With the ability to deal with both numeric and nominal information, rough set theory (RST), which can express knowledge in a rule-based form, has been one of the most important techniques in data analysis. However, applications of rough set theory for analyzing electricity loads are not widely discussed. Thus, this investigation employs rough set theory to analyze electricity loads. Additionally, to reduce the time generating reducts by rough set theory, linear discriminant analysis (LDA) is used to generate a reduct for rough set model. Therefore, this study designs a hybrid discriminant analysis and rough set model (DARST) to provide decision rules representing relations in an electric load information system. In this investigation, nine condition factors and variations of electricity loads are employed to examine the feasibility of the hybrid model. Experimental results reveal that the proposed model can efficiently and accurately analyze the relation between condition variables and variations of electricity loads. Consequently, the proposed model is a promising alternative for developing an electric load information system and offers decision rules base for the utility management as well as operations staff.

Rough set theory with discriminant analysis in analyzing electricity loads

Anomaly detection is one of the important challenges of network security associated today. We present a novel hybrid technique called G-LDA to identify the anomalies in network traffic. We propose a hybrid technique integrating Latent Dirichlet Allocation and genetic algorithm namely the G-LDA process. Furthermore, feature selection plays an important role in identifying the subset of attributes for determining the anomaly packets. The proposed method is evaluated by carrying out experiments on KDDCUP'99 dataset. The experimental results reveal that the hybrid technique has a better accuracy for detecting known and unknown attacks and a low false positive rate.

A hybrid anomaly detection model using G-LDA

Indoor positioning systems based on the fingerprint method are widely used due to the large number of existing devices with a wide range of coverage. However, extensive positioning regions with a massive fingerprint database may cause high computational complexity and error margins, therefore clustering methods are widely applied as a solution. However, traditional clustering methods in positioning systems can only measure the similarity of the Received Signal Strength without being concerned with the continuity of physical coordinates. Besides, outage of access points could result in asymmetric matching problems which severely affect the fine positioning procedure. To solve these issues, in this paper we propose a positioning system based on the Spatial Division Clustering (SDC) method for clustering the fingerprint dataset subject to physical distance constraints. With the Genetic Algorithm and Support Vector Machine techniques, SDC can achieve higher coarse positioning accuracy than traditional clustering algorithms. In terms of fine localization, based on the Kernel Principal Component Analysis method, the proposed positioning system outperforms its counterparts based on other feature extraction methods in low dimensionality. Apart from balancing online matching computational burden, the new positioning system exhibits advantageous performance on radio map clustering, and also shows better robustness and adaptability in the asymmetric matching problem aspect.

https://www.mdpi.com/1424-8220/14/1/1850/pdf

A Spatial Division Clustering Method and Low Dimensional Feature Extraction Technique Based Indoor Positioning System

In Wireless Local Area Network (WLAN) positioning system, the most popular solution for RSS-based positioning is the fingerprinting architecture In this paper, we present a novel algorithm, known as Semi-supervised Discriminant Embedding (SDE), to reconstruct a radio map by using real-time signalstrength values received at random points Instead of deploying dense reference points, our approach takes advantage of less labeled data and partial unlabeled data to transform into lowerdimensional feature signals Through solving the objective functions optimization, with strong discriminative features in Receive Signal Strength (RSS) are retained in the low-dimensional space We conducted experiments in our office area with a realistic WLAN environment Compared to the traditional methods, the experimental results show that the proposed algorithm has considerable accuracy improvement in the same positioning environment Furthermore, the results also show the size of training samples can be greatly reduced in the proposed algorithm in order to achieve the similar accuracy of traditional approaches That is, the cost of collecting fingerprints in the offline stage and calibrating database in the online stage are thus reduced

Semi-Supervised Positioning Algorithm in Indoor WLAN Environment

Green wireless local area network WLAN is an emerging technology to achieve both the purposes of power conservation and high-speed accessing to the Internet because of the working on-demand strategy adoption and high density access points APs deployment. Although it is good news to data traffic service, Green WLAN brings severe challenges to the indoor localization service based on fingerprint algorithm. Redundant APs will greatly enlarge the radio map and introduce a much heavier computation burden to the terminal for localization in the online phase. In addition, APs in Green WLAN are powered on and off to make balances between data traffic service demand and energy saving goals so that the received signal strength RSS sampled online and recorded in the radio map offline are rarely matched in the same detected AP number, which leads to asymmetric matching problem occurring in the fingerprint algorithm. In this paper, we propose to make a nonlinear dimensionality reduction on the RSS by local discriminant embedding algorithm to realize both the computation burden decreasing and asymmetric matching problem resolving for the fingerprint algorithm in Green WLAN. The simulation results show that our proposed methods could effectively reduce the computation burden in the online phase and make the fingerprint algorithm operate more robustly when the RSS is reduced to the intrinsic dimensionality in Green WLAN. Copyright © 2013 John Wiley & Sons, Ltd.

Green wireless local area network received signal strength dimensionality reduction and indoor localization based on fingerprint algorithm

It is difficult to give an accurate judgement of whether the traffic model fit the actual traffic. The traditional method is to compare the Hurst parameter, data histogram and autocorrelation function. The method of comparing Hurst parameter cannot give exact results and judgement. The method of comparing data histogram and autocorrelation only gives a qualitative judgement. Based on linear discriminant analysis we proposed a novel arithmetic. Utilizing this arithmetic we analysed some sets of data with large and little differences. We also analysed some sets of data generated by network simulator. The analysis result is accurate. Comparing with traditional method, this arithmetic is useful and can conveniently give an accurate judgement for complex network traffic trace. Copyright © 2005 John Wiley & Sons, Ltd.

Linear discriminant analysis in network traffic modelling

Linear discriminant analysis in network traffic modelling: Research Articles

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Linear discriminant analysis in network traffic modelling

Linear discriminant analysis in network traffic modelling: Research Articles