Unnatural Pattern Research Articles

Using Curve-Registration Information for Profile Monitoring

The quality characteristics in manufacturing processes are often represented in terms of spatially- or time-ordered data, called “profiles”, which are characterized by amplitude and phase variability. In this context, curve registration plays a key role, as it allows separating the two kinds of between-profiles variability and reducing any undesired inflation of the natural phase variability. In the mainstream literature, warping functions are not generally considered in the monitoring process, even though this may cause significant information loss. We propose a novel approach for profile monitoring that combines the functional principal component analysis and the use of parametric warping functions. The key idea is to jointly monitor the stability over time of the registered profiles (i.e., the information related to amplitude variability) and the registration coefficients (i.e., the information related to phase variability). This allows improving the capability of detecting unnatural pattern modifications, thanks to a better characterization of the overall natural variability. The benefits of a proper management of functional data registration, together with the advantages over the most common approaches used in the literature, are demonstrated by means of Monte Carlo simulations. The proposed methodology is finally applied to a real industrial case study relying on a dataset acquired in waterjet cutting processes under different health conditions of the machine tool.

A novel optic flow pattern speeds split-belt locomotor adaptation

Visual input provides vital information for helping us modify our walking pattern. For example, artificial optic flow can drive changes in step length during locomotion and may also be useful for augmenting locomotor training for individuals with gait asymmetries. Here we asked whether optic flow could modify the acquisition of a symmetric walking pattern during split-belt treadmill adaptation. Participants walked on a split-belt treadmill while watching a virtual scene that produced artificial optic flow. For the Stance Congruent group, the scene moved at the slow belt speed at foot strike on the slow belt and then moved at the fast belt speed at foot strike on the fast belt. This approximates what participants would see if they moved over ground with the same walking pattern. For the Stance Incongruent group, the scene moved fast during slow stance and vice versa. In this case, flow speed does not match what the foot is experiencing, but predicts the belt speed for the next foot strike. Results showed that the Stance Incongruent group learned more quickly than the Stance Congruent group even though each group learned the same amount during adaptation. The increase in learning rate was primarily driven by changes in spatial control of each limb, rather than temporal control. Interestingly, when this alternating optic flow pattern was presented alone, no adaptation occurred. Our results demonstrate that an unnatural pattern of optic flow, one that predicts the belt speed on the next foot strike, can be used to enhance learning rate during split-belt locomotor adaptation.

Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be the combination of several basic patterns, which leads to severe performance degradations in these methods. To address this problem, some independent component analysis (ICA) based schemes have been proposed. However, some limitations are observed in these algorithms, such as lacking of the capability of monitoring univariate processes with only one key measurement, misclassifications caused by the inherent permutation and scaling ambiguities, and inconsistent solution. This paper proposes a novel hybrid approach based on singular spectrum analysis (SSA) and support vector machine (SVM) to identify concurrent CCPs. In the proposed method, the observed data is first separated by SSA into multiple basic components, and then these separated components are classified by SVM for pattern recognition. The scheme is suitable for univariate concurrent CCPs identification, and the results are stable since it does not have shortcomings found in the ICA-based schemes. Furthermore, it has good generalization performance of dealing with the small samples. Superior performance of the proposed algorithm is achieved in simulations.

On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes

The effective recognition of unnatural control chart patterns (CCPs) is one of the most important tools to identify process problems. In multivariate process control, the main problem of multivariate quality control charts is that they can detect an out of control event but do not directly determine which variable or group of variables has caused the out of control signal and how much is the magnitude of out of control. Recently machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. This study presents a modular model for on-line analysis of out of control signals in multivariate processes. This model consists of two modules. In the first module using a support vector machine (SVM)-classifier, mean shift and variance shift can be recognized. Then in the second module, using two special neural networks for mean and variance, it can be recognized magnitude of shift for each variable simultaneously. Through evaluation and comparison, our research results show that the proposed modular performs substantially better than the traditional corresponding control charts. The main contributions of this work are recognizing the type of unnatural pattern and classifying the magnitude of shift for mean and variance in each variable simultaneously.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

A double neural network approach for the identification and parameter estimation of control chart patterns

The exhibited pattern on a control chart is classified as either natural or unnatural pattern. The presence of an unnatural pattern is evidence that a process is out of control. This paper devises neural networks as an intelligent tool to automate the identification of the different control chart patterns, and to accurately estimate their parameters. Two neural networks, named 'NN-1' and 'NN-2', are integrated together to perform the identification and the parameter estimation. The first stage 'NN-1' is developed to identify the existing pattern in the control data, and the second stage 'NN-2' is used to estimate the parameters of that pattern. NN-1 is developed to identify the five basic control chart patterns; namely: natural, upward shift, downward shift, upward trend, and downward trend. The probability of success in identifying the correct control charts pattern and its parameters is used to evaluate the performance of both NN-1and NN-2. Performance results of NN-1 and NN-2 are compared with other previous leading research work. Comparisons show that the proposed neural network approach yield better probability of success than the others.

On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents a hybrid learning-based model for on-line analysis of out-of-control signals in multivariate manufacturing processes. This model consists of two modules. In the first module using a support vector machine-classifier, type of unnatural pattern can be recognized. Then by using three neural networks for shift mean, trend and cycle it can be recognized magnitude of mean shift, slope of trend and cycle amplitude for each variable simultaneously in the second module. The performance of the proposed approach has been evaluated using two examples. The output generated by trained hybrid model is strongly correlated with the corresponding actual target value for each quality characteristic. The main contributions of this work are recognizing the type of unnatural pattern and classification major parameters for shift, trend and cycle and for each variable simultaneously by proposed hybrid model.

On the Use of the FuzzyARTMAP Neural Network for Pattern Recognition in Statistical Process Control using a Factorial Design

Time-series statistical pattern recognition is of prime importance in statistics, especially in quality control techniques for manufacturing processes. A frequent problem in this application is the complexity when trying to determine the behaviour (pattern) from sample data. There have been identified standard patterns which are commonly present when using the X chart; its detection depends on human judgement supported by norms and graphical criteria. In the last few years, it has been demonstrated that Artificial Neural Networks (ANN’s) are useful to predict the type of time-series pattern instead of the use of rules. However, the ANN control parameters have to be fixed to values that maximize its performance. This research proposes an experimental design methodology to determine the most appropriate values for the control parameters of the FuzzyARTMAP ANN such as: learning rate (β ) and network vigilance (ρa, ρb, ρab) in order to increment the neural network efficiency during unnatural pattern recognition.

Development of fuzzy process control charts and fuzzy unnatural pattern analyses

Many problems in scientific investigation generate nonprecise data incorporating nonstatistical uncertainty. A nonprecise observation of a quantitative variable can be described by a special type of membership function defined on the set of all real numbers called a fuzzy number or a fuzzy interval. A methodology for constructing control charts is proposed when the quality characteristics are vague, uncertain, incomplete or linguistically defined. Fuzzy set theory is an inevitable tool for fuzzy control charts as well as other applications subjected to uncertainty in any form. The vagueness can be handled by transforming incomplete or nonprecise quantities to their representative scalar values such as fuzzy mode, fuzzy midrange, fuzzy median, or fuzzy average. Then crisp methods may be applied to those representative values for control chart decisions as “in control” or “out of control”. Transforming the vague data by using one of the transformation methods may result in biased decisions since the information given by the vague data is lost by the transformation. Such data needs to be investigated as fuzzy sets without transformation, and the decisions based on the vague data should not be concluded with an exact decision. A “direct fuzzy approach (DFA)” to fuzzy control charts for attributes under vague data is proposed without using any transformation method. Then, the unnatural patterns for the proposed fuzzy control charts are defined using the probabilities of fuzzy events.

A control chart pattern recognition system using a statistical correlation coefficient method

This paper presents a control chart pattern recognition system using a statistical correlation coefficient method. Pattern recognition techniques have been widely applied to identify unnatural patterns in control charts. Most of them are capable of recognizing a single unnatural pattern for different abnormal types. However, before an unnatural pattern occurs, a change point from normal to abnormal may appear at any point in control charts for most practical cases. Moreover, concurrent patterns where two unnatural patterns simultaneously exist may also occur in a control chart pattern recognition system. Our statistical correlation coefficient approach is a simple mechanism for recognizing these unnatural control chart patterns with good performance. This approach is also an effective method for the control chart pattern recognition without a tedious training process.

Control chart pattern recognition using average of cumulative sum

The statistical process control chart is a monitoring technique useful in determining whether a process is behaving as intended or if there are some unnatural causes of variation. A process is considered to be out of control if a point falls outside the control limits or a series of points exhibit an unnatural pattern (also known as nonrandom variation). Analysis of unnatural patterns is an important aspect of control charting. It is well known that a particular unnatural pattern on a control chart is often associated with a specific set of assignable causes. Identification of unnatural patterns can greatly narrow the set of possible causes that must be investigated, hence facilitating the rapid diagnosis and corrective action.

Grey relation pattern recogniser for X-bar control charts

This paper has developed an on-line pattern recogniser to detect unnatural patterns on control charts. The recogniser can assist in the correction of assignable causes. The recogniser presented here is based on the grey relation model which is used to calculate the degree of relative similarity between observations routinely collected from X-bar charts and each unnatural pattern, such as a trend or cycle. According to the degree of relative similarity, the recogniser will identify whether an unnatural pattern exists in the data. To explain the design process and the evaluation of the performance of the recogniser, a set of commonly encountered unnatural patterns, such as trends, cycles, systematic, stratification, mixtures and sudden-shifts, was applied in this paper. The performance of the recogniser was evaluated by simulation. The results showed that the recogniser has effectiveness in detecting the following patterns trend, cycle, sudden-shift, systematic, and stratification.

On-line control chart pattern detection and discrimination—a neural network approach

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. A common difficulty in existing control chart pattern recognition approaches is that of discrimination between different types of patterns which share similar features. This paper proposes an artificial neural network based model, which employs a pattern discrimination algorithm to recognise unnatural control chart patterns. The pattern discrimination algorithm is based on several special-purpose networks trained for specific recognition tasks. The performance of the proposed model was evaluated by simulation using two criteria: the percentage of correctly recognised patterns and the average run length (ARL). Numerical results show that the false recognition problem has been effectively addressed. In comparison with previous control chart approaches, the proposed model is capable of superior ARL performance while the type of the unnatural pattern can also be accurately identified.

AN UNSUPERVISED LEARNING NEURAL ALGORITHM FOR IDENTIFYING PROCESS BEHAVIOR ON CONTROL CHARTS AND A COMPARISON WITH SUPERVISED LEARNING APPROACHES

The applications of supervised pattern recognition techniques on control charts have shown a substantial improvement in the ability to utilize the information of the chart more effectively than conventional run rules. One major assumption underlying this methodology is that the user has a set of well-defined patterns to detect and a sufficient number of training examples. In practice, however, sufficient training examples may not be readily available, owing either to the inability to simulate these patterns or to the lack of real process data. This paper presents a new approach to detect and identify unnatural patterns on control charts based on the unsupervised self-organizing neural paradigm. The unsupervised methodology is based on ART1 networks. The paper discusses training and testing algorithms to train and test the network using a set of unlabelled natural patterns obtained from the process during normal operation. A comparison is also presented between this unsupervised approach and a major supervised methodology, namely, the statistical learning technique. For the unsupervised methodology, the false alarm rate has substantially improved over that of the supervised methodology, while the rate of identification is higher for the supervised system. The higher rate of identification has been achieved at the cost of providing additional unnatural pattern information to implement the supervised system strategy. © 1997 Elsevier Science Ltd

A neural network approach for the analysis of control chart patterns

Control chart pattern recognition is an important aspect of statistical process control (SPC). The presence of unnatural patterns indicates that a process is affected by assignable causes, and corrective actions should be taken. This paper describes two types of pattern recognizers based on different neural network architectures: a multilayer perceptron trained by back-propagation and a modular neural network. The pattern recognizers were developed to take the advantage of the fact that a particular unnatural pattern is often associated with a set of assignable causes. Identification of unnatural patterns can greatly narrow the set of possible causes that must be investigated, and thus the diagnostic search could be reduced in length. The performances of the proposed pattern recognizers were evaluated through Monte Carlo simulations on the basis of appropriate performance measures. An extensive evaluation indicates that the proposed pattern recognizers could recognize multiple unnatural patterns for which they were trained. The modular neural network provides a better recognition accuracy than back-propagation when there will be strong interference effects. The notable features of the proposed pattern recognizers include the compactness of input/output representation, the ability to detect unnatural patterns when the signal-to-noise ratio is low, the outstanding capability to detect unnatural patterns starting anywhere in the sequence of data and the directional invariance property in the sense that patterns of different orientations could be detected equally well.

Automated process monitoring using statistical pattern recognition techniques on X‐bar control charts

Quality control charts are statistical process control tools aimed at monitoring a (manufacturing) process to detect any deviations from normal operation and to aid in process diagnosis and correction. The information presented on the chart is a key to the successful implementation of a quality process correction system. Pattern recognition methodology has been pursued to identify unnatural behaviour on quality control charts. This approach provides the ability to utilize patterning information of the chart and to track back the root causes of process deviation, thus facilitating process diagnosis and maintenance. Presents analysis and development of a statistical pattern recognition system for the explicit identification of unnatural patterns on control charts. Develops a set of statistical pattern recognizers based on the likelihood ratio approach and on correlation analysis. Designs and implements a training algorithm to maximize the probability of identifying unnatural patterns, and presents a classification procedure for real‐time operation. Demonstrates the system performance using a set of newly defined measures, and obtained results based on extensive experiments illustrate the power and usefulness of the statistical approach for automating unnatural pattern detection on control charts.

Unnatural pattern recognition on control charts using correlation analysis techniques

This paper presents analysis and development of a pattern recognition system for identifying unnatural patterns on quality control charts. The system is based on correlation analysis, where a set of optimal matched filters are generated. To illustrate the design methodology and operation of the system, a set of commonly encountered patterns is utilized, such as the trend, the systematic, and the cyclic patterns. A training algorithm that minimizes the probabilities of Type I and Type II errors is presented. To evaluate the system performance, a testing algorithm as well as a set of newly-defined performance measures are introduced. The obtained results have shown the effectiveness of correlation analysis for control chart pattern recognition.

Surface-Water Flow to Everglades National Park

A complex system of transfer and storage has evolved to supply water to all users in southern Florida. Everglades National Park, at the southern extreme of the system, complicates regional water management. Water delivered to the national park has increased more than 50 percent in recent years, but the additional volume has produced more numerous extreme high flows and alteration of the natural wet-dry seasonal cycle that is crucial to the park's ecology. FLOODS, drought, and saltwater encroachment have been the principal water-resources problems in southern Florida for several decades. Solutions to these problems have been complicated in recent years because of increasing concerns to save the Everglades, the premier wetland in the United States, and to protect Everglades National Park. These issues have exacerbated the usual competition among urban, agricultural, and environmental water uses, because the national park is at the southwestern end of the Florida drainage system. The park covers 5,250 square kilometers and is a main component of the remaining natural wetlands (Fig. 1). The future characteristics of the park are linked directly to regional water and land uses that influence the quantity and quality of water delivered to the park (Gardner and Lugo 1976; Blake 1980). One of the most evident features of the competition for water in southern Florida is the complex, highly managed system of canals, levees, pump stations, and water-control structures that provide the water supply and the flood protection for the region (Rhoads, Shih, and Hamrick 1987). These facilities give much of the southern Florida landscape an unnatural pattern of water moving in geometrically straight lines over long distances. The purpose of this article is to assess the changes in water delivery to Everglades National Park resulting from expansion of water-transfer facilities since 1940. The analysis of alterations of hydrologic conditions attributable to human manipulation is based on flow through the Tamiami Canal outlets.

The Biology Education of Schoolchildren

More than one hundred years ago, the winged words of A. I. Herzen sounded like an alarm throughout the entire world: Without natural science there is no salvation for modern humanity! In the light of the latest achievements of the exact sciences, these words not only have not faded, but, on the contrary, have become truly prophetic for both living and future generations. Now - in the period of the swift approach of the century of biology - the question of the schoolchildren's education in biology, which was raised so opportunely on the pages of Sovetskaia pedagogika (1964, No. 9) by Corresponding Member of the RSFSR Academy of Pedagogical Sciences N. M. Verzilin, is all the more acute. We are witnesses to this unnatural pattern: the volume of scientific knowledge grows all the time, but the number of hours allotted to the study of natural science not only does not remain stable, but even decreases consistently. It has been reduced to a minimum now, and it is no longer possible to set the matter righ...