Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections

The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.

Prediction of the concentration of chlorophyll-α for Liuhai urban lakes in Beijing City

The weekly water quality monitor data of Liuhai lakes between April 2003 and November 2004 in Beijing City were used as an example to build an artificial neural networks （ANN） model and a multi-varieties regression model respectively for predicting the fresh water algae bloom. The different predicted abilities of the two methods in Liuhai lakes were compared. A principle analysis method was first used to select the input variables of the models to avoid the phenomenon of collinearity in the data. The results showed that the input variables for the artificial neural networks were T, TP, transparency（SD）, DO, chlorophyll-a （Chl-a）,pH and the output variable was Chl-a. A three layer Levenberg-Marguardt feed forward leaming algorithm in ANN was used to model the eutrophication process of Liuhai lakes. 20 nodes in hidden layer and 1 node of output for the ANN model had been optimized by trial and error method. A sensitivity analysis of the input variables was performed to evaluate their relative significance in determining the predicted values. The correlation coefficient between predicted value and observed value in all data and in test data were 0.717 and 0.816 respectively in the artificial neural networks. The stepwise regression method was used to simulate the linear relation between Chl-a and temperature, of which the correlation coefficient was 0.213. By comparing the results of the two models, it was found that neural network models were able to simulate non-linear behavior in the water eutrophication process of Liuhai lakes reasonably and could successfully estimate some extreme values from calibration and test data sets.

Review on residual stress and its effects on manufacturing of aluminium alloy structural panels with typical multi-processes

In the aerospace industry,integrated aluminium alloy plates and stiffened panels with high accuracy and performance attract significant interest.To manufacture these panels as integrity with high accuracy,multiple processes need to be utilised,such as machining,welding and forming.During the whole manufacturing chain,residual stresses can be generated and redistributed in the components among different processes.The residual stress would significantly affect the shapes and properties of the final products.Currently,these great effects are not well considered in the design and manufacturing processes.This paper aims to draw a general understanding of the residual stress generated in the pre-manufacturing processes and its effects on subsequent manufacturing processes.The mechanisms and distributions of residual stresses generated in typical premanufacturing processes of structural panels,including machining,welding and additive manufacturing(AM),are firstly summarised.The detailed effects of generated residual stresses on distortion and application properties in subsequent manufacturing processes are then concluded.In addition,current methods developed for the investigation of residual stress effect in multi-processes manufacturing are critically reviewed,including experimental,analytical,finite element(FE)and machine learning methods.Furthermore,the future development trend of methods for residual stress consideration and control in the design of manufacturing processes is summarised,providing comprehensive guidance to achieve the high accurate manufacturing of aluminium alloy structural components.

On Numerical methods for determination of Earth gravity field model using mass satellite gravity gradiometry data

On the basis of Space-Wise Least Square method, three numerical methods including Cholesky de- composition, pre-conditioned conjugate gradient and Open Multi-Processing parallel algorithm are applied into the determination of gravity field with satellite gravity gradiometry data. The results show that, Cholesky de- composition method has been unable to meet the requirements of computation efficiency when the computer hardware is limited. Pre-conditioned conjugate gradient method can improve the computation efficiency of huge matrix inversion, but it also brings a certain loss of precision. The application of Open Multi-Processing parallel algorithm could achieve a good compromise between accuracy and computation efficiency.

不可逾越的长城——由IDF 2003再谈Hyper Threading技术的未来

自从Intel推出Hyper Threading(超线程)技术以来,以超强的性能改变了以往一切,桌面处理器又一次改朝换代,Intel并不只是在频率上提升处理器性能,同时在其它领域也让处理器有质的飞跃,继MMX、SSE、SSEII之后,Intel把原来只用在工作站上的Xeon CPU的Hyper Threading技术也引入到桌面处理器中,HT技术可以让一个CPU当成两个使用?这也许在以往一些HT技术中就介绍过,但今天写这个文章,主要是分析HT的工作原理以及Single Threading与Mutil-Tasking的区别,在多核心处理器时代HT又将会如何?以此来对HT的未来做一个最后的展望。

Explanation of Multi-process Competition for SRS Gain Anomaly

1 Introduction The explanation of the gain anomaly of the stimulated Raman scattering (SRS) in nonself-focusing medium has been one of the perplexing problems for all the researchers in quantum electronics, especially the explanation of the Grun’s experimental result.

Iso-UniK:lightweight multi-process unikernel through memory protection keys

Unikernel,specializing a minimalistic libOS with an application,is an attractive design for cloud computing.However,the Achilles’heel of unikernel is the lack of multi-process support,which makes it less flexible and applicable.Many applications rely on the process abstraction to isolate different components.For example,Apache with the multi-processing module isolates a request handler in a process to guarantee security.Prior art tackles the problem by simulating multi-process with multiple unikernels,which is incompatible with existing cloud providers and also introduces high overhead.This paper proposes Iso-UniK,a new unikernel design enabling multi-task applications with the support of both functionality and isolation.Iso-UniK leverages a recent hardware feature,named Intel Memory Protection Key(Intel MPK),to provide lightweight and efficient isolation for multi-process in unikernel.Our design has three benefits compared with previous approaches.First,Iso-UniK does not need hypervisor support and is thus compatible with existing cloud computing platforms;second,Iso-UniK promises fast system calls with only 45 cycles;last,a process can be isolated with a flexible configuration.We have implemented a prototype based on OSv,a unikernel system supporting unmodified applications.Iso-UniK can achieve fast fork operation with only 66μs for multi-process applications.Our evaluation shows that the isolation and multi-process support in Iso-UniK will not damage the applications’performance.

Iso-UniK:lightweight multi-process unikernel through memory protection keys

Unikernel,specializing a minimalistic libOS with an application,is an attractive design for cloud computing.However,the Achilles’heel of unikernel is the lack of multi-process support,which makes it less flexible and applicable.Many applications rely on the process abstraction to isolate different components.For example,Apache with the multi-processing module isolates a request handler in a process to guarantee security.Prior art tackles the problem by simulating multi-process with multiple unikernels,which is incompatible with existing cloud providers and also introduces high overhead.This paper proposes Iso-UniK,a new unikernel design enabling multi-task applications with the support of both functionality and isolation.Iso-UniK leverages a recent hardware feature,named Intel Memory Protection Key(Intel MPK),to provide lightweight and efficient isolation for multi-process in unikernel.Our design has three benefits compared with previous approaches.First,Iso-UniK does not need hypervisor support and is thus compatible with existing cloud computing platforms;second,Iso-UniK promises fast system calls with only 45 cycles;last,a process can be isolated with a flexible configuration.We have implemented a prototype based on OSv,a unikernel system supporting unmodified applications.Iso-UniK can achieve fast fork operation with only 66μs for multi-process applications.Our evaluation shows that the isolation and multi-process support in Iso-UniK will not damage the applications’performance.

A multi process value-based reinforcement learning environment framework for adaptive traffic signal control

Realising adaptive traffic signal control(ATSC)through reinforcement learning(RL)is an important means to easetraffic congestion.This paper finds the computing power of the central processing unit(CPU)cannot fully usedwhen Simulation of Urban MObility(SUMO)is used as an environment simulator for RL.We propose a multi-process framework under value-basedRL.First,we propose a shared memory mechanism to improve exploration efficiency.Second,we use the weight sharing mechanism to solve the problem of asynchronous multi-process agents.We also explained the reason shared memory in ATSC does not lead to early local optima of the agent.Wehave verified in experiments the sampling efficiency of the 10-process method is 8.259 times that of the single process.The sampling efficiency of the 20-process method is 13.409 times that of the single process.Moreover,the agent can also converge to the optimal solution.