A tunable support vector machine assembly classifier for epileptic seizure detection

Automating the detection of epileptic seizures could reduce the significant human resources necessary for the care of patients suffering from intractable epilepsy and offer improved solutions for closed-loop therapeutic devices such as implantable electrical stimulation systems. While numerous detection algorithms have been published, an effective detector in the clinical setting remains elusive. There are significant challenges facing seizure detection algorithms. The epilepsy EEG morphology can vary widely among the patient population. EEG recordings from the same patient can change over time. EEG recordings can be contaminated with artifacts that often resemble epileptic seizure activity. In order for an epileptic seizure detector to be successful, it must be able to adapt to these different challenges. In this study, a novel detector is proposed based on a support vector machine assembly classifier (SVMA). The SVMA consists of a group of SVMs each trained with a different set of weights between the seizure and non-seizure data and the user can selectively control the output of the SVMA classifier. The algorithm can improve the detection performance compared to traditional methods by providing an effective tuning strategy for specific patients. The proposed algorithm also demonstrates a clear advantage over threshold tuning. When compared with the detection performances reported by other studies using the publicly available epilepsy dataset hosted by the University of BONN, the proposed SVMA detector achieved the best total accuracy of 98.72%. These results demonstrate the efficacy of the proposed SVMA detector and its potential in the clinical setting.     

入侵检测系统分类法技术研究

首先阐述了IDS的发展历史，接着从IDS分类法的发展对现有的IDS分类法进行了介绍，回顾常见的IDS分类，并对IBM提出的IDS分类法进行了介绍和简要分析。最后通过分析现状对IDS的发展趋势进行了探讨。     

基于图像识别的微型倾斜传感器

通过人眼观察进行识读的传统水准器式倾斜仪表，直观但精度不高、灵活性不强。通过引入CCD图像传感器采集水准器中气泡图像并结合图像识别算法分析计算气泡图形边缘，研究开发了具备倾斜角度数值输出能力的新型倾斜传感器。并进一步通过采用DSP处理器并应用嵌入式设计方法，设计了该类型倾斜传感器的小型化原型，从而显著地提高了测量精度和应用的灵活性．克服了传统仪表的不足。     

高噪声条件下基于投影的二维条码倾斜检测

图像倾斜纠正是二维条码自动识别过程中的重要步骤,最常用的倾斜角检测方法是基于Hough变化的方法,实际应用中Hough变换峰值扩散效应显著,同时在速度上也难以适应实时应用的需求。提出使用投影法对倾斜的二维条码图像进行倾斜角判断,并通过加速策略提高检测速度,能够在高噪声条件下快速得到精确的结果。     

基于移动Agent的新型分布式入侵检测系统

通过阐述入侵检测系统的基本概念和移动Agent的优点,并将移动Agent技术与分布式技术相结合,提出了一种基于移动Agent的新型分布式入侵检测系统DIDSBMA,它具有传统入侵检测技术无法比拟的优势,极大地提高了系统的稳定性、可测量性和容错性,真正实现了分布式入侵检测。     

入侵检测技术研究

论文介绍了入侵检测技术的概念,分类和通用入侵检测框架,详细分析了各种入侵检测技术。并对入侵检测技术的未来发展方向进行了探讨。     

入侵检测系统检测效率的研究

目前的入侵检测系统(IDS)主要存在检测准确率低、检测速度较慢等缺点。本文以提高检测的准确率和速度为目的对入侵检测系统中的各功能模块进行分析,给出了一些关于提高入侵检测系统检测效率的方法。     

一种基于对等网络的入侵检测系统

本文设计了一种基于P2P网络的分布式入侵检测系统,有效地实现分布式入侵检测,各个IDS子系统分别监视部分网络通信,协同工作以提高系统的安全性。     

无取向硅钢热轧板形控制与轧制特性的关系

无取向硅钢板形质量要求日趋严苛。热轧是控制无取向硅钢成品横截面厚差偏大、尤其是边部减薄显著的关键工序。通过测试并分析无取向硅钢板形控制与专有轧制特性的关系,提出可通过采用合理的ASR非对称自补偿工作辊和常规工作辊的辊形配置及相应的窜辊策略可有效控制带钢边部减薄和凸度,使板形质量达到用户要求。