82 research outputs found
Integrated design of run-to-run PID controller and SPC monitoring for process disturbance rejection
An integrated design methodology has been developed for a run-to-run PID controller and SPC monitoring for the purpose of process disturbance rejection. In the paper, the process disturbance is assumed to be an ARMA (1,1) process. A detailed procedure is developed to design a PID controller which minimizes process variability. The performance of the PID controller is also discussed. A joint monitoring of input and output, using Bonferroni's approach, is then designed for the controlled process. The ARL performance is studied. One major contribution of the paper is to develop a complete procedure and design plots, which serve as tools to conduct all the aforementioned tasks. An example is provided to illustrate the integrated design approach.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45925/1/10756_2004_Article_236231.pd
Missing Data Imputation with Graph Laplacian Pyramid Network
Data imputation is a prevalent and important task due to the ubiquitousness
of missing data. Many efforts try to first draft a completed data and second
refine to derive the imputation results, or "draft-then-refine" for short. In
this work, we analyze this widespread practice from the perspective of
Dirichlet energy. We find that a rudimentary "draft" imputation will decrease
the Dirichlet energy, thus an energy-maintenance "refine" step is in need to
recover the overall energy. Since existing "refine" methods such as Graph
Convolutional Network (GCN) tend to cause further energy decline, in this work,
we propose a novel framework called Graph Laplacian Pyramid Network (GLPN) to
preserve Dirichlet energy and improve imputation performance. GLPN consists of
a U-shaped autoencoder and residual networks to capture global and local
detailed information respectively. By extensive experiments on several
real-world datasets, GLPN shows superior performance over state-of-the-art
methods under three different missing mechanisms. Our source code is available
at https://github.com/liguanlue/GLPN.Comment: 12 pages, 5 figure
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
Correlated time series analysis plays an important role in many real-world
industries. Learning an efficient representation of this large-scale data for
further downstream tasks is necessary but challenging. In this paper, we
propose a time-step-level representation learning framework for individual
instances via bootstrapped spatiotemporal representation prediction. We
evaluated the effectiveness and flexibility of our representation learning
framework on correlated time series forecasting and cold-start transferring the
forecasting model to new instances with limited data. A linear regression model
trained on top of the learned representations demonstrates our model performs
best in most cases. Especially compared to representation learning models, we
reduce the RMSE, MAE, and MAPE by 37%, 49%, and 48% on the PeMS-BAY dataset,
respectively. Furthermore, in real-world metro passenger flow data, our
framework demonstrates the ability to transfer to infer future information of
new cold-start instances, with gains of 15%, 19%, and 18%. The source code will
be released under the GitHub
https://github.com/bonaldli/Spatiotemporal-TS-Representation-LearningComment: Accepted to IEEE CASE 202
Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
Low-rank tensor decomposition and completion have attracted significant
interest from academia given the ubiquity of tensor data. However, the low-rank
structure is a global property, which will not be fulfilled when the data
presents complex and weak dependencies given specific graph structures. One
particular application that motivates this study is the spatiotemporal data
analysis. As shown in the preliminary study, weakly dependencies can worsen the
low-rank tensor completion performance. In this paper, we propose a novel
low-rank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework
by introducing the -norm penalty and Graph Laplacian penalty to model
the weakly dependency on graph. We further propose an efficient optimization
algorithm based on the Block Coordinate Descent for efficient estimation. A
case study based on the metro passenger flow data in Hong Kong is conducted to
demonstrate improved performance over the regular tensor completion methods.Comment: Accepted at AAAI 202
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
Passenger clustering based on trajectory records is essential for
transportation operators. However, existing methods cannot easily cluster the
passengers due to the hierarchical structure of the passenger trip information,
including multiple trips within each passenger and multi-dimensional
information about each trip. Furthermore, existing approaches rely on an
accurate specification of the clustering number to start. Finally, existing
methods do not consider spatial semantic graphs such as geographical proximity
and functional similarity between the locations. In this paper, we propose a
novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can
preserve the hierarchical structure of the multi-dimensional trip information
and cluster them in a unified one-step manner with the ability to determine the
number of clusters automatically. The spatial graphs are utilized in community
detection to link the semantic neighbors. We further propose a tensor version
of Collapsed Gibbs Sampling method with a minimum cluster size requirement. A
case study based on Hong Kong metro passenger data is conducted to demonstrate
the automatic process of cluster amount evolution and better cluster quality
measured by within-cluster compactness and cross-cluster separateness. The code
is available at https://github.com/bonaldli/TensorDPMM-G.Comment: Accepted in ACM SIGSPATIAL 2023. arXiv admin note: substantial text
overlap with arXiv:2306.1379
Impact of Information Sharing on Statistical Quality
on and practice of information sharing is now having a significant impact on many aspects of supply chains. Nevertheless, few investigations focus on the impact of information sharing on product and process quality. Furthermore, it is still not clear how and what information should be shared or used, and how to quantify the benefits of information sharing in terms of quality improvement. In this research, a “matching problem ” is used to demonstrate the impact of information sharing on quality. We quantify and compare the impact of different information-sharing strategies on process and product quality, and suggest that real-time information sharing may lead to dramatic quality improvement for an assembly process, the example here being a two-stage supply chain. The proposed approach to evaluate information sharing in terms of quality improvement can be extended to a more complex supply chain. Index Terms—Automatic process control, engineering process control, statistical process control, supply chain management. I
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