基于MCFM模型的集贸市场火灾致灾因素分析

为降低大型集贸市场的火灾风险,探究大型集贸市场火灾致灾因素,运用解释结构模型分析各因素间的内在联系。结合交叉影响分析法和马尔可夫链预测构建定量分析模型,获得火灾致灾因素的稳态概率矩阵。研究结果表明：马尔可夫修正概率更加准确,该模型可明确大型集贸市场火灾致灾因素排查和管控的重点。     

云环境下安全密文区间检索方案的新设计

针对目前密文区间检索多次断言存在信息泄露等问题,有单断言的密文区间检索方案(SRQSAE方案),并证明方案在唯密文攻击下的隐私安全性。对SRQSAE方案的安全性进行了分析,分析结果表明SRQSAE方案并不能隐藏搜索关键字的大小关系排序。通过在每次生成搜索索引或陷门消息时引入不同随机数的方法,提出了单断言的密文区间检索新方案。新方案对搜索关键字、区间的大小关系提供了很好的隐私保护;而且新方案在安全性提高的同时,并不以损失效率为代价。     

含腐蚀缺陷油气管道评价技术研究进展

由于外部环境及输送介质的影响,油气管道在运行过程中不可避免地会出现腐蚀缺陷,腐蚀处会产生应力集中的现象,从而削弱管道的承压能力。因此,必须对含腐蚀缺陷的管道进行剩余强度及剩余寿命评价,评价结果可以为缺陷管线的维修计划提供重要依据。综述了国内外主要的腐蚀缺陷管道评价规范,分析了不同规范的适用范围。从单个腐蚀和群腐蚀两个方面展开论述,归纳了含腐蚀缺陷管道评价技术的最新研究进展,并介绍了目前的研究热点——群腐蚀缺陷管道剩余强度评价方法以及腐蚀缺陷间相互影响准则(interaction rule)。另外,论述了目前腐蚀管道剩余寿命预测的常用方法,包括灰色预测法以及可靠度函数分析法。最后,总结了腐蚀缺陷管道评价技术研究进展,并对未来的研究工作提出了展望。     

Extended vertical lists for temporal pattern mining from multivariate time series

In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists is introduced. The method is based on an extension of the level‐wise property, which requires a more complex pattern to start at positions within a record where all of the subpatterns of the pattern start. The approach is built around a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records and links them to appropriate positions of a specific subpattern of the pattern called the prefix. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for Temporal Pattern Mining; however, the increase in speed comes at the expense of increased memory usage.     

A new interval diagnosis method: Application to the spacecraft rendezvous phase of the Mars sample return mission

This paper presents a new optimized interval principal component analysis applied to detect and isolate actuators faults of an autonomous spacecraft involved in the rendezvous phase of the Mars sample return mission. Based on the exploitation of various arithmetic and interval analysis properties, the new interval model is built by solving the interval eigenpairs problem via a resolution of a parametric linear programming problem. The detection and isolation phases are performed by extending the classic methods to interval-valued data. The proposed method is applied to detect and isolate actuators faults that can occur on the spacecraft's thrusters. Based on data provided by a “high fidelity” industrial simulator developed by Thales Alenia Space, the obtained results proved the effectiveness of the proposed interval fault diagnosis method on detecting and isolating thrusters' faults.     

基于SpringBoot框架的中长期水文预报系统设计与开发

鉴于中长期水文预报能有效为宏观防洪形势分析和水资源管理提供科学依据,提出了Java环境下基于SpringBoot框架开发的中长期水文预报系统。该系统集成了包含随机森林、支持向量机、小波分析、贝叶斯模型平均等多种模型方法的Python模型库以提高开发效率;采用了基于RabbitMQ技术的异种语言调用与基于RESTful技术的轻量级接口设计以降低系统响应时间。并搭建了全国主要江河流域共10个断面的中长期水文预报系统作为示例进行分析,实现了中长期水文预报业务化运行,可为同类系统开发提供参考示范。     

Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor

A semiconductor distributor that plays a third-party role in the supply chain will buy diverse components from different suppliers, warehouse and resell them to a number of electronics manufacturers with vendor-managed inventories, while suffering both risks of oversupply and shortage due to demand uncertainty. However, demand fluctuation and supply chain complexity are increasing due to shortening product life cycle in the consumer electronics era and long lead time for capacity expansion for high-tech manufacturing. Focusing realistic needs of a leading distributor for semiconductor components and modules, this study aims to construct a UNISON framework based on deep reinforcement learning (RL) for dynamically selecting the optimal demand forecast model for each of the products with the corresponding demand patterns to empower smart production for Industry 3.5. Deep RL that integrates deep learning architecture and RL algorithm can learn successful policies from the dynamic and complex real world. The reward function mechanism of deep RL can reduce negative impact of demand uncertainty. An empirical study was conducted for validation showing practical viability of the proposed approach. Indeed, the developed solution has been in real settings.     

An optimal integrated lot sizing and maintenance strategy for multi-machines system with energy consumption

This paper proposes an integrated model for multi-machines dynamic lot sizing aiming to produce a single item, considering the energy consumption during the production horizon. The objective is to find, firstly, the optimal lot size as well as the number of machines that satisfy a random demand under given service level and secondly, maintenance plan depended to production planning to minimise the total production, energy and maintenance costs. In fact, the problem of energy consumption is one of the most evoked topics especially with the decision of many governments to reduce theirs (For example France is willing to reduce the total consumption by 20% by 2020). The keys of this study are to consider, firstly, the correlation between the forecasting of demand, the variation of the working machines as well as their production rates under energy constraint and secondly the correlation between the production cadences and the maintenance strategy of all machines.     

考虑边信息的多层贝叶斯需求预测模型

随着工业互联网经济的发展,需求的不确定性日益增大,为提高需求预测的准确性,提出一个考虑边信息的多层贝叶斯需求预测模型(DFSI)。DFSI模型通过构造隐层的网络结构以实现对客户需求更加精确的刻画,该隐层结构主要包含两组参数:一组用于描述客户需求在时间上固有的连续性特征,另一组则用于融合相关的边信息特征。进一步,以贝叶斯推断为理论基础,以最大化后验概率为目标,推导出了DFSI的优化目标,并基于梯度下降方法设计了相应的求解算法。使用京东商城及某制造企业的真实销售数据对提出的模型进行了检验。结果显示,与常用的需求预测模型相比,DFSI能获得更好的预测结果。     

基于负荷预测的0.4 kV配网低电压风险评估方法

我国现阶段的配电网网架结构依然十分薄弱,智能化水平低,缺乏先进的检测技术和高效的运维模式。在现阶段低压电气信息不开源的情况下,为解决0.4 kV配网无差别运维效率低的问题,文章分析了适用于低压配网状态评估的评价维度,并基于负荷预测结果提出了低压台区低电压风险评估方法,引入了微增容量所引起的压降比,实现对低电压风险的定期管控。该文对于优化低压配电网运维资源配置、指导低压配网差异化运维模式的建立,以及提高低压配电网运维整治效率具有一定的参考价值。