An Adaptive Neuro-Fuzzy Inference System-Based Approach for Oil and Gas Pipeline Defect Depth Estimation

Abstract-To determine the severity of metal-loss defects in oil and gas pipelines, the depth of potential defects, along with their length, needs first to be estimated. For this purpose, pipeline engineers use intelligent Magnetic Flux Leakage (MFL) sensors that scan the metal pipelines and collect defect-related data. However, due to the huge amount of the collected MFL data, the defect depth estimation task is cumbersome, timeconsuming, and error-prone. In this paper, we propose an adaptive neuro-fuzzy inference system (ANFIS)-based approach to estimate defect depths from MFL signals. Depth-related features are first extracted from the MFL signals and then are used to train the neural network to tune the parameters of the membership functions of the fuzzy inference system. A hybrid learning algorithm that combines least-squares and back propagation gradient descent method is adopted. Moreover, to achieve an optimal performance by the proposed approach, highly-discriminant features are selected from the obtained features by using the weight-based support vector machine (SVM). Experimental work has shown that encouraging results are obtained. Within error-tolerance ranges of ±15%, ±20%, ±25%, and ±30%, the depth estimation accuracies obtained by the proposed technique are 80.39%, 87.75%, 91.18%, and 95.59%, respectively. Moreover, further improvement can be easily achieved by incorporating new and more discriminant features

A Machine Learning Approach for Big Data in Oil and Gas Pipelines

Abstract-Experienced pipeline operators utilize Magnetic Flux Leakage (MFL) sensors to probe oil and gas pipelines for the purpose of localizing and sizing different defect types. A large number of sensors is usually used to cover the targeted pipelines. The sensors are equally distributed around the circumference of the pipeline; and every three millimeters the sensors measure MFL signals. Thus, the collected raw data is so big that it makes the pipeline probing process difficult, exhausting and error-prone. Machine learning approaches such as neural networks have made it possible to effectively manage the complexity pertaining to big data and learn their intrinsic properties. We concentrate, in this work, on the applicability of artificial neural networks in defect depth estimation and present a detailed study of various network architectures. Discriminant features, which characterize different defect depth patterns, are first obtained from the raw data. Neural networks are then trained using these features. The Levenberg-Marquardt back-propagation learning algorithm is adopted in the training process, during which the weight and bias parameters of the networks are tuned to optimize their performances. Compared with the performance of pipeline inspection techniques reported by service providers such as GE and ROSEN, the results obtained using the method we proposed are promising. For instance, within ±10% error-tolerance range, the proposed approach yields an estimation accuracy at 86%, compared to only 80% reported by GE; and within ±15% error-tolerance range, it yields an estimation accuracy at 89% compared to 80% reported by ROSEN

2-Hydr­oxy-3,3-dimethyl-7-nitro-3,4-dihydro­isoquinolin-1(2H)-one

In the title compound, C11H12N2O4, a new hydroxamic acid which belonging to the isoquinole family, the heterocyclic ring adopts a half-chair conformation. The nitro group is essentially coplanar with the aromatic ring. Inter­molecular O—H⋯O hydrogen bonds assemble the mol­ecules around inversion centres to form pseudo-dimers

Semantic Map Based Web Search Result Visualization

The problem of information overload has become more pressing with the emergence of the increasingly more popular Internet services. The main information retrieval mechanisms provided by the prevailing Internet Web software are based on either keyword search (e.g., Google and Yahoo) or hypertext browsing (e.g., Internet Explorer and Netscape). The research presented in this paper is aimed at providing an alternative concept-based categorization and search capability based on a combination of meta-search and self-organizing maps. Kohonen's self-organizing map is very well known as a clustering and dimension reduction tool. Clustering can be used for categorization of search results. Dimension reduction can be used for visualization and for reducing information in order to ease search

MASACAD: A multi-agent approach to information customization for the purpose of academic advising of students

The growth and advancement in the Internet and the World Wide Web has led to an explosion in the amount of available information. This staggering amount of information has made it extremely difficult for users to locate and retrieve information that is actually relevant to their task at hand. Dealing with this problem of “information overload” will need tools to customize the information space. In this paper we present MASACAD, a multi-agent system that learns to advise students by mining the Web and discuss important problems in relationship to information customization systems and smooth the way for possible solutions. The main idea is to approach information customization using a multi-agent paradigm in combination with a number of aspects from the domains of machine learning, user modeling, and Web mining

MASACAD: a multiagent based approach to information customization

MASACAD is a multiagent information customization system that adopts the machine-learning paradigm to advise students by mining the Web. In the distributed problem-solving paradigm, systems can distribute among themselves the processes necessary to accomplish a given task. Given the number of problems that distributed processing can address, AI researchers have directed significant effort toward developing specialized problem-solving systems that can interact in their search for a solution. The multiagent-system paradigm embodies this approach

A Goal-Oriented Behaviour-Based Control Architecture For Autonomous Mobile Robots Allowing Learning

. When dealing with autonomous robots, the definition of autonomy is important. Autonomous agents require decision-making capabilities in order to perform difficult tasks in a complex environment. In addition, learning from experience is required in order to improve dealing with future tasks. Over the past few years, several different architectures for autonomous mobile robots have been proposed in the literature. Most of them can be classified into two categories: a functionbased one that decomposes the control system into functional modules and a behaviour-based one that decomposes it into task-achieving behaviours. In this paper, an approach for combining these two categories, i.e., combining reactivity as one of the main characteristics of behaviour-based systems with goal-oriented mechanisms as one of the main characteristics of function-based systems, is presented and possibilities of integrating learning strategies are discussed. The paper gives a short survey of existing system..