Intergration of control chart and pattern recognizer for bivariate quality control

Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing multivariate statistical process control schemes are only effective in rapid detection but suffer high false alarm. This is referred to as imbalanced performance monitoring. The problem becomes more complicated when dealing with small mean shift particularly in identifying the causable variables. In this research, a scheme that integrated the control charting and pattern recognition technique has been investigated toward improving the quality control (QC) performance. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and Statistical Features-ANN models, and monitoring-diagnosis approach based on single stage and two stages techniques. The study focuses on correlated process mean shifts for cross correlation function, ρ = 0.1, 0.5, 0.9, and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated design, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme provides superior performance, namely the Average Run Length for grand average ARL1 = 7.55 7.78 ( for out-of-control) and ARL0 = 491.03 (small mean shift) and 524.80 (large mean shift) in control process and the grand average for recognition accuracy (RA) = 96.36 98.74. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts

Long-term Monitoring Plan for the Central Nevada Test Area

The groundwater flow and transport model of the Faultless underground nuclear test conducted at the Central Nevada Test Area (CNTA) was accepted by the state regulator and the environmental remediation efforts at the site have progressed to the stages of model validation and long-term monitoring design. This report discusses the long-term monitoring strategy developed for CNTA. Subsurface monitoring is an expensive and time-consuming process, and the design approach should be based on a solid foundation. As such, a thorough literature review of monitoring network design is first presented. Monitoring well networks can be designed for a number of objectives including aquifer characterization, parameter estimation, compliance monitoring, detection monitoring, ambient monitoring, and research monitoring, to name a few. Design methodologies also range from simple hydrogeologic intuition-based tools to sophisticated statistical- and optimization-based tools. When designing the long-term monitoring well network for CNTA, a number of issues are carefully considered. These are the uncertainty associated with the subsurface environment and its implication for monitoring design, the cost associated with monitoring well installation and operation, the design criteria that should be used to select well locations, and the potential conflict between different objectives such as early detection versus impracticality of placing wells in the vicinity of the test cavity. Given these considerations and the literature review of monitoring design studies, a multi-staged approach for development of the long-term monitoring well network for CNTA is proposed. This multi-staged approach will proceed in parallel with the validation efforts for the groundwater flow and transport model of CNTA. Two main stages are identified as necessary for the development of the final long-term monitoring well network for the site. The first stage is to use hydrogeologic expertise combined with model simulations and probability based approaches to select the first set of monitoring wells that will serve two purposes. The first is to place the wells in areas likely to encounter migration pathways thereby enhancing the probability of detecting radionuclide migration in the long run. The second objective is crucial in the short run and is aimed at using this set of wells to collect validation data for the model. The selection criteria should thus balance these two objectives. Based on the results of the validation process that progresses concurrently with the first monitoring stage, either more wells will be needed in this first stage or the second stage will be initiated. The second monitoring design stage will be based on an optimum design methodology that uses a suitable statistical approach, combined with an optimization approach, to augment the initial set of wells and develop the final long-term monitoring network. The first-stage probabilistic analysis conducted using the CNTA model indicates that the likelihood of migration away from the test cavity is very low and the probability of detecting radionuclides in the next 100 years is extremely low. Therefore, it is recommended to place one well in the downstream direction along the model longitudinal centerline (i.e., directly north of the working point), which is the location with the highest probability of encountering the plume. Lack of significant plume spreading, coupled with the extremely low velocities, suggests that this one well is sufficient for the first stage. Data from this well, and from additional wells located with validation as the prime objective, will benefit the model validation process. In the long run, this first monitoring well is going to be crucial for the long-term monitoring of the site (assuming that the flow model is validated), as it will be the most likely place to detect any plume migration away from the cavity

Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks

Rehabilitation is important to improve quality of life for mobility-impaired patients. Smart walkers are a commonly used solution that should embed automatic and objective tools for data-driven human-in-the-loop control and monitoring. However, present solutions focus on extracting few specific metrics from dedicated sensors with no unified full-body approach. We investigate a general, real-time, full-body pose estimation framework based on two RGB+D camera streams with non-overlapping views mounted on a smart walker equipment used in rehabilitation. Human keypoint estimation is performed using a two-stage neural network framework. The 2D-Stage implements a detection module that locates body keypoints in the 2D image frames. The 3D-Stage implements a regression module that lifts and relates the detected keypoints in both cameras to the 3D space relative to the walker. Model predictions are low-pass filtered to improve temporal consistency. A custom acquisition method was used to obtain a dataset, with 14 healthy subjects, used for training and evaluating the proposed framework offline, which was then deployed on the real walker equipment. An overall keypoint detection error of 3.73 pixels for the 2D-Stage and 44.05mm for the 3D-Stage were reported, with an inference time of 26.6ms when deployed on the constrained hardware of the walker. We present a novel approach to patient monitoring and data-driven human-in-the-loop control in the context of smart walkers. It is able to extract a complete and compact body representation in real-time and from inexpensive sensors, serving as a common base for downstream metrics extraction solutions, and Human-Robot interaction applications. Despite promising results, more data should be collected on users with impairments, to assess its performance as a rehabilitation tool in real-world scenarios.Comment: Accepted for publication in Expert Systems with Application

Automated sleep stage classification in sleep apnoea using convolutional neural networks

A sleep disorder is a condition that adversely impacts one\u27s ability to sleep well on a regular schedule. It also occurs as a consequence of numerous neurological sicknesses. These types of disorders can be investigated using laboratory-based polysomnography (PSG) signals. The detection of neurological disorders is exact and efficient thanks to the automated monitoring of sleep relegation stages. This automation method publicly presents a flexible deep learning model and machine learning approach utilizing raw electroencephalogram (EEG) signals. The deep learning model is a Deep Convolutional Neural Network (CNN) that analyses invariant time capacities and frequency actualities and collects assessment adaptations. It also captures the inviolate and long brief length setting conditions between the epochs and the degree of sleep stage relegation. This method uses an innovative function to calculate data loss and misclassified errors found while training the network for the sleep stage, considering the restrictions found in the publicly available sleep datasets. It is used in conjunction with machine learning techniques to forecast the best approach for the process. Its effectiveness is determined by using two open-source, public databases available from PhysioNet: two recordings with 5402 epoch counts. The technique used in this approach achieves an accuracy of 90.70%, precision of 90.50%, recall of 92.70%, and F-measure of 90.60%. The proposed method is more significant than existing models like AlexNet, ResNet, VGGNet, and LeNet. The comparative study of the models could be adopted for clinical use and modified based on the requirements

Diagnosis of bivariate process variation using an integrated mspc-ann scheme

Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing multivariate statistical process control schemes are only effective in rapid detection but suffer high false alarm. This is referred to as imbalanced performance monitoring. The problem becomes more complicated when dealing with small mean shift particularly in identifying the causable variables. In this research, a scheme that integrated the control charting and pattern recognition technique has been investigated toward improving the quality control (QC) performance. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and Statistical Features-ANN models, and monitoring-diagnosis approach based on single stage and two stages techniques. The study focuses on correlated process mean shifts for cross correlation function, ρ = 0.1, 0.5, 0.9, and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated design, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme provides superior performance, namely the Average Run Length for grand average ARL1 = 7.55 ̴ 7.78 ( for out-of-control) and ARL0 = 4λ1.03 (small shifts) and 524.80 (large shifts) in control process and the grand average for recognition accuracy (RA) = λ6.36 ̴ λ8.74. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts

Progressive damage assessment and network recovery after massive failures

After a massive scale failure, the assessment of damages to communication networks requires local interventions and remote monitoring. While previous works on network recovery require complete knowledge of damage extent, we address the problem of damage assessment and critical service restoration in a joint manner. We propose a polynomial algorithm called Centrality based Damage Assessment and Recovery (CeDAR) which performs a joint activity of failure monitoring and restoration of network components. CeDAR works under limited availability of recovery resources and optimizes service recovery over time. We modified two existing approaches to the problem of network recovery to make them also able to exploit incremental knowledge of the failure extent. Through simulations we show that CeDAR outperforms the previous approaches in terms of recovery resource utilization and accumulative flow over time of the critical service