Analysis of deformation characteristic in multi-way loading forming process of aluminum alloy cross valve based on finite element model

The deformation characteristic in the forming process of aluminum alloy 7075 cross valve under multi-way loading was investigated by numerical simulation method. The results indicate that there exist 4 deformation patterns in the multi-way loading forming process of cross valve, such as forward extrusion, backward extrusion, forward-lateral extrusion and backward-lateral extrusion; one or several patterns occur at different forming stages depending on loading path. In general, the main deformation pattern is forward extrusion or backward extrusion at the initial stage; the main deformation pattern is backward extrusion at the intermediate stage, and the backward extrusion and forward-lateral extrusion occur at the final stage. In order to improve the cavity fill and reduce the forming defects, the lateral extrusion deformation should be increased at the initial and intermediate stages, and the forward extrusion deformation at the final forging stage should be reduced or avoided.

On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.

An Improved Adaptive Multi-way Principal Component Analysis for Monitoring Streptomycin Fermentation Process

Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.

Phase Analysis and Identification Method for Multiphase Batch Processes with Partitioning Multi-way Principal Component Analysis (MPCA) Model

Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.

Finite element simulation of aluminum alloy cross valve forming by multi-way loading

Deformation behavior,temperature evolution and coupled effects have a significant influence on forming process and quality of component formed,which are very complex in forming process of aluminum alloy 7075 cross valve under multi-way loading due to the complexity of loading path and the multiplicity of associated processing parameters.A model of the process was developed under DFEORM-3D environment based on the coupled thermo-mechanical finite element method.The comparison between two process models,the conventional isothermal process model and the non-isothermal process model developed in this study,was carried out,and the results indicate that the thermal events play an important role in the aluminum alloy forming process under multi-way loading.The distributions and evolutions of the temperature field and strain filed are obtained by non-isothermal process simulation.The plastic zone and its extension in forming process of cross valve were analyzed.The results may provide guidelines for the determination of multi-way loading forming scheme and loading conditions of the forming cross valve components.

Decomposition of Point-Symmetry Using Ordinal Quasi Point-Symmetry for Ordinal Multi-Way Tables

For multi-way tables with ordered categories, the present paper gives a decomposition of the point-symmetry model into the ordinal quasi point-symmetry and equality of point-symmetric marginal moments. The ordinal quasi point-symmetry model indicates asymmetry for cell probabilities with respect to the center point in the table.

Efficient Processing of Multi-way Joins Using MapReduce

Multi-way join is critical for many big data applications such as data mining and knowledge discovery. Even though lots of research have been devoted to processing multi-way joins using MapReduce, there are still several problems in general to be further improved, such as transferring numerous unpromising intermediate data and lacking of better coordination mechanisms. This work proposes an efficient multi-way joins processing model using MapReduce, named Sharing-Coordination-MapReduce (SC-MapReduce), which has the functions of sharing and coordination. Our SC-MapReduce model can filter the unpromising intermediatedata largely by using the sharing mechanism and optimize the multiple tasks coordination of multi-way joins. Extensive experiments show that the proposed model is efficient, robust and scalable.

Estimation of multipath parameters in wireless communications using multi-way compressive sensing

This paper addresses the problem of joint angle and delay estimation(JADE) in a multipath communication scenario. A low-complexity multi-way compressive sensing(MCS) estimation algorithm is proposed. The received data are firstly stacked up to a trilinear tensor model. To reduce the computational complexity,three random compression matrices are individually used to reduce each tensor to a much smaller one. JADE then is linked to a low-dimensional trilinear model. Our algorithm has an estimation performance very close to that of the parallel factor analysis(PARAFAC) algorithm and automatic pairing of the two parameter sets. Compared with other methods, such as multiple signal classification(MUSIC), the estimation of signal parameters via rotational invariance techniques(ESPRIT), the MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also does not require the channel fading information, which means the proposed algorithm is blind and robust, therefore it has a higher working efficiency.Simulation results indicate the proposed algorithm have a bright future in wireless communications.

Research of the multi-way connectivity probability for platoon-based vehicle-to-infrastructure communication network

Vehicles travelling as platoons can reduce the huge traffic jams on the highway. Platoon members can share vehicle information such as speed and acceleration via vehicular ad hoc networks （VANETs） communication to maintain a constant inter-vehicle and inter-platoon distances. However, connectivity is a fundamental measurement to indicate the linking quality of VANETs. This paper analyzes the access and connectivity probability between the vehicles and the road side units （RSUs） of the multi-way platoon-based VANETs with roadside infrastructure. We denote the connectivity probability as the probability that the vehicles on the highway can access to at least one RSU besides the road within a designated number of hops. Moreover, besides considering the connection on the same road, we study the connection between the vehicles and the RSU via vehicles on the nearby neighbor roads. The analytical results have been validated by simulations and results show that the connectivity probability can be improved when there are platoons in a network. Meanwhile, the connectivity probability is higher in the multi-way vehicle-to-infrastructure （V2I） communication network than that in a one-way V2I communication network. The results in this paper can help to reduce the jams on the highway and achieve intelligent driving. Then the safety and comfort of the drivers and passengers on the highway can be improved. Moreover, these results can provide forceful theoretical support to the future intelligent transportation system （ITS） design.

A Robust Statistical Batch Process Monitoring Framework and Its Application

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.

ASCS Online Fault Detection and Isolation Based on an Improved MPCA

Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.

Faulty Sensor Detection and Reconstruction for a PVC Making Process

Based on principal component analysis, this paper presents an application of faulty sensor detection and reconstruction in a batch process, polyvinylchloride (PVC) making process. To deal with inconsistency in process data, it is proposed to use the dynamic time warping technique to make the historical data synchronized first,then build a consistent multi-way principal component analysis model. Fault detection is carried out based on squared prediction error statistical control plot. By defining principal component subspace, residual subspace and sensor validity index, faulty sensor can be reconstructed and identified along the fault direction. Finally, application results are illustrated in detail by use of the real data of an industrial PVC making process.

Traffic Assignment Forecast Model Research in ITS

As an important role in the development of ITS, traffic assignment forecast is always the research focus. Based on the analysis of classic traffic assignment forecast models, an improved traffic assignment forecast model, multi-ways probability and capacity constraint (MPCC) is presented. Using the new traffic as- signment forecast model to forecast the traffic volume will improve the rationality and veracity of traffic as- signment forecast.