Lumiproxy: A Hybrid Representation of Image-Based Models

In this paper,we present a hybrid representation of image-based models combining the textured planes and the hierarchical points.Taking a set of depth images as input,our method starts from classifying input pixels into two categories,indicating the planar and non-planar surfaces respectively.For the planar surfaces,the geometric coefficients are reconstructed to form the uniformly sampled textures.For nearly planar surfaces,some textured planes,called lumiproxies, are constructed to represent the equiva...     

大规模三维云实时模拟方法

针对虚拟环境中大规模三维云渲染开销大的问题,提出一种大规模三维云实时模拟方法.在云建模方面,利用Navier-Stokes流体力学公式模拟云的动态生成,提出一种基于八叉树的模型化简策略,减少了云模型粒子数;在渲染阶段,提出一种基于Cell的绘制更新策略,结合Impostor技术自动混合绘制三维云与Impostor,实现了大规模三维云的实时模拟.实验结果表明,该方法基于物理的方法模拟云,同时在绘制阶段根据视点的移动实时更新,效果逼真;与同类方法相比,基于Cell的绘制策略更新时计算量更小,有效地避免了绘制更新时常见的抖动和跳变问题.     

一种三维实体模型的离散表示方法

在计算机中,传统的三维几何模型只能描述表面数据,要描述模型内部属性时,这种模型显得无能为力。体图形学(VolumeGraphics,简称VG)是计算机图形学中新发展起来的一个重要分支。三维模型的体素表示是体图形学中的基本方法,体素化是体图形学中一个不可缺少的前处理过程,其任务是把物体的表面几何形式表示离散成最接近该物体的体素表示形式,产生体数据集。该文将基于Z-buffer扩展的深度缓存原理扩展到x,y,z三个方向上,实现了复杂三维网格模型的体素化表示。实验表明该算法具有很高的执行效率,并且算法的复杂度不依赖于模型的复杂度。     

自适应于局部几何特征的三维模型水印算法

文章针对三维模型提出了一种稳健的三角网格数字水印算法,通过修改从模型中心到各顶点的向量长度来嵌入数字水印。该算法具有如下特点:以全局几何特征作为嵌入对象;将一比特的水印信息分布到整个模型之中;水印的嵌入强度自适应于模型的局部特征。实验结果表明该水印算法能抵抗网格简化攻击,噪声攻击,剪切攻击,以及它们的混合攻击,具有很高的稳健性。     

FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

In this paper, we introduce a new algorithm for incremental learning of a specific form of Takagi–Sugeno fuzzy systems proposed by Wang and Mendel in 1992. The new data-driven online learning approach includes not only the adaptation of linear parameters appearing in the rule consequents, but also the incremental learning of premise parameters appearing in the membership functions (fuzzy sets), together with a rule learning strategy in sample mode. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The modifications include an automatic generation of new clusters based on the nature, distribution, and quality of new data and an alternative strategy for selecting the winning cluster (rule) in each incremental learning step. Antecedent and consequent learning are connected in a stable manner, meaning that a convergence toward the optimal parameter set in the least-squares sense can be achieved. An evaluation and a comparison to conventional batch methods based on static and dynamic process models are presented for high-dimensional data recorded at engine test benches and at rolling mills. For the latter, the obtained data-driven fuzzy models are even compared with an analytical physical model. Furthermore, a comparison with other evolving fuzzy systems approaches is carried out based on nonlinear dynamic system identification tasks and a three-input nonlinear function approximation example.      

Digital Preservation in Grids and Clouds: A Middleware Approach

Digital preservation is the persistent archiving of digital assets for future access and reuse, irrespective of the underlying platform and software solutions. Existing preservation systems have a strong focus on Grids, but the advent of cloud technologies offers an attractive option. We describe a middleware system that enables a flexible choice between a Grid and a cloud for ad-hoc computations that arise during the execution of a preservation workflow and also for archiving digital objects. The choice between different infrastructures remains open during the lifecycle of the archive, ensuring a smooth switch between different solutions to accommodate the changing requirements of the organization that needs its digital assets preserved. We also offer insights on the costs, running times, and organizational issues of cloud computing, proving that the cloud alternative is particularly attractive for smaller organizations without access to a Grid or with limited IT infrastructure.     

基于混杂系统方法的一类采样数据系统鲁棒故障检测

针对具有连续时间过程噪声和离散时间测量噪声的采样数据系统, 提出了一种新的鲁棒故障检测直接设计方法. 首先利用具有有限跳变的线性系统作为残差产生器, 采样数据系统的鲁棒故障检测设计问题被描述成采样数据滤波问题, 然后给出有限跳变线性系统有界实引理的线性矩阵不等式(LMI)表达形式, 基于此, 推导出采样数据系统鲁棒故障检测滤波器的存在条件及设计参数, 并将所提方法推广到具有结构不确定性的采样数据系统上. 所设计的滤波器能够保证残差与故障之间误差最小, 并对过程噪声、测量噪声、结构不确定性等因素鲁棒. 最后, 通过数值仿真对所提方法的可行性进行了验证.     

Deep Multimodal Fusion: A Hybrid Approach

We propose a novel hybrid model that exploits the strength of discriminative classifiers along with the representation power of generative models. Our focus is on detecting multimodal events in time varying sequences as well as generating missing data in any of the modalities. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative space which allows for data generation and joint feature representation that discriminative models lack. We propose a new model that jointly optimizes the representation space using a hybrid energy function. We employ a Restricted Boltzmann Machines (RBMs) based model to learn a shared representation across multiple modalities with time varying data. The Conditional RBMs (CRBMs) is an extension of the RBM model that takes into account short term temporal phenomena. The hybrid model involves augmenting CRBMs with a discriminative component for classification. For these purposes we propose a novel Multimodal Discriminative CRBMs (MMDCRBMs) model. First, we train the MMDCRBMs model using labeled data by training each modality, followed by training a fusion layer. Second, we exploit the generative capability of MMDCRBMs to activate the trained model so as to generate the lower-level data corresponding to the specific label that closely matches the actual input data. We evaluate our approach on ChaLearn dataset, audio-mocap, as well as the Tower Game dataset, mocap-mocap as well as three multimodal toy datasets. We report classification accuracy, generation accuracy, and localization accuracy and demonstrate its superiority compared to the state-of-the-art methods.     

Solving Rational Expectations Models with Informational Subperiods: A Perturbation Approach

This paper presents an algorithm to solve up to the second order of approximation rational expectations models with informational subperiods, and provides simple examples to demonstrate how the algorithm works.     

A Multiobjective Evolutionary-Simplex Hybrid Approach for the Optimization of Differential Equation Models of Gene Networks

This paper describes genetic and hybrid approaches for multiobjective optimization using a numerical measure called fuzzy dominance. Fuzzy dominance is used when implementing tournament selection within the genetic algorithm (GA). In the hybrid version, it is also used to carry out a Nelder-Mead simplex-based local search. The proposed GA is shown to perform better than NSGA-II and SPEA-2 on standard benchmarks, as well as for the optimization of a genetic model for flowering time control in rice. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks. The hybrid version also compares well with ParEGO on a few other benchmarks. The proposed hybrid algorithm is then applied to estimate the parameters of an elaborate gene network model of flowering time control in Arabidopsis. Overall solution quality is quite good by biological standards. Tradeoffs are discussed between accuracy in gene activity levels versus in the plant traits that they influence. These tradeoffs suggest that data mining the Pareto front may be useful in bioinformatics.     

一种混合智能的Interbay系统调度方法

为提高晶圆制造Interbay物料运输系统的多目标调度性能,提出一种混合智能的Interbay系统调度方法。采用Takagi-Sugeno模糊逻辑方法对晶圆卡运输成本模型的参数权重进行自适应调节,用匈牙利方法对Interbay系统的运输小车和晶圆工件进行优化指派。实验结果表明,该方法在Interbay系统和晶圆加工系统的多目标综合性能方面优于传统方法。     

Robust Output Feedback Model Predictive Control: A Stochastic Approach

This paper addresses the robust explicit model predictive control scheme for linear systems with input and output constraint in the presence of disturbances and noise. Conditions for disturbance rejection are established by incorporating a full state/disturbance observer. The separation principle is applied to design an optimal observer in the unconstrained problem. Then, an efficient algorithm is developed to explicitly design observer gains by minimizing a quadratic performance criterion. It is shown that the solution includes a set of regions with piecewise affine functions of the state and reference vectors and a set of regions with optimal observers. In the proposed method, two sets of partitions associated with the control law and the observer gains are obtained. Therefore, the online computation includes finding the active regions of both observer and control law partitions in which the current state is located. The proposed technique is particularly attractive for a wide range of practical problems where the exact model of the actual system is not available.     

Evacuation Transportation Planning Under Uncertainty: A Robust Optimization Approach

This paper considers evacuation via surface transportation networks in an uncertain environment. We focus on demand uncertainty which can lead to significant infeasibility cost during evacuation, where loss of life or property may appear. We develop a robust linear programming model based on a robust optimization approach where hard constraints are guaranteed within an appropriate uncertainty set. The robust counterpart solutions have been shown tractable. We show that the robustness in evacuation is important and a robust solution outperforms a nominal deterministic solution in both quality and feasibility.     

Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach

We have developed an easy-to-use and cost-effective system to construct textured 3D animated face models from videos with minimal user interaction. This is a particularly challenging task for faces due to a lack of prominent textures. We develop a robust system by following a model-based approach: we make full use of generic knowledge of faces in head motion determination, head tracking, model fitting, and multiple-view bundle adjustment. Our system first takes, with an ordinary video camera, images of a face of a person sitting in front of the camera turning their head from one side to the other. After five manual clicks on two images to indicate the position of the eye corners, nose tip and mouth corners, the system automatically generates a realistic looking 3D human head model that can be animated immediately (different poses, facial expressions and talking). A user, with a PC and a video camera, can use our system to generate his/her face model in a few minutes. The face model can then be imported in his/her favorite game, and the user sees themselves and their friends take part in the game they are playing. We have demonstrated the system on a laptop computer live at many events, and constructed face models for hundreds of people. It works robustly under various environment settings.     

Nationwide Freight Generation Models: A Spatial Regression Approach

This paper investigates the application of linear regression models and modeling techniques in predicting freight generation at the national level within the U.S. Specifically, the paper seeks to improve the performance and fit of linear regression models of freight generation. We provide insight into different variable transformation techniques, evaluate the use of spatial regression variables, and apply a spatial regression modeling methodology to correct for spatial autocorrelation. We conclude that the spatial regression model is the preferred specification for freight generation at the national level. The proliferation of Geographic Information Systems (GIS) within planning agencies affords more widespread use of spatial regression and our results indicate this technique would provide improvement to models that have been traditionally limited by insufficient data.     

A Robust Approach for Structure from Planar Motion by Stereo Image Sequences

This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions.