Antenna array design for covariance-based direction-finding methods

Angle-of-arrival estimates based on the analysis of a spatial covariance matrix are particularly vulnerable to error in the presence of coherent signal interference. When this condition exists, the wave interference field is statistically nonstationary, and estimation techniques that are based upon an assumption of stationarity tend to produce poor results. A method is proposed for the design of antenna arrays that exploits the effects of time-averaging and spatial smoothing to approximate the condition of statistical stationarity and thereby enhance the performance of bearing estimation methods     

高光谱图像异常检测算法研究进展

异常检测算法不需要利用光谱的先验知识,而能直接检测出与周围景物光谱存在明显差异的光谱信号所在位置,是高光谱遥感应用领域一个重要研究方向,在民用和军事上都有重要的理论价值和应用前景。深入分析了目前主要的异常检测算法,并对各算法的优、缺点分别进行了评述。最后,指出了此领域今后的研究方向。     

A class of Monte-Carlo-based statistical algorithms for efficient detection of repolarization alternans

Cardiac repolarization alternans is an electrophysiologic condition identified by a beat-to-beat fluctuation in action potential waveform. It has been mechanistically linked to instances of T-wave alternans, a clinically defined ECG alternation in T-wave morphology, and associated with the onset of cardiac reentry and sudden cardiac death. Many alternans detection algorithms have been proposed in the past, but the majority have been designed specifically for use with T-wave alternans. Action potential duration (APD) signals obtained from experiments (especially those derived from optical mapping) possess unique characteristics, which requires the development and use of a more appropriate alternans detection method. In this paper, we present a new class of algorithms, based on the Monte Carlo method, for the detection and quantitative measurement of alternans. Specifically, we derive a set of algorithms (one an analytical and more efficient version of the other) and compare its performance with the standard spectral method and the generalized likelihood ratio test algorithm using synthetic APD sequences and optical mapping data obtained from an alternans control experiment. We demonstrate the benefits of the new algorithm in the presence of Gaussian and Laplacian noise and frame-shift errors. The proposed algorithms are well suited for experimental applications, and furthermore, have low complexity and are implementable using fixed-point arithmetic, enabling potential use with implantable cardiac devices.     

Three state-of-the-art methods for condition monitoring

This paper describes and compares three different state-of-the-art condition monitoring techniques: first principles, feature extraction, and neural networks. The focus of the paper is on the application of the techniques, not on the underlying theory. Each technique is described briefly and is accompanied by a discussion on how it can be applied properly. The discussion is finished with an enumeration of the advantages and disadvantages of the technique. Two condition monitoring cases, taken from the marine engineering field, are explored: condition monitoring of a diesel engine, using only the torsional vibration of the crank shaft, and condition monitoring of a compression refrigeration plant, using many different sensors. Attention is also paid to the detection of sensor malfunction and to the user interface. The experience from the cases shows that all techniques are showing promising results and can be used to provide the operator with information about the monitored machinery on a higher level. The main problem remains the acquisition of the required knowledge, either from measured data or from analysis     

Network intrusion and fault detection: a statistical anomaly approach

With the advent and explosive growth of the global Internet and electronic commerce environments, adaptive/automatic network/service intrusion and anomaly detection in wide area data networks and e-commerce infrastructures is fast gaining critical research and practical importance. We present and demonstrate the use of a general-purpose hierarchical multitier multiwindow statistical anomaly detection technology and system that operates automatically, adaptively, and proactively, and can be applied to various networking technologies, including both wired and wireless ad hoc networks. Our method uses statistical models and multivariate classifiers to detect anomalous network conditions. Some numerical results are also presented that demonstrate that our proposed methodology can reliably detect attacks with traffic anomaly intensity as low as 3-5 percent of the typical background traffic intensity, thus promising to generate an effective early warning.     

基于流量统计特征的端口扫描检测算法

根据网络流量的统计特征提出一种慢速端口扫描行为检测算法，以主机数和端口数的比值及被访问主机端口集合之间的相似度为基础，采用非参数累积和CUSUM算法及小波变换方法对流量统计特征进行分析，进而判断是否存在端口扫描行为。实验结果表明，所提取的网络流量特征及算法可以有效地检测异常行为，该方法和Snort相比较具有低的漏报率和误报率。     

一种基于LSTM自动编码机的工业系统异常检测方法

在工业互联网的环境下,自动有效的异常检测方法对工业系统的安全、稳定生产具有重要的意义。传统的异常检测方法存在需要大量标注样本、不适应高维度时序数据等不足,提出一种基于LSTM自动编码机的工业系统异常检测方法。为克服现有方法依赖标注样本的不足,提出采用自动编码机,通过无监督的方式学习大量正常样本的特征和模式,在此基础上通过对样本进行重构和计算重构误差的方式进行异常检测。其次,为克服现有方法不适应高维度时序数据的不足,提出采用双向LSTM作为编码器,进而挖掘多维时序数据的潜在特征。基于一个真实造纸工业的数据集的实验表明,所提方法在各项指标上都对现有无监督异常检测方法有一定的提升,检测的总体精度达到了93.4%。     

时间序列不确定数据流中异常数据检测方法

结合小波分析和不确定聚类方法的优点,提出一种基时间序列不确定数据流的异常数据检测方法,该方法主要考虑数据流中元组的不确定性,同时平衡检测的计算代价与检测精度。仿真实验证明,该检测方法能够良好地适应数据流的不确定性。在一定条件下可获得相当好的检测效果。     

An efficient system for anomaly detection using deep learning classifier

In this paper, a deep learning-based anomaly detection (DLAD) system is proposed to improve the recognition problem in video processing. Our system achieves complete detection of abnormal events by involving the following significant proposed modules a Background Estimation (BE) Module, an Object Segmentation (OS) Module, a Feature Extraction (FE) Module, and an Activity Recognition (AR) Module. At first, we have presented a BE (Background Estimation) module that generated an accurate background in which two-phase model is generated to compute the background estimation. After a high-quality background is generated, the OS model is developed to extract the object from videos, and then, object tracking process is used to track the object through the overlapping detection scheme. From the tracked objects, the FE module is extracted for some useful features such as shape, wavelet, and histogram to the abnormal event detection. For the final step, the proposed AR module is classified as abnormal or normal event using the deep learning classifier. Experiments are performed on the USCD benchmark dataset of abnormal activities, and comparisons with the state-of-the-art methods validate the advantages of our algorithm. We can see that the proposed activity recognition system has outperformed by achieving better EER of 0.75 % when compared with the existing systems (20 %). Also, it shows that the proposed method achieves 85 % precision rate in the frame-level performance.     

An anomaly intrusion detection method using fourier transform

A set of discrete points obtained from audit records on a behavior session is processed with Fourier transform. The criterion of selecting Fourier transform coefficients is introduced, and is used to find a unified value from the set of coefficients. This unified value is compared with a threshold to determine whether the session is abnormal. Finally simple test results are reported.     

An invariance property of some subspace-based detection algorithms

Subspace fitting estimates involve multidimensional parameter estimating functions having a particular product form. Based on estimating functions, the statistical local approach builds detection algorithms that enjoy a simple invariance property. This article investigates that invariance property for the detectors built on those two concepts     

Multi-task learning for video anomaly detection

We propose a multi-task learning framework for video anomaly detection based on a novel pipeline. Our model contains two crossing streams, one stream employs the backbone of Attention-R2U-net for future frame prediction, while the other is designed based on an encoder–decoder network to reconstruct the input optical flow maps. In addition, the latent layers of the two streams are merged together and assigned with a Deep SVDD-based loss at each location individually. Through the combination of these three tasks, the two-stream-crossing pipeline can be trained end-to-end to provide a comprehensive evaluation for the anomaly targets. Experimental results on several popular benchmark datasets show that our model outperforms the state-of-the-art competing models, which can be applied to different types of anomalous targets and meanwhile achieves remarkable precision.     

场景统计类红外图像非均匀性校正算法研究

场景统计类非均匀性校正算法对场景分布进行假定,获得探测单元接收红外能量的一、二阶矩,进而估计探测单元的响应参数,校正非均匀性。分析比较了现有的场景统计类非均匀性校正算法,并应用交互多模(IMM)算法校正非均匀性,实验结果表明以连续图像序列作为观测数据时,能够有效地校正非均匀性,扩展了卡尔曼滤波法的适用范围。对各种方法进行仿真,表明删算法具有较好的收敛特性。     

UFKLDA: An unsupervised feature extraction algorithm for anomaly detection under cloud environment

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high‐dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non‐Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C‐SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.     

磁异探测法探测海底光缆

为提高海底光缆的探测效率,克服有源磁探测法的缺点,首先从海底光缆自身的铠装出发,提出了磁异探测这种无源磁探测的方法,接着对该方法的原理进行了深入分析,并利用无限长圆柱体模型和磁偶极子磁场模型对其进行了理论推导,其结果对今后开展仿真和实验具有较强的指导意义。     

A novel ensembled technique for anomaly detection

Anomaly detection is a technique that works to detect those instances of data that do not comply with the data model. In this paper the problem of anomaly detection in networked traffic data is considered, and a novel ensembled technique for anomaly detection is proposed. The proposed technique uses a combination of fuzzy K‐means clustering algorithm, extended Kalman filter, and support vector machines to detect the anomalies. In the proposed technique, fuzzy membership functions are used instead of crisp clusters to compute the best set of features by fuzzy k‐means algorithm. These features are then optimized with a nonlinear Bayesian approach known as extended Kalman filter. The resultant optimized set of features is then provided as an input to the support vector machine classifier that detects the network anomalies. The proposed technique is validated by using 2 benchmark datasets, ie, DARPA 1998 and KDD CUP 1999. Experimental results indicate that the proposed technique performs quite well as compared to its traditional counterparts in accuracy, detection rate, false positive rate, and F‐score.     

Clustering-based label estimation for network anomaly detection

A substantial body of work has been done to identify network anomalies using supervised and unsupervised learning techniques with their unique strengths and weaknesses. In this work, we propose a new approach that takes advantage of both worlds of unsupervised and supervised learnings. The main objective of the proposed approach is to enable supervised anomaly detection without the provision of the associated labels by users. To this end, we estimate the labels of each connection in the training phase using clustering. The “estimated” labels are then utilized to establish a supervised learning model for the subsequent classification of connections in the testing stage. We set up a new property that defines anomalies in the context of network anomaly detection to improve the quality of estimated labels. Through our extensive experiments with a public dataset (NSL-KDD), we will prove that the proposed method can achieve performance comparable to one with the “original” labels provided in the dataset. We also introduce two heuristic functions that minimize the impact of the randomness of clustering to improve the overall quality of the estimated labels.     

An evaluation of test methods for the detection and control ofinterconnect reliability

A number of fast, wafer-level test methods exist for interconnect reliability evaluation. The relative abilities of four such methods to detect the quality and reliability of the interconnect over very short test times are evaluated in this work. Four different test structure designs are also evaluated, and the results are compared with package-level median time to failure (MTF) results. The isothermal test method combined with standard wafer-level electromigration accelerated test (SWEAT)-type test structures is shown to be the most suitable combination for defect detection and interconnect reliability control over short test times     

Epsilon-optimal non-Bayesian anomaly detection for parametric tomography

The non-Bayesian detection of an anomaly from a single or a few noisy tomographic projections is considered as a statistical hypotheses testing problem. It is supposed that a radiography is composed of an imaged nonanomalous background medium, considered as a deterministic nuisance parameter, with a possibly hidden anomaly. Because the full voxel-by-voxel reconstruction is impossible, an original tomographic method based on the parametric models of the nonanomalous background medium and radiographic process is proposed to fill up the gap in the missing data. Exploiting this "parametric tomography," a new detection scheme with a limited loss of optimality is proposed as an alternative to the nonlinear generalized likelihood ratio test, which is untractable in the context of nondestructive testing for the objects with uncertainties in their physical/geometrical properties. The theoretical results are illustrated by the processing of real radiographies for the nuclear fuel rod inspection.     

一种改进的3GPP-LTE系统MIMO检测算法研究

本文对3GPP-LTE项目中MIMO技术进行了分析和探讨,针对目前的检测算法进行了研究和比较,提出一种易于实用的改进复数列表球形译码检测算法ICLSD,并对该算法的性能进行了分析.