A Combined Stochastic and Greedy Hybrid Estimation Capability for Concurrent Hybrid Models with Autonomous Mode Transitions

Robotic and embedded systems have become increasingly pervasive in applicationsranging from space probes and life support systems to robot assistants. In order to act robustly in the physical world, robotic systems must be able to detect changes in operational mode, such as faults, whose symptoms manifest themselves only in the continuous state. In such systems, the state is observed indirectly, and must therefore be estimated in a robust, memory-efficient manner from noisy observations.Probabilistic hybrid discrete/continuous models, such as Concurrent Probabilistic Hybrid Automata (CPHA) are convenient modeling tools for such systems. In CPHA, the hidden state is represented with discrete and continuous state variables that evolve probabilistically. In this paper, we present a novel method for estimating the hybrid state of CPHA that achieves robustness by balancing greedy and stochastic search. The key insight is that stochastic and greedy search methods, taken together, are often particularly effective in practice.To accomplish this, we first develop an efficient stochastic sampling approach for CPHA based on Rao-Blackwellised Particle Filtering. We then propose a strategy for mixing stochastic and greedy search. The resulting method is able to handle three particularly challenging aspects of real-world systems, namely that they 1) exhibit autonomous mode transitions, 2) consist of a large collection of concurrently operating components, and 3) are non-linear. Autonomous mode transitions, that is, discrete transitions that depend on thecontinuous state, are particularly challenging to address, since they couple the discrete and continuous state evolution tightly. In this paper we extend the class of autonomous mode transitions that can be handled to arbitrary piecewise polynomial transition distributions.We perform an empirical comparison of the greedy and stochastic approaches to hybrid estimation, and then demonstrate the robustness of the mixed method incorporated with our HME (Hybrid Mode Estimation) capability. We show that this robustness comes at only a small performance penalty

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

SYSTEM-LEVEL HYBRID FAULT DIAGNOSABILITY WITH GENERAL TEST INVALIDATION

On the basis of a self-checking system model with general test invalidation the problem of diagnosability in the case of permanent and intermittent faults known as hybrid fault situation is discussed. Two hybrid fault models have been introduced that take into consideration the behaviour of the faulty tester. On the basis of the relationship that exists between the permanent and hybrid fault models, given the number of all units in a system, the upper bound of the number of diagnosable faulty units is defined without restriction on the test connection assignment

Integrating Learning from Examples into the Search for Diagnostic Policies

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO* algorithm to solve this MDP. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on five benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on todays desktop computers

Design and optimisation of a (FA)Q-learning-based HTTP adaptive streaming client

In recent years, HTTP (Hypertext Transfer Protocol) adaptive streaming (HAS) has become the de facto standard for adaptive video streaming services. A HAS video consists of multiple segments, encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network conditions. Current HAS client heuristics are, however, hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, a (frequency adjusted) Q-learning HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behaviour corresponding to the current network environment in order to optimise the quality of experience. Furthermore, the client has been optimised both in terms of global performance and convergence speed. Thorough evaluations show that the proposed client can outperform deterministic algorithms by 11-18% in terms of mean opinion score in a wide range of network configurations