Static and dynamic policies with RFID for the scheduling of retrieval and storage warehouse operations

Warehouses are essential components of logistics and supply chains. The performance of warehouse operations significantly affects the efficiency of the whole chain it belongs to. Radio frequency identification (RFID) is an emerging technology capable of providing real-time information about the location and properties of tagged object(s), such as people, equipment or products. The objective of this article is threefold, to propose and compare different offline and online policies for the scheduling of warehouse operations, to design a tool that allows the decision maker to compare policies and environments without putting them into practice, and to study the benefits that can be obtained if RFID is used in a particular type of warehouse. To this end, we have developed a stylised model that captures and generalises the main characteristics of the structure, routing and sequencing operations of a given real warehouse. The model incorporates several realistic features never or rarely discussed in the literature in the presence of RFID, for example, due dates in the orders that have to be performed and congestion in the warehouse due to the presence of multiple vehicles performing the orders. We have also developed a set of heuristic routing and sequencing procedures that take and, alternatively, do not take into account real time information, and compare their performance via simulation on a set of randomly generated, although realistic, warehouse scenarios. Computational results show the effect in terms of due data fulfilment and tardiness minimisation if the RFID technology is installed and offline and online management policies are considered.     

Scheduling to Minimize Staleness and Stretch in Real-Time Data Warehouses

We study scheduling algorithms for loading data feeds into real time data warehouses, which are used in applications such as IP network monitoring, online financial trading, and credit card fraud detection. In these applications, the warehouse collects a large number of streaming data feeds that are generated by external sources and arrive asynchronously. Data for each table in the warehouse are generated at a constant rate, different tables possibly at different rates. For each data feed, the arrival of new data triggers an update that seeks to append the new data to the corresponding table; if multiple updates are pending for the same table, they are batched together before being loaded. At time τ, if a table has been updated with information up to time r≤τ, its staleness is defined as τ−r.     

A concurrent test architecture for massively parallel computers andits error detection capability

Presents new principles for online monitoring in the context of multiprocessors (especially massively parallel processors) and then focuses on the effect of the aliasing probability on the error detection process. In the proposed test architecture, concurrent testing (or online monitoring) at the system level is accomplished by enforcing the run-time testing of the data and control dependences of the algorithm currently being executed on the parallel computer. In order to help in this process, each message contains both source and destination addresses. At each message source, the sequence of destination addresses of the outgoing messages is compressed on a block basis. At the same time, at each destination, the sequence of source addresses of all incoming messages is compressed, also on a block basis. Concurrent compression of the instructions executed by the PEs is also possible. As a result of this procedure, an image of the data dependences and of the control flow of the currently running algorithm is created. This image is compared, at the end of each computational block, with a reference image created at compilation time. The main results of this work are in proposing new principles for the online system-level testing of multiprocessor systems, based on signaturing and monitoring the data dependences together with the control dependences, and in providing an analytical model and analysis for the address compression process used for monitoring the data routing process     

Hybrid system of ART and RBF neural networks for online clustering

An online clustering task is considered for machine state monitoring purpose. In the previous authors’ researches a classical ART-2 network was tested for online classification of operational states in the context of a wind turbine monitoring. Some drawbacks, however, were found when a data stream size had been increased. This case is investigated in this paper. Classical ART-2 network can cluster data incorrectly when data points are compared by using Euclidean distance. Furthermore, ART-2 network can lose accuracy when data stream is processed for long time. The way of improving the ART-2 network is considered and two main steps of that are taken. At first, the stereographic projection is implemented. At the second step, a new type of hybrid neural system which consists of ART-2 and RBF networks with data processed by using the stereographic projection is introduced. Tests contained elementary scenarios for low-dimensional cases as well as higher dimensional real data from wind turbine monitoring. All the tests implied that an efficient system for online clustering had been found.     

描述实时系统需求的模型

本文提出一个描述实时系统需求的模型。在这个模型中,层次式有穷状态机械表示成规则和模板的形式,且一个模板对应于一个状态机。由于与状态机相关的规则和信息可被写入到模板中,故用此模型写出的需求规格说明书可由多个模板组成,而且易于理解和阅读。最后,本文讨论了此模型的特点。     

NeuroNet: collaborative intraoperative guidance and control

Neurophysiological monitoring assesses CNS structure function relationships during surgery. NeuroNet supports remote performance of this task through real time multimodal data processing and multimedia network communication. The system is fully integrated, transparently combining the collection, processing, and presentation of real time data sources, including all physiological monitoring functions, with non real time functions and extensive online database information. Workstations are mounted in instrumentation racks and configured with appropriate electronics to support various data acquisition tasks including electroencephalograms (EEGs), electromyograms (EMGs), and multimodality evoked potentials. Multiple racks can be used in parallel on the same case if the number of variables to be monitored exceeds the capacity of a single tack. The data acquired on these systems is transparently accessible, in real time, across the network for both review and analysis     

Seven steps to optimizing data warehouse performance

Most operational systems store data in a normalized model in which certain rules eliminate redundancy and simplify data relationships. While beneficial for the online transaction processing workload, this model can inhibit those same OLTP databases from running analytical queries effectively. Because the analytical systems did not need to support the OLTP workload, many developers began preplanning for the answer sets. Preplanning, however, created problems in four areas: creating summary tables of preaggregated data, placing indexes in the system to eliminate scanning large data volumes, putting data into one table instead of having tables that join together, and storing the data in sorted order. All these activities require prior knowledge of the analysis and reports being requested. Unfortunately, most data warehouse implementations ignore the longer-term goals of analysis and flexibility in the rush to provide initial value. Taking time to consider the project's real purpose, then building a correct foundation for it, can assure a better future for the data warehouse. To meet user demands for more timely and flexible analysis, companies can use a step-by-step approach to move from maintaining detailed information to using summary-level data     

基于短文本情感增强的在线学习者成绩预测方法

当前利用短文本情感信息进行在线学习成绩预测的研究存在以下问题: 1)当前情感分类模型无法有效适应在线学习社区的短文本特征, 分类效果较差; 2)利用短文本情感信息定量预测在线学习成绩的研究在准确性上还有较大的提升空间. 针对以上问题, 本文提出了一种短文本情感增强的成绩预测方法. 首先, 从单词和句子层面建模短文本语义, 并提出基于学习者特征的注意力机制以识别不同学习者的语言表达特点, 得到情感概率分布向量; 其次, 将情感信息与统计、学习行为信息相融合, 并基于长短时记忆网络建模学习者的学习状态; 最后, 基于学习状态预测学习者成绩. 在三种不同类别课程组成的真实数据集上进行了实验, 结果表明本文方法能有效对学习社区短文本进行情感分类, 且能够提升在线学习者成绩预测的准确性. 同时, 结合实例分析说明了情感信息、学习状态与成绩之间的关联.     

Predictive on-line monitoring of continuous processes

For safety and product quality, it is important to monitor process performance in real time. Since traditional analytical instruments are usually expensive to install, a process model can be used instead to monitor process behavior. In this paper, a monitoring approach using a multivariate statistical modeling technique, namely multi-way principal component analysis (MPCA), is studied. The method overcomes the assumption that the system is at steady state and it provides a real time monitoring approach for continuous processes. The monitoring approach using MPCA models can detect faults in advance of other monitoring approaches. Several issues which are important for the proposed approach, such as the model input structure, data pretreatment, and the length of the predictive horizon are discussed. A multi-block extension of the basic methodology is also treated and this extension is shown to facilitate fault isolation. The Tennessee Eastman process is used for demonstrating the power of the new monitoring approach.     

惯性测量组合在线诊断系统研究

为了对武器中的惯性测量组合实施监视与诊断,提出了一套实时在线的故障诊断系统;该系统利用TEAMS-RT与LabVIEW软件和多信号模型建模,实时采集测试点的信号并进行分析,得出惯性测量组合的工作状态;最后通过Simulink模拟产生故障,对整个系统实行检验,结果表明系统正确地推断出故障;所设计的系统,具有在线、实时的特点,可在惯性测量组合的工作过程中及时发现并隔离故障.     

A data-based framework for fault detection and diagnostics of non-linear systems with partial state measurement

A novel framework based on the use of dynamic neural networks for data-based process monitoring, fault detection and diagnostics of non-linear systems with partial state measurement is presented in this paper. The proposed framework considers the presence of three kinds of states in a generic system model: states that can easily be measured in real time and in-situ, states that are difficult to measure online but can be measured offline to generate training data, and states that cannot be measured at all. The motivation for such a categorization of state variables comes from a wide class of problems in the manufacturing and chemical industries, wherein certain states are not measurable without expensive equipments or offline analysis while some other states may not be accessible at all. The framework makes use of a recurrent neural network for modeling the hidden dynamics of the system from available measurements and uses this model along with a non-linear observer to augment the information provided by the measured variables. The performance of the proposed method is verified on a synthetic problem as well as a benchmark simulation problem.     

空间Cube计算方法

随着卫星勘测、遥感影像、GPS等系统的广泛应用,目前各行各业拥有了大量的地理空间数据。空间数据仓库技术将较为成熟的数据仓库和联机分析处理技术应用到空间信息领域,以有效地支持空间分析和决策。空间Cube的构建与维护是空间数据仓库和空间联机分析处理的一个核心问题。文章在介绍空间数据仓库模型和空间Cube的基础上,结合空间聚集计算的特点,给出了几种空间Cube计算的有效方法。     

基于VR技术的变电站二次回路在线监测方法研究

针对传统的变电站二次回路监测方法存在实时性差、监测过程准确率低的问题,设计了一种基于VR技术的变电站二次回路在线监测方法.首先,利用VR技术模拟变电站实际情况,对环境信息进行三维采集,建立相应的虚拟环境模型,根据其中的各主要状态参量实现变电站监测信息融合;在此基础上,建立回路状态判断指标,对变电站二次回路状态进行判定,得到变电站二次回路安全等级信息.将其与警戒值进行对比,若等级信息超出警戒值,则代表回路中存在故障,需及时处理或检查,若等级信息小于该警戒值则代表回路状态安全,由此实现基于VR技术的变电站二次回路在线监测方法的设计.实验对比结果表明,该监测方法在监测实时性和准确性两个方面具有明显优势,能够及时、准确地发现变电站二次回路中的故障问题,为变电站检修和维护提供支持.     

流式细胞仪藻类在线监测系统—架构与应用

已有的在线型浮游植物流式细胞仪主要适用于深水湖泊或海洋,构建一种适用于浅水湖泊的流式细胞仪藻类在线监测系统。该系统主要由取排水、前处理、检测、清洗和控制5个部分组成,采用蠕动泵取水以保证藻类的完整性,并利用超声处理方式打散微囊藻群体,便于微囊藻细胞计数。藻类在线监测的数据可及时传回实验室,且使用者可以通过远程控制,实时查看系统运行情况,修改检测参数。系统日常运行中主要需要清洗维护,维护过程简单,但较耗时。目前该系统已成功应用于典型浅水湖湾太湖贡湖湖区,说明其适用于浅水湖泊的藻类在线监测,有一定的推广意义。     

On-demand data assimilation of large-scale spatial temporal systems using sequential Monte Carlo methods

The proliferation of sensors is generating rapidly increasing quantities of data like never before. These extensive amounts of data can provide useful information for more accurate state inference of large-scale spatial temporal systems. Sequential Monte Carlo methods are used to assimilate the observed data from sensors to improve the state estimation of large-scale spatial temporal systems, which highly rely on the available real time observation data. In many scenarios, the real time data are limited in space and time. Therefore, it is important to effectively obtain critical sensor data in real time and then dynamically feed them into the running model. In this paper, we propose the on-demand data assimilation method for large-scale spatial temporal systems, in which we quantify the spatial states using run-time state quantification methods and decide if we need to trigger data assimilation on demand and obtain more relevant real time data when the state uncertainty is high. The effectiveness of the developed framework is evaluated based on large-scale wildfire spread simulations.     

模型检测新技术研究

1 引言软件是否可信赖已成为一个国家的经济、国防等系统能否正常运转的关键因素之一,尤其在一些诸如核反应堆控制、航空航天以及铁路调度等安全悠关(safety-critical)领域更是如此。这类系统要求绝对安全可靠,不容半点疏漏,否则将导致灾难性后果。如1996年6月4日,欧洲航天局阿丽亚娜(Ariane)501火箭因为其控制软件的规范和设计错误而导致发射37秒后爆炸。类似的报道屡见不鲜,如何确保这些系统的可靠性成为计算机科学与控制论领域共同关注的一个焦点问题。     

一种基于Boosting的在线回归算法

针对实时数据的在线处理问题,提出了一种基于Boosting的在线回归算法,通过对学习机适宜度置信区间的定义,建立了对概念漂移的实时判断方法,利用最新流入的数据块,及时对集成算法中的个体学习机进行逐一迭代更新,从而起到在线学习的效果。通过对标准数据库的数据建立仿真模型,验证这种在线回归算法可以与离线Boosting回归算法达到相似的精度,同时占用较少的存储记忆单元,提高学习速度,能够对学习机参数进行及时调整;该算法还可引入到工业生产中,对生产数据起到实时监控的作用。     

智能化网络考试监控系统开发

近年来随着高等教育事业的快速发展,为了节约教育成本、提高教学质量和教育的信息化程度,网络化、无纸化考试在高校得到了快速推广。但由于计算机网络是一个开放的系统,考试中电子文档资料可以十分隐秘地被复制和在学生间传递,造成很多严重的作弊行为。如何防范当前网络考试中考生的作弊行为及保障考试结果的公平性成为了急需解决的重要问题。本论文所开发的智能化网络考试监控系统克服了现有考试监控系统的不足,将各种网络考试实时监控信息机地结合在一起,实现了作弊行为检测、作弊行为发现及作弊行为报警三位一体的立体化管理。同时,所开发的网络考试监控系统独立于现有各网络考试系统,在不影响现有考试系统正常工作的情况下,实现了考试作弊行为的有效监控,使其具有良好的可推广性及可维护性。     

Robust online signal extraction from multivariate time series

Robust regression-based online filters for multivariate time series are proposed and their performance in real time signal extraction settings is discussed. The focus is on methods that can deal with time series exhibiting trends, level changes, outliers and a high level of noise as well as periods of a comparatively steady state. The new filter is based on a robust two-step online procedure, and it recognises that the data are often measured on a discrete scale. The relevant properties and the performance of this new filter are discussed and investigated by means of simulations and by a medical application.     

Scheduling the truckload operations in automated warehouses with alternative aisles for pallets

In this study, the scheduling of truck load operations in automated storage and retrieval systems is investigated. The problem is an extension of previous ones such that a pallet can be retrieved from a set of alternative aisles. It is modelled as a flexible job shop scheduling problem where the loads are considered as jobs, the pallets of a load are regarded as the operations, and the forklifts used to remove the retrieving items to the trucks are seen as machines. Minimization of maximum loading time is used as the objective to minimize the throughput time of orders and maximize the efficiency of the warehouse. A priority based genetic algorithm is presented to sequence the retrieving pallets. Permutation coding is used for encoding and a constructive algorithm generating active schedules for flexible job shop scheduling problem is applied for decoding. The proposed methodology is applied to a real problem arising in a warehouse installed by a leading supplier of automated materials handling and storage systems.