640 research outputs found
Rejoinder
Ferrer, A. (2014). Rejoinder. Quality Engineering. 26(1):99-101. doi:10.1080/08982112.2014.846107S99101261Box, G. E. P. (1976). Science and Statistics. Journal of the American Statistical Association, 71(356), 791-799. doi:10.1080/01621459.1976.10480949Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128. doi:10.1016/j.chemolab.2012.04.003Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304Ferrer, A. (2013). Latent Structures-Based Multivariate Statistical Process Control: A Paradigm Shift. Quality Engineering, 26(1), 72-91. doi:10.1080/08982112.2013.846093Megahed, F. M., Wells, L. J., Camelio, J. A., & Woodall, W. H. (2012). A Spatiotemporal Method for the Monitoring of Image Data. Quality and Reliability Engineering International, 28(8), 967-980. doi:10.1002/qre.1287Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002SCHALL, S., & CHANDRA, J. (1987). Multivariate quality control using principal components. International Journal of Production Research, 25(4), 571-588. doi:10.1080/00207548708919862Shi, J., & Zhou, S. (2009). Quality control and improvement for multistage systems: A survey. IIE Transactions, 41(9), 744-753. doi:10.1080/07408170902966344Westerhuis, J. A., Kourti, T., & MacGregor, J. F. (1998). Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 12(5), 301-321. doi:10.1002/(sici)1099-128x(199809/10)12:53.0.co;2-sWold, S., Kettaneh, N., & Tjessem, K. (1996). Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection. Journal of Chemometrics, 10(5-6), 463-482. doi:10.1002/(sici)1099-128x(199609)10:5/63.0.co;2-lWold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-
Latent Structures based-Multivariate Statistical Process Control: a paradigm shift
The basic fundamentals of statistical process control (SPC)
were proposed by Walter Shewhart for data-starved production environments
typical in the 1920s and 1930s. In the 21st century, the traditional
scarcity of data has given way to a data-rich environment typical of highly
automated and computerized modern processes. These data often exhibit
high correlation, rank deficiency, low signal-to-noise ratio, multistage and
multiway structures, and missing values. Conventional univariate and multivariate
SPC techniques are not suitable in these environments. This article
discusses the paradigm shift to which those working in the quality improvement
field should pay keen attention. We advocate the use of latent
structure based multivariate statistical process control methods as efficient
quality improvement tools in these massive data contexts. This is a strategic
issue for industrial success in the tremendously competitive global market.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Ferrer, A. (2014). Latent Structures based-Multivariate Statistical Process Control: a paradigm shift. Quality Engineering. 26(1):72-91. https://doi.org/10.1080/08982112.2013.846093S7291261Aparisi, F., Jabaioyes, J., & Carrion, A. (1999). Statistical properties of the lsi multivariate control chart. Communications in Statistics - Theory and Methods, 28(11), 2671-2686. doi:10.1080/03610929908832445Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview. Quality and Reliability Engineering International, 23(5), 517-543. doi:10.1002/qre.829Bharati, M. H., & MacGregor, J. F. (1998). Multivariate Image Analysis for Real-Time Process Monitoring and Control. Industrial & Engineering Chemistry Research, 37(12), 4715-4724. doi:10.1021/ie980334lBharati, M. H., MacGregor, J. F., & Tropper, W. (2003). Softwood Lumber Grading through On-line Multivariate Image Analysis Techniques. Industrial & Engineering Chemistry Research, 42(21), 5345-5353. doi:10.1021/ie0210560Bisgaard, S. (2012). The Future of Quality Technology: From a Manufacturing to a Knowledge Economy & From Defects to Innovations. Quality Engineering, 24(1), 30-36. doi:10.1080/08982112.2011.627010Box, G. E. P. (1954). Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification. The Annals of Mathematical Statistics, 25(2), 290-302. doi:10.1214/aoms/1177728786Camacho, J., & Ferrer, A. (2012). Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: theoretical aspects. Journal of Chemometrics, 26(7), 361-373. doi:10.1002/cem.2440Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128. doi:10.1016/j.chemolab.2012.04.003Efron, B., & Gong, G. (1983). A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. The American Statistician, 37(1), 36-48. doi:10.1080/00031305.1983.10483087Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304Fuchs, C. (1998). Multivariate Quality Control. doi:10.1201/9781482273731Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. doi:10.1016/0003-2670(86)80028-9Helland, I. S. (1988). On the structure of partial least squares regression. Communications in Statistics - Simulation and Computation, 17(2), 581-607. doi:10.1080/03610918808812681Höskuldsson, A. (1988). PLS regression methods. Journal of Chemometrics, 2(3), 211-228. doi:10.1002/cem.1180020306Hunter, J. S. (1986). The Exponentially Weighted Moving Average. Journal of Quality Technology, 18(4), 203-210. doi:10.1080/00224065.1986.11979014Edward Jackson, J. (1985). Multivariate quality control. Communications in Statistics - Theory and Methods, 14(11), 2657-2688. doi:10.1080/03610928508829069Jackson, J. E., & Mudholkar, G. S. (1979). Control Procedures for Residuals Associated With Principal Component Analysis. Technometrics, 21(3), 341-349. doi:10.1080/00401706.1979.10489779Process analysis and abnormal situation detection: from theory to practice. (2002). IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Kourti, T. (2006). Process Analytical Technology Beyond Real-Time Analyzers: The Role of Multivariate Analysis. Critical Reviews in Analytical Chemistry, 36(3-4), 257-278. doi:10.1080/10408340600969957Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699Liu, R. Y. (1995). Control Charts for Multivariate Processes. Journal of the American Statistical Association, 90(432), 1380-1387. doi:10.1080/01621459.1995.10476643Liu, R. Y., Singh, K., & Teng*, J. H. (2004). DDMA-charts: Nonparametric multivariate moving average control charts based on data depth. Allgemeines Statistisches Archiv, 88(2), 235-258. doi:10.1007/s101820400170Liu, R. Y., & Tang, J. (1996). Control Charts for Dependent and Independent Measurements Based on Bootstrap Methods. Journal of the American Statistical Association, 91(436), 1694-1700. doi:10.1080/01621459.1996.10476740LOWRY, C. A., & MONTGOMERY, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800-810. doi:10.1080/07408179508936797MacGregor, J. F. (1997). Using On-Line Process Data to Improve Quality: Challenges for Statisticians. International Statistical Review, 65(3), 309-323. doi:10.1111/j.1751-5823.1997.tb00311.xMason, R. L., Champ, C. W., Tracy, N. D., Wierda, S. J., & Young, J. C. (1997). Assessment of Multivariate Process Control Techniques. Journal of Quality Technology, 29(2), 140-143. doi:10.1080/00224065.1997.11979743Montgomery, D. C., & Woodall, W. H. (1997). A Discussion on Statistically-Based Process Monitoring and Control. Journal of Quality Technology, 29(2), 121-121. doi:10.1080/00224065.1997.11979738Nelson, P. R. C., Taylor, P. A., & MacGregor, J. F. (1996). Missing data methods in PCA and PLS: Score calculations with incomplete observations. Chemometrics and Intelligent Laboratory Systems, 35(1), 45-65. doi:10.1016/s0169-7439(96)00007-xNomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Prats-Montalbán, J. M., Ferrer, A., Malo, J. L., & Gorbeña, J. (2006). A comparison of different discriminant analysis techniques in a steel industry welding process. Chemometrics and Intelligent Laboratory Systems, 80(1), 109-119. doi:10.1016/j.chemolab.2005.08.005Prats-Montalbán, J. M., & Ferrer, A. (2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026Bisgaard, S., Doganaksoy, N., Fisher, N., Gunter, B., Hahn, G., Keller-McNulty, S., … Wu, C. F. J. (2008). The Future of Industrial Statistics: A Panel Discussion. Technometrics, 50(2), 103-127. doi:10.1198/004017008000000136Stoumbos, Z. G., Reynolds, M. R., Ryan, T. P., & Woodall, W. H. (2000). The State of Statistical Process Control as We Proceed into the 21st Century. Journal of the American Statistical Association, 95(451), 992-998. doi:10.1080/01621459.2000.10474292Tracy, N. D., Young, J. C., & Mason, R. L. (1992). Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 24(2), 88-95. doi:10.1080/00224065.1992.12015232Wierda, S. J. (1994). Multivariate statistical process control—recent results and directions for future research. Statistica Neerlandica, 48(2), 147-168. doi:10.1111/j.1467-9574.1994.tb01439.xWold, S. (1978). Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models. Technometrics, 20(4), 397-405. doi:10.1080/00401706.1978.10489693Woodall, W. H. (2000). Controversies and Contradictions in Statistical Process Control. Journal of Quality Technology, 32(4), 341-350. doi:10.1080/00224065.2000.11980013Woodall, W. H., & Montgomery, D. C. (1999). Research Issues and Ideas in Statistical Process Control. Journal of Quality Technology, 31(4), 376-386. doi:10.1080/00224065.1999.11979944Yu, H., & MacGregor, J. F. (2003). Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods. Chemometrics and Intelligent Laboratory Systems, 67(2), 125-144. doi:10.1016/s0169-7439(03)00065-0Yu, H., MacGregor, J. F., Haarsma, G., & Bourg, W. (2003). Digital Imaging for Online Monitoring and Control of Industrial Snack Food Processes. Industrial & Engineering Chemistry Research, 42(13), 3036-3044. doi:10.1021/ie020941
Climatic regions as an indicator of forest coarse and fine woody debris carbon stocks in the United States
<p>Abstract</p> <p>Background</p> <p>Coarse and fine woody debris are substantial forest ecosystem carbon stocks; however, there is a lack of understanding how these detrital carbon stocks vary across forested landscapes. Because forest woody detritus production and decay rates may partially depend on climatic conditions, the accumulation of coarse and fine woody debris carbon stocks in forests may be correlated with climate. This study used a nationwide inventory of coarse and fine woody debris in the United States to examine how these carbon stocks vary by climatic regions and variables.</p> <p>Results</p> <p>Mean coarse and fine woody debris forest carbon stocks vary by Köppen's climatic regions across the United States. The highest carbon stocks were found in regions with cool summers while the lowest carbon stocks were found in arid desert/steppes or temperate humid regions. Coarse and fine woody debris carbon stocks were found to be positively correlated with available moisture and negatively correlated with maximum temperature.</p> <p>Conclusion</p> <p>It was concluded with only medium confidence that coarse and fine woody debris carbon stocks may be at risk of becoming net emitter of carbon under a global climate warming scenario as increases in coarse or fine woody debris production (sinks) may be more than offset by increases in forest woody detritus decay rates (emission). Given the preliminary results of this study and the rather tenuous status of coarse and fine woody debris carbon stocks as either a source or sink of CO<sub>2</sub>, further research is suggested in the areas of forest detritus decay and production.</p
Crime geo-surveillance in microscale urban environments: NetSurveillance
Events and phenomena such as crime incidents and outbreak of an epidemic tend to form concentration of high risks known as hotspots. Geosurveillance is an increasingly popular notion for detecting and monitoring the emergence of and changes in hotspots. Yet the existing range of methods are not designed to accurately detect emerging risks at the micro-scale of street-address level. This study proposes NetSurveillance, a method for monitoring the emergence of significant concentration of events along the intricate network of urban streets. Through a simulation test, the study demonstrates the high accuracy of NetSurveillance in detecting such clusters, and outperforms its conventional counterpart conclusively when applied at the individual street address level. Empirical analysis of drug incidents from Chicago also illustrates its capacity to identify rapid outburst of crimes as well as a more gradual build-up of such concentration, and their disappearance, either as a one-off or as part of a recurrent hotbed
The stellar atmosphere simulation code Bifrost
Context: Numerical simulations of stellar convection and photospheres have
been developed to the point where detailed shapes of observed spectral lines
can be explained. Stellar atmospheres are very complex, and very different
physical regimes are present in the convection zone, photosphere, chromosphere,
transition region and corona. To understand the details of the atmosphere it is
necessary to simulate the whole atmosphere since the different layers interact
strongly. These physical regimes are very diverse and it takes a highly
efficient massively parallel numerical code to solve the associated equations.
Aims: The design, implementation and validation of the massively parallel
numerical code Bifrost for simulating stellar atmospheres from the convection
zone to the corona.
Methods: The code is subjected to a number of validation tests, among them
the Sod shock tube test, the Orzag-Tang colliding shock test, boundary
condition tests and tests of how the code treats magnetic field advection,
chromospheric radiation, radiative transfer in an isothermal scattering
atmosphere, hydrogen ionization and thermal conduction.
Results: Bifrost completes the tests with good results and shows near linear
efficiency scaling to thousands of computing cores
The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States
We synthesize insights from current understanding of drought impacts at stand‐to‐biogeographic scales, including management options, and we identify challenges to be addressed with new research. Large stand‐level shifts underway in western forests already are showing the importance of interactions involving drought, insects, and fire. Diebacks, changes in composition and structure, and shifting range limits are widely observed. In the eastern US, the effects of increasing drought are becoming better understood at the level of individual trees, but this knowledge cannot yet be confidently translated to predictions of changing structure and diversity of forest stands. While eastern forests have not experienced the types of changes seen in western forests in recent decades, they too are vulnerable to drought and could experience significant changes with increased severity, frequency, or duration in drought. Throughout the continental United States, the combination of projected large climate‐induced shifts in suitable habitat from modeling studies and limited potential for the rapid migration of tree populations suggests that changing tree and forest biogeography could substantially lag habitat shifts already underway. Forest management practices can partially ameliorate drought impacts through reductions in stand density, selection of drought‐tolerant species and genotypes, artificial regeneration, and the development of multistructured stands. However, silvicultural treatments also could exacerbate drought impacts unless implemented with careful attention to site and stand characteristics. Gaps in our understanding should motivate new research on the effects of interactions involving climate and other species at the stand scale and how interactions and multiple responses are represented in models. This assessment indicates that, without a stronger empirical basis for drought impacts at the stand scale, more complex models may provide limited guidance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134257/1/gcb13160_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134257/2/gcb13160.pd
Chemistry in a gravitationally unstable protoplanetary disc
Until now, axisymmetric, alpha-disc models have been adopted for calculations
of the chemical composition of protoplanetary discs. While this approach is
reasonable for many discs, it is not appropriate when self-gravity is
important. In this case, spiral waves and shocks cause temperature and density
variations that affect the chemistry. We have adopted a dynamical model of a
solar-mass star surrounded by a massive (0.39 Msun), self-gravitating disc,
similar to those that may be found around Class 0 and early Class I protostars,
in a study of disc chemistry. We find that for each of a number of species,
e.g. H2O, adsorption and desorption dominate the changes in the gas-phase
fractional abundance; because the desorption rates are very sensitive to
temperature, maps of the emissions from such species should reveal the
locations of shocks of varying strengths. The gas-phase fractional abundances
of some other species, e.g. CS, are also affected by gas-phase reactions,
particularly in warm shocked regions. We conclude that the dynamics of massive
discs have a strong impact on how they appear when imaged in the emission lines
of various molecular species.Comment: 10 figures and 3 tables, accepted for publication in MNRA
Transfer Matrices and Partition-Function Zeros for Antiferromagnetic Potts Models. IV. Chromatic polynomial with cyclic boundary conditions
We study the chromatic polynomial P_G(q) for m \times n square- and
triangular-lattice strips of widths 2\leq m \leq 8 with cyclic boundary
conditions. This polynomial gives the zero-temperature limit of the partition
function for the antiferromagnetic q-state Potts model defined on the lattice
G. We show how to construct the transfer matrix in the Fortuin--Kasteleyn
representation for such lattices and obtain the accumulation sets of chromatic
zeros in the complex q-plane in the limit n\to\infty. We find that the
different phases that appear in this model can be characterized by a
topological parameter. We also compute the bulk and surface free energies and
the central charge.Comment: 55 pages (LaTeX2e). Includes tex file, three sty files, and 22
Postscript figures. Also included are Mathematica files transfer4_sq.m and
transfer4_tri.m. Journal versio
Recommended from our members
Assessing the stability of tree ranges and influence of disturbance in eastern US forests
Shifts in tree species ranges may occur due to global climate change, which in turn may be exacerbated
by natural disturbance events. Within the context of global climate change, developing techniques to
monitor tree range dynamics as affected by natural disturbances may enable mitigation/adaptation of
projected impacts. Using a forest inventory across the eastern U.S., the northern range margins of tree
distributions were examined by comparing differences in the 95th percentile locations of seedlings to
adults (i.e., trees) by 0.5° longitudinal bands over 5-years and by levels of disturbance (i.e., canopy gap
formation). Our results suggest that the monitoring of tree range dynamics is complicated by the limits
of forest inventory data across varying spatial/temporal scales and the diversity of tree species/environments
in the eastern U.S. The vast majority of tree and seedling latitudinal comparisons across measurement
periods and levels of disturbance in the study were not statistically different from zero (53 out of 60
comparisons). A potential skewing of ranges towards a northern limit was suggested by the stability of
northern margins of tree ranges found in this study and shifts in mean locations identified in previous
work. Only a partial influence of disturbances on tree range dynamics during the course of the 5-years
was found in this study. The results of this study underscore the importance of continued examination
of the role of disturbance in tree range dynamics and refined range monitoring techniques given future
forest extent and biodiversity implications.Keywords: Disturbance, Seedlings, Tree range retreat, Climate change, Tree species migration, Canopy gapsKeywords: Disturbance, Seedlings, Tree range retreat, Climate change, Tree species migration, Canopy gap
From Profile to Surface Monitoring: SPC for Cylindrical Surfaces Via Gaussian Processes
Quality of machined products is often related to the shapes of surfaces
that are constrained by geometric tolerances. In this case, statistical
quality monitoring should be used to quickly detect unwanted deviations
from the nominal pattern. The majority of the literature has focused on
statistical profile monitoring, while there is little research on
surface monitoring. This paper faces the challenging task of moving from
profile to surface monitoring. To this aim, different parametric
approaches and control-charting procedures are presented and compared
with reference to a real case study dealing with cylindrical surfaces
obtained by lathe turning. In particular, a novel method presented in
this paper consists of modeling the manufactured surface via Gaussian
processes models and monitoring the deviations of the actual surface
from the target pattern estimated in phase I. Regardless of the specific
case study in this paper, the proposed approach is general and can be
extended to deal with different kinds of surfaces or profiles
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