Objective: The present study proposes a new epileptic seizure prediction method through integrating heart rate variability (HRV) analysis and an anomaly monitoring technique. Methods: Because ...excessive neuronal activities in the preictal period of epilepsy affect the autonomic nervous systems and autonomic nervous function affects HRV, it is assumed that a seizure can be predicted through monitoring HRV. In the proposed method, eight HRV features are monitored for predicting seizures by using multivariate statistical process control, which is a well-known anomaly monitoring method. Results: We applied the proposed method to the clinical data collected from 14 patients. In the collected data, 8 patients had a total of 11 awakening preictal episodes and the total length of interictal episodes was about 57 h. The application results of the proposed method demonstrated that seizures in ten out of eleven awakening preictal episodes could be predicted prior to the seizure onset, that is, its sensitivity was 91%, and its false positive rate was about 0.7 times per hour. Conclusion: This study proposed a new HRV-based epileptic seizure prediction method, and the possibility of realizing an HRV-based epileptic seizure prediction system was shown. Significance: The proposed method can be used in daily life, because the heart rate can be measured easily by using a wearable sensor.
Objective: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on ...heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. Methods: Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. Result: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. Conclusion: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. Significance: The proposed method can contribute to preventing accidents caused by drowsy driving.
•A multistage degradation model based on Wiener process is proposed•The model integrated unit-to-unit, temporal and measurement three variability•The control chart of statistical process control is ...applied to stage division•Use Expectation Maximization to update model parameters lacking prior information
The prediction of remaining useful life (RUL) of rolling bearing is core content of equipment prognosis and health management. For rolling bearings, the degradation trend can be divided into multiple stages, and each stage has uncertain changes. Therefore, a new approach of bearing RUL prediction based on stochastic process model is proposed in this paper. Firstly, a new stochastic degradation model is established, which integrates the characteristics of multistage and multi-variability of degradation trend. Then, the statistical process control (SPC) is applied to stage division for the first time, which divides degradation stages and adaptively switches degradation models. At the same time, in the absence of prior information, update model parameters online by using parameters estimation method based on expectation maximization (EM) algorithm and predict RUL distribution in different degradation stages. Finally, the effectiveness of this approach is verified by empirical study of simulation example and XJTU-SY bearing data. The results show that this approach can divide different stages of rolling bearing and provide RUL prediction of corresponding stages.
Sustainability issues are challenging the cement industry due to its high emission of greenhouse gas, intensive energy consumption, and depletion of resources. One of the strategies to mitigate the ...problem is to improve process control techniques and optimize resources. The objective of this paper is to survey the approach and evolution of statistical process control chart techniques and study their significance and limitations in the case of optimization of cement production. The main research question this study address is "What are the significances and limitations of statistical process control chart methods to the optimization of cement process?" The methodology of the study followed the literature survey with meta-analysis and focused on identifying the statistical process control chart design techniques and their application to cement industries. The result of the survey indicated that statistical and mathematical algorithms are encapsulated by advanced soft computing methods; however, still, it is the foundation for advanced process control methods. Moreover, it is found that statistical process control has a theoretical and technical gap in the application of the cement industry. The theoretical gap identified in the literature is that in the case of a complex production system the techniques recognize the occurrence of the out-of-control case in the production process but are not able to identify the cause of variation. The technical gap in the statistical process control techniques is that there are several important theoretical control chart techniques, but they are not researched well on how to apply to the real world.
Artificial intelligence (AI), which is characterized by ability to learn and adapt continuously, can enhance the fault tolerance and robustness in process control. Application of high-efficiency ...physical fields such as microwave, radio frequency, infrared radiation and ultrasonic fields can result in efficient production of dried fruit and vegetable products with high quality. Whether the combination of AI technology and efficient physical field can obtain better dried products of fruits and vegetables, and how AI can be applied in the drying process of fruit and vegetable, has attracted extensive attention.
The application of artificial intelligence technology to assist the efficient physical field in the drying of fruits and vegetables is the development trend of fruit and vegetable drying industry in the future. This paper aims to provide a concise overview of recent research in the rapidly emerging area of AI-assisted drying of fruits and vegetables using physical fields to provide energy for drying process. A selection of AI technologies is introduced such as sensor technology, computer vision systems as well as a few relevant AI technologies used in the drying process of fruits or vegetables. Afterwards, it summarizes the application of artificial intelligence in the physical drying of fruits and vegetables, and how to improve the shortcomings of highly efficient physical field drying of fruits and vegetables with AI.
The application of high efficiency physical field in the drying process of fruits and vegetables can solve the problems of large energy consumption, uneven drying, poor sensory evaluation, and large nutrient loss. The drying process and the corresponding drying model of fruits and vegetables can be detected and controlled online, and the optimum drying scheme can be determined using artificial intelligence technology. The most important thing is to make up for the shortcomings of highly efficient physical field drying of fruits and vegetables. The artificial intelligence technology has a promising application prospect to assist the efficient physical field drying of fruits and vegetables.
•Introduce artificial intelligence technologies, including sensor technology, computer vision system and other technologies.•The application of artificial intelligence (sensor, computer vision, ANN, GA) in fruit and vegetable drying was reviewed.•The application of artificial intelligence technology combined in fruit and vegetable drying using efficient physical field.•The development suggestion of artificial intelligence drying technology based on efficient physical field was provided.
Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from ...a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs.
Graphical abstract
Novel quality improvement(QI) methods are needed to optimize healthcare costs and value. Our goal was to determine if Statistical Process Control(SPC), an industrial QI tool, could transform length ...of stay(LOS) into a process measure, identify outliers, and their impact on surgical outcomes.
SPC was performed on an institutional colorectal resection database 1/1/13-5/1/2018 to identify outliers and compare outcome variables across outliers and non-outliers. Control charts analyzed the process performance of LOS over time. Control limits were set at ± 1 standard deviation(SD) from the mean. Measures were stable within these limits.
LOS was stable, with consistent annual rates and variation of outliers. Outliers had identifiable causes of variation that were significantly different from non-outliers(p < 0.05). The variation resulted in more complications, readmissions, and reoperations in outliers(p < 0.05).
SPC can be applied to LOS, a stable process measure with decreasing variability over time, and easy outlier identification. Identifying outliers can facilitate targeted quality improvement.
•Statistical process control (SPC) is an industrial tool that can be easily applied to surgical quality improvement.•SPC can graphically transform length of stay (LOS) into a processs measure.•LOS became a stable process with outliers easily identifiable.•The outliers were common causes variation, and the variation was reduced over time.•The SPC exercise demonstrated the LOS process is primed for quality improvement intervention.
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