Nearby Points Research Articles

Target Cambodia

Target Cambodia

Efficient Evaluation of the Physical-Optics Integrals for Conducting Surfaces Using the Uniform Stationary Phase Method

The computational time to evaluate the physical optics (PO) expression by numerical integration increases rapidly with the increase of electrical size of scattering surfaces. However, the computational time of PO integrals for electrically large object can be greatly reduced by using the stationary phase method, which is independent of the wavenumber. For this method, the theory and numerical implementations for isolated critical points have been well developed. However, for cases of nearby critical points, there are still a few issues to be considered, especially, in numerical implementations. In this paper, we mainly study the numerical implementations for several most common cases of nearby critical points. In particular, the cases of two nearby inner stationary phase points and complex inner stationary phase points are discussed in more details. Such cases occur frequently when the scattering surface includes convex-concave parts, but the numerical implementations to such cases have not been reported to our knowledge. The difficulty lies in how to identify whether two inner stationary points and complex inner stationary points on the surfaces with arbitrary shapes are close to each other or not. A strategy is designed to solve this difficulty. By validation in some typical examples, we find that the stationary phase method is robust enough to evaluate the PO integrals accurately. Finally, some interesting phenomena observed in numerical validations are interpreted.

Climate effects of the Three Gorges Reservoir as simulated by a high resolution double nested regional climate model

Abstract High resolution multi-annual regional climate model (RegCM3) experiments were performed to simulate the effects of the Three Gorges Reservoir (TGR) on the climate of the surrounding areas. The model was run in double nested mode. Firstly a 50 km resolution simulation was conducted over the China domain driven by the coarse resolution NCEP/NCAR re-analysis. Then the output of the simulation was used to drive the model over the Three Gorges Area (TGA) at a resolution of 10 km. SUB-BATS scheme was employed in the 10 km simulation to represent the land surface at 2 km. Two 10 km simulation, one with and the other without the inland water in TGR were conducted. Comparison of the simulations against observation were firstly carried out to validation the model performances over TGA. The 10 km sensitivity experiments with and without the TGR showed that little or negligible effects can be found except directly over the TGR. Most of the simulated effects are noisy and not statistically significant, except for cooling over the TGR water body in both June–July–August (JJA) and December–January–February (DJF). The cooling leads to an/a insignificant decrease/slight decrease of precipitation over the TGR and nearby grid points, respectively. The cooling is larger in JJA compared to DJF. As a typical river-like reservoir, the width and coverage of the TGR does not have significant influence on the local climate over the area.

Efficient Block-Based Parameterized Timing Analysis Covering All Potentially Critical Paths

In order for the results of timing analysis to be useful, they must provide insight and guidance on how the circuit may be improved so as to fix any reported timing problems. A limitation of many recent variability-aware timing analysis techniques is that, while they report delay distributions, or verify multiple corners, they do not provide the required guidance for re-design. We propose an efficient block-based parameterized timing analysis technique that can accurately capture circuit delay at every point in the parameter space, by reporting all paths that can become critical. Using an efficient pruning algorithm, only those potentially critical paths are carried forward, while all other paths are discarded during propagation. This allows one to examine local robustness to parameters in different regions of the parameter space, not by considering differential sensitivity at a point (that would be useless in this context) but by knowledge of the paths that can become critical at nearby points in parameter space. We give a formal definition of this problem and propose a technique for solving it, which improves on the state of the art, both in terms of theoretical computational complexity and in terms of runtime on various test circuits.

Automated and Efficient Method for Extraction of Tunnel Cross Sections Using Terrestrial Laser Scanned Data

An automated and efficient method for extraction of tunnel cross sections using terrestrial laser scanner (TLS) data is presented. In this process, a three-dimensional (3D) point cloud acquired from the TLS is projected onto a horizon plane, converted to a two-dimensional (2D) planar image, and skeletonized to estimate the tunnel centerline. Stations from which cross sections are extracted are estimated from that centerline, which is vectorized and smoothed for better estimation. From those stations, cross-sectional planes are estimated, and point groups surrounding the planes are extracted. By using these point groups, the azimuth angles of the cross-sectional planes are adjusted, and the final cross sections are generated by projecting the nearby points to the adjusted planes. The performance of the proposed method was evaluated by applying it to a real tunnel and comparing the results with those from a conventional method that uses a total station. In the results, the cross sections were extracted at the stations corresponding to the conventional method. The proposed method proved itself to offer advantages, including detailed description and improved surveying and data processing efficiencies.

Analysis of Structure and Composition of Bacterial Core Communities in Mature Drinking Water Biofilms and Bulk Water of a Citywide Network in Germany

The bacterial core communities of bulk water and corresponding biofilms of a more than 20-year-old drinking water network were compared using 16S rRNA single-strand confirmation polymorphism (SSCP) fingerprints based on extracted DNA and RNA. The structure and composition of the bacterial core community in the bulk water was highly similar (>70%) across the city of Braunschweig, Germany, whereas all biofilm samples contained a unique community with no overlapping phylotypes from bulk water. Biofilm samples consisted mainly of Alphaproteobacteria (26% of all phylotypes), Gammaproteobacteria (11%), candidate division TM6 (11%), Chlamydiales (9%), and Betaproteobacteria (9%). The bulk water community consisted primarily of Bacteroidetes (25%), Betaproteobacteria (20%), Actinobacteria (16%), and Alphaproteobacteria (11%). All biofilm communities showed higher relative abundances of single phylotypes and a reduced richness compared to bulk water. Only biofilm communities sampled at nearby sampling points showed similar communities irrespective of support materials. In all of our bulk water studies, the community composition determined from 16S rRNA was completely different from the 16S rRNA gene-based community composition, whereas in biofilms both molecular fractions resulted in community compositions that were similar to each other. We hypothesize that a higher fraction of active bacterial phylotypes and a better protection from oxidative stress in drinking water biofilms are responsible for this higher similarity.

Refining Gaussian mixture model based on enhanced manifold learning

Gaussian mixture model (GMM) has been widely used for data analysis in various domains including text documents, face images and genes. GMM can be viewed as a simple linear superposition of Gaussian components, each of which represents a data cluster. Recent models, namely Laplacian regularized GMM (LapGMM) and locally consistent GMM (LCGMM) have been proposed to preserve the local manifold structure of the data for modeling Gaussian mixtures, and show superior performance than the original GMM. However, these two models ignore the global manifold structure without consideration of the widely separated points. In this paper, we introduce refined Gaussian mixture model (RGMM), which explicitly places separated points far apart from each other as well as brings nearby points closer together according to the probability distributions of Gaussians, in the hope of fully discovering the discriminating power of manifold learning for estimating Gaussian mixtures. We use EM algorithm to optimize the maximum likelihood function of RGMM. Experimental results on three real-world data sets demonstrate the effectiveness of RGMM in data clustering.

A novel pre-authentication scheme based on fast channel switching in IEEE 802.11 WLANs

Abstract In the case of a 3G-WLAN interworking architecture, handoff latency is mainly caused by the delays incurred when a mobile station (STA) transfers a current security context to the target access network or establishes connectivity to a new access point (AP). Existing handoff optimization schemes have mostly focused on reducing the scanning time which is to discover nearby access points. Even though the time taken by a mobile STA to authenticate the target APs contributes to the total handoff delay, the schemes to optimize the authentication time have not yet been fully investigated for providing seamless connectivity to mobile users performing handoff in WLANs. As a solution, pre-authentication, a technique performing the authentication of target APs before the completion of the handoff procedure, has attracted considerable attention to reduce the overall delay related with a handoff, which is performed within inter-subnet and/or inter-administrative domain levels. Therefore, in this article, we propose a novel pre-authentication scheme to minimize the handoff delay in 3G-WLAN interworking architecture. The proposed scheme pre-authenticates a new AP directly through a switched channel in a short period of time. Our evaluation shows that the proposed scheme outperforms the existing authentication schemes defined in the IEEE 802.11 standards in terms of authentication delay, signaling cost, and mobility rate. The proposed mechanism offers an additional advantage in that it is easy to implement and deploy by simply modifying the device driver modules of clients' WLAN interfaces.

Least squares twin support vector machine classification via maximum one-class within class variance

A twin support vector machine (TWSVM), as an effective classification tool, tries to find two non-parallel planes that can be produced by solving two quadratic programming problems (QPPs). The QPPs lead to higher computational costs. The least squares twin SVM (LSTSVM), as a variant of TWSVM, attempts to avoid the above deficiency and obtain two non-parallel planes directly by solving two sets of linear equations. Both TWSVM and LSTSVM operate directly on patterns using two optimizations with constraints and, respectively, use such constraints to estimate the distance between each plane for its own class and patterns of other classes. However, such approaches weaken the geometric interpretation of the generalized proximal SVM (GEPSVM) so that in many Exclusive Or examples for different distributions, they may obtain the worse classification performance. Moreover, in addition to failing to discover the local geometry inside the samples, they are sensitive to outliers. In this paper, inspired by several geometrically motivated learning algorithms and the advantages of LSTSVM, we first propose a new classifier, called LSTSVM classification via maximum one-class within-class variance (MWSVM), which is specially designed for avoiding the aforementioned deficiencies and keeping the advantages of LSTSVM. The new method directly incorporates the one-class within-class variance to the classifier, such that it is expected that the genuine geometric interpretation of GEPSVM can be kept in LSTSVM. Undoubtedly, like LSTSVM, MWSVM may lead to a worse classification performance in many cases, especially when the outliers are present. Therefore, a localized version (LMWSVM) of MWSVM is further proposed to remove the outliers effectively. Another advantage of LMWSVM is that it takes the nearby points which are closest to each other as a training set, such that the MWSVM classifier is determined by smaller size of training samples than that of LSTSVM. Naturally, it can reduce the storage space of LSTSVM, especially when extended to nonlinear cases. Experiments carried out on both toy and real-world problems disclose the effectiveness of both MWSVM and LMWSVM.

Nonexistence Results of Sign-changing solutions for a Supercritical Problem of the Scalar Curvature Type

Abstract This paper is devoted to the study of the nonlinear elliptic problem with supercritical critical exponent (Pε) : −Δu = K|u|4/(n−2)+εu in Ω, u = 0 on ∂Ω, where Ω is a smooth bounded domain in ℝn, n ≥ 3, K is a C3 positive function and ε is a positive real parameter. We show first that in dimension 3, for ε small, (Pε) has no sign-changing solutions with low energy which blow up at two points. For n ≥ 4, we prove that there are no sign-changing solutions which blow up at two nearby points. We also show that (Pε) has no bubble-tower sign-changing solutions.

Preserving geometric properties in reconstructing regions from internal and nearby points

The problem of reconstructing a region from a set of sample points is common in many geometric applications, including computer vision. It is very helpful to be able to guarantee that the reconstructed region “approximates” the true region, in some sense of approximation. In this paper, we study a general category of reconstruction methods, called “locally-based reconstruction functions of radius α,” and we consider two specific functions, Jα(S) and Fα(S), within this category. We consider a sample S, either finite or infinite, that is specified to be within a given Hausdorff distance δ of the true region R, and we prove a number of theorems which give conditions on R, δ that are sufficient to guarantee that the reconstructed region is an approximation of the true region. Specifically, we prove:1.For any R, if F is any locally-based reconstruction method of radius α where α is small enough, and if the Hausdorff distance from S to R is small enough, then the dual-Hausdorff distance from F(S) to R, the Hausdorff distance between their boundaries, and the measure of their symmetric difference are guaranteed to be small.2.If R is r-regular, then for any ϵ,ϕ>0, if α is small enough, and the Hausdorff distance from S to R is small enough, then each of the regions Jα(S) and Fα(S) is ϵ-similar to R and is an (ϵ,ϕ)-approximation in tangent of R.

A Genetic Algorithm with Weighted Average Normally-Distributed Arithmetic Crossover and Twinkling

Genetic algorithms have been extensively used as a global optimization tool. These algorithms, however, suffer from their generally slow convergence rates. This paper proposes two approaches to address this limitation. First, a new crossover technique, the weighted average normally-distributed arithmetic crossover (NADX), is introduced to enhance the rate of convergence. Second, twinkling is incorporated within the crossover phase of the genetic algorithms. Twinkling is a controlled random deviation that allows only a subset of the design variables to undergo the decisions of an optimization algorithm while maintaining the remaining variable values. Two twinkling genetic algorithms are proposed. The proposed algorithmsare compared to simple genetic algorithms by using various mathematical and engineering design test problems. The results show that twinkling genetic algorithms have the ability to consistently reach known global minima, rather than nearby sub-optimal points, and are able to do this with competitive rates of convergence.

The Chaotic Prediction for Aero‐Engine Performance Parameters Based on Nonlinear PLS Regression

The prediction of the aero‐engine performance parameters is very important for aero‐engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT) time series which come from actual air‐borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS) based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS) method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

QUADRISECANT APPROXIMATION OF HEXAGONAL TREFOIL KNOT

It is known that every nontrivial knot has at least two quadrisecants. Given a knot, we mark each intersection point of each of its quadrisecants. Replacing each subarc between two nearby marked points with a straight line segment joining them, we obtain a polygonal closed curve which we will call the quadrisecant approximation of the given knot. We show that for any hexagonal trefoil knot, there are only three quadrisecants, and the resulting quadrisecant approximation has the same knot type.

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user’s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.

A unique property of single-link distance and its application in data clustering

We prove a unique property of single-link distance, based on which an algorithm is designed for data clustering. The property states that a single-link cluster is a subset with inter-subset distance greater than intra-subset distance, and vice versa. Among the major linkages (single, complete, average, centroid, median, and Ward's), only single-link distance has this property. Based on this property we introduce monotonic sequences of iclusters (i.e., single-link clusters) to model the phenomenon that a natural cluster has a dense kernel and the density decreases as we move from the kernel to the boundary. A monotonic sequence of iclusters is a sequence of nested iclusters such that an icluster in the sequence is a dominant child (in terms of size) of the icluster before it. Our data clustering algorithm is monotonic sequence based. We classify a dataset of one monotonic sequence into to two classes by splitting the sequence into two parts: the kernel part and the surrounding part. For a data set of multiple monotonic sequences, each leaf monotonic sequence represents the kernel of a class, which then “grows” by absorbing nearby non-kernel points. This algorithm, proved by experiments, compares favorable in effectiveness to other clustering algorithms.

A Tailored Finite Point Method for Solving Steady MHD Duct Flow Problems with Boundary Layers

AbstractIn this paper we propose a development of the finite difference method, called the tailored finite point method, for solving steady magnetohydrodynamic (MHD) duct flow problems with a high Hartmann number. When the Hartmann number is large, the MHD duct flow is convection-dominated and thus its solution may exhibit localized phenomena such as the boundary layer. Most conventional numerical methods can not efficiently solve the layer problem because they are lacking in either stability or accuracy. However, the proposed tailored finite point method is capable of resolving high gradients near the layer regions without refining the mesh. Firstly, we devise the tailored finite point method for the scalar inhomogeneous convection-diffusion problem, and then extend it to the MHD duct flow which consists of a coupled system of convection-diffusion equations. For each interior grid point of a given rectangular mesh, we construct a finite-point difference operator at that point with some nearby grid points, where the coefficients of the difference operator are tailored to some particular properties of the problem. Numerical examples are provided to show the high performance of the proposed method.

Surface Hopping Excited-State Dynamics Study of the Photoisomerization of a Light-Driven Fluorene Molecular Rotary Motor.

We report a theoretical study of the photoisomerization step in the operating cycle of a prototypical fluorene-based molecular rotary motor (1). The potential energy surfaces of the ground electronic state (S0) and the first singlet excited state (S1) are explored by semiempirical quantum-chemical calculations using the orthogonalization-corrected OM2 Hamiltonian in combination with a multireference configuration interaction (MRCI) treatment. The OM2/MRCI results for the S0 and S1 minima of the relevant 1-P and 1-M isomers and for the corresponding S0 transition state are in good agreement with higher-level calculations, both with regard to geometries and energetics. The S1 surface is characterized at the OM2/MRCI level by locating two S0-S1 minimum-energy conical intersections and nearby points on the intersection seam and by computing energy profiles for pathways toward these MECIs. Semiclassical Tully-type trajectory surface hopping (TSH) simulations with on-the-fly OM2/MRCI calculations are carried out to study the excited-state dynamics after photoexcitation to the S1 state. Fast relaxation to the ground state is observed through the conical intersection regions, predominantly through the lowest-energy one with a strongly twisted central C═C double bond and pyramidalized central carbon atom. The excited-state lifetimes for the direct and inverse photoisomerization reactions (1.40 and 1.79 ps) and the photostationary state ratio (2.7:1) from the TSH-OM2 simulations are in good agreement with the available experimental data (ca. 1.7 ps and 3:1). Excited-state lifetimes, photostationary state ratio, and dynamical details of the TSH-OM2 simulations also agree with classical molecular dynamics simulations using a reparametrized optimized potentials for liquid simulations (OPLS) all-atom force field with ad-hoc surface hops at predefined conical intersection points. The latter approach allows for a more extensive statistical sampling.

Simulating mode transitions during breakdown in liquids

The primary new information from this investigation is the simulation of a transition from one streamer mode to another during propagation. The transition is characteristic of experimental data, but has not heretofore been described in a model. This demonstrates that the conceptual framework describing prebreakdown streamers in liquids as stochastic growth of a branching fractal tree in point-plane geometry is capable of simulating a wide range of streamer propagation behaviors in insulating liquids. The work also improved the approach in the model for scaling the potential at nearby grid points and explored some of the influences of computational choices, specifically needle length and grid size, on the predicted results.

Design and analysis of a ranking approach to private location-based services

Users of mobile services wish to retrieve nearby points of interest without disclosing their locations to the services. This article addresses the challenge of optimizing the query performance while satisfying given location privacy and query accuracy requirements. The article's proposal, SpaceTwist, aims to offer location privacy for k nearest neighbor ( k NN) queries at low communication cost without requiring a trusted anonymizer. The solution can be used with a conventional DBMS as well as with a server optimized for location-based services. In particular, we believe that this is the first solution that expresses the server-side functionality in a single SQL statement. In its basic form, SpaceTwist utilizes well-known incremental NN query processing on the server. When augmented with a server-side granular search technique, SpaceTwist is capable of exploiting relaxed query accuracy guarantees for obtaining better performance. We extend SpaceTwist with so-called ring ranking, which improves the communication cost, delayed termination, which improves the privacy afforded the user, and the ability to function in spatial networks in addition to Euclidean space. We report on analytical and empirical studies that offer insight into the properties of SpaceTwist and suggest that our proposal is indeed capable of offering privacy with very good performance in realistic settings.