Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced     

Evaluating software reuse alternatives: a model and its application to an industrial case study

We propose a model that enables software developers to systematically evaluate and compare all possible alternative reuse scenarios. The model supports the clear identification of the basic operations involved and associates a cost component with each basic operation in a focused and precise way. The model is a practical tool that assists developers to weigh and evaluate different reuse scenarios, based on accumulated organizational data, and then to decide which option to select in a given situation. The model is currently being used at six different companies for cost-benefit analysis of alternative reuse scenarios; we give a case study that illustrates how it has been used in practice.     

Sequential Bayesian bit error rate measurement

As bit error rates decrease, the time required to measure a bit error rate (BER) or perform a BER test (i.e., to determine that a particular communications device's BER is less than some acceptable limit) increases dramatically. One cause of long measurement times is the difficulty of deciding a priori how many bits to measure to establish the BER to within a predetermined confidence interval width. This paper explores a new approach to deciding how many bits to measure, namely a sequential Bayesian approach. As measurement proceeds, the posterior distribution of BER is checked to see if the conclusion can be made that the BER rate is known to be within the desired range with high enough probability. Desired properties of the posterior distribution such as the maximum a postiori estimate and confidence limits can be computed quickly using off-the-shelf numerical software. Examples are given of using this method on bit error data measured with an Agilent 81250 parallel BER tester.     

A XRD study of Co/Au multilayers using a laboratory microdiffractometer

In this paper we show that by the analysis of 2D images collected with a laboratory X-ray microdiffractometer it is possible to non-destructively evaluate the structure, the microstructure, and the preferred orientation of films. In particular, the structural analysis of Co/Au multilayers on Si(1 0 0) deposited at different Ar pressures are reported and discussed.     

Formaldehyde content of milk in goats fed formaldehyde-treated soybean oil-meal

Formaldehyde is used in ruminant feeding for different purposes including the protection of dietary proteins from ruminal degradation. The formaldehyde content of milk of goats fed various levels of formaldehyde-treated soybean oil-meal has been determined by using a sensitive HPLC method. Results showed a significant linear correlation between ingested formaldehyde and formaldehyde concentration in milk. About 0.02% of ingested formaldehyde was excreted in milk, as free formaldehyde.