U.S. Energy Conservation Program: Organization and Management

Program management has become an important issue today. The governments in many countries and on different levels use different programs whose essence sometimes is very different. The current stage of modern development includes a scientific-technological revolution (STR) in which large-scale complex problems, which cannot be solved by ordinary separate government actions, have emerged. There are several reasons for taking the energy conservation program of the USA as an object of the present study. This program is deeply connected with the economy and all key industries, it is a large-scale non-conventional program with an element of crisis in it, it has top-level government priority, and represents an example for studying the American economy. In this article the attempt is made to understand the program using the systems approach. The author does not aim to describe all governmental actions in detail or the technical questions connected with the problem of energy conservation. There is also no program evaluation here, although it is one of the most important components of program management. The accent is rather on the decomposition of the system into related subsystems, definition of the actors involved, and interactions between them

Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video

This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algorithms for this kind of models require processing of the whole data through several passes. It is computationally intractable for massive or sequential data. We design the batch and online inference, based on the Gibbs sampling, for our model. It allows to process sequential data, incrementally updating the model by a new observation. The model is applied to abnormal behaviour detection in video sequences. A new abnormality measure is proposed for decision making. The proposed method is compared with the method based on the non-dynamic Hierarchical Dirichlet Process, for which we also derive the online Gibbs sampler and the abnormality measure. The experimental results show that the consideration of the dynamics in a topic model improves the classification performance for abnormal behaviour detection

Abnormal behaviour detection in video using topic modeling

The growth of the number of surveillance systems makes it is impossible to process data by human operators thereby autonomous algorithms are required in a decision-making procedure. A novel dynamic topic modeling approach for abnormal behaviour detection in video is proposed. Activities and behaviours in the scene are described by the topic model where temporal dynamics for behaviours is assumed. Here we implement Expectation-Maximisation algorithm for inference in the model and show in the experiments that it outperforms the Gibbs sampling inference scheme that is originally proposed in [1]

Learning methods for dynamic topic modeling in automated behaviour analysis

Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators’ load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximization approach and variational Bayes inference are proposed. Theoretical derivations of the posterior estimates of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localization procedure, elegantly embedded in the topic modeling framework. It is shown that the developed learning algorithms can achieve 95% success rate. The proposed framework can be applied to a number of areas, including transportation systems, security, and surveillance

Anomaly detection in video with Bayesian nonparametrics

A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper introduces a new abnormality measure for decision making. The proposed method is evaluated on both synthetic and real data. The comparison with a non-dynamic model shows the superiority of the proposed dynamic one in terms of the classification performance for anomaly detection

Bayesian neural networks for sparse coding

Deep learning is actively used in the area of sparse coding. In current deep sparse coding methods uncertainty of predictions is rarely estimated, thus providing the results that lack the quantitative justification. Bayesian learning provides the way to estimate the uncertainty of predictions in neural networks (NNs) by imposing the prior distributions on weights, propagating the resulting uncertainty through the layers and computing the posterior distributions of predictions. We propose a novel method of propagating the uncertainty through the sparsity-promoiting layers of NNs for the first time. We design a Bayesian Learned Iterative Shrinkage-Thresholding network (BayesLIsTA). An efficient posterior inference algorithm based on probabilistic backpropagation is developed. Experiments on sparse coding show that the proposed framework provides both accurate predictions and sensible estimates of uncertainty in these predictions

Structured Sparse Modelling with Hierarchical GP

In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data

Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences

Video analytics requires operating with large amounts of data. Compressive sensing allows to reduce the number of measurements required to represent the video using the prior knowledge of sparsity of the original signal, but it imposes certain conditions on the design matrix. The Bayesian compressive sensing approach relaxes the limitations of the conventional approach using the probabilistic reasoning and allows to include different prior knowledge about the signal structure. This paper presents two Bayesian compressive sensing methods for autonomous object detection in a video sequence from a static camera. Their performance is compared on real datasets with the non-Bayesian greedy algorithm. It is shown that the Bayesian methods can provide more effective results than the greedy algorithm in terms of both accuracy and computational time

Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors

This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian process describes such structure and the interdependencies are represented via the covariance matrices of the prior distributions. The inference is based on the expectation propagation method and the theoretical derivation of the posterior distribution is provided in this paper. The inference framework is thoroughly evaluated over synthetic, real video, and electroencephalography (EEG) data where the spatio-temporal evolving patterns need to be reconstructed with high accuracy. It is shown that it achieves 15% improvement of the F-measure compared with the alternating direction method of multipliers, spatio-temporal sparse Bayesian learning method and the one-level Gaussian process model. Additionally, the required memory for the proposed algorithm is less than in the one-level Gaussian process model. This structured sparse regression framework is of broad applicability to source localization and object detection problems with sparse signals