scholarly journals Large-Scale CT Inspection of Feed-Through EMI Filters for Space Application

Author(s):  
John Bescup

Abstract This paper describes a project to develop and deploy a systematic screening methodology involving computed tomography (CT) to inspect a set of electromagnetic interference (EMI) filter components for a spacecraft application. The goal was to deploy the nondestructive CT test to replace the destructive test method typically deployed for such components. The paper describes the development of test criteria, fixturing, inspection process, and data analysis, including quantitative image analysis of voids and cracks. The initial results indicated that the parts would not pass the requirements established in the test design. A waiver was written to the project clarifying that if the parts were to be used in the assembly, they should be considered as simple conductors with EMI filtering capability viewed as an added benefit rather than a guaranteed design requirement.

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 90-92
Author(s):  
Kae Doki ◽  
Yuki Funabora ◽  
Shinji Doki

Every day we are seeing an increasing number of robots being employed in our day-to-day lives. They are working in factories, cleaning our houses and may soon be chauffeuring us around in vehicles. The affordability of drones too has come down and now it is conceivable for most anyone to own a sophisticated unmanned aerial vehicle (UAV). While fun to fly, these devices also represent powerful new tools for several industries. Anytime an aerial view is needed for a planning, surveillance or surveying, for example, a UAV can be deployed. Further still, equipping these vehicles with an array of sensors, for climate research or mapping, increases their capability even more. This gives companies, governments or researchers a cheap and safe way to collect vast amounts of data and complete tasks in remote or dangerous areas that were once impossible to reach. One area UAVs are proving to be particularly useful is infrastructure inspection. In countries all over the world large scale infrastructure projects like dams and bridges are ageing and in need of upkeep. Identifying which ones and exactly where they are in need of patching is a huge undertaking. Not only can this work be dangerous, requiring trained inspectors to climb these megaprojects, it is incredibly time consuming and costly. Enter the UAVs. With a fleet of specially equipped UAVs and a small team piloting them and interpreting the data they bring back the speed and safety of this work increases exponentially. The promise of UAVs to overturn the infrastructure inspection process is enticing, but there remain several obstacles to overcome. One is achieving the fine level of control and positioning required to navigate the robots around 3D structures for inspection. One can imagine that piloting a small UAV underneath a huge highway bridge without missing a single small crack is quite difficult, especially when the operators are safely on the ground hundreds of meters away. To do this knowing exactly where the vehicle is in space becomes a critical variable. The job can be made even easier if a flight plan based on set waypoints can be pre-programmed and followed autonomously by the UAV. It is exactly this problem that Dr Kae Doki from the Department of Electrical Engineering at Aichi Institute of Technology, and collaborators are focused on solving.


2019 ◽  
Vol 7 ◽  
Author(s):  
Brian Stucky ◽  
James Balhoff ◽  
Narayani Barve ◽  
Vijay Barve ◽  
Laura Brenskelle ◽  
...  

Insects are possibly the most taxonomically and ecologically diverse class of multicellular organisms on Earth. Consequently, they provide nearly unlimited opportunities to develop and test ecological and evolutionary hypotheses. Currently, however, large-scale studies of insect ecology, behavior, and trait evolution are impeded by the difficulty in obtaining and analyzing data derived from natural history observations of insects. These data are typically highly heterogeneous and widely scattered among many sources, which makes developing robust information systems to aggregate and disseminate them a significant challenge. As a step towards this goal, we report initial results of a new effort to develop a standardized vocabulary and ontology for insect natural history data. In particular, we describe a new database of representative insect natural history data derived from multiple sources (but focused on data from specimens in biological collections), an analysis of the abstract conceptual areas required for a comprehensive ontology of insect natural history data, and a database of use cases and competency questions to guide the development of data systems for insect natural history data. We also discuss data modeling and technology-related challenges that must be overcome to implement robust integration of insect natural history data.


Author(s):  
Alexander Miropolsky ◽  
Anath Fischer

The inspection of machined objects is one of the most important quality control tasks in the manufacturing industry. Contemporary scanning technologies have provided the impetus for the development of computational inspection methods, where the computer model of the manufactured object is reconstructed from the scan data, and then verified against its digital design model. Scan data, however, are typically very large scale (i.e., many points), unorganized, noisy, and incomplete. Therefore, reconstruction is problematic. To overcome the above problems the reconstruction methods may exploit diverse feature data, that is, diverse information about the properties of the scanned object. Based on this concept, the paper proposes a new method for denoising and reduction in scan data by extended geometric filter. The proposed method is applied directly on the scanned points and is automatic, fast, and straightforward to implement. The paper demonstrates the integration of the proposed method into the framework of the computational inspection process.


2014 ◽  
Vol 584-586 ◽  
pp. 894-898
Author(s):  
Ping Zhang ◽  
Guan Guo Liu ◽  
Chao Ming Pang ◽  
Bing Du ◽  
Hong Gen Qin

The X ray computed tomography (X-CT) was applied to test the cracking resistance of cement paste, and the hydration process was monitored to study the effect of fly ash on the early age cracking performance. The results showed that the hydration heat reduced with the increase of fly ash under the same water-cement ratio. Within 24h, the porosity increased with time. The addition of fly ash increased the proportion of large holes and then changed the internal stress state. Using X-CT test method and by comparing the number of cracks, the sample with 20% FA was found to have the most serious cracks, whereas the sample with 30% FA had the best crack resistance.


Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2528 ◽  
Author(s):  
Hiroshi Yamazaki ◽  
Ichiro Kurose ◽  
Michiko Nishiyama ◽  
Kazuhiro Watanabe

In this paper, a novel pendulum-type accelerometer based on hetero-core fiber optics has been proposed for structural health monitoring targeting large-scale civil infrastructures. Vibration measurement is a non-destructive method for diagnosing the failure of structures by assessing natural frequencies and other vibration patterns. The hetero-core fiber optic sensor utilized in the proposed accelerometer can serve as a displacement sensor with robustness to temperature changes, in addition to immunity to electromagnetic interference and chemical corrosions. Thus, the hetero-core sensor inside the accelerometer measures applied acceleration by detecting the rotation of an internal pendulum. A series of experiments showed that the hetero-core fiber sensor linearly responded to the rotation angle of the pendulum ranging within (−6°, 4°), and furthermore the proposed accelerometer could reproduce the waveform of input vibration in a frequency band of several Hz order.


2016 ◽  
Vol 20 (1) ◽  
pp. 27
Author(s):  
Hongyi Li ◽  
Di Zhao ◽  
Shaofeng Xu ◽  
Pidong Wang ◽  
Jiaxin Chen

In this paper, we study the spectral characteristics and global representations of strongly nonlinear, non-stationary electromagnetic interferences (EMI), which is of great significance in analysing the mathematical modelling of electromagnetic capability (EMC) for a large scale integrated system. We firstly propose to use Self-Organizing Feature Map Neural Network (SOM) to cluster EMI signals. To tackle with the high dimensionality of EMI signals, we combine the dimension reduction and clustering approaches, and find out the global features of different interference factors, in order to finally provide precise mathematical simulation models for EMC design, analysis, forecasting and evaluation. Experimental results have demonstrated the validity and effectiveness of the proposed method.


2018 ◽  
Vol 7 (2.4) ◽  
pp. 17
Author(s):  
C Shyamala Kumari ◽  
S Florence ◽  
K Prema ◽  
L Leema Priyadharshini

In this era of technology the number of gadgets such as mobilephones, smartwatches, digital glasses and health trackers are used extensively by people in a large scale. The data traffic is abundant as the available radio frequency is limited, so the demand for the wireless network is keep on increasing. In order to meet the future demands there was a development in the optical communication method known as Li-Fi(Light-Fidelity).This will offer more bandwidth than the RF spectrum. They make use of LEDs to transmit the data. The main advantages of the Li-Fi is that there is no need of licensing and has a huge amount of unregulated bandwidth and there is zero electromagnetic interference so that the health hazards are nullified.


2019 ◽  
Vol 489 (3) ◽  
pp. 3582-3590 ◽  
Author(s):  
Dmitry A Duev ◽  
Ashish Mahabal ◽  
Frank J Masci ◽  
Matthew J Graham ◽  
Ben Rusholme ◽  
...  

ABSTRACT Efficient automated detection of flux-transient, re-occurring flux-variable, and moving objects is increasingly important for large-scale astronomical surveys. We present braai, a convolutional-neural-network, deep-learning real/bogus classifier designed to separate genuine astrophysical events and objects from false positive, or bogus, detections in the data of the Zwicky Transient Facility (ZTF), a new robotic time-domain survey currently in operation at the Palomar Observatory in California, USA. Braai demonstrates a state-of-the-art performance as quantified by its low false negative and false positive rates. We describe the open-source software tools used internally at Caltech to archive and access ZTF’s alerts and light curves (kowalski ), and to label the data (zwickyverse). We also report the initial results of the classifier deployment on the Edge Tensor Processing Units that show comparable performance in terms of accuracy, but in a much more (cost-) efficient manner, which has significant implications for current and future surveys.


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