Information recommendation using ICA

We describe an information filtering system using independent component analysis (ICA). A document–word matrix is generally sparse and has an ambiguity of synonyms. To solve this problem, we propose a method to use document vectors represented by independent components. An independent component generated by ICA is considered as a topic. In practice, we map the document vectors into a topics space. Since some independent components are useless for recommendation, we select the necessary components from all independent components by a maximum distance algorithm (MDA). Although Euclidean distance is usually used by MDA, we propose topic selection by cosine-distance-based MDA to solve the mismatch of similarities in information filtering. We create a user profile from the transformed data with a genetic algorithm (GA). Finally, we recommend documents with the user profile and evaluate the accuracy by imputation precision. We have carried out an evaluation experiment to confirm the practicality of the proposed method.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

Information filtering using SVD and ICA

We propose an information filtering system for documents by a user profile using latent semantics obtained by singular value decomposition (SVD) and independent component analysis (ICA). In information filtering systems, it is useful to analyze the latent semantics of documents. ICA is one method to analyze the latent semantics. We assume that topics are independent of each other. Hence, when ICA is applied to documents, we obtain the topics included in the documents. By using SVD remove noises before applying ICA, we can improve the accuracy of topic extraction. By representation of the documents with those topics, we improve recommendations by the user profile. In addition, we construct a user profile with a genetic algorithm (GA) and evaluate it by 11-point average precision. We carried out an experiment using a test collection to confirm the advantages of the proposed method. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

局部梯度轮廓变换的高光谱异常检测

目的 高光谱异常检测由于其重要的应用价值,引起了研究人员的广泛关注,但大部分的检测算法,往往直接利用输入的高光谱遥感影像所携带的光谱信息或者空谱信息进行检测。考虑到由于成像过程的限制,如成像条件的复杂性以及光谱通道众多导致的每个通道光子数量有限等问题,所获取的高光谱遥感影像往往在一定程度上偏离真实场景,而这也制约了异常检测的精度。针对此问题,本文提出了一种局部梯度轮廓变换的高光谱遥感影像异常检测算法。方法 为了在不影响算法性能的基础上减少计算复杂度,首先选取部分可能的异常像元,只对这些局部的异常像元可能位置进行梯度轮廓变换。其次,将变换后的梯度轮廓用于指导原始高光谱遥感影像的空域增强。最后,对增强后的高光谱遥感影像进行检测。通过将局部梯度轮廓用于影像的增强,避免了成像过程中由于细节损失而造成检测精度受限的情况。结果 实验在来自4个数据集的6幅高光谱遥感影像上进行了性能验证。首先利用经典的Global-RX （Reed Xiaoli）检测算法同时检测本文算法增强后的影像和原始影像,分别取得的平均AUC （area under curve）值为0.987 1和0.933 6,本文算法带来了0.053 5的精度提升;同时,通过与其他3种预处理方法进行比较,证明了本文局部梯度轮廓变换方法的有效性;更进一步,利用基于协同表示CRD （collaborative representation-based detector）的检测器对增强后的影像和原始影像分别进行检测,分别取得的平均AUC值为0.990 7和0.977 5,检测结果再次验证了本文算法能够有效提升影像的检测精度;通过对比,实验数据表明本文所采用的局部梯度轮廓变换可减少约37.82%的时间复杂度。结论 本文算法通过将局部的梯度轮廓进行变换并用于指导原始影像的增强过程,使得影像的空间轮廓信息更为锐利,更为接近真实场景,从而获得异常检测结果的提升。     

Analysis and optimization of service availability in a HA cluster with load-dependent machine availability

Calculations of service availability of a high-availability (HA) cluster are usually based on the assumption of load- independent machine availabilities. In this paper, we study the issues and show how the service availabilities can be calculated under the assumption that machine availabilities are load dependent. We present a Markov chain analysis to derive the steady-state service availabilities of a load-dependent machine availability HA cluster. We show that with a load-dependent machine availability, the attained service availability is now policy dependent. After formulating the problem as a Markov decision process, we proceed to determine the optimal policy to achieve the maximum service availabilities by using the method of policy iteration. Two greedy assignment algorithms are studied: least load and first derivative length (FDL) based, where least load corresponds to some load balancing algorithms. We carry out the analysis and simulations on two cases of load profiles: In the first profile, a single machine has the capacity to host all services in the HA cluster; in the second profile, a single machine does not have enough capacity to host all services. We show that the service availabilities achieved under the first load profile are the same, whereas the service availabilities achieved under the second load profile are different. Since the service availabilities achieved are different in the second load profile, we proceed to investigate how the distribution of service availabilities across the services can be controlled by adjusting the rewards vector.     

以本体构造中文信息过滤中的需求模型

在信息过滤系统中,用户模板是机器可理解的用户需求表示形式,是否能准确地反映出用户的真实需求将直接影响着过滤系统的性能。在向量空间模型中,用户的模板表现为一组带权重的特征词集,但由于在这样的用户模板中缺少必要的语义信息,很难准确地反映出用户的需求。本文提出了以本体构造需求模板的方法,以本体的形式定义需求中概念间的语义关联关系,将向量空间模型中的特征向量定义为本体中的实例,通过实例间的关联路径计算特征项间的语义关联,并通过特征项间的语义关联计算出文档与模板的语义关联度。     

Modeling of first-order photobleaching kinetics using Krylov subspace spectral methods

We solve the first order 2-D reaction–diffusion equations which describe binding-diffusion kinetics using the photobleaching scanning profile of a confocal laser scanning microscope, approximated by a Gaussian laser profile. We show how to solve the first-order photobleaching kinetics partial differential equations (PDEs) using a time-stepping method known as a Krylov subspace spectral (KSS) method. KSS methods are explicit methods for solving time-dependent variable-coefficient partial differential equations. They approximate Fourier coefficients of the solution using Gaussian quadrature rules in the spectral domain. In this paper, we show how a KSS method can be used to obtain not only an approximate numerical solution, but also an approximate analytical solution when using initial conditions that come from pre-bleach steady states and also general initial conditions, to facilitate asymptotic analysis. Analytical and numerical results are presented. It is observed that although KSS methods are explicit, it is possible to use a time step that is far greater than what the CFL condition would indicate.     

Predicting IR personalization performance using pre-retrieval query predictors

Code reviews consist in proof-reading proposed code changes in order to find their shortcomings such as bugs, insufficient test coverage or misused design patterns. Code reviews are conducted before merging submitted changes into the main development branch. The selection of suitable reviewers is crucial to obtain the high quality of reviews. In this article we present a new method of recommending reviewers for code changes. This method is based on profiles of individual programmers. For each developer we maintain his/her profile. It is the multiset of all file path segments from commits reviewed by him/her. It will get updated when he/she presents a new review. We employ a similarity function between such profiles and change proposals to be reviewed. The programmer whose profile matches the change most is recommended to become the reviewer. We performed an experimental comparison of our method against state-of-the-art techniques using four large open-source projects. We obtained improved results in terms of classification metrics (precision, recall and F-measure) and performance (we have lower time and space complexity).     

Linear feature extraction using adaptive least‐squares template matching and a scalable slope edge model

This paper presents a linear feature extraction method. Least squares template matching (LSTM) is adopted as the computational tool to fit the linear features with a scalable slope edge (SSE) model, which is based on an explicit function to define the blurred edge profile. In the SSE model, the magnitude of the grey gradient and the edge scale can be described by three parameters; additionally, the edge position can be obtained strictly by the ‘zero crossing’ location of the profile model. In our method the edge templates are locally and adaptively generated by estimating the three parameters via fitting the image patches with the model, accordingly the linear feature can be positioned with high accuracy by using LSTM. We derived the computational models to rectify straight line and spline curve features and tested those algorithms using the synthetic and real remotely sensed images. The experiments using synthetic images show that the method can position the linear features with the mean geometric error of pixel location of less than one pixel in certain noise levels. Examples of semiautomatic extraction of buildings and linear objects from real imagery are also given and demonstrate the potential of the method.     

A Data Analysis-Based Approach for Detecting Intruders

ABSTRACT

We introduce a novel anomaly intrusion detection method based on Linear Discriminant Analysis (LDA). This approach searches for those vectors in the underlying space that best discriminate among users' profile classes. The discrimination rules are based on linear combinations of the observed users' profiles, called discriminant factors. This new approach provides for the ability to learn and later determine whether a new profile does or does not correspond to those of known users. Unlike many researchers we used realistic data to learn the behaviors of four students' classes. After that we apply LDA to get an appropriate discrimination between the student classes. Thus one can easily determine if a new student is legitimate or not by projecting its profile onto the profile subspace. Simulations show that our approach outperforms both Principal Components Analysis (PCA) and Electre Tri methods.     

Fast optimization of computer-generated holograms using conjugate gradient

Holographic display, as an ideal three-dimensional (3D) display method, can provide a realistic stereo-perception sense, and is expected to be used in the next generation of augmented reality (AR) prototypes without complex optical devices. While holographic 3D displays have been proposed in the past decades, high-quality holographic reconstructed images have always relied on well-designed generation algorithms, and it is still a challenge to obtain an accurate phase profile fast. In this work, we utilize a first-order optimization algorithm to optimize the phase profile using the conjugate gradient direction instead of the steepest gradient direction to update the phase parameters. We validate this proposed method by comparing the current widely used stochastic gradient method and the wirtinger holography method. The simulation and experimental results illustrate the superiority of the proposed method, which can achieve faster optimization with the same quality metrics.     

Information filtering using a probabilistic model

We propose an information filtering system based on a probabilistic model. We make an assumption that a document consists of words which occur according to a probability distribution, and regard a document as a sample drawn according to that distribution. In this article, we adopt a multinomial distribution and represent a document as probability which has random values as the words in the document. When an information filtering system selects information, it uses the similarity between the user's interests (a user profile) and a document. Since our proposed system is constructed under the probabilistic model, the similarity is defined using the Kullback Leibler divergence. To create the user profile, we must optimize the Kullback Leibler divergence. Since the Kullback Leibler divergence is a nonlinear function, we use a genetic algorithm to optimize it. We carry out experiments and confirm effectiveness of the proposed method. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

An automated pottery archival and reconstruction system

Motivated by the current requirements of archaeologists, we are developing an automated archival system for archaeological classification and reconstruction of ceramics. Our system uses the profile of an archaeological fragment, which is the cross‐section of the fragment in the direction of the rotational axis of symmetry, to classify and reconstruct it virtually. Ceramic fragments are recorded automatically by a 3D measurement system based on structured (coded) light. The input data for the estimation of the profile is a set of points produced by the acquisition system. By registering the front and the back views of the fragment the profile is computed and measurements like diameter, area percentage of the complete vessel, height and width are derived automatically. We demonstrate the method and give results on synthetic and real data. Copyright © 2003 John Wiley & Sons, Ltd.     

极化相干层析反演森林垂直结构原理与方法研究

层析技术对于垂直结构信息提取具有独特的优势,已被广泛应用于森林垂直结构参数提取\,混合环境分离等。极化相干层析(Polarization Coherence Tomography,PCT)是一种依靠先验知识基于干涉复相干通过傅立叶-勒让德展开式重建目标垂直结构的层析技术。单基线或双基线PCT技术依然可以获取森林垂直结构剖面,由于基线数量过少,很多学者对这一方法存在质疑。研究目的是分析单基线PCT技术提取森林垂直结构剖面的原理\,方法与目前存在的问题。具体方法如下:①应用森林模拟数据提取不同森林场景平均后向散射能量垂直分布;②采用PCT技术获取模拟场景尺度森林垂直结构剖面(相对反射率垂直分布);③分析平均后向散射能量垂直分布与森林垂直结构剖面的异同;④总结森林垂直结构剖面含义及物理意义。结果表明:PCT技术重建的森林垂直结构剖面能够近似表征平均后向散射能量垂直分布轮廓;不同极化方式其森林垂直结构剖面与平均后向散射能量垂直分布也不同;平均后向散射能量垂直分布包含更多的细节信息。     

The feedrate scheduling of NURBS interpolator for CNC machine tools

This paper proposes an off-line feedrate scheduling method of CNC machines constrained by chord tolerance, acceleration and jerk limitations. The off-line process for curve scanning and feedrate scheduling is realized as a pre-processor, which releases the computational burden in real-time task. The proposed method first scans a non-uniform rational B-spline (NURBS) curve and finds out the crucial points with large curvature (named as critical point) or G⁰ continuity (named as breakpoint). Then, the NURBS curve is divided into several NURBS sub-curves using curve splitting method which guarantees the convergence of predictor–corrector interpolation (PCI) algorithm. The suitable feedrate at critical point is adjusted according to the limits of chord error, centripetal acceleration and jerk, and at breakpoint is adjusted based on the formulation of velocity variation. The feedrate profile corresponding to each NURBS block is constructed according to the block length and the given limits of acceleration and jerk. In addition, feedrate compensation method for short NURBS blocks is performed to make the jerk-limited feedrate profile more continuous and precise. Because the feedrate profile is established in off-line, the calculation of NURBS interpolation is extremely efficient for CNC high-speed machining. Finally, simulations and experiments with two free-form NURBS curves are conducted to verify the feasibility and applicability of the proposed method.     

Strongly Subgame-Consistent Core in Stochastic Games

This paper investigates stochastic games on finite tree graphs. A given n-player normal-form game is defined at each node of a tree. Transition to a next node of the tree is random and depends on the strategy profile realized in a current game. We construct a cooperative solution of the game by maximizing the total expected payoff of the players. The core is used as the solution concept of the cooperative game. We introduce the definition of a strongly subgame-consistent (strongly time-consistent) core. Finally, we suggest a method for designing a cooperative distribution procedure of an imputation from the core that guarantees its strong subgame consistency.     

Computation of optical modes in axisymmetric open cavity resonators

The computation of optical modes inside axisymmetric cavity resonators with a general spatial permittivity profile is a formidable computational task. In order to avoid spurious modes the vector Helmholtz equations are discretised by a mixed finite element approach. We formulate the method for first and second order Nédélec edge and Lagrange nodal elements. We discuss how to accurately compute the element matrices and solve the resulting large sparse complex symmetric eigenvalue problems. We validate our approach by three numerical examples that contain varying material parameters and absorbing boundary conditions (ABC).     

On the influence of the wall shear stress vector form on hemodynamic indicators

Hemodynamic indicators such as the averaged wall shear stress (AWSS) and the oscillatory shear index (OSI) are well established to characterize areas of arterial walls with respect to the formation and progression of aneurysms. Here, we study two different forms for the wall shear stress vector from which AWSS and OSI are computed. One is commonly used as a generalization from the two-dimensional setting, the latter is derived from the full decomposition of the wall traction force given by the Cauchy stress tensor. We compare the influence of both approaches on hemodynamic indicators by numerical simulations under different computational settings. Namely, different (real and artificial) vessel geometries, and the influence of a physiological periodic inflow profile. The blood is modeled either as a Newtonian fluid or as a generalized Newtonian fluid with a shear rate dependent viscosity. Numerical results are obtained by using a stabilized finite element method. We observe profound differences in hemodynamic indicators computed by these two approaches, mainly at critical areas of the arterial wall.

     

基于雷达–扫描器/惯性导航系统的微小型无人机室内组合导航

本文提出一种基于雷达–扫描器/惯性导航系统(radar-scanner/INS)的微小型无人机室内导航方法.为提高算法的实时性,采用基于扩展卡尔曼滤波(EKF)的DC同步定位与构图技术(SLAM)实现定位和构图;在更新状态值的扫描匹配过程中提出启发性逻辑来筛选激光雷达数据,以提高算法对无人机因姿态和高度变化而引起的轮廓地图波动的抗干扰性;在特征匹配的过程中选取合理的地图轮廓特征,并利用扫描匹配的结果和特征匹配的传递性提出了精度较高的引导配对,以提高特征配对在三维环境下的准确性;最后,将DC SLAM与惯性导航系统进行基于EKF的组合滤波,给出无人机的全状态估计.通过与GPS/INS组合导航对比以及室内飞行验证,本文提出的方法能够满足无人机飞行控制对导航实时性和精度的要求.     

Analysis of heuristic validity,efficiency and applicability of the profile distance method for implementation in decision support systems

This article seeks to enhance acceptance of the profile distance method (PDM) in decision support systems. The PDM is a multiple attributive-based decision making as well as a multiple method approach to support complex decision making and uses a heuristic to avoid computationally complex global optimization. We elaborate on the usability of the method and question the heuristic used. We present a bi-section algorithm, which efficiently supports the discovery of transition profiles needed in a user-friendly and practical application of the method. Additionally, we provide empirical evidence showing that the proposed heuristic is efficient and delivers results within 5% of the global optimizer for a wide range of datasets.