Research on Nonline-of-Sight Positioning Method of Intelligent Mobile Terminal Based on Intelligent Monitoring Architecture of Multimedia Sensor Network

With the continuous development of social economy, information resources have become more and more valued resources. Based on the intelligent monitoring architecture of the multimedia sensor network, this article proposes a nonline-of-sight positioning method that can fit the characteristics of autonomous movement for the object of intelligent terminal, that is, first draw the corresponding position trajectory according to the speed attribute of the node. On this basis, according to the relative position trajectory and radio frequency signal positioning, the two-by-two positioning of position and direction is comprehensively realized, and the positioning result is obtained; the positioning accuracy is evaluated according to the positioning of the radio frequency signal, and the false positioning result of the distorted radio frequency signal is stripped out to reduce the error influences. Practical results show that the method is effective and can meet the needs of positioning accuracy.

Implementation of Convolutional Neural Networks for Warp Detection in 3D Printed Components manufactured via Fused Filament Fabrication: A Bayesian-Based Automated Approach

Fused Filament Fabrication (FFF) is an additive manufacturing technique commonly used in industry to produce complicated structures sustainably. Although promising, the technology frequently suffers from defects, including warp deformation compromising the structural integrity of the component and, in extreme cases, the printer itself. To avoid the adverse effects of warp deformation, this thesis explores the implementation of deep neural networks to form a closed-loop in-process monitoring architecture using Convolutional Neural Networks (CNN) capable of pausing a printer once a warp is detected. Any neural network, including CNNs, depend on their hyperparameters. Hyperparameters can either be optimized using a manual or an automated approach. A manual approach, although easier to program, is often time-consuming, inaccurate and computationally inefficient, necessitating an automated approach. To evaluate this statement, classification models were optimized through both approaches and tested in a laboratory scaled manufacturing environment. The automated approach utilized a Bayesianbased optimizer yielding a mean accuracy of 100% significantly higher than 36% achieved by the other approach.

Implementation of Convolutional Neural Networks for Warp Detection in 3D Printed Components manufactured via Fused Filament Fabrication: A Bayesian-Based Automated Approach

Fused Filament Fabrication (FFF) is an additive manufacturing technique commonly used in industry to produce complicated structures sustainably. Although promising, the technology frequently suffers from defects, including warp deformation compromising the structural integrity of the component and, in extreme cases, the printer itself. To avoid the adverse effects of warp deformation, this thesis explores the implementation of deep neural networks to form a closed-loop in-process monitoring architecture using Convolutional Neural Networks (CNN) capable of pausing a printer once a warp is detected. Any neural network, including CNNs, depend on their hyperparameters. Hyperparameters can either be optimized using a manual or an automated approach. A manual approach, although easier to program, is often time-consuming, inaccurate and computationally inefficient, necessitating an automated approach. To evaluate this statement, classification models were optimized through both approaches and tested in a laboratory scaled manufacturing environment. The automated approach utilized a Bayesianbased optimizer yielding a mean accuracy of 100% significantly higher than 36% achieved by the other approach.

The CMS monitoring infrastructure and applications

The globally distributed computing infrastructure required to cope with the multi-petabyte datasets produced by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at CERN comprises several subsystems, such as workload management, data management, data transfers, and submission of users’ and centrally managed production requests. To guarantee the efficient operation of the whole infrastructure, CMS monitors all subsystems according to their performance and status. Moreover, we track key metrics to evaluate and study the system performance over time. The CMS monitoring architecture allows both real-time and historical monitoring of a variety of data sources. It relies on scalable and open source solutions tailored to satisfy the experiment’s monitoring needs. We present the monitoring data flow and software architecture for the CMS distributed computing applications. We discuss the challenges, components, current achievements, and future developments of the CMS monitoring infrastructure.