高功率激光焊接光致等离子体的检测

系统地归纳了高功率激光焊接过程中光致等离子体的声、光、电、热等四种特征信号,综述了国外在等离子体信号检测方面的试验方法及研究进展,分析了未来的发展趋势。     

早期火灾图像监测技术的应用与比较

本文对可提供图像信息的火灾物理现象进行了具体分析，讨论了利用摄像技术进行早期火灾探测的可能性、使用条件以及图像监测技术在火灾探测中的地位和作用，进行了若干种图像监测技术的对比实验，得出了相应的结论。     

透射式跑道能见度激光测量仪的研究

介绍了一种利用激光测量跑道水平及斜视能见度的智能化仪器。该仪器主要由光学系统、信号放大与处理系统及显示系统三部分组成。简要介绍了该仪器的基本结构、工作原理和主要技术指标，讨论了其中的技术难点及其相应的解决办法。     

水下成像的现状和发展动向

本文以下水成像在军事，海洋开发工程应用为基础，论述了水下观测和成像中采用微光电视摄像机和激光器件及其系统的技术现状和发展动向。     

Novel All Digital Scheme for Open Loop Fiber Optic Gyroscopes

ＮｏｖｅｌＡｌＤｉｇｉｔａｌＳｃｈｅｍｅｆｏｒＯｐｅｎＬｏｏｐＦｉｂｅｒＯｐｔｉｃＧｙｒｏｓｃｏｐｅｓＹＡＮＧＹｕａｎｈｏｎｇ（Ｔｈｅ５ｔｈＲｅｓｅａｒｃｈＤｉｖｉｓｉｏｎｏｆＢｅｉｊｉｎｇＵｎｉｖｅｒｓｉｔｙｏｆＡｅｒｏｎａｕｔｓ＆Ａｓｔｒｏｎａｕ...     

基于自适应对比度增强的红外小目标检测网络

红外探测系统需要尽早发现目标以便及时拦截,但是红外图像上的小目标检测是一个挑战十足的任务。为了提高检测准确率,提出一种基于自适应对比度增强的红外小目标检测方法。为了利用自注意力机制和卷积各自的优势,设计了一个高效的特征提取网络和一个面向小目标的检测头。同时为了解决实际应用中出现的弱目标,在检测子网络前添加了一个图像预处理子网络,该模块可以自适应地调节图像对比度。在红外空中小目标数据集上的实验表明,提出的方法能达到93.76%的检测精度,与经典的检测方法相比,能够更好地平衡检测精度和召回率,证明了方法的巨大应用潜力。     

基于背景感知的相关滤波抗遮挡优化算法

针对相关滤波目标跟踪中由于目标遮挡、出视野引起的跟踪失败等问题,以背景感知相关滤波(background aware correlation filters, BACF)算法为基础,提出一种抗遮挡优化算法。首先特征提取部分融合方向梯度直方图(histogram of oriented gradient, HOG)特征与颜色名称(color name, CN)特征;其次提出层级遮挡优化算法,在目标跟踪过程中根据平均峰值相关能量(average peak to correlation energy,APCE)和最大响应峰值判断目标外观是否发生较大变化,并通过巴氏距离进一步计算目标相邻滤波器模板特征相似程度,以此判断造成目标外观变化的真正原因,进而决定是否更新模板;针对目标出视野问题,提出模板筛查策略,跟踪过程中每隔K帧对滤波器模板进行置信度考查,若目标丢失,则生成新的模板并对目标进行全局搜索重新抓取目标。通过在目标跟踪数据集OTB2015上测试,优化算法精确度与成功率分别为83.0%与78.8%,有效提高了算法性能。     

Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

In this paper, an advanced and optimized Light Gradient Boosting Machine (LGBM) technique is proposed to identify the intrusive activities in the Internet of Things (IoT) network. The followings are the major contributions: i) An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network; ii) An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem. Here, a Genetic Algorithm (GA) with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space; iii) Finally, the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency. Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.     

Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.