图像处理中模糊算法问题的分析

图像处理是计算机常用技术之一,模糊算法的运用有助于提高图像处理的效果。传统图像处理存在诸多弊端,影响了用户获取信息的便捷性。本文分析了图像处理的基本操作内容,并阐述了模糊算法在图像处理流程中的运用,以进一步提升原始图像处理的效果。     

未知环境下基于约束点的移动机器人路径规划

针对未知环境中障碍物种类多样性和位置不确定性的特点,提出了基于约束点的路径规划方法。首先对机器人在未知环境中检测到的局部障碍物信息进行分类和几何特征属性描述,得其约束点信息,然后引入改进后的A*算法,将其搜索范围局限于约束点上,计算约束点的评价函数值后得到子目标点,机器人到达子目标点后,若陷入死区,则采取回溯路径策略,重新选择子目标点,否则根据该点所属的障碍物种类采取跨越或绕行避障策略,最后移动机器人在未知环境中顺利到达目标点。仿真研究说明本文提出的路径规划方法具有可行性和有效性。     

Parallel depth first search. Part II. Analysis

This paper presents the analysis of a parallel formulation of depth-first search. At the heart of this parallel formulation is a dynamic work-distribution scheme that divides the work between different processors. The effectiveness of the parallel formulation is strongly influenced by the work-distribution scheme and the target architecture. We introduce the concept of isoefficiency function to characterize the effectiveness of different architectures and work-distribution schemes. Many researchers considered the ring architecture to be quite suitable for parallel depth-first search. Our analytical and experimental results show that hypercube and shared-memory architectures are significantly better. The analysis of previously known work-distribution schemes motivated the design of substantially improved schemes for ring and shared-memory architectures. In particular, we present a work-distribution algorithm that guarantees close to optimal performance on a shared-memory/-network-with-message-combining architecture (e.g. RP3). Much of the analysis presented in this paper is applicable to other parallel algorithms in which work is dynamically shared between different processors (e.g., parallel divide-and-conquer algorithms). The concept of isoefficiency is useful in characterizing the scalability of a variety of parallel algorithms.This work was supported by Army Research Office Grant No. DAAG29-84-K-0060 to the Artificial Intelligence Laboratory, and Office of Naval Research Grant N00014-86-K-0763 to the Computer Science Department at the University of Texas at Austin.     

低温裂纹张开位移预测方法的讨论

把低温影响下的COD试验数据用灰色理论中的累加方法进行累加，可以使一组没有规律的数据成为一条光滑的曲线．然后利用人工神经网络和自适应模糊推理系统两种方法进行预测，算例的结果表明用一次累加后的数列预测精度较高。     

温度对Ni—P化学镀层结合力影响的研究

用弯曲法、划痕法研究了镀后加热温度对Ni-P化学镀层结合力的影响,结果表明:镀后退火温度越高,镀层结合力越好,但温度过高时,镀层硬度将下降。用金相观察及电子探针分析,探讨了镀后退火温度影响结合力的机理。结果指出:退火时将在镀层和基体界面上发生Ni与Fe的互扩散,形成Ni-Fe合金扩散层,从而提高了结合力。为了能在保证镀层硬度的前提下提高结合力,本文提出了二次镀覆的设想,获得较好试验结果。     

高纯钨中杂质元素的化学光谱法测定

采用以一定比例的ＨＮＯ３和ＨＣｌ为介质，使钨沉淀成钨酸与杂质分离，然后用碳粉吸附杂质再进行光谱测定的方法，对高纯钨（包括金属钨、三氧化钨、钨酸、钨酸铵）中１５个杂质元素Ｆｅ、Ｎｉ、Ｃｕ、Ｍｎ、Ａｌ、Ｖ、Ｃｒ、Ｃｏ、Ｃａ、Ｍｓ、Ｐｂ、Ｓｎ、Ｂｉ、Ｃｄ、Ｂｅ进行了测定，取得了理想的结果，测定下限可达（０．１～０．５）×１０（－６）。     

应用人工神经网络理论建立热粘塑性材料的本构关系

本文将人工神经网络（ＡＮＮ）用于建立热粘塑性材料的本构关系，意在探索出一种描述材料变形力学行为的新方法。文中给出了应用人工神经网络建立热粘塑性材料本构关系的ＢＰ模型和学习算法过程，并应用于４５号钢在高温和高速变形条件下的流动应力计算。计算结果与实测结果比较表明，二者吻合良好。因此，应用人工神经网络建立热粘塑性材料的本构关系具有重要的工程应用价值。     

基于随机森林的多站融合项目优先业务筛选机制

在“双循环”新发展格局和“电力数字化”战略中顺势发展“多站融合”项目。基于多站融合布局,提出了一个多站融合项目区域性优先业务筛选的技术方法,从经济发展与能源产业视角,依据各区域属性特征将中国31个省市划分为四类区域,并结合多站融合项目业务清单所梳理的三大类业务范畴,利用随机森林算法的大数据分析法,通过选择区域数据样本,形成了多站融合项目区域性优先业务筛选机制,进而依据优先业务匹配组合预测,实现了各区域优先发展业务的精准甄别和科学选择。实证分析验证了基于随机森林算法的多站融合项目区域性优先业务筛选模型的科学合理性以及有效可靠性,以期为多站融合推进提供有益参考。     

基于改进U-net和CNN的绝缘子自爆检测方法研究

针对绝缘子自爆故障人工检测效率低,成本高的问题,基于改进U-net和卷积神经网络（CNN）模型,提出一种可有效识别绝缘子自爆故障的双阶段目标检测算法。首先,在语义分割阶段使用改进U-net模型,通过翻倍提高图像分辨率的方法有效提高图像分割精度。其次,在图像分类阶段提出更适合所提问题且有效提高分类准确度的新型CNN模型。最后,使用无人机拍摄的绝缘子图片为实验数据进行实验。实验结果表明所提算法识别精度较高。     

A power fluctuation evaluation method of PV plants based on RankBoost ranking

Fluctuation evaluation is an important task in promoting the accommodation of photovoltaic (PV) power generation. This paper proposes an evaluation method to quantify the power fluctuation of PV plants. This consists of an index system and a ranking method based on the RankBoost algorithm. Eleven indices are devised and included in the index system to fully cover diverse fluctuation features. By handling missing and invalid data effectively, the ranking method fuses multiple indices automatically and provides a systematic and comprehensive comparison of power fluctuation. Simulation results based on power data from six PV plants indicate that the evaluation list obtained by the RankBoost ranking method is better represented and more comprehensive than that derived by the equal weight method.