人机系统可靠性分析的发展

人机系统可靠性在很大程度上取决于人的可靠性,因此,人的失误及其可靠性分析的研究就成为多学科交叉渗透的面向２１世纪的重点研究领域,已上升到一门新学科的高度．本文在论证人可靠性分析的重要性和迫切性的基础上,详细地评述了人可靠性分析的历史背景、研究内容及其应用范围．最后,文中展望了人可靠性分析的研究方向并拓展出相关的研究领域,指出了该学科得以深入发展的研究途径．     

电网检修排序问题

研究一个从实践中提出的电网检修排序（调度）问题,并证明了该问题的一般情形是ＮＰ－困难的,对其特殊情形给出了多项式时间的算法．     

INVENTORY ANALYSIS OF AN UNRELIABLE PRODUCTION-INVENTORY SYSTEM

1.IntroductionDuringrecentyears,variousmodelsrelatedtounreliableproduction-inventorysystemshaveappearedintheliterature(seeII--5]).PosnerandBerg[4]studiedabasicmodelwhichinvolvesonemaChinewithconstalltproductionratewhichoperatesaslongastheaccumulatedinventoryisbelowapresetthreshold,andisidleotherwise.Byemployinglevel-crossingtechniques,theyderivedanexplicitclosedformsolutionforthesteady-statedistributionoftheinventorylevel.FUlthermore,Berg,PosnerandZhaoll]studiedsomeextensionsoftheabovebasi…     

Spiking neural P systems with anti-spikes and without annihilating priority as number acceptors

Spiking neural P systems with anti-spikes （ASN P systems） are variant forms of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, spikes and anti-spikes wil immediately annihilate each other in a maximal way. In this paper, a restricted variant of ASN P systems, cal ed ASN P systems without anni-hilating priority, is considered, where the annihilating rule is used as the standard rule, i.e., it is not obligatory to use in the neuron associated with both spikes and anti-spikes. If the annihilating rule is used in a neuron, the annihilation wil consume one time unit. As a result, such systems using two categories of spiking rules （identified by （a, a） and （a,a^-）） can achieve Turing completeness as number accepting devices.     

短时交通流预测FSMSVR模型

针对城市道路短时交通流的复杂非线性特点和以往的预测仅考虑典型交通条件（无交通事故等突发事件）的现状,结合交通流的特征,提出了一种有限状态机支持向量回归模型（finite state machine of support vector regression model,FSMSVR）的短时交通流预测机制. 通过线性回归算法和指数平滑算法划分交通流状态,根据各状态特点结合支持向量回归算法建立有限状态机工作机制,实现涵盖典型和非典型交通条件的短时交通流预测. 通过实验例证,对比了FSMSVR模型和传统SVR模型对城市道路6min交通流的预测,研究结果表明,该预测机制能够提高预测精确度,在非典型条件下有着较好的预测表现.     

基于支持向量机的典型宽带电磁干扰源识别

由于民航周围电磁环境复杂, 一旦产生电磁干扰(electromagnetic interference,EMI), 就不易被排查, 特别是随机性较强的宽带干扰。对此, 提出一种基于支持向量机(support vector machine, SVM)的干扰源识别方法。通过实时测量干扰信号的频谱数据, 并分析其特点, 选择包络因子、频谱能量、频谱峰值、均值和方差5个特征向量, 用主成分分析法降低数据冗余程度, 最后采用SVM来判断干扰源类型。仿真结果证明, 所提算法能有效识别6类典型机场宽带干扰源, 识别精度可达98.33%。     

Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods

Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk.     

法律智能合约平台模型的研究与设计

现有的智能合约技术本质上只是一段链上代码,而真正的智能合约应是法律合约的数字化,可以实现合约条款的自动执行.为此,介绍了法律智能合约的重要科技,包括法律考量、预言机、事件模型等基础设施,在分析相关机构工作的基础上提出了法律智能合约的5个标准开发步骤,通过对法律流程标准的研究提出了设计智能合约模版的方法.为解决智能合约模...     

支持向量机在围岩变形监测中的应用

根据支特向量机优越的非线性拟合性能,建立变形量的时间序列预测模型,滚动预测围岩变形量,提高了预测模型的训练速度和预测推广能力。该方法用于西乡-固戍盾构段围岩变形预测,并与BP神经网络预测进行比较。结果表明这种模型可预测区间较长且具有较高的准确度,能够科学地指导现场施工和监测。     

Establishing Efficient C2C E-alliance Based on Mobile Agent

In order to alleviate difficulties of conducting consumer-to-consumer(C2C)e-commerce transaction,establishing efficient e-alliance was proposed.E-alliance is the union of e-commerce sites.It is constructed by mobile agents.The mobile agent architecture was discussed.The process of selecting suitable e-commerce site to e-alliance was presented based on support vector machine(SVM)and fuzzy method.A prototype of the proposed system is implemented on a web platform.To enable data exchange between e-alliance and...