一种基于多Agent的分布式入侵检测系统设计

在分析现有基于Agent的入侵检测系统的基础上,提出了一种基于多Agent分布式入侵检测系统模型。该模型采用了分布检测、分布响应的模式,各Agent之间具有良好的相对独立性。通过多Agent技术的思想建立系统总体结构,给出了模型的各个组成部分,并对结构中各种Agent与中心控制台的功能设计进行了分析。同时对涉及到特征匹配算法、动态选举算法、协同算法进行了初步的设计与分析。系统可充分利用各Agent的协同完成入侵检测任务,实时响应,可有效地改进传统IDS。     

基于二维小波变换的车牌边缘检测的研究

在Matlab环境下运用基于二维小波变换边缘检测方法提取车牌信息的边缘。实验证明这种方法对车牌边缘信息提取的效果比传统的边缘提取算子提取车牌信息边缘的效果要好,特别是对伪边缘和图片噪声的过滤作用比较明显。     

话题发现与追踪技术研究

话题发现与追踪以新闻流为处理对象,采用基于事件的信息组织方式进行研究,一直是自然语言处理领域里的热点。该研究借鉴大量相关研究尤其是信息检索中的经典模型和方法,取得了很大成功。首先介绍了话题发现与追踪的主要研究内容、评价方法以及发展历史;然后对其多个研究内容提出一个统一研究框架,并对该框架中的关键技术进行了详细分析;最后指出该领域中的关键问题及难点,并对未来研究做出展望。     

基于决策树及遗传算法的人工免疫入侵检测算法

针对传统的否定选择算法中生成大量无效抗体,并且抗体之间缺乏多样性的问题,设计了基于决策树及遗传算法的人工免疫入侵检测算法。将决策树和遗传算法引入传统的否定选择算法中,利用决策树计算抗原和抗体之间的亲和力,提出了新fitness的计算公式,并利用抗体浓度衡量抗体集的多样性,将低浓度抗体代替高浓度抗体,实现了抗体的多样性,确保了当抗体集的数量一定时尽可能覆盖最大的非自体集空间,以提高抗体集的性能。     

Economic modeling of board test strategies

A long and deep recession, coupled with continuous competitive pressure to reduce costs, is forcing many companies to review their test strategies. Testing costs have become a more significant proportion of the overall manufacturing cost even though manufacturing yields have increased dramatically over the past ten or twelve years. This causes attention to be focused on testing costs as a key source of cost reduction. The increased use of DFT and the integration of design and test are very positive moves towards controlling testing costs but other methods employed can often backfire. The increased use of low priced testers is one such method. The pressure to reduce costs, higher process yields and exhortations that testing adds no value can lead the test engineering manager to take the cheap route. In reality this can often turn out to be an expensive decision. The only way to avoid expensive mistakes is to perform an economic analysis of the alternative courses of action. In most cases this is done, but not always in the right manner or with the necessary amount of detail to make the comparisons meaningful. This article discusses the need for effective cost analysis of test strategies and highlights some of the pitfalls.     

Ship detection based on multi-scale weighted fusion

Ship surveillance plays an important role in ensuring the safety of maritime transportation and navigation. Due to the influence of factors such as waves and special weather, the existing detection methods still cannot balance the accuracy, speed and the parameters of the model in the changeable and complex marine environment. To solve this problem, this paper proposes an improved real-time method based on YOLOv5, which has few parameters and achieves high detection accuracy with little memory and computation cost. Collaborative Attention (CA) mechanism is added to the network structure, which enables the model to more accurately locate and identify target regions. We also design a Spatial Pyramid Pooling module (SPP) and a weighted pyramid network called Bidirectional Feature Pyramid Network (BiFPN) based on the characteristics of the ships to better fuse feature information. Transformer encoder is introduced to capture long-distance dependencies and preserve global and local features to the greatest extent. Furthermore, the ability of our proposed structure to localize objects at each stage is improved through integrating the output of multiple modules. The experimental results show that, the comprehensive performance of this method is better than the existing technology in ship detection on different evaluation criteria.     

地铁站场景下的可行驶区域检测

在室内环境下的机器人视觉导航任务中,可行驶区域检测是不可或缺的一部分,这是保证自动驾驶任务实现的基础.目前较多的解决方法是对数据集中出现过的障碍物进行识别来检测可行驶区域,缺乏灵活性,因此本文提出了一种针对地铁站等室内平坦地面的可行驶区域检测方法,提高实用性.本文采用经典的MobileNetV3网络对采集到的前方图像进行分类,判断是否为地面区域.由于室内地面的地标、箭头等贴纸的影响,因此需要对非地面区域进一步判断,与常规的立体障碍物进行区分.本文利用连续帧之间的特征点匹配获得相机移动距离,并利用直线拟合计算斜率的方法达到区分立体障碍物与平面地标的目的.实验表明,本文提出的方法能较好地检测机器人前方可行驶区域,具有较高的实用价值.     

Transfer Learning on Deep Neural Networks to Detect Pornography

While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts both pornographic and non-pornographic contents. The videos were from different ethnicities and skin color which ensures the models can detect any kind of video. A VGG16, Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49, 0.49 and 0.78 respectively. Finally, utilizing transfer learning, a convolutional neural network was designed and yielded an accuracy of 0.98.     

基于多流网络一致性的视频显著性检测

现有的视频显著性检测算法通常采用双流结构提取视频的时空线索,其中运动信息作为双流结构的一个分支,在显著物体发生剧烈或慢速移动时存在运动估计准确率低的问题,并且不合理的训练数据或方案使得权重偏向单个分支结构。提出一种基于多流网络一致性的视频显著性检测算法MSNC。设计并使用一种新的三重网络结构提取预选目标区域的颜色信息、时序信息和先验特征,通过先验特征补偿运动流的缺陷,并提高运动线索的利用率。采用多流一致性融合模型优化三流分支,得到不同特征的最佳融合方案。同时通过循环训练策略平衡三重网络的权重,以避免网络过度拟合单流分支,从而有效地提高运动估计和定位的准确率。在Davis数据集上的实验结果表明,相比PCSA、SSAV、MGA等算法,该算法的鲁棒性更优,其maxF和S-Measure值分别达到0.893和0.912,MAE仅为0.021。