Mazura Mat Din

Automated Matching Systems and Correctional Method for Improved Inspection Data Quality

Advances in computing technology, and data gathering tools provides a great opportunity in engineering area such as civil structure analysis domain to better understand its phenomenon. Our case study utilize these advances in pipeline structure in order to study the corrosion behavior that been one of the problem that leads to its failure. The availability of ILI data from MFL tools provides a better insight of corrosion process by using an efficient systems and data analysis method in order to extract important information regarding the condition of the pipeline. Our paper will discuss an implementation of automated matching systems and data correctional method that shown a promising result to improve the quality of data for future reliability assessment. The automated matching systems was evaluated using linear regression method for its sensitivity analysis whereby a modified corrosion rate method was used along with linear prediction method to verify the accuracy of the corrected data. Issues and advantage gain from this research is threefold; timeliness, accuracy, and consistencies in data sampling. This is a preliminary work towards a reliable pipeline assessment method.

Improved Inspection Data Quality Using Modified Corrosion Rate Method for Offshore Pipeline Assessment

Data quality is crucial to any data analysis task. Information collected from many channels prone to disturbance, inconsistent, missing values and redundant information. In our case, these errors arise in metal loss data collected at different point of time using dissimilar sensors and devices in offshore pipeline structure. Furthermore, data collection and analysis are often time consuming and expensive making it undesirable for recollection. Thus, rather than discard the corrupted data, we need to evaluate and enhanced its quality by correcting the errors as much as possible. In this paper, we discuss how data is pre-processed by means of correcting the data to make it ready for further analysis. A modified corrosion rate method was used to enhance the quality of data coupled with a linear prediction method to verify the accuracy of the corrected data. Result shows that the proposed method can minimize the effects of uncertainties on the reliability of the inspection data.

A taxonomy on intrusion alert aggregation techniques

As security threats advance in a drastic way, most of the organizations apply various intrusion detection systems (IDSs) to optimize detection and to provide comprehensive view of intrusion activities. But IDS produces huge number of duplicated alerts information that overwhelm security operator. Alert aggregation addresses this issue by reducing, fusing and clustering the alerts. Techniques from a different scope of disciplines have been proposed by researchers for different aspects of aggregation. In this paper we present a comprehensive review on proposed alert aggregation techniques. Our main contribution is to classify the literature based on the techniques applied to aggregate the alerts.