Ontologies as computational artifacts have been seen as a solution to FAIRness due to their characteristics, applications, and semantic competencies. Conceptualizations of complex and vast domains can be fragmented in...
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ISBN:
(纸本)9783031472619;9783031472626
Ontologies as computational artifacts have been seen as a solution to FAIRness due to their characteristics, applications, and semantic competencies. Conceptualizations of complex and vast domains can be fragmented in different ways and can compose what is known as ontology networks. Thus, the ontologies produced can relate to each other in many different ways, making the ontological artifacts themselves subject to FAIRness. The problem is that in the Ontology Engineering process, stakeholders take different perspectives of the conceptualizations, and this causes ontologies to have biases that are sometimes more ontological and sometimes more related to the domain. Besides, usually, Ontology Engineers provide well-grounded reference ontologies, but rarely are they implemented. At the same time, Domain Specialists produce operational ontologies storing large amounts of valid data but with naive ontological support or even without any. We address this problem of lack of consensual conceptualization by proposing a reference conceptual model (O4OA) that considers ontological-related and domain-related perspectives, knowledge, and commitment necessary to facilitate the process of Ontological analysis, including the analysis of ontologies composing an ontology network. Indeed, O4OA is a (meta)ontology grounded in the Unified Foundational Ontology (UFO) and supported by well-known ontological classification standards, guides, and FAIR principles. We demonstrate how this approach can suitably promote conceptual clarification and terminological harmonization in this area through our framework proposal and its case studies.
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 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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Numerous supplementary Shewhart monitoring designs have emerged, customized to data that follows specific non-normal distributions like the Rayleigh distribution (RD). The Rayleigh distribution has a variety of applic...
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Numerous supplementary Shewhart monitoring designs have emerged, customized to data that follows specific non-normal distributions like the Rayleigh distribution (RD). The Rayleigh distribution has a variety of applications in modeling theory of communication, physical sciences, diagnostic imaging, life testing, reliability analysis, applied statistics and clinical studies. The exponential weighted moving average (EWMA) design is frequently advocated in the literature because of its ability to swiftly detect smaller process alterations. However, the common EWMA chart may not perform optimally in detecting all changes in the process parameters. To address this limitation, this study introduces an adaptive EWMA structure for monitoring quality characteristics following the RD, called the adaptive Rayleigh EWMA (AREWMA) chart. To determine the design parameters of the AREWMA chart, a Markov chain model is utilized. Analytical results are then used to assess the performance of the AREWMA chart in comparison to existing competitors. The comparative analysis illustrates the strengths of the proposed AREWMA chart in detecting shifts of various magnitudes during parameter monitoring. Finally, we present a practical application of the proposed AREWMA chart in the manufacturing industry, utilizing real data on the time of failure eld-tracking of devices in a system. Our analysis demonstrates the effectiveness of the AREWMA chart in detecting a range of shifts in the manufacturing process, highlighting its utility for continuous monitoring and quality control.
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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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 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.
We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (mu PC) performance when manufactured at scale for chemical and biochemic...
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We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (mu PC) performance when manufactured at scale for chemical and biochemical analysis of volatile organic compounds (VOCs). To test the proposed model, a medium-sized university-led production batch of 30 wafers of chips were subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process step leading to the final functional chemical sensor chip. We implemented a principal component analysis (PCA) model to score individual chip chemical performance, and we observed that the first two principal components represent 74.28% of chemical testing variance with 111 of 118 viable chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing parameters and steps. In our analysis, we find the amount of sorbent mass present in the chip (variable importance score = 2.6) and heater and the RTD resistance values (variable importance score = 1.1) to be the manufacturing parameters with the greatest impact on chemical performance. Other non-obvious latent manufacturing parameters also had quantified influence. Statistical distributions for each manufacturing step will allow future large-scale production runs to be statistically sampled during production to perform QA/QC in a real-time environment. We report this study as the first data-driven, model-based production of a microfabricated chemical sensor.
The proceedings contain 104 papers. The special focus in this conference is on Computer Aided Systems Theory. The topics include: Influence of Spike Encoding, Neuron Models and Quantization on SNN Perfo...
ISBN:
(纸本)9783031829512
The proceedings contain 104 papers. The special focus in this conference is on Computer Aided Systems Theory. The topics include: Influence of Spike Encoding, Neuron Models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture Models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-processing of Myoelectric Signals for the control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for Learning Medicine Through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine Learning Methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications;advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, Therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine Learning Based Parameter Estimation of Energy Models in Digital Production Environments;efficient Classification of Live Sensor data on Low-Energy IoT Devices with Simple Machine Learning Methods;machine Learning Using a Hybrid Quantum Classical Algorithm with Amplitude data Encoding;quantitative Trend analysis of Reinforcement Learning Algorithms in Production Systems;Using AutomationML for Advanced Simulation in Industrial Automation;AR Digital Twin Demonstrator for Industrial Robotics Education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -
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