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.

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.

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.

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.

Automatic Measurement and Control Design of Sea Wave Energy Storage and Seawater Desalination Device

The application of seawater desalination has become more and more popular. The reverse osmosis membrane method is one of its applications, but it consumes more energy resources and produces freshwater with high cost. The four-column floating bed we designed has low investment and is simple and easy to operate. The sea wave energy storage and seawater desalination device produced by this method has greatly reduced the cost of freshwater produced. This paper focuses on the design of the automatic measurement and control loop of the device.

The Complexity and On-Line Algorithm for Automated Storage and Retrieval System with Stacker Cranes on One Rail

This paper considers an on-line scheduling and routing problem concerning the automated storage and retrieval system from tobacco industry. In this problem, stacker cranes run on one common rail between two racks. Multiple input/output-points are located at the bottom of the racks. The stacker cranes transport bins between the input/output-points and cells on the racks to complete requests generated over time. Each request should be accomplished within its response time. The objective is to minimize the time by which all the generated requests are completed. Under a given physical layout, the authors study the complexity of the problem and design on-line algorithms for both one-stacker-crane model and two-stacker-crane model. The algorithms axe validated by instances and numerical simulations.

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.

An Approach for Content Retrieval from Web Pages Using Clustering Techniques

Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is not an appropriate, because it gives ambiguous information for the user query, and the user cannot get relevant information within the stipulated time. To overcome these issues, we propose a new methodology for information retrieval EPCRR by providing the top most exact information to the user, by using the collaborative clustered automated filter which makes use of the collaborative data set and filter works on the prediction by providing the highest ranking for the exact data retrieved. The retrieval works on the basis of recommendation of data which consists of relevant data set with highest priority from the cluster of data which is on high usage. In this work, we make use of the automated wrapper which works similar to the meta crawler functionality and it obtains the content in the semantic usage data format. Obtained information from the user to the agent will be ranked based on the Enabled Pile clustered data with respect to the metadata information from the agent and end-user. The information is given to the end-user with the top most ranking data within the stipulated time and the remaining top information will be moved to the data repository for future use. The data collected will remain stable based on the user preference and works on the intelligence system approach in which the user can choose any information under any instances and can be provided with suitable high range of exact content. In this approach, we find that the proposed algorithm has produced better results than existing work and it costs less online computation time.

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.

Scheduling Storage Process of Shuttle-Based Storage and Retrieval Systems Based on Reinforcement Learning

The Shuttle-Based Storage and Retrieval System(SBS/RS)has been widely studied because it is currently the most efficient automated warehousing system.Most of the related existing studies are focused on the prediction and improvement of the efficiency of such a system at the design stage.Hence,the control of existing SBS/RSs has been rarely investigated.In existing SBS/RSs,some empirical rules,such as storing loads column by column,are used to control or schedule the storage process.The question is whether or not the control of the storage process in an existing system can be improved further by using a different approach.The storage process is controlled to minimize the makespan of storing a series of loads into racks.Empirical storage rules are easy to control,but they do not reach the minimum makespan.In this study,the performance of a control system that uses reinforcement learning to schedule the storage process of an SBS/RS with fixed configurations is evaluated.Specifically,a reinforcement learning algorithm called the actor-critic algorithm is used.This algorithm is made up of two neural networks and is effective in making decisions and updating itself.It can also reduce the makespan relative to the existing empirical rules used to improve system performance.Experiment results show that in an SBS/RS comprising six columns and six tiers and featuring a storage capacity of 72 loads,the actor-critic algorithm can reduce the makespan by 6.67%relative to the column-by-column storage rule.The proposed algorithm also reduces the makespan by more than 30%when the number of loads being stored is in the range of 7–45,which is equal to 9.7%–62.5%of the systems’storage capacity.

Storage and retrieval of ultraslow optical solitons in coherent atomic system

We investigate the propagation of intense probe pulses in a lifetime broadened A-type three-level atomic system with a configuration of electromagnetically induced transparency. We find that ultraslow optical solitons formed by a balance between dispersion and nonlinearity can be stored and retrieved in the system by switching off and on a control field. Such pulses are robust during storage and retrieval, and hence may have potential applications in optical and quantum information processing.

Cellular automata modelling of phase-change memories

A novel approach to modelling phase-transition processes in phase change materials used for optical and electrical data storage applications is presented. The model is based on a cellular automaton （CA） approach to predict crystallization behaviour that is linked to thermal and electrical simulations to enable the study of the data writing and erasing processes. The CA approach is shown to be able to predict the evolution of the microstructure during the rapid heating and cooling cycles pertinent to data storage technology, and maps crystallization behaviour on the nanoscale. A simple example based on possible future nonvolatile phase-change random access solid-state memory is presented.

An Efficient Dynamic Proof of Retrievability Scheme

Data security is a significant issue in cloud storage systems. After outsourcing data to cloud servers, clients lose physical control over the data. To guarantee clients that their data is intact on the server side, some mechanism is needed for clients to periodically check the integrity of their data. Proof of retrievability （PoR） is designed to ensure data integrity. However, most prior PoR schemes focus on static data, and existing dynamic PoR is inefficient. In this paper, we propose a new version of dynamic PoR that is based on a B＋ tree and a Merkle hash tree. We propose a novel authenticated data structure, called Cloud Merkle B＋ tree （CMBT）. By combining CMBT with the BES signature, dynamic operations such as insertion, deletion, and modification are supported. Compared with existing PoR schemes, our scheme improves worst-case overhead from O（n） to O（log n）.

Dynamic Proofs of Retrievability Based on Partitioning-Based Square Root Oblivious RAM

With the development of cloud storage,the problem of efficiently checking and proving data integrity needs more consideration.Therefore,much of growing interest has been pursed in the context of the integrity verification of cloud storage.Provable data possession(PDP)and Proofs of retrievablity(POR)are two kinds of important scheme which can guarantee the data integrity in the cloud storage environments.The main difference between them is that POR schemes store a redundant encoding of the client data on the server so as to she has the ability of retrievablity while PDP does not have.Unfortunately,most of POR schemes support only static data.Stefanov et al.proposed a dynamic POR,but their scheme need a large of amount of client storage and has a large audit cost.Cash et al.use Oblivious RAM(ORAM)to construct a fully dynamic POR scheme,but the cost of their scheme is also very heavy.Based on the idea which proposed by Cash,we propose dynamic proofs of retrievability via Partitioning-Based Square Root Oblivious RAM(DPoR-PSR-ORAM).Firstly,the notions used in our scheme are defined.The Partitioning-Based Square Root Oblivious RAM(PSR-ORAM)protocol is also proposed.The DPOR-PSR-ORAM Model which includes the formal definitions,security definitions and model construction methods are described in the paper.Finally,we give the security analysis and efficiency analysis.The analysis results show that our scheme not only has the property of correctness,authenticity,next-read pattern hiding and retrievabiltiy,but also has the high efficiency.

Improved phase retrieval in holographic data storage based on a designed iterative embedded data

Embedded data are used to retrieve phases quicker with high accuracy in phase-modulated holographic data storage(HDS).We propose a method to design an embedded data distribution using iterations to enhance the intensity of the high-frequency signal in the Fourier spectrum.The proposed method increases the antinoise performance and signal-to-noise ratio(SNR)of the Fourier spectrum distribution,realizing a more efficient phase retrieval.Experiments indicate that the bit error rate(BER)of this method can be reduced by a factor of one after 10 iterations.

Literature Review on Storage and Retrieval Models of Mental Lexical

心理词汇也称为内部词汇或者心理字典，指的是储存于大脑中的词汇。桂诗春（2000）认为研究心理词汇应该主要关注词汇是怎样储存在记忆里，又是如何被提取的？为此学者们提出很多不同的模型理论。本文从储存模型和提取模型两个角度着手，总结概括原子球理论，分级网络模型，扩散激活模拟，并行提取模型和窜行提取模型，深入讨论它们的研究现状，存在问题以及进一步的研究建议。总结得出随着这些模型的不断修正，他们当初所强调的假设越来越微弱，它们之间的距离也越来越短。

信息技术在果蔬冷库系统中的应用

冷库是延长果蔬贮藏期的关键设施,而应用信息技术可以实现果蔬冷库的冷藏装置自动化控制、果蔬温度数据采集分析以及冷库内设备的物联网应用等功能,进而达到精确调控果蔬冷库的温度、降低能耗、提高管理效率的目的。随着人工智能算法和区块链技术的发展,冷库系统将更加智能化和网络化,不仅可以优化果菜的贮存和运输环境,还能实现果蔬从种植到消费者手中的全程质量可追溯。本文探讨了信息技术在果蔬冷库系统中的应用及未来发展趋势,包括自动化控制、数据采集分析、物联网技术和人工智能算法。这些技术的应用提高了冷库运营的效率和安全性,为实现果蔬贮存的预测优化和质量可追溯性提供了有力支持,有助于保障食品安全和供应链的稳定。

基于改进启发式算法的四向穿梭车系统双端预编组研究

四向穿梭车存储系统是一种新型密集存储系统,可以满足电商环境下仓储快速响应的需求。四向库具有双端检索货物的功能,以释放库存潜力并提升出入库的效率。由于系统的多深位特性,出库过程中会涉及额外的检索操作,因此提前对货物进行预编组排序至关重要。针对四向库预编组问题,设计了一种改进的启发式算法,在非高峰出库时段通过双端检索排序使货物达到有序状态,减少出库移动步数和检索时间,从而提高仓储作业的效率。结果表明:相较于L公司现有算法,该算法平均减少了12.82%的移动步数;采用矩形布局和双端出库布局可以进一步减少移动步数,分别平均减少了35.44%和12.82%的移动次数。

在ArcGIS环境下基于Python实现钻孔数据批量导入及检索方法的研究

该文以地震工程钻孔数据的批量处理为案例,探讨了如何结合Arc GIS10.8版本中的Python脚本编程以及ArcToolbox工具箱,开发自动化的数据批量导入和检索程序。这一过程通过对数据的导入和筛选,有效提高了工作流程的效率。