Inversion of Seabed Geotechnical Properties in the Arctic Chukchi Deep Sea Basin Based on Time Domain Adaptive Search Matching Algorithm

The chirp sub-bottom profiler,for its high resolution,easy accessibility and cost-effectiveness,has been widely used in acoustic detection.In this paper,the acoustic impedance and grain size compositions were obtained based on the chirp sub-bottom profiler data collected in the Chukchi Plateau area during the 11th Arctic Expedition of China.The time-domain adaptive search matching algorithm was used and validated on our established theoretical model.The misfit between the inversion result and the theoretical model is less than 0.067%.The grain size was calculated according to the empirical relationship between the acoustic impedance and the grain size of the sediment.The average acoustic impedance of sub-seafloor strata is 2.5026×10^(6) kg(s m^(2))^(-1)and the average grain size(θvalue)of the seafloor surface sediment is 7.1498,indicating the predominant occurrence of very fine silt sediment in the study area.Comparison of the inversion results and the laboratory measurements of nearby borehole samples shows that they are in general agreement.

Development of System for Simultaneously Present Multiple Videos That Enables Search by Absolute Time

We propose a system for simultaneously presenting numerous pieces of video content with absolute time metadata attached for effectively utilizing increasing number of pieces of video content. The proposed stored format used for the system is based on the MMT （MPEG （moving picture experts group） media transport） standardized method, which makes it possible to search by absolute time, and it is a format easy to access by HTTP. We implemented software that can simultaneously present multiple video files according to that format. The constructed system was able to provide service within a feasible processing time.

公式分组索引搜索的D-Search算法

本文提出了一个公式分组索引搜索的D-Search算法。该算法查找N个元素所需要的期望时间为O(n)。

一种模糊项目调度问题的变宽Beam Search算法

具有弹性约束和模糊工期的项目调度问题是一类具有实际应用意义但难于解决的问题.传统BS(Beam Search)方法存在常数线宽大小影响求解效果的问题.本文建立了此类项目调度问题的模型,并将序优化理论思想与BS方法结合,提出一种通过计算和改变线宽,兼顾解的质量和求解速度的变宽BS调度算法.仿真计算结果表明,该算法能够在较短时间内得到满意的调度结果,适用于实际模糊项目调度决策.

VRPTW的扰动恢复及其TABUSEARCH算法

本文对带时间窗的车辆路线安排扰动恢复问题进行了讨论,分析了各种可能的扰动:增加减少客户,时间窗、客户需求及路线可行性的扰动,构造了扰动模型.利用禁忌搜索算法对问题进行求解,同时通过对模型参数重新设置,得到了多个满足要求的不同的解,这样使解更具有实际可行性和有效性.

一种带时间窗和容量约束的车辆路线问题及其TabuSearch算法

本文提出一种带时间窗和容量约束的车辆路线问题 (CVRPTW ) ,并利用TabuSearch快速启式算法 ,针对Solomon提出的几个标准问题 ,快捷地得到了优良的数值结果。

Tabu Search算法在优化配送路线问题中的应用

将ＴＳ算法应用到物流系统的配送路线优化问题中。在给出了此类问题的描述后，着重阐述了ＴＳ启发式算法的设计，编程实现此算法的要点。最后，用模拟算例对设计的算法进行了验证，计算结果是比较理想的。

ElasticSearch分布式搜索引擎在地名地址检索中的应用

针对传统全文搜索引擎Lucene无法满足海量地名地址数据高速分布式检索的问题,本文通过对分布式搜索引擎ElasticSearch的集群架构、索引分片与复制集、地理要素索引存储、数据检索及位置聚合等关键点进行详细研究,结合地名地址库数据结构的特点,提出了一种面向Web电子地图开发的分布式地名地址搜索服务构架,实现了地名地址模糊搜索、专题搜索、视野内搜索、周边搜索等功能。该架构具备可伸缩性、容错性、支持大规模并发访问的特点。

A Novel Search Engine for Internet of Everything Based on Dynamic Prediction

In recent years, with the rapid development of sensing technology and deployment of various Internet of Everything devices, it becomes a crucial and practical challenge to enable real-time search queries for objects, data, and services in the Internet of Everything. Moreover, such efficient query processing techniques can provide strong facilitate the research on Internet of Everything security issues. By looking into the unique characteristics in the IoE application environment, such as high heterogeneity, high dynamics, and distributed, we develop a novel search engine model, and build a dynamic prediction model of the IoE sensor time series to meet the real-time requirements for the Internet of Everything search environment. We validated the accuracy and effectiveness of the dynamic prediction model using a public sensor dataset from Intel Lab.

Fast Iterative Closest Point-Simultaneous Localization and Mapping(ICP-SLAM)with Rough Alignment and Narrowing-Scale Nearby Searching

Two deficiencies in traditional iterative closest pointsimultaneous localization and mapping( ICP-SLAM) usually result in poor real-time performance. On one hand, relative position between current scan frame and global map cannot be previously known. As a result, ICP algorithm will take much amount of iterations to reach convergence. On the other hand,establishment of correspondence is done by global searching, which requires enormous computational time. To overcome the two problems,a fast ICP-SLAM with rough alignment and narrowing-scale nearby searching is proposed. As for the decrease of iterative times,rough alignment based on initial pose matrix is proposed. In detail,initial pose matrix is obtained by micro-electro-mechanical system( MEMS) magnetometer and global landmarks. Then rough alignment will be applied between current scan frame and global map at the beginning of ICP algorithm with initial pose matrix. As for accelerating the establishment of correspondence, narrowingscale nearby searching with dynamic threshold is proposed,where match-points are found within a progressively constrictive range.Compared to traditional ICP-SLAM,the experimental results show that the amount of iteration for ICP algorithm to reach convergence reduces to 92. 34% and ICP algorithm runtime reduces to 98. 86% on average. In addition,computational cost is kept in a stable level due to the eliminating of the accumulation of computational consumption. Moreover,great improvement can also been achieved in SLAM quality and robustness.

基于HBase+ ElasticSearch的海量交通数据实时存取方案设计

交通流数据具有数据海量、存储和交互速率快等特征,因此其数据的采集、存储及检索成为了车辆远程监控平台中的关键问题。采用LVS集群技术进行数据采集负载均衡,队列缓存处理I/O时延,HBase进行分布式数据存储;针对Hadoop实时在线数据处理不足的问题,整合Elastic Search并构建了分层索引。通过关键技术的设计和实现,车辆监控由400辆扩展到上万辆,PB级数据在线查询速度提升了10~20倍,验证了方案的高效性。

Taboo Search Algorithm for Item Assignment in Synchronized Zone Automated Order Picking System

The idle time which is part of the order fulfillment time is decided by the number of items in the zone; therefore the item assignment method affects the picking efficiency. Whereas previous studies only focus on the balance of number of kinds of items between different zones but not the number of items and the idle time in each zone. In this paper, an idle factor is proposed to measure the idle time exactly. The idle factor is proven to obey the same vary trend with the idle time, so the object of this problem can be simplified from minimizing idle time to minimizing idle factor. Based on this, the model of item assignment problem in synchronized zone automated order picking system is built. The model is a form of relaxation of parallel machine scheduling problem which had been proven to be NP-complete. To solve the model, a taboo search algorithm is proposed. The main idea of the algorithm is minimizing the greatest idle factor of zones with the 2-exchange algorithm. Finally, the simulation which applies the data collected from a tobacco distribution center is conducted to evaluate the performance of the algorithm. The result verifies the model and shows the algorithm can do a steady work to reduce idle time and the idle time can be reduced by 45.63% on average. This research proposed an approach to measure the idle time in synchronized zone automated order picking system. The approach can improve the picking efficiency significantly and can be seen as theoretical basis when optimizing the synchronized automated order picking systems.

Network Decomposition and Maximum Independent Set Part Ⅱ: Application Research

According to the researches on theoretic basis in part Ⅰ of the paper, the spanning tree algorithms solving the maximum independent set both in even network and in odd network have been developed in this part, part Ⅱ of the paper. The algorithms transform first the general network into the pair sets network, and then decompose the pair sets network into a series of pair subsets by use of the characteristic of maximum flow passing through the pair sets network. As for the even network, the algorithm requires only one time of transformation and decomposition, the maximum independent set can be gained without any iteration processes, and the time complexity of the algorithm is within the bound of O(V3). However, as for the odd network, the algorithm consists of two stages. In the first stage, the general odd network is transformed and decomposed into the pseudo-negative envelope graphs and generalized reverse pseudo-negative envelope graphs alternately distributed at first; then the algorithm turns to the second stage, searching for the negative envelope graphs within the pseudo-negative envelope graphs only. Each time as a negative envelope graph has been found, renew the pair sets network by iteration at once, and then turn back to the first stage. So both stages form a circulation process up to the optimum. Two available methods, the adjusting search and the picking-off search are specially developed to deal with the problems resulted from the odd network. Both of them link up with each other harmoniously and are embedded together in the algorithm. Analysis and study indicate that the time complexity of this algorithm is within the bound of O(V5).

Hybrid Discrete Harmony Search Algorithm for Flow Shop Scheduling with Limited Buffers

The flow shop scheduling problem with limited buffers( LBFSP) widely exists in manufacturing systems. A hybrid discrete harmony search algorithm is proposed for the problem to minimize total flow time. The algorithm presents a novel discrete improvisation and a differential evolution scheme with the jobpermutation-based representation. Moreover,the discrete harmony search is hybridized with the problem-dependent local search based on insert neighborhood to balance the global exploration and local exploitation. In addition, an orthogonal experiment design is employed to provide a receipt for turning the adjustable parameters of the algorithm. Comparisons based on the Taillard benchmarks indicate the superiority of the proposed algorithm in terms of effectiveness and efficiency.

基于深度神经网络的7065铝合金厚板应力检测模型

针对工业生产中传统超声应力检测法对铝合金厚板在不同拉伸率和不同温度条件下存在的测量误差的问题,以7065铝合金厚板为实验对象,提出一种在不同拉伸率和不同温度条件下的基于树突神经网络的应力预测模型与传统超声检测法融合的应力检测模型,然后使用改进的GSA-GRNN对该应力检测模型进行温度补偿。以南南铝公司生产的7065铝合金厚板为研究对象,使用恒温槽为超声检测提供恒温环境,分别对不同拉伸率、不同温度下的7065铝合金厚板进行超声检测,将声时差、拉伸率作为输入参数,应力作为输出参数,创建一个基于树突神经网络的应力检测模型,然后将应力检测模型的输出作为输入,使用改进的GSA-GRNN建立温度补偿模型对应力检测模型进行温度补偿。研究结果表明:融合了传统超声声时差的检测模型均方根误差为0.84636,相关系数为0.99743,和其他神经网络模型对比,该模型拥有更好的精度;在对该模型进行温度补偿后,模型的应力均方根误差和相关系数分别可以达到0.78848和0.99844,模型的精度得到了进一步的提升。证明基于数据驱动的神经网络融合传统超声检测可以有效降低检测误差,同时省去传统检测方法人工计算应力的时间,提高了检测效率。研究结果可以为基于数据驱动的应力检测模型提供进一步的优化参考。

基于能量均衡的非均匀分簇调度算法

针对无线传感器网络节点能量有限且不可充电的问题,本文提出基于能量均衡的非均匀分簇调度算法(EBNC_CHES)延长网络寿命。EBNC_CHES在麻雀搜索算法的基础上,引入时间竞争机制减少冗余信息获取和能耗传递的同时,采取K-means非均匀分簇均衡簇间网络能量消耗,引入改进的麻雀搜索在簇头选举中均衡簇内能耗。仿真结果表明:该算法与LEACH、EECHS_ISSADE和EESSTBRP相比,冗余信息降低了81%、80%、55%,能耗利用率提高了133%、50%、11.4%,寿命延长了52.8%、43.5%、12.2%。此算法能减少冗余信息,降低网络能耗、延长网络寿命。

考虑机会充电与行程时间可靠性的区域多车型电动公交调度优化

为提高电动公交系统运营效率,降低运营成本,本文提出一种考虑机会充电和行程时间可靠性的电动公交调度优化方法。首先,基于区域调度场景,提出在线路始末站配备快速充电桩,利用车次接续时间进行机会充电的策略;然后,考虑行程时间的随机波动,以表征特定可靠性的预留行程时间作为模型输入生成调度方案,同时,将发车延误成本纳入目标函数中,综合考虑公交企业从规划到运营阶段的整体效益,构建以总成本最小为目标的区域多车型电动公交调度优化模型,针对模型特点,设计自适应大邻域搜索算法进行求解;最后,以北京市大兴区4条公交线路为例,验证模型和算法的有效性。结果表明:基于本文方法得到的最优调度方案相较于传统单线路单车型调度方案,能使企业日均总成本下降37.93%,平均每辆车的发车延误时长减少5.63 min,说明本文所提方法能有效降低企业成本,提升公交系统可靠性。相较于不考虑机会充电和行程时间可靠性的区域多车型运营模式,本文最优方案能使总成本下降28.67%。此外,通过灵敏度分析,建议公交企业以240 kW的充电功率进行快速充电资源的配置,以90%的行程时间可靠性进行电动公交调度方案的编制。

面向准时化生产的船体分段排产分析

针对国内某典型船厂存在的智能化程度低、作业方案可控性差和库存成本高等问题,以减少船体分段的库存时间、满足分段的开工时间限制和保证胎位负荷均衡为目标建立数学模型,并引入遗传算法解决此类NP-Hard问题。在算法运行过程中采用混沌映射、精英保留策略和自适应交叉变异算子增强算法的全局寻优性和收敛性。对某船厂的实际生产数据进行计算和对比分析,结果表明,采用改进算法所得结果比船厂排产计划库存时间更少,资源利用率更高,该算法有效。

A Hybrid Estimation of Distribution Algorithm for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times

A hybrid estimation of distribution algorithm (EDA) with iterated greedy (IG) search (EDA-IG) is proposed for solving the unrelated parallel machine scheduling problem with sequence-dependent setup times (UPMSP-SDST). For makespan criterion, some properties about neighborhood search operators to avoid invalid search are derived. A probability model based on neighbor relations of jobs is built in the EDA-based exploration phase to generate new solutions by sampling the promising search region. Two types of deconstruction and reconstruction as well as an IG search are designed in the IG-based exploitation phase. Computational complexity of the algorithm is analyzed, and the effect of parameters is investigated by using the Taguchi method of design-of-experiment. Numerical tests on 1640 benchmark instances are carried out. The results and comparisons demonstrate the effectiveness of the EDA-IG. Especially, the bestknown solutions of 531 instances are updated. In addition, the effectiveness of the properties is also demonstrated by numerical comparisons. © 2014 Chinese Association of Automation.

Convergence Analysis of Cuckoo Search by Creating Markov Chain

Cuckoo search(CS) has been used successfully for solving global optimization problems.From a theoretical point of view,the convergence of the CS is an important issue.In this paper,convergence analysis of CS was studied.The transition probability characteristics of the population to construct a Markov chain were analyzed.The homogeneity of the Markov chain was derived based on stochastic process theory.Then it was proved to be an absorbing state Markov chain.Consequently,the global convergence of CS was deduced based on conditions of convergence sequence and total probability formula,and the expected convergence time was given.Finally,a series of experiments were conducted.Experimental results were analyzed and it is observed that CS seems to perform better than PSO.