Data Driven Vibration Control:A Review

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

ARCHITECTURE OF DYNAMIC DATA DRIVEN SIMULATION FOR WILDFIRE AND ITS REALIZATION

Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.

Deformation and failure in nanomaterials via a data driven modelling approach

A data driven computational model that accounts for more than two material states has been presented in this work. Presented model can account for multiple state variables, such as stresses,strains, strain rates and failure stress, as compared to previously reported models with two states.Model is used to perform deformation and failure simulations of carbon nanotubes and carbon nanotube/epoxy nanocomposites. The model capability of capturing the strain rate dependent deformation and failure has been demonstrated through predictions against uniaxial test data taken from literature. The predicted results show a good agreement between data set taken from literature and simulations.

Data Driven Customer Segmentation for Vietnamese SMEs in Big Data Era

Almost Vietnamese big businesses often use outsourcing services to do marketing researches such as analysing and evaluating consumer intention and behaviour,customers’satisfaction,customers’loyalty,market share,market segmentation and some similar marketing studies.One of the most favourite marketing research business in Vietnam is ACNielsen and Vietnam big businesses usually plan and adjust marketing activities based on ACNielsen’s report.Belong to the limitation of budget,Vietnamese small and medium enterprises(SMEs)often do marketing researches by themselves.Among the marketing researches activities in SMEs,customer segmentation is conducted by tools such as Excel,Facebook analytics or only by simple design thinking approach to help save costs.However,these tools are no longer suitable for the age of data information explosion today.This article uses case analysing of the United Kingdom online retailer through clustering algorithm on R package.The result proves clustering method’s superiority in customer segmentation compared to the traditional method(SPSS,Excel,Facebook analytics,design thinking)which Vietnamese SMEs are using.More important,this article helps Vietnamese SMEs understand and apply clustering algorithm on R in customer segmenting on their given data set efficiently.On that basis,Vietnamese SMEs can plan marketing programs and drive their actions as contextualizing and/or personalizing their message to their customers suitably.

Data Driven Uncertainty Evaluation for Complex Engineered System Design

Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.

DATA DRIVEN控制方式图象理解系统的结构性能及改进

本文是以图象理解系统实例分析入手,较详尽地论述了采用DATADRIVEN控制方式的线画解释图象理解系统的硬软件结构,并在评估了系统的可靠性基础上,提出了采用数据驱动和模型驱动双向控制的新观点.

Data driven composite shape descriptor design for shape retrieval with a VoR-Tree

We develop a data driven method(probability model) to construct a composite shape descriptor by combining a pair of scale-based shape descriptors. The selection of a pair of scale-based shape descriptors is modeled as the computation of the union of two events, i.e.,retrieving similar shapes by using a single scale-based shape descriptor. The pair of scale-based shape descriptors with the highest probability forms the composite shape descriptor. Given a shape database, the composite shape descriptors for the shapes constitute a planar point set.A VoR-Tree of the planar point set is then used as an indexing structure for efficient query operation. Experiments and comparisons show the effectiveness and efficiency of the proposed composite shape descriptor.

Theoretical Mechanisms,Practice Foundations,and Policy Options for Big Data Driven High-quality Economic Growth in China

In the transition of China’s economy from high-speed growth to high-quality growth in the new era,economic practices are oriented to fostering new growth drivers,developing new industries,and forming new models.Based on the data flow,big data effectively integrates technology,material,fund,and human resource flows and reveals new paths for the development of new growth drivers,new industries and new models.Adopting an analytical framework with"macro-meso-micro"levels,this paper elaborates on the theoretical mechanisms by which big data drives high-quality growth through efficiency improvements,upgrades of industrial structures,and business model innovations.It also explores the practical foundations for big data driven high-quality growth including technological advancements of big data,the development of big data industries,and the formulation of big data strategies.Finally,this paper proposes policy options for big data promoting high-quality growth in terms of developing digital economy,consolidating the infrastructure construction of big data,expediting convergence of big data and the real economy,advocating for a big data culture,and expanding financing options for big data.

Data-driven modeling on anisotropic mechanical behavior of brain tissue with internal pressure

Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.

Full field reservoir modeling of shale assets using advanced data-driven analytics

Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism（sorption process and flow behavior in complex fracture systems- induced or natural） leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called ＂hard data＂ directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The ＂hard data＂ refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of ＂soft data＂（non-measured, interpretive data such as frac length, width,height and conductivity） in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.

An online data driven actor-critic-disturbance guidance law for missile-target interception with input constraints

In this article,we develop an online robust actor-critic-disturbance guidance law for a missile-target interception system with limited normal acceleration capability.Firstly,the missiletarget engagement is formulated as a zero-sum pursuit-evasion game problem.The key is to seek the saddle point solution of the Hamilton Jacobi Isaacs(HJI)equation,which is generally intractable due to the nonlinearity of the problem.Then,based on the universal approximation capability of Neural Networks(NNs),we construct the critic NN,the actor NN and the disturbance NN,respectively.The Bellman error is adjusted by the normalized-least square method.The proposed scheme is proved to be Uniformly Ultimately Bounded(UUB)stable by Lyapunov method.Finally,the effectiveness and robustness of the developed method are illustrated through numerical simulations against different types of non-stationary targets and initial conditions.

Vision for energy material design:A roadmap for integrated data-driven modeling

The application scope and future development directions of machine learning models(supervised learning, transfer learning, and unsupervised learning) that have driven energy material design are discussed.

Data driven models for compressive strength prediction of concrete at high temperatures

The use of data driven models has been shown to be useful for simulating complex engineering processes,when the only information available consists of the data of the process.In this study,four data-driven models,namely multiple linear regression,artificial neural network,adaptive neural fuzzy inference system,and K nearest neighbor models based on collection of 207 laboratory tests,are investigated for compressive strength prediction of concrete at high temperature.In addition for each model,two different sets of input variables are examined:a complete set and a parsimonious set of involved variables.The results obtained are compared with each other and also to the equations of NIST Technical Note standard and demonstrate the suitability of using the data driven models to predict the compressive strength at high temperature.In addition,the results show employing the parsimonious set of input variables is sufficient for the data driven models to make satisfactory results.

Elastoplastic constitutive modeling under the complex loading driven by GRU and small-amount data

In this paper,a data-driven method to model the three-dimensional engineering structure under the cyclic load with the one-dimensional stress-strain data is proposed.In this method,one-dimensional stress-strain data obtained under uniaxial load and different loading history is learned offline by gate recurrent unit(GRU)network.The learned constitutive model is embedded into the general finite element framework through data expansion from one dimension to three dimensions,which can perform stress updates under the three-dimensional setting.The proposed method is then adopted to drive numerical solutions of boundary value problems for engineering structures.Compared with direct numerical simulations using the J2 plasticity model,the stress-strain response of beam structure with elastoplastic materials under forward loading,reverse loading and cyclic loading were predicted accurately.Loading path dependent response of structure was captured and the effectiveness of the proposed method is verified.The shortcomings of the proposed method are also discussed.

Data Driven Model-Free Adaptive Control Method for Quadrotor Trajectory Tracking Based on Improved Sliding Mode Algorithm

In order to solve the problems of dynamic modeling and complicated parameters identification of trajectory tracking control of the quadrotor,a data driven model-free adaptive control method based on the improved sliding mode control(ISMC)algorithm is designed,which does not depend on the precise dynamic model of the quadrotor.The design of the general sliding mode control(SMC)algorithm depends on the mathematical model of the quadrotor and has chattering problems.In this paper,according to the dynamic characteristics of the quadrotor,an adaptive update law is introduced and a saturation function is used to improve the SMC.The proposed control strategy has an inner and an outer loop control structures.The outer loop position control provides the required reference attitude angle for the inner loop.The inner loop attitude control ensures rapid convergence of the attitude angle.The effectiveness and feasibility of the algorithm are verified by mathematical simulation.The mathematical simulation results show that the designed model-free adaptive control method of the quadrotor is effective,and it can effectively realize the trajectory tracking control of the quadrotor.The design of the controller does not depend on the kinematic and dynamic models of the unmanned aerial vehicle(UAV),and has high control accuracy,stability,and robustness.

Data driven prediction of oil reservoir fluid properties

Accuracy of the fluid property data plays an absolutely pivotal role in the reservoir computational processes.Reliable data can be obtained through various experimental methods,but these methods are very expensive and time consuming.Alternative methods are numerical models.These methods used measured experimental data to develop a representative model for predicting desired parameters.In this study,to predict saturation pressure,oil formation volume factor,and solution gas oil ratio,several Artificial Intelligent(AI)models were developed.582 reported data sets were used as data bank that covers a wide range of fluid properties.Accuracy and reliability of the model was examined by some statistical parameters such as correlation coefficient(R2),average absolute relative deviation(AARD),and root mean square error(RMSE).The results illustrated good accordance between predicted data and target values.The model was also compared with previous works and developed empirical correlations which indicated that it is more reliable than all compared models and correlations.At the end,relevancy factor was calculated for each input parameters to illustrate the impact of different parameters on the predicted values.Relevancy factor showed that in these models,solution gas oil ratio has greatest impact on both saturation pressure and oil formation volume factor.In the other hand,saturation pressure has greatest effect on solution gas oil ratio.

Design and Application of Cloud Test Platform for Launch Vehicle Electrical System Based on Data-Driven Approach

In order to improve efficiency of the integrated test of a launch vehicle electrical system while meeting the requirement of high-density,a cloud test platform for the electrical system was designed based on a data-driven approach,using secure private cloud technology and virtualization technology.The platform has a general hardware and software architecture,which integrates the functions of graphical editing,automated testing,data processing,fault diagnosis and so on.It can realize multi-task parallel testing.Compared with the traditional test mode,the platform has obvious advantages on testing eficiency and effectiveness.

Data-driven simulation in fluids animation: A survey

The field of fluid simulation is developing rapidly,and data-driven methods provide many frameworks and techniques for fluid simulation.This paper presents a survey of data-driven methods used in fluid simulation in computer graphics in recent years.First,we provide a brief introduction of physical based fluid simulation methods based on their spatial discretization,including Lagrangian,Eulerian,and hybrid methods.The characteristics of these underlying structures and their inherent connection with data driven methodologies are then analyzed.Subsequently,we review studies pertaining to a wide range of applications,including data-driven solvers,detail enhancement,animation synthesis,fluid control,and differentiable simulation.Finally,we discuss some related issues and potential directions in data-driven fluid simulation.We conclude that the fluid simulation combined with data-driven methods has some advantages,such as higher simulation efficiency,rich details and different pattern styles,compared with traditional methods under the same parameters.It can be seen that the data-driven fluid simulation is feasible and has broad prospects.