Low Latency Anomaly Detection with Imperfect Models

The problem of anomaly detection deals with detecting abrupt changes/anomalies in the distribution of sequentially observed data in a stochastic system. This problem applies to many applications, such as signal processing, intrusion detection, quality control, medical diagnosis, etc. A low latency anomaly detection algorithm, which is based on the framework of quickest change detection (QCD), aims at minimizing the detection delay of anomalies in the sequentially observed data while ensuring satisfactory detection accuracy. Moreover, in many practical applications, complete knowledge of the post-change distribution model might not be available due to the unexpected nature of the change. Hence, the objective of this dissertation is to study low latency anomaly detection or QCD algorithms for systems with imperfect models such that any type of abnormality in the system can be detected as quickly as possible for reliable and secured system operations. This dissertation includes the theoretical foundations behind these low latency anomaly detection algorithms along with real-world applications. First, QCD algorithms are designed for detecting changes in systems with multiple post-change models under both Bayesian and non-Bayesian settings. Next, a QCD algorithm is studied for real-time detection of false data injection attacks in smart grids with dynamic models. Finally, a QCD algorithm for detecting wind turbine bearing faults is developed by analyzing the statistical behaviors of stator currents generated by the turbines. For all the proposed algorithms, analytical bounds of the system performance metrics are derived using asymptotic analysis and the simulation results show that the proposed algorithms outperform existing algorithms

Improvement of Geometric Quality Inspection and Process Efficiency in Additive Manufacturing

Additive manufacturing (AM) has been known for its ability of producing complex geometries in flexible production environments. In recent decades, it has attracted increasing attention and interest of different industrial sectors. However, there are still some technical challenges hindering the wide application of AM. One major barrier is the limited dimensional accuracy of AM produced parts, especially for industrial sectors such as aerospace and biomedical engineering, where high geometric accuracy is required. Nevertheless, traditional quality inspection techniques might not perform well due to the complexity and flexibility of AM fabricated parts. Another issue, which is brought up from the growing demand for large-scale 3D printing in these industry sectors, is the limited fabrication speed of AM processes. However, how to improve the fabrication efficiency without sacrificing the geometric quality is still a challenging problem that has not been well addressed. In this work, new geometric inspection methods are proposed for both offline and online inspection paradigms, and a layer-by-layer toolpath optimization model is proposed to further improve the fabrication efficiency of AM processes without degrading the resolution. First, a novel Location-Orientation-Shape (LOS) distribution derived from 3D scanning output is proposed to improve the offline inspection in detecting and distinguishing positional and dimensional non-conformities of features. Second, the online geometric inspection is improved by a multi-resolution alignment and inspection framework based on wavelet decomposition and design of experiments (DOE). The new framework is able to improve the alignment accuracy and to distinguish different sources of error based on the shape deviation of each layer. In addition, a quickest change point detection method is used to identify the layer where the earliest change of systematic deviation distribution occurs during the printing process. Third, to further improve the printing efficiency without sacrificing the quality of each layer, a toolpath allocation and scheduling optimization model is proposed based on a concurrent AM process that allows multiple extruders to work collaboratively on the same layer. For each perspective of improvements, numerical studies are provided to emphasize the theoretical and practical meanings of proposed methodologies

Using a mean changing stochastic processes exit-entry model for stock market long-short prediction

Stochastic processes is one of the key operations research tools for analysis of complex phenomenon. This paper has a unique application to the study of mean changing models in stock markets. The idea is to enter and exit stock markets like Apple Computer and the broad S&P500 index at good times and prices (long and short). Research by Chopra and Ziemba showed that mean estimation was far more important to portfolio success than variance or co-variance estimation. The idea in the stochastic process model is to determine when the mean changes and then reverse the position direction. This is applied to Apple Computer stock in 2012 when it rallied dramatically then had a large fall and Apple Computer and the S&P500 in the 2020 Covid-19 era. The results show that the mean changing model greatly improves on a buy and hold strategy even for securities that have has large gains over time but periodic losses which the model can exploit. This type of model is also useful to exit bubble-like stock markets and a number of these in the US, Japan, China and Iceland are described. An innovation in this paper is the exit entry long short feature which is important in financial markets

Principles for the post-GWAS functional characterisation of risk loci

Several challenges lie ahead in assigning functionality to susceptibility SNPs. For example, most effect sizes are small relative to effects seen in monogenic diseases, with per allele odds ratios usually ranging from 1.15 to 1.3. It is unclear whether current molecular biology methods have enough resolution to differentiate such small effects. Our objective here is therefore to provide a set of recommendations to optimize the allocation of effort and resources in order maximize the chances of elucidating the functional contribution of specific loci to the disease phenotype. It has been estimated that 88% of currently identified disease-associated SNP are intronic or intergenic. Thus, in this paper we will focus our attention on the analysis of non-coding variants and outline a hierarchical approach for post-GWAS functional studies

In Datacenter Performance, The Only Constant Is Change

All computing infrastructure suffers from performance variability, be it bare-metal or virtualized. This phenomenon originates from many sources: some transient, such as noisy neighbors, and others more permanent but sudden, such as changes or wear in hardware, changes in the underlying hypervisor stack, or even undocumented interactions between the policies of the computing resource provider and the active workloads. Thus, performance measurements obtained on clouds, HPC facilities, and, more generally, datacenter environments are almost guaranteed to exhibit performance regimes that evolve over time, which leads to undesirable nonstationarities in application performance. In this paper, we present our analysis of performance of the bare-metal hardware available on the CloudLab testbed where we focus on quantifying the evolving performance regimes using changepoint detection. We describe our findings, backed by a dataset with nearly 6.9M benchmark results collected from over 1600 machines over a period of 2 years and 9 months. These findings yield a comprehensive characterization of real-world performance variability patterns in one computing facility, a methodology for studying such patterns on other infrastructures, and contribute to a better understanding of performance variability in general.Comment: To be presented at the 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid, http://cloudbus.org/ccgrid2020/) on May 11-14, 2020 in Melbourne, Victoria, Australi

Sequential sensor installation for Wiener disorder detection

We consider a centralized multisensor online quickest disorder detection problem where the observation from each sensor is a Wiener process gaining a constant drift at a common unobservable disorder time. The objective is to detect the disorder time as quickly as possible with small probability of false alarms. Unlike the earlier work on multisensor change detection problems, we assume that the observer can apply a sequential sensor installation policy. At any time before a disorder alarm is raised, the observer can install new sensors to collect additional signals. The sensors are statistically identical, and there is a fixed installation cost per sensor. We propose a Bayesian formulation of the problem. We identify an optimal policy consisting of a sequential sensor installation strategy and an alarm time, which minimize a linear Bayes risk of detection delay, false alarm, and new sensor installations. We also provide a numerical algorithm and illustrate it on examples. Our numerical examples show that significant reduction in the Bayes risk can be attained compared to the case where we apply a static sensor policy only. In some examples, the optimal sequential sensor installation policy starts with 30% less number of sensors than the optimal static sensor installation policy and the total percentage savings reach to 12%. © 2016 INFORMS

Pedestrian Mobility Mining with Movement Patterns

In street-based mobility mining, pedestrian volume estimation receives increasing attention, as it provides important applications such as billboard evaluation, attraction ranking and emergency support systems. In practice, empirical measurements are sparse due to budget limitations and constrained mounting options. Therefore, estimation of pedestrian quantity is required to perform pedestrian mobility analysis at unobserved locations. Accurate pedestrian mobility analysis is difficult to achieve due to the non-random path selection of individual pedestrians (resulting from motivated movement behaviour), causing the pedestrian volumes to distribute non-uniformly among the traffic network. Existing approaches (pedestrian simulations and data mining methods) are hard to adjust to sensor measurements or require more expensive input data (e.g. high fidelity floor plans or total number of pedestrians in the site) and are thus unfeasible. In order to achieve a mobility model that encodes pedestrian volumes accurately, we propose two methods under the regression framework which overcome the limitations of existing methods. Namely, these two methods incorporate not just topological information and episodic sensor readings, but also prior knowledge on movement preferences and movement patterns. The first one is based on Least Squares Regression (LSR). The advantage of this method is the easy inclusion of route choice heuristics and robustness towards contradicting measurements. The second method is Gaussian Process Regression (GPR). The advantages of this method are the possibilities to include expert knowledge on pedestrian movement and to estimate the uncertainty in predicting the unknown frequencies. Furthermore the kernel matrix of the pedestrian frequencies returned by the method supports sensor placement decisions. Major benefits of the regression approach are (1) seamless integration of expert data and (2) simple reproduction of sensor measurements. Further advantages are (3) invariance of the results against traffic network homeomorphism and (4) the computational complexity depends not on the number of modeled pedestrians but on the traffic network complexity. We compare our novel approaches to state-of-the-art pedestrian simulation (Generalized Centrifugal Force Model) as well as existing Data Mining methods for traffic volume estimation (Spatial k-Nearest Neighbour) and commonly used graph kernels for the Gaussian Process Regression (Squared Exponential, Regularized Laplacian and Diffusion Kernel) in terms of prediction performance (measured with mean absolute error). Our methods showed significantly lower error rates. Since pattern knowledge is not easy to obtain, we present algorithms for pattern acquisition and analysis from Episodic Movement Data. The proposed analysis of Episodic Movement Data involve spatio-temporal aggregation of visits and flows, cluster analyses and dependency models. For pedestrian mobility data collection we further developed and successfully applied the recently evolved Bluetooth tracking technology. The introduced methods are combined to a system for pedestrian mobility analysis which comprises three layers. The Sensor Layer (1) monitors geo-coded sensor recordings on people’s presence and hands this episodic movement data in as input to the next layer. By use of standardized Open Geographic Consortium (OGC) compliant interfaces for data collection, we support seamless integration of various sensor technologies depending on the application requirements. The Query Layer (2) interacts with the user, who could ask for analyses within a given region and a certain time interval. Results are returned to the user in OGC conform Geography Markup Language (GML) format. The user query triggers the (3) Analysis Layer which utilizes the mobility model for pedestrian volume estimation. The proposed approach is promising for location performance evaluation and attractor identification. Thus, it was successfully applied to numerous industrial applications: Zurich central train station, the zoo of Duisburg (Germany) and a football stadium (Stade des Costières Nîmes, France)