红外弱小目标的匹配方法研究与仿真

研究红外图像中的目标匹配问题。针对传统的红外图像中目标匹配效果不理想,匹配不准确。当红外图像中是弱小目标时,背景灰度值与目标灰度值非常相近,灰度直方图均衡化无法实现目标与背景的完整分离,导致漏匹配率较高的问题。为提高目标的匹配精度,提出一种形态学预处理的红外目标匹配方法,通过形态学算法提取出图像中的形状信息,利用形状信息将红外图像中的目标与背景完整分离,避免了只依靠灰度图像信息造成的不能完整分离目标与背景带来的漏匹配问题。进行仿真的结果证明,采用的形态学与处理的匹配方法能够完整将目标与背景分离,成功完成目标匹配,取得了满意的结果。     

空心铝型材挤压过程计算机仿真系统

试开发了一套基于计算机辅助设计和数值模拟的空心铝型材挤压过程计算机仿真系统,它涵盖了模具工艺设计的全过程,可以集成原有的和新建立的工艺设计知识。该系统包括空心型材挤压模具及坯料的参数化几何造型模块,并能分析分流桥的截面形状、分流孔的布置、焊合室的高度、工作带长度和阻流或者助流结构对金属流速分布的影响,模具的弹性变形对工作带有效长度和模孔尺寸的影响。     

操作参数对煤泥脱水效果的影响

探究不同操作参数对煤泥脱水效果的影响,通过改变操作参数进行单因素试验,分别探究入料浓度、入料流量、转鼓转速对煤泥脱水效果的影响,并通过正交试验确定三者的主次序列.结果表明,随着入料浓度的增加,煤泥的脱水效果越来越好,产品的含水率逐渐降低;在一定的入料流量范围内,随着入料流量的增加,产品的含水率降低,但是超过某一流量后,含水率会有所增加;在转鼓最大允许范围内,产品水分随着转速的增加而降低.通过正交试验可以得知影响脱水效果的主次因素依次为转鼓转速、入料流量、入料浓度.     

高压交流线路避雷器劣化状态数值仿真

高压交流线路避雷器的劣化分析是保障其运行安全的重要评估技术.针对避雷器出现的劣化问题,提取其伏安特性中的小电流区间,建立数学表征模型,并以模型中的时间常数表征氧化锌的劣化程度.结合晶粒物理结构、Voronoi网格分析晶粒及晶粒间的电气连接特性,推演RC等效电路模型并简化电路.开展数值仿真计算,获得不同劣化程度下避雷器的伏安特性曲线以及交流电压作用下的阻性电流变化规律,分析了容性电流分量与谐波电压对总泄漏电流的影响.泄漏电流波形的畸变率可判断避雷器的劣化程度,模型可辅助优化基于泄漏电流的避雷器在线监测与诊断技术.     

Analysis of D.C Parameters of Short-Channel Heterostructure Double Gate Junction-Less MOSFET Circuits Considering Quantum Mechanical Effects

Silicon - In this article, the electrical behavior of short channel SiGe Heterostructure Junction-Less DG-MOSFET have been studied by incorporating the quantum mechanical effect and short channel...     

边缘AI的霜雪识别系统设计与实现

为提高霜雪识别系统的识别率,开展了基于边缘AI的霜雪识别系统设计。综合海思嵌入式处理器Hi3559A高算力、低功耗特点,硬件部分采用模块复用的方法降低功耗和资源消耗,软件部分,从模型训练、模型量化、模型部署入手对MobileNetV2图像分类网络和ISP自适应处理算法进行改进,最终识别率99.7%,预处理时间0.3 s,图像分类时间0.8 s,模组平均功率2 W,鲁棒性好、性能稳定。由此可见Hi3559A智能相机模组可有效纠正雪深探测仪的疑误数据。     

Quantum DCS系统及其应用体会

中国联合水泥公司3000t／d熟料生产线电气控制系统采用了法国施耐德公司的Quantum DCS系统，实现对全厂所有设备的工艺操作和控制。全面介绍了QuantumDCS系统的特点、功能系统构成网络，并详细总结了确保该系统稳定可靠运行的使用和维护的经验。     

A Simple D 2-Sampling Based PTAS for k-Means and Other Clustering Problems

Given a set of points \(P \subset\mathbb{R}^{d}\) , the k-means clustering problem is to find a set of k centers \(C = \{ c_{1},\ldots,c_{k}\}, c_{i} \in\mathbb{R}^{d}\) , such that the objective function ∑ _x∈P e(x,C)², where e(x,C) denotes the Euclidean distance between x and the closest center in C, is minimized. This is one of the most prominent objective functions that has been studied with respect to clustering. D ²-sampling (Arthur and Vassilvitskii, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA’07, pp. 1027–1035, SIAM, Philadelphia, 2007) is a simple non-uniform sampling technique for choosing points from a set of points. It works as follows: given a set of points \(P \subset\mathbb{R}^{d}\) , the first point is chosen uniformly at random from P. Subsequently, a point from P is chosen as the next sample with probability proportional to the square of the distance of this point to the nearest previously sampled point. D ²-sampling has been shown to have nice properties with respect to the k-means clustering problem. Arthur and Vassilvitskii (Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA’07, pp. 1027–1035, SIAM, Philadelphia, 2007) show that k points chosen as centers from P using D ²-sampling give an O(logk) approximation in expectation. Ailon et al. (NIPS, pp. 10–18, 2009) and Aggarwal et al. (Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, pp. 15–28, Springer, Berlin, 2009) extended results of Arthur and Vassilvitskii (Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA’07, pp. 1027–1035, SIAM, Philadelphia, 2007) to show that O(k) points chosen as centers using D ²-sampling give an O(1) approximation to the k-means objective function with high probability. In this paper, we further demonstrate the power of D ²-sampling by giving a simple randomized (1+?)-approximation algorithm that uses the D ²-sampling in its core.     

Grafting of methyl methacrylate onto guar gum by hydrogen peroxide initiation

Graft copolymerization of methyl methacrylate (MMA) onto guar gum (GG) in aqueous slurry has been carried out using hydrogen peroxide (H₂O₂) as initiator. The copolymers were characterized by infrared spectroscopy. The grafting parameters like percent grafting, grafting efficiency, percent add-on, and the grafting frequency were determined, and the effect of reaction time, concentration of initiator, and [GG]/[MMA] ratios on the grafting parameters have been discussed. The decrease in % add-on at increasing concentration of H₂O₂ indicated enhancement in the rate of homopolymerization of methyl methacrylate.     

Micropore characterization using the Dubinin-Astakhov equation to analyze high pressure CO2 (273 K) adsorption data

The characterization of the pore structure of microporous materials is of interest because of the usefulness of these materials in many applications. Of these, the characterization of carbon adsorbents is particularly problematic because of the presence of small pores with size on the order of small molecules (micropores) along with a wide distribution of pore sizes, and their non-crystalline structure. In this paper, we present results obtained using the Dubinin-Astakhov equation to analyze data from high pressure CO₂ adsorption at 273 K to characterize two sets of microporous carbons. Our results support the conclusions of previous workers that the Dubinin-Astakhov (DA) equation is able to linearize adsorption data that gives rise to curved Dubinin-Radushkevich plots. However, when applied over different ranges of relative pressure on the adsorption isotherm, the Dubinin-Astakhov plots result in different values of micropore volume and characteristic adsorption potential. Furthermore, DA analysis of CO₂ (273 K) adsorption data over a wide range of pressures (10^–3–22000 Tort), gives results different from DA analysis of CO₂ (273 K) isotherms measured at low pressures only (10^–3–830 Tort). It would appear desirable to apply the DA equation to data that reflects the entire range of micropore filling on the adsorption isotherm, as opposed to data over a limited relative pressure range. For CO₂ adsorption at 273 K, this would necessitate adsorption studies at high pressures, to about 28 atm. Micropore volumes obtained in this manner, agreed with the total pore volumes determined by nitrogen (77 K) adsorption for all the activated carbons studied.