Novel cued search strategy based on information gain for phased array radar

A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then, two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy, respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Feature selection has been widely used in data mining and machine learning.Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly.In this article,a feature selection algorithm with local search strategy based on the forest optimization algorithm,namely FSLSFOA,is proposed.The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest.Next,the fitness function is improved,which not only considers the classification accuracy,but also considers the size of the feature subset.To avoid falling into local optimum,a novel global seeding method is attempted,which selects trees on the bottom of candidate set and gives the algorithm more diversities.Finally,FSLSFOA is compared with four feature selection methods to verify its effectiveness.Most of the results are superior to these comparative methods.

Effective arithmetic optimization algorithm with probabilistic search strategy for function optimization problems

This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adjustable parameter is also developed to balance the exploration and exploitation operations.In addition,a jump mechanism is included in the PSAOAto assist individuals in jumping out of local optima.Using 29 classical benchmark functions,the proposed PSAOA is extensively tested.Compared to the AOA and other well-known methods,the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.

Speed Reduction Mitigation Strategies on Rural Highways at Two-Way Stop Control Intersections and Curves

Studies conducted on crashes at rural intersections of state highways and local roads/streets with two-way stop control have proved that the problem was likely poor judgment by the stopped driver on the minor approach of the speed of an oncoming vehicle and/or safe gap in traffic on the major highway. Several mitigating strategies have been suggested and studied in various studies to improve safety at the two-way stop control rural intersections and at other rural highway segments. There are also several low-cost techniques used, mostly in foreign countries, to slow drivers on major highways as they enter small towns and villages, e.g., narrowing pavement lines, establishing visual gateways, etc. Lowered speeds would decrease crash severity. In this study, four strategies including solar speed display units, mobile speed trailers, optical speed bars, and colored pavement were tested and assessed in some locations in Kansas. The results of this research indicate that both solar speed display units and mobile speed trailers are effective in speed reduction at the desired points, but optical speed bars and colored pavements do not yield reliable results. Additional studies and longer term studies, should be conducted.

Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning

Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.

基于Shark-Search和Hits算法的主题爬虫研究

主题爬虫是实现垂直搜索引擎的核心技术。介绍主题爬虫的两个重要爬行算法:基于网页内容评价的Shark-Search算法和基于网页链接关系的Hits算法,并分析了各自的优缺点,提出了一种新的主题爬行策略:将上述两种算法的优点结合起来即将基于网页内容评价和基于网页链接关系算法结合起来判断待下载url的优劣,并实现了一个主题爬虫。这种新策略正好弥补了两个算法各自的不足。通过与Shark-Search算法和Hits算法实现的主题爬虫对比,发现用新算法实现的主题爬虫查准率比这两种算法高。

The Cochrane Library“New search”图标的特点及相关检索策略分析

目的对标有"New search"的Cochrane系统评价在检索方面的特点和检索策略进行统计分析,探讨这些特点和检索策略是否对更新系统评价有帮助。方法在John Wiley&Sons公司2009年第1期The Cochrane Library的"Advanced Search"检索模块,选择"New search"选项进行检索,并将检索结果转入到ProCite参考文献管理软件,然后逐一浏览The Cochrane Library中每一检索结果的"What’snew"、"History"及"Appendix"部分,并将此部分内容加到ProCite参考文献管理软件的相关字段中以便统计分析。结果共检出140条标注有"New search"图标的系统评价,其中新检索总频次274次,平均1.96次/篇;两年内至少进行一次新检索的有58篇(41.43%);有61篇(43.57%)附有检索策略,其中检索最多的数据库是MEDLINE,共56篇(91.80%),其次是EMBASE47篇(77.05%)、CENTRAL 45篇(73.77%)。在将检索策略作为附件的系统评价中,多数没有正确标注针对各个数据库最近一次检索时间和所检数据库的时间范围。结论虽然有些Cochrane系统评价更新时存在更新不及时、检索策略标注相关信息不完善等问题,但部分内容对系统评价员更新检索仍有帮助。

Artificial bee colony algorithm with comprehensive search mechanism for numerical optimization

The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing ex- ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability P,. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self- adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en- hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition- based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func- tions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.

A novel adjustable multiple cross-hexagonal search algorithm for fast block motion estimation

In this paper,we propose a novel adjustable multiple cross-hexagonal search(AMCHS) algorithm for fast block motion estimation. It employs adjustable multiple cross search patterns(AMCSP) in the first step and then uses half-way-skip and half-way-stop technique to determine whether to employ two hexagonal search patterns(HSPs) subsequently. The AMCSP can be used to find small motion vectors efficiently while the HSPs can be used to find large ones accurately to ensure prediction quality. Simulation results showed that our proposed AMCHS achieves faster search speed,and provides better distortion performance than other popular fast search algorithms,such as CDS and CDHS.

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.

Two-Phase Bidirectional Dual-Relay Selection Strategy for Wireless Relay Networks

In this article,we introduce a new bi-directional dual-relay selection strategy with its bit error rate(BER)performance analysis.During the first step of the proposed strategy,two relays out of a set of N relay-nodes are selected in a way to optimize the system performance in terms of BER,based on the suggested algorithm which checks if the selected relays using the maxmin criterion are the best ones.In the second step,the chosen relay-nodes perform an orthogonal space-time coding scheme using the two-phase relaying protocol to establish a bi-directional communication between the communicating terminals,leading to a significant improvement in the achievable coding and diversity gain.To further improve the overall system performance,the selected relay-nodes apply also a digital network coding scheme.Furthermore,this paper discusses the analytical approximation of the BER performance of the proposed strategy,where we prove that the analytical results match almost perfectly the simulated ones.Finally,our simulation results show that the proposed strategy outperforms the current state-of-the-art ones.

Application of Rollout Strategy to Test Points Selection for Integer-Coded Fault Wise Table

Test points selection for integer-coded fault wise table is a discrete optimization problem. The global minimum set of test points can only be guaranteed by an exhaustive search which is eompurationally expensive. In this paper, this problem is formulated as a heuristic depth-first graph search problem at first. The graph node expanding method and rules are given. Then, rollout strategies are applied, which can be combined with the heuristic graph search algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the greedy heuristic algorithms. The proposed rollout-based test points selection algorithm is illustrated and tested using an analog circuit and a set of simulated integer-coded fault wise tables. Computa- tional results are shown, which suggest that the rollout strategy policies are significantly better than other strategies.

基于ElasticSearch和语义相似度匹配的教学资源搜索策略

【目的】整合多种教学资源,并在此场景下设计和实现一种高效准确的搜索策略,帮助用户获取丰富的教学内容。【应用背景】教学资源类型众多,数量庞大,用户对于准确检索的需求日益增长,仅基于ElasticSearch进行搜索的效果不尽人意。【方法】在对用户输入的Query进行预处理和分词后,通过ER-BERT语义相似度模型在Query库中匹配出n条近似结果,将其输入到ElasticSearch并构建相关度计算公式,最后按照综合评估的最终得分将匹配结果进行排序。【结果】利用知识图谱技术整合复杂的教学资源,并在此基础上实现了一种基于ElasticSearch和语义相似度匹配的教学资源搜索策略,在保证检索速度的同时可以根据用户检索Query的语义信息进行检索。【结论】实验结果表明使用该教学资源搜索策略增加了检索结果的数量,并在保证检索速度的同时提升了结果的准确性,显著改善了用户的搜索体验。

Research of Rural Power Network Reactive Power Optimization Based on Improved ACOA

In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.

Local Search Algorithm with Hybrid Neighborhood and Its Application to Job Shop Scheduling Problem

A new local search method with hybrid neighborhood for Job shop scheduling problem is developed. The proposed hybrid neighborhood is not only efficient in local search, but also can help overcome entrapments while search procedure get trapped at local optima and carry the search to areas of the feasible set with better prospect. New strategies used for breaking out of entrapments are presented and they are helpful for the procedure to improve local optima. A performance comparison of the proposed method with some best-performing algorithms on all 10-job, 10-machine benchmark problems and the other two problems generated by Fisher and Thompson (ie., FT6 and FT20)is made. The experiment results show the better optimal performance of the proposed algorithm.

基于改进Tabu Search算法的配电线路无功运行优化系统控制策略研究

在配电网无功补偿优化过程中,Tabu Search算法能够高效地将最优解搜索出来,因此,将Tabu Search算法应用于配电网无功补偿优化具有现实可操作性。本文对Tabu Search方法的原理进行了详细介绍,因为农村配电网线路具备很多特点,综合考量其并联电容器投切等问题及特点,本文在Tabu Search的基础上,进行了一系列改进,用来解决配电网的投切优化问题。提出了基于电容器投切分组的二进制编码优化,基于无功补偿的损耗降低特点,根据功率传输方向,对权重根据由低到高的顺序排列,然后进行二进制编码,从二进制编码串的末端进行移动;Tabu Search非常依赖于初始解,提出了当前时段处于运行退出状态时,根据无功缺额电容器的配置方式进行。

Chinese Keyword Search by Indexing in Relational Databases

In this paper, we propose a new method based on index to realize IR-style Chinese keyword search with ranking strategies in relational databases. This method creates an index by using the related information of tuple words and presents a ranking strategy in terms of the nature of Chinese words. For a Chinese keyword query, the index is used to match query search words and the tuple words in index quickly, and to compute similarities between the query and tuples by the ranking strategy, and then the set of identifiers of candidate tuples is generated. Thus, we retrieve top-N results of the query using SQL selection statements and output the ranked answers according to the similarities. The experimental results show that our method is efficient and effective.

Word Search and Its Results in Oral Speech of Elderly People

The author studies the markers of searching for a word in spontaneous oral speech of Russian-speaking elderly women. The paper puts a question about age-related features of searching strategies of elderly people. While searching the informants, as a rule, recollect the only word （a noun, including their own name）, they do not choose the most suitable nominations from the available options. They often refuse to search and realize an alternative speech strategy.

神经架构搜索综述

近几年,深度学习因具有强大的表征能力,已经在许多领域中取得了突破性的进展,而神经网络的架构对它的性能至关重要。然而,高性能的神经网络架构设计严重依赖研究人员的先验知识和经验,神经网络参数量庞大,难以设计最优的神经网络架构,因此自动神经架构搜索(NAS)获得了极大的关注。NAS是一种使用机器学习的方法,可以在不需要大量人力的情况下,自动搜索最优网络架构的技术,是未来神经网络设计的重要手段之一。NAS本质上是一个搜索优化问题,通过对搜索空间、搜索策略和性能评估策略的设计,自动搜索最优的网络结构。从搜索空间、搜索策略和性能评估策略这3个方面详细且全面地分析、比较和总结目前NAS的研究进展,方便读者快速了解神经架构搜索的发展过程和各项技术的优缺点,并提出NAS未来可能的研究发展方向。

Elastic Full Waveform Inversion Based on the Trust Region Strategy

In this paper, we investigate the elastic wave full-waveform inversion (FWI) based on the trust region method. The FWI is an optimization problem of minimizing the misfit between the observed data and simulated data. Usually, the line search method is used to update the model parameters iteratively. The line search method generates a search direction first and then finds a suitable step length along the direction. In the trust region method, it defines a trial step length within a certain neighborhood of the current iterate point and then solves a trust region subproblem. The theoretical methods for the trust region FWI with the Newton type method are described. The algorithms for the truncated Newton method with the line search strategy and for the Gauss-Newton method with the trust region strategy are presented. Numerical computations of FWI for the Marmousi model by the L-BFGS method, the Gauss-Newton method and the truncated Newton method are completed. The comparisons between the line search strategy and the trust region strategy are given and show that the trust region method is more efficient than the line search method and both the Gauss-Newton and truncated Newton methods are more accurate than the L-BFGS method.