基于纹理特征的指纹识别算法

基于纹理特征的指纹识别方法,具有计算量小的特点。本文对该指纹识别算法进行了系统研究,提出了一套新颖的方向图修正算法,对于噪声相对较小的情况具有良好的效果。本文对多窗口法求块方向图给出了减少运算量的方法;在方向滤波器设计方面,给出了一个方向滤波器的闭合等式,可以简化方向滤波器设计,大大减少运算量,并取得了较好的增强效果;在中心点求取方面,提出了一种新的粗搜索算法,可以减少计算量,加快中心区域搜索速度。     

舰载武器系统局部故障分析方法与研究

针对舰载电子与武器装备结构复杂、设备数量庞大、分布舱室多、检测点分散不集中等问题,在充分调研论证基础上,以系统局部故障分析研究为切入点,提出适用于舰船武器装备特点的局部故障的分析方法;对系统的局部故障我们采用故障树的方法进行界定,并通过核心路径跟踪技术来确定检测点;采用核心路径在线故障检测方法,有效地节省测试资源,提高测试效率。     

Soft sensing modelling based on optimal selection of secondary variables and its application

The composition of the distillation column is a very important quality value in refineries, unfortunately, few hardware sensors are available on-line to measure the distillation compositions. In this paper, a novel method using sensitivity matrix analysis and kernel ridge regression （KRR） to implement on-line soft sensing of distillation compositions is proposed. In this approach, the sensitivity matrix analysis is presented to select the most suitable secondary variables to be used as the soft sensor＇s input. The KRR is used to build the composition soft sensor. Application to a simulated distillation column demonstrates the effectiveness of the method.     

固态功率组件热耦合效应研究

文中基于热叠加原理研究了固态功率组件中多个离散分布的集中热源的热耦合效应,并证明强迫对流下应用热叠加原理计算的温度场与整场系统数值模拟的结果相当吻合,用它来进行热耦合效应的分析研究是有效的.     

基于双特征粒子滤波的驾驶员脸部跟踪

用单一特征跟踪目标时,有时会出现目标跟踪失败或准确性不高的情况。融合了轮廓特征与Gabor小波特征,并在粒子滤波的框架下,提出了一种有效的视频图像双特征参数融合的人脸跟踪方法。实验结果表明,该方法能够在可变光照及脸部旋转变化等的情况下,当一特征失去对脸部特征的有效跟踪时,另一特征也会有效的跟踪到目标,从而实现比只利用单一特征目标跟踪的方法更稳定可靠的目标跟踪。     

Learning knowledge representation with meta knowledge distillation for single image super-resolution

Although the deep CNN-based super-resolution methods have achieved outstanding performance, their memory cost and computational complexity severely limit their practical employment. Knowledge distillation (KD), which can efficiently transfer knowledge from a cumbersome network (teacher) to a compact network (student), has demonstrated its advantages in some computer vision applications. The representation of knowledge is vital for knowledge transferring and student learning, which is generally defined in hand-crafted manners or uses the intermediate features directly. In this paper, we propose a model-agnostic meta knowledge distillation method under the teacher–student architecture for the single image super-resolution task. It provides a more flexible and accurate way to help teachers transmit knowledge in accordance with the abilities of students via knowledge representation networks (KRNets) with learnable parameters. Specifically, the texture-aware dynamic kernels are generated from local information to decompose the distillation problem into texture-wise supervision for further promoting the recovery quality of high-frequency details. In addition, the KRNets are optimized in a meta-learning manner to ensure the knowledge transferring and the student learning are beneficial to improving the reconstructed quality of the student. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation-related distillation methods and can help super-resolution algorithms achieve better reconstruction quality without introducing any extra inference complexity.     

CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis

As a representative deep learning network, Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and many good results have been reported. In Prognostics and Health Management (PHM) field, the CNN’s input size is usually designed as a 1D vector or 2D square matrix, and the convolution kernel size is also defined as a square shape like 3 × 3 and 5 × 5, which are directly adopted from the image recognition. Though satisfying results can be obtained, CNN with such parameter specifications is not optimal and efficient. To this end, this paper elaborated the physical characteristics of bearing acceleration signals to guide the CNN design. First, the fault period under different fault types and shaft rotation frequency were used to determine the size of CNN’s input. Next, an exponential function was involved in fitting the envelope of decaying acceleration signal during each fault period, and signal length within different decaying ratios was used to define the CNN’s kernel size. Finally, the designed CNN was validated with the Case Western Reserve University bearing dataset and Paderborn University bearing dataset. Results confirm that the physics-guided CNN (PGCNN) with rectangular input shape and rectangular convolution kernel works better than the baseline CNN with higher accuracy and smaller uncertainty. The feasibility of designing CNN parameters with physics-guided rules derived from bearing fault signal analysis has also been verified.     

基于系统调用序列学习的内核模糊测试

操作系统内核是计算机系统中最基本的软件组件, 它控制和管理计算机硬件资源, 并提供访问和管理其他应用程序所需的接口和服务. 操作系统内核的安全性直接影响整个计算机系统的稳定性和可靠性. 内核模糊测试是一种高效、准确的安全漏洞检测方法. 然而目前内核模糊测试工作中, 存在系统调用间关系的计算开销过大且容易误判, 以及系统调用序列构造方式缺乏合理能量分配以至于很难探索低频系统调用的问题. 本文提出以N-gram模型学习系统调用间关系, 根据系统调用的出现频次信息和TF-IDF信息优先探索出现频次低或者TF-IDF值高的系统调用. 我们以极低的开销, 在Linux 4.19和5.19版本的24 h实验中分别提升了15.8%、14.7%的覆盖率. 此外, 我们挖掘到了一个已知CVE (CVE-2022-3524)、8个新崩溃, 其中一个获得了CNNVD编号(CNNVD-2023-84723975).     

A CBIR system based on saliency driven local image features and multi orientation texture features

In Content-based Image Retrieval (CBIR), the user provides the query image in which only a selective portion of the image carries the foremost vital information known as the object region of the image. However, the human visual system also focuses on a particular salient region of an image to instinctively understand its semantic meaning. Therefore, the human visual attention technique can be well imposed in the CBIR scheme. Inspired by these facts, we initially utilized the signature saliency map-based approach to decompose the image into its respective main object region (ObR) and non-object region (NObR). ObR possesses most of the vital image information, so block-level normalized singular value decomposition (SVD) has been used to extract salient features of the ObR. In most natural images, NObR plays a significant role in understanding the actual semantic meaning of the image. Accordingly, multi-directional texture features have been extracted from NObR using Gabor filter on different wavelengths. Since the importance of ObR and NObR features are not equal, a new homogeneity-based similarity matching approach has been devised to enhance retrieval accuracy. Finally, we have demonstrated retrieval performances using both the combined and distinct ObR and NObR features on seven standard coral, texture, object, and heterogeneous datasets. The experimental outcomes show that the proposed CBIR system has a promising retrieval efficiency and outperforms various existing systems substantially.