三防设计及其在数字对讲机结构设计中的应用

通过对数字对讲机在严酷环境条件下产生故障模式的原因分析,得出结论:三防设计的缺失是导致对讲机失效最主要因素之一。本文以手持式数字对讲机的结构设计为研究对象,运用三防设计的设计原则和方法,优选材料,减小结构设计对产品性能的影响。从而,大大提高数字对讲机在恶劣环境中的适应性,降低产品故障率。     

仿真优化的资源分配算法研究综述

针对仿真优化过程中资源分配的问题,沿着其发展路径综述了几种主要的仿真优化资源分配算法,主要阐述了模拟预算最优分配算法,最后讨论了该领域的发展前景。     

仿真优化的资源分配算法研究综述

针对仿真优化过程中资源分配的问题,沿着其发展路径综述了几种主要的仿真优化资源分配算法,主要阐述了模拟预算最优分配算法,最后讨论了该领域的发展前景.     

过热区对晶体生长界面特征的影响研究

长晶过程中,成晶质量与晶面形状有直接的关系,形成凸或水平的晶面有助于得到高质量的晶体。文章采用布里奇曼法,利用数值计算,运用移动网格模拟晶面的演化过程,得出不同的过热区域对晶体生长的流场分布的影响。     

LCD改进算法在模拟电路故障诊断中的应用研究

提出了一种利用改进LCD算法提取模拟电路故障特征的方法。首先,针对LCD算法估计包络曲线时未考虑其凹凸性的问题,提出了一种改进的LCD算法并尽心了必要的随噪声影响仿真分析;其次,利用该算法对模拟电路被测信号进行分解,得到若干具有物理意义的分量;最后,使用基本尺度熵算法得到故障特征。运用Pearson相关性指标的仿真结果验证了该方法的有效性。     

过热区对晶体生长界面特征的影响研究

长晶过程中,成晶质量与晶面形状有直接的关系,形成凸或水平的晶面有助于得到高质量的晶体.文章采用布里奇曼法,利用数值计算,运用移动网格模拟晶面的演化过程,得出不同的过热区域对晶体生长的流场分布的影响.     

基于小波系数区域相关性的电能质量扰动检测

软阈值法已经在电能质量扰动检测中得到了应用,但该方法受噪声强度影响较大,当噪声强度较大时,该方法会失效。信号相邻尺度的小波系数具有较强的相关性,但受小波系数偏移影响较大,区域相关算法在保持相关算法优点的同时能够克服小波系数偏移带来的影响。文中结合软阈值法和区域相关法的优点,提出了一种新的电能质量扰动检测算法。仿真结果表明,该算法能够弥补软阈值法的不足,可以在强噪声干扰下对电能质量扰动准确定位。     

数字对讲机在邯钢运输部的应用

模拟对讲机在邯钢运输部已经运行多年,在提高了铁路运输的劳动生产效率的同时,一些固有的短板也凸显了出来。比如频率易受到外界干扰、超长车列容易掉信号等。数字对讲机能够很好地解决以上问题,加上中继台的配合,在解决频率干扰的同时,扩大了无线调车通信距离,提高无线调车的工作效率。     

手持对讲机应实行严格管理,健全质保体系

手持对讲机是一种用途广泛、用户涉及面广的移动通讯工具,大量应用于公安、大型厂矿、森林防火、抢险救灾、交通运输、银行、宾馆等部门。我国的手持对讲机生产基础薄弱,加之近年来,蜂房通讯、无线寻呼通讯网迅速发展,外国进口手持对讲机大量进入中国市场,致使我国对讲机产品生产企业的形势急转直下,在市场竞争中纷纷落马,大部分停产或改为经营进口对     

城市区域交通状态判别方法研究

随着新一代智能化交通控制系统的出现,城市区域交通状态判别方法的研究显得尤为重要。本文在已知各个子区交通状态的基础上,用层次分析法确定出各个子区在区域中所占的权重,然后用加权平均法得出区域的交通状态。最后利用VISSIM软件模拟验证了本文算法的合理性。     

DRAKE双子星内话系统简介及案例分析

语音通信交换系统是地空通信的终端部分,通过对接入的VHF、HF、地面电话、卫星电话等各种通信资源进行交换和管理,为管制员与空中飞机之间提供地空无线电语音通信及管制区之间的电话移交及协调通信。论文以海口现场情况为例,主要阐述DRAKE双子星内话系统的组成,并列举一例重大案例分析,为维护人员提供一点思路。     

浅谈高校留级学生教育管理对策

留级学生已成为高校校园里的一个特殊群体,留级对于高校、社会、家庭都存在着不利的一面,因此开展留级生的教育工作是高校教育的重要任务,也是和谐校园的重要主题,具有较大的现实意义。本文主要阐述高校留级生的现状,全面分析了高校留级生的留级原因;并对如何有效开展留级生的思想教育工作;提出一些管理措施。     

Application of quantum computing in discrete portfolio optimization

This study proposes a novel and more efficient quantum algorithm for portfolio optimization using quantum combinatorial optimization (QCO) techniques. A recent construction developed in 2021 has sparked the field of financial portfolio optimization through the Quantum Walk Optimization Algorithm (QWOA). In this study, we investigated the complexity and efficiency of quantum optimization algorithms with a special interest in QWOA. The objective is to minimize investment risk by having a good combination of assets in the portfolio. We also focused on reducing the number of iterations while attaining a high-quality resolution through contraction of the solution space to ease computations. The concept of QWOA was extended by constructing a newly outperforming scheme known as the “Quantum Mix Optimization Algorithm (QMOA).” QMOA algorithm codes were provided for the implementation and simulation of numerical results. In addition, the efficiency of QMOA, which is better than the existing QCO algorithms, was discussed. For instance, the least QWOA number of computations required to execute the initial state equation was p > 2, whereas this value was p ≥ 2 in the proposed QMOA.     

车辆数不确定的软时间窗车辆路径问题的改进遗传算法

综合考虑车辆数和行驶距离两种优化目标,提出了VRPSTW的多目标优化模型,同时提出了解决VRPSTW问题的一种改进遗传算法。在算法中,通过适应度函浸透的变化,较好地解决了多目标优化的问题;通过对交叉算子改进,增加了算法的寻优能力,同时又克服了算法对群体多样性的要求;针对遗传算法局部搜索能力弱的问题。加入了2-opt局部搜索方法,很好地弥补了遗传算法的不足。经过实验,本方法能较好地解决VRPSTW问题,从而对运榆决策提供有力支持。     

Bayesian network models for incomplete and dynamic data

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper, we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and in practical applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.     

Reconfiguring a set of coverage-providing facilities under travel time uncertainty

We study networks of facilities that must provide coverage under conditions of uncertainty with respect to travel times and customer demand. We model this uncertainty through a set of scenarios. Since opening new facilities and/or closing existing ones is often quite expensive, we focus on optimal re-configuration of the network, that is finding a facility set that achieves desired thresholds with respect to expected and minimal coverage, while retaining as many of the existing facilities as possible. We illustrate our model with an example of Toronto Fire Service. We demonstrate that relocating just a few facilities can have the same effect as opening a similar number of new ones. We develop exact and approximate solution approaches and test them with computational experiments. Algorithm based on Tabu Search (with certain novel components) appears to be particularly successful for this problem. We also analyze the multi-objective version of the problem, where the expected and minimum coverage levels are treated as objectives in addition to the objective of maximizing the number of pre-existing facilities in the final location set.     

A modified deferred acceptance algorithm for conditionally lexicographic-substitutable preferences

We study a class of preferences that generalize substitutable ones and allows complementarities in a college admissions model. We show that a stable matching exists under this class of preferences by using an algorithm which we call the Conditional Deferred Acceptance Algorithm. Furthermore, we show that the algorithm is strategy-proof for the student side if we assume in addition a law of aggregate demand adapted to our setting.     

An algorithm for panel ANOVA with grouped data

In this paper, we present an algorithm suitable for analysing the variance of panel data when some observations are either given in grouped form or are missed. The analysis is carried out from the perspective of ANOVA panel data models with general errors. The classification intervals of the grouped observations may vary from one to another, thus the missing observations are in fact a particular case of grouping. The proposed Algorithm (1) estimates the parameters of the panel data models; (2) evaluates the covariance matrices of the asymptotic distribution of the time-dependent parameters assuming that the number of time periods, T, is fixed and the number of individuals, N, tends to infinity and similarly, of the individual parameters when T → ∞ and N is fixed; and, finally, (3) uses these asymptotic covariance matrix estimations to analyse the variance of the panel data.     

“引导探究”式教学法探讨

长期以来,我国的传统的教学模式一直是教师主动灌输,而学生被动的学习,这样不仅抑制了学生学习的积极性,更没有办法充分发挥学生们的创造性思维和创新能力。因此,本文引入了新的教学方法,即引导探究式教学方法。将传统的教学方法引导探究式教学方法分别在《磁场对运动电荷的作用》这门课程教学的应用,我们自然看到了传统教学的弊端以及引导探究式教学方法的优势。为了更好的培养学生的综合素质,我们应当大力推广引导探究式教学在授课过程中的应用。     

遗传算法在Lorentz型重叠峰分解中的尝试

遗传算法作为一种鲁棒性很强的优化方法,由于不依赖于问题的具体领域,因此广泛应用于很多学科,在重叠峰信号的分解中也被越来越多的讨论和尝试。本文从分析计算机模拟Lorentz重叠峰入手,介绍遗传算法程序的设计、流程及数据分析等过程,并在遗传算法的改进方面作出了一些尝试。