Inverse Maxwell Distribution and Statistical Process Control: An Efficient Approach for Monitoring Positively Skewed Process

(1) Background: The literature discusses the inverse Maxwell distribution theoretically without application. Control charting is promising, but needs development for inverse Maxwell processes. (2) Methods: Thus, we develop the VIM control chart for monitoring the inverse Maxwell scale parameter and studied its statistical properties. The chart’s performance is evaluated using power curves and run length properties. (3) Results: Further, we use simulated data to compare the shift detection capability of our chart with Weibull, gamma, and lognormal charts. (4) Conclusion: The analysis demonstrates our chart’s efficiency for monitoring skewed processes. Finally, we apply our chart for monitoring real world lifetimes of car brake pads.

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Improving the performance of a radio-frequency localization system in adverse outdoor applications

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-02001-6 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Marcelo N. de Sousa ◽

Ricardo Sant’Ana ◽

Rigel P. Fernandes ◽

Julio Cesar Duarte ◽

José A. Apolinário ◽

...

Keyword(s):

Random Forest ◽

Ray Tracing ◽

Real World ◽

Practical Implication ◽

Real Life ◽

Simulated Data ◽

Real Data ◽

Gradient Boosting ◽

Real World Data ◽

Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

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SURVEILLANCE OF DIABETES PREVALENCE RATE THROUGH THE DEVELOPMENT OF A MARKOV-BASED CONTROL CHART

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519412500832 ◽

2012 ◽

Vol 12 (04) ◽

pp. 1250083

Author(s):

PERSHANG DOKOUHAKI ◽

RASSOUL NOOROSSANA

Keyword(s):

Control Chart ◽

Control Charts ◽

High Performance ◽

Discrete Data ◽

Correlated Data ◽

Statistical Process ◽

Control Charting ◽

Attribute Quality ◽

Related Process

In the field of statistical process control (SPC), usually two issues are addressed; the variables and the attribute quality characteristics control charting. Focusing on discrete data generated from a process to be monitored, attributes control charts would be useful. The discrete data could be classified into two categories; the independent and auto-correlated data. Regarding the independence in the sequence of discrete data, the typical Shewhart-based control charts, such as p-chart and np-chart would be effective enough to monitor the related process. But considering auto-correlation in the sequence of the data, such control charts would not workanymore. In this paper, considering the auto-correlated sequence of X1, X2,…, Xt,… as the sequence of zeros or ones, we have developed a control chart based on a two-state Markov model. This control chart is compared with the previously developed charts in terms of the average number of observations (ANOS) measure. In addition, a case study related to the diabetic people is investigated to demonstrate the applicability and high performance of the developed chart.

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Origin–Destination Flow Estimation from Link Count Data Only

Sensors ◽

10.3390/s20185226 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5226

Author(s):

Subhrasankha Dey ◽

Stephan Winter ◽

Martin Tomko

Keyword(s):

Real World ◽

Count Data ◽

Road Traffic ◽

Physical Space ◽

Simulated Data ◽

Accuracy Score ◽

Travel Times ◽

Estimation Model ◽

Flow Estimation ◽

Link Count

All established models in transportation engineering that estimate the numbers of trips between origins and destinations from vehicle counts use some form of a priori knowledge of the traffic. This paper, in contrast, presents a new origin–destination flow estimation model that uses only vehicle counts observed by traffic count sensors; it requires neither historical origin–destination trip data for the estimation nor any assumed distribution of flow. This approach utilises a method of statistical origin–destination flow estimation in computer networks, and transfers the principles to the domain of road traffic by applying transport-geographic constraints in order to keep traffic embedded in physical space. Being purely stochastic, our model overcomes the conceptual weaknesses of the existing models, and additionally estimates travel times of individual vehicles. The model has been implemented in a real-world road network in the city of Melbourne, Australia. The model was validated with simulated data and real-world observations from two different data sources. The validation results show that all the origin–destination flows were estimated with a good accuracy score using link count data only. Additionally, the estimated travel times by the model were close approximations to the observed travel times in the real world.

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Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420925304 ◽

2020 ◽

Vol 17 (3) ◽

pp. 172988142092530

Author(s):

Feng Youyang ◽

Wang Qing ◽

Yang Gaochao

Keyword(s):

Optimization Algorithm ◽

Real World ◽

Bayes Factor ◽

Simulated Data ◽

Schur Complement ◽

Factor Graph ◽

Data Sets ◽

Loop Closure ◽

Graph Optimization ◽

Pose Graph Optimization

Pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universität München (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures; Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

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Damn You, Little h! (Or, Real-World Applications of the Hubble Constant Using Observed and Simulated Data)

Publications of the Astronomical Society of Australia ◽

10.1017/pasa.2013.31 ◽

2013 ◽

Vol 30 ◽

Cited By ~ 25

Author(s):

Darren J. Croton

Keyword(s):

Data Analysis ◽

Galaxy Formation ◽

Real World ◽

Simulated Data ◽

Theoretical Data ◽

Hubble Constant ◽

Uncertain Parameters ◽

Modern Age ◽

Real World Applications ◽

Better Than

AbstractThe Hubble constant, H0, or its dimensionless equivalent, “little h”, is a fundamental cosmological property that is now known to an accuracy better than a few per cent. Despite its cosmological nature, little h commonly appears in the measured properties of individual galaxies. This can pose unique challenges for users of such data, particularly with survey data. In this paper we show how little h arises in the measurement of galaxies, how to compare like-properties from different datasets that have assumed different little h cosmologies, and how to fairly compare theoretical data with observed data, where little h can manifest in vastly different ways. This last point is particularly important when observations are used to calibrate galaxy formation models, as calibrating with the wrong (or no) little h can lead to disastrous results when the model is later converted to the correct h cosmology. We argue that in this modern age little h is an anachronism, being one of least uncertain parameters in astrophysics, and we propose that observers and theorists instead treat this uncertainty like any other. We conclude with a ‘cheat sheet’ of nine points that should be followed when dealing with little h in data analysis.

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Statistical Process Control Charting Applied to the Analysis of Human Performance in Computer-Supported Environments

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129403800434 ◽

1994 ◽

Vol 38 (4) ◽

pp. 350-354

Author(s):

Janine A. Purcell

Keyword(s):

Process Control ◽

Statistical Process Control ◽

Transfer Of Learning ◽

Human Performance ◽

Individual Subject ◽

Statistical Process ◽

Control Charting ◽

Human Operators ◽

Machine Systems ◽

Order Of Training

To develop usable Human-Machine Systems, we need Tools to evaluate and measure the length of learning periods, error rate, response time, and transfer of learning in the human operators of these systems (Whiteside, Bennett, and Holtzblatt, 1988). This research explores the use of Statistical Process Control (SPC) charts as a tool to visualize and analyze performance in a decision-making task. The data submitted to control charting was collected in an experiment that explored the effect of order of training or experience in working with alternate display formats. Results for an individual subject as well as a summary for one of the four experimental groups are discussed. Suggestions for further applications of these techniques are offered.

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Using the Statistical Process Control to evaluate the learning process in Higher Education / Aplicación del Control Estadístico de Procesos para la evaluación de procesos de aprendizaje en la Educación Superior

Brazilian Journal of Business ◽

10.34140/bjbv3n5-017 ◽

2021 ◽

Vol 3 (5) ◽

pp. 3730-3749

Author(s):

Ana Gessa-Perera ◽

Eyda Lucía Marín-Ramírez ◽

María del Pilar Sancha-Dionisio

Keyword(s):

Higher Education ◽

Process Control ◽

Statistical Process Control ◽

Teaching Strategies ◽

Learning Process ◽

Learning Experience ◽

Learning Material ◽

Statistical Process ◽

Computer Based Learning ◽

Control Charting

The purpose of this paper is to propose an approach for incorporating Statistical Process Control (SPC) charting technique to monitor and continuously improve the learning processes by monitoring the satisfaction of students who used an interactive computer-based learning material, which was produced to solve problems in the Operation Management course. By applying SPC methods, the authors examine the sources of process variation (common or special causes) and analyse the ability of teaching strategies to ensure the acquisition and development of the competencies and skills demanded in current university studies. A total of 184 students participated in the learning experience. The findings show that the learning process is under control and therefore the variation of process is due to common causes. However, the Capability Analysis carried out, reveals that process has not enough capacity to achieve the specifications required by the teachers involved in the educational project. This quantitative approach will not only allow self-assessment, but can be used for comparative purposes (other teaching strategies, colleges, etc.). Thus, the control charting is a complementary assessment technique that should be included within the current Quality and Learning Assurance Systems at higher education institutions.

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