Learning Tractable Probabilistic Models for Fault Localization

In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple statistical models, and fail to generalize across multiple programs. In this work, we propose Tractable Fault Localization Models (TFLMs) that can be learned from data, and probabilistically infer the location of the bug. While most previous statistical debugging methods generalize over many executions of a single program, TFLMs are trained on a corpus of previously seen buggy programs, and learn to identify recurring patterns of bugs. Widely-used fault localization techniques such as TARANTULA evaluate the suspiciousness of each line in isolation; in contrast, a TFLM defines a joint probability distribution over buggy indicator variables for each line. Joint distributions with rich dependency structure are often computationally intractable; TFLMs avoid this by exploiting recent developments in tractable probabilistic models (specifically, Relational SPNs). Further, TFLMs can incorporate additional sources of information, including coverage-based features such as TARANTULA. We evaluate the fault localization performance of TFLMs that include TARANTULA scores as features in the probabilistic model. Our study shows that the learned TFLMs isolate bugs more effectively than previous statistical methods or using TARANTULA directly.Comment: Fifth International Workshop on Statistical Relational AI (StaR-AI 2015

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

Anyon models in quantum codes and topological superconductors

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Física Teórica, leída el 27-11-2020En esta tesis estudiamos dos temas relacionados con la información cuántica y la topología: los superconductores topológicos y la corrección cuántica topológica de errores. Los superconductores topológicos han sido ampliamente estudiados, hecho parcialmente motivado por la búsqueda de fermiones de Majorana en sistemas de materia condensada. Estas cuasipartículas son anyones no abelianos y se pueden utilizar para el procesamiento de información cuántica. Recientemente se han presentado varias propuestas y experimentos en los que se obtienen superconductores topológicos mediante la construcción de heteroestructuras. Dichas heteroestructuras generalmente consisten en un superconductor de onda s acoplado a un semiconductor. En la publicación [P1] exploramos la posibilidad de diseñar un superconductor topológico utilizando un superconductor padre de onda d acoplado a un gas de electrones bidimensional con interacción de espín-órbita y un campo Zeeman. Hallamos una expresión analítica de los estados de Majorana y comparamos estos resultados con los obtenidos cuando se usa un superconductor de ondas convencional.In this thesis we study two main topics related to the interplay between quantum information and topology: topological superconductors and topological quantum error correction. Topological superconductors have been extensively studied, partly motivated by the search of a condensed-matter realization of Majorana fermions. These quasiparticles are non-Abelian anyons and can be used for quantum information processing. There have been several proposals and experiments where topological superconductors are realized by building heterostructures. These heterostructures usually consist of an s-wave superconductor proximity-coupled to a semiconductor. In publication [P1] we explore the possibility of engineering a topological superconductor using a d-wave parent superconductor coupled to a two-dimensional electron gas with spin-orbit coupling and a Zeeman field. We determine an analytical expression of the Majorana states and compare these results to the ones obtained using a conventional s-wave superconductor...Fac. de Ciencias FísicasTRUEunpu

Localization Techniques for Water Pipeline Leakages: A Review

Pipeline leakages in water distribution network (WDN) is one of the prominent issues that has gain an interest among researchers in the past few years. Time and accuracy play an important role in leak localization as it has huge impact to the human population and economic point of view. The complexity of WDN has prompt numerous techniques and methods been introduced focusing on the accuracy and efficacy. In general, localization techniques can be divided into two broad categories; external and internal systems. This paper reviews some of the techniques that has been explored and proposed including the limitations of each techniques. Â

An Image Compression Method Based on Wavelet Transform and Neural Network

Image compression is to compress the redundancy between the pixels as much as possible by using the correlation between the neighborhood pixels so as to reduce the transmission bandwidth and the storage space. This paper applies the integration of wavelet analysis and artificial neural network in the image compression, discusses its performance in the image compression theoretically, analyzes the multi-resolution analysis thought, constructs a wavelet neural network model which is used in the improved image compression and gives the corresponding algorithm. Only the weight in the output layer of the wavelet neural network needs training while the weight of the input layer can be determined according to the relationship between the interval of the sampling points and the interval of the compactly-supported intervals. Once determined, training is unnecessary, in this way, it accelerates the training speed of the wavelet neural network and solves the problem that it is difficult to determine the nodes of the hidden layer in the traditional neural network. The computer simulation experiment shows that the algorithm of this paper has more excellent compression effect than the traditional neural network method

Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy