神经网络在煤矿安全决策支持系统中的研究及应用

主要研究人工神经网络在煤矿安全决策支持系统中的应用。将BP网络用于瓦斯突出,解决了瓦斯突出判别困难、难以建立数学模型的难题,为煤矿瓦斯突出研究找到了一种新的建模和预测方法。     

改进分块支持向量聚类在入侵检测中的应用

支持向量聚类(SVC)是在支持向量机的思想上发展而来一种聚类方法,针对其处理大规模数据集速度缓慢的缺点,提出了一种改进的分块支持向量聚类算法。改进的算法分为三个阶段:前期的预处理,中期的分块算法,后期的改进标类算法。提出的方法显著加快了SVC的速度,在保持原来SVC算法的优点的基础上,对大规模数据集以及非均匀分布数据集等具有良好的效果。将其应用到网络入侵检测,实验结果表明改进的算法行之有效。     

宇宙主胁量限定粒子质量、微观临界尺寸和真空光速（胁量力学初导IV）

扩展到任意质量体的宇宙主胁量描述。宇宙主胁量同质量之间只差一个常量系数。宇宙主胁量创造了粒子的质量，造就了粒子的微观临界尺寸，构筑了恒定而有限的真空光速。     

榴弹弹丸质点弹道性能分析与可视化仿真

为提高榴弹设计中的计算分析精度，加快弹种的研发速度，建立了一个弹丸质点弹道模型提出了相应的算法和软件实现；本软件除具有同类软件的计算功能外，还以可视化的形式模拟弹丸在空中的飞行全过程，并生成各种特性曲线，同时具有智能捕捉功能、查询功能。     

基于屏栅电离室的双参数谱仪研制及其应用

以屏栅电离室为基础，同时引出其阳级和阴极信号，研制了1台能够同时记录两路信号的双参数谱仪，在DEC－3000工作站上编制程序，实现了双维谱数据的获取、显示和存储。利用所建立的谱仪，测量了α放射源的双维谱，计算了α粒子的角分布，并讨论了如何利用角分布来校正活度测量中自吸收和反散射的影响以及改善电离室阳极能谱的能量分辨率。     

自适应神经模糊系统在托砖架最高温度预测中的应用

气流床气化炉在气化工业中得到了广泛应用，托砖架用于支撑气化炉耐火砖的重量，必须有足够的强度，温度是托砖架强度的重要影响因素。传统方法使用有限单元法计算，需要编制复杂的计算机程序。本文使用已有软件计算出托砖架最高温度点温度，对一个自适应神经模糊系统(ANFIS)进行训练，然后对未知数据进行预测，取得了准确的结果。     

Dissymmetric warfare versus asymmetric warfare

This paper presents two concepts: asymmetric warfare and dissymmetric warfare. NATO and US Military operations in recent wars in Kosovo and Afghanistan are analysed by means of comparison with old Chinese strategic thinking. No‐Loss‐Strategy and virtual warfare as well as the transformation from asymmetric to dissymmetric and from dissymmetric to asymmetric warfare are reflected upon.     

井下粉尘多参数检测的自适应方法

为提高井下粉尘计重含量检测精度并同时获得粒度分布参数,根据井下粉尘衍射光的角谱特征,提出了一种适合于在单片机上运行的算法。首先将衍射光角谱归一化,使得不同浓度但粒度分布相同的尘样具有相同的归一化角谱,而该角谱与给定模式的角谱的贴近程度则用差值平方和表征。由几个优选模式求得的计重含量或粒度分布取加权平均即可获得待求参数。考虑到现场环境中噪声抑制能力和对尘样多样性的适应能力至关重要,本文中最后给出了针对这两个方面的仿真实验结果。     

Parallel global optimization with the particle swarm algorithm

Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima-large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available.