Comparison of two Bayesian-point-estimation methods in multiple-source localization

Environmental uncertainty represents the limiting factor in matched-field localization. Within a Bayesian framework, both the environmental parameters, and the source parameters are considered to be unknown variables. However, including environmental parameters in multiple-source localization greatly increases the complexity and computational demands of the inverse problem. In the paper, the closed-form maximumlikelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. This paper compares two Bayesian-point-estimation methods： the maximum a posteriori（MAP） approach and the marginal posterior probability density（PPD） approach to source localization. The MAP approach determines the sources locations by maximizing the PPD over all source and environmental parameters. The marginal PPD approach integrates the PPD over the unknowns to obtain a sequence of marginal probability distribution over source range or depth.Monte Carlo analysis of the two approaches for a test case involving both geoacoustic and water-column uncertainties indicates that：（1） For sensitive parameters such as source range, water depth and water sound speed, the MAP solution is better than the marginal PPD solution.（2） For the less sensitive parameters, such as,bottom sound speed, bottom density, bottom attenuation and water sound speed, when the SNR is low, the marginal PPD solution can better smooth the noise, which leads to better performance than the MAP solution.Since the source range and depth are sensitive parameters, the research shows that the MAP approach provides a slightly more reliable method to locate multiple sources in an unknown environment.

Almost Fixed Points and Fixed Points of Upper [Lower] Semicontinuous Multimap on Locally G-convex Spaces

In this paper, we use the well known KKM type theorem for generalized convex spaces due to Park （Elements of the KKM theory for generalized convex spaces, Korean J. Comp. Appl. Math., 7（2000）, 1-28） to obtain an almost fixed point theorem for upper [resp., lower] semicontinuous multimaps in locally G-convex spaces, and then give a fixed point theorem for upper semicontinuous multimap with closed Γ-convex values.

An improved local radial point interpolation method for transient heat conduction analysis

The smoothing thin plate spline （STPS） interpolation using the penalty function method according to the optimization theory is presented to deal with transient heat conduction problems. The smooth conditions of the shape functions and derivatives can be satisfied so that the distortions hardly occur. Local weak forms are developed using the weighted residual method locally from the partial differential equations of the transient heat conduction. Here the Heaviside step function is used as the test function in each sub-domain to avoid the need for a domain integral. Essential boundary conditions can be implemented like the finite element method （FEM） as the shape functions possess the Kronecker delta property. The traditional two-point difference method is selected for the time discretization scheme. Three selected numerical examples are presented in this paper to demonstrate the availability and accuracy of the present approach comparing with the traditional thin plate spline （TPS） radial basis functions.

FIXED POINT THEOREMS OF LOCAL CONTRACTION MAPPINGS ON MENGER SPACES

In this paper,we introduce the concept ofε-chainable PM-space,and give severalfixed point theorems of one-valued and multivalued local contraction mapping on the kindof spaces.

A Robust Wi-Fi Fingerprinting Indoor Localization Coping with Access Point Movement

A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.

基于Adaptive Sigma-Point滤波的智能导弹对目标协同定位方法研究

在对智能导弹协同作战过程详细描述的基础上,提出了一种利用多枚智能导弹协同对目标定位的方法。考虑目标运动模型为静止模型和运动模型两种情况,将Sigma-Point滤波方法与过程噪声的自适应估计方法相结合,融合目标运动状态和目标到达角信息,克服了常规Sigma-Point滤波性能依赖于目标运动模型的先验统计信息的缺点。通过比较目标可能存在区域面积的大小,确定了参与协同定位的智能导弹数目和编队构型。仿真研究表明,多枚智能导弹协同对目标定位可以显著提高定位精度和速度。

弱空间式Locale

空间式locale范畴SLoc是locale范畴Loc的余反射满子范畴,但对locale乘积不封闭．本文引入弱空间式locale,证明弱空间式locale范畴WSloc为范畴Loc的余反射满子范畴,且对locale秉积封闭．还证明了一个locale A是空间式的当且仅当它的枝映射localeN(A)是弱空间式的;一个空问式locale的每一个子locale都是空间式的当且仅当它的每一个子locale是弱空间式的．最后,证明了弱空间式性在定向函子下保持不变．

MONOTONE POINTS IN ORLICZ-BOCHNER SEQUENCE SPACES

In Orlicz-Bochner sequence spaces endowed with Orlicz norm and Luxemburg norm, points of lower monotonicity, upper monotonicity, lower local uniform monotonicity and upper local uniform monotonicity are characterized.

RSSI-based Algorithm for Indoor Localization

Wireless node localization is one of the key technologies for wireless sensor networks. Outdoor localization can use GPS, AGPS (Assisted Global Positioning System) [6], but in buildings like supermarkets and underground parking, the accuracy of GPS and even AGPS will be greatly reduced. Since Indoor localization requests higher accuracy, using GPS or AGPS for indoor localization is not feasible in the current view. RSSI-based trilateral localization algorithm, due to its low cost, no additional hardware support, and easy-understanding, it becomes the mainstream localization algorithm in wireless sensor networks. With the development of wireless sensor networks and smart devices, the number of WIFI access point in these buildings is increasing, as long as a mobile smart device can detect three or three more known WIFI hotspots’ positions, it would be relatively easy to realize self-localization (Usually WIFI access points locations are fixed). The key problem is that the RSSI value is relatively vulnerable to the influence of the physical environment, causing large calculation error in RSSI-based localization algorithm. The paper proposes an improved RSSI-based algorithm, the experimental results show that compared with original RSSI-based localization algorithms the algorithm improves the localization accuracy and reduces the deviation.

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

In existing methods for segmented images,either edge point extraction or preservation of edges,compromising contrast images is so sensitive to noise.The Degeneration Threshold Image Detection(DTID)framework has been proposed to improve the contrast of edge filtered images.Initially,DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast images.This filter decomposes input images into base layers in the DTID framework.With minimal filtering time,Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge preservation.In the DTID framework,Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to noise.This Shift-Invariant Filtering estimates value across edges for removing outliers(i.e.,noise preserving base layers of the contrast image).The intensity values are calculated in the base layer of the contrast image for accurately detecting nearby spatial locations using Shift-Invariant base Pass Domain Filter(SIDF).At last,Affine Planar Transformation is applied to detect edge filtered contrast images in the DTID framework for attaining a high quality of the image.It normalizes the translation and rotation of images.With this,Degeneration Threshold Image Detection maximizes average contrast enhancement quality and performs an experimental evaluation of factors such as detection accuracy,rate,and filtering time on contrast images.Experimental analysis shows that the DTID framework reduces the filtering time taken on contrast images by 54%and improves average contrast enhancement quality by 27%compared to GUMA,HMRF,SWT,and EHS.It provides better performance on the enhancement of average contrast enhancement quality by 28%,detection accuracy rate by 26%,and reduction in filtering time taken on contrast images by 30%compared to state-of-art methods.

EQUILIBRIUM EXISTENCE FOR MULTI-LEADER-FOLLOWER GENERALIZED CONSTRAINED MULTIOBJECTIVE GAMES IN LOCALLY FC-UNIFORM SPACES

In this article, we introduce and study some new classes of multi-leader-follower generalized constrained multiobjective games in locally FC-uniform spaces where the number of leaders and followers may be finite or infinite and the objective functions of the followers obtain their values in infinite-dimensional spaces. Each leader has a constrained correspondence. By using a collective fixed point theorem in locally FC-uniform spaces due to author, some existence theorems of equilibrium points for the multi-leader-follower generalized constrained multiobjective games are established under nonconvex settings. These results generalize some corresponding results in recent literature.

Resonant Modes of One-Dimensional Metamaterial Containing Helmholtz Resonators with Point Defect

The metamaterial constructed by Helmholtz resonators (HR) has low-frequency acoustic forbidden bands and possesses negative mass density and effective bulk modulus at particular frequencies. The resonant modes in one-dimensional HR structure with point defect were studied using finite element method (FEM). The results show that the acoustic energy is localized between the resonant HR and the opening in the local-resonant-type gap. There is a high pressure area around the defect resonator at the frequency of defect mode. In the Bragg type gap, the energy mainly distributes in the waveguide with harmonic attenuation due to the multi-scattering. Phase opposition demonstrates the existence of negative dynamic mass density. Local negative parameter is observed in the pass band due to the defect mode. Based on further investigation of the acoustic intensity and phase distributions in the resonators corresponding to two different forbidden bands, only one local resonant mode is verified, which is different from the three-component local resonant phononics. This work will be useful for understanding the mechanisms of acoustic forbidden bands and negative parameters in the HR metamaterial, and of help for designing new functional acoustic devices.

An invariant interest point detector under image affine transformation

For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.

Generalized constrained multiobjective games in locally FC-uniform spaces

A new class of generalized constrained multiobjective games is introduced and studied in locally FC-uniform spaces without convexity structure where the number of players may be finite or infinite and all payoff functions get their values in an infinite-dimensional space. By using a Himmelberg type fixed point theorem in locally FC-uniform spaces due to author, some existence theorems of weak Paxeto equilibria for the generalized constrained multiobjective games are established in locally FC-uniform spaces. These theorems improve, unify and generalize the corresponding results in recent literatures.

A Fuzzy Decision Based WSN Localization Algorithm for Wise Healthcare

Wise healthcare is a typical application of wireless sensor network(WSN), which uses sensors to monitor the physiological state of nursing targets and locate their position in case of an emergency situation. The location of targets need to be determined and reported to the control center,and this leads to the localization problem. While localization in healthcare field demands high accuracy and regional adaptability, the information processing mechanism of human thinking has been introduced,which includes knowledge accumulation, knowledge fusion and knowledge expansion. Furthermore, a fuzzy decision based localization approach is proposed. Received signal strength(RSS) at references points are obtained and processed as position relationship indicators, using fuzzy set theory in the knowledge accumulation stage; after that, optimize degree of membership corresponding to each anchor nodes in different environments during knowledge fusion; the matching degree of reference points is further calculated and sorted in decision-making, and the coordinates of several points with the highest matching degree are utilized to estimate the location of unknown nodes while knowledge expansion. Simulation results show that the proposed algorithm get better accuracy performance compared to several traditional algorithms under different typical occasions.

基于中子位置灵敏探测器对点源的三维定位方法研究

使用中子法对密封容器内的放射源进行定位具有重要的现实意义。本研究使用4根矩形分布的中子位置灵敏探测器对探测空间内AmLi源进行定位,首先使用蒙特卡罗方法设计了探测器的慢化屏蔽体;然后设计了延迟电路加到探测器的一端,根据探测器两端探测到中子信号的时间差来确定源y轴(探测器轴向)的位置坐标,并对每个探测器的轴向位置函数进行了刻度;利用探测器围绕密封容器搭建测量空间,使用相邻探测器探测到中子计数率的比值来确定x轴和z轴(另两个方向)的位置坐标并进行了函数刻度。测量时,首先选择计数率最大的探测器来确定源的轴向坐标,再根据与此探测器相邻两根探测器计数率的比值来确定源的另两个方向的坐标,实现了对源的定位。使用该方法测量了点源在探测器空间内的5个不同位置,每个坐标轴上的定位偏差均在1.5 cm之内,并对造成偏差的原因进行了分析。该方法验证了使用中子位置灵敏探测器对放射源进行定位的可行性,并为进一步测量核设施工艺设备内核材料的位置打下了良好的基础。

基于特征点改进的视觉SLAM定位研究

为改善视觉SLAM系统在低纹理环境下定位精度较低的现象,提出一种改进的ORB特征点提取策略和一种关键帧选择机制;首先采用多尺度分析和基于局部灰度的特征检测方法克服一般ORB算法缺乏尺度和旋转描述的缺点;其次提出一种基于高斯模糊的图像信息增强方法解决传统ORB特征点提取方法在纹理信息不被突出环境下容易失效的问题,并对图像进行象限分割使特征点均匀分布;最后为剔除劣质关键帧,设计了一种综合时间因素与特征点数量因素的关键帧选择机制;将提出的方法移植到ORB_SLAM2上,并在TUM数据集上测试,实验结果表明,视觉SLAM系统的定位误差平均降低14.688%,证实了该方法的有效性。

融合细粒度特征编码的点云分类分割网络

有效获取点云特征是分析和处理三维点云场景的关键。针对目前深度学习方法特征信息提取不充分,难以捕捉深层次语义信息的问题,提出了一种融合细粒度特征编码的网络来提高点云分类与分割任务的准确率。首先,特征提取模块包含2个子模块:一个是扩张图卷积模块,相比图卷积能够提取更丰富的几何信息;另一个是细粒度特征编码模块,能够获取局部区域的细节特征。其次,通过可学习参数将二者动态融合,有效地学习每个点的上下文信息。最后,将提取的所有特征相加,通过通道亲和注意力模块来强调不同通道,协助特征图来避免可能的冗余。在ModelNet40及ScanObjectNN数据集上进行点云分类实验,总体分类精度分别为93.3%和80.0%。在ShapeNet Part数据集上进行点云部件分割实验,平均交并比为85.6%。实验结果表明,与目前主流方法相比,该网络具有较优的性能。

用于多通道单分子定位的高精度图像配准方法

单分子定位技术可以绕过光学系统的衍射限制,在生物样品的单粒子追踪和超分辨显微成像中得到了广泛应用.多通道单分子定位采用多个成像通道,可以实现对不同目标的同时追踪或多色超分辨成像,也可以提升单粒子追踪的轴向深度或实现更高的定位精度和密度.但各通道图像间的差异会影响协同定位或定量分析,因此图像配准是其图像数据预处理的关键环节;且由于单分子定位精度高,其对多通道图像配准精度的要求也很高.现有技术一般采用基于控制点的配准方法,且多采用复杂而精密的方式来获取基准物网格图像用于定位得到控制点对,以实现高精度图像配准,对样品或实验设备要求高,难以直接推广.为此,本文基于局部非线性变换和误匹配点剔除,发展了一种可以直接采用随机分布荧光珠样品作为基准物的高精度图像配准方法,通过在特征匹配和变换模型参数估计的过程中对控制点进行监测和迭代筛选,以剔除因单分子定位不准确或精度差而导致未精确匹配的控制点对,从而消除以随机分布荧光珠样品作为基准物时对于控制点准确获取和精确匹配所带来的不良影响,同时采用基于局部加权平均的二阶多项式拟合进行变换模型参数估计,以更好地适用于不同通道间存在局部非线性形变的情形.结果表明,采用该方法只需要3次迭代,就可以将未准确定位和精确匹配的控制点对找到并剔除,从而实现更准确的变换模型参数估计,将配准精度提高一个数量级,在图像局部非线性形变情况严重的正交像散双通道单分子定位成像系统中实现了约6 nm的配准精度.