An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.     

Identification of approximately duplicate material records in ERP systems

The quality of master data is crucial for the accurate functioning of the various modules of an enterprise resource planning (ERP) system. This study addresses specific data problems arising from the generation of approximately duplicate material records in ERP databases. Such problems are mainly due to the firm’s lack of unique and global identifiers for the material records, and to the arbitrary assignment of alternative names for the same material by various users. Traditional duplicate detection methods are ineffective in identifying such approximately duplicate material records because these methods typically rely on string comparisons of each field. To address this problem, a machine learning-based framework is developed to recognise semantic similarity between strings and to further identify and reunify approximately duplicate material records – a process referred to as de-duplication in this article. First, the keywords of the material records are extracted to form vectors of discriminating words. Second, a machine learning method using a probabilistic neural network is applied to determine the semantic similarity between these material records. The approach was evaluated using data from a real case study. The test results indicate that the proposed method outperforms traditional algorithms in identifying approximately duplicate material records.     

Design and development of inventory knowledge discovery system

Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.     

省域高速公路联网监控系统方案设计

省域高速公路的联网监控是高速公路建设的主要趋势,文中设计了省域联网监控系统,阐述了设计的主要目标、整体方案以及各个子系统的功能和构成.     

快速货运轴辐式网络研究

介绍了快速货运中各种轴辐式网络及其特点,总结了轴辐式网络问题的研究现状,并对其研究趋势做出了分析。     

计算机网络安全及防范技术

文章主要阐述了网络安全产品的特点,计算机信息网络攻击和入侵的特点、方法及其安全防范手段。     

基于VPN技术的军事物流信息系统应用研究

传统的联网方式已难以适应现代信息化技术发展时军事物流信息化的要求,VPN（虚拟专用网络）技术提供了一种既安全可靠又节省成本的组网方式。通过对VPN技术及其在信息系统应用中的优势进行相关研究。提出了军事物流信息系统建设中VPN实施方案。最后结合具体实例对VPN规划与使用进行了论述。     

福建省加权网络应急物资储备点选址

文章在对应急物资储备点选址模型进行概述的基础上,对福建省应急物资储备点进行了分析。选择集合覆盖和加权网络作为理论基础实现储备点选址。并给出福建省应急物资储备点选址的最优方案和遍历应急系统所有区域所需要的时间。     

混凝土搅拌站属机械设备冬季使用保养探讨

混凝土搅拌站的良好运作和它自身的保养维护是密不可分的,混凝土搅拌站的设备需要精心呵护,进而才能够最大限度降低机器的故障发生率和提高设备使用寿命。本文主要论述了搅拌站正常使用与保养过程中的注意事项、使用搅拌站柴油机过程中的注意事项、混凝土输送泵、混凝土搅拌车及装载机的使用注意事项及混凝土搅拌站负责人员要注意的清理保养项目。     

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well-performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well-established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes’ interactions with exogenous factors. To assist related work, we made the code available in a dedicated repository.