Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique

The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic production.Thus,it is necessary to monitor tool condition for a.machining process to have superior quality and economic production.In the pre-sent study,fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique.Cutting force and vibration signals were acquired to monitor tool condition during machining.A set of four tooling conditions namely healthy,worn flank,broken insert and extended tool overhang have been considered for the study.The machine learning technique was applied to both vibration and cutting force signals.Discrete wavelet features of the signals have been extracted using discrete wavelet trans formation(DWT).This transformation represents a large dataset into approximation coeffcients which contain the most useful information of the dataset.Significant features,among features extracted,were selected using J48 decision tree technique.Clas-sification of tool conditions was carried out us ing Naive Bayes algorithm.A 10 fold cross validation was incorporated to test the validity of classifier.A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.

Privacy Quantification Model Based on the Bayes Conditional Risk in Location-Based Services

The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms （LPPMs） have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary＇s estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.

MERGING FUZZY STATISTICAL DATA WITH IMPRECISE PRIOR INFORMATION

Solving complex decision problems requires the usage of information from different sources. Usually this information is uncertain and statistical or probabilistic methods are needed for its processing. However, in many cases a decision maker faces not only uncertainty of a random nature but also imprecision in the description of input data that is rather of linguistic nature. Therefore, there is a need to merge uncertainties of both types into one mathematical model. In the paper we present methodology of merging information from imprecisely reported statistical data and imprecisely formulated fuzzy prior information. Moreover, we also consider the case of imprecisely defined loss functions. The proposed methodology may be considered as the application of fuzzy statistical methods for the decision making in the systems analysis.