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Introduction
It is already the XIIIth International Workshop on “Intelligent Statistical Quality Con-trol” (ISQC 2019) following:
year place
2016 Hamburg, Germany
2013 Sydney, Australia
2010 Seattle, USA
2007 Beijing, China
2004 Warsaw, Poland
2001 Waterloo, Canada
1998 Wurzburg, Germany
1995 Osaka, Japan
1990 Baton Rouge, USA
1986 Lyngby, Danmark
1983 Kent, UK
1980 Berlin, Germany
This time, it is hosted by the City University (CityU) of Hong Kong, in Hong Kong.We hope that it will be as successful as the previous ones. On the next pages of thisbooklet some useful information are collected.
Scientific program committee
Professor S. Knoth, Germany
Professor F. Megahed, U.S.A.
Professor W. Schmid, Germany
Professor K.-L. Tsui, Hong Kong
Professor J. Wei, China
Professor W. H. Woodall, U.S.A.
Hong Kong, August 2019.
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Miscellaneous
Workshop venue etc.
Room 6-209, Lau Ming Wai Academic Building of the City University of Hong Kong.
Complimentary Lunches and Dinner Venue
12 Aug 2019 (Mon) Western style semi-buffet @ City Top, 9/F, BOC Complex
12 Aug 2019 (Mon) Conference banquet @ Tsim Sha Tsui / Hung Mom / Mongkok East (To be advised)
13 Aug 2019 (Tue) Chinese style lunch @ Chinese Restaurant, 8/F, BOC Complex
14 Aug 2019 (Wed) Dim Sum @ Chinese Restaurant, 8/F, BOC Complex
Free Campus Wifi
Wireless Access via ‘Wi-Fi,HK via CityU’. Or EDUroam ...
Useful coordinates
Workshop URL: http://www.cityu.edu.hk/csie/ISQC2019/
Transport to CityU: https://www.cityu.edu.hk/wayfinder/GettingToU/
Useful local contacts [Hong Kong Country Code: +852]:
• Kwok-L. Tsui (room YEUNG-P6623, 3442-2177, [email protected])
• Inez Zwetsloot (room YEUNG-P6606, 3442-6155, [email protected])
• Lolli Lee (room AC1-P7314, 3442-7310, [email protected]).
• CityU Security Office (3442-8888)
• Emergency Services: Police, Fire and Ambulance (999)
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Schedule
Monday, Aug 12
08:50 – 09:00 Opening
09:00 – 09:30 AR Driscoll, WH Woodall, C Zou(Virginia Tech, Nankai U)
Applications of the Conditional False AlarmRate Metric in Process Monitoring
09:30 – 10:00 D Han, F Tsung, J Xian, Y Ye(Shanghai Jiao Tong U, HKUST)
A Method of Optimizing the Control Charts forFinite Sequence of Observations
10:00 – 10:30 M Morais, S Knoth, CJ Cruz, CWeiß (U Lisboa, HSU Hamburg)
ARL-unbiased CUSUM schemes to Monitor Bi-nomial Counts
10:30 – 11:00 Break
11:00 – 11:30 F Tsung (HKUST Hong Kong) Quality Big Data
11:30 – 12:00 R Goedhart (U Amsterdam) Design Considerations and Tradeoffs for She-whart Control Charts
12:00 – 12:30 SF Yang, SW Lu(National Chengchi U)
An Average Loss Control Chart Under a SkewedProcess Distribution
12:30 – 14:00 Lunch
14:00 – 14:30 O Hryniewicz, K Kaczmarek-Majer,K Opara (PAN Warsaw)
Monitoring Two-state Processes Using Indi-rectly Observed Data
14:30 – 15:00 W Huang, W Jiang, C Shi(Hangzhou Dianzi U, Shanghai JiaoTong U)
Product’s Warranty Claim Monitoring underVariable Intensity Rates
15:00 – 15:30 T Mahmood, RA Sanusi, M Xie(CityU Hong Kong)
A Flexible Monitoring Method for High-YieldProcesses
15:30 – 16:00 Break
16:00 – 16:30 S Knoth (HSU Hamburg) On the Calculation of the ARL for Beta EWMAControl Charts
16:30 – 17:00 L Shu, J Fan (U Macau) A Distribution-Free Control Chart for Monitor-ing High-Dimensional Processes based on Inter-point Distances
18:30 – 20:30 Conference Banquet
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Tuesday, Aug 13
09:00 – 09:30 KL Tsui (CityU Hong Kong) Personalized Health Management for ElderlyCare
09:30 – 10:00 Y Okhrin, W Schmid, I Semeniuk(U Augsburg, EUV Viadrina Frank-furt Oder)
Monitoring Image Processes
10:00 – 10:30 P Otto (U Hannover) Parallelized Monitoring of Dependent Spa-tiotemporal Processes
10:30 – 11:00 Break
11:00 – 11:30 D Wang, X Zhang(Peking U)
A Dynamic Field Monitoring Approach: An Ap-plication to Thermal Field Monitoring
11:30 – 12:00 K Nishina (Aich Inst. Tech.) Quality Control Activities Are a Challenge forReducing Variability
12:00 – 12:30 Y Zhao, H Yan, SE Holte, RPKerani, Y Mei(..., Georgia Tech)
Rapid Detection of Hot-spot by Tensor Decom-position with Application to Weekly GonorrheaData
12:30 – 14:00 Lunch
14:00 – 14:30 Z Wang, IM Zwetsloot(CityU Hong Kong)
Exploring the usefulness of Functional DataAnalysis for Health Surveillance
14:30 – 15:00 FF Gan, WL Koh, JJ Ang(NU Singapore)
Monitoring Performances of Surgeons Usinga New Risk-adjusted Exponentially WeightedMoving Average Control Chart
15:00 – 15:30 R Sparks, A Joshi, C Paris, SKarimi (CSIRO Sydney)
An Approach to Monitoring Time BetweenEvents When Events are Frequent
15:30 – 16:00 Break
16:00 – 16:30 E Yashchin (IBM) Target-setting in Multi-Stage Processes Moni-toring
16:30 – 17:00 MB Perry, Z Wang (U Alabama) A CUSUM Control Chart for Autocorrelated Bi-nary Sequences
18:00 – 20:00 Dinner (Self-Financed)
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Wednesday, Aug 14
09:30 – 10:00 A Steland (RWTH Aachen) On Statistical Quality Control Procedures forHigh-Dimensional Time Series
10:00 – 10:30 FM Megahed, LA Jones-Farmer, MMohamed, SE Rigdon (Miami U,St. Louis U)
A Statistical (Process Monitoring) Perspectiveon Human Performance Modeling
10:30 – 11:00 Break
11:00 – 11:30 C Li, X Wang, L Li, M Xie(CityU Hong Kong)
On dynamically monitoring aggregate warrantyclaims for early detection of reliability problems
11:30 – 12:00 W Koessler, HJ Lenz, XD Wang(HU Berlin, FU Berlin)
Is the Benford Law useful for Data Quality As-sessment?
12:00 – 12:30 S Ross (USC Viterbi) Some Ideas on Selecting the Best Population
12:30 – 14:00 Lunch
14:00 – 14:30 SH Steiner, J MacKay, K Fan(U Waterloo)
Assessing a Binary Measurement System withOperator and Random Part Effects
14:30 – 15:00 T Suzuki, J Takeshita, M Ogawa,XN Lu, Y Ojima(Tokyo U Science, AIST)
Analysis of Measurement Precision Experimentwith Categorical Variables
16:30 – 19:30 Social Program (Self-Financed)
Legend of abstract markers (used throughout the following pages)
label meaning
h available in proceedings
ö available in file repository
I work in progress
1 might be published later (in Springer book or elsewhere)
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Abstracts
Assessing laboratory effects in key comparisons with two transfer standardsmeasured in two petals: A Bayesian approach h
Olha Bodnar and Clemens Ester
Division of Applied Mathematics, Malardalen University, Sweden, [email protected]; Physikalisch-Technische
Bundesanstalt, Berlin, Germany, [email protected]
Quality management plays an important role in measurement science, especially inlaboratory comparisons. One of the main goals is the identification of the laborato-ries which underestimate their uncertainties, i.e. the laboratories whose measurementresults considerably deviate from those obtained by other participating laboratories.
We propose a new statistical method for analyzing data from laboratory comparisonwhen two transfer standards are measured in two petals. The approach is based ona generalization of the classical random effects model, a popular procedure in metrol-ogy. A Bayesian inference of the model parameters as well as of the random effects issuggested. The latter can be viewed as potential laboratory effects which are assessedthrough the proposed analysis. While the prior for the laboratory effects naturally isassigned as a Gaussian distribution, the non-informative Berger & Bernardo referenceprior is taken for the remaining model parameters. The results are presented in terms ofthe posterior distribution derived for the laboratory effects in a closed-form expression.We also provide the Bayesian estimators for the model parameters and their credibleintervals. The proposed method paves the way for applying the established randomeffects model also for data obtained from the measurement of several transfer stan-dards in several petals, and it is illustrated for measurements of two 500mg transferstandards carried out in key comparison CCM.M-K7.
Applications of the Conditional False Alarm Rate Metric in ProcessMonitoring h
Anne R. Driscoll, William H. Woodall and Changliang Zou
Virginia Tech, Blacksburg, USA, [email protected], [email protected];
Nankai University, China, [email protected]
The conditional false alarm rate at a particular time is the probability of a false alarmat that time conditional on no false alarm at any previous time. Only the Shewhartcontrol chart designed with known in-control parameters has constant conditional falsealarm rates, although other charts can be designed to have any desired pattern ofsuch rates. The important advantage of the use of this metric is when sample sizes,population sizes or other covariate information affecting chart performance vary overtime. In these cases, the control limit at a particular time can be obtained after thecorresponding covariate value is known through control of the conditional false alarmrate. This allows one to control the in-control performance of the chart without theneed to model and forecast covariate values. The approach is illustrated using therisk-adjusted Bernoulli CUSUM chart.
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Monitoring Performances of Surgeons Using a New Risk-adjustedExponentially Weighted Moving Average Control Chart h
Fah F. Gan, Wei L. Koh, Janice J. Ang
Dep. of Statistics and App. Prob., National University of Singapore, [email protected]
Risk-adjusted charting procedures which take into account patients’ health conditionshave been developed in the literature. One important class of risk-adjusted chartingprocedures is based on the likelihood ratio statistic obtained by testing the odds ratioof mortality. The likelihood ratio statistic essentially converts the binary outcomes ofdeath and survival into penalty and reward scores respectively which are dependent onthe predicted risk of death of a patient. For cardiac operations, the risk distributionis highly right skewed resulting in penalty scores in a narrow range for majority of thepatients. Similar is true for the reward scores. This means effectively there is littlerisk adjustment for majority of the patients. In this paper, we propose a risk-adjustedstatistic which is the ratio of surgical outcome to the estimated probability of deathfrom an operation as the monitoring statistic. The main characteristic of this statisticis that the penalty score is substantially higher if a patient with low risk dies, and thepenalty score decreases sharply as the risk increases. The chart based on this statisticwill be compared with the risk-adjusted cumulative sum chart in terms of average runlength. Finally, this chart is used to analyze the performances of two surgeons.
Design considerations and tradeoffs for Shewhart control charts h
Rob Goedhart
IBIS UvA, Dep. of Operations Management, University of Amsterdam, The Netherlands, [email protected]
When in-control parameters are unknown, they have to be estimated using a referencesample. The control chart performance in Phase II, which is generally measured interms of the Average Run Length (ARL) or False Alarm Rate (FAR), will vary acrosspractitioners due to the use of different reference samples in Phase I. This variationis especially large for small sample sizes. Although increasing the amount of PhaseI data improves the control chart performance, others have shown that the amountrequired to achieve a desired in-control performance is infeasibly high. This holds evenwhen the actual distribution of the data is known. When the distribution of the datais unknown, it has to be estimated as well, along with its parameters. This yields evenmore uncertainty in control chart performance when parametric models are applied.
With these issues in mind, several choices have to be made in order to control theperformance of control charts. First, one has to choose a design criterion. For this cri-terion two approaches are possible, namely an unconditional approach (in expectation)or a conditional approach (minimum performance with specified probability). Both ofthese approaches can also be combined with different performance measures, such asthe FAR, ARL, or other similar characteristics. After deciding on a design criterionand performance measure, one has to determine the estimation method to achieve it.This includes decisions on estimators to be used, but more importantly also on theaccompanying parameter and distributional assumptions made, as well as the PhaseI sample size. For example, when sample sizes are small, parametric methods withappropriate distributional assumptions obviously perform much better than nonpara-metric alternatives. However, in that case the appropriateness of these distributional
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assumptions is also more difficult to validate, while deviations from these assumptionscan have substantial impact on performance. For this reason, tradeoffs have to bemade between the sample size, the distributional assumptions, and the strictness of theperformance criterion. We evaluate and discuss the consequences of these tradeoffs forvarious settings and methods.
A Method of Optimizing the Control Charts for Finite Sequence ofObservations 1
Dong Han, Fugee Tsung, Jinguo Xian, Yunfei YeDepartment of Statistics, Shanghai Jiao Tong University, China, [email protected]; Department of Indus-
trial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Hong Kong, sea-
[email protected]; Department of Statistics, Shanghai Jiao Tong University, China, [email protected]
We propose a method for optimizing the control charts with a given set of chartingstatistics to detect a change in distribution of finite sequence of observations. Theoptimized control chart is proved to have the smallest average value of some kind ofdetection delay among all control charts with the false alarm rate no less than a givenvalue. The method is illustrated by numerical simulations of the three optimized controlcharts, Shewhart, CUSUM and EWMA charts, in detecting mean shifts.
Monitoring two-state processes using indirectly observed data h
Olgierd Hryniewicz, Katarzyna Kaczmarek-Majer, Karol OparaSystems Research Institute, Polish Academy of Sciences, Warsaw, Poland, [email protected],
[email protected], [email protected]
In all classical SPC procedures used for monitoring two-state processes it is assumedthat the value of the observed quality characteristic, 0 or 1, is known. This assumptionis usually true in the case of production processes. In some cases, however, the quality ofmonitored items has to be predicted using results of other measurements. This happens,e.g., when quality of monitored items can be evaluated using destructive measurements.An appropriate classifier is built from training data, and used for evaluation purposesin SPC procedures. The situation becomes more complicated when the accuracy ofclassification (prediction) is low. In such cases, ensembles of classifiers may be used,and the final classification can be established using, e.g., a voting procedure. Thesituation becomes even more complicated when we have to combine the results obtainedby different classifiers, which use for classification purposes different sets of explanatoryvariables. This situation happens in health- related processes, where unknown state ofa patient has to be predicted using different sets of symptoms. In theory, one can buildone classifier using all possible types of measurements. However, due to the problem ofincomplete observational data sets this approach may be not effective. A competitiveapproach may consist in a certain fusion of the classification results obtained by severalclassifiers. In this fusion, however, we have to take into account that the results of suchclassifications are inter-correlated, and may be of different accuracy.
In classical SPC we define an in-control state of a monitored process in terms of statisti-cal characteristics of the probability distribution describing the results of measurements(average value, fraction nonconforming, etc.) that describe natural variability of a mon-itored process. However, for processes described by indirectly observed data, such as,
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e.g., health-related processes, the source of randomness is mainly attributed to errorsof classification (diagnosis). In the paper, we propose a new idea of monitoring suchprocesses, where the state of a monitored process, 0 or 1, is evaluated using the numberof positive evaluations yielded by an ensemble of inter-correlated classifiers. The appli-cation of the proposed procedure will be illustrated with a real example of monitoring ofpsychiatric patients suffering from the bipolar disorder. In this case, classifiers are builtusing data obtained in time periods when the state of a patient is only partially known,e.g., evaluated by a psychiatrist only in certain time moments. In order to overcomethe problem of incomplete data, a classical control chart is applied for choosing trainingdata used for the construction of classifiers. Then, in remaining time periods, the stateof a patient is monitored using a specially designed control chart for data representingpredictions of patient’s health state. The aim of such monitoring procedure is to reveala possible change of this state.
Weighted Likelihood-based Claim Monitoring under Variable IntensityRates h
Wenpo Huang, Jiang Wei, Chenyou Shi
School of Mechanical Engineering, Northwestern Polytechnical University, China, [email protected];
Department of Operations Management, Shanghai Jiao Tong University, China, [email protected]
Product manufacturers pay great attentions to the number of warranty claims of soldproduct as high claims incur additional operational costs. Poisson distribution hasbeen widely used to model the claim number with the pooled Poisson intensity ratebeing referred as the nominal failure intensity rate. Since product used by differentcustomers are heterogeneous, failure intensity rates varies from product to product. Thecount of warranty claims are often skewed and overdispersed. Negative binomial (NB)distribution which is the compound of the Poisson-gamma distribution has been widelyused to model the overdispersed count data. However the use of the NB distributionholds the assumption that product’s intensity rates are randomly distributed from timeto time which is unrealistic in many cases. In this paper, the impact of time-varyingintensity rates is evaluated. We show that conventional control limits based on theNB distribution-based Shewhart chart should be lowered to accommodate the reducedvariation of counts when product’s intensity rates become constant from time to time.
Joint optimization of adaptive multivariate control chart and maintenancepolicies for an imperfect production system 1
Shuo Huang, Jun Yang, Min Xie
School of Reliability and Systems Engineering, Beihang University, Beijing, China, and Department of Systems
Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong,
[email protected], [email protected]
This paper considers an integrated problem of adaptive statistical monitoring schemewith maintenance polices for an imperfect production system. The system is assumedto produce the same type of item with multiple quality characteristics, and the multipleindependent assignable causes may affect the quality state of the process. Therefore,an adaptive multivariate control chart is utilized to monitoring the process. When anout-of control (OOC) signal is issued by the control chart, the minimum maintenance is
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conducted, while corrective maintenance is initiated once a failure of the equipment isoccurred. Moreover, the time-based preventive maintenance policy is also considered.Based on these realistic assumptions and by using Markov process, the optimizationmodel has been developed to find the optimal warning limit, control limit, samplingscheme, and maintenance polies by minimizing the expected total costs per time. Thecomparison study with other traditional models is carried out to verify the superiorityof our proposed model. Finally, a real case example is presented to illustrate theapplication of the model.
Spatial cluster detection in mobility networks: a copula approach ö
Heeyoung Kim, Rong Duan, Sungil Kim, Jaehwan Lee, Guang-Qin Ma
Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea, [email protected];
Huawei Technologies, Shenzhen, China, [email protected]; Ulsan National Institute of Science and Tech-
nology, Republic of Korea, [email protected]; Korea Advanced Institute of Science and Technology, Dae-
jeon, Republic of Korea; AT&T Laboratories, Bedminster, USA
In mobility network capacity planning, characterizing the mobility network traffic is oneof the most challenging tasks. Besides the growth trend and multiple periodic temporalpatterns for normal traffic, the problem is complicated by the occasionally intensetraffic for special events and its dynamic spatial relationships. Identifying the areasthat have different traffic patterns compared with their neighboring areas is a problemof spatial hotspot detection. In the paper, a copula-based method is proposed: using amultivariate extreme value copula, the upper tail dependence of the traffic distributionsof neighboring cell towers is evaluated, and then a cluster of multiple time series (i.e.multiple cell towers) with high upper tail dependence is detected. The method proposedis validated by using synthetic data as well as real mobility traffic data.
On the Calculation of the ARL for Beta EWMA Control Charts h
Sven Knoth
Helmut Schmidt U Hamburg, Germany, [email protected]
Accurate calculation of the Average Run Length (ARL) for EWMA (exponentiallyweighted moving average) charts might be a tedious task. The omnipresent Markovchain approach is a common and effective tool to perform these calculations — seeLucas & Saccucci (1990) and Saccucci & Lucas (1990) for its application in case ofEWMA charts. However, Crowder (1987a) and Knoth (2005) provided more sophisti-cated methods from the rich literature of numerical analysis to solve the ARL integralequation. These algorithms lead to very fast implementations for determining the ARLwith high accuracy such as Crowder (1987b) or the R package spc (Knoth 2019) withits functions xewma.arl() and sewma.arl(). Crowder (1987a) utilized the popularNystrom method (Nystrom 1930) which fails for bounded random variables present, forexample, within an EWMA chart monitoring the variance. For the latter, Knoth (2005)utilized the so-called collocation method. It turns out that the numerical problems areeven more severe for beta distributed random variables, which are bounded from bothsides, typically on (0, 1). Here, we illustrate these subtleties and provide extensionsfrom Knoth (2005) to achieve high accuracy in an efficient way.
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References
1. Crowder (1987a). ”A simple method for studying run-length distributions ofexponentially weighted moving average charts”. Technometrics, 29(4), 401–407.
2. Crowder (1987b). ”Average run lengths of exponentially weighted moving av-erage control charts”. Journal of Quality Technology, 19(3), 161–164.
3. Knoth (2005). ”Accurate ARL computation for EWMA-S2 control charts”.Statistics and Computing, 15(4), 341–352.
4. Knoth (2019). spc: Statistical Process Control – Collection of Some UsefulFunctions. R package version 0.6.1.
5. Lucas & Saccucci (1990). ”Exponentially weighted moving average controlschemes: Properties and enhancements”. Technometrics, 32(1), 1–12.
6. Nystrom (1930). ”Uber die praktische Auflosung von Integralgleichungen mitAnwendungen auf Randwertaufgaben”. Acta Mathematica, 54(1), 185–204.
7. Saccucci & Lucas (1990). ”Average run lengths for exponentially weightedmoving average control schemes using the Markov chain approach”. Journal ofQuality Technology, 22, 154–162.
Is the Benford Law useful for Data Quality Assessment? h
Wolfgang Kossler, Hans-Joachim Lenz, Xing D. WangInstitut fur Informatik, Humboldt-Universitat Berlin, Germany, [email protected]; Fachbereich
Wirtschaftswissenschaft, Freie Universitat Berlin, Germany, [email protected]
Data quality and data fraud are of increasing concern in the digital world. The Ben-ford Law is world-wide used for detecting non-conformance or data fraud of numericaldata. It says that the first non-zero digit, D1, of a data item from a universe is notuniformly distributed. The shape is roughly logarithmically decaying starting withP (D1 = 1) ∼= 0, 3. It is self-evident that Benford’s law should not be applied for de-tecting manipulated or faked data before having examined the goodness of fit of theprobability model while the business process is free of manipulations, i.e. is ’undercontrol’. In this paper we are concerned with the goodness of fit phase, not withfraud detection itself. We selected five empirical numerical data sets of various samplesizes being publicly accessible as a kind of benchmark, and evaluated the performanceof three statistical tests. The tests include the chi-square goodness of fit test, whichis used in business as a standard test, the Kolmogorov-Smirnov test, and the MADtest as originated by Nigrini (1992). We are analyzing further whether the invarianceprinciples of Benford’s law might improve the tests or not.
A Flexible Monitoring Method for High-Yield Processes 1
Tahir Mahmood, Ridwan A. Sanusi, Min XieDepartment of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon,
Hong Kong, [email protected], [email protected], [email protected]
In recent years, advancement in technology brought revolutionary change in the man-ufacturing processes. Therefore, manufacturing systems produce a large number of
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conforming items with a small number of non-conforming items. Resulting datasetusually contains a large number of zeros with a small number of count observations.It is claimed that the excess number of zeros may cause over-dispersion in the data(i.e., when variance exceeds mean), which is not entirely correct. Actually, an excessnumber of zeros reduce the mean of a dataset which causes inflation in the dispersion.Hence, modeling and monitoring of the products from high-yield processes have be-come a challenging task for quality inspectors. From these highly efficient processes,produced items are mostly zero-defect and modeled based on the zero-inflated distri-butions (e.g., zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB)distributions). A control chart based on the ZIP distribution is used to monitor thezero-defect process. However, when additional over-dispersion exist in the zero-defectdataset, a control chart based on the ZINB distribution is a better alternative. Usually,it is difficult to ensure that data is over-dispersed or under-dispersed. Hence, a flex-ible distribution named zero-inflated Conway–Maxwell–Poisson (ZICMP) distributionis used to model over or under-dispersion zero-defect dataset. In this study, controlcharts are designed based on the ZICMP distribution, which provides a flexible mon-itoring method for quality practitioners. A simulation study is designed to access theperformance of the proposed monitoring methods and their comparison with existingcounterparts.
A Statistical (Process Monitoring) Perspective on Human PerformanceModeling I
Fadel M. Megahed, L. Allison Jones-Farmer, Manar Mohamed, Steven E. Rigdon
Miami University, Oxford, OH, USA, [email protected], [email protected],
[email protected]; Saint Louis University, St. Louis, MO, USA, [email protected]
With the continued technological advancements in mobile computing, sensors, andartificial intelligence methodologies, cyber-physical convergence is becoming more per-vasive. Consequently, personal device data can be used as a proxy for the “humanoperator”, creating a digital signature of their typical usage. Examples of such datasources include: wearable sensors, motion capture devices, and sensors embedded inwork stations. Our motivation behind presenting this paper study is to encourage thequality community to investigate relevant research problems that pertain to humanoperators. To frame our discussion, we examine three application areas (with distinctdata sources and characteristics) for “human performance modeling”: (a) identificationof physical human fatigue using wearable sensors/accelerometers; (b) capturing changesin a driver’s safety performance based on fusing on-board sensor data with online APIdata; and (c) human authentication for cyber-security applications. Through three casestudies, we identify opportunities for applying statistical (process monitoring) method-ologies and opportunities for future work. To encourage future examination by thequality community, we host our data, code and analysis on an online repository.
Rapid Detection of Hot-spot by Tensor Decomposition with Application toWeekly Gonorrhea Data h
Yujie Zhao, Hao Yan, Sarah E. Holte, Roxanne P. Kerani, Yajun Mei
School of Industrial and Systems Engineering, Georgia Tech, Atlanta, GA, USA, [email protected]; School
of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA,
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[email protected]; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA,
USA, [email protected]; Department of Medicine, University of Washington, and Public Health-Seattle &
King County, Seattle, WA, USA, [email protected]; School of Industrial and Systems Engineering, Georgia Tech,
Atlanta, GA, USA, [email protected]
In many bio-surveillance and healthcare applications, data sources are measured frommany spatial locations repeatedly over time, say, daily/weekly/monthly. In these ap-plications, we are typically interested in detecting hot-spots, which are defined as somestructured outliers that are sparse over the spatial domain but persistent over time.In this paper, we propose a tensor decomposition method to detect when and wherethe hot-spots occur. Our proposed methods represent the observed raw data as athree-dimensional tensor including a circular time dimension for daily/weekly/monthlypatterns, and then decompose the tensor into three components: smooth global trend,local hot-spots, and residuals. A combination of LASSO and fused LASSO is used toestimate the model parameters, and a CUSUM procedure is applied to detect whenand where the hot-spots might occur. The usefulness of our proposed methodology isvalidated through numerical simulation and a real-world dataset in the weekly numberof gonorrhea cases from 2006 to 2018 for 50 states in the United States.
ARL-unbiased CUSUM schemes to monitor binomial counts h
Manuel Morais, Sven Knoth, Camila Jeppesen Cruz, Christian Weiß
CEMAT & Department of Mathematics, Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal,
[email protected], [email protected]; Institute of Mathematics and Statistics, Department of
Economics and Social Sciences, Helmut Schmidt University Hamburg, Germany, [email protected], weissc@hsu-
hh.de
Abstract Counted output, such as the number of defective items per sample, is of-ten assumed to have a marginal binomial distribution. The integer and asymmetricalnature of this distribution and the value of its target mean hinders the quality controlpractitioner from dealing with a chart for the process mean with an pre-stipulated in-control average run length (ARL) and the ability to swiftly detect not only increasesbut also decreases in the process mean.
In this paper we propose ARL-unbiased cumulative sum (CUSUM) schemes to swiftlydetect both increases and decreases in the mean of independent and identically dis-tributed binomial counts and first-order autoregressive binomial output. These schemestake longer (in average) to trigger a false alarm than to detect any shifts in the processmean and their in-control ARL coincide with the pre-specified in-control ARL.
We use the R statistical software to provide compelling illustrations of all these CUSUMschemes.
We also discuss the extension of this ARL-unbiased design for other independent andautocorrelated types of counts.
Quality control activities are a challenge for reducing variability h
Ken Nishina
Department of Business Administration, Aich Institute of Technology, Jiyugaoka, Chikusa-ku Nagoya, Japan,
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It is well known that reducing variability is the basis of quality control activities. Theproduction process can be roughly regarded as a value chain, which is composed ofcustomer’s voice, product planning, product design, and product manufacturing. Inthe outcome of the value chain three kinds of variability can be considered. The firstone is the variability before the shipment to the market. Another one is the variabilityafter the shipment to the market and the last one is the variability of satisfaction of themarket. Quality control activities can be regarded as process of thinking about whatcan be done to reduce the three variabilities and how to take actions to ensure qualityfor customers by implementing these prior steps. Many proposals and improvementsin the value chain have been implemented to reduce variabilities. In this research, astructure of the variabilities in the value chain and a relationship among the variabilitiesare shown; then a classification and a systematization of approaches of reduction of thevariabilities are discussed.
The variability before the shipment to the market, the variability after the shipmentto the market and the variability of satisfaction of the market are caused by the noisesin the manufacturing processes, the noises in the surrounding environment and theindividual feeling of customers, respectively. Many situations can be considered buttheir structures consist of causes, effects and their causality. In particular situationsan action for the causes can be taken, but in another situations no action can be takenfor the causes. By considering many situations for reducing the variability, and byclassifying them, the following four approaches can be systematized;
• Approach A: Taking action for effects,
• Approach B: Taking action for causes,
• Approach C: Observing the situation of causes and taking action for effects inaccordance to the situation,
• Approach D: Taking action for the causality.
In summary, this research shows how the four approaches have been utilized to reducethe variabilities are shown by overlooking the value chain.
Parallelized Monitoring of Dependent Spatiotemporal Processes h
Philipp OttoInstitute of Cartography and Geoinformatics, Faculty of Civil Engineering and Geodetic Science, Leibniz Uni-
versity Hannover, Germany, [email protected]
With the growing availability of high-resolution spatial data, such as high-definitionimages, three-dimensional point clouds of light detection and ranging (LIDAR) scan-ners, or communication and sensor networks, it might become challenging to detectchanges and simultaneously account for spatial interactions in a timely manner. Todetect local changes in the mean of isotropic spatiotemporal processes with locally con-strained dependence structures, we have proposed a monitoring procedure that can becompletely run on parallel processors. This allows for fast detection of local changes(i.e., in the case that only a few spatial locations are affected by the change). Dueto parallel computation, high-frequency data could also be monitored. Hence, we ad-ditionally focused on the processing time required to compute the control statistics.
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Finally, the performance of the charts has been analyzed using a series of Monte Carlosimulation studies.
A CUSUM control chart for autocorrelated binary sequences 1
Marcus B. Perry, Zhi Wang
Department of Info Systems, Statistics, and Management Science, The University of Alabama, USA,
Recent advances in data acquisition and storage technologies have allowed for the rapidcollection of data over time. It is well known that an increase in the sampling frequencywill often induce positive autocorrelation into the data sequence. This presents a sig-nificant challenge to the modern quality practitioner when using traditional controlcharts, i.e., a traditionally designed control chart applied to positively autocorrelateddata will yield a false alarm rate much greater than expected. For continuous variables,the most common strategy for monitoring an autocorrelated process is residual-based,i.e., monitor the residuals obtained after fitting an assumed time series model to anin-control historical data set. However, model misspecification is all too common inpractice, and consequently, leads to even more difficulty in controlling the false alarmrate of such monitoring schemes. To more precisely control the false alarm rate, onecan take an alternative approach. Specifically, one can monitor the resulting (and oftenautocorrelated) binary sequence obtained from “hard clipping” a continuous process.By hard clipping we mean that we transform the continuous process into a two-valuedprocess, i.e., a binary sequence. The advantage of monitoring the hard-clipped processinstead of the original continuous process is that one does not need to specify a timeseries model a priori, and therefore, the false alarm rate for a given monitoring strategywill not be affected by model misspecification. The obvious disadvantage is the loss ofinformation induced by the clipping operation. In this effort, we assume this tradeoffis acceptable, and derive a CUSUM control chart for autocorrelated binary sequencesthat is directly applicable to the monitoring of hard-clipped processes. We study theaverage run length (ARL) performance of our new control chart, relative to existingCUSUM strategies, and demonstrate its application on both simulated and real data.
Statistical Methods & Tools Enabling Measurement Quality h
Antonio Possolo
National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA, [email protected]
An overview, illustrated with examples, of applications of statistical methods thatsupport measurement quality and guarantee the intercomparability of measurementsmade worldwide, in all fields of commerce, industry, science, and technology, includingmedicine.
These methods enable a rigorous definition of measurement uncertainty, and providethe means to evaluate it quantitatively, both for qualitative measurands (for example,the sequence of nucleobases in a DNA strand) and for quantitative measurands (forexample, the mass fraction of arsenic in rice).
Measurement quality comprises multiple attributes: calibration against reliable stan-dards, traceability to the international system of units, measurement uncertainty that
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realistically captures contributions from all significant sources of uncertainty, and fitnessfor purpose of the measurement results (measured values and associated uncertainties).
Statistical methods play key roles in the quality system that validates the measurementservices (reference materials, calibrations, reference data, and reference instruments)provided by NIST. And these services in turn support measurement quality in labo-ratories, plants, and monitoring stations throughout the world, contributing to ensurefood safety, to manufacture reliable products, and to monitor industrial and naturalprocesses accurately.
Of particular interest are statistical tools to evaluate measurement uncertainty and forconsensus building, which serves to blend measurement results obtained independentlyfor the same measurand, and also to intercompare measurement capabilities of differentlaboratories. In this presentation we will describe the principles, models, and capabili-ties of the NIST Uncertainty Machine and of the NIST Consensus Builder, web-basedtools that make freely available to metrologists everywhere state-of-the-art technologyto evaluate and maintain measurement quality.
Some Ideas on Selecting the best Population 1
Sheldon Ross
USC Viterbi, School of Engineering, University of Southern California, USA, [email protected]
Consider the classical problem in which there are n populations, where the membersof population i have values from a Bernoulli distribution with unknown parameter pi.The idea is to sample from these populations, stopping at some point and declaringwhich population has the largest parameter. We want a procedure that has both a highprobability of a correct decision and a relatively small mean number of samplings. Wealso consider the normal case, where population i values have unknown mean mi andunknown variance vi.
Monitoring Image Processes – Overview and Comparison Study h
Yarema Okhrin, Wolfgang Schmid, Ivan Semeniuk
Department of Statistics, University of Augsburg, Germany, [email protected]; Department
of Statistics, European University Viadrina, Frankfurt(Oder), Germany, [email protected],
In this paper an overview of recent developments on monitoring image processes is pre-sented. We consider a quite general model where in the in-control state spatially corre-lated pixels are monitored. The described control charts are based on non-overlappingregions of interest. This leads to a dimension reduction but, nevertheless, we still facea high-dimensional data set. We consider residual charts and charts based on the gen-eralized likelihood ratio (GLR) approach. For the calculation of the control statistic ofthe latter chart the inverse of the covariance matrix of the process must be determined.However, in a high-dimensional setting this is time consuming and moreover, the em-pirical covariance matrix does not behave well in such a case. This is the reason whytwo further control charts are considered which can be regarded as modifications of theGLR statistic. Within an extensive simulation study the presented control charts arecompared with each other using the median run length as a performance criterion.
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A Distribution-Free Control Chart for Monitoring High-DimensionalProcesses based on Interpoint Distances ö
Lianjie Shu and Jinyu FanFaculty of Business Administration, University of Macau, Taipa, Macau, [email protected]
The high dimensionality presents a new challenge to the traditional tools in multivariatestatistical process control. Various tests for mean vectors in high dimensional situa-tions have been discussed recently; however, they have been rarely adapted to processmonitoring. This paper develops a distribution-free control chart based on interpointdistances for monitoring mean vectors in highdimensional settings. The proposed ap-proach is very general as it represents a class of nonparametric control charts basedon distances. Numerical results show that the proposed control chart is efficient indetecting mean shifts in both symmetric and heavy-tailed distributions.
An Approach to Monitoring Time Between Events When Events areFrequent h
Ross Sparks, Aditya Joshi, Cecile Paris, Sarvnaz KarimiCSIRO Australia, Data61, Sydney, Australia, [email protected]
This paper focuses on monitor plans aimed at the early detection of the increase inthe frequency of events. The literature recommends either monitoring the Time Be-tween Events (TBE) if events are rare or counting the number of events per unit non-overlapping time intervals if events are not rare. However, recently work has suggestedthat monitoring counts in preference to TBE is not recommended even when countsare moderately high. Monitoring TBE is the real-time option for outbreak detection,because outbreak information is accumulated when an event occurs. This is preferredto waiting for the end of a period to count events. If the TBE reduces significantly, thenthe incidence of these events increases significantly. This paper explores TBE when thedaily counts are quite high. We consider the case when TBEs are Weibull distributed.The paper derive TBE plans that can be used when the daily counts are quite high (inthe thousands per day).
Assessing a Binary Measurement System with Operator and Random PartEffects h
Stefan H. Steiner, Jock MacKay, Kevin FanDept. of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada,
Consider the assessment of a binary measurement system with multiple operators whena gold standard measurement system is also available (for the assessment study). Dataare collected as in a gauge repeatability and reproducibility plan for a continuous mea-surement system and each operator in the study measures a number of parts multipletimes. We characterize the performance of the measurement system by estimating theprobabilities of accepting a non-conforming part and of rejecting a conforming part.To model the data, we assume that some parts are more difficult to correctly classifythan others and so choose to use random part effects. We consider two cases, modelingthe operator effects as fixed or random. For each, we study a conditional and marginal
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model and their corresponding estimates of the parameters of interest. We also provideguidance on the planning of the assessment study in terms of the number of parts,number of operators and number of repeated measurements.
On Statistical Quality Control Procedures for High-Dimensional TimeSeries 1
Ansgar Steland
Institute of Statistics, RWTH Aachen University, Aachen, Germany, [email protected]
Quality control and monitoring in industry as well as sciences has to deal with datasets and data streams characterized by a large number of quality features comparedto the number of observations. Areas of applications range from environmentrics, e.g.to analyze and monitor climate variables, to industrial assembly-line production wherehigh-dimensional sensors such as cameras or pressure and temperature sensors recordimages, measurement curves and multivariate data describing the quality of manufac-turing in real-time.
We discuss models, procedures and approaches designed to analyze and control suchhigh-dimensional quality features. As a particular class of models suited to describesuch data, we shall deal with high-dimensional vector autoregressive models for whitenoise as well as colored noise processes. The question arises how one can reduce thedimensionality of the data. An effective approach to handle a large number of variablesis to consider projections of the data. Here various methods ranging from knownfixed bases, e.g. derived from Walsh functions heavily used in data compression, todata-dependent techniques such as auto-encoders from machine learning or (sparse)PCA coming from statistics, can be used. Some of these techniques and ideas can beconsidered classic, but for high-dimensional data classic algorithms and statistical toolsassuming fixed dimension fail and need to be modified appropriately.
The statistical methods discussed in this talk project the data on axes satisfying certainsparsity constraints to allow for valid statistical inference. The proposed procedureswill be illustrated to some extent by real data from environmental and industrial ap-plications and will be investigated by simulations.
Analysis of Measurement Precision Experiment with Categorical Variablesh
Tomomichi Suzuki, Jun-ichi Takeshita, Mayu Ogawa, Xiao-Nan Lu, YoshikazuOjima
Department of Industrial Administration, Tokyo University of Science, Japan, [email protected]; Research Insti-
tute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology
(AIST), Onogawa, Tsukuba, Ibaraki, Japan, [email protected]; Department of Industrial Administration,
Tokyo University of Science, Japan, [email protected], [email protected], [email protected]
Evaluating performance of a measurement method is essential in metrology. Conceptsof repeatability and reproducibility are introduced in ISO 5725 series including how torun and analyse experiments (usually collaborative studies) to obtain these precisionmeasures. ISO 5725-2 describe precision evaluation in quantitative measurements butnot in qualitative measurements. Some methods have been proposed for qualitative
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measurements cases such as Wilrich (2010), de Mast et al. (2010), Bashkansky et al.(2010). Item response theory (Muraki 1992) is another methodology that can be usedto analyse qualitative data. Utilizing these methods, analysis of measurement precisionexperiment is investigated when the measurements involve categorical variables.The data analysed are from the precision experiment of intratracheal administrationtesting (AIST 2018) whose objectives were to study the precision of the standardizedtest method for evaluating the pulmonary toxicity of nanomaterials. In such experi-ments, dose-response relationship also need to be considered which makes the situationmore complicated. We discuss how these data should be analysed using actual data.
References
1. International Organization for Standardization (1994). ISO 5725-1 Accuracy(trueness and precision) of measurement methods and results – Part 1: Generalprinciples and definitions. Geneva: ISO.
2. International Organization for Standardization (1994). ISO 5725-2 Accuracy(trueness and precision) of measurement methods and results – Part 2: Basicmethod for the determination of repeatability and reproducibility of a standardmeasurement method. Geneva: ISO.
3. P.-Th. Wilrich (2010). “The determination of precision of qualitative measure-ment methods by interlaboratory experiments”. Accreditation and Quality As-surance 15, 439–444.
4. J. de Mast and W. van Wieringen (2010). “Modeling and Evaluating Repeatabil-ity and Reproducibility of Ordinal Classifications”. Technometrics 52(1), 94–106.
5. E. Bashkansky, T. Gadrich and I. Kuselman (2012). “Interlaboratory comparisonof test results of an ordinal or nominal binary: analysis of variation”. Accredita-tion and Quality Assurance 17, 239–444.
6. E. Muraki, (1992). “A Generalized Partial Credit Model: Application of an EMAlgorithm”. Applied Psychological Measurement 16(2), 159–176.
7. The National Institute of Advanced Industrial Science and Technology (AIST)(2018). “Annual Report on a Project ‘Survey on standardization of intratrachealadministration study for nanomaterials and relatedissues’ ”. In Japanese, accessed 2019-05-31: http://www.meti.go.jp/meti\
_lib/report/H29FY/000102.pdf.
Personalized Health Management for Elderly Care 1
Kwok-L. Tsui
School of Data Science, Dept. of Systems Engineering & Engineering Management, City University of Hong
Kong, Kowloon, Hong Kong, [email protected]
Due to the advancement of computation power, sensor technologies, and data collec-tion devices, the field of systems monitoring and health management have been evolvedover the past several decades under different names and application domains, such asstatistical process control (SPC), process monitoring, health surveillance, prognostics
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and health management (PHM), engineering asset management (EAM), personalizedmedicine, etc. There are tremendous opportunities in interdisciplinary research of per-sonalized health management through integration of SPC, system informatics, dataanalytics, etc. In this talk we will present our views and experience in the challengesand opportunities, and applications of health monitoring and management for elderlycare.
Quality Big Data 1
Fugee TsungDepartment of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology,
Clear Water Bay, Kowloon, Hong Kong, [email protected]
This talk will present and discuss the challenges and opportunities that quality engi-neers and managers face in the era of big data. The ability to separate signal and noisein the data-rich-information-poor environment would be the key, especially for indus-trial big data. Emerging research issues include data fusing with heterogeneous datasources, statistical transfer learning, and statistical process control and monitoring forbig data streams.
A Dynamic Field Monitoring Approach: An Application to Thermal FieldMonitoring ö
Di Wang, Xi ZhangDept. of Industrial Engineering and Management, Peking University, 5 Yiheyuan Rd., Haidian, Beijing, China,
Field monitoring serves as an importance supervision tool in a variety of engineeringdomains. An efficient monitoring would trigger an alarm timely once it detects an out-of-control event by learning the state change from the collected sensor data. However,in practice, multiple sensor data may not be gathered appropriately into a database forsome unexpected reasons, leading to a large number of missing data as well as inaccurateor delayed detection, and this fact poses a great challenge for field monitoring in sensornetworks. To address this issue, we develop a multitask-learning based field monitoringmethod to achieve an efficient spatial detection. Specifically, we adopt a log likelihoodratio (LR)-based MCUSUM control chart given spatial correlation among neighboringregions within the monitored field. To deal with the missing data problem, we integratea multitask learning model into the LR-based MCUSUM control chart in the sensornetwork. Both simulation and real case studies are conducted to validate our proposedapproach, and the results show that our approach outperforms the benchmark methodwithout considering multitask learning.
Exploring the usefulness of Functional Data Analysis for HealthSurveillance h
Zezhong Wang, Inez Maria ZwetslootDepartment of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon,
Hong Kong, [email protected], [email protected]
Health surveillance is the process of ongoing systematic collection, analysis, interpre-
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tation, and dissemination of health data for the purpose of preventing and controllingdisease, injury, and other health problems. Health surveillance data is often recordedcontinuously over a selected time interval or intermittently at several discrete timepoints. These can often be treated as functional data, and hence functional data analy-sis (FDA) can be applied to model and analyze these type of health data. One objectivein health surveillance is early event detection. Statistical process monitoring tools areoften used for online event detecting. In this paper, we explore the usefulness of FDAfor prospective health surveillance and propose two strategies for monitoring using con-trol charts. We apply these strategies to monthly ovitrap index data. These vector dataare used in Hong Kong as part of its dengue control plan.
A Diagnostic Procedure For High-Dimensional Data Streams Via MissedDiscovery Rate Control ö
Dongdong Xiang
Faculty of Economics and Management, East China Normal University, Shanghai, China, [email protected]
Monitoring complex systems involving high-dimensional data streams provides quickreal-time detection of abnormal changes of system performance but accurate and ef-ficient diagnosis of the streams responsible has also become increasingly important inmany data-rich statistical process control (SPC) applications. Existing diagnostic pro-cedures, designed for low/moderate dimensional multivariate process, may miss toomuch important information in the out-of-control (OC) streams with a high signal-to-noise ratio (SNR) or waste too many resources.
An Average Loss Control Chart Under a Skewed Process Distribution h
Su-Fen Yang, Shan-Wen Lu
Department of Statistics, National Chengchi University Taipei, Taiwan,
[email protected], [email protected]
In global market, the loss of products is a crucial factor separating competitive com-panies in numerous industries. Firms widely employ a loss function to measure theloss caused by a deviation of the quality variable from the target value. From theview of Taguchi’s philosophy, monitoring this deviation from the process target valueis important. In practice, there are many quality data with the distributions of notnormal but skewed. This paper aims at developing an average loss control chart formonitoring quality loss variation under skewed distributions. It is equivalently in de-tecting the difference of process location and target and/or dispersion. Both the caseswith average loss and exponentially weighted moving average (EWMA) average losscontrol charts are considered. The statistical properties of the proposed control chartsare investigated. The out-of-control process detection performance of the proposed losscontrol charts is measured using the average run length. The average loss and EWMAaverage loss control charts illustrate the best performance in detection out-of-controlloss variation for a left-skewed process distribution.
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Target-setting in Multi-Stage Processes Monitoring 1
Emmanuel YashchinIBM, Thomas J. Watson Research Ctr., Yorktown Heights, USA, [email protected]
We consider early warning systems (EWS) for monitoring multi-stage data, in whichdownstream variables undergo changes associated with upstream process stages. In suchapplications, the EWS monitoring arm acts as a search engine that analyses a numberof data streams for each monitored variable, as the problems of change detection andidentification of the change-causing stage are handled jointly. Given massive amounts ofdata involved in analysis, it is important to achieve an acceptable balance between falsealarms and sensitivity requirements, by focusing on changes of practical significance.The role of the target-setting arm of EWS is to ensure and maintain this balance viasuitable selection of control scheme parameters. In this paper, we discuss principles ofdeveloping and managing targets, with examples from a supply chain operation.
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Index
Ang, Janice J., 7
Bodnar, Olha, 6
Cruz, Camila Jeppesen, 12
Driscoll, Anne R., 6Duan, Rong, 10
Elster, Clemens, 6
Fan, Jinyu, 16Fan, Kevin, 17
Gan, Fah F., 7Goedhart, Rob, 7
Han, Dong, 8Holte, Sarah E., 12Hryniewicz, Olgierd, 8Huang, Shuo, 9Huang, Wenpo, 9
Jones-Farmer, L. Allison, 11Joshi, Aditya, 16
Kaczmarek-Majerm Katarzyna, 8Karimi, Sarvnaz, 16Kerani, Roxanne P., 12Kim, Heeyoung, 10Kim, Sungil, 10Knoth, Sven, 10, 12Koh, Wei L., 7Kossler, Wolfgang, 10
Lee, Jaehwan, 10Lenz, Hans-Joachim, 10Lu, Shan-Wen, 21Lu, Xiao-Nan, 18
Ma, Guang-Qin, 10MacKay, Jock, 17Mahmood, Tahir, 11Megahed, Fadel M., 11Mei, Yajun, 12Mohamed, Manar, 11Morais, Manuel, 12
Nishina, Ken, 13
Ogawa, Mayu, 18Ojima, Yoshikazu, 18Okhrin, Yarema, 16Opara, Karol, 8Otto, Philipp, 14
Paris, Cecile, 16Peryy, Marcus B., 14Possolo, Antonio, 15
Rigdon, Steven E., 11Ross, Sheldon, 15
Sanusi, Ridwan A., 11Schmid, Wolfgang, 16Semeniuk, Ivan, 16Shi, Chenyou, 9Shu, Lianjie, 16Sparks, Ross, 16Steiner, Stefan H., 17Steland, Ansgar, 17Suzuki, Tomomichi, 18
Takeshita, Jun-ichi, 18Tsui, Kwok-L., 19Tsung, Fugee, 8, 19
Wang, Di, 19Wang, Xing D., 10Wang, Zezhong, 20Wang, Zhi, 14Wei, Jiang, 9Weiß, Christian, 12Woodall, William H., 6
Xian, Jinguo, 8Xiang, Dongdong, 20Xie, Min, 9, 11
Yan, Hao, 12Yang, Jun, 9Yang, Su-Fen, 21Yashchin, Emmanuel, 21Ye, Yunfei, 8
Zhang, Xi, 19Zhao, Yujie, 12Zou, Changliang, 6Zwetsloot, Inez Maria, 20
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