多堆厂址始发事件分析探讨

多堆厂址一级概率安全评价（PSA）研究中，机组数目的增加使得建模工作量剧增，给整个核电厂的风险评估带来困难。结合已有基础，本文研究了多堆厂址始发事件分析的筛选方法，提出利用堆芯损伤频率（CDF）上下限值评估方法，分析厂址内不同机组数对厂址CDF的影响。结果表明，双机组厂址适合优先进行具体分析。针对双机组核电站，对多堆厂址内各始发事件进行筛选。结果表明，丧失厂外电、丧失热阱等事件适合建模分析，并对其他筛选结果给出后续分析建议，为多堆厂址一级PSA后续事故序列建模工作提供了重要基础。     

An E-Business Event Stream Mechanism for Improving User Tracing Processes

With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets; namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets.     

Distributed two‐stage state estimation with event‐triggered strategy for multirate sensor networks

This paper investigates the state estimation issue for a class of wireless sensor networks (WSNs) with the consideration of limited energy resources. First, a multirate estimation model is established, and then, a new event‐triggered two‐stage information fusion algorithm is developed based on the optimal fusion criterion weighted by matrices. Compared with the existing methods, the presented fusion algorithm can significantly reduce the communication cost in WSNs and save energy resources of sensors efficiently. Furthermore, by presetting a desired containment probability over the interval [0,1] with the developed event‐triggered mechanism, one can obtain a suitable compromise between the communication cost and the estimation accuracy. Finally, a numerical simulation for the WSN tracking system is given to demonstrate the effectiveness of the proposed method.     

高效液相色谱-氢化物发生原子荧光光谱法测定食品中多形态硒含量

目的 探究急性心肌梗死(AMI)患者经皮冠状动脉介入术(PCI)后血清成纤维细胞生长因子21(FGF-21)、髓过氧化物酶(MPO)水平与预后的关系。方法 选择2016年1月至2018年12月在我院接受PCI术治疗的108例AMI患者作为研究对象。根据随访过程中是否发生主要不良心血管事件(MACE),病人分为预后不良组(25例)和预后良好组(83例)。采用酶联免疫吸附法检测受试者血清FGF-21水平,采用胶体金免疫层析法检测受试者血清MPO水平。分析FGF-21、MPO与AMI患者PCI术后MACE发生的关系及诊断MACE发生的效能。结果 预后不良组血清FGF-21、MPO水平均高于预后良好组,差异均有统计学意义(P＜0.05)。FGF-21＋MPO诊断AMI患者PCI术后MACE发生的ROC曲线下面积(AUC)为0.860,高于FGF-21、MPO单独诊断AMI患者PCI术后MACE发生的AUC。FGF-21＞140.41 ng/L和FGF-21≤140.41 ng/L的患者,MACE发生率分别为39.58%和10.00%,差异有统计学意义(P＜0.001)。MPO＞419.42 μg/L和MPO≤419.42 μg/L的患者,MACE发生率分别为35.00%和8.33%,差异有统计学意义(P＝0.001)。Cox单因素及Cox多因素分析显示FGF-21、MPO与AMI患者PCI术后MACE发生密切相关(均P＜0.05)。结论 AMI患者PCI术后血清FGF-21、MPO水平与预后有关。高水平的FGF-21、MPO与MACE发生密切相关。     

Retracted: State estimation of Takagi-Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

This article concerns the event-triggered fuzzy filter design for Takagi-Sugeno (T-S) fuzzy systems subject to deception attacks under the stochastic multiple time-varying delays. A sequence of random variables, which are mutually independent but obey the Bernoulli distribution, is introduced to account for the randomly occurring communication delays. In order to efficiently utilize limited network communication bandwidth resources, the event-triggering scheme is adopted. A fuzzy filter with the attacked input signal is presented. Moreover, due to communication delays caused by event-triggering schemes and transmission, the filter adopts non-synchronous premise variables with the system. Then, by utilizing a model transformation technique, the fuzzy systems are developed. Furthermore, using the piecewise Lyapunov functional method technique, the resulting criterion provides sufficient conditions to ensure that fuzzy systems under deception attacks are stochastically stable with an H_∞ performance. Accordingly, the conditions for the co-design of the fuzzy filter and event-triggering schemes are given. Finally, numerical simulation with the industrial process provided to verify the proposed event-triggered design.     

Efficient estimation of extreme quantiles using adaptive kriging and importance sampling

This study considers an efficient method for the estimation of quantiles associated to very small levels of probability (up to O(10⁻⁹)), where the scalar performance function J is complex (eg, output of an expensive-to-run finite element model), under a probability measure that can be recast as a multivariate standard Gaussian law using an isoprobabilistic transformation. A surrogate-based approach (Gaussian Processes) combined with adaptive experimental designs allows to iteratively increase the accuracy of the surrogate while keeping the overall number of J evaluations low. Direct use of Monte-Carlo simulation even on the surrogate model being too expensive, the key idea consists in using an importance sampling method based on an isotropic-centered Gaussian with large standard deviation permitting a cheap estimation of small quantiles based on the surrogate model. Similar to AK-MCS as presented in the work of Schöbi et al., (2016), the surrogate is adaptively refined using a parallel infill criterion of an algorithm suitable for very small failure probability estimation. Additionally, a multi-quantile selection approach is developed, allowing to further exploit high-performance computing architectures. We illustrate the performances of the proposed method on several two to eight-dimensional cases. Accurate results are obtained with less than 100 evaluations of J on the considered benchmark cases.     

Agent-based monitoring service for management of disruptive events in supply chains

Schedules of supply chains are generated with buffers to absorb the effect of disruptive events that could occur during their execution. Schedules can be systematically repaired through specific modifications within buffers by using appropriate decision models that consider the distributed nature of a supply chain. To this aim, information of disruptive events at occurrence or in advance allows decision models to make better decisions. To detect and predict disruptive events along a schedule execution, a service-oriented monitoring subsystem that uses a reference model for defining monitoring models was proposed. This subsystem offers services for collecting execution data of a schedule and environment data, and assessing them to detect/anticipate disruptive events. Because of the distributed nature and the complexity of these services functionalities, this paper presents an agent-based approach for their implementation. This technology allows dealing with supply chain monitoring by structuring monitoring subsystem functionalities as a set of autonomous entities. These entities are able to perform tailored plans created at execution time to concurrently monitor different schedules. A case study is described to try out the implemented prototype system.     

Simulation and prediction of hydrological processes based on firefly algorithm with deep learning and support vector for regression

ABSTRACT

Hydrological processes are hard to accurately simulate and predict because of various natural and human influences. In order to improve the simulation and prediction accuracy of the hydrological process, the firefly algorithm with deep learning (DLFA) was used in this study to optimise the parameters of support vector for regression (SVR) automatically, and a prediction model was established based on DLFA and SVR. The hydrological process of Huangfuchuan in Fugu County, Shanxi Province was taken as the research object to verify the performance of the prediction model, and the results were compared with those by the other six prediction models. The experimental results showed that the proposed prediction model achieved improved prediction performance compared with the other six models.     

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。     

基于差频检测技术的高速AD单粒子翻转评估方法研究

本文基于差频检测的原理，提出一种在高频动态输入模式下，对高速高精度模数转换器（AD）的抗单粒子翻转效应进行评估的测试方法，并以一款8位3 GSPS高速AD为测试对象，设计开发了一套高速AD单粒子翻转效应测试系统，对目标器件进行了重离子试验。通过对试验结果的图像和错误数据进行分析，评估参试器件的抗辐照性能参数，为抗辐照高速高精度AD的加固设计提供数据支撑。