A model-based control system is developed for continuous countercurrent tangential chromatography. The mechanistic model is formulated as a distributed parameter system. The computational cost of the mechanistic model...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
A model-based control system is developed for continuous countercurrent tangential chromatography. The mechanistic model is formulated as a distributed parameter system. The computational cost of the mechanistic model is reduced by reformulating the partial differential equations as ordinary differential equations via the method of characteristics. The model parameters are fit to experimental data for the capture of a monoclonal antibody from clarified bioreactor material. An control problem is formulated for the objective of maximizing system productivity subject to a constraint on the protein recovery, and analyzed to provide insight into the process parameters that strongly affect the closed-loop performance.
Aiming at the model of underwater vehicle, a magnetic field feature fusion method based on principal component analysis (PCA) is proposed, focusing on: Non-stationary, nonlinear and non-Gaussian magnetic field time do...
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An intelligent system is described for the formation of advice to managerial production staff on quality control of polymeric films of a wide range in a routine mode and in case of emergency situations that are associ...
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ISBN:
(数字)9798331532178
ISBN:
(纸本)9798331532185
An intelligent system is described for the formation of advice to managerial production staff on quality control of polymeric films of a wide range in a routine mode and in case of emergency situations that are associated with defective films. The system is designed to control extrusion calendering production of films. The production is distinguished by its multi-stage nature, flexibility of the hardware design of the stages, the presence of a recycle for returning waste for processing, a large number and complexity of connections between the parameters of raw materials, equipment, technological mode and product quality indicators, the lack of quality control of the intermediate (extrudate) from which the film is made. Production data is characterized by a large volume, high generation velocity and a variety of sources. The complexity of the control object has required the creation of algorithms for processing its big data to predict product quality based on machine learning methods and the construction of multivariate models for describing an industrial facility. Regression analysis, artificial neural networks and boosting decision trees are used to build predictive models. The choice of method depends on the type of distribution law of production data, their volume and requirements for accuracy and processing speed. The models for describing an industrial facility are deterministic mathematical models of technological processes at the production stages for calculating unmonitored quality indicators of extrudate and film, a base of expert knowledge about emergency situations, their reasons and recommendations for elimination for forming advice to operators, as well as a data bank of film types, production line configurations, monitored and calculated production parameters for adjusting the system to hardware and technological design of production and the range of its products. Testing using data from industrial production of polyvinyl chloride-based packaging films
Imitation learning (IL) can generate computationally efficient sensorimotor policies from demonstrations provided by computationally expensive model-based sensing and control algorithms. However, commonly employed IL ...
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ISBN:
(数字)9781665479271
ISBN:
(纸本)9781665479271
Imitation learning (IL) can generate computationally efficient sensorimotor policies from demonstrations provided by computationally expensive model-based sensing and control algorithms. However, commonly employed IL methods are often data-inefficient, requiring the collection of a large number of demonstrations and producing policies with limited robustness to uncertainties. In this work, we combine IL with an output feedback robust tube model predictive controller (RTMPC) to co-generate demonstrations and a data augmentation strategy to efficiently learn neural network-based sensorimotor policies. Thanks to the augmented data, we reduce the computation time and the number of demonstrations needed by IL, while providing robustness to sensing and process uncertainty. We tailor our approach to the task of learning a trajectory tracking visuomotor policy for an aerial robot, leveraging a 3D mesh of the environment as part of the data augmentation process. We numerically demonstrate that our method can learn a robust visuomotor policy from a single demonstration-a two-orders of magnitude improvement in demonstration efficiency compared to existing IL methods.
The development and application of high-throughput sequencing technology and bioinformatics have promoted the understanding of complex microbial communities in various ecosystems. However, complex environment configur...
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Capability analysis of manufacturing processes needs to appropriately model quality characteristics (QCs). The sample data for modeling can be divided into three categories: (a) original measurement data of QCs, (b) t...
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ISBN:
(数字)9798350386097
ISBN:
(纸本)9798350386103
Capability analysis of manufacturing processes needs to appropriately model quality characteristics (QCs). The sample data for modeling can be divided into three categories: (a) original measurement data of QCs, (b) transformed or normalized measurement data, and (c) data of a composite QC obtained through aggregating several QCs. Generally, different categories of data require different types of distribution models. The purpose of this paper is to identify potential candidate distributions for the first and second categories of data. This is done through fitting seven realworld datasets to eight candidate distributions, which can be divided into two types: (a) common distributions like the Weibull distribution, and (b) the folded-normal distribution (FND) and its variants such as generalized and power halfnormal distributions. Main conclusions are that the normal assumption does not hold for the data considered in this paper, the measurement data can be appropriately modeled by some of the common distributions, and the transformed data can be appropriately modeled by the FND or its variants. These are useful for QC modeling in process capability analysis.
The proceedings contain 73 papers. The special focus in this conference is on Electrical Engineering and Information Technologies for Rail Transportation. The topics include: Electromagnetic Wind Energy Harvester for ...
ISBN:
(纸本)9789819993147
The proceedings contain 73 papers. The special focus in this conference is on Electrical Engineering and Information Technologies for Rail Transportation. The topics include: Electromagnetic Wind Energy Harvester for Condition Monitoring System of High-Speed Train Bogies;an Exploration of Voltage-Type Rotor Magnetic Field Indirect Vector control;method to Detect Arc Across Pantograph-Catenary Structure Atop Train Based on Frequency Features of Entry Current;a Foreign Object Detection Method for Railway Overhead Lines Based on Few-Shot Learning;transformer-Aware Graph Convolution Networks for Relation Extraction of Railway Safety Risk;a Comprehensive Study on Train Operation Dynamics and Passenger Comfort Optimization;model-Driven Study of Intelligent Passenger Information System for Urban Rail Transit;research on Positioning of Permanent Magnet Maglev Trains Based on Weighted Adaptive Kalman Information Fusion;Real-Time Low-Light Image Enhancement Method for Train Driving Scene Based on Improved Zero-DCE;analysis of Dynamic Characteristics of Dropper in Catenary System;a Power Flow Optimization Method for Urban Rail Flexible Traction Power Supply System Considering Train Dwell Time;a Mechanism and data-Driven Hybrid Mechanical Model of Rotary Arm Positioning Rubber Joint;high Speed Train Bracket Arm Visualization Experiment System;design of Cabinet-Level Refrigeration System in Subway Station Communication Signal Room;active control of Pantograph Sliding Mode Under Fluctuating Wind Excitation;Time Synchronized Sensor Network with IEEE1588 for Vibration Measurement in Structural Health Monitoring of Railway System;modeling and Implementation of EMU Traction System;traffic Operation Status Research Based on Multi-source data Fusion;isochronous Deterministic Ethernet System Research;wave Propagation in the Overhead Conductor Rail System.
The peptide coupling reaction is one of the most critical steps in the solid phase synthesis of therapeutic peptides/proteins. Improper reaction conditions can result in several common impurities such as single amino ...
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The peptide coupling reaction is one of the most critical steps in the solid phase synthesis of therapeutic peptides/proteins. Improper reaction conditions can result in several common impurities such as single amino acid deletions, additions, N-terminus modifications, and D-isomers, all while potentially impacting the active pharmaceutical ingredient critical quality attributes. In this work, we developed a first-principle mechanistic reaction kinetics model for the solid-phase peptide/protein coupling reaction based on well-established reaction mechanisms and experimental data from literature. Utilizing the reaction kinetics model, we present a systematic, quality by design approach for the coupling reaction control strategy. Critical process parameters are identified via univariate analysis and the design space is designated via multivariate risk assessment. The presented approach provides a novel solution for designing solid-phase peptide/protein synthesis control strategies and identifying normal operating ranges for each process parameter, as well as the associated design space.
In the construction of new power systems, data as a production factor plays an increasingly important role. In order to improve the efficiency of data utilization, power grid enterprises have established a data manage...
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A major issue for project management, besides handling schedules and deadlines, is the process of finding and extracting the most relevant information from a variety of different software solutions used by the differe...
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ISBN:
(纸本)9781510675216;9781510675223
A major issue for project management, besides handling schedules and deadlines, is the process of finding and extracting the most relevant information from a variety of different software solutions used by the different stakeholders. This often leads to enormously large Excel sheets that try to identify the most up-to-date versions of the documents needed, which are extremely time-consuming to maintain, inefficient for finding inf ormation, and prone to err ors. The contemporary methodology we introduce represents a paradigm shift in project management, eschewing the traditional model of isolated databases for documents, such as requirements, CAD data, and Gantt chart schedules. Instead, we propose an integrated database architecture that consolidates all project management needs into a single, user-friendly repository. This, coupled with a web-based interface, facilitates the retrieval of relevant information in a straightforward and dependable manner. Illustrated through the case study of ELT-MICADO, we present the implementation of this strategy using Siemens Teamcenter, an industry standard software solution that is adapted to our specific needs. This exposition is not intended as an endorsement of the product but rather as an exemplification of one potential solution. It is acknowledged that alternative software solutions may offer comparable functionality and performance.
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