Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance

This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.

Recent Progress on Data-Based Optimization for MineralProcessing Plants

In the globalized market environment, increasingly significant economic and environmental factors withincomplex industrial plants impose importance on the optimization of global production indices; such opti-mization includes improvements in production efficiency, product quality, and yield, along with reductionsof energy and resource usage. This paper briefly overviews recent progress in data-driven hybrid intelli-gence optimization methods and technologies in improving the performance of global production indicesin mineral processing. First, we provide the problem description. Next, we summarize recent progress indata-based optimization for mineral processing plants. This optimization consists of four layers： optimiza-tion of the target values for monthly global production indices, optimization of the target values for dailyglobal production indices, optimization of the target values for operational indices, and automation systemsfor unit processes. We briefly overview recent progress in each of the different layers. Finally, we point outopportunities for future works in data-based optimization for mineral processing plants.

Data-Based Optimal Tracking of Autonomous Nonlinear Switching Systems

In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance function.First,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled data.The linear-in-parameter(LIP)neural network is taken as the value function approximator.Considering the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions.Moreover,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is derived.Finally,the effectiveness of the algorithm is demonstrated with three simulation examples.

The COVID-19 and the“European Solution”of Data-based Approach to Governance:Public Security or Data Protection?

To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.

Data-based intelligent modeling and control for nonlinear systems

With the ever increasing complexity of industrial systems,model-based control has encountered difficulties and is facing problems,while the interest in data-based control has been booming.This paper gives an overview of data-based control,which divides it into two subfields,intelligent modeling and direct controller design.In the two subfields,some important methods concerning data-based control are intensively investigated.Within the framework of data-based modeling,main modeling technologies and control strategies are discussed,and then fundamental concepts and various algorithms are presented for the design of a data-based controller.Finally,some remaining challenges are suggested.

Realization of the Function of Basic Rights to Balance Society--taking personal Information Rights as an example

The main function of basic rights is to defend against the state’s public power.the traditional theory of basic rights con-structs logic based on the dualistic framework of“state power and individual rights,”and deals with the dualistic horizontal relationship between“the state and individuals.”However,the increasing com-plexity of modern society has led to the emergence of different inter-ests and needs within society and the formation of new social powers,resulting in an unequal relationship between private subjects.In the digital era,this phenomenon has become particularly prominent,ev-idenced by the rise of data-based power and the frequent and serious intrusion of personal information by private subjects.In this context,the value of basic rights should radiate within society and function as a balancing force.taking the protection of personal information as an example,while innovating the idea of regulating society through basic rights,the state’s obligation to protect personal information should be further strengthened.Meanwhile,the direct effect of personal infor-mation rights,which are basic human rights,on the private subjects of data-based power should be established,so as to realize their function in balancing the interests of all parties in society.

Digital Servo Control of a Robotic Excavator

An electro-hydraulic control system is designed and implemented for a robotic excavator known as the Lancaster University Computerised and Intelligent Excavator （LUCIE）. The excavator is being developed to autonomously dig trenches without human intervention. Since the behavior of the excavator arm is dominated by the nonlinear dynamics of the hydraulic actuators and by the large and unpredictable external disturbances when digging, it is difficult to provide adequate accurate, quick and smooth movement under traditional control methodology, e.g., PI/PID, which is comparable with that of an average human operator. The data-based dynamic models are developed utilizing the simplified refined instrumental variable （SRIV） identification algorithm to precisely describe the nonlinear dynamical behaviour of the electro-hydraulic actuation system. Based on data-based model and proportional-integral-plus （PIP） methodology, which is a non-minimal state space method of control system design based on the true digital control （TDC） system design philosophy, a novel control system is introduced to drive the excavator arm accurately, quickly and smoothly along the desired path. The performance of simulation and field tests which drive the bucket along straight lines both demonstrate the feasibility and validity of the proposed control scheme.

Optimization and statistical analysis of Au-ZnO/Al_2O_3 catalyst for CO oxidation

In our former work [Catal. Today 174 （2011） 127], 12 heterogeneous catalysts were screened for CO oxidation, and Au-ZnO/Al2O3 was chosen and optimized in terms of weight loadings of Au and ZnO. The present study follows on to consider the impact of process parameters （catalyst preparation and reaction conditions）, in conjunction with catalyst composition （weight loadings of Au and ZnO, and the total weight of the catalyst）, as the optimization of the process parameters simultaneously optimized the catalyst composition. The optimization target is the reactivity of this important reaction. These factors were first optimized using response surface methodology （RSM） with 25 experiments, to obtain the optimum： 100 mg of 1.0%Au-4.1%ZnO/Al2O3 catalyst with 220℃ calcination and 100℃ reduction. After optimization, the main effects and interactions of these five factors were studied using statistical sensitivity analysis （SA）. Certain observations from SA were verified by reaction mechanism, reactivity test and/or characterization techniques, while others need further investigation.

Web Data Cube Construction in Multidimensional On-line Analytical Processing Environment

This paper investigates how to integrate Web data into a multidimensional data warehouse （cube） for comprehensive on-line analytical processing （OLAP） and decision making. An approach for Web data-based cube construction is proposed, which includes Web data modeling based on MIX （ Metadam based Integration model for data X-change ）, generic and specific mapping rules design, and a transformation algorithm for mapping Web data to a multidimensional array. Besides, the structure and implementation of the prototype of a Web data base cube are discussed.

The IRIS Development Platform and Proposed Object-Oriented Data Base

Various code development platforms, such as the ATHENA Framework [1] of the ATLAS [2] experiment encounter lengthy compilation/linking times. To augment this situation, the IRIS Development Platform was built as a software development framework acting as compiler, cross-project linker and data fetcher, which allow hot-swaps in order to compare various versions of software under test. The flexibility fostered by IRIS allowed modular exchange of software libraries among developers, making it a powerful development tool. The IRIS platform used input data ROOT-ntuples [3];however a new data model is sought, in line with the facilities offered by IRIS. The schematic of a possible new data structuring—as a user implemented object oriented data base, is presented.

中国学术会议文献数据库主题标引的实践

本文介绍了中国学术会议文献数据库的特点和主题标引的基本原则。该库是一种按文献类型收集的包含各学科专业的综合性文献数据库。该库在建成之后,不仅提供计算机检索服务,还可提供计算机编排《中国学术会议文献通报》及主题索引。文中认为会议文献除按照《汉语主题词表标引手册》一书中规定的标引规则进行标引外,还需根据会议文献的不同类型,采用不同的模式标引。并例举了论述某项生产发展问题的综述性论文,论述具体工艺和技术的专题性论文,以及文摘集等各种类型的标引实例,同时进行了分析比较。最后对做好标引工作提出了建议。

外篇:日本的漫画发展逻辑——日本同人志、Data-base消费论对独立漫画的启示

在漫画市场极度发达的日本,与动漫画相关的书本大致分为两类,一种是"商业志",就是一般在市场流通、在书店能够买得到的、通常有国际书号的出版物,另一种是"同人志",泛指一些同好们按照版权原作,进行二次创作,自费出版,在同人志贩卖会交易的作品。因此,日本的分类与概念,重点不在销售量以及销售量决定的主流或非主流,反而是销售渠道;无关"商业市场逻辑"与"独立文化"的对立,反而是消费模式的变迁。在日本语境,"独立漫画"勉强可翻译成"次文化漫画"(sabu-karu komikkuサブカルミック),其实也是商业出版。所以备受漫画学者关注的是第二种现象,同人志的出现。

Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines

Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.

Virtual sensing method for monitoring vibration of continuously variable configuration structures using long short-term memory networks

Vibration monitoring by virtual sensing methods has been well developed for linear timeinvariant structures with limited sensors.However,few methods are proposed for Time-Varying(TV)structures which are inevitable in aerospace engineering.The core of vibration monitoring for TV structures is to describe the TV structural dynamic characteristics with accuracy and efficiency.This paper propose a new method using the Long Short-Term Memory(LSTM)networks for Continuously Variable Configuration Structures(CVCSs),which is an important subclass of TV structures.The configuration parameters are used to represent the time-varying dynamic characteristics by the‘‘freezing"method.The relationship between TV dynamic characteristics and vibration responses is established by LSTM,and can be generalized to estimate the responses with unknown TV processes benefiting from the time translation invariance of LSTM.A numerical example and a liquid-filled pipe experiment are used to test the performance of the proposed method.The results demonstrate that the proposed method can accurately estimate the unmeasured responses for CVCSs to reveal the actual characteristics in time-domain and modal-domain.Besides,the average one-step estimation time of responses is less than the sampling interval.Thus,the proposed method is promising to on-line estimate the important responses of TV structures.

Power quality investigation of a solar PV transformer-less grid-connected system fed DVR

This paper presents a single stage transformerless grid-connected solar photovoltaic （PV） system with an active and reactive power control. In the absence of active input power, the grid-tied voltage source converter （VSC） is operated in a reactive power generation mode, which powers the control circuitry, and maintains a regulated DC voltage to the VSC. A data-based maximum power point tracking （MPPT） control scheme which performs power quality control at a maximum power by reducing the total harmonic distortion （THD） in grid injected current as per IEEE-519/1547 standards is implemented. A proportionalintegral （P1） controller based dynamic voltage restorer （DVR） control scheme is implemented which controls the grid side converter during single-phase to ground fault. The analysis includes the grid current THD along with the corresponding variation of the active and reactive power during the fault condition. The MPPT tracks the actual variable DC link voltage while deriving the maximum power from the solar PV array, and maintains the DC link voltage constant by changing the modulation index of the VSC. Simulation results using Matlab/Simulink are presented to demonstrate the feasibility and validations of the proposed novel MPPT and DVR control systems under different environmental conditions.

Machine learning modeling for proton exchange membrane fuel cell performance

Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, various machine learning methods are used to develop data-based models for PEMFC performance attributes and internal states. Techniques such as Artificial Neural Network (ANN) and Support Vector Machine Regressor (SVR) are used to predict the cell voltage, membrane resistance, and membrane hydration level for various operating conditions. Varying input features such as cell current, temperature, reactant pressures, and humidity are introduced to evaluate the accuracy of the model, especially under extreme conditions. Two different sets of data are considered in this study, which are acquired from, a physics-based semiempirical model and a 1-D reduced-dimension Computational Fluid Dynamics model, respectively. The aspect of data preprocessing and hyperparameter tuning procedures are investigated that are extensively used to calibrate the artificial neural network layers and support vector regressor to predict the fuel cell attributes. ANN clearly shows an advantage in comparison with SVR, especially on a multivariable output regression. However, the SVR is advantageous to model simple regressions as it greatly reduces the level of computation without sacrificing accuracy. Data-based models for PEMFC are successfully developed on both the data sets by adapting advanced modeling techniques and calibration procedures such as ANN incorporating the dropout technique, resulting in an R2 ≥ 0.99 for all the predicted variables, demonstrating the ability to build accurate data-based models solely on data from validated physics-based models, reducing the dependency on extensive experimentation.