Non-Parametric Nonlinear System Identification: A Data-Driven Orthogonal Basis Function Approach

In this paper, a data driven orthogonal basis function approach is proposed for non-parametric FIR nonlinear system identification. The basis functions are not fixed a priori and match the structure of the unknown system automatically. This eliminates the problem of blindly choosing the basis functions without a priori structural information. Further, based on the proposed basis functions, approaches are proposed for model order determination and regressor selection along with their theoretical justifications.      

A Data-Driven Approach to Manage the Length of Stay for Appendectomy Patients

Skyrocketing patient-care costs demand that health-care institutions improve their resource-utilization effectiveness and efficiency. The length of an inpatient's stay has direct significant impacts on patient-care costs, service quality, and outcomes. Despite attempts to manage the length of stay (LOS) for frequently performed surgical procedures (e.g., appendectomies), many service providers cannot achieve the target range allowed by the managed care system. We take a data-driven approach to predict which appendectomy patients will likely have a LOS beyond that reimbursable by the underlying managed care system. We use a support vector machine to construct a generic prediction system and then extend that system by incorporating a resampling or cost-sensitive method to address the imbalanced sample problem. Using 557 appendectomy cases from a tertiary medical center in Taiwan, we examine the effectiveness of the generic prediction system compared with the effectiveness of its extensions. The results suggest the viability of a data-driven approach to manage LOS by enabling service providers to identify in advance those patients who will likely need extended stays. The comparative analyses also show the relative advantages of the oversampling and cost-sensitive methods for addressing the imbalanced sample problem. The findings have important implications for research and practice.     

A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity

Standard simulation in classical mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy,...), whereas the second one consists of models that scientists have extracted from collected, natural or synthetic data. Even if one can be confident on the first type of equations, the second one contains modeling errors. Moreover, this second type of equations remains too particular and often fails in describing new experimental results. The vast majority of existing models lack of generality, and therefore must be constantly adapted or enriched to describe new experimental findings. In this work we propose a new method, able to directly link data to computers in order to perform numerical simulations. These simulations will employ axiomatic, universal laws while minimizing the need of explicit, often phenomenological, models. This technique is based on the use of manifold learning methodologies, that allow to extract the relevant information from large experimental datasets.     

基于软传感器的信息融合

为了利用”凝视视觉几何约束”的信息来确定移动机器人的位置和方位角,就需要解决数据融合的问题。”几何约束”不是来自真实传感器的直接可测量的数据,这种特殊形式的信息不能被直接融合。为此目的,该文提出了一种融合特殊形式信息的新途径,也即利用”软传感器”的方法来融合来自”几何约束”的信息。软传感器的输出与其它真实传感器的输出一起经过扩展信息滤波器最终实现融合。文中最后提供了利用该方法进行移动机器人定位的计算机仿真例子。仿真结果表明了软传感器信息融合方法的可行性和有效性。软传感器可以广泛应用在很多类似的信息融合问题中。     

A Novel Soft Clustering Approach for Gene Expression Data

Gene expression data represents a condition matrix where each row represents the gene and the column shows the condition. Micro array used to detect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messenger RNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with the volume of data containing imprecision and outliers increases the challenges in dealing with them. Clustering methods are hence essential to identify the patterns present in massive gene data. Many techniques involve hierarchical, partitioning, grid based, density based, model based and soft clustering approaches for dealing with the gene expression data. Understanding the gene regulation and other useful information from this data can be possible only through effective clustering algorithms. Though many methods are discussed in the literature, we concentrate on providing a soft clustering approach for analyzing the gene expression data. The population elements are grouped based on the fuzziness principle and a degree of membership is assigned to all the elements. An improved Fuzzy clustering by Local Approximation of Memberships (FLAME) is proposed in this work which overcomes the limitations of the other approaches while dealing with the non-linear relationships and provide better segregation of biological functions.     

Canis: A High-Level Language for Data-Driven Chart Animations

In this paper, we introduce Canis, a high-level domain-specific language that enables declarative specifications of data-driven chart animations. By leveraging data-enriched SVG charts, its grammar of animations can be applied to the charts created by existing chart construction tools. With Canis, designers can select marks from the charts, partition the selected marks into mark units based on data attributes, and apply animation effects to the mark units, with the control of when the effects start. The Canis compiler automatically synthesizes the Lottie animation JSON files [Aira], which can be rendered natively across multiple platforms. To demonstrate Canis’ expressiveness, we present a wide range of chart animations. We also evaluate its scalability by showing the effectiveness of our compiler in reducing the output specification size and comparing its performance on different platforms against D3.     

基于数据驱动方法的汉语文本-可视语音合成

计算机文本-可视语音合成系统(TTVS)可以增强语音的可懂度,并使人机交互界面变得更为友好.给出一个基于数据驱动方法(基于样本方法)的汉语文本-可视语音合成系统,通过将小段视频拼接生成新的可视语音.给出一种构造汉语声韵母视觉混淆树的有效方法,并提出了一个基于视觉混淆树和硬度因子的协同发音模型,模型可用于分析阶段的语料库选取和合成阶段的基元选取.对于拼接边界处两帧图像的明显差别,采用图像变形技术进行平滑并.结合已有的文本-语音合成系统(TTS),实现了一个中文文本视觉语音合成系统.     

A Pixel-Based Framework for Data-Driven Clothing

We propose a novel approach to learning cloth deformation as a function of body pose, recasting the graph-like triangle mesh data structure into image-based data in order to leverage popular and well-developed convolutional neural networks (CNNs) in a two-dimensional Euclidean domain. Then, a three-dimensional animation of clothing is equivalent to a sequence of two-dimensional RGB images driven/choreographed by time dependent joint angles. In order to reduce nonlinearity demands on the neural network, we utilize procedural skinning of the body surface to capture much of the rotation/deformation so that the RGB images only contain textures of displacement offsets from skin to clothing. Notably, we illustrate that our approach does not require accurate unclothed body shapes or robust skinning techniques. Additionally, we discuss how standard image based techniques such as image partitioning for higher resolution can readily be incorporated into our framework.     

A Manifold Learning Approach for Integrated Computational Materials Engineering

Image-based simulation is becoming an appealing technique to homogenize properties of real microstructures of heterogeneous materials. However fast computation techniques are needed to take decisions in a limited time-scale. Techniques based on standard computational homogenization are seriously compromised by the real-time constraint. The combination of model reduction techniques and high performance computing contribute to alleviate such a constraint but the amount of computation remains excessive in many cases. In this paper we consider an alternative route that makes use of techniques traditionally considered for machine learning purposes in order to extract the manifold in which data and fields can be interpolated accurately and in real-time and with minimum amount of online computation. Locallly Linear Embedding is considered in this work for the real-time thermal homogenization of heterogeneous microstructures.     

平行控制: 数据驱动的计算控制方法

本文简述平行控制的理念、概念及基本方法与应用, 主要强调虚实互动的平行扩展方式与同时计算的并行划分方式的不同之处. 平行控制方法是ACP理论在控制领域中的具体应用, 其核心是利用人工系统进行建模和表示、通过计算实验进行分析和评估、最后借助平行执行实现对复杂系统的控制和管理. 这一控制方法是反馈控制, 特别是自适应控制方法向复杂系统问题扩展的自然结果, 是一种迈向数据驱动控制和计算控制的必然且有效途径.     

Apollo:Rapidly Picking the Optimal Cloud Configurations for Big Data Analytics Using a Data-Driven Approach

Big data analytics applications are increasingly deployed on cloud computing infrastructures,and it is still a big challenge to pick the optimal cloud configurations in a cost-effective way.In this paper,we address this problem with a high accuracy and a low overhead.We propose Apollo,a data-driven approach that can rapidly pick the optimal cloud configurations by reusing data from similar workloads.We first classify 12 typical workloads in BigDataBench by characterizing pairwise correlations in our offline benchmarks.When a new workload comes,we run it with several small datasets to rank its key characteristics and get its similar workloads.Based on the rank,we then limit the search space of cloud configurations through a classification mechanism.At last,we leverage a hierarchical regression model to measure which cluster is more suitable and use a local search strategy to pick the optimal cloud configurations in a few extra tests.Our evaluation on 12 typical workloads in HiBench shows that compared with state-of-the-art approaches,Apollo can improve up to 30％ search accuracy,while reducing as much as 50％ overhead for picking the optimal cloud configurations.     

A Soft Computing Approach to Road Classification

Current learning approaches to computer vision have mainly focussed on low-level image processing and object recognition, while tending to ignore high-level processing such as understanding. Here we propose an approach to object recognition that facilitates the transition from recognition to understanding. The proposed approach embraces the synergistic spirit of soft computing, exploiting the global search powers of genetic programming to determine fuzzy probabilistic models. It begins by segmenting the images into regions using standard image processing approaches, which are subsequently classified using a discovered fuzzy Cartesian granule feature classifier. Understanding is made possible through the transparent and succinct nature of the discovered models. The recognition of roads in images is taken as an illustrative problem in the vision domain. The discovered fuzzy models while providing high levels of accuracy (97%), also provide understanding of the problem domain through the transparency of the learnt models. The learning step in the proposed approach is compared with other techniques such as decision trees, naïve Bayes and neural networks using a variety of performance criteria such as accuracy, understandability and efficiency.     

Statistical Mechanics of Online Learning of Drifting Concepts: A Variational Approach

We review the application of statistical mechanics methods to the study of online learning of a drifting concept in the limit of large systems. The model where a feed-forward network learns from examples generated by a time dependent teacher of the same architecture is analyzed. The best possible generalization ability is determined exactly, through the use of a variational method. The constructive variational method also suggests a learning algorithm. It depends, however, on some unavailable quantities, such as the present performance of the student. The construction of estimators for these quantities permits the implementation of a very effective, highly adaptive algorithm. Several other algorithms are also studied for comparison with the optimal bound and the adaptive algorithm, for different types of time evolution of the rule.     

High Performance Regulator for Fractional Order Systems: A Soft Variable Structure Control Approach

In this paper, soft variable structure control of fractional order systems is proposed for achieving high regulation rates and short settling times. Using the appropriate fractional order Lyapunov approach, a controller based on continuous fractional switch are designed to achieve high control performance. Due to the alleviation of chattering phenomenon soft VSC that is proposed for fractional order systems are quite useful in future for the practical systems.     

A Software-Reuse Approach to Build Monitor Programs for Soft Real-Time Applications

Thispaper describes the implementation of an integration frameworkto build real-time monitor programs based on software reuse.A custom-made real-time monitor program can be built very quicklyby integrating existing monitor programs on commercial operatingsystems, where limited real-time system support is provided.We address issues related to software control, output re-processing,data age, and interactivities of program executions. The feasibilityof this framework is demonstrated by the construction of an integrationenvironment and a monitor program built by the environment forsoft real-time applications in the Microsoft Windows operatingsystems. We also measure the incurred system overheads of thisapproach.     

Data-Driven Based Fault Prognosis for Industrial Systems: A Concise Overview

Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial systems. According to the results of fault prognosis, the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance. With the increased complexity and the improved automation level of industrial systems, fault prognosis techniques have become more and more indispensable. Particularly, the data-driven based prognosis approaches, which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data, gain great attention from different industrial sectors. In this context, the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems. Firstly, the characteristics of different prognosis methods are revealed with the data-based ones being highlighted. Moreover, based on the different data characteristics that exist in industrial systems, the corresponding fault prognosis methodologies are illustrated, with emphasis on analyses and comparisons of different prognosis methods. Finally, we reveal the current research trends and look forward to the future challenges in this field. This review is expected to serve as a tutorial and source of references for fault prognosis researchers.      

Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts

With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns, relationships, and knowledge embedded in such large ST datasets. In this survey, we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis. The focus is on outlining various state-of-the-art spatio-temporal data mining techniques, and their applications in various domains. We start with a brief overview of spatio-temporal data and various challenges in analyzing such data, and conclude by listing the current trends and future scopes of research in this multi-disciplinary area. Compared with other relevant surveys, this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives. We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.

     

针穿刺软组织变形有限元仿真

为了提高机器人辅助针穿刺精度,提出一种对软组织变形分析的准静态有限元法.该方法基于针受力下软组织变形机理,将软组织变形的连续动态过程分解成离散的准静态过程;采用重叠单元法在ANSYS中建立了软组织变形的二维和三维准静态有限元模型,并进行了仿真实验.最后通过实验结果验证了该方法的有效性和可行性.     

Onboard：以数据驱动的敏捷软件开发协同工具

Scrum是一种兼顾计划性与灵活性的敏捷开发过程,能让软件开发团队具有快速工作和响应变化的能力.软件开发生命周期中每一个环节都会产生大量的数据,如果能记录下这些数据进行分析,并通过可视化等手段展示和反馈,则能进一步促进团队管理、项目管理,提高开发效率.现有的软件开发管理工具中,项目管理和代码管理往往是相互独立的,这导致了数据的分散和未充分利用.为推广以Scrum为核心、以数据为驱动的敏捷软件开发过程,开发了一款基于云服务的Onboard敏捷软件开发协同工具, 利用代码提交和任务的关联,创造性地将敏捷过程管理、源代码管理和项目管理有机地整合到一起,支持端到端的软件全生命周期管理,从而能记录下软件开发过程中产生的所有数据并提取有价值的信息,为中小软件开发团提供一站式的敏捷开发管理与协同服务.1)介绍了Onboard的设计理念;2)围绕着“如何利用软件开发过程中产生的数据更好地支持敏捷开发过程”和“如何评估团队成员贡献度”两大课题,全面介绍了数据可视化和数据分析在Onboard敏捷软件开发协同工具中的应用,并针对一系列相关问题提出了解决方案;3)对值得进一步研究的问题进行了展望.