Prof. Irad E. Ben-Gal- List of Selected Abstracts
1. PROBABILISTIC SEQUENTIAL METHODOLOGY FOR DESIGNING A FACTORIAL SYSTEM
WITH MULTIPLE RESPONSES. (pdf File)
International
Journal of Production Research, Vol. 37(12) 2703-2724.
Abstract
This paper addresses the problem of
optimizing a factorial system with multiple responses. A heuristic termed probabilistic
sequential methodology (PSM) is proposed. The PSM identifies those designs
that maximize the likelihood of satisfying a given set of functional
requirements. It is based on sequential experimentation, statistical inference
and a probabilistic local search. The PSM comprises three main steps: (1)
screening and estimating the main location and dispersion effects by applying
fractional factorial experiments (FFE) techniques; (2) based on these effects,
establishing probabilistic measures for different combinations of factor-
Keywords: probability search, design of experiments, optimization, ranking and selection, statistical inference, location effects, dispersion effects, Flexible Manufacturing Systems.
2. ON THE UNCERTAINTIES OF DECENTRALIZED CONTROLLERS IN A TRANSFER PRODUCTION LINE. (pdf File)
Ben-Gal I., Khmelnitsky E., (2000).
IIE Transactions on Design and Manufacturing, 32, 953-961,
Abstract
In this paper, an information theoretic
approach is applied to analyze the performance of a decentralized control
system. The control system plays the role of a correcting device which
decreases the uncertainties associated with state variables of a production
line by applying an appropriate ``correcting signal'' for each deviation from
the target. In particular, a distributed feedback control policy is considered
to govern a transfer production line, which consists of machines and buffers
and processes a single part type in response to a stochastic demand. It is
shown how the uncertainty of the demand propagates dynamically into the
production system, causing uncertainties associated with buffer
Keywords: decentralize
and centralize controllers, entropy measure, control complexity, stochastic
demands, feedback control policy, transfer lines, information theory.
3. AN APPLICATION OF INFORMATION THEORY AND ERROR-CORRECTING CODES TO
FRACTIONAL FACTORIAL EXPERIMENTS. (pdf
File)
Journal
of Statistical Planning and Inference. 92/1-2, 267-282.
Abstract
The objective of design
of experiments (DOE) is addressed by introducing an information optimality
criterion, which is based on concepts adopted from information theory.
In particular, experiments are specified to maximize the information in the
system responses about estimators of the system parameters. It is shown that
one has to maintain certain resolution of the design matrix to maximize
the information, obtainable by a design, about a system described by a linear
model with interactions. The correspondence between error-correcting codes
and fractional factorial experiments provides a method to attain the
required resolution with a smaller fractional factorial experiment by
increasing the number of
Keywords: Design of Experiments, information in experiments, H-optimal designs, coding theory, design matrix, fractional factorial experiments, error correcting codes, design resolution, alphabetic optimality.
4. DESIGN OF CONTROL AND MONITORING RULES FOR STATE DEPENDENT PROCESSES (pdf File)
Ben-Gal I.,
Shmilovici A., Morag G. (2000),
The International Journal for Manufacturing Science and Production, 3, NOS. 2-4, pp. 85-93.
Abstract
Most statistical process control (SPC) methods assume that the
faulty products are the result of independent and identically distributed error
modes in the production system. This paper proposes a general framework for
extending SPC methods to autocorrelated and state
dependent processes. The suggested methodology does not require an a-priory knowledge of the process
distribution. A context identification model is used to estimate the
probabilities of different process outputs based on the contexts in which they
appear. The Kullback-Leibler measure for the discrimination
between distributions is adapted for dealing with distribution that can be
described with context models. It is demonstrated that the Kullback-Leibler
statistic is approximately Chi-square distributed with the number of degrees of
fre
Keywords: Process
control, control charts, context algorithm, variable order Markov models,
decision trees, Kullback-Leibler, SPC,
auto-correlated data.
5. SEQUENTIAL DOE VIA DYNAMIC PROGRAMMING (pdf File)
Ben-Gal I., Caramanis M. (2002)
IIE Transactions on Quality and Reliability, (2002) 34, 1087-1100.
The paper considers a sequential design of experiments (DOE) scheme. Our objective is to maximize both information and economic measures over a feasible set of experiments. Optimal DOE strategies are developed by introducing information criteria based on measures adopted from information theory. The evolution of acquired information along various stages of experimentation is analyzed for linear models with a Gaussian noise term. We show that for particular cases, although the amount of information is unbounded, the desired rate of acquiring information decreases with the number of experiments. This observation implies that at a certain point in time it is no longer efficient to continue experimenting. Accordingly, we investigate methods of stochastic dynamic programming under imperfect state information as appropriate means to obtain optimal experimentation policies. We propose cost-to-go functions that model the trade-off between the cost of additional experiments and the benefit of incremental information. We formulate a general stochastic dynamic programming framework for design of experiments and illustrate it by analytic and numerical implementation examples.
Keywords: adaptive design of experiment,
dynamic experimentation, cost of experiments, measure for uncertainty, stochastic dynamic
programming, dynamic programming for imperfect state information, alphabetic
optimality criteria, value of information.
6. SELF-CORRECTING INSPECTION PROCEDURE UNDER INSPECTION ERRORS. (pdf File)
Ben-Gal
I., Herer Y., Raz T. (2002)
IIE Transactions on Quality and Reliability, 2002, 34(6), pp. 529-540.
Abstract
In this
paper we present a novel treatment of the inspection-system design problem when
inspection is unreliable and subject to classification errors. Our approach,
based on the theory of Error-Correcting Codes (ECC), leads to the development
of a Self- Correcting Inspection (SCI) decision rule that does not require
complete knowledge of inspection error probabilities. We show that the proposed
rule assures correct classification, if the number of inspection errors is less
than a certain number. We analyze the performance of the SCI decision rule
under different inspection situations, including some situations that are
uncommon in the field of error-correcting codes. Then, we show how the
underlying mathematical structure can be applied to determine the number of
inspections and the
Keywords: unreliable inspection, error-correcting codes, classification of errors, cost of experiments, value of information, reliability, sensors.
7. THE ERGONOMIC DESIGN OF WORKSTATION USING RAPID PROTOTYPING AND RESPONSE SURFACE METHODOLOGY. (pdf File)
Ben-Gal
I., Bukchin J. (2001)
IIE Transactions on Design and Manufacturing, 2001; 34 (4) : 375-391
Abstract
The increasing use of
computerized tools for virtual manufacturing in workstation design has two main
advantages over traditional methods; first, it enables the designer to examine
a large number of design solutions; and second, simulation of the work task may
be performed in order to obtain the values of various performance measures. In
this paper a new structural methodology for the workstation design is
presented. Factorial experiments and the response surface methodology are
integrated in order to reduce the number of examined design solutions and
obtain an estimate for the best design configuration with respect to
multi-objective requirements.
Keywords: work stations,
ergonomic design, rapid prototyping, design of
experiments, computer simulations, multi-objective criteria, bio-mechanic
design, fractional factorial experiments, and statistical inference.
8. THROUGHPUT OF MULTIPLE PART-TYPE SYSTEMS: A SUBJECTIVE LINEAR MEASURE (pdf File)
Hevron A., Khmelnitsky E., Ben-Gal I.,
(2005)
The International Journal for Manufacturing Science and Production (IJMSP), Vol. 6, no. 3, pp. 143-151.
Abstract
The term throughput, which is commonly used as a performance measure of various production systems, has not been uniquely defined for multiple part-type systems. In some cases, the procedures that were developed to maximize throughput of multiple part-type systems are difficult to evaluate and to justify. In other cases the inaccurate definition of this term motivated incorrect operational concepts. This paper discusses some of the problems in the traditional definition of throughput and suggests a new, more suitable definition for a multiple-product manufacturing system.
Keywords:
FMS, performance measure, productivity, workload, make span, cycle time, multiple
part-types, multiple models, production line, optimization, throughput measure,
production
systems, design.
9. CSPC: A MONITORING PROCEDURE FOR STATE DEPENDENT PROCESSES
Technometrics, Vol. 45, no. 4, pp. 293-311.
Abstract
Most Statistical Process
Control (SPC) methods are not suitable for monitoring non-linear and
state-dependent processes. This paper introduces the Context-based SPC (CSPC)
methodology for state-dependent data generated by a finite-memory source. The
key idea of the CSPC is to monitor the statistical attributes of a process by
comparing two context trees at any monitoring period of time. The first is a reference
tree that represents the 'in control' reference behavior of the process;
the second is a monitored tree, generated periodically from a sample of
sequenced observations,
that represents the behavior of the process at that period. The Kullback-Leibler (KL) statistic is used to measure the
relative 'distance' between these two trees, and an analytic distribution of
this statistic is derived. Monitoring the KL statistic indicates whether there
has been any significant change in the process that requires intervention. An
example of buffer-
Keywords: Nonparametric Statistical process control (SPC), distribution free
SPC, Context Tree, Kullback-Leibler statistic,
control charts,
context algorithm, variable order Markov models, decision trees,
auto-correlated data, dependent
data, stochastic processes, data analysis, production systems, queueing systems.
10. STATISTICAL PROCESS CONTROL VIA CONTEXT MODELING OF FINITE STATE
PROCESSES: AN APPLICATION TO PRODUCTION MONITORING (pdf File)
Ben-Gal I., Singer
G. (2004)
IIE Transactions on Quality and Reliability, vol. 36,
no. 5, pp.401-415.
Abstract
Conventional Statistical Process Control (SPC) schemes fail to monitor nonlinear and finite-state processes that often result from feedback-controlled processes. SPC methods that are designed to monitor autocorrelated processes usually assume a known model (often an ARIMA) that might poorly describe the real process. In this paper, we present a novel SPC methodology based on context modeling of finite-state processes. The method utilizes a series of context-tree models to estimate the conditional distribution of the process output given the context of previous observations. The Kullback-Leibler divergence statistic is derived to indicate significant changes in the trees along the process. The method is implemented in a simulated flexible manufacturing system in order to detect significant changes in its production mix ratio output.
Keywords: Nonparametric Statistical process
control (SPC), distribution free SPC, engineering process control, feedback
control, Context Tree, Kullback-Leibler statistic, control charts, context
algorithm, variable order Markov models, decision trees, auto-correlated data,
dependent data, stochastic processes, data analysis, production systems,
11. AN UPPER BOUND OF THE WEIGHT-BALANCED TESTING PROCEDURE WITH MULTIPLE
TESTERS (pdf File)
IIE Transactions on Quality and Reliability, vol. 36, no. 5, 481-491.
Abstract
This paper presents the performance of the Weight-Balanced Testing (WBT) algorithm with multiple testers. The WBT algorithm aims to minimize the expected number of (round of) tests and has been proposed for coding, memory storage, search and testing applications. It often provides reasonable results if used with a single tester. Yet, the performance of the WBT algorithm with multiple testers and particularly its upper bound have not been previously analyzed, despite the large body of literature that exists on the WBT algorithm, and the recent papers that suggest its use in various testing applications. Here we demonstrate that WBT algorithm with multiple testers is far from being the optimal search procedure. The main result of this paper is the generalization of the upper bound on the expected number of tests previously obtained for a single-tester WBT algorithm. For this purpose, we first draw an analogy between the WBT algorithm and alphabetic codes; both being represented by the same Q-ary search tree. The upper bound is then obtained on the expected path length of a Q-ary tree, which is constructed by the WBT algorithm. Applications to the field of testing and some numerical examples are presented for illustrative purposes.
Keywords: decision trees, dynamic testing, adaptive search, weight balance algorithm, queueing systems, multiple sensors, search trees, alphabetic codes, Huffman codes, Hu-Tucker codes, Vitter algorithm, data compression, entropy, information theory.
12. USING A PSEUDO-STOCHASTIC APPROACH FOR MULTIPLE-PARTS SCHEDULING ON AN
UNRELIABLE MACHINE. (pdf File)
Herbon A., Khmelnitsky E., Ben-Gal I.
(2005)
IIE Transactions on Operations Engineering, vol. 37, no. 3, pp. 189 - 199.
Abstract
In this paper we follow previous “pseudo-stochastic” approaches that solve stochastic control problems by using deterministic optimal control methods. Similarly to the certainty equivalence principle, the suggested model maximizes a given profit function of the expected system outcome. However, unlike the certainty equivalence principle, we model the expected influences of all future events, including those that are expected beyond the planning horizon, as encapsulated by their density functions and not only by their mean values. The model is applied to the optimal scheduling of multiple part-types on a single machine that is subjected to random failures and repairs. The objective of the scheduler is to maximize the profit function of the produced multiple-part mix. A numerical study is performed to evaluate the suggested pseudo-stochastic solutions under various conditions. These solutions are compared to a profit upper bound of the stochastic optimal control solutions.
Keywords:
pseudo-stochastic, multiple part type, scheduling, unreliable machine,
stochastic control, uncertainty equivalence principle, stochastic optimization,
numerical methods, dynamic programming.
13. IDENTIFICATION OF TRANSCRIPTION FACTOR BINDING SITES WITH VARIABLE-ORDER BAYESIAN NETWORKS (pdf File)
Ben-Gal I., Shani A., Gohr A., Grau J., Arviv S., Shmilovici A., Posch S. and Grosse I., (2005)
Bioinformatics,
vol. 21, no. 11, 2657-2666.
Abstract
Motivation: We propose a new class of variable-order Bayesian network (VOBN) models for the identification of transcription factor binding sites (TFBSs). The proposed models generalize the widely used position weight matrix (PWM) models, Markov models and Bayesian network models. In contrast to these models, where for each position a fixed subset of the remaining positions is used to model dependencies, in VOBN models, these subsets may vary based on the specific nucleotides observed, which are called the context. This flexibility turns out to be of advantage for the classification and analysis of TFBSs, as statistical dependencies between nucleotides in different TFBS positions (not necessarily adjacent) may be taken into account
efficiently—in a position-specific and context-specific manner.
Results: We apply the VOBN model to a set of 238 experimentally verified sigma-70 binding sites in Escherichia coli. We find that the VOBN model can distinguish these 238 sites from a set of 472 intergenic ‘nonpromoter’ sequences with a higher accuracy than fixed-order Markov models or Bayesian trees. We use a replicated stratified-holdout experiment having a fixed true-negative rate of 99.9%. We find that for a foreground inhomogeneous VOBN model of order 1 and a background homogeneous variable-order Markov (VOM) model of order 5, the obtained mean true-positive (TP) rate is 47.56%. In comparison, the best TP rate for the conventional models is 44.39%, obtained from a foreground PWM model and a background 2nd-order Markov model. As the standard deviation of the estimated TP rate is ~0.01%, this improvement is highly significant.
Availability: All
datasets are available upon request from the authors. A web server for the VOBN
and VOM models is available http://www.eng.tau.ac.il/~bengal/
Keywords:
context tree, variable order Markov models, probability weight matrix,
hidden and interpolated Markov models, Bayesian networks, context specific,
promotes, non-promoters, DNA modeling, transcription factor binding sites
(TFBS), bioinformatics, statistical dependence, nucleotides, bases, sigma 70,
E. coli, inhomogeneous models.
14. ECONOMIC OPTIMIZATION OF OFF-LINE INSPECTION IN A PROCESS SUBJECT TO FAILURE AND RECOVERY. (pdf File)
Finkelshtein A.,
Herer Y., Raz T., Ben-Gal I. (2005)
IIE
Transactions on Quality and Reliability, 37, 995–1009.
Abstract
In certain types of processes, verification of the quality of the output units is possible only after the entire batch has been processed. We develop a model that prescribes which units should be inspected and how the units that were not inspected should be disposed of, in order to minimize the expected sum of inspection costs and disposition error costs, for processes that are subject to random failure and recovery. The model is based on a dynamic programming algorithm that has a low computational complexity. The study also includes a sensitivity analysis under a variety of cost and probability scenarios, supplemented by an analysis of the smallest batch that requires inspection, the expected number of inspections, and the performance of an easy to implement heuristic.
Keywords:
Offline / online quality control, offline online inspection, unreliable
inspection, adaptive testing, dynamic programming, value of information,
production process, transfer line
15. USING A COMPRESSIBILITY MEASURE TO DISTINGUISH CODING AND NONCODING DNA (pdf File)
Shmilovici A., Ben-Gal I.,(2004)
Abstract
DNA sequences consist of protein coding and noncoding regions. Recognition of coding regions is an important phase in gene-finding procedures. This paper presents a new method for distinguishing coding and noncoding DNA regions. The proposed method implements compressibility measures that results from Variable Order Markov (VOM) models. In contrast to fixed-order Markov models, where the model order is identical for all positions and for all contexts, in VOM models the order may vary – based on a nucleotide position and its contexts. As a result, VOM models are more flexible with respect to model parameterization. Preliminary experimental results on benchmark datasets demonstrate that the proposed methodology classifies coding and noncoding DNA more accurately than traditional coding measures presented in the literature.
Keywords: DNA compression, variable order Markov
model, coding and noncoding DNA, context tree, sequence analysis, gene
prediction, gene annotation, sequencing errors, detection
and correction.
16. ON THE USE OF DATA COMPRESSION MEASURES TO ASSESS ROBUST DESIGNS (pdf File)
IEEE
Trans. on Reliability,
Vol. 54, no. 3, 381-388.
Abstract
In this paper we suggest a potential use of data compression measures, such as the Entropy and the Huffman Coding, to assess the effects of noise factors on the reliability of tested systems. In particular, we extend the Taguchi method for robust design by computing the entropy of the percent contribution values of the noise factors. The new measures are computed already at the parameter-design stage and together with the traditional S/N ratios enable the specification of a robust design. Assuming that (some of) the noise factors should be naturalized, the entropy of a design reflects the potential efforts that will be required in the tolerance-design stage to reach a more reliable system. Using a small example, we illustrate the contribution of the new measure that might alter the designer decision in comparison with the traditional Taguchi method and ultimately obtain a system with a lower quality loss.
Assuming that the percent contribution values can reflect the probability of a noise factor to trigger a disturbance in the system response, a series of probabilistic algorithms can be applied to the robust design problem. We focus on the Huffman coding algorithm and show how to implement this algorithm such that the designer obtains the minimal expected number of tests in order to find the disturbing noise factor. The entropy measure, in this case, provides the lower bound on the algorithm's performance.
Keywords: Control & noise
factors, experimentation, compression rate, robust designs, Taguchi method, S/N
ratio, entropy, performance measure, Information theory, Huffman coding.
17. ASSESSING THE EFFICIENT MARKET HYPOTHESIS VIA A UNIVERSAL PREDICTION STATISTIC
Shmilovici A. and
Abstract
In an efficient market, it is assumed that all available information is fully reflected in the security prices. In recent years weak-form tests are primarily concerned with the time-series predictability of stock returns. Non of which was able to reject the weak-form of market efficiency. The purpose of this research, is to offer another test of the weak form of the market efficiency hypothesis. The test is based on the stochastic complexity of a time series as a measure for the number of bits needed to represent the information in the time series. The length of the data after compression is a practical measure for the stochastic complexity of the data. The key idea is that in an efficient market, compression of the time series is not possible (that is the stochastic complexity is high). In this research, Rissanen’s context tree algorithm is used to identify recurring patterns in the data, and use them for compression. If the series are compressible, this indicates potential market inefficiency. The weak market efficiency hypothesis was tested for 13 international stock indices, and for all the stocks composing the Tel-Aviv 25 index. Using sliding windows of 50, 75 and 100 consecutive trading days, it was found that for 10 international stock indices, and for 60% to 84% of the stocks tested, compressibility beyond a random is possible. These findings imply that the market is inefficient in some of the trading days that may be used to generate excess returns.
Keywords: Stochastic Complexity, the
efficient market hypothesis, context tree, stocks prediction,
stocks behavior, revenues, variable-order Markov models, portfolio analysis..
18. USING A VOM MODEL FOR RECONSTRUCTING POTENTIAL CODING REGIONS IN EST SEQUENCES
Shmilovici A. and
Abstract
This paper presents a new method for annotating coding and noncoding DNA regions using Variable Order Markov (VOM) models. In contrast to fixed-order Markov models, where the model order is identical for all positions and for all contexts, in VOM models the order may vary. As a result, VOM models are more flexible with respect to model parameterization and can be trained on relatively short sequences, and on low-quality datasets, such as Expressed Sequence Tags (ESTs) that are considered in this paper. Furthermore, the proposed method is found to be robust to random substitution errors in the genetic sequences. Accordingly, the paper presents a modified VOM model for detecting and correcting insertion and deletion sequencing errors.
Keywords: variable order Markov model, coding and noncoding DNA, context tree, gene annotation, sequencing
error, detection and correction, gene finding, sequence analysis, gene
prediction
19. DESIGNING EXPERIMENTS FOR ROBUST OPTIMIZATION PROBLEMS: THE VS-OPTIMALITY CRITERION. (pdf File)
Ginsburg H. and
Ben-Gal I. (2006)
IIE
Transactions on Quality and Reliability, vol 38, 445 – 461.
Abstract
We suggest an experimentation strategy for robust design of empirically fitted models. The suggested approach is used to design experiments that minimize the variance of the optimal robust solution. The new design of experiment (DOE) optimality criterion, termed Vs-optimal, prioritizes the estimation of the model's coefficients, such that the variance of the optimal solution is minimized by the performed experiments. It is discussed how the new criterion is related to known optimality criteria. We present an analytical formulation of the suggested approach for linear models and a numerical procedure for higher-order or non-polynomial models. In comparison to conventional robust-design methods, our approach provides more information on the robust solution by numerically generating its multidimensional distribution. Moreover, in a case study, the proposed approach results in a better robust solution in comparison with these standard methods.
Keywords: Robust design, robust
optimization, design of experiments, DOE alphabetic optimality criteria, design
matrix, computerized experiments, Taguchi, Box, signal to noise ratio,D-optimality, experimental design.
20. THE FUNNEL EXPERIMENT: A MARKOV-BASED SPC APPROACH
Singer G. and
Abstract
This paper extends the classical funnel experiment that was used by Deming to promote Statistical Process Control (SPC). The popular example illustrate that feedback control rules violate the independence assumption, but it did not indicate how to implement SPC in such cases. We extend the funnel example by introducing a simple feedback-control rule. We analyze the resulting non-linear process to which traditional SPC methods cannot be applied, then we use a simple Markov-based SPC approach to model and monitor the controlled process.
Keywords: funnel experiment,
Deming, Quality control, feedback control, simulation, simulated experiments,
statistical process control (spc), engineering
process control (epc), integration, FMS.
21. GENE-FINDING WITH THE VOM MODEL (pdf File)
Shohat-Zaidenraise
K.O., Shmilovici A., Ben-Gal I. (2006)
Journal of Computational Methods in Sciences and Engineering.
Abstract
We present the architecture of an elementary gene-finding algorithm that is based on a Variable Order Markov model (VOM). The VOM model is a generalization of the traditional Markov model that can cope with varying memory dependencies. The VOM model is more efficient in terms of its parameterization and therefore can be trained on relatively short sequences. Experiments with the proposed gene-finder on three prokaryotic genomes indicate its potential advantage on the detection of short genes.
Keywords:
Gene
finding, variable order Markov models, context tree, sequence analysis, gene
prediction, coding noncoding DNA,
gene annotation, sequencing errors, detection and correction
22. ROBUST ECO-DESIGN: A NEW APPLICATION FOR QUALITY ENGINEERING
Katz R., Ben-Gal I., Bukchin J. (2005)
Abstract
The method of robust
design has long been used for the design of systems that are insensitive to
noises. In this paper we demonstrate how this approach can be used to obtain a
robust eco-design (ecological design). In our case study, we implement the
robust design principles to the design of a factory smoke-stack, relying on the
Gaussian Plume model (GPM). The GPM is a known model for describing pollutant
dispersal from a point source, subject to various atmospheric conditions. The
mean-square-errors (MSE) of the accumulated pollution values around a given target
is defined as the performance measure and used to adjust the design parameters.
Both analytical and numerical approaches are used to evaluate the MSE measure
over the design space. We demonstrate how the non-linearity in the GPM can be
exploited to reach a respectively low MSE value by a cheaper design
configuration. We focus and analyze the differences between the manufacturer
viewpoint and the environmentalist viewpoint with respect to the considered
eco-design problem.
Keywords:
Eco
design, sustainable design, Taguchi method, green manufacturing, Ecology and
production, air pollution, cleaner production, design
for the environment,
end-of -pipe
23. BACKUP STRATEGY FOR ROBOTS' FAILURES IN AN AUTOMOTIVE ASSEMBLY SYSTEM
Kahan T., Bukchin Y., Menassa R., Ben-Gal
I.
Abstract
Robotic assembly lines
are often characterized by robots' failures that reduce the throughput rate of
the line. A common scenario under such failures is a full stoppage of the line during
the robots' repair period. This paper presents a backup strategy in which
working robots backup the failed ones by performing the latter's tasks. The
strategy aims at minimizing the throughput loss by utilizing the robots'
flexibility and effectively managing the redundancies in the system. In case no
backup is available, the tasks of the failed robot are performed manually,
resulting in an impaired quality and a reduced throughput rate. The need for an efficient robotic backup
strategy is common to automotive welding assembly lines. The paper considers
such lines and formulates a Mixed Integer Linear Programming (MILP) model for
minimizing the cycle time when a failure occurs. Experiments illustrate the
effect of the problem parameters on the cycle time. The performance of the
proposed approach is then compared with heuristic rules in a stochastic
environment, where the failure rates and the repair times are randomly
distributed. A quality factor, which is affected by the amount of manual work,
is also evaluated. The proposed algorithm is found superior to these rules with
regard to both the throughput and the quality factor.
Keywords:
Assembly
lines, Cycle-time reduction, Throughput reduction, Optimization of production
systems, optimization by simulation, Robots reliability, Automotive industry.
24. ROBUST GENE EXPRESSION CLUSTERING VIA MUTUAL INFORMATION DISTANCE MEASURE
Abstract
Motivation: The definition of a distance measure plays a key role for a successful clustering of gene expression pro-files. In this paper we compare the robustness of the Mutual Information (MI) measure to that of the Euclidean distance and the Pearson correlation coefficient.
Results: It is found that the MI measure yields a
more significant differentiation among erroneous clustering solutions. The
proposed measure is also used to analyze the performance of several known
clustering algorithms.
Keywords:
Gene
expression data, clustering, genetics profiles, distance measures, distance
metrics, homogeneity and separation.
25. VOMBAT: PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES USING VARIABLE ORDER BAYESIAN TREES (pdf File)
Grau J.,
Nucleic Acids Research, Vol. 34, Web Server issue, W529–W533
Abstract
Variable order Markov models and variable order Bayesian trees have been proposed for the recognition of transcription factor binding sites, and it could be demonstrated that they outperform traditional models such as position weight matrices, Markov models, and Bayesian trees. We develop a web-server for the recognition of DNA binding sites based on variable order Markov models and variable order Bayesian
trees offering the following functionality: (i) given data sets with annotated binding sites and genomic background sequences, variable order Markov models and variable order Bayesian trees can be trained; (ii) given a set of trained models, putative DNA binding sites can be predicted in a given set of genomic sequences; and (iii) given a data set with annotated binding sites and a data set with genomic background sequences, cross-validation experiments for different model combinations with different parameter settings can be performed. Several of the offered services are computationally demanding, such as genome-wide predictions of DNA binding sites in mammalian genomes or sets of 104-fold cross-validation experiments for different model combinations based on problem-specific data sets. In order to execute
these
jobs, and in order to serve multiple users at the same time, the web-server is
attached to a Linux cluster with 150 processors.
VOMBAT is available at http://pdw-24.ipk-gatersleben.de:8080/VOMBAT/.
Keywords:
context tree, variable order Markov models, probability weight
matrix, hidden and interpolated Markov models, Bayesian networks, context
specific, promotes, non-promoters, DNA modeling, transcription factor binding
sites (TFBS), bioinformatics, statistical dependence, nucleotides, bases, sigma
70, E. coli, inhomogeneous models.
26. STATISTICAL PROCESS CONTROL OF THE STOCHASTIC COMPLEXITY OF DISCRETE PROCESSES
Shmilovici A. and Ben-Gal I.,
Communications
on Dependability and Quality Management in Engineering, 8(3), 55-61.
Abstract
Changes in stochastic
processes often affect their description length, and reflected by their
stochastic complexity measures. Monitoring the stochastic complexity of a sequence
(or, equivalently, its code length) can detect process changes that may be
undetectable by traditional SPC methods. The context tree is proposed here as a
universal compression algorithm for measuring the stochastic complexity of a
state-dependent discrete process. The advantage of the proposed method is in
the reduced number of samples that are needed for reliable monitoring.
Keywords:
Process
control; Control charts; Stochastic complexity;
Context tree algorithm. Variable order Markov.