基于单片机的地下车库车位监测系统的设计

提出了一种基于单片机的地下车位监测系统, 应用于地下车库车位实时监测. 该系统以单片机为基础, 采集车位照明灯光信号被遮挡前后的硅光电池的电压信号, 用电压比较电路处理硅光电池的电压信号, 并将处理之 后的电信号传送至单片机, 最后将车位实时数据在液晶屏上显示. 本系统将硅光电池和车库照明系统有机结合起 来, 提升了照明灯光的应用价值; 实现了车位信息的实时监测, 提高了车库的运作效率     

有热漏时定常态不可逆卡诺热机功率效率特性

对变热漏情况下定常态不可逆卡诺热机进行了有限时间热力学分析。导出最佳效率时的功率与等熵温比指数、效率与等熵温比指数的关系式,以及最佳面积比,进而得到最佳效率与功率关系。对最佳功率与最佳效率时的热机性能进行分析和讨论,给出一些有益的结论。     

地磁车辆检测器抗邻车位干扰的磁场角度关系分析应用

利用地磁车检器进行停车位检测时,车检器检测轴方向、停车位方向以及地磁场方向之间的角度关系,影响车检器不同轴向的峰值与灵敏度,随意设置会产生邻车位干扰或对磁场扰动的灵敏度低等问题。本研究基于双轴向地磁车检器,在不同停车位方向下,对车检器各个轴向进行测试,找出的最佳指向在保证正向轴高峰值高灵敏度的同时,使侧向轴低峰值低灵敏度,在提高检测精度的同时,减少邻车位干扰问题。研究给出了经验公式,在各地实践中,均得到了良好的效果。     

改进的分层A*算法在停车场路径寻优中的应用

在大型停车场车位路径寻优中,传统的评价指标是距离最短作为最优指标,并不可虑实际路况,为此引入了切实可行的时间较优评价指标,既能简化引导系统硬件设计,又能节省软件开销。首先,建立了停车场网络图R(P,C,D,V)数学模型,并把网络中的所有节点分成两层,分别构建了不同的估价函数,采用改进的A*算法,提高了以时间较优为评价指标的路径搜索效率和搜索精度。最后,程序运行结果表明,针对不同的路段属性参数,算法总能较快的找到时间较优路径。     

基于丙烯酰胺为单体的红敏光致聚合物衍射效率增强的研究

目前以丙烯酰胺为单体的光聚物体系在高分辨力下衍射效率不高。从光聚物聚合机理出发．探讨了其主要原因。从单体聚合难易程度角度提出了加入新成分丙烯酸、使用聚合度低的成膜物、增加膜的厚度和优化凉板时间等四种促进单体聚合的措施并从实验上加以了验证。结果表明：在一定条件下。成膜物使用聚合度为341的聚乙烯醇比聚合度为1750的聚乙烯醇体系衍射效率提高了约60％;加入丙烯酸后衍射效率也提高了10％左右,且使用聚合度低的成膜物和加入丙烯酸后体系更不易结晶;厚度和凉板时间均有一个最佳范围。经过优化实验后得到了空间频率在2000lp／mm时衍射效率大于90％,3000lp／mm时大于55％的结果,且利用制备出的材料拍摄出透射和平面反射全息网,说明该材料适合于日益发展的全息显示。     

脉冲激光二极管泵浦调Q激光器泵浦时间特性研究

从调Q激光器的速率方程出发,对脉冲激光二极管泵浦的调Q激光器在不同泵浦功率及不同透过率情况下的最佳泵浦时间进行了理论分析。计算结果表明,在透过率为20%,腔内损耗为0.05的条件下,1—3倍阈值泵浦功率时,最高效率对应的最佳泵浦时间为1—1.5倍Nd∶YAG荧光寿命;7倍阈值泵浦功率及以上时,最佳泵浦时间约为荧光寿命的一半或者更短。在脉冲泵浦能量相同时,采用较短的泵浦时间(即泵浦功率较高),调Q输出的能量转换效率较高。     

基于KNX的地下停车场智能监控及车位引导系统

针对目前地下停车场的自动化监控和管理方式单一,缺乏智能化的停车位引导以及停车场内照明、通风、泵站设施管理方式落后等问题,提出一种基于KNX的地下停车场智能监控及车位引导系统;通过将各个KNX节点设备挂载到KNX总线上,构建KNX系统,通过挂接在KNX总线上的智能终端实现对整个地下停车场的监控与管理,即实现对停车场照明、通风设备的管理和监控,以及车辆检测、车位检测以及车辆进入车位的智能引导功能;经实践验证,系统运行稳定可靠,实时性好,抗干扰能力强。     

蜂巢蓄热体换热性能的实验研究

实验研究了高温空气燃烧系统使用的蜂巢蓄热体热回收率、最佳换向时间、流动阻力等性能参数及其随几何尺寸的变化规律;提出供风效率的概念,表征流经蓄热体的实际供风量与总供风量的差别,并对热回收率进行了修正。本试验为蓄热体应用与设计提供了依据。     

一种主动磁场停车位检测新装置

针对大型停车场中车位检测装置的能耗问题和可靠性问题,提出了一种主动磁场停车位检测新装置;使用永磁体建立主动磁场,通过设计磁路,使得停车位上车辆的存在会改变通过传感器的磁力线的闭合路径,选用低功耗的磁传感器检测通过其本身的磁通量,以输出的磁感应强度值的大小为依据判断停车位上是否停有车辆;计算与仿真结果表明:该车位检测装置可行且检测结果可靠;其克服了地磁场的干扰,解决了能耗问题,成本低,可靠性高,有利于提高大型停车场的管理质量。     

核黄素与蒽酮衍生物之间的能量传递现象的研究

依据Förster的能量传递理论,研究了核黄素和蒽酮衍生物之间的能量转移。研究结果表明：核黄素与蒽酮衍生物之间存在着显著的能量转移,而且二者的能量转移效率受加热温度、加热时间和质量配比的影响;核黄素与蒽酮衍生物最佳质量比为1∶3;随着加热温度和加热时间的增大,能量转移效率减小。     

A Second-row Parking Paradox

We consider two variations of the discrete car parking problem where at every vertex of ℤ cars (particles) independently arrive with rate one. The cars can park in two lines according to the following parking (adsorption) rules. In both models a car which arrives at a given vertex tries to park in the first line first. It parks (sticks) whenever the vertex and all of its nearest neighbors are not occupied yet. A car that cannot park in the first line will attempt to park in the second line. If it is obstructed in the second line as well, the attempt is discarded. In the screening model a) a car cannot pass through parked cars in the second line with midpoints adjacent to its vertex of arrival. In the model without screening b) cars park according to the same rules, but parking in the first line cannot be obstructed by parked cars in the second line. We show that both models are solvable in terms of finite-dimensional ODEs. We compare numerically the limits of first- and second-line densities, with time going to infinity. While it is not surprising that model a) exhibits an increase of the density in the second line from the first line, more remarkably this is also true for model b), albeit in a less pronounced way.     

Burning Cars in a Parking Lot

Knuth’s parking scheme is a model in computer science for hashing with linear probing. One may imagine a circular parking lot with n sites; cars arrive at each site with unit rate. When a car arrives at a vacant site, it parks there; otherwise it turns clockwise and parks at the first vacant site which is found. We incorporate fires into this model by throwing Molotov cocktails on each site at a smaller rate n ^−α , where 0 < α < 1 is a fixed parameter. When a car is hit by a Molotov cocktail, it burns and the fire propagates to the entire occupied interval which turns vacant. We show that with high probability when n → ∞, the parking lot becomes saturated at a time close to 1 (i.e. as in the absence of fire) for α > 2/3, whereas for α < 2/3, the average occupation approaches 1 at time 1 but then quickly drops to 0 before the parking lot is ever saturated. Our study relies on asymptotics for the occupation of the parking lot without fires in certain regimes which may be of independent interest.     

Random Parking, Sequential Adsorption,¶and the Jamming Limit

Identical cars are dropped sequentially from above into a large parking lot. Each car is positioned uniformly at random, subject to non-overlap with its predecessors, until jamming occurs. There have been many studies of the limiting mean coverage as the parking lot becomes large, but no complete proof that such a limit exists, until now. We prove spatial laws of large numbers demonstrating that for various multidimensional random and cooperative sequential adsorption schemes such as the one above, the jamming limit coverage is well-defined. Received: 18 August 2000 / Accepted: 13 November 2000     

Parking Cars with Spin but no Length

The car parking problem is a one-dimensional model of random packing. Cars arrive to park on a block of length x, sequentially. Each car has, independently, spin up or spin down, w.p. 0 < p 1, for spin up and q = 1 – p for spin down, respectively. Each car tries to park at a uniformly distributed random point t [0, x]. If t is within distance 1 of the location of a previously parked car of the same spin, or within distance a of the location of a previously parked car of the opposite spin, then the new car leaves without parking and the next car arrives, until saturation. We study the problem analytically as well as numerically. The expected number of up spins c(p, a) per unit length for sufficiently large x is neither monotonic in p for fixed a, nor is it monotone in a for fixed p, in general. An intuitive explanation is given for this nonmonotonicity.     

Parking on a Random Tree

Consider an infinite tree with random degrees, i.i.d. over the sites, with a prescribed probability distribution with generating function G(s). We consider the following variation of Rényi’s parking problem, alternatively called blocking RSA (random sequential adsorption): at every vertex of the tree a particle (or “car”) arrives with rate one. The particle sticks to the vertex whenever the vertex and all of its nearest neighbors are not occupied yet. We provide an explicit expression for the so-called parking constant in terms of the generating function. That is, the occupation probability, averaged over dynamics and the probability distribution of the random trees converges in the large-time limit to (1−α ²)/2 with .     

Random Parking, Euclidean Functionals, and Rubber Elasticity

We study subadditive functions of the random parking model previously analyzed by the second author. In particular, we consider local functions S of subsets of ${\mathbb{R}^d}$ and of point sets that are (almost) subadditive in their first variable. Denoting by ξ the random parking measure in ${\mathbb{R}^d}$ , and by ξ ^R the random parking measure in the cube Q _R =  (?R, R) ^d , we show, under some natural assumptions on S, that there exists a constant ${\overline{S} \in \mathbb{R}}$ such that $$\lim_{R \to +\infty} \frac{S(Q_R, \xi)}{|Q_R|} \, = \, \lim_{R \to +\infty} \frac{S(Q_R, \xi^{R})}{|Q_R|} \, = \, \overline{S}$$ almost surely. If ${\zeta \mapsto S(Q_R, \zeta)}$ is the counting measure of ${\zeta}$ in Q _R , then we retrieve the result by the second author on the existence of the jamming limit. The present work generalizes this result to a wide class of (almost) subadditive functions. In particular, classical Euclidean optimization problems as well as the discrete model for rubber previously studied by Alicandro, Cicalese, and the first author enter this class of functions. In the case of rubber elasticity, this yields an approximation result for the continuous energy density associated with the discrete model at the thermodynamic limit, as well as a generalization to stochastic networks generated on bounded sets.     

文化框架下多群智能优化算法的云作业调度

作业调度是一种云计算核心技术,为了获得更优的云计算作业调度方案,提出一种文化框架下多群智能优化算法的云作业调度方法。首先构建云作业调度问题的数学模型,然后借助文化算法模型,粒子群算法组成信仰空间,人工鱼群算法组成群体空间,两者之间并行演化,相互促进,对云计算作业调度数学模型进行求解,最后通过仿真实验测试算法的性能。结果表明,本文加快了算法的收敛速度,获得了更优的云计算作业调度方案,大幅度缩短少云计算作业完成时间,具有一定的实用价值。     

基于时变阈值过程神经网络的太阳黑子数预测

太阳黑子活动直接影响着外层空间环境的变化，为保证航天飞行任务的安全必须对其进行有效预测.为此，提出了一种基于时变阈值过程神经网络的时间序列预测模型.为简化模型的计算复杂度，开发了一种基于正交基函数展开的学习算法.文中分析了模型的泛函逼近能力，并以Mackey-Glass时间序列预测为例验证了所提模型及其学习算法的有效性.最后，将该预测模型用于太阳活动第23周太阳黑子数平滑月均值预测，取得了满意的结果，应用结果同时表明：所提预测方法与其他传统预测方法相比预测精度有所提高，具有一定的理论和实用价值. 关键词： 太阳黑子数 时变阈值过程神经网络 时间序列预测 泛函逼近     

基于改进教学优化算法的Hermite正交基神经网络混沌时间序列预测

针对传统预测模型对混沌时间序列预测精度低、收敛速度慢及模型结构复杂的问题, 提出了基于改进教学优化算法的Hermite正交基神经网络预测模型. 首先, 将自相关法和Cao方法相结合对混沌时间序列进行相空间重构, 以获得重构延迟时间向量; 其次, 以Hermite正交基函数为激励函数构成Hermite正交基神经网络, 作为预测模型; 最后, 将模型参数优化问题转化为多维空间上的函数优化问题, 利用改进教学优化算法对预测模型进行参数优化, 以建立预测模型并进行预测分析. 分别以Lorenz 系统和Liu系统为模型, 通过四阶Runge-Kutta法产生混沌时间序列作为仿真对象, 并进行单步及多步预测对比实验. 仿真结果表明, 与径向基函数神经网络、回声状态网络、最小二乘支持向量机及基于教学优化算法的Hermite正交基神经网络预测模型相比, 所提预测模型具有更高的预测精度、更快的收敛速度和更简单的模型结构, 验证了该模型的高效性, 便于推广和应用.