree in given monotonic order. A different coding scheme based on the branches of a regular binary tree with n-nodes is proposed. It differs from the coding scheme generally used and makes no distinction between internal nodes and terminal nodes. A code of a regular binary tree with nnodes is formed by labeling the left branches by O’s and the right branches by l’s and then traversing these branches in pre-order. Root is always assumed to be on a left branch.

Backtracking Routing Mechanism for Improving Link Failure Recovery

Failure-insensitive routing is a good mechanism to avoid packet dropping and disconnection of forwarding when some links fail, but multiple failure links may bring routing loop for the mechanism. Backtracking routing algorithm based on inverse shortest path tree rooted at destination is presented. The feasible restoration routing is obtained through searching from the start of the failure link and tracing back to the leaves of the shortest path tree with the destination as the root. The packets are forwarded from the mounted point with smaller sequence to the mount point with bigger sequence to decrease the possible of loop in case of multi-failures. The simulations and analysis indicate that backtracking routing algorithm improves the network survivability especially for large network, at the cost of the computation complexity in the same order as failure insensitive routing.

Enumeration of Strength Three Orthogonal Arrays and Their Implementation in Parameter Design

This paper describes the construction and enumeration of mixed orthogonal arrays (MOA) to produce optimal experimental designs. A MOA is a multiset whose rows are the different combinations of factor levels, discrete values of the variable under study, having very well defined features such as symmetry and strength three (all main interactions are taken in consideration). The applied methodology blends the fields of combinatorics and group theory by applying the ideas of orbits, stabilizers and isomorphisms to array generation and enumeration. Integer linear programming was used in order to exploit the symmetry property of the arrays under study. The backtrack search algorithm was used to find suitable arrays in the underlying space of possible solutions. To test the performance of the MOAs, an engineered system was used as a case study within the stage of parameter design. The analysis showed how the MOAs were capable of meeting the fundamental engineering design axioms and principles, creating optimal experimental designs within the desired context.

Software Tools for Some Natural Language Texts Computer Processing

Software tools are developed for computer realization of syntactic, semantic, and morphological models of natural language texts, using rule based programming. The tools are efficient for a language, which has free order of words and developed morphological structure like Georgian. For instance, a Georgian verb has several thousand verb-forms. It is very difficult to express rules of morphological analysis by finite automaton and it will be inefficient as well. Resolution of some problems of full morphological analysis of Georgian words is impossible by finite automaton. Splitting of some Georgian verb-forms into morphemes requires non-deterministic search algorithm, which needs many backtrackings. To minimize backtrackings, it is necessary to put constraints, which exist among morphemes and verify them as soon as possible to avoid false directions of search. Software tool for syntactic analysis has means to reduce rules, which have the same members in different order. The authors used the tool for semantic analysis as well. Thus, proposed software tools have many means to construct efficient parser, test and correct it. The authors realized morphological and syntactic analysis of Georgian texts by these tools. In the presented paper, the authors describe the software tools and its application for Georgian language.

Prediction and Backtracking Models of Life Cycle Cost Based on School Facility Conditions in South Korea

After the introduction of BTL （Build-Transfer-Lease） projects in 2005, most construction projects of school facilities have been implemented in BTL system. However, concern about whether the school facilities can be managed appropriately during the 20 year as operation and management period is increasing. Therefore, the necessity of reference for evaluation standard on operating costs and the establishment of LCC （life cycle costing） prediction models is coming to the fore. In this respect, the goal of this study was to extract the variables for LCC-related models and conduct analyses of the correlations of the variables using statistical analysis tool, in order to establish LCC prediction and backtracking model based on BTL project cases of school facilities. The prediction and backtracking model of LCC will be a key for budget equalization or optimum range as one way of estimating method using LCC by year and school type. In the future, it would provide the accurate reference for analyzing and managing the actual input costs against the plan and evaluating the practical cost for long-term facility management plan as the predictive management.

Superlinearly Convergent Affine Scaling Interior Trust-Region Method for Linear Constrained LC^1 Minimization

We extend the classical affine scaling interior trust region algorithm for the linear constrained smooth minimization problem to the nonsmooth case where the gradient of objective function is only locally Lipschitzian. We propose and analyze a new affine scaling trust-region method in association with nonmonotonic interior backtracking line search technique for solving the linear constrained LC1 optimization where the second-order derivative of the objective function is explicitly required to be locally Lipschitzian. The general trust region subproblem in the proposed algorithm is defined by minimizing an augmented affine scaling quadratic model which requires both first and second order information of the objective function subject only to an affine scaling ellipsoidal constraint in a null subspace of the augmented equality constraints. The global convergence and fast local convergence rate of the proposed algorithm are established under some reasonable conditions where twice smoothness of the objective function is not required. Applications of the algorithm to some nonsmooth optimization problems are discussed.