A Two-Level Detection Algorithm for Optical Fiber Vibration

Optical fiber vibration is detected by the coherent optical time domain reflection technique. In addition to the vibration signals, the reflected signals include clutters and noises, which lead to a high false alarm rate. The ＂cell averaging＂ constant false alarm rate algorithm has a high computing speed, but its detection performance will be declined in nonhomogeneous environments such as multiple targets. The ＂order statistics＂ constant false alarm rate algorithm has a distinct advantage in multiple target environments, but it has a lower computing speed. An intelligent two-level detection algorithm is presented based on ＂cell averaging＂ constant false alarm rate and ＂order statistics＂ constant false alarm rate which work in serial way, and the detection speed of ＂cell averaging＂ constant false alarm rate and performance of ＂order statistics＂ constant false alarm rate are conserved, respectively. Through the adaptive selection, the ＂cell averaging＂ is applied in homogeneous environments, and the two-level detection algorithm is employed in nonhomogeneous environments. Our Monte Carlo simulation results demonstrate that considering different signal noise ratios, the proposed algorithm gives better detection probability than that of ＂order statistics＂.

Quantum Spin Liquid Phase in the Shastry–Sutherland Model Detected by an Improved Level Spectroscopic Method

We study the spin-1/2 two-dimensional Shastry–Sutherland spin model by exact diagonalization of clusters with periodic boundary conditions, developing an improved level spectroscopic technique using energy gaps between states with different quantum numbers. The crossing points of some of the relative(composite) gaps have much weaker finite-size drifts than the normally used gaps defined only with respect to the ground state, thus allowing precise determination of quantum critical points even with small clusters. Our results support the picture of a spin liquid phase intervening between the well-known plaquette-singlet and antiferromagnetic ground states, with phase boundaries in almost perfect agreement with a recent density matrix renormalization group study, where much larger cylindrical lattices were used [J. Yang et al., Phys. Rev. B 105, L060409(2022)]. The method of using composite low-energy gaps to reduce scaling corrections has potentially broad applications in numerical studies of quantum critical phenomena.

Advanced topics on RF amplitude and phase detection for low-level RF systems

Low-level radio frequency(LLRF)systems stabilize the electromagnetic field in the RF cavities used for beam acceleration in particle accelerators.Reliable,accurate,and precise detection of RF amplitude and phase is particularly important to achieve high field stability for pulsed accelerators of free-electron lasers(FEL).The digital LLRF systems employ analog-to-digital converters to sample the frequency down-converted RF signal and use digital demodulation algorithms to calculate the RF amplitude and phase.Different sampling strategies and demodulation algorithms have been developed for these purposes and are introduced in this paper.This article focuses on advanced topics concerning RF detection,including accurate RF transient measurement,wideband RF detection,and RF detection with an asynchronous trigger,local oscillator,or clock.The analysis is based on the SwissFEL measurements,but the algorithms introduced are general for RF signal detection in particle accelerators.

Environmental Sound Recognition Using Double-Level Energy Detection

The performance of classic Mel-frequency cepstral coefficients (MFCC) is unsatisfactory in noisy environment with different sound sources from nature. In this paper, a classification approach of the ecological environmental sounds using the double-level energy detection (DED) was presented. The DED was used to detect the existence of the sound signals under noise conditions. In addition, MFCC features from the frames which were detected the presence of the sound signals by DED were extracted. Experimental results show that the proposed technology has better noise immunity than classic MFCC, and also outperforms time-domain energy detection (TED) and frequency-domain energy detection (FED) respectively.

Malicious Node Detection Using Confidence Level Evaluation in a Grid-Based Wireless Sensor Network

In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid form a cluster with a cluster head. Each cluster head maintains the confidence levels of its member nodes based on their readings and reflects them in decision-making. Two thresholds are used to distinguish between false alarms due to malicious nodes and events. In addition, the center of an event region is estimated, if necessary, to enhance the event and malicious node detection accuracy. Experimental results show that the scheme can achieve high malicious node detection accuracy without sacrificing normal sensor nodes.

基于PS-Level Set的嘴唇几何形状定位模型

针对面向唇读的水平集模型在嘴唇分割中存在边界过收敛和过早收敛的问题,文中提出了一种改进的基于先验知识的水平集模型(简称为PS-Level Set)来进行嘴唇几何形状的定位.PS-Level Set模型利用改进的差值能量函数引入嘴唇形状的先验信息.在曲线演化过程中,反复比较演化曲线和先验曲线的差距,使曲线的演化形状逐渐逼近先验模型形状,从而更精确地收敛于目标物体实际轮廓.实验表明,用PS-Level Set模型定位嘴唇几何形状的准确率比用水平集模型提高了8.38%.

高斯混合函数区域匹配引导的Level Set纹理图像分割

基于高斯混合模型颜色匹配及多尺度图像增强,文中提出了有效的边缘停止函数用于引导levelset函数演化,有效地解决了纹理图像的分割问题.文中首先提出基于高斯混合模型颜色分布的边缘停止函数,通过计算levelset演化窄带区域与用户给定交互区域的相似性,根据其相似性来引导levelset快速演化;然后,提出一个定义在多尺度图像梯度上的边缘停止函数,使得levelset能精确地分割出图像的边缘;最后,结合以上两种边缘停止函数的优点,提出一个边缘停止函数的混合模型,根据图像颜色、边缘特征自适应地引导levelset函数演化.实验结果表明,文中提出的算法不仅能有效地检测出纹理目标区域,同时需要计算出纹理区域精确、光滑的边界.

基于改进的level set嘴唇轮廓定位方法

根据嘴唇的几何分布特性,提出一种基于多方向的水平集方法(Multi-Level set)来进行嘴唇轮廓的定位。Multi-Level set方法通过对嘴唇图像多个方向的滤波得到新的边缘检测函数来增强嘴唇轮廓的梯度信息,然后利用能量函数最小化来使初始曲线向嘴唇轮廓靠近,达到说话人嘴唇轮廓的精确定位。实验证明用Multi-Level set方法定位嘴唇轮廓的准确率比level set提高了7.32%。

融入注意力机制的水位检测算法

传统图像处理的水位检测算法易受环境因素的影响,因此提出一种融入注意力机制的水位检测算法。首先,利用融入CBAM注意力机制的YOLO v5目标检测模型获取尺身数字类别及其坐标信息;其次,通过融入ECA注意力机制的DeepLabv3+语义分割模型实现水尺和背景的分割;再次,使用边缘检测算法得出水位线信息;最后,根据尺身数字信息和水位线信息将像素值换算为真实水位值。实验结果表明,优化水位检测算法与人工读数误差在2 cm以内,满足水位检测标准要求。

基于Canny算子Level set方法的肝脏CT图像分割

文中主要实现了一种基于Canny边缘检测水平集图像分割方法,这种方法的基本思想是通过低维到高维的映射,实现对不均匀的受噪声影响的肝脏CT图像的分割。该方法具有适应性强和精确度高的特点,能准确地提取到目标的边缘,实验也取得了比较好的分割结果。

基于Level Set的海岸线检测

采用海岸线做为地标进行静止卫星导航是一种重要的遥感图像自动导航方法,此方法在静止卫星遥感图像中选取特定区域,检测其中的海陆分界线,与全球海岸线标准模板库的相应区域进行匹配,依据计算获得的偏移量调整卫星姿态.根据静止卫星图像自动导航过程的特点,本文提出了一种适用于红外通道的基于水平集算法的海陆分界线检测方法,本方法依据地标模板库进行初始化,采用一种可扩展区域能量泛函推动曲线进行演化,直至收敛.实验结果表明,本方法具有迭代次数少,稳定性好,精度高的特点.

零信任环境下的多层次身份认证数据流安全检测算法

身份认证数据流中的敏感信息可能在传输过程中被攻击者截获,并用于恶意目的,导致隐私泄露、身份盗用等风险,为确保网络安全性,提高主体身份认证安全性,提出零信任环境下的多层次身份认证数据流安全检测算法。采用改进的文档指纹检测算法实现多层次身份认证过程中主体和客体交互数据流安全监测。通过Rabin-Karp算法实现身份认证数据文档的分块,采用Winnow算法划分身份认证数据分块文档边界后,得到身份认证数据文档指纹,将其与指纹库中的指纹进行匹配对比,识别出多层次身份认证数据流中的异常数据,实现多层次身份认证数据流安全检测。实验结果表明,该算法具有较好的身份认证数据流安全检测能力,有效地降低了网络威胁频率,提升了网络安全性。

复杂场景下无水尺水位的影像水位反演智能检测方法

实现精细化水务管控和洪涝灾害预警,需要实时、准确感知水位突变事件。现有技术不能满足夜晚、雾霾、雨天、雪天、漂浮物遮挡及阴影等复杂恶劣环境下的水位识别需求。为此,本文提出一种融合改进YOLOv5与卡尔曼滤波原理的无水尺水位智能检测技术:①引入YOLOv5对水位线(水岸分界线)进行检测,并利用线性拟合方法获得实际水位线;②针对水位线在延伸方向无限大而在其法向无限小特点,提出强化中尺度特征的多层级特征融合方法改进原YOLOv5算法;③利用卡尔曼滤波引入水位历史信息作为先验知识,提高本技术对复杂恶劣环境的泛化性能;④将图像中事先标定的固定的标志物加入到深度学习网络中训练,根据标志位真实尺寸解算实际水位高程,实现无水尺检测方案。相关试验和实践表明,改进的YOLOv5更加轻量化;本文所述水位智能检测技术斜率准确性为97.3%,较原算法提高了2.4%;截距准确性为99.3%,较原算法提高了0.5%;在夜晚、雾霾、雨天、雪天、漂浮物遮挡及阴影等复杂恶劣环境下可以自动、准确识别出水位高程,误差小于0.1 m。

A New Method of Small Moving Target Detection and Its Performance Analysis

This paper describes a new method of small moving target detection and analyzes the performance of this algorithm. The method is based on multi-level threshold decision-making and sliding trajectory confidence testing technology. The parameters of the algorithm are also given. Experiments have been conducted, the results show that the algorithm has advantages of high detection probability, simple structure, and excellent real-time performance.

Coal–rock interface detection on the basis of image texture features

Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence matrix,twenty-two texture features were extracted from the images of coal and rock.Data dimension of the feature space reduced to four by feature selection,which was according to a separability criterion based on inter-class mean difference and within-class scatter.The experimental results show that the optimized features were effective in improving the separability of the samples and reducing the time complexity of the algorithm.In the optimized low-dimensional feature space,the coal–rock classifer was set up using the fsher discriminant method.Using the 10-fold cross-validation technique,the performance of the classifer was evaluated,and an average recognition rate of 94.12%was obtained.The results of comparative experiments show that the identifcation performance of the proposed method was superior to the texture description method based on gray histogram and gradient histogram.

THE PRELIMINARY DESIGN OF LOW-LEVEL WINDSHEAR SIMULATION SYSTEM ON GROUND

Infrared windshear prediction is one of airborne LLWS remote sensing methods.Before it is applied,the prediction system should be tested on ground to prove it’s feasibility.The LLWS simulation system isused to check whether the infrared windshear prediction system operate properly.In this paper,according tothe requirement of the LLWS detection and the characteristic of LLWS＂source＂,we will analyze the theoryof the LLWS simulation and give a basic description of the system construction in which we pay more atten-tion to optical simulation and flight simulation.The optical simulation is used to simulate the atmospherc in-frared radiation as a LLWS occurs.The flight simulation is used to simulate the aircraft airspeed,pitch,alti-tude etc..The works presented here are necessary for the LLWS infrared prediction system.

基于计算机视觉的常用玻璃量器自动检定装置

玻璃量器作为常规配套设备,其需求量日益增长,常用玻璃量器的检定任务越来越重,传统的人工检定已不能满足日益增长的业务需求。该文提出了一种图像法和反馈控制技术相结合的容量示值检定自动化技术,即一种基于图像处理的常用玻璃量器检定自动化装置,主要解决加液自动化功能、容量示值读数自动化功能。开发集数据采集、处理、原始记录生成、报告打印等多功能于一体的软件系统。

透明容器液面机器视觉检测方法

机器视觉检测容器内液体体积的关键在于液面图像识别。根据目标成像特征,研究了基于液面特征和基于参考特征的透明容器液面机器视觉检测方法。通过图像实例与成像机理分析发现,利用液面灰度特征和边缘特征的液面检测方法受液面形态和成像质量的影响较大,而利用外部参照物折射成像出现分离带的液面检测方法在工业相机平视液面时亦容易导致检测失败,且自上而下的搜索方式存在检测误差大的问题。为了提高检测稳定性,提出了一种基于外部参考成像特征的液面检测新方法。该方法基于透明容器影响参照物成像的规律作为检测原理,通过检测参照物的行间梯度,利用液面处梯度最大的特征,自下而上搜索和识别液面。新检测方法不仅增强了液面识别的鲁棒性,还提高了液面检测的准确性。

Clinical Application of Sex Hormone in Different Physiological Periods in the Diagnosis of Infertility Patients

Background: Infertility is characterized by the inability to conceive after a year of regular unprotected intercourse. Aims: This study aimed to investigate the diagnostic value of sex hormone levels during different physiological periods in the diagnosis of infertility patients. Methods: From December 2019 to May 2021, a total of 93 infertility patients were admitted and selected as the observation group. Among them, 31 cases were in the follicular stage, 31 cases in the ovulation stage, and 31 cases in the luteal stage. Ninety-three healthy women for fertility evaluation due to male infertility were selected as the control group. The control group included 31 women in the follicular phase, 31 women in the ovulatory phase, and 31 women in the luteal phase. The levels of sex hormones (prolactin (PRL), luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol (E2), testosterone (T), and progesterone (P)) during different physiological phases were compared between the observation and control groups. Results: The follicular phase showed no significant difference in LH levels between the observation group and the control group. The observation group showed higher levels of PRL and P compared to the control group, while the levels of FSH, E2, and T were lower in the observation group compared to the control group. The ovulation phase showed no significant difference in PRL levels between the two groups. The observation group showed lower levels of LH, FSH, E2, T, and P compared to the control group. The luteal phase showed no statistical difference in E2 levels between the two groups. The observation group showed higher levels of PRL, LH, and FSH compared to the control group, while the levels of T and P were lower in the observation group compared to the control group. Conclusion: Infertile women show variations in hormone levels compared to the normal levels during the follicular phase, ovulatory phase, and luteal phase.

基于FinBERT和Bi-GRU的篇章级事件检测模型

现有的篇章级事件检测方法一般分为候选实体抽取和事件检测两个子任务,候选实体抽取任务的精度影响着事件检测任务的结果。针对现有方法候选实体抽取精度不足的问题,提出了基于FinBERT和Bi-GRU的篇章级事件检测模型。首先,采用FinBERT预训练模型提升词嵌入向量对金融语义的表示能力,从而增强模型对金融语义的感知。其次,将上一层获取到的语义表示输入Bi-GRU模型和多头注意力机制中捕获全局和局部特征,通过CRF进行解码并标注出候选实体和实体类型。最后,根据抽取的候选实体信息判断篇章中存在的预定义事件。实验结果表明,本文方法提升了候选实体抽取任务的精度,取得了较好的篇章级事件检测结果。