Constructing Internet coordinate system based on delay measurement

Network distance estimation via Euclidean coordinate system has been widely studied over the past decade. Basically, each node in the Internet is assigned a set of coordinates and the network distances such as round trip time can be estimated by the Euclidean distances among the nodes. The accuracy of the Euclidean coordinate systems is usually measured with various performance metrics such as relative errors. It is well known that the estimation has intrinsic errors due to the triangle inequality violations among the network distances. Since there have been abundant analysis on the accuracy of the direct distance estimation, i.e., the network distance between two nodes, in this paper, we turn our focus to the path length estimation. Basically, for an overlay path with multiple links, the path length is the sum of the distances of the links along the path. Accurate path length estimation can be exploited for many path selection problems in the peer to peer systems. For example, selecting one hop relay node can be easily implemented via the Euclidean coordinate systems. In this paper, we present a rigorous mathematical analysis on the estimation accuracy especially of the path lengths. We consider the errors as random variables and show that the path length estimation is much more accurate than the direct distance estimation through a rigorous statistical analysis. We also provide simulation results that conform to the mathematical analysis.

A whole new class of location-aware services may be envisaged by the deployment of a geographic location service for Internet hosts. We focus on a technique that relies on delay measurements and the exploitation of a possible correlation between geographic distance and network delay. Our investigation shows that such a correlation becomes stronger as connectivity becomes richer. Host locations are inferred by comparing delay patterns of geographically distributed landmarks (hosts with a known geographic location) with the delay pattern of the target host to be located. The problem we deal with is how to best measure the similarity between the delay patterns of the landmarks and the one observed for the target host. The location estimation of the target host is the location of the landmark that presents the most similar delay pattern with respect to the one of the target host. We define and evaluate three similarity models. Experiments show that the previously adopted Euclidean distance is outperformed by other similarity models, resulting in a more accurate Internet host location from delay measurements.

Factor analysis aims to reduce the amount of information from the original variable without losing important information. A factor is a collection of reduced variables. Several methods can be used in factor analysis, the most common of which are principal component analysis and maximum likelihood. The principal component analysis technique was used in this study. This technique was chosen because it better represents the purpose of the analysis than others. A case study of beef gravel is used to supplement the explanation of factor analysis using the Principal Component Analysis (PCA) technique. The goal of this study was to identify the most important factors influencing beef gravel quality to maintain product quality. The main factor is the volume of soaking water, and the second is the volume of soaking two dishes. These two factors have a cumulative variance proportion of 55%, indicating that they can influence the quality of the gravel. In contrast, the rest can be influenced by other factors not revealed in the research model.

Urmia Lake in the northwestern of Iran is a hypersaline water body and has become an environmentally important issue especially due to the presence of an infrequent aquatic species, Artemia Urmiana. During the last three decades, several considerable man-made changes including river damming and construction of a causeway across the lake affected the lake salinity. This article aims to propose a new approach of salinity modeling using a reduced-order model based on MIKE21 simulation model, in conjunction with principal component analysis (PCA) technique. At first, spatial variation of salinity in the lake was simulated by MIKE21 to prepare the input information for the PCA. Then, the dominant modes of salinity were determined by PCA technique while MIKE21 simulated results were compared with the output of developed reduced order model. Findings indicated that MIKE21's results closely matched the experimental data collected by field study. Also, the first 10 PCs among 974 modes computed by the reduced order model conserved approximately over 93% of the system variance. Therefore, the reduced order model was sufficient to capture the variation of salinity in the lake using a few first PCs. In other words, it was generally found that improvements in the simulated salinity in the lake provided by reduced order model were comparable to MIKE21 simulations.

Introduction: One of the stringent assumptions about covariates in the Cox hazard and Logistic regression modelling is that they should be independent. Incorporating correlated covariates as such into the model might distort the precision of the estimates due to multicollinearity. One way to deal with multicollinearity is by using Principal Component Analysis (PCA) technique. Aim: To demonstrate the application of PCA in dealing with correlated covariates while modelling time to event and casecontrol study data. Materials and Methods: This study was conducted at Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India, from February 2021 to January 2022. Two datasets were used for the demonstration i.e., data relates to a time to event outcome and a case-control study with binary outcome in which lipids were the correlated covariates. Three sets of Cox regression models were used to demonstrate change in hazard ratios with 95% Confidence Intervals (CI) for evaluating the effect of intervention at a different time of lipid measurement. Model I has evaluated treatment/ Body Mass Index (BMI) effect on the outcome by ignoring the effect of lipid parameters. Model II has evaluated treatment/ BMI effect on the outcome by incorporating lipid variables but ignoring multicollinearity. Model III has evaluated treatment/ BMI effect on the outcome by incorporating lipid variables through principal component analysis and thus adjusting for multicollinearity. Similarly, a logistic regression model was performed by using the same three sets of models to evaluate the effect of exposure (BMI). The comparability of lipids between the two groups for both datasets was tested using Hotelling’s T-squared statistic. Results: The lipids measured at 12th, 24th and 36th months between the two groups in the first data set as well as between cases and controls in the second data set were statistically significant. In the first dataset, at baseline, the Hazard Ratio’s (HR’s) were statistically similar irrespective of the models used; while decreasing successively with narrowing 95% CI’s as moving from model I to model III for the lipid measured at 12th, 24th and 36th months. Further, at 24th and 36th months, the HR in model-III found to be significant. In the second data set, the Odds Ratio (OR) were significant for all the three models and it was almost similar for model I and II but in model III it was elevated. Conclusion: The multicollinearity issue should be properly addressed before including correlated covariates in the Cox regression hazard and Logistic regression model. The PCA technique would be a favourable method.

Nowadays, micro-machining processes are playing an important role because of the technological developments in the MEMS, electrical, medical and aerospace industries. In this research work, the principal component analysis (PCA) technique is used to examine and explains the influences of three process parameters (spindle speed, feed rate and depth of cut) on the output responses such as MRR and Ra in micro-end milling process. Also this approach is used for the optimisation of multiple responses in micro-end milling operation to achieve maximum metal removal rate (MRR) and minimum surface roughness (Ra). Further, the optimised machining parameters were determined considering the multiple response objective using multiple response performance index value. The predicted value of multiple response performance index (Z) is 0.6412 which is calculated by using additive model. The measured value of multiple response performance index is 0.707, which shows good prediction accuracy. From the experimental result, it was concluded that, the PCA technique is suitable for the optimisation of multi-response problem.

The insulated gate bipolar transistor (IGBT) is a crucial component of power converters (PCVs) and is commonly used in several PCVs topologies. On the other hand, the investigation and the study of the IGBT component show several changes within its behavior and lifetime, while this component is highly influenced by the operating conditions. Indeed, the monitoring of this component is necessary to minimize unexpected downtime of the wind energy system (WES). However, an accurate prediction of IGBTs remaining useful life (RUL) is the key enabler for life-time-optimized operation. Consequently, this work proposes a new prognostic approach for online IGBTs monitoring that adopts the time-domain analysis to extract useful information that is used as an input in the generation of the health indicator. Moreover, this approach is based on combining both of principal component analysis (PCA) technique and the feedforward neural network (FFNN) technique. PCA is used to reduce features extracted from IGBTs and the FFNN is implemented to achieve online regression of the trend parameter obtained from the PCA technique. To investigate and evaluate the performance of our idea we used the NASA Ames Laboratory Prognostics Center of Excellence IGBTs accelerated aging database. Finally, the achieved results clearly show the strength of the new trend parameter for IGBTs RUL prediction. The most notable strong correlation within the proposed approach is in relation to accuracy value, with an acceptable average accuracy rate of 60.4%.

In this paper, an application of the Principal Component Analysis (PCA) technique to data-driven approach is addressed. The proposed scheme is applied to OFSP-based adaptive output feedback control systems. It is well recognized that an adaptive control system based on OFSP conditions can achieve asymptotic stability via a static output feedback. However, these conditions are very restrictive on most realistic systems. Therefore, a parallel feedforward compensator (PFC) is introduced to alleviate such restrictions, but remains a steady state error which is removed by utilizing the feedforward input. Besides, the data-driven approach is one of effective control strategies for nonlinear systems in the process control, and the control parameters can be updated rapidly at each equilibrium point. However, there is no research work that had been done in terms of applying the data-driven approach in an adaptive control system based on OFSP. Therefore, once the robust stability of the adaptive control system based on OFSP can be guaranteed, the adaptive gains can be optimized by the data-driven approach such that the output performance is able to be improved. Moreover, the PCA technique is considered to imporove the data-driven approach in terms of computional cost. The proposed scheme is verified through a numerical example, the results of which demonstrate the effectiveness.

In range-free localization scheme of wireless sensor networks, estimating the distance to the anchor nodes having the actual location is common to compute the position of unknown node. Since the range-free scheme is based on the topology information, the accuracy of distance estimation is considerably affected by node density or node deployment. In this paper, we propose a geometric range-free localization algorithm which estimates the unknown positions geometrically by topological information without considering the distance estimation. To achieve this, we propose an optimal anchor node selection algorithm which selects the anchor nodes connected topologically well for the geometrical location estimation. Simulation results show that the proposed algorithm offers considerably an improved performance compared to the other existing studies.

Geolocation techniques aim at determining the geographic location of an Internet host based on its IP address. Currently, measurement-based geolocation techniques disregard the buffering delays that may be introduced at each hop along the path taken by probe packets. To fill this gap, we propose the GeoBuD (Geolocation using Buffering Delay estimation) approach. Although the network delay and the geographic distance between two Internet hosts have been shown to be related to some extent, leveraging buffering delay estimation at each hop for geolocation purposes is challenging for two reasons. First, correctly estimating the buffering delay at intermediate hops along a traceroute path for geolocation purposes depends on the accurate estimation of the geolocation of the intermediate routers. Second, even given an a priori knowledge of the location of the routers, estimating the buffering delays is difficult due to the coarse-grained information provided by delay measurements. Relying on traceroute measurements, we show that leveraging buffering delay estimation improves accuracy in the measurement-based geolocation of Internet hosts as well as the confidence that the geolocation service associates to each estimation.Keywordsgeolocationbuffering delay estimationtraceroutemultilateration

The turbulent energy spectrum of molecular clouds in a variety of environments is measured via principal component analysis (PCA) of spectral line imaging observations at millimeter wavelengths. Molecular clouds with known distances have been previously shown to accurately obey a universal scale dependence of turbulent velocity dispersion over spatial scales of 1-50 pc, via both standard object-based analysis and, more recently, PCA. The PCA-based spectrum is accurately obeyed such that it may be used as a distance estimator for molecular clouds with ~30% accuracy, where the error budget is contributed to strongly by input H II region distances used for the calibration. The use of 13CO spectral line data for distance estimation is examined and compared to the distance calibration established for 12CO observations. We show that distances estimated using 13CO are in good agreement with those obtained using 12CO, with a possible ~10% distance overestimation for 13CO relative to the 12CO calibration. Several molecular clouds with known distances are subjected to PCA, and we demonstrate that the universal spectrum is closely respected by all clouds; PCA-based distances estimated under the assumption of exact adherence to the universal spectrum are derived and are shown to be in excellent agreement with optically estimated distances. We examine the possibility that the PCA distance estimation method may be used to solve the kinematic distance ambiguity in the inner Galaxy. We establish how PCA may be used to diagnose severe blending of near/far emission and, in cases of little or no blending, to accurately establish the near or far distance. The inner Galaxy results provide initial support for the global validity of the universal PCA spectrum previously demonstrated for the outer Galaxy only. In conjunction with the accurate velocity information provided by millimeter wavelength spectral line data, PCA can provide useful information for studies of Galactic structure and kinematics.

We present a method for the analysis of solar Stokes profiles based on the technique of principal component analysis (PCA). We consider the development of a PCA/interpolation technique for obtaining estimates of the solar magnetic field vector when the underlying physical parameter distribution is not well determined. Unlike previous applications of PCA, we consider separate distributions of the PCA components as applied to the source of the underlying plasma, namely, the umbra, penumbra, and quiet photosphere. We also isolate the PCA for the Fe I λ6301.5 line. In some previous work, PCA was applied to the entire 6300-6305 A spectral region that includes the telluric lines. The method is tested on photospheric (Fe I λ6301.5) observations of NOAA Active Region 9240 and compared with the results of the traditional High Altitude Observatory Milne-Eddington inversion code. We also consider a PCA-perturbation analysis of the data and how it can assist the interpretation of the results, discuss current challenges faced by the inversion codes, and suggest important areas for future development.

SUMMARY Reciprocal averaging is a technique of indirect ordination, related both to weighted averages and to principal components analysis and other eigenvector techniques. A series of tests with simulated community gradients (coenoclines), simulated community patterns (coenoplanes), and sets of vegetation samples was used to compare ordination performance of reciprocal averaging (RA) with non-standardized and standardized principal components analysis (PCA) and polar or Bray-Curtis ordination (PO). Of these, non-standardized PCA is most vulnerable to effects of beta diversity, giving distorted ordinations of sample sets with three or more half-changes. PO and RA give good ordinations to five or more half-changes, and standardized PCA is intermediate. Sample errors affect all these techniques more at low than at high beta diversity, but PCA is most vulnerable to effects of sample errors. All three techniques could ordinate well a small (1-5 x 1-5 half-changes) simulated community pattern; and PO and RA could ordinate larger patterns (4 5 x 4-5 half-changes) well. PCA distorts larger community patterns into complex surfaces. Given a rectangular pattern (1-5 x 4-5 halfchanges), RA distorts the major axis of sample variation into an arch in the second axis of ordination. Clusters of samples tend to distort PCA ordinations in rather unpredictable ways, but they have smaller effects on RA, and none on PO. Outlier samples do not affect PO (unless used as endpoints), but can cause marked deterioration in RA and PCA ordinations. RA and PO are little subject to the involution of axis extremes that affects nonstandardized PCA. Despite the arch effect, RA is much superior to PCA at high beta diversities and on the whole preferable to PCA at low beta diversities. Second and higher axes of PCA and RA may express ecologically meaningless, curvilinear functions of lower axes. When curvilinear displacements are combined with sample error, axis interpretation is difficult. None of the techniques solves all the problems for ordination that result from the curvilinear relationships characteristic of community data. For applied ordination research consideration of sample set properties, careful use of supporting information to evaluate axes, and comparison of results of RA or PCA with PO and direct ordination are suggested.

Aims: We show the use of principal component analysis (PCA) and Fourier decomposition (FD) method as tools for variable star diagnostics and compare their relative performance in studying the changes in the light curve structures of pulsating Cepheids and in the classification of variable stars. Methods: We have calculated the Fourier parameters of 17,606 light curves of a variety of variables, e.g., RR Lyraes, Cepheids, Mira Variables and extrinsic variables for our analysis. We have also performed PCA on the same database of light curves. The inputs to the PCA are the 100 values of the magnitudes for each of these 17,606 light curves in the database interpolated between phase 0 to 1. Unlike some previous studies, Fourier coefficients are not used as input to the PCA. Results: We show that in general, the first few principal components (PCs) are enough to reconstruct the original light curves compared to FD method where 2 to 3 times more number of parameters are required to satisfactorily reconstruct the light curves. The computation of required number of Fourier parameters on the average needs 20 times more CPU time than the computation of required number of PCs. Therefore, PCA does have some advantage over the FD method in analysing the variable stars in a larger database. However in some cases, particularly in finding the resonances in Fundamental mode (FU) Cepheids, the PCA results show no distinct advantages over the FD method. We also demonstrate that the PCA technique can be used to classify variables into different variability classes in an automated, unsupervised way, a feature that has immense potential for larger databases of the future.

We review a neural network implementation of the statistical technique of Principal Component Analysis (PCA) and Factor Analysis. We now derive a new method based on Kernel Principal Components Analysis (KPCA) and extend the Kernel PCA method to sparsified Kernel PCA. We then apply two methods to the data set which is composed of 10 faces in a mixture of poses. We wish to identify only the most significant poses on a data set. We found the better result from the sparsified Kernel PCA method.