乳化液浓度超声波在线检测技术的研究

介绍了一种基于超声波技术的乳化液浓度在线检测技术,通过检测超声波的传播速度和乳化液的温度,并利用乳化液浓度、温度和超声波声速三者之间的曲线拟合关系,来实现乳化液浓度的在线连续监测,为实现乳化液的自动配比奠定了坚实的基础。     

双闭环直流调速系统的工程设计与仿真

介绍直流调速系统的工程设计方法,利用MATLAB软件,对直流调速系统进行数学建模和系统仿真的研究。     

计算机数据库的入侵检测技术实现

计算机数据库的入侵检测技术对计算机的安全运行有着很大的帮助,这是一项积极主动防御外来侵害的高新技术,对计算机数据库可以进行多重保护。检测技术不仅能够对外来的病毒入侵一一攻克,还能对内部的潜在病毒进行查杀,对数据进行保护。     

Efficient random saliency map detection

Most image retargeting algorithms rely heavily on valid saliency map detection to proceed. However, the inefficiency of high quality saliency map detection severely restricts the application of these image retargeting methods. In this paper, we propose a random algorithm for efficient context-aware saliency map detection. Our method is a multiple level saliency map detection algorithm that integrates multiple level coarse saliency maps into the resulting saliency map and selectively updates unreliable regio...     

基于网络检测平台应对网络灾害探讨

网络灾害作为互联网给人类社会带来的各种潜在风险和负面效应之一,正在影响着社会各个行业。除了在网络安全技术、网络道德和相关法律等方面开展研究外,网络检测平台通过检测网络拓扑结构和其他网络动态属性,构建网络地图,可以为网络灾害的预防和控制提供相关依据。     

Efficient collision detection using a dual OBB-sphere bounding volume hierarchy

We present an efficient algorithm for collision detection between static rigid objects using a dual bounding volume hierarchy which consists of an oriented bounding box (OBB) tree enhanced with bounding spheres. This approach combines the compactness of OBBs and the simplicity of spheres. The majority of distant objects are separated using the simpler sphere tests. The remaining objects are in close proximity, where some separation axes are significantly more effective than others. We select 5 from among the 15 potential separating axes for OBBs. Experimental results show that our algorithm achieves considerable speedup in most cases with respect to the existing OBB algorithms.     

Skin detection for single images using dynamic skin color modeling

Up-to-date skin detection techniques use adaptive skin color modeling to overcome the varying skin color problem. Most methods for tracking skin regions in videos utilize the correlation between contiguous frames. This paper proposes a new approach for detecting skin in a single image. This approach uses a local skin model to shift a globally trained skin model to adapt the final skin model to the current image. Experimental results show that the proposed method can achieve better accuracy. Two improvements for speeding up the processing are also discussed.     

Local normalized linear summation kernel for fast and robust recognition

Kernel-based methods are effective for object detection and recognition. However, the computational cost when using kernel functions is high, except when using linear kernels. To realize fast and robust recognition, we apply normalized linear kernels to local regions of a recognition target, and the kernel outputs are integrated by summation. This kernel is referred to as a local normalized linear summation kernel. Here, we show that kernel-based methods that employ local normalized linear summation kernels can be computed by a linear kernel of local normalized features. Thus, the computational cost of the kernel is nearly the same as that of a linear kernel and much lower than that of radial basis function (RBF) and polynomial kernels. The effectiveness of the proposed method is evaluated in face detection and recognition problems, and we confirm that our kernel provides higher accuracy with lower computational cost than RBF and polynomial kernels. In addition, our kernel is also robust to partial occlusion and shadows on faces since it is based on the summation of local kernels.     

New Riemannian techniques for directional and tensorial image data

This paper develops new geometrical filtering and edge detection algorithms for processing non-Euclidean image data. We view image data as residing on a Riemannian manifold, and we work with a representation based on the exponential map for this manifold together with the Riemannian weighted mean of image data. We show how the weighted mean can be efficiently computed using Newton's method, which converges faster than the gradient descent method described elsewhere in the literature. Based on geodesic distances and the exponential map, we extend the classical median filter and the Perona-Malik anisotropic diffusion technique to smooth non-Euclidean image data. We then propose an anisotropic Gaussian kernel for image filtering, and we also show how both the median filter and the anisotropic Gaussian filter can be combined to develop a new edge preserving filter, which is effective at removing both Gaussian noise and impulse noise. By using the intrinsic metric of the feature manifold, we also generalise Di Zenzo's structure tensor to non-Euclidean images for edge detection. We demonstrate the applications of our Riemannian filtering and edge detection algorithms both on directional and tensor-valued images.     

Anisotropic diffusion with generalized diffusion coefficient function for defect detection in low-contrast surface images

In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect, because the intensity difference between the unevenly illuminated background and the defective region is hardly observable and no clear edges are present between the defect and its surroundings.The proposed anisotropic diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed method can effectively and efficiently detect small defects in various low-contrast surface images.