Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time series of process variables may have an important indication about the process operation.For example,in a batch process,the change points can correspond to the operations and phases defined by the batch recipe.Hence identifying change points can assist labelling the time series data.Various unsupervised algorithms have been developed for change point detection,including the optimisation approachwhich minimises a cost functionwith certain penalties to search for the change points.The Bayesian approach is another,which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point.The paper investigates how the two approaches for change point detection can be applied to process data analytics.In addition,a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data.The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data.The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions,which will be useful for other machine learning tasks.

Fault detection and diagnosis for data incomplete industrial systems with new Bayesian network approach

For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.

Packet Cache-Forward Method Based on Improved Bayesian Outlier Detection for Mobile Handover in Satellite Networks

In this paper, we propose a Packet Cache-Forward(PCF) method based on improved Bayesian outlier detection to eliminate out-of-order packets caused by transmission path drastically degradation during handover events in the moving satellite networks, for improving the performance of TCP. The proposed method uses an access node satellite to cache all received packets in a short time when handover occurs and forward them out in order. To calculate the cache time accurately, this paper establishes the Bayesian based mixture model for detecting delay outliers of the entire handover scheme. In view of the outliers' misjudgment, an updated classification threshold and the sliding window has been suggested to correct category collections and model parameters for the purpose of quickly identifying exact compensation delay in the varied network load statuses. Simulation shows that, comparing to average processing delay detection method, the average accuracy rate was scaled up by about 4.0%, and there is about 5.5% cut in error rate in the meantime. It also behaves well even though testing with big dataset. Benefiting from the advantage of the proposed scheme in terms of performance, comparing to conventional independent handover and network controlled synchronizedhandover in simulated LEO satellite networks, the proposed independent handover with PCF eliminates packet out-of-order issue to get better improvement on congestion window. Eventually the average delay decreases more than 70% and TCP performance has improved more than 300%.

Mutation detection and fast identification of switching system based on data-driven method

In the engineering field,switching systems have been extensively studied,where sudden changes of parameter value and structural form have a significant impact on the operational performance of the system.Therefore,it is important to predict the behavior of the switching system,which includes the accurate detection of mutation points and rapid reidentification of the model.However,few efforts have been contributed to accurately locating the mutation points.In this paper,we propose a new measure of mutation detection—the threshold-based switching index by analogy with the Lyapunov exponent.We give the algorithm for selecting the optimal threshold,which greatly reduces the additional data collection and the relative error of mutation detection.In the system identification part,considering the small data amount available and noise in the data,the abrupt sparse Bayesian regression(abrupt-SBR)method is proposed.This method captures the model changes by updating the previously identified model,which requires less data and is more robust to noise than identifying the new model from scratch.With two representative dynamical systems,we illustrate the application and effectiveness of the proposed methods.Our research contributes to the accurate prediction and possible control of switching system behavior.

Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data.

Application of Bayesian Dynamic Forecast in Anomaly Detection

A macroscopical anomaly detection method based on intrusion statistic and Bayesian dynamic forecast is presented. A large number of alert data that cannot be dealt with in time are always aggregated in control centers of large-scale intrusion detection systems. In order to improve the efficiency and veracity of intrusion analysis, the intrusion intensity values are picked from alert data and Bayesian dynamic forecast method is used to detect anomaly. The experiments show that the new method is effective on detecting macroscopical anomaly in large-scale intrusion detection systems.

Leak Detection in Water Distribution Systems Using Bayesian Theory and Fisher’s Law

A leak detection method based on Bayesian theory and Fisher’s law was developed for water distribution systems. A hydraulic model was associated with the parameters of leaks (location, extent). The randomness of parameter values was quantified by probability density function and updated by Bayesian theory. Values of the parameters were estimated based on Fisher’s law. The amount of leaks was estimated by back propagation neural network. Based on flow characteristics in water distribution systems, the location of leaks can be estimated. The effectiveness of the proposed method was illustrated by simulated leak data of node pressure head and flow rate of pipelines in a test pipe network, and the leaks were spotted accurately and renovated on time.

Change-Point Detection for General Nonparametric Regression Models

A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.

Speech Signal Detection Based on Bayesian Estimation by Observing Air-Conducted Speech under Existence of Surrounding Noise with the Aid of Bone-Conducted Speech

In order to apply speech recognition systems to actual circumstances such as inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult, some countermeasure methods for surrounding noise are indispensable. In this study, a signal detection method to remove the noise for actual speech signals is proposed by using Bayesian estimation with the aid of bone-conducted speech. More specifically, by introducing Bayes’ theorem based on the observation of air-conducted speech contaminated by surrounding background noise, a new type of algorithm for noise removal is theoretically derived. In the proposed speech detection method, bone-conducted speech is utilized in order to obtain precise estimation for speech signals. The effectiveness of the proposed method is experimentally confirmed by applying it to air- and bone-conducted speeches measured in real environment under the existence of surrounding background noise.

Using Bayesian Game Model for Intrusion Detection in Wireless Ad Hoc Networks

Wireless ad ho network is becoming a new research fronter, in which security is an important issue. Usually some nodes act maliciously and they are able to do different kinds of Denial of Service (Dos). Because of the limited resource, intrusion detection system (IDS) runs all the time to detect intrusion of the attacker which is a costly overhead. In our model, we use game theory to model the interactions between the intrusion detection system and the attacker, and a realistic model is given by using Bayesian game. We solve the game by finding the Bayesian Nash equilibrium. The results of our analysis show that the IDS could work intermittently without compromising on its effectiveness. At the end of this paper, we provide an experiment to verify the rationality and effectiveness of the proposed model.

Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs

Early attack detection is essential to ensure the security of complex networks,especially those in critical infrastructures.This is particularly crucial in networks with multi-stage attacks,where multiple nodes are connected to external sources,through which attacks could enter and quickly spread to other network elements.Bayesian attack graphs(BAGs)are powerful models for security risk assessment and mitigation in complex networks,which provide the probabilistic model of attackers’behavior and attack progression in the network.Most attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring,which is unrealistic given the ever-growing complexity of threats.This paper derives the optimal minimum mean square error(MMSE)attack detection and monitoring policy for the most general form of BAGs.By exploiting the structure of BAGs and their partial and imperfect monitoring capacity,the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman filtering.An adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined value.Exact and efficient matrix-form computations of the proposed policies are provided,and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.

Multi-sources information fusion algorithm in airborne detection systems

To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.

A new oil spill detection algorithm based on Dempster-Shafer evidence theory

Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these features.Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes,some oil spill detection algorithms do not consider the environmental factors.To distinguish oil spills and look-alikes more accurately based on environmental factors and image features,a new oil spill detection algorithm based on Dempster-Shafer evidence theory was proposed.The process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian model.The Faster-region convolutional neural networks(RCNN)model was used for oil spill detection based on the convolution features.The detection results of the two models were fused at decision level using Dempster-Shafer evidence theory.The establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1798 image samples and environmental information records related to the image samples.The analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate,with an identifi cation rate greater than 75%and a false alarm rate lower than 19%from experiments.A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed algorithm.The evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.

Semantic Units Based Event Detection in Soccer Videos

A semantic unit based event detection scheme in soccer videos is proposed in this paper.The scheme can be characterized as a three-layer framework. At the lowest layer, low-level featuresincluding color, texture, edge, shape, and motion are extracted. High-level semantic events aredefined at the highest layer. In order to connect low-level features and high-level semantics, wedesign and define some semantic units at the intermediate layer. A semantic unit is composed of asequence of consecutives frames with the same cue that is deduced from low-level features. Based onsemantic units, a Bayesian network is used to reason the probabilities of events. The experiments forshoot and card event detection in soccer videos show that the proposed method has an encouragingperformance.

A New Probability of Detection Model for Updating Crack Distribution of Offshore Structures

There exists model uncertainty of probability of detection for inspecting ship structures with nondestructive inspection techniques. Based on a comparison of several existing probability of detection (POD) models, a new probability of detection model is proposed for the updating of crack size distribution. Furthermore, the theoretical derivation shows that most existing probability of detection models are special cases of the new probability of detection model. The least square method is adopted for determining the values of parameters in the new POD model. This new model is also compared with other existing probability of detection models. The results indicate that the new probability of detection model can fit the inspection data better. This new probability of detection model is then applied to the analysis of the problem of crack size updating for offshore structures. The Bayesian updating method is used to analyze the effect of probability of detection models on the posterior distribution of a crack size. The results show that different probabilities of detection models generate different posterior distributions of a crack size for offshore structures.

A Multi-Leveled Approach to Intrusion Detection and the Insider Threat

When considering Intrusion Detection and the Insider Threat, most researchers tend to focus on the network architecture rather than the database which is the primary target of data theft. It is understood that the network level is adequate for many intrusions where entry into the system is being sought however it is grossly inadequate when considering the database and the authorized insider. Recent writings suggest that there have been many attempts to address the insider threat phenomena in regards to database technologies by the utilization of detection methodologies, policy management systems and behavior analysis methods however, there appears to be a lacking in the development of adequate solutions that will achieve the level of detection that is required. While it is true that Authorization is the cornerstone to the security of the database implementation, authorization alone is not enough to prevent the authorized entity from initiating malicious activities in regards to the data stored within the database. Behavior of the authorized entity must also be considered along with current data access control policies. Each of the previously mentioned approaches to intrusion detection at the database level has been considered individually, however, there has been limited research in producing a multileveled approach to achieve a robust solution. The research presented outlines the development of a detection framework by introducing a process that is to be implemented in conjunction with information requests. By utilizing this approach, an effective and robust methodology has been achieved that can be used to determine the probability of an intrusion by the authorized entity, which ultimately address the insider threat phenomena at its most basic level.

A study of fault detection way for computer networks based on multiple stages

To relieve traliic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered alter multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method.

基于贝叶斯优化ResNet-BiLSTM的多电飞行器故障检测技术

针对多电飞行器在复杂运行模式下故障检测的精确性与实时性问题,开发了基于贝叶斯优化ResNet-BiLSTM模型的故障检测技术。首先设计以残差网络为核心的多层次特征提取模块,实现故障特征的多层次表达,以提升故障检测的实时性;接着构建以双向长短期记忆网络为核心的多尺度特征提取模块,获取长期依赖的故障特征信息,以提升故障检测的准确性;进而提出基于贝叶斯优化的超参数寻优方法,进一步提升故障特征的学习效果;最后,设计多电飞行器故障检测实验方案,并开展不同故障模式下的性能分析。结果表明,所提方法可有效实现多种故障模式下的故障诊断,并且检测准确率和实时性均优于现有方法,有助于提升多电飞行器故障检测性能,为飞行安全提供有力的技术保障。

基于墙角族语义尺寸链的绑架定位研究

针对目前原始自适应蒙特卡洛定位(Adaptive Monte Carlo Localization,AMCL)在相似环境下绑架检测容易出错且重定位极易失败等问题,提出基于墙角族语义尺寸链的改进AMCL算法.融合机器人多传感器信息和Gmapping算法构建二维栅格地图,基于Yolov5获取室内环境的目标检测框和类别信息,结合GrabCut算法和贝叶斯方法构建增量式语义映射地图;通过墙角的凸、凹和墙角相对于机器人的方位角对墙角进行分类,充分发掘语义映射地图中各墙角之间、墙角与室内物体之间的类别和位置关系,构建墙角族语义尺寸链和相应检索表;在定位过程中,基于墙角族语义尺寸链进行全局预定位,提出绑架检测机制进行绑架检测,在检测到绑架事件发生后,基于改进AMCL算法实现定位自恢复.最后,通过真实环境下的绑架实验验证了本文方法的有效性,实验表明,所提方法的全局定位准确率、全局定位速率、绑架检测准确率和绑架后定位准确率在相似环境下分别提升了42%、214%、88%和72%;在非相似环境下分别提升了44%、152%、12%和92%;在长走廊环境下分别提升了36%、426%、26%和68%.