Search results for: S. Chakraborti

Items from 1 to 20 out of 38 results

article

Exact two‐sided statistical tolerance limits for sample variances

Martin G.C. Sarmiento, S. Chakraborti, Eugenio K. Epprecht

Quality and Reliability Engineering International > 34 > 6 > 1238 - 1253

Statistical tolerance intervals are widely used in the industry and in various areas of sciences, especially in conformity assessment and acceptance of products or processes in terms of quality. When the interest is in precision, a tolerance interval for the variance is useful. In this paper, we consider two‐sided tolerance intervals for the population of sample variances for data that arise from...

article

Size and shape dependences of the colloidal silver nanoparticles on the light sources in photo-mediated citrate reduction technique

S K Panda, S Chakraborti, R N Basu

Bulletin of Materials Science > 2018 > 41 > 4 > 1-7

Colloidal silver nanoparticles (Ag NPs) with different sizes and shapes were synthesized by photo-mediated citrate reduction of aqueous $$\hbox {AgNO}_{3}$$ AgNO3 solution under UV, sun and visible–infrared (VIS–IR) light sources. Pale yellow colour colloid was obtained under VIS–IR light exposure, whereas reddish yellow colour colloid was yielded under UV light. The colloid prepared under natural...

article

Nonparametric signed‐rank control charts with variable sampling intervals

M.L.I. Coelho, M.A. Graham, S. Chakraborti

Quality and Reliability Engineering International > 33 > 8 > 2181 - 2192

Variable sampling interval (VSI) charts have been proposed in the literature for normal theory (parametric) control charts and are known to provide performance enhancements. In the VSI setting, the time between monitored samples is allowed to vary depending on what is observed in the current sample. Nonparametric (distribution‐free) control charts have recently come to play an important role in statistical...

article

Detecting changes in location using distribution‐free control charts with big data

R. Sparks, S. Chakraborti

Quality and Reliability Engineering International > 33 > 8 > 2577 - 2595

This paper proposes a simple distribution‐free control chart for monitoring shifts in location when the process distribution is continuous but unknown. In particular, we are concerned with big data applications where there are sufficient in‐control data that can be used to specify certain quantiles of interest which, in turn, are used to assess whether the new, incoming data to be monitored are in...

article

Designing Phase I Shewhart $\bar{X}$ charts: Extended tables and software

Y. Yao, C.W. Hilton, S. Chakraborti

Quality and Reliability Engineering International > 33 > 8 > 2667 - 2672

Control charts play an important role in Phase I studies, which are conducted to establish process control and generate reference data for parameter estimation and calculation of prospective (Phase II) control limits. Researchers have tabulated the necessary charting constants for the normal theory–based Phase I Shewhart

\bar{X}

chart for the process mean to achieve a desired nominal false alarm probability...

article

Vertically Aligned Silicon Nanowire Array Decorated by Ag or Au Nanoparticles as SERS Substrate for Bio-molecular Detection

S. Chakraborti, R. N. Basu, S. K. Panda

Plasmonics > 2018 > 13 > 3 > 1057-1080

This review article summerises preparation techniques of vertically aligned silicon nanowire (Si NW) arrays through metal-assisted chemical etching (MacEtch) process and plasmonic nanoparticles (Ag and Au) with the perspective of the fabrication of surface-enhanced Raman scattering (SERS)-active substrates which are highly efficient for bio-molecular detection. At first, basic methods and mechanisms...

article

Effects of Parameter Estimation on the Multivariate Distribution‐free Phase II Sign EWMA Chart

Y. H. Dovoedo, S. Chakraborti

Quality and Reliability Engineering International > 33 > 2 > 431 - 449

Multivariate nonparametric control charts can be very useful in practice and have recently drawn a lot of interest in the literature. Phase II distribution‐free (nonparametric) control charts are used when the parameters of the underlying unknown continuous distribution are unknown and can be estimated from a sufficiently large Phase I reference sample. While a number of recent studies have examined...

article

A Generally Weighted Moving Average Signed‐rank Control Chart

N. Chakraborty, S. Chakraborti, S.W. Human, N. Balakrishnan

Quality and Reliability Engineering International > 32 > 8 > 2835 - 2845

The idea of process monitoring emerged so as to preserve and improve the quality of a manufacturing process. In this regard, control charts are widely accepted tools in the manufacturing sector for monitoring the quality of a process. However, a specific distributional assumption for any process is restrictive and often criticised. Distribution‐free control charts are efficient alternatives when information...

article

Monitoring the Process Mean When Standards Are Unknown: A Classic Problem Revisited

M. D. Diko, S. Chakraborti, M. A. Graham

Quality and Reliability Engineering International > 32 > 2 > 609 - 622

One of the most common applications in statistical process monitoring is the use of control charts to monitor a process mean. In practice, this is often performed with a Shewhart

\bar{X}

chart along with a Shewhart R (or an S) chart. Thus, two charts are typically used together, as a scheme, each using the 3‐sigma limits. Moreover, the process mean and standard deviation are often unknown and need to...

article

Distribution-free Phase II Mann–Whitney control charts with runs-rules

J. C. Malela-Majika, S. Chakraborti, M. A. Graham

The International Journal of Advanced Manufacturing Technology > 2016 > 86 > 1-4 > 723-735

The addition of runs-rules has been recommended to improve the performance of classical, normal theory Shewhart-type control charts, for detecting small to moderate size shifts. In this paper, we consider adding both standard and improved runs-rules to enhance the performance of the distribution-free Phase II Shewhart-type chart based on the well-known Mann–Whitney statistic proposed by Chakraborti...

article

Improved Phase I Control Charts for Monitoring Times Between Events

N. Kumar, S. Chakraborti

Quality and Reliability Engineering International > 31 > 4 > 659 - 668

In many situations, the times between certain events are observed and monitored instead of the number of events particularly when the events occur rarely. In this case, it is common to assume that the times between events follow an exponential distribution. Control charts are one of the main tools of statistical process control and monitoring. Control charts are used in phase I to assist operating...

article

A median run length-based double-sampling X ¯ $$ \overline{X} $$ chart with estimated parameters for minimizing the average sample size

W. L. Teoh, Michael B. C. Khoo, Philippe Castagliola, S. Chakraborti

The International Journal of Advanced Manufacturing Technology > 2015 > 80 > 1-4 > 411-426

The existing control charts with estimated parameters have been widely studied from the perspective of the average run length (ARL). However, when parameters are estimated, the shape and the skewness of the run length distribution change with the magnitude of the mean shift, the number of phase I samples and sample sizes. Therefore, in this paper, we argue that the median run length (MRL) and the...

article

Synthetic Phase II Shewhart‐type Attributes Control Charts When Process Parameters are Estimated

Philippe Castagliola, Shu Wu, Michael B. C. Khoo, S. Chakraborti

Quality and Reliability Engineering International > 30 > 3 > 315 - 335

The performance of attributes control charts (such as c and np charts) is usually evaluated under the assumption of known process parameters (i.e., the nominal proportion of nonconforming units or the nominal number of nonconformities). However, in practice, these process parameters are rarely known and have to be estimated from an in‐control phase I data set. In this paper, we derive the run length...

article

A New Distribution‐free Control Chart for Joint Monitoring of Unknown Location and Scale Parameters of Continuous Distributions

S. Chowdhury, A. Mukherjee, S. Chakraborti

Quality and Reliability Engineering International > 30 > 2 > 191 - 204

While the assumption of normality is required for the validity of most of the available control charts for joint monitoring of unknown location and scale parameters, we propose and study a distribution‐free Shewhart‐type chart based on the Cucconi[] statistic, called the Shewhart‐Cucconi (SC) chart. We also propose a follow‐up diagnostic procedure useful to determine the type of shift the process...

article

Optimal design of the double sampling X¯ chart with estimated parameters based on median run length

W.L. Teoh, Michael B.C. Khoo, Philippe Castagliola, S. Chakraborti

Computers & Industrial Engineering > 2014 > 67 > Complete > 104-115

The double sampling (DS) X¯ chart when the process parameters are unknown and have to be estimated from a reference Phase-I dataset is studied. An expression for the run length distribution of the DS X¯ chart is derived, by conditioning and taking parameter estimation into account. Since the shape and the skewness of the run length distribution change with the magnitude of the mean shift, the number...

chapter

Multifunctional lab-on-a-tube (LOT) probe for simultaneous neurochemical and electrophysiological activity measurements

C. Li, P. M Wu, S. J. Prince, Z. Wu, more

2013 Transducers & Eurosensors XXVII: The 17th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS & EUROSENSORS XXVII) > 880 - 883

2013 Transducers & Eurosensors XXVII: The 17th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS & EUROSENSORS XXVII)

A multifunctional lab-on-a-tube (LOT) probe for simultaneous in vivo neurochemical and electrophysiological activity measurements is presented. Developed heterostructured platinum/iridium oxide (Pt/IrOx) microelectrodes have shown a superior signal-to-noise ratio and around 80 times slower polarization rate than platinum microelectrodes with the same surface area. Oxygen and glucose biosensors have...

article

Pituicytoma: Report of three cases with review of literature

S. Chakraborti, A. Mahadevan, A. Govindan, K. Sridhar, more

Pathology - Research and Practice > 2013 > 209 > 1 > 52-58

Pituicytoma is a rare low-grade tumor (WHO Grade I) of pituicytes involving the sellar–suprasellar region, and originating from the specialized glial cells of the neurohypophysis. Clinically and radiologically, they closely mimic pituitary adenoma or meningioma. Diagnosis requires histopathological examination of the resected tissue. This uncommon glial neoplasm is a rarity, with only 57 cases reported...

article

A letter to the Editor about “A comparison study of three nonparametric control charts to detect shift in location parameters,” by Das, N. (2009), International Journal of Advanced Manufacturing Technology, 41 (7-8), 799–807

M. A. Graham, F. C. Delport, S. Chakraborti

The International Journal of Advanced Manufacturing Technology > 2013 > 69 > 9-12 > 2227-2228

article

Distribution-free exponentially weighted moving average control charts for monitoring unknown location

M.A. Graham, A. Mukherjee, S. Chakraborti

Computational Statistics and Data Analysis > 2012 > 56 > 8 > 2539-2561

Distribution-free (nonparametric) control charts provide a robust alternative to a data analyst when there is lack of knowledge about the underlying distribution. A two-sided nonparametric Phase II exponentially weighted moving average (EWMA) control chart, based on the exceedance statistics (EWMA-EX), is proposed for detecting a shift in the location parameter of a continuous distribution. The nonparametric...

article

Two simple Shewhart‐type multivariate nonparametric control charts

J. M. Boone, S. Chakraborti

Applied Stochastic Models in Business and Industry > 28 > 2 > 130 - 140

In many quality control applications, the necessary distributional assumptions to correctly apply the traditional parametric control charts are either not met or there is simply no enough information or evidence to verify the assumptions. It is well known that the performance of many parametric control charts can be seriously degraded in situations like this. Thus, control charts that do not require...

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INFONA - science communication portal

Search results for: S. Chakraborti

Exact two‐sided statistical tolerance limits for sample variances

Size and shape dependences of the colloidal silver nanoparticles on the light sources in photo-mediated citrate reduction technique

Nonparametric signed‐rank control charts with variable sampling intervals

Detecting changes in location using distribution‐free control charts with big data

Designing Phase I Shewhart $\bar{X}$ charts: Extended tables and software

Vertically Aligned Silicon Nanowire Array Decorated by Ag or Au Nanoparticles as SERS Substrate for Bio-molecular Detection

Effects of Parameter Estimation on the Multivariate Distribution‐free Phase II Sign EWMA Chart

A Generally Weighted Moving Average Signed‐rank Control Chart

Monitoring the Process Mean When Standards Are Unknown: A Classic Problem Revisited

Distribution-free Phase II Mann–Whitney control charts with runs-rules

Improved Phase I Control Charts for Monitoring Times Between Events

A median run length-based double-sampling X ¯ $$ \overline{X} $$ chart with estimated parameters for minimizing the average sample size

Synthetic Phase II Shewhart‐type Attributes Control Charts When Process Parameters are Estimated

A New Distribution‐free Control Chart for Joint Monitoring of Unknown Location and Scale Parameters of Continuous Distributions

Optimal design of the double sampling X¯ chart with estimated parameters based on median run length

Multifunctional lab-on-a-tube (LOT) probe for simultaneous neurochemical and electrophysiological activity measurements

Pituicytoma: Report of three cases with review of literature

A letter to the Editor about “A comparison study of three nonparametric control charts to detect shift in location parameters,” by Das, N. (2009), International Journal of Advanced Manufacturing Technology, 41 (7-8), 799–807

Distribution-free exponentially weighted moving average control charts for monitoring unknown location

Two simple Shewhart‐type multivariate nonparametric control charts

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