An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and Linear Regression

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.

Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.

A method for identifying outliers in data observed from sonars

A new method is proposed for identifying outliers in the direction-of-arrival (DOA) data of a source observed from a linear array sonar. Suppose a source moves uniformly along a straight line. The method for identifying outliers consists of three steps, (i) Divide the data into groups, each with four sample points, and delete certain two sample points from every group by means of a robust method pesented in this paper. When the percentage of the outliers is less than 50%, there exists at least one group in which the remaining two sample points are 'good' . (ii) Estimate the DOA and its Change rate, (θ0 , θ0 ), using the remaining two simple points of every group, and computethe objective functions of M-etsimator using the resulting estimates of all groups respectively. A 'good' estimate of (θ0 , θ0 ), which minmizes the objective function is then obtained, (iii) Iterate the M-estimator with the 'good' estimate of (θ0 , θ0 ) as the initial value, obtain an accurate estimate of (θ0 , θ0),and identify outliers in the observed data using the residuals calculated from the accurate estimate of (θ0 , θ0 ). The breakdown point of the method is 50%. Thesimulation examples given in the paper verify the reliability of the method.

A MapReduced-Based and Cell-Based Outlier Detection Algorithm

Outlier detection is a very important type of data mining,which is extensively used in application areas.The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data,but also uses lots of machine resources,which results in the imbalance of the machine load.This paper presents an algorithm of the MapReduce-based and cell-based outlier detection,combined with the single-layer perceptron,which achieves the parallelization of outlier detection.These experiments show that this improved algorithm is able to effectively improve the efficiency of the outlier detection as well as the accuracy.