Sequencing method for dual-shuttle flow-rack automated storage and retrieval systems

The dual-retrieval （DR） operation sequencing problem in the flow-rack automated storage and retrieval system （AS/RS） is modeled as an assignment problem since it is equivalent to pairing outgoing unit-loads for each DR operation. A recursion symmetry Hungarian method （RSHM）, modified from the Hungarian method, is proposed for generating a DR operation sequence with minimal total travel time, in which symmetry marking is introduced to ensure a feasible solution and recursion is adopted to break the endless loop caused by the symmetry marking. Simulation experiments are conducted to evaluate the cost effectiveness and the performance of the proposed method. Experimental results illustrate that compared to the single-shuttle machine, the dual-shuttle machine can reduce more than 40% of the total travel time of retrieval operations, and the RSHM saves about 5% to 10% of the total travel time of retrieval operations compared to the greedy-based heuristic.

Travel time and alternative configurations analysis for automated storage/retrieval systems based on geometrical method

In order to evaluate the efficiency of the automated storage/retrieval system(AS/RS)accurately,and compare different layouts of the AS/RS using mean travel time,under randomized storage conditions,an exact,geometry-based analytical model is presented.The model can be used to compute the expected single-command and dual-command travel time for a storage/retrieval(S/R)machine which can travel simultaneously horizontally and vertically as it moves along a storage aisle.The rack may be either square in time or non square in time.Additionally,the alternative layouts of the AS/RS and travel-time models are examined.Comparing with setting the I/O point at the left-lower corner of the rack,setting the I/O point at any point at the vertical edge can help enhance the efficiency of the AS/RS.

The Cloud Storage Ciphertext Retrieval Scheme Based on ORAM

Due to its characteristics distribution and virtualization, cloud storage also brings new security problems. User＇s data is stored in the cloud, which separated the ownership from management. How to ensure the security of cloud data, how to increase data availability and how to improve user privacy perception are the key issues of cloud storage research, especially when the cloud service provider is not completely trusted. In this paper, a cloud storage ciphertext retrieval scheme based on AES and homomorphic encryption is presented. This ciphertext retrieval scheme will not only conceal the user retrieval information, but also prevent the cloud from obtaining user access pattern such as read-write mode, and access frequency, thereby ensuring the safety of the ciphertext retrieval and user privacy. The results of simulation analysis show that the performance of this ciphertext retrieval scheme requires less overhead than other schemes on the same security level.

A Multi-stage Heuristic Algorithm for Matching Problem in the Modified Miniload Automated Storage and Retrieval System of E-commerce

E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can＇t fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.

IOTA-Based Data Encryption Storage and Retrieval Method

At present,the traditional blockchain for data storage and retrieval reflects the characteristics of slow data uploading speed,high cost,and transparency,and there are a lot of corresponding problems,such as not supporting private data storage,large data operation costs,and not supporting Data field query.This paper proposes a method of data encryption storage and retrieval based on the IOTA distributed ledger,combined with the fast transaction processing speed and zero-value transactions of the IOTA blockchain,through the Masked Authenticated Messaging technology,so that the data is encrypted in the data stream.The form is stored in the distributed ledger,quickly retrieved through the field index mechanism established by the data form,and the data operation is carried out on the chain.Experimental results show that this system has high storage,encryption and retrieval performance,and good practicability.

X-RESTORE: Middleware for XML's Relational Storage and Retrieve

We propose a new approach to store and query XML data in an RDBMS basing on the idea of the numbering scheme and inverted list. O ur approach allows us to quickly determine the precedence, sibling and ancestor/ descendant relationships between any pair of nodes in the hierarchy of XML, and utilize path index to speed up calculating of path expressions. Examples have de monstrated that our approach can effectively and efficiently support both XQuery queries and keyword searches. Our approach is also flexible enough to support X ML documents both with Schema and without Schema, and applications both retrieva l and update. We also present the architecture of middleware for application acc essing XML documents stored in relations, and an algorithm translating a given X ML document into relations effectively.

A Sentence Retrieval Generation Network Guided Video Captioning

Currently,the video captioning models based on an encoder-decoder mainly rely on a single video input source.The contents of video captioning are limited since few studies employed external corpus information to guide the generation of video captioning,which is not conducive to the accurate descrip-tion and understanding of video content.To address this issue,a novel video captioning method guided by a sentence retrieval generation network(ED-SRG)is proposed in this paper.First,a ResNeXt network model,an efficient convolutional network for online video understanding(ECO)model,and a long short-term memory(LSTM)network model are integrated to construct an encoder-decoder,which is utilized to extract the 2D features,3D features,and object features of video data respectively.These features are decoded to generate textual sentences that conform to video content for sentence retrieval.Then,a sentence-transformer network model is employed to retrieve different sentences in an external corpus that are semantically similar to the above textual sentences.The candidate sentences are screened out through similarity measurement.Finally,a novel GPT-2 network model is constructed based on GPT-2 network structure.The model introduces a designed random selector to randomly select predicted words with a high probability in the corpus,which is used to guide and generate textual sentences that are more in line with human natural language expressions.The proposed method in this paper is compared with several existing works by experiments.The results show that the indicators BLEU-4,CIDEr,ROUGE_L,and METEOR are improved by 3.1%,1.3%,0.3%,and 1.5%on a public dataset MSVD and 1.3%,0.5%,0.2%,1.9%on a public dataset MSR-VTT respectively.It can be seen that the proposed method in this paper can generate video captioning with richer semantics than several state-of-the-art approaches.

DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms.Image and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge graph.In the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge graphs.The representation learning method is sig-nificant to the application of multi-modal knowledge graphs.This paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval.Firstly,use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency.Secondly,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of information.Finally,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval.The paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.

On-Demand Photon Storage and Retrieval with a Solid-State Photon Molecule at Room Temperature

Deterministically achieving on-chip photon storage and retrieval is a fundamental challenge for integrated photonics.Moreover,this requirement is increasingly urgent as photon storage and retrieval is crucial to realize truly scalable room-temperature quantum computing.However,most of existing quantum memories integrated on chips must either work at cryogenic temperature or else are strongly coupled with the environment,which hugely reduces the efficiency.Here,we propose an on-chip room-temperature quantum memory comprising three coupled microcavities,which presents an ideal dark state decoupled by a waveguide,thereby allowing on-demand photon storage and retrieval with high efficiency and high fidelity simultaneously.Furthermore,we demonstrate that the single-photon temporal duration can be increased or decreased by a factor of 10^(3),thereby enabling many crucial quantum applications.Our error-robust approach highlights the potential for a solid-state photonic molecule for use as on-chip quantum memory and for optical quantum computing.

AS/RS在建设物资仓储环节的应用与研究

在当前的电网建设和升级过程中,物资仓储管理的效率直接关系到整个工程项目的进展速度和成本控制。自动化仓储系统(AS/RS)的应用,为电网建设物资仓储带来了革命性的改进。文章深入探讨了AS/RS的技术原理、功能结构及在电网建设物资仓储中的具体应用。通过仓储需求分析,提出了基于AS/RS的设计方案,并对其实施与运行管理进行了详细讨论。并通过对比分析,阐述了AS/RS在提高仓储效率、降低管理成本等方面的显著优势。研究结果表明,AS/RS的引入为电网建设物资的仓储管理提供了一种高效、节能、成本可控的解决方案,具有重要的实践价值和广泛的应用前景。

混合蛙跳算法在旋转货架货位优化中的应用

以集群旋转式货架存储货物的自动化立体仓库为研究对象,以提高货架整体和运动状态的单层稳定性、货物相关性和出入库效率等基本货位优化原则为目标,提出一种通用性较强的多参数混合蛙跳算法,对多目标优化问题进行建模,使用MATLAB工具进行仿真。通过实验结果的对比可知,多参数混合蛙跳算法比混合蛙跳算法具有一定的优越性,并且对于分析其他问题的适应性较强,对于多目标优化问题具有一定的理论意义。

具有多层级货架的自动小车存取系统排队网建模与分析

为了满足多楼层生产车间之间的物料运输需求,同时减少企业成本,自动存取系统的设计问题变得愈发重要。带有多层级货架的自动小车存取系统因其优秀的吞吐能力和柔性,具有很好的应用前景。为了分析多层车间中带有多层级货架自动小车存取系统并行作业的性能,在分析自动导引小车(AGV)和电梯、伸缩臂作业方式的基础上,构建了半开排队网络模型,并设计了基于近似均值分析法的近似求解算法。该方法首先将半开排队网内部网络分为不同的单独网络模型,并分别重构为等效的闭排队网络模型。这些网络模型最终被聚合成一个节点,与外部的同步节点联合构建闭排队网络模型求解吞吐率。最后,针对内部网络的单独节点进行分析计算,得到其他系统性能指标。通过设计仿真实验,验证了所提方法的有效性和准确性。

自动化立体仓库退库货位优化问题及其求解算法

针对自动化立体仓库出库作业过程中剩余货物退库问题,以堆垛机作业总能耗最小化为目标,以退库货位分配为决策变量,建立了自动化立体仓库退库货位优化模型,提出了基于深度强化学习的自动化立体仓库退库货位优化框架。在该框架内,以立体仓库实时存储信息和出库作业信息构建多维状态,以退库货位选择构建动作,建立自动化立体仓库退库货位优化的马尔科夫决策过程模型;将立体仓库多维状态特征输入双层决斗网络,采用决斗双重深度Q网络(dueling double deep Q-network,D3QN)算法训练网络模型并预测退库动作目标价值,以确定智能体的最优行为策略。实验结果表明D3QN算法在求解大规模退库货位优化问题上具有较好的稳定性。

RS纠删码在云存储中的应用

云存储作为一种旨在为用户提供高效、廉价、安全、可扩展、可定制的存储服务的存储模式,已然成为存储服务的发展趋势.但是因为其云端存储节点的不可控性,云存储在如何保障数据安全性的问题上面临着挑战.通过将RS纠删码引入云存储中,采用分片聚合机制,为云存储的数据安全性保障提供了一种新的解决方案.

基于RS和GIS分析干旱区土地利用/覆盖变化对陆地植被碳储量的影响——以新疆鄯善县绿洲为例

土地利用馒被变化是影响陆地碳循环的一个重要因子。依据鄯善平原绿洲群1990、1999两年9月TM与SPOT4（P）的融合影像以及2004年9月SPOT5（XS／XI）影像数据，采用混合分类法对研究区土地覆被进行分类，估算其变化面积以及林地与各土地覆被类型的面积转换。结果显示：1990～1999年、1999～2004年研究区林地面积年变化率分别为＋4．59％、-1．70％，植被碳库1999年比1990年增加了264．13×10^3t，2004年较之1999年减少了70．75×10^3t，但2004年植被碳储量较1990年上升212．04×10^3t，研究区总体上为一小的净碳汇。15a间天然草地的植被碳储量一直下降，并在后期呈现更为严峻的态势。说明在极端干旱区，生态系统的稳定仍面临极大挑战。如何实现丰富矿产资源的开采与人居生存环境相协调，做好土地、水资源管理与生态环境保护工作，仍需更为深入、审慎的研究。

纳米光纤-半导体量子点分子耦合系统中光孤子的存储与读取

本文解析研究了基于纳米光纤-半导体量子点分子耦合系统中时间光孤子存储和读取.结果表明,由于半导体量子点分子中点间隧穿诱导透明效应,系统中的光吸收被大大抑制.同时,纳米光纤的横向约束能增强光与系统的相互作用,增强的系统非线性响应能平衡色散效应得到稳定的时间光孤子.进一步研究表明,通过关闭和打开点间隧穿耦合,系统中可实现光孤子的高效率和高保真度的存储与读取.上述结果对于固体量子材料中全光信息处理的实际应用具有一定的指导意义和潜在的应用价值.

InfoWorks RS软件在河道洪水模拟中的应用

以淮河中游鲁台子淮南区间河段为例,采用InfoWorks RS软件模拟分析不同行蓄洪区启闭方案情景下,50年一遇设计洪水引起的河道主要防洪断面水位变化过程,并按最高水位最小为原则确定最佳的行蓄洪区启用方案,为确定最佳的行蓄洪区启用方案与防洪决策提供依据。

MARS软件在数值预报模式产品数据管理中的应用

该文简单介绍MARS(Meteorological Archival and Retrieval System)软件的架构及组成部分,并基于MARS软件,介绍了数值预报产品数据管理系统设计与实现。通过在GRIB2数据段对应描述层次类型和气象要素的表格文件中定义新的层次类型和气象要素,在MARS软件解析GRIB2数据所需要查找的相关模板表格文件中定义气象要素对应的产品类型号、要素类别号、要素号、要素名称和要素名称缩写,说明GRIB2数据模板和代码表创建方法。以GRAPES模式产品GRIB2数据为例,实现应用MARS软件管理GRAPES模式产品GRIB2数据。结合存储管理TSM(Tivoli Storage Manager)软件,研究开发MARS软件和TSM软件连接接口程序,实现数据的直接迁移和回调。以T639L60模式产品为例,通过测试检索性能,MARS软件检索数值预报产品速度超过当前国家级气象数据管理系统数据检索速度1倍,说明MARS软件检索数值预报产品数据具有时效优势。

基于改进人工蜂群算法的四向穿梭车仓储系统货位优化研究

目的针对四向穿梭车仓储系统中多设备并行作业的特点和高效作业的需求,对其货位优化进行研究。方法建立了考虑入库效率、货架稳定性、作业均衡度和货物关联性4个因素的货位分配组合优化模型,设计了改进人工蜂群算法求解该模型。利用多目标Pareto支配解概念生成初始解,融合遗传算法和变邻域搜索算法思想进行解的优化搜索,以提高算法的性能。为更好地确定算法参数,进行了算法参数敏感性试验。针对3种(大、中、小)入库规模和9种不同仓位下的入库,进行了改进人工蜂群算法、遗传算法和蚁群算法3种算法的求解。结果入库规模越大,改进人工蜂群算法优势越明显,大规模下相比遗传算法和蚁群算法,其性能分别提升了13.14%和19.32%;不同仓位下相比另2种算法,其性能提升了6.38%~26.76%。结论文中所建立的货位分配优化模型更接近仓储系统的实际工作状态,所提出的算法能够有效进行入库货位分配,提高了仓库运作效率。

面向云存储容错系统的RS再生码

面向云存储容错系统提出了一种RS再生纠删码,该编码继承了RS编码容多错的可靠性,又能实现容三错的高效性。对RS再生码中单节点故障混合修复方法进行了介绍,并求出了混合修复时磁盘读取数的理论下界。从理论上对RS再生码的存储开销、译码效率、修复带宽进行了性能评估。实验结果表明,RS再生纠删码比同类纠删码的修复性能有较大的提升,特别是采用混合修复算法以后,系统单故障恢复时间下降20.8%~28.2%。