Graphical Models for Human Motion Modelling

Buildings are subject to faults in their heating, ventilation and air-conditioning (HVAC) systems that can lead to excessive energy wastage, poor indoor climate, equipment failures and high maintenance costs. Field studies have shown that employing fault detection, diagnosis and prognosis (FDDP) tools followed up with equipment services and corrections can help achieve up to 40% of energy savings within the HVAC system and improve indoor climate, increase equipment lifecycle and reduce maintenance costs. The increasing adoption of building automation systems (BAS), Internet of Things (IoT) and other smart technologies in recent years have allowed large amounts of data to be continuously collected from building systems. This data-rich environment, along with the surge in data analytics and machine learning tools, has made cost-effective data-driven FDDP strategies possible. Compared to purely physics-based methods, data-driven methods require less explicit knowledge of the underlying physical system, thus are often easier to develop, and can learn certain intricate relationships that exist among data. Within the reported data-driven FDDP methods, there exists a few research gaps: 1) data imputation methods that leverage mutual information from correlated measurements to defy poor data quality from BAS have not been utilized efficiently; 2) there lacks a systematic and scalable fault diagnosis framework that incorporates probabilistic temporal relationships to track fault evolution; 3) existing fault diagnosis strategies typically focus on traditional rule-based control strategies and their scalability for advanced control strategies such as Guideline 36 have not been explored yet; 4) active threats information such as cyber-attacks, are typically not incorporated in an FDDP framework; 5) fault prognosis strategies to preemptively identify gradual faults for predictive maintenance have rarely been studied. This research attempts to address the above-mentioned research gaps through the following: Data Imputation: reported data imputation methods that are suitable for handling and repairing multi-source BAS data are evaluated. Data collected from a medium-sized, mixed-use institution building situated in Stockholm, Sweden and a small commercial building simulated in a laboratory setup is used to evaluate five different data imputation methods. Results demonstrate that incorporating time-lagged cross-correlations within the k-Nearest Neighbor (kNN) method helps to significantly improve the imputation accuracy and minimize the impact of repaired data on data-driven algorithms. Dynamic Bayesian Network (DBN)-based Framework for Cyber-Physical Fault Diagnosis: a DBN framework with discretized conditional probabilities parameters to represent the temporal relationships among building measurements is developed. Both domain knowledge and machine learning methods are used to develop the DBN structure and parameter model. The developed framework is evaluated for both traditional rule-based and Guideline 36 controls using datasets from a real building, a laboratory building, and a virtual testbed. Results show that the developed DBN framework is effective in diagnosing and isolating faults in systems even with different control strategies. The framework also successfully distinguishes whether system abnormalities originate from cyber-attacks or naturally occurring physical faults. Potential future direction to improve fault isolation using modified DBN topological structure is also reported in this study. DBN-based Framework for Fault Prognosis: an extension of the DBN framework in conjunction with Robust Multivariate Temporal (RMT) variate selection is proposed for fault prognosis. The RMT variate selection is used to extract localized temporal features from high dimensional datasets to determine the best inputs for training forecasting models. The expected fault-free behavior of multiple target variates, selected using domain knowledge, is forecasted using incoming data. The prediction errors generated from the forecasting phase are used as evidences in the DBN inference to estimate future fault probabilities. Gradual faults simulated in the virtual testbed are used to evaluate the prognosis framework. Results show that the developed framework is effective in prognosing gradual faults by leveraging the trending growth on the prediction errors. The research presented in this thesis contributes to the overall objective of developing a robust and cost-effective DBN-based framework for fault diagnosis and prognosis of building HVAC systems. Potential solutions to other existing challenges of implementing data-driven FDDP strategies, such as obtaining high-quality datasets, handling and repairing missing data, establishing a baseline model for detecting abnormalities despite other disturbances such as weather and internal conditions changes, and extracting temporal features from timeseries data are also examined.

Contributions to the Estimation of Mixed-State Conditionally Heteroscedastic Latent Factor Models: A Comparative Study

In this paper, we describe a dynamic Bayesian network or DBN based approach to both two-hand gestures and one-hand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by fail-safe steps of motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to the model. In an experiment with ten isolated gestures, we obtained a recognition rate upwards of 99.59% with cross validation. The proposed model is believed to have a strong potential for successful applications to other related problems such as sign languages.

Hand gesture recognition based on dynamic Bayesian network framework

Purpose – Change propagation is the major source of schedule delays and cost overruns in design projects. One way to mitigate the risk of change propagation is to impose a design freeze on components at some point prior to completion of the process. The purpose of this paper is to propose a model-driven approach to optimal freeze sequence identification based on change propagation risk. Design/methodology/approach – A dynamic Bayesian network was used to represent the change propagation process within a system. According to the model, when a freeze decision is made with respect to a component, a probabilistic inference algorithm within the Bayesian network updates the uncertain state of each component. Based on this mechanism, a set of algorithm was developed to derive optimal freeze sequence. Findings – The authors derived the optimal freeze sequence of a helicopter design project from real product development process. The experimental result showed that our proposed method can significantly improve the effectiveness of freeze sequencing compared with arbitrary freeze sequencing. Originality/value – The methodology identifies the optimal sequence for resolution of entire-system uncertainty in the most effective manner. This mechanism, in progressively updating the state of each component, enables an analyzer to continuously evaluate the effectiveness of the freeze sequence.

: Graphical models fuse probability theory and graph theory in such a way as to permit efficient representation and computation with probability distributions. They intuitively capture statistical relationships among random variables and provide a succinct formalism that allows for the development of tractable algorithms for statistical inference. In recent years, certain types of graphical models, particularly Bayesian networks and dynamic Bayesian networks (DBNs), have been applied to various problems in missile defense that involve decision making under uncertainty and estimation in dynamic systems, such as data association, multitarget tracking, and classification. While the set of problems addressed in the missile defense arena is quite diverse, all require mathematically sound machinery for dealing with uncertainty. The graphical model regime provides a robust, flexible framework for representing and computationally handling uncertainty in real-world problems. While the graphical model regime is relatively new, it has deep roots in many fields, as the formalism generalizes many commonly used stochastic models, including Kalman filters and hidden Markov models. In this report, we describe the mathematical foundations of graphical models and statistical inference, focusing on the concepts and techniques that are most useful to the problem of decision making in dynamic systems under uncertainty. In general, statistical inference on a graphical model is an NP-Hard problem, so there have been large research efforts that involve developing algorithms for performing inference efficiently for certain classes of models, or obtaining approximations for quantities of interest using algorithms for approximate inference. Due to the breadth of problems, a broad class of algorithms has been of interest to researchers over the past several years. As such, the need arose for an extensible and efficient software library for performing statistical inference on graphical models.

Modeling interactions of multiple co-occurring objects in a complex activity is becoming increasingly popular in the video domain. The Dynamic Bayesian Network (DBN) has been applied to this problem in the past due to its natural ability to statistically capture complex temporal dependencies. However, standard DBN structure learning algorithms are generatively learned, require manual structure definitions, and/or are computationally complex or restrictive. We propose a novel structure learning solution that fuses the Granger Causality statistic, a direct measure of temporal dependence, with the Adaboost feature selection algorithm to automatically constrain the temporal links of a DBN in a discriminative manner. This approach enables us to completely define the DBN structure prior to parameter learning, which reduces computational complexity in addition to providing a more descriptive structure. We refer to this modeling approach as the Granger Constraints DBN (GCDBN). Our experiments show how the GCDBN outperforms two of the most relevant state-of-the-art graphical models in complex activity classification on handball video data, surveillance data, and synthetic data.

A new anomaly detection scheme based on growing structure multiple model system (GSMMS) is proposed in this paper to detect and quantify the effects of anomalies. The GSMMS algorithm combines the advantages of growing self-organizing networks with efficient local model parameter estimation into an integrated framework for modeling and identification of general nonlinear dynamic systems. The identified model then serves as a foundation for building an effective anomaly detection and fault diagnosis system. By utilizing the information about system operation region provided by the GSMMS, the residual errors can be analyzed locally within each operation region. This local decision making scheme can accommodate for unequally distributed residual errors across different operational regions. The performance of the newly proposed method is evaluated through anomaly detection and quantification in an electronically controlled throttle system, which is simulated using a high-fidelity engine simulation software package provided by a major automotive manufacturer for control system development.

Artificial neural networks have been consolidated as a reliable technique to analyze behaviors of economic and financialvariables. Its first use in finance was on the early 90’s. Through theseyears, itsuse as a tool for modeling and description of general nonlinear dynamical systems hasbeen consolidated as an effective and ¿ relatively fast technique, thanks to the great development experienced in computer systems. In this paper we use a Differential Neural Networks (DNN) as a tool of analysis and description of the behavior of exchange rate between the mexican peso and the U.S. dollar during the period from January 3 of 2000 to October 5 of 2011. The use of this technique very common in the analysis of complex nonlinear dynamical systems in the areas of engineering and biology, showed an excellent performance in the description of the peso-dollar exchange developed in this work.

This paper introduces the multiattribute utility theory to the control Lyapunov function design framework. As an illustration we focus on the problem of multi-target assignment. With this formulation, we use a global multiattribute utility function as a multivariate objective function that should be minimized for the agents to achieve their objectives. The objectives represent deviations of each agent from specified targets. We provide closed form feedback control laws, based on the multiattribute utility function, for general nonlinear multi-agent system models affine in control. Finally, we present simulation results and conduct sensitivity analysis for two different models that are affine in control; basic kinematic model and nonlinear nonholonomic unicycle model.

Quantifying the value of structural health monitoring information with measurement bias impacts in the framework of dynamic Bayesian Network

Reliability analysis is an integral part of system design and operating. Moreover, it can be an input to optimize maintenance policies. Recently, Bayesian Networks (BN) and Dynamic Bayesian Networks (DBN) have been proved relevant to represent complex systems and perform reliability studies. The major drawback of this approach comes from the constraint on the state sojourn times which are necessarily exponentially distributed, as in usual markovian approaches. Therefore, a new formalism was previously introduced in the literature to avoid this constraint: the Graphical Duration Models (GDM). This paper aims to quantify the reliability estimation error due to an exponential approximation when the system follows other kinds of sojourn time's distributions. Finally results obtained by DBN and GDM will be compared.

A New Approach to Learning in Dynamic Bayesian Networks (DBNs)

The totally blind image restoration problem is solved using an expectation maximization (EM) based learning technique. A new formulation on simultaneous recursive image and blur parameter identification and image restoration is given in a dynamic Bayesian network (DBN) framework. This technique incorporates optimal Kalman smoothing equations for maximum likelihood (ML) parameter identification and state estimation. Because of the computationally heavy processing of smoothing, we use a filtering approximation of the Kalman instead of Kalman smoothing. Experimental results are given using a 64/spl times/64 Lena image which is both noisy and artificially blurred.

We present a novel method for automatic classification of seismological data streams, focusing on the detection of earthquake signals. We consider the approach as being a first step towards a generic method that provides for classifying a broad range of seismic patterns by modeling the interrelationships between essential features of seismograms in a graphical model. Through a continuous Wavelet transform the features are extracted, yielding a time-frequency-amplitude decomposition. The extracted features obey certain Markov properties, which allows us to form a joint distribution in terms of a Dynamic Bayesian Network. We performed experiments using real seismic data recorded at different stations in the European Broadband Network, for which we achieve an average classification accuracy of 95%.KeywordsDirected Acyclic GraphContinuous Wavelet TransformSeismic SignalDynamic Bayesian NetworkSeismic Wave PropagationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.