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Position information is vital for wireless sensor networks in many applications. In this paper, based on the barycentric coordinate system, we incorporate a term constrains sensors to remain the intrinsic structure revealed by range measurements between neighboring nodes into the STRESS function, which is the cost function optimized by the distributed weighted-multidimensional scaling algorithm (dwMDS)...
Network Intrusion Detection Systems (NIDS) monitor internet traffic to detect malicious activities including but not limited to denial of service attacks, network accesses by unauthorized users, attempts to gain additional privileges and port scans. The amount of data that must be analyzed by NIDS is too large. Prior studies developed feature selection and feature extraction techniques to reduce the...
The rapid increase in computer technology and the availability of very large scale in power quality monitoring data have motivated the utility engineers to extract the useful information that is hidden into the data. This information might be critical issues for diagnoses the power quality problems, predicting expecting failure and alarming for dangerous situation. This paper identifies the underlying...
In this paper, we proposed a localization algorithm in wireless sensor networks based on MDS combined with weight. The way of determining weight is according to RSSI which is classified by an experiential method. In the new method, the shortest path matrix could be modified in order to obtain better accuracy. Although without range measuring, it is proved that a better accuracy could be obtained in...
The self-organizing-feature-maps (SOM) algorithm is a typical dimensionality reduction technique. The SOM algorithm adopts neighborhood learning to form a topological ordering among data points. In other words, self-organizing feature maps highly preserve topological relationships in the lower-dimensional space. Using SOM as a feature extraction method for the k nearest neighbor classifier is appropriate,...
A new knowledge mining framework based on multivariate analyses is proposed to discover and simulate the school grading policy. The framework comprises three major steps. Firstly, factor analysis is adopted to separate the scores of several different subjects into grading-related ones and grading-unrelated ones. Secondly, multidimensional scaling is employed for dimensionality reduction to facilitate...
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