The integration of robotics in manufacturing has significantly enhanced productivity and safety by performing hazardous tasks. However, it also introduces new risks and accident profiles that require thorough analysis...
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In creating integrated automated production control systems, there is an open question of simulation fidelity distribution between MES- and APC-systems according to the requirement for speed and decision-making horizo...
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ISBN:
(纸本)9783031734168;9783031734175
In creating integrated automated production control systems, there is an open question of simulation fidelity distribution between MES- and APC-systems according to the requirement for speed and decision-making horizon at each level. Operational planning forms a set of discrete operations organized by time and indicators. The period of the operations includes the transient and steady-state mode of the manufacturing process, but does not affect the values of transient mode indicators. Transient mode is the "noise" for operational production planning. The period of scheduled operations should be determined for petroleum refineries so that the level of the "noise" does not exceed the required production planning accuracy. The paper presents a method of calculating the transient mode parameters and the analysis results for refinery process time series, enabling us to determine the minimum operation period for the required operational production planning accuracy in the MES-system. Presents response time, average control action period and obtained estimates of transient speed on the example unit for each group of real refinery processes.
The proceedings contain 14 papers. The special focus in this conference is on Multibody System Dynamics. The topics include: modeling, Simulation, Optimization of the DLR Scout Rover to Enable Extraterrestrial Cave Ex...
ISBN:
(纸本)9789819775248
The proceedings contain 14 papers. The special focus in this conference is on Multibody System Dynamics. The topics include: modeling, Simulation, Optimization of the DLR Scout Rover to Enable Extraterrestrial Cave Exploration;simulation and control of Shape Memory Alloy Spring Actuator in a Flexible Tube Manipulator;design and control of All Pneumatic Virtual Motion Simulator;design of Flexure Robotic Hand for Teleoperation;design and Development of a Novel Rotary Actuator Based on Shape Memory Alloy and Permanent Magnet System;UAV Landing on General Moving Platforms Without Markers;innovative Two-Axle Vehicle with Improved Ride Comfort via Blended Active Vibration control;a Linear Frequency Domain Solver Workflow for Fast Simulation of Transmission Systems;multibody Dynamic Study of Subassembly Transfer Flask Under Seismic Excitation;a Finite Element analysis Study on the Effect of Tool Stiffness in Incremental Forming process;mobile Haptic Device for Large Virtual Environments;multibody Dynamics Software-Based Simulation of a Game for a Robotics Competition.
Gas cylinders, serving in high-pressure environments for extended periods, are prone to significant residual stress in their thin-walled tank regions during the additive manufacturing and subsequent rolling strengthen...
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Path tracking control is a fundamental technology in autonomous vehicle applications, but it faces significant challenges related to vehicle modeling and external disturbances. In this paper, an improved model-free ad...
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This paper intends to use cloud computing technology combined with multi-source heterogeneous data to study extensive dataanalysis and modeling methods for dense environments. This paper aims to improve the mining an...
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ISBN:
(纸本)9783031705069;9783031705076
This paper intends to use cloud computing technology combined with multi-source heterogeneous data to study extensive dataanalysis and modeling methods for dense environments. This paper aims to improve the mining and detection of massive Internet of Things (IoT) big data in the cloud environment. Firstly, relevant statistical characteristics and correlation rules are extracted from massive IoT big data. Secondly, a multi-source heterogeneous network model based on block is proposed. This paper uses a multi-source isomer model to process the collected data, and it proposes a semantic ontology decomposition method for big data in dense IoT scenarios in a cloud environment, and establishes its association rule knowledge base. Meanwhile, the multi-source heterogeneous information transmission mechanism, and then the dense IoT in the cloud environment is analyzed and mined with big data. Experiments show the proposed algorithm performs better anti-jamming when applied in dense IoT environments. This method has better mining accuracy and less time cost.
Given the rapid increase in climate change, investigating the impact of climate change on the transmission mechanism of tick-borne diseases is imperative. In order to fully capture the influence of the seasonal variat...
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Given the rapid increase in climate change, investigating the impact of climate change on the transmission mechanism of tick-borne diseases is imperative. In order to fully capture the influence of the seasonal variation of temperature, environmental disturbances and the co-feeding transmission on the spread of tick-borne diseases, we propose a novel stochastic dynamical model that couples the mean-reverting Ornstein-Uhlenbeck temperature equation with periodic input to the tick-borne disease model. Through theoretical analysis, we derive sufficient conditions for the extinction of tick populations and the eradication of tick-borne diseases, as well as the stochastic persistence conditions of the system. In numerical simulations, we find that the periodic Ornstein-Uhlenbeck temperature equation can effectively fit the actual temperature data in low, medium, and high latitude regions of China. In risk assessment, we find that at the spatial perspective, low-latitude areas have a higher risk of tick-borne diseases, requiring enhanced control measures;from a temporal perspective, compared to the past, the current stage presents a greater risk of tick-borne diseases when preventive measures are not implemented. Additionally, we observe that larger noise of environment for tick populations favors the extinction of tick populations, while smaller temperature fluctuations, noise on infected hosts and ticks, as well as higher temperature regression rate, are more likely to lead to the extinction of tick-borne diseases. These findings provide crucial insights into understanding the impact of climate change on the transmission mechanism of tick-borne diseases.
In the world of Industry 4.0, The technique known as Predictive maintenance (PdM) has emerged as a vital tool in enhancing manufacturing productivity and reducing plant loss. This research paper explores the part that...
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In today's world, gaining digital competence plays a substantial role in improving both the quality for 21st-century citizens, technology permeates daily life, relationships, work, and learning, educational instit...
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The success of deep learning in transient stability assessment (TSA) heavily relies on high-quality training data. However, the label information in TSA datasets is vulnerable to contamination through false label inje...
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The success of deep learning in transient stability assessment (TSA) heavily relies on high-quality training data. However, the label information in TSA datasets is vulnerable to contamination through false label injection (FLI) cyberattacks, resulting in degraded performance of deep TSA models. To address this challenge, a Multi-Module Robust (MMR) TSA method is proposed to rectify the supervised training process misguided by FLI attacks in an unsupervised manner. In MMR, a supervised classification module and an unsupervised clustering module are alternately trained to improve the clustering friendliness of representation leaning, achieving accurate clustering assignments. By leveraging the clustering assignments, we construct a training label corrector to rectify the injected false labels and correct the misguided supervised classification, thereby improving the performance of deep TSA models. However, there is still a gap on accuracy and convergence speed between MMR and FLI-free deep TSA models. To narrow this gap, we further propose a human-in-the-loop training strategy, named MMRHIL. In MMR-HIL, potential false samples can be detected by modeling the training loss with a Gaussian distribution. From these samples, the most likely false samples and most ambiguous samples are selected and relabeled by a TSA expert-guided annotator and then subjected to penalized optimization, aimed at improving accuracy and convergence speed. Extensive experiments indicate that MMR and MMR-HIL both exhibit powerful robustness against FLI attacks. Moreover, the contaminated labels can be effectively corrected, demonstrating superior resilience of the proposed methods.
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