Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of ...process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.
A most critical and important issue surrounding the design of automatic control systems with the successively increasing complexity is guaranteeing a high system performance over a wide operating ...range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solutions, advanced fault detection and identification (FDI) technology is receiving considerable attention. The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.
Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data ...mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.
Large-scale processes have become common, and fault detection for such processes is imperative. This work studies the data-driven distributed local fault detection problem for large-scale processes ...with interconnected subsystems and develops a genetic algorithm (GA)-regularized canonical correlation analysis (CCA)-based distributed local fault detection scheme. For each subsystem, the GA-regularized CCA is first performed with its all coupled systems, which aims to preserve the maximum correlation with the minimal communication cost. A CCA-based residual is then generated, and corresponding statistic is constructed to achieve optimal fault detection for the subsystem. The distributed fault detector performs local fault detection for each subsystem using its own measurements and the information provided by its coupled subsystems and therefore exhibits a superior monitoring performance. The regularized CCA-based distributed fault detection approach is tested on a numerical example and the Tennessee Eastman benchmark process. Monitoring results indicate the efficiency and feasibility of the proposed approach.
In this paper, the key performance indicator (KPI)-based multivariate statistical process monitoring and fault diagnosis (PM-FD) methods for linear static processes are surveyed and evaluated using ...the multivariate statistics framework. Based on their computational characteristics, the possible methods will be broadly classified into three categories: direct, linear regression-based, and PLS-based. The three categories are respectively presented in the first part, then the comparison study in aspects of their interconnections, geometric properties, and computational costs are shown, and finally their performance for PM-FD of KPIs is evaluated using a new evaluation index called expected detection delay, where a numerical case and the Tennessee Eastman process are used to provide a demonstration of the evaluation result.
To ensure the reliability and safety of modern industrial process monitoring, computer vision-based soft measurement has received considerable attention due to its nonintrusive property. ...State-of-the-art computer vision-based approaches mostly rely on feature embedding from deep neural networks. However, this kind of feature extraction suffers from noise effects and limitation of labeled training instances, leading to unsatisfactory performance in real industrial process monitoring. In this article, we develop a novel hybrid learning framework for feature representation based on knowledge distillation and supervised contrastive learning. First, we attempt to transfer the abundant semantic information in handcrafted features to deep learning feature-based network by knowledge distillation. Then, to enhance the feature discrimination, supervised contrastive learning is proposed to contrast many positive pairs against many negative pairs per anchor. Meanwhile, two important mechanisms, memory queue-based negative sample augmentation and hard negative sampling, are added into the supervised contrastive learning model to assist the proper selection of negative samples. Finally, a flotation process monitoring problem is considered to illustrate and demonstrate the effectiveness of the proposed method.
The replication of coronavirus, a family of important animal and human pathogens, is closely associated with the cellular membrane compartments, especially the endoplasmic reticulum (ER). Coronavirus ...infection of cultured cells was previously shown to cause ER stress and induce the unfolded protein response (UPR), a process that aims to restore the ER homeostasis by global translation shutdown and increasing the ER folding capacity. However, under prolonged ER stress, UPR can also induce apoptotic cell death. Accumulating evidence from recent studies has shown that induction of ER stress and UPR may constitute a major aspect of coronavirus-host interaction. Activation of the three branches of UPR modulates a wide variety of signaling pathways, such as mitogen-activated protein (MAP) kinase activation, autophagy, apoptosis, and innate immune response. ER stress and UPR activation may therefore contribute significantly to the viral replication and pathogenesis during coronavirus infection. In this review, we summarize the current knowledge on coronavirus-induced ER stress and UPR activation, with emphasis on their cross-talking to apoptotic signaling.
Background
Stereotactic radiosurgery (SRS) relies on small fields to ablate lesions. Currently, linac based treatment is delivered via circular cones using a 6 MV beam. There is interest in both ...lower energy photon beams, which can offer steeper dose fall off as well as higher energy photon beams, which have higher dose rates, thus reducing radiation delivery times. Of interest in this study is the 2.5 MV beam developed for imaging applications and both the 6 and 10 MV flattening‐filter‐free (FFF) beams, which can achieve dose rates up to 2400 cGy/min.
Purpose
This study aims to assess the benefit and feasibility among different energy beams ranging from 2.5 to 10 MV beams by evaluating the dosimetric effects of each beam and comparing the dose to organs‐at‐risk (OARs) for two separate patient plans. One based on a typical real patient tremor utilizing a 4 mm cone and the other a typical brain metastasis delivered with a 10 mm cone.
Methods
The Monte Carlo codes BEAMnrc/DOSXYZnrc were used to generate beams of 2.5 MV, 6 MV‐FFF, 6 MV‐SRS, 6 MV, 10 MV‐FFF, and 10 MV from a Varian TrueBeam except 6 MV‐SRS, which is taken from a Varian TX model linear accelerator. Each beam's energy spectrum, mean energy, %dd curve, and dose profile were obtained by analyzing the simulated beams. Calculated patient dose distributions were compared among six different energy beam configurations based on a realistic treatment plan for thalamotomy and a conventional brain metastasis plan. Dose to OARs were evaluated using dose–volume histograms for the same target dose coverage.
Results
The mean energies of photons within the primary beam projected area were insensitive to cone sizes and the values of percentage depth–dose curves (%dd) at d = 5 cm and SSD = 95 cm for a 4 mm (10 mm) cone ranges from 62.6 (64.4) to 82.2 (85.7) for beam energy ranging from 2.5 to 10 MV beams, respectively. Doses to OARs were evaluated among these beams based on real treatment plans delivering 15 000 and 2200 cGy to the target with a 4 and 10 mm cone, respectively. The maximum doses to the brainstem, which is 10 mm away from the isocenter, was found to be 434 (300), 632 (352), 691 (362), 733 (375), 822 (403), and 975 (441) cGy for 2.5 MV, 6 MV‐FFF, 6 MV‐SRS, 6 MV, 10 MV‐FFF, and 10 MV beams delivering 15 000 (2200) cGy target dose, respectively.
Conclusion
Using the 6 MV‐SRS as reference, changes of the maximum dose (691 cGy) to the brain stem are −37%, −9%, +6%, +19%, and 41% for 2.5 MV, 6 MV‐FFF, 6 MV, 10 MV‐FFF, and 10 MV beams, respectively, based on the thalamotomy plan, where the “‐” or “+” signs indicate the percentage decrease or increase. Changes of the maximum dose (362 cGy) to brain stem, based on the brain metastasis plan are much less for respective beam energies. The sum of 21 arcs beam‐on time was 39 min on our 6 MV‐SRS beam with 1000 cGy/min for thalamotomy. The beam‐on time can be reduced to 16 min with 10 MV‐FFF.
Background
With radiotherapy having entered the era of image guidance, or image‐guided radiation therapy (IGRT), imaging procedures are routinely performed for patient positioning and target ...localization. The imaging dose delivered may result in excessive dose to sensitive organs and potentially increase the chance of secondary cancers and, therefore, needs to be managed.
Aims
This task group was charged with: a) providing an overview on imaging dose, including megavoltage electronic portal imaging (MV EPI), kilovoltage digital radiography (kV DR), Tomotherapy MV‐CT, megavoltage cone‐beam CT (MV‐CBCT) and kilovoltage cone‐beam CT (kV‐CBCT), and b) providing general guidelines for commissioning dose calculation methods and managing imaging dose to patients.
Materials & Methods
We briefly review the dose to radiotherapy (RT) patients resulting from different image guidance procedures and list typical organ doses resulting from MV and kV image acquisition procedures.
Results
We provide recommendations for managing the imaging dose, including different methods for its calculation, and techniques for reducing it. The recommended threshold beyond which imaging dose should be considered in the treatment planning process is 5% of the therapeutic target dose.
Discussion
Although the imaging dose resulting from current kV acquisition procedures is generally below this threshold, the ALARA principle should always be applied in practice. Medical physicists should make radiation oncologists aware of the imaging doses delivered to patients under their care.
Conclusion
Balancing ALARA with the requirement for effective target localization requires that imaging dose be managed based on the consideration of weighing risks and benefits to the patient.
The high-temperature stability and mechanical properties of refractory molybdenum alloys are highly desirable for a wide range of critical applications. However, a long-standing problem for these ...alloys is that they suffer from low ductility and limited formability. Here we report a nanostructuring strategy that achieves Mo alloys with yield strength over 800 MPa and tensile elongation as large as ~ 40% at room temperature. The processing route involves a molecular-level liquid-liquid mixing/doping technique that leads to an optimal microstructure of submicrometre grains with nanometric oxide particles uniformly distributed in the grain interior. Our approach can be readily adapted to large-scale industrial production of ductile Mo alloys that can be extensively processed and shaped at low temperatures. The architecture engineered into such multicomponent alloys offers a general pathway for manufacturing dispersion-strengthened materials with both high strength and ductility.
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