系统的最优诊断参数临界值和最优检测周期

综合研究一类系统的检测策略和故障诊断问题。假定系统有3种运行状态：正常、异常和故障。系统开始工作后，每隔一段随机时间对它检测一次，以便它是正常还是异常。检测结果有可能出现错误。系统处于正常和异常状态的时间分别服从参数不同的指数分布，系统的修理时间是连续型随机变量，检测间时间为任意随机变量。利用概率分析、补充变量和最优化方法，求出了系统基于可靠性的最优诊断参数临界值和最优检测周期。     

组合体中补线题目校验机制的设计与实现

采用分类方法，对在计算机上实现组合体补线题目的匹配校验问题进行了较为详细的讨论，提出了校验机制的设计思路，并给出了相应的检验算法．     

基于灰色PSO-ANFIS模型的船舶横摇运动预报分析

为了准确高效的预测船舶在波浪中的航行状态以保证人员、货物和船舶的安全,提出了一种基于灰色模型粒子群优化算法的自适应神经模糊推理系统(Grey Particle Swarm Optimization-Adaptive Neural-fuzzy Inference System, GPSO -ANFIS)。GPSO-ANFIS预测模型使用模糊C均值聚类算法对输入样本进行聚类分析,得到模糊规则数量并建立神经模糊推理系统,再使用粒子群优化算法对建立的预测系统进行优化训练,从而得到最优的预测系统模型。其中灰色模型用于横摇数据的预处理,以便削弱横摇状态中的非线性影响因素。最后通过实船“育鲲”轮的横摇数据进行仿真实验,实验结果验证了GPSO-ANFIS模型的实用性和可行性,具有较高的预测精度。并为船舶航行智能化提供了一种有价值的理论依据。     

用背散射法对ECAF壁面沉积层的研究

应用康普顿背散射法对静电旋风空气滤清器的壁面沉积层进行在线研究,并分析壁面沉积规律与除尘效率之间的关系.在电旋风除尘器中,当气流入口速度一定时,壁面上的沉积层随气流含尘浓度的增加和运行时间的延长而加厚,从入口向下筒壁沉积层厚度也逐渐增加.除尘效率随入口气流速度的增加而提高,随气流含尘浓度的增加而下降.当入口处气流速度和气流的含尘浓度一定时,逐渐加厚的壁面沉积层就会使除尘空间的电晕电流降低,使静电的作用大大减弱,进而使得除尘效率降低.     

一种新型天然气水合物层钻井录井用脱气器

在天然气水合物层钻井过程中,水合物在井筒返出的过程中将分解成气体,通过地面气测录井技术可有效识别层位、检测水合物层丰度,故在天然气水合物层钻井过程中气测录井就显得尤为重要。气测录井数据精确与否,主要取决于仪器的精度和效率。针对天然气水合物层钻井的新型开采方式设计了一种专门用于天然气水合物层钻井过程中的脱气器,与陆地油田所用的脱气器相比它具有脱气效率高、测量准确、使用方便快捷、安全可靠等优点。详细介绍了这种脱气器的原理及优势,给出了这种脱气器的结构示意图和三视图,并通过对圆筒和支架的理论强度校核及静力学仿真对该脱气器进行强度分析。最终,通过理论研究和仿真技术结合得到了现场所需要的脱气器的应用要求。     

泥石流拦砂坝排泄孔优化及其数值模拟研究

为改善拦砂坝排水孔堵塞问题,提出了以调节泥石流流量为主的排水孔优化策略;并通过数值模拟初步研究了横、纵断面形态对优化后的排水孔(排泄孔)排泄能力的影响,以及排泄孔对不同性质的泥石流的排泄效果。通过对横断面形态、纵断面形态及浆体容重三因子的正交组合,进行了80组模拟试验,以平均排泄流量珚Q和坝前泥深下降速率为主要评价指标,结果表明具有合理宽深比的矩形横断面相对最优。对容重较小的流体,窄深型的矩形断面排泄能力最好;而对高容重流体,则正方形断面相对更好。在可流动范围内,排泄效果随着排泄孔纵比降的增大而增大;而增大沿程排泄孔横断面截面积(变截面一坡到底喇叭型)可提高相同纵比降排泄孔的排泄能力。同时,随着流体容重的增加,排泄孔的排泄能力逐渐降低。     

Exemplification and the use-values of cases and case studies

This paper provides an account of the ‘use-value’ of case-based research by showing how social scientists exploit cases, and case studies, in a variety of practices of inference and extension. The critical basis for making such extensions relies on the power of a case, or the account given of a case (the case-study account), to exemplify certain features of the social world in ways which prove valuable for further analysis: either of the same case, or in many domains beyond the original case study. Framing use-values in terms of exemplification compares favourably with understanding reasoning beyond the case either as a form of analogical reasoning or in taking cases as experimentable objects.     

异形汽车座椅弹簧的刚度及应力分析

以某型异形汽车椅弹簧为研究对象,针对其刚度特性及应力进行理论计算.利用Hypermesh与ANSYS联合仿真研究该异形汽车座椅弹簧的结构特点,并对其弹簧的刚度特性进行应力分析和强度校核.结果表明:基于弹簧串联理论得到的弹簧刚度和仿真结果更接近;汽车座椅弹簧轴向载荷与轴向变形量呈近似线性关系,刚度随着轴向力的增大而小幅增大,在满载工况下,汽车椅弹簧能满足强度要求,符合实际应用要求.利用有限元软件对弹簧进行应力分析及强度校核,并对切应力计算公式提出修正系数,使结果更加合理.     

The ecological rationality of explanatory reasoning

There is growing evidence that explanatory considerations influence how people change their degrees of belief in light of new information. Recent studies indicate that this influence is systematic and may result from people’s following a probabilistic update rule. While formally very similar to Bayes’ rule, the rule or rules people appear to follow are different from, and inconsistent with, that better-known update rule. This raises the question of the normative status of those updating procedures. Is the role explanation plays in people’s updating their degrees of belief a bias? Or are people right to update on the basis of explanatory considerations, in that this offers benefits that could not be had otherwise? Various philosophers have argued that any reasoning at deviance with Bayesian principles is to be rejected, and so explanatory reasoning, insofar as it deviates from Bayes’ rule, can only be fallacious. We challenge this claim by showing how the kind of explanation-based update rules to which people seem to adhere make it easier to strike the best balance between being fast learners and being accurate learners. Borrowing from the literature on ecological rationality, we argue that what counts as the best balance is intrinsically context-sensitive, and that a main advantage of explanatory update rules is that, unlike Bayes’ rule, they have an adjustable parameter which can be fine-tuned per context. The main methodology to be used is agent-based optimization, which also allows us to take an evolutionary perspective on explanatory reasoning.