大型立式玻璃磨边机整机动态特性分析

大型立式玻璃磨边机的动态特性影响玻璃磨边质量和磨边效率。利用Solidworks软件建立某大型立式玻璃磨边机三维实体模型,导入ANSYS软件中,对整机结构有限元动力学分析,得到该磨边机前六阶模态参数,分析了各阶振型对磨边机动态特性的影响,为磨边机结构的改进设计提供了理论依据。     

莱钢冷轧轧机弯辊控制系统分析

对莱钢冷轧六辊单机架的弯辊系统进行研究与分析,介绍板形控制系统的原理和使用效果。     

带平衡约束矩形布局优化问题的深度强化学习算法

带平衡约束的矩形布局问题源于卫星舱设备布局设计,属于组合优化问题。深度强化学习利用奖赏机制,通过数据训练实现高性能决策优化。针对布局优化问题,提出一种基于深度强化学习的新算法DAR及其扩展算法IDAR。DAR用指针网络输出定位顺序,再利用定位机制给出布局结果,算法的时间复杂度是O（n³）;IDAR算法在DAR的基础上引入迭代机制,算法时间复杂度是O（n⁴）,但能给出更好的结果。测试表明DAR算法具有较好的学习能力,用小型布局问题进行求解训练所获得的模型,能有效应用在大型问题上。在两个大规模典型算例的对照实验中,提出算法分别超出和接近目前最优解,具有时间和质量上的优势。     

Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models

Engineering with Computers - The indirect and accurate determination of blast-induced rock movement has important significance in the reduction of ore loss and dilution and in the protection of...     

AdaDT: An adaptive decision tree for addressing local class imbalance based on multiple split criteria

Applied Intelligence - As it is well known, decision tree is a kind of data-driven classification model, and its primary core is the split criterion. Although a great deal of split criteria have...     

基于Shannon熵的飞机交流发电机振动分析

飞机交流发电机是飞机关键部件之一,监测其健康状况具有重要意义。通过设计实验数据获取平台采集交流发电机在转速上升(从6000转/分到8500转/分)、平稳运行(7500转/分)及转速下降(8500转/分到6000转/分)三个阶段的振动信号,利用符号序列Shannon熵的计算方法以及符号化方法设定合适参数对振动信号进行了处理,得出了交流发电机在三个阶段的振动信号的Shannon熵;通过初步的分析表明,交流发电机在正常状态下三个阶段的Shannon熵都比较有规律的在一定范围内变化,一旦发生振动异常将会引起Shannon熵突变;经过分析讨论,Shannon熵的突变与否是可以用来反映交流发电机在机械方面是否存在故障隐患的。     

常见计算机窃密技术分析及安全防范措施

随着信息化和网络技术的不断发展,以计算机为工具来盗取用户信息的犯罪日益增加。尤其是通过网络窃取上网计算机涉密信息,已经成为国内外不法分子窃取我国秘密信息的重要手段。本文对国内外计算机窃密与反窃密技术的各种手段进行研究与分析,并通过理论与实际的相结合,提出安全与实用并重的防范措施。     

Detection of sudden pedestrian crossings for driving assistance systems

In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps.     

Generalized extreme learning machine acting on a metric space

Functional data learning is an extension of traditional data learning, that is, learning the data chosen from the Euclidean space ${\mathbb{R}^{n}}$ to a metric space. This paper focuses on the functional data learning with generalized single-hidden layer feedforward neural networks (GSLFNs) acting on some metric spaces. In addition, three learning algorithms, named Hilbert parallel overrelaxation backpropagation (H-PORBP) algorithm, ν-generalized support vector regression (ν-GSVR) and generalized extreme learning machine (G-ELM) are proposed to train the GSLFNs acting on some metric spaces. The experimental results on some metric spaces indicate that GELM with additive/RBF hidden-nodes has a faster learning speed, a better accuracy, and a better stability than HPORBP algorithm and ν-GSVR for training the functional data. The idea of GELM can be used to extend those improved extreme learning machines (ELMs) that act on the Euclidean space ${\mathbb{R}^{n}, }$ such as online sequential ELM, incremental ELM, pruning ELM and so on, to some metric spaces.