Non-Recursive Base Conversion Using a Deterministic Markov Process

We prove that non-recursive base conversion can always be implemented by using a deterministic Markov process. Our paper discusses the pros and cons of recursive and non-recursive methods, in general. And we include a comparison between non-recursion and a deterministic Markov process, proving that the Markov process is twice as efficient.

Remaining useful life estimation based on Wiener degradation processes with random failure threshold

Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold(RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization(EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

A FUZZY MATCH BASED EXPERT SYSTEM FOR SYNTHESIS OF SEPARATION PROCESSES

A predictive parallel search algorithm,the fuzzy match inference strategy,is implemented ina prototype expert system.Selection of separation technologies and sequencing of separators are beingapproached in an integrated manner.The fuzzy match mechanism results in a relatively smaller subsetof favored schemes,constituting a hyperstructure for further quantitative evaluation and combinationoptimization.An industrial application example of aromatics extraction separation is presented.

Analyzing and Designing Business Processes Management System Based on Transformation of Instantiated Ontology’s State

The paper discusses efforts of finding a simple and transparent method to analyze business processes and developing its management system architecture. By introducing ontology and its state, the approach tried to find a unified representation and a flexible choreography of business processes. The main idea of the paper is the transformation of ontology＇s states, which are the most important scenarios of enterprises. The business activity composition, that is, case composition based on an AI technique, Case Based Reason （CBR）, which is to solve new problems by retrieving solutions to previous problems, and then store the modified solution. The main interest in CBR relies on that it allows a system to avoid past failures and exploit past successes.

Variable selection-based SPC procedures for high-dimensional multistage processes

Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift.

Analysis of sea level changes in the Caspian Sea related to Cosmo-geophysical processes based on satellite and terrestrial data

Analysis results of the average annual sea levels in the Caspian Sea obtained from ground and satellite observations, corresponding to solar activity characteristics, magnetic field data, and length of day are presented. Spectra of the indicated processes were investigated and their approximation models were also built. Previously assumed statistical relationships between space-geophysical processes and Caspian Sea level(CSL) changes were confirmed. A close connection was revealed between the low-frequency models of the solar and geomagnetic activity parameters and the CSL changes. Predictions extending into the next decades showed a high probability of an increase in the CSL and a decrease of the compared space-geophysical parameters.

基于Process-Based LCA方法的我国装配式住宅物化阶段碳排放计算模型研究

以沈阳的装配式住宅建设项目为案例工程,对预制装配生产建造方式的节能效益和碳排放进行研究。将装配式住宅物化阶段碳排放作为研究对象进行量化计算,分析了物化阶段碳排放路径及碳排放源,确定了碳排放的计算边界,通过基于过程评价法(Process-based LCA)确定了装配式住宅物化阶段的碳排放量化计算流程与计算方法,建立了装配式住宅物化阶段碳排放计算模型。依据物化阶段的过程划分进行了数据收集,结合IPCC等机构发布的碳排放因子数据,计算得出案例装配式住宅物化阶段的碳排放量,并与传统住宅物化阶段碳排放量的计算结果进行对比分析。计算模型可为我国装配式住宅的环境性能评价提供相关的数据和技术支持。

Effect of Al_(3)Zr particles on hot-compression behavior and processing map for Al-Cu-Li based alloys at elevated temperatures

The influence of Al3Zr particles on the hot compression behavior and processing map(PM)of Al-Cu-Li based alloys under isothermal plane-strain compression in the temperature range of 400-500℃and at the strain rates of 0.01-10 s^-1 was investigated.The corresponding microstructure was analyzed using optical microscopy(OM),electron back-scattered diffraction(EBSD)and transmission electron microscope(TEM).The results showed that dynamic recovery(DRV)played a greater role than dynamic recrystallization(DRX)in dynamic softening.At low temperatures,the Al3Zr particles were the significant barriers to inhibit DRV and DRX grain growth.When the temperature reached 500℃,the Al3Zr particles readily spread along grain boundaries just like a necklace due to the dissolution of Al3Zr particles and rapid diffusion of Zr through grain boundary,resulting in generating the macroscopic cracks and forming an instability domain at 490-500℃,0.01 s^-1 in the PM.

Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.

Spray Atomized and Codeposited Al-Li Based Metal-matrix Composites Processing and Properties

In spray atomization and codeposition, a molten stream of metal is disintegrated into a fine dispersion of droplets by high velocity gas jets. The resulting semi-solidified droplets are directed towards a substrate where they impact and collect as rapidly solidified splats. Relatively high rates of solidification are achieved as a result of the thinness of the splats and the rapid heat extraction during flight and upon impacting with the substrate. The processing method uses codeposition of the metallic semi-solidified droplets (metallic matrix) with the injected reinforcement ceramic particles. In the present paper, the microstructures, mechanical properties, interfacial properties, thermal stability and aging behaviour of spray atomized and codeposited Al-Li-X MMC's (injected X=SiC, Al2O3) are reported and correlated to the processing conditions.

A Model for the Design Process Gene Based on the Extensible Basic-element and the Mutation of the Design Process Gene

In this paper a method is developed to model the design process gene based on the extensible basic-element with the purpose of design process optimization and reuse. First, the principle of genetic engineering based design process optimization and reuse is put forward and analyzed. Second, the extensible basic-element model of the design process gene is established based on the models of the design process base and the base pair through analyzing the concept and structure of the design process gene and the extensible basic-element as well as its extensibility. Third, the features of divergence and scalability of the extensible basic-element model of the design process gene are discussed for carrying out the extension translation to the design process gene by way of inserting, deleting and updating design process bases. Finally, an example of building extensible basic-element models for the design process base, base pair and design process gene in mechanical product design and the mutation process of the design process gene in airplane design is presented which demonstrates the application of the method proposed in this paper.

Case Based Reasoning Intelligent System for Network Computer Aided Process Planning

Computer aided process planning system played a key role for integrating design and manufacturing or assembly systems properly considering available resources and design constraints.To take advantage of the enterprise resource,the web CAPP framework was established.Case based reasoning and multi agent system were integrated in the system.The multi agent mecha- nism was discussed in the paper.And an instance of case base was introduced.They made the system run independently and contin- uously in the network environment of process planning problems.

Hierarchy-Based Team Software Process Simulation Model

According to the characteristic of Team Software Process （TSP）, it adopts a hierarchy-based model combined discrete event model with system dynamics model. This model represents TSP as form of two levels, the inner level embodies the continuity of the software process, the outer embodies the software development process by phases, and the structure and principle of the model is explained in detail, then formalization description of the model is offered. At last, an example is presented to demonstrate the simulation process and result. This model can simulate team software process from various angles, supervise and predict the software process. Also it can make the management of software development become more scientific and improve the quality of software.

Simulation of nucleation and coalescence of second phase droplets during earth-based solidification process of immiscible alloys

The solidified microstructure of immiscible alloys strongly depends on the nucleation, diffusional growth, especially the coalescence of the second phase droplets in the miscibility gap. A numerical model based on the discrete multi-particle approach was developed to simulate the nucleation and coalescence mode of the second phase droplets during the earth-based processing of immiscible alloys (in this case, the effect of gravity cannot be neglected). The cooling rate is the major factor influencing the coalescence mode. Under the super-rapid or rapid solidification condition (>104K/s), Brownian collision is the dominant coalescence mode. Marangoni collision becomes the dominant mode under the sub-rapid solidification condition (>102K/s). In the conventional slow cooling scope(101K/s), Stokes collision becomes the dominant coalescence mode, correspondingly, leading to a serious phase segregation.

CMAC BASED NONLINEAR PROCESS CONTROL SYSTEMS,PART I : CMAC WITH GENERAL BASIS FUNCTIONS FOR MODELLING

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On-line detection system of weld-based rapid prototyping process based on structured light

In order to realize the closed-loop control for rapid prototyping process based on gas metal arc welding, the geometric parameters of weld beads should be detected. In this study, a vision sensor system consisting of a linear laser projector and a CCD camera was designed to collect images of weld beads. Then, an image processing approach which combines with a Gaassian filter and an improved gravity method was used to extract the centerline of a light stripe based on VC ＋＋. Feature points of the centerline were identical directly by means of an image fusion algorithm. Experimental results show that image fusion is an effective approach to measure the width and height of the weld bead with high accuracy. This method can identify beads effectively in multi-pass welding and avoid designing different modes to suit all kinds of shapes.

Multi-objective optimization based optimal setting control for industrial double-stream alumina digestion process

The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.

Low-cycle Fatigue Behavior of Ni-based Superalloy GH586 with Laser Shock Processing

Low-cycle fatigue behavior of Ni-based superalloy GH586 with laser shock processing（LSP） was investigated. The residual stress of the specimens treated with LSP was assessed by X-ray diffraction method. The microstructure and fracture morphology were characterized by using an optical microscope（OM）, a scanning electron microscope（SEM）, and a transmission electron microscope（TEM）. The results indicated that the maximum residual compressive stress was at about 1 mm from the shocking spot center, where the residual compressive stress was slightly lower. High density tangling dislocations, dislocation walls, and dislocation cells in the microstructure of the specimens treated with LSP effectively prevented fatigue cracks propagation. The fatigue life was roughly twice as long as that of the specimens without LSP. The fatigue crack initiation（FCI） in specimens treated with LSP was observed in the lateral section and the subsurface simultaneously. The fatigue striation in the fracture treated with LSP was narrower than that in the untreated specimens. Moreover, dimples with tear ridges were found in the fatigued zones of the LSP treated specimens, which would be caused by severe plastic deformation.