Performance Analysis of Distributed Neyman-Pearson Detection Systems

The performance of a distributed Neyman-Pearson detection system is considered with the decision rules of the sensors given and the decisions from different sensors being mutually independent conditioned on both hypothese. To achieve the better performance at the fusion center for a general detection system of n 〉 3 sensor configuration, the necessary and sufficient conditions are derived by comparing the probability of detec- tion at the fusion center with that of each of the sensors, with the constraint that the probability of false alarm at the fusion center is equal to that of the sensor. The conditions are related with the performances of the sensors and using the results we can predict the performance at the fusion center of a distributed detection system and can choose appropriate sensors to construct efficient distributed detection systems.

Maximum Likelihood Detection for Detect-and-Forward Relay Channels

This paper introduces a simple combining technique for cooperative relay scheme which is based on a Detect-and-Forward (DEF) relay protocol. Cooperative relay schemes have been introduced in earlier works but most of them ignore the quality of the source-relay (S-R) channel in the detection at the destination, although this channel can contribute heavily to the performance of cooperation schemes. For optimal detection, the destination has to account all possible error events at the relay as well. Here we present a Maximum Likelihood criterion (ML) at the destination which considers closed-form expressions for each symbol error rate (SER) to facilitate the detection. Computer simulations show that significant diversity gain and Packet Error Rate (PER) performance can be achieved by the proposed scheme with good tolerance to propagation errors from noisy relays. In fact, diversity gain is increased with additional relay nodes. We compare this scheme against the baseline Cooperative-Maximum Ratio Combining (C-MRC).

Code Smell Detection Using Whale Optimization Algorithm

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their capabilities.Code smells arise as one of the obstacles in the software industry.They are characteristics of software source code that indicate a deeper problem in design.These smells appear not only in the design but also in software implementation.Code smells introduce bugs,affect software maintainability,and lead to higher maintenance costs.Uncovering code smells can be formulated as an optimization problem of finding the best detection rules.Although researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be improved.Previous research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their types.Our research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection rules.Applying this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass variance.The proposed framework adopts if-then detection rules during the software development life cycle.Those rules identify the types for both medium and large projects.Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes.The proposed detection framework has an average of 94.24%precision and 93.4%recall.These accurate values are better than other search-based algorithms of the same field.The proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and cost.Additionally,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

Robust Speech Endpoint Detection in Airplane Cockpit Voice Background

A method of robust speech endpoint detection in airplane cockpit voice background is presented. Based on the analysis of background noise character, a complex Laplacian distribution model directly aiming at noisy speech is established. Then the likelihood ratio test based on binary hypothesis test is carried out. The decision criterion of conventional maximum a posterior incorporating the inter-frame correlation leads to two separate thresholds. Speech endpoint detection decision is finally made depend on the previous frame and the observed spectrum, and the speech endpoint is searched based on the decision. Compared with the typical algorithms, the proposed method operates robust in the airplane cockpit voice background.

COMPUTER-AIDED DETECTION OF THE EPILEPTIC WAVES IN EEG:A REALIZED STRATEGY BY ADOPTING MULTI-METHOD

In this thesis, a strategy realizing the computer-aided detection (CAD) of the epileptic waves in EEG isintroduced. The expert criterion, continuous wavelet transformation, neural networks, and characteristic parametermeasuremente these modern signa1 processing weapons were synthesized togetLher to form a so-called multi-method.It was estimated that the advantages of all the powerful techniques could be exploited systematically. Therefore, theCAD’s capacities in the long-term monitoring, trCaAnent and control of epilepsy might be enhanced. In this strategy,the raw EEG signals were uniformed and the expelt criterion were applied to discard most of aItifacts in them at first,and then the signals were pre-processed by continuous wavelet transformation. Some characteristic parameters wereextracted from the raw signals and the pre-processed ones. Consequently groups of eighteen parameters were sent totrain or test BP networks. By applying this theme a correct-detection rate of 84.3% for spike and sharp waves, and88.9% for sPike and sharp slow waves were obtained. In the next step, some non-linear tools wtll also be equippedwith the CAD system.

JOINT EARLY DETECTION AND EARLY STOPPING SCHEME FOR COMPLEXITY REDUCTION OF TURBO DECODING

In this paper,a Joint Early Detection and Early Stopping (JEDES) approach for effectively reducing the complexity of turbo decoding with negligible performance loss is proposed. It combines the effectiveness of both early detection and early stopping techniques. Our simulation results demon-strated that the proposed JEDES scheme based on cyclic redundancy check and trellis splicing can achieve a complexity saving of 15% to 20% at practical bit error rates over the idealized GENIE stopping scheme,which is widely accepted as the theoretically best possible early stopping scheme.

复合绝缘子交流盐雾闪络的可见光判据研究

绝缘子闪络事故一旦发生,严重危害电网安全运行。为探索放电检测和电弧发展成闪络预警的新方法,采用图像处理技术研究复合绝缘子交流盐雾闪络过程中不同阶段的电弧演化特性,提出复合绝缘子表面放电和电弧发展成闪络的可见光判据。首先进行10 kV复合绝缘子交流盐雾闪络试验,拍摄黑暗背景和明亮背景下的电弧演化视频,接着分析并发现局部电弧发展阶段和临闪阶段的电弧熄灭重燃特征差异,最后提出用放电像素数作为可见光图像放电程度评估参数,并通过图像处理技术提取不同背景下各阶段电弧演化过程的放电像素数时间序列。结果表明,放电像素数能准确反映绝缘子表面放电状态,放电像素数时间序列与各阶段电弧熄灭重燃规律高度匹配,可将放电像素数不为0和放电像素数时间序列谷值不为0分别作为复合绝缘子表面放电和盐雾闪络的可见光判据。

基于优化小波分量的高铁桥墩沉降异常探测方法

针对高铁桥墩沉降数据中传统异常值探测方法存在探测结果不理想的情况,提出一种基于优化小波分量的高铁桥墩沉降异常值探测方法。所提方法在拉依达准则法的基础上,通过优化小波分量降低小波分量中的噪声影响并充分利用小波分量中的有效信息。利用模拟实验数据和工程实例数据进行实验,并与拉依达准则法、IQR法、MAD法以及4种传统小波组合方法进行对比分析。实验结果表明,文中所提方法相较于单一异常值探测方法和其它小波组合方法能够探测出更多的异常值,且探测结果更加可靠。

基于改进小波阈值去噪和CEEMDAN-HT融合的谐波检测技术

针对电网环境噪声导致自适应噪声的CEEMDAN谐波检测精度低的问题,文中提出了一种基于改进小波阈值去噪和CEEMDAN-HT融合的谐波检测技术。利用修正因子T j自适应调整阈值,通过可调参数τ调节阈值函数软、硬特性,并将改进的小波阈值去噪方法应用于谐波信号的预处理。经预处理的信号再进行CEEMDAN分解,可有效抑制模态混叠束缚。运用相关度判据去除虚假分量,并利用Hilbert变换解调包含谐波特征的分量,准确提取其幅频信息。经MATLAB仿真可知,改进小波阈值去噪与CEEMDAN-HT的融合算法可将稳态谐波检测平均误差被控制在1%以下,暂态谐波检测平均误差被控制在2.1%以下,呈现出良好的抗噪性能。

埋地钢质管道直流杂散电流干扰研究综述

随着轨道交通、高压直流输电系统的建设,埋地钢质管道受到越来越多的直流杂散电流干扰,由此引发的安全问题受到政府和企业的重视。本文以直流杂散电流为对象,论述了直流干扰的不同来源以及对埋地钢质管道的腐蚀机理;综述了不同来源的直流杂散电流干扰规律的研究现状;总结了现行标准中的直流杂散电流评价准则,并提出了建议;综述了各种直流杂散电流检测与防护技术,分析了各自技术的适用范围及特点;并且对直流杂散电流干扰规律以及检测防护研究方向进行了展望。

基于Grubbs准则的改进的2D CA-CFAR

在不同程度的噪声存在下,现代雷达通过恒虚警率(constant false alarm rate, CFAR)检测算法最大化提升目标的检测概率。针对车载毫米波雷达目标检测过程中传统2维单元均值类恒虚警率(two-dimensional cell averaging CFAR,2D CA-CFAR)检测算法在相邻多目标条件下出现的目标遮蔽问题,提出了一种基于格拉布斯(Grubbs)准则的2D CA-CFAR的改进算法。该方法利用Grubbs准则剔除参考窗中的奇异值和干扰目标,从而获得更加准确的检测阈值。仿真结果表明:改进的算法极大地降低了多目标环境中传统2D CA-CFAR检测器的“遮盖效应”,验证了所提算法的有效性。

大气颗粒物重金属检测预处理及测定方法的应用对比

在工业化时代,重金属污染愈加严重,国内外对此问题也愈加重视。大气环境中,重金属极易附着于大气颗粒物,并随着颗粒物进入人体,对人体健康造成危害。本文论述了大气颗粒物中重金属检测的预处理方法和主要检测技术。通过对大气颗粒物重金属的国家环境监测方法标准进行分析发现,在精密度和准确度方面,电感耦合等离子体质谱法更占优势;从检测过程方面看,X射线荧光光谱法的样品预处理等更为便利;同时X射线荧光光谱法和电感耦合等离子体原子发射光谱法所需费用相对较高。

Edge Detectors Based on Pauta Criterion with Application to Hybrid Compact-WENO Finite Difference Scheme

In the last two decades,many edge detection methods have been developed and widely used in image processing for edge detection and the hybrid compactWENO finite difference(hybrid)schemes for solving the system of hyperbolic conservation laws with solutions containing both discontinuous and complex fine-scale structures.However,many edge detection methods include the problem-dependent parameters such as the high order multi-resolution(MR)analysis(Harten,JCP,49(1983)).Therefore,we combined the Tukey’s boxplot method with MR analysis(Gao et al.,JSC,73(2017))to overcome this problem in a sense.But the Tukey’s boxplot method needs to sort the data at the beginning of Runge-Kutta time integration method,which is relatively time-consuming and inefficient.In this study,we employ the PauTa criterion and remove the problem-dependent parameters in the MR analysis.Furthermore,two new edge detection approaches,which are based on second-order central difference scheme and Ren’s idea(Ren et al.,JCP,192(2003)),are also proposed.The accuracy,efficiency and robustness of the hybrid scheme with the new edge detectors are verified by numerous classical one-and two-dimensional examples in the image processing and compressible Euler equations with discontinuous solutions.

大坝监测数据多维度LSTM异常检测与恢复

大坝安全监测是大坝安全的重要保障,对监测数据进行异常检测与恢复可有效避免对大坝状态的错误估计和判断,具有重要现实意义。近年来基于深度学习方法的大坝监测数据异常检测受到广泛研究,但现存方法存在数据利用不足、信息挖掘不充分等问题。因此,本文提出一种多维度LSTM异常检测与恢复方法,该方法用LSTM输入多个测点的大坝监测数据对单测点数据进行预测,有效利用了不同测点间的相关信息;最后利用拉依达准则对目标测点进行异常检测。本文利用大渡河瀑布沟水电站真空激光准直监测数据进行案例验证,通过与单维度的LSTM异常检测与恢复方法相比较,验证了所提方法能有效地检测数据异常和预测恢复正常数据,是一种有效的大坝监测数据异常检测与恢复方法。

Duffing振子在水工结构动力响应信号检测应用中的一种混沌判据

【目的】Duffing振子是一种典型的非线性动力系统,混沌状态的判断是利用Duffing振子进行水工结构动力响应信号检测的基础准则。Duffing振子系统进入混沌状态时,系统状态时序图和相图会变得更为无序和复杂,可以对系统状态进行直观反映,但直接将二者作为系统混沌状态的判断依据,存在较为模糊和主观性较大的问题。为了对Duffing振子系统状态时序图和相图的复杂程度进行定量描述,以便更准确地利用二者对Duffing振子系统混沌状态的判断,【方法】提出了一种利用系统内状态量的时序图和相图盒维数的指数组合的指标,将该指标是否发生突变作为利用Duffing振子进行信号检测时的混沌状态的判断依据。【结果】所提出的Duffing振子混沌状态识别指标在Duffing方程的仿真分析、正弦波仿真信号检测和悬臂梁锤击动力响应信号检测试验中分别表现出22.611%、17.514%和30.975%的相对变化率。同时振子对应的李雅普诺夫指数也由负转正,但Duffing方程的仿真分析结果显示李雅普诺夫指数作为识别指标更为敏感,在振子系统的时序图和相图未明显表现出混沌状态时,其已经由负转正。【结论】由试验结果可知:可以根据所提出识别指标是否发生突变进行识别振子混沌状态,达到信号检测的目的。同时,所提出的混沌状态识别指标以时序图和相图的复杂程度直接作为判断标准,与其他指标相比更为直接和合理。悬臂梁动力响应信号检测试验初步验证了所选取的识别指标应用到具体工作中的可行性,有望应用于结构损伤识别,共振发现等工作中。

卡尔曼滤波能耗预测模型及其应用

为满足工业能耗管控对能耗测量数据的自适应异常检测和缺失替代需求,提出一种基于卡尔曼滤波算法的能耗预测模型。结合应用场景特点,描述模型对应的卡尔曼滤波递推方程组的计算变量和模型参数,提出模型参数的获取方法,即采用K-均值聚类算法对原始的功率和能耗历史样本进行聚类,使用格拉布斯准则剔除聚类样本中的异常数据,基于剩余聚类样本实施无偏估计得到模型参数。实验数据表明,卡尔曼滤波能耗预测模型适用于工业能耗管控的相关需求。

基于最大后验概率准则的天基星座网络多用户检测方法

天基星座网络中用户可以随意进出自由组网,活动用户的数量、身份和发送的数据随机变化,对接收用户的性能造成很大影响。多用户检测是指识别每个活动用户的身份并检测每个活动用户发送的数据。传统方法假设活动用户数等于网络可以容纳的最大用户数,这种方法模型比较简单且误码率性能不够理想。提出了一种基于最大后验概率准则的多用户检测方法,该方法将用户的活动状态建模为马尔科夫链,并将多用户检测转换为在网格图中使用BCJR算法搜索最优路径。仿真结果表明,当活动状态错误概率和符号错误概率达到10^(-3)时,本文所提方法与对照方法相比分别获得2.6 dB和1 dB信噪比增益。

IPCLRC约束的时序地表覆盖数据不一致探测

目前的地表覆盖数据不一致性研究多是针对单期或者更新期在空间方面的不一致性探测,在具有多期的时序地表覆盖数据中,进行时间连续性不一致探测的研究较少。本文提出适用于地表覆盖属性变化的改进拉依达准则,并结合地类时序转换逻辑规则提出改进拉依达逻辑规则结合法,对时序地表覆盖数据进行不一致探测和验证;以GlobeLand30数据集为例,选取雄安新区和合肥市为研究区,利用谷歌高清历史影像、Landsat影像等对探测结果进行了抽样验证。结果表明:改进拉依达准则能更好地拟合时序地表覆盖属性变化频数且包括的时序属性变化种类更多;本方法有90%以上的准确率和29%~69%的探测率。

信号检测论与贝叶斯决策理论的关系

信号检测论被广泛用于解释个体在不同类型认知任务中的决策过程。然而,经典信号检测论的重要不足之处在于难以进一步解释个体设置报告标准的过程对应怎样的内在心理机制。本文从贝叶斯决策理论的视角出发,深入探讨个体在信号检测任务中的决策规则。首先基于贝叶斯定理介绍贝叶斯决策理论的基本观点。随后探讨贝叶斯决策理论如何解释理想观察者的决策规则,以及在实际的信号检测任务中个体的决策结果与理想观察者之间的偏离。其次探讨经典信号检测论和贝叶斯决策理论在不等方差信号检测模型中的差异。最后简要介绍支持贝叶斯决策理论的实证研究证据。

压水堆核电厂控制棒导向卡磨损机理与检测标准研究

为了解决核电厂控制棒导向卡磨损问题,以对控制棒组件安全分析提供支持,本研究基于国外核电厂导向卡磨损数据和标准,介绍了磨损寿命边界的计算、标准的划分以及视觉法和超声法两种检测技术,阐明了导向卡磨损机理,检测标准以及验收准则。结果表明:三代核电的导向卡磨损机理与检测标准,大部分可沿用二代改进型压水堆的标准。针对导向筒结构的不同,有必要开展进一步的研究工作,据此完善相应的检测标准。