Process Mean Shift Detection Using Prediction Error Analysis

1998 ◽  
Vol 120 (3) ◽  
pp. 489-495 ◽  
Author(s):  
S. J. Hu ◽  
Y. G. Liu
Keyword(s):  
Error Analysis ◽  
Prediction Error ◽  
Control Charts ◽  
Mean Shift ◽  
Measurement Data ◽  
Step Function ◽  
False Alarms ◽  
Prediction Errors ◽  
Function Type ◽  
Shift Detection

Autocorrelation in 100 percent measurement data results in false alarms when the traditional control charts, such as X and R charts, are applied in process monitoring. A popular approach proposed in the literature is based on prediction error analysis (PEA), i.e., using time series models to remove the autocorrelation, and then applying the control charts to the residuals, or prediction errors. This paper uses a step function type mean shift as an example to investigate the effect of prediction error analysis on the speed of mean shift detection. The use of PEA results in two changes in the 100 percent measurement data: (1) change in the variance, and (2) change in the magnitude of the mean shift. Both changes affect the speed of mean shift detection. These effects are model parameter dependent and are obtained quantitatively for AR(1) and ARMA(2,1) models. Simulations and examples from automobile body assembly processes are used to demonstrate these effects. It is shown that depending on the parameters of the AMRA models, the speed of detection could be increased or decreased significantly.

scholarly journals Gender differences in refraction prediction error of five formulas for cataract surgery

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yibing Zhang ◽  
Tingyang Li ◽  
Aparna Reddy ◽  
Nambi Nallasamy
Keyword(s):  
Gender Differences ◽  
Cataract Surgery ◽  
Prediction Error ◽  
Statistical Difference ◽  
Independent Predictor ◽  
Study Data ◽  
Prediction Errors ◽  
Optical Biometry ◽  
Iol Power ◽  
Lens Power

Abstract Objectives To evaluate gender differences in optical biometry measurements and lens power calculations. Methods Eight thousand four hundred thirty-one eyes of five thousand five hundred nineteen patients who underwent cataract surgery at University of Michigan’s Kellogg Eye Center were included in this retrospective study. Data including age, gender, optical biometry, postoperative refraction, implanted intraocular lens (IOL) power, and IOL formula refraction predictions were gathered and/or calculated utilizing the Sight Outcomes Research Collaborative (SOURCE) database and analyzed. Results There was a statistical difference between every optical biometry measure between genders. Despite lens constant optimization, mean signed prediction errors (SPEs) of modern IOL formulas differed significantly between genders, with predictions skewed more hyperopic for males and myopic for females for all 5 of the modern IOL formulas tested. Optimization of lens constants by gender significantly decreased prediction error for 2 of the 5 modern IOL formulas tested. Conclusions Gender was found to be an independent predictor of refraction prediction error for all 5 formulas studied. Optimization of lens constants by gender can decrease refraction prediction error for certain modern IOL formulas.

Differential pial and penetrating arterial responses examined by optogenetic activation of astrocytes and neurons

2021 ◽  
pp. 0271678X2110103
Author(s):  
Nao Hatakeyama ◽  
Miyuki Unekawa ◽  
Juri Murata ◽  
Yutaka Tomita ◽  
Norihiro Suzuki ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Acetylcholine Receptors ◽  
Step Function ◽  
Muscarinic Acetylcholine Receptors ◽  
Cell Type ◽  
Function Type ◽  
Neuron Activation ◽  
Cell Type Specific ◽  
Arterial Responses

A variety of brain cells participates in neurovascular coupling by transmitting and modulating vasoactive signals. The present study aimed to probe cell type-dependent cerebrovascular (i.e., pial and penetrating arterial) responses with optogenetics in the cortex of anesthetized mice. Two lines of the transgenic mice expressing a step function type of light-gated cation channel (channelrhodopsine-2; ChR2) in either cortical neurons (muscarinic acetylcholine receptors) or astrocytes (Mlc1-positive) were used in the experiments. Photo-activation of ChR2-expressing astrocytes resulted in a widespread increase in cerebral blood flow (CBF), extending to the nonstimulated periphery. In contrast, photo-activation of ChR2-expressing neurons led to a relatively localized increase in CBF. The differences in the spatial extent of the CBF responses are potentially explained by differences in the involvement of the vascular compartments. In vivo imaging of the cerebrovascular responses revealed that ChR2-expressing astrocyte activation led to the dilation of both pial and penetrating arteries, whereas ChR2-expressing neuron activation predominantly caused dilation of the penetrating arterioles. Pharmacological studies showed that cell type-specific signaling mechanisms participate in the optogenetically induced cerebrovascular responses. In conclusion, pial and penetrating arterial vasodilation were differentially evoked by ChR2-expressing astrocytes and neurons.

scholarly journals A Location Map Reduced Reversible Data Hiding Method Using Prediction Error Modification

2012 ◽  
Vol 6-7 ◽  
pp. 428-433
Author(s):  
Yan Wei Li ◽  
Mei Chen Wu ◽  
Tung Shou Chen ◽  
Wien Hong
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Experimental Results ◽  
Cover Image ◽  
Prediction Errors ◽  
Secret Data ◽  
Location Map ◽  
Comparable Image Quality

We propose a reversible data hiding technique to improve Hong and Chen’s (2010) method. Hong and Chen divide the cover image into pixel group, and use reference pixels to predict other pixel values. Data are then embedded by modifying the prediction errors. However, when solving the overflow and underflow problems, they employ a location map to record the position of saturated pixels, and these pixels will not be used to carry data. In their method, if the image has a plenty of saturated pixels, the payload is decreased significantly because a lot of saturated pixels will not joint the embedment. We improve Hong and Chen’s method such that the saturated pixels can be used to carry data. The positions of these saturated pixels are then recorded in a location map, and the location map is embedded together with the secret data. The experimental results illustrate that the proposed method has better payload, will providing a comparable image quality.

scholarly journals A Homeostatic Model of Subjective Cognitive Decline

2018 ◽  
Vol 8 (12) ◽  
pp. 228 ◽  
Author(s):  
Akiko Mizuno ◽  
Maria Ly ◽  
Howard Aizenstein
Keyword(s):  
Cognitive Decline ◽  
Prediction Error ◽  
Primary Source ◽  
Subjective Cognitive Decline ◽  
Prediction Errors ◽  
Functional Neuroanatomy ◽  
System A ◽  
Prediction Error Minimization ◽  
Breakdown Model ◽  
Normal Cognition

Subjective Cognitive Decline (SCD) is possibly one of the earliest detectable signs of dementia, but we do not know which mental processes lead to elevated concern. In this narrative review, we will summarize the previous literature on the biomarkers and functional neuroanatomy of SCD. In order to extend upon the prevailing theory of SCD, compensatory hyperactivation, we will introduce a new model: the breakdown of homeostasis in the prediction error minimization system. A cognitive prediction error is a discrepancy between an implicit cognitive prediction and the corresponding outcome. Experiencing frequent prediction errors may be a primary source of elevated subjective concern. Our homeostasis breakdown model provides an explanation for the progression from both normal cognition to SCD and from SCD to advanced dementia stages.

The Embodiment of Concepts

2018 ◽  
pp. 640-660
Author(s):  
Michiel Van Elk ◽  
Harold Bekkering
Keyword(s):  
Prediction Error ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Conceptual Representation ◽  
Recurrent Processing ◽  
Different Dimensions ◽  
Prior Models ◽  
Processing Framework

We characterize theories of conceptual representation as embodied, disembodied, or hybrid according to their stance on a number of different dimensions: the nature of concepts, the relation between language and concepts, the function of concepts, the acquisition of concepts, the representation of concepts, and the role of context. We propose to extend an embodied view of concepts, by taking into account the importance of multimodal associations and predictive processing. We argue that concepts are dynamically acquired and updated, based on recurrent processing of prediction error signals in a hierarchically structured network. Concepts are thus used as prior models to generate multimodal expectations, thereby reducing surprise and enabling greater precision in the perception of exemplars. This view places embodied theories of concepts in a novel predictive processing framework, by highlighting the importance of concepts for prediction, learning and shaping categories on the basis of prediction errors.

scholarly journals Prefrontal solution to the bias-variance tradeoff during reinforcement learning

2020 ◽  
Author(s):  
Dongjae Kim ◽  
Jaeseung Jeong ◽  
Sang Wan Lee
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Brain Regions ◽  
Decision Task ◽  
Prediction Errors ◽  
Model Based ◽  
Model Free ◽  
Bias Variance ◽  
The Brain

AbstractThe goal of learning is to maximize future rewards by minimizing prediction errors. Evidence have shown that the brain achieves this by combining model-based and model-free learning. However, the prediction error minimization is challenged by a bias-variance tradeoff, which imposes constraints on each strategy’s performance. We provide new theoretical insight into how this tradeoff can be resolved through the adaptive control of model-based and model-free learning. The theory predicts the baseline correction for prediction error reduces the lower bound of the bias–variance error by factoring out irreducible noise. Using a Markov decision task with context changes, we showed behavioral evidence of adaptive control. Model-based behavioral analyses show that the prediction error baseline signals context changes to improve adaptability. Critically, the neural results support this view, demonstrating multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free learning.One sentence summaryA theoretical, behavioral, computational, and neural account of how the brain resolves the bias-variance tradeoff during reinforcement learning is described.

A control chart for severity index to detect drought compares favourably to logistic regression

2011 ◽  
Vol 42 (1) ◽  
pp. 1-9
Author(s):  
Jared Townsley ◽  
Justin R Chimka
Keyword(s):  
Logistic Regression ◽  
Control Chart ◽  
Control Charts ◽  
Severity Index ◽  
Drought Severity ◽  
Prediction Errors ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Accessible Method ◽  
Made In

We describe the discovery of how a traditional control chart for the Palmer Drought Severity Index (PDSI) to detect drought compares favourably to a theoretically appropriate statistical (logistic regression) model of drought as a function of PDSI. Our empirical results are based on monthly observations of PDSI, precipitation and temperature made in Kansas since 1895. Results from the study suggest that a relatively simple statistical approach based on Shewhart control charts may provide a more accessible method for relevant government agencies to predict droughts, improving resource management and preparation. Moreover, utilizing such an approach over more sophisticated methods may come at little expense regarding prediction errors.

Condition monitoring based on modified CUSUM and EWMA control charts

2018 ◽  
Vol 24 (1) ◽  
pp. 119-132 ◽  
Author(s):  
Suzana Paula Gomes Fernando da Silva Lampreia ◽  
José Fernando Gomes Requeijo ◽  
José António Mendonça Dias ◽  
Valter Martins Vairinhos ◽  
Patrícia Isabel Soares Barbosa
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Failure Detection ◽  
Parameters Estimation ◽  
False Alarms ◽  
Model Parameters ◽  
Content Type ◽  
Catastrophic Failures ◽  
The Right ◽  
Weighted Moving Average

Purpose The application of condition-based maintenance on selected equipment can allow online monitoring using fixed, half-fixed or portable sensors. The collected data not always allow a straightforward interpretation and many false alarms can happen. The paper aims to discuss these issues. Design/methodology/approach Statistical techniques can be used to perform early failure detection. With the application of Cumulative Sum (CUSUM) Modified Charts and the Exponentially Weighted Moving Average (EWMA) Charts, special causes of variation can be detected online and during the equipment functioning. Before applying these methods, it is important to check data for independence. When the independence condition is not verified, data should be modeled with an ARIMA (p, d, q) model. Parameters estimation is obtained using the Shewhart Traditional Charts. Findings With data monitoring and statistical methods, it is possible to detect any system or equipment failure trend, so that we can act at the right time to avoid catastrophic failures. Originality/value In this work, an electro pump condition is monitored. Through this process, an anomaly and four stages of aggravation are forced, and the CUSUM and EWMA modified control charts are applied to test an online equipment monitoring. When the detection occurs, the methodology will have rules to define the degree of intervention.

The Associative Learning Prediction Error Feedback Loop as Reversible Driving Belt of the Bioenergetics of Longevity

2021 ◽  
Author(s):  
Matthew S Price
Keyword(s):  
Associative Learning ◽  
Prediction Error ◽  
Feedback Loop ◽  
Telomerase Activity ◽  
Behavioral Strategy ◽  
Prediction Errors ◽  
Error Feedback ◽  
Treatment Expectations ◽  
Telomere Shortening ◽  
Inhibitory Learning

Leukocyte telomere shortening is a useful biomarker of biological and cellular age that occurs at an accelerated rate in anxiety disorders and posttraumatic stress disorder (PTSD). Intriguingly, inhibitory learning — the systematic exposure to noxious stimuli that serves as a basis for many treatments for anxiety, phobia, and PTSD —reduces relative telomeres attrition rates and increases protective telomerase activity in a manner predictive of treatment response. How does inhibitory learning, a behavioral strategy, modulate organismal chromosomal activity? Inhibitory learning may induce repeated mismatch between treatment expectations, intrasession states, and eventual outcome. Nevertheless, inhibitory learning can incentivize repetition of the behavior. Thus, this paper aims to conceptualize inhibitory learning as involving a ‘prediction error feedback loop’, i.e., a series of self-perpetuating prediction errors — mismatches between expectations and outcomes — that enhances neural inhibitory regulation to effectuate extinction. Inhibitory learning is necessarily predicated upon an opposing process – excitatory learning – that may be conceptualized as a prediction error feedback loop that operates in reverse to inhibitory learning and enhances neural excitability as arousal. Together, excitatory and inhibitory learning may be elements of an associative learning prediction error feedback loop responsible for modulating neural bioenergetic rates, leading to changes in downstream cellular signaling that could explain reduced or increased rates of leukocyte telomere shortening and telomerase activity from each behavioral strategy, respectively.

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