A Study on Comprehensive Modeling for Errors in on-line Measuring System of Machining Center

Based on the theory of multi-body system (MBS), bine’s and huston’s methods are applied to an on-line measuring system of machining center in this paper. Through the study on modeling technique, the comprehensive model for errors calculation in an on-line measuring System of machining center have been built for the first time. Using this model, the errors can be compensated by soft.ware and the measuring accuracy can be enhanced without any more inveSt. This model can be used in all kinds of machining center.

Modeling of Microstructure Evolution and Mechanical Properties of Steel Plates Produced by Thermo-Mechanical Control Process and Its On-line Application

An integrated metallurgical model was developed to predict microstructure evolution and mechanical properties of low-carbon steel plates produced by TMCP. The metallurgical phenomena occurring during TMCP and mechanical properties were predicted for different process parameters. In the later passes full recrystallization becomes difficult to occur and higher residual strain remains in austenite after rolling. For the reasonable temperature and cooling schedule, yield strength of 30 mm plain carbon steel plate can reach 310 MPa. The first on-line application of prediction and control of microstructure and properties (PCMP) in the medium plate production was achieved. The predictions of the system are in good agreement with measurements.

Mathematical Model of Dynamic Operation and Optimum Control of Billet Reheating Furnace

This paper provides a mathematical model for the billet reheating process in furnace.A new optimum method is brought up that the objective function is the integral value of enthalpy increasing process of a billet.Different delays are simulated and calculated,some proper delay strategies are ob- tained.The on-line computer control model is de- veloped.The real production conditions simulated, the temperature deviation of drop out billet from the target temperature is kept within±15℃.

Model predictive control with an on-line identification model of a supply chain unit

A model predictive controller was designed in this study for a single supply chain unit.A demand model was described using an autoregressive integrated moving average(ARIMA) model,one that is identified on-line to forecast the future demand.Feedback was used to modify the demand prediction,and profit was chosen as the control objective.To imitate reality,the purchase price was assumed to be a piecewise linear form,whereby the control objective became a nonlinear problem.In addition,a genetic algorithm was introduced to solve the problem.Constraints were put on the predictive inventory to control the inventory fluctuation,that is,the bullwhip effect was controllable.The model predictive control(MPC) method was compared with the order-up-to-level(OUL) method in simulations.The results revealed that using the MPC method can result in more profit and make the bullwhip effect controllable.

Force-reflecting teleoperation of robots based on on-line correction of a virtual model

Virtual reality is an effective method to eliminate the influence of time delay.However,it depends on the precision of the virtual model.In this paper,we introduce a method that corrects the virtual model on-line to establish a more precise model.The geometric errors of the virtual model were corrected on-line by overlapping the graphics over the images and also by syncretizing the position and force information from the remote.Then the sliding average least squares(SALS)method was adopted to determine the mass,damp,and stiffness of the remote environment and use this information to amend the dynamic model of the environment.Experimental results demonstrate that the on-line correction method we proposed can effectively reduce the impact caused by time delay,and improve the operational performance of the teleoperation system.

TOPICS AND TRENDS OF THE ON-LINE PUBLIC CONCERNS BASED ON TIANYA FORUM

Many social events spread fast through the Internet and arouse wide community discussions. Those on-line public opinions emerge into diverse topics along the time. Moreover, the strength of the topics is fluctuating. How to catch both primary topics and trend of topics over the shifting on-line discussions are not only of theoretical importance for scientific research, but also of practical importance for societal management especially in current China. To try the cutting-edge text analytic technologies to deal with unstructured on-line public opinions and provide support for social problem-solving in the big data era is worth an endeavour. This paper applies dynamic topic model （DTM） to explore the changing topics of new posts collected from Tianya Zatan Board of Tianya Club, the most influential Chinese BBS in China's Mainland. By analysis of the hot and cold terms trends, we catch the topics shift of main on-line concerns with illustrations of topics of school bus and environment in December of 2011. An algorithm is proposed to compute the strength fluctuation of each topic. With visualized analysis of the respective main topics in several months of 2012, some patterns of the topics fluctuation on the board are summarized.

在线结构断点估计的非平稳时间序列模型(英文)

Non-stationary time series could be divided into piecewise stationary stochastic signal. However, the number and locations of breakpoints, as well as the approximation function of the respective segment signal are unknown. To solve this problem, a novel on-line structural breaks estimation algorithm based on piecewise autoregressive processes is proposed. In order to find the "best" combination of the number, lengths, and orders of the piecewise autoregressive (AR) processes, the Akaikes Information Criterion (AIC) and Yule-Walker equations are applied to estimate an AR model fit to the data. Numerical results demonstrate that the proposed estimation algorithm is suitable for different data series. Furthermore, the algorithm is used in a clinical study of electroencephalogram (EEG) with satisfactory results, and the ability to deal with real-time data is the most outstanding characteristic of on-line structural breaks estimation algorithm proposed.

STUDY ON THE ON－LINE OPTIMAL CONTROL OF PARAMETERS OF THREE－CONE BLAST HOLE DRILLS

A model recognition method for the on-line optimal control of the parameters ofthree-cone blast drills is developed. It takes a few of on-line measurements and has a rapidoptimization speed. The mathematic model for on-line optimal control of the parameters and thedetermination of the parameters in the model are also presented.

Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context

This paper models the complex simultaneous localization and mapping（SLAM） problem through a very flexible Markov random field and then solves it by using the iterated conditional modes algorithm. Markovian models allow to incorporate： any motion model; any observation model regardless of the type of sensor being chosen; prior information of the map through a map model; maps of diverse natures; sensor fusion weighted according to the accuracy. On the other hand, the iterated conditional modes algorithm is a probabilistic optimizer widely used for image processing which has not yet been used to solve the SLAM problem. This iterative solver has theoretical convergence regardless of the Markov random field chosen to model. Its initialization can be performed on-line and improved by parallel iterations whenever deemed appropriate. It can be used as a post-processing methodology if it is initialized with estimates obtained from another SLAM solver. The applied methodology can be easily implemented in other versions of the SLAM problem, such as the multi-robot version or the SLAM with dynamic environment. Simulations and real experiments show the flexibility and the excellent results of this proposal.

Towards online optimisation of solid oxide fuel cell performance: Combining deep learning with multi-physics simulation

The use of solid oxide fuel cells(SOFCs)is a promising approach towards achieving sustainable electricity pro-duction from fuel.The utilisation of the hydrocarbons and biomass in SOFCs is particularly attractive owing to their wide distribution,high energy density,and low price.The long-term operation of SOFCs using such fuels remains difficult owing to a lack of an effective diagnosis and optimisation system,which requires not only a precise analysis but also a fast response.In this study,we developed a hybrid model for an on-line analysis of SOFCs at the cell level.The model combines a multi-physics simulation(MPS)and deep learning,overcoming the complexity of MPS for a model-based control system,and reducing the cost of building a database(compared with the experiments)for the training of a deep neural network.The maximum temperature gradient and heat generation are two target parameters for an efficient operation of SOFCs.The results show that a precise predic-tion can be achieved from a trained AI algorithm,in which the relative error between the MPS and AI models is less than 1%.Moreover,an online optimisation is realised using a genetic algorithm,achieving the maximum power density within the limitations of the temperature gradient and operating conditions.This method can also be applied to the prediction and optimisation of other non-liner,dynamic systems.