Localization of Thermal Wellbore Defects Using Machine Learning

Defect detection and localization are key to preventing environmentally damaging wellbore leakages in both geothermal and oil/gas applications. In this work, a multi-step, machine learning approach is used to localize two types of thermal defects within a wellbore model. This approach includes a COMSOL heat transfer simulation to generate base data, a neural network to classify defect orientations, and a localization algorithm to synthesize sensor estimations into a predicted location. A small-scale physical wellbore test bed was created to verify the approach using experimental data. The classification and localization results were quantified using this experimental data. The classification predicted all experimental defect orientations correctly. The localization algorithm predicted the defect location with an average root mean square error of 1.49 in. The core contributions of this work are 1) the overall localization architecture, 2) the use of centroid-guided mean-shift clustering for localization, and 3) the experimental validation and quantification of performance.

CHILES. VII. Deep Imaging for the CHILES Project, an SKA Prototype

Radio astronomy is undergoing a renaissance, as the next generation of instruments provides a massive leap forward in collecting area and therefore raw sensitivity. However, to achieve this theoretical level of sensitivity in the science data products, we need to address the much more pernicious systematic effects, which are the true limitation. These become all the more significant when we consider that much of the time used by survey instruments, such as the Square Kilometre Array (SKA), will be dedicated to deep surveys. CHILES is a deep H i survey of the COSMOS field, with 1000 hr of Very Large Array time. We present our approach for creating the image cubes from the first epoch, with discussions of the methods and quantification of the data quality from 946 to 1420 MHz—a redshift range of 0.5−0. We lay out the problems we had to solve and describe how we tackled them. These are important because CHILES is the first deep wide-band multiepoch H i survey and has relevance for ongoing and future surveys. We focus on the accumulated systematic errors in the imaging, as the goal is to deliver a high-fidelity image that is only limited by the random thermal errors. To understand and correct these systematic effects, we ideally manage them in the domain in which they arise, and that is predominately the visibility domain. CHILES is a perfect test bed for many of the issues we can expect for deep imaging with the SKA or ngVLA, and we discuss the lessons we have learned.

Pioneering Evaluation of GaN Transistors in Geostationary Satellites

In this paper, we present the results of a 6-year experiment in space that studied the effects of radiation in GaN electronics in geostationary orbit. Four GaN transistors in a Colpitts oscillator configuration were flown in the Component Technology Test-Bed aboard the Alphasat telecommunication satellite. A heuristic analysis was performed by observing the variation in the power output of the oscillators with the total ionizing dose gathered during the mission. The total ionizing dose was measured with a RADFET placed close to the GaN devices. The experiment showed that GaN is a robust technology that can be used in the space radiation environment of a geostationary orbit. The work presented here starts with a brief introduction of the subject, the motivation and the main goal. This is followed by the description of the experimental setup, including the details of the oscillator design and simulations, as well as the implementation of the test bead and the Components Technology Test Bed. Finally, the results obtained during the 6 years of experience in space are discussed.

Design of Smart Grid Test Bed Using OPNET and PLC

In this design unit, a design to test the performances of varying models was developed for the simulations in the PLC-base data link layer. The design includes a smart home and a Smart Grid environment where a comparison between Zigbee and WiMax-based models can be performed. The Smart Grid Test Bed has been designed using OPNET and Power Line Communication is proposed in this book. It is being designed to allow test bed experiments in four layers among OSI 7 layers. This chapter is organized as follows: The Physical Layer and Datalink Layer for Smart Grid Test Bed in Section 1; the Transport Layer for Smart Grid Test Bed in Section 2; and finally, Application Layer for Smart Grid Test Bed in Section.

Implementation of Smart Grid Test Bed Using OPNET and PLC

In this chapter, a design that allows testing of the performances of various models was developed with OPNET for the simulations in the PLC-base data link layer. As the model proposed earlier, the design includes a smart home and a Smart Grid environment where a comparison between Zigbee and WiMax-based models can be performed. The Smart Grid Test Bed has been implemented using OPNET and Power Line Communication is proposed in this book. It is being designed to allow Test Bed experiments in four layers among seven OSI layers. This chapter is organized as follows: the physical layer and datalink layer for Smart Grid Test Bed in Section 1; the transport layer for Smart Grid Test Bed in Section 2; and finally, application layer for Smart Grid Test Bed in Section 3.

A Machine Learning Approach for Anomaly Detection to Secure Smart Grid Systems

The rapid industrial growth in cyber-physical systems has led to upgradation of the traditional power grid into a network communication infrastructure. The benefits of integrating smart components have brought about security issues as attack perimeter has increased. In this chapter, firstly, the authors train the network on the results generated by the uncompromised grid network result dataset and then extract valuable features by the various system calls made by the kernel on the grid and after that internal operations being performed. Analyzing the metrics and predicting how the call lists are differing in call types, parameters being passed to the OS, the size of the system calls, and return values of the calls of both the systems and identifying benign devices from the compromised ones in the test bed are done. Predictions can be accurately made on the device behavior in the smart grid and calculating the efficiency of correct detection vs. false detection according to the confusion matrix, and finally, accuracy and F-score will be computed against successful anomaly detection behavior.

EXPERIMENTAL STUDY ON AIR FLOW FIELD CHARACTERISTICS OF SUCTION METERING DEVICE

The vacuum seed metering device absorbs seeds by using the negative pressure generated by vacuum air flow. Therefore, it is of great significance to study the variation law of pyrolysis gas flow field to improve its seed metering performance. In this paper, the common disc and composite disc were selected as the research objects and tested on the indoor test-bed. The negative pressure was measured by U-type barometer, and the effects of fan speed, suction hole size, seed hole structure and air chamber thickness on the air flow field were studied. Firstly, the influence of fan rotation frequency on vacuum chamber negative pressure is studied, and the variation law of negative pressure in vacuum chamber and fan port of common disc and composite disc under the same frequency is compared. Secondly, the suction holes in the vacuum chamber were numbered, the negative pressure distribution of the suction holes was measured, and the influence of the number and diameter of the suction holes on the negative pressure of the vacuum chamber was studied. Finally, the negative pressure was measured at the distance of 0 to 10 mm from the suction hole to study the effect of seed hole structure on the air flow field. Moreover, increase the additional thickness of the vacuum chamber from 0 to 40 mm to study the influence of the chamber thickness on the distribution of the gas flow field. This paper makes a comprehensive experimental analysis on the influencing factors of air flow field of air suction seed metering device, necessary for future design of air suction seed metering device.

A macroscopic link between interhemispheric tract myelination and cortico-cortical interactions during action reprogramming

Myelination has been increasingly implicated in the function and dysfunction of the adult human brain. Although it is known that axon myelination shapes axon physiology in animal models, it is unclear whether a similar principle applies in the living human brain, and at the level of whole axon bundles in white matter tracts. Here, we hypothesised that in humans, cortico-cortical interactions between two brain areas may be shaped by the amount of myelin in the white matter tract connecting them. As a test bed for this hypothesis, we use a well-defined interhemispheric premotor-to-motor circuit. We combined TMS-derived physiological measures of cortico-cortical interactions during action reprogramming with multimodal myelin markers (MT, R1, R2* and FA), in a large cohort of healthy subjects. We found that physiological metrics of premotor-to-motor interaction are broadly associated with multiple myelin markers, suggesting interindividual differences in tract myelination may play a role in motor network physiology. Moreover, we also demonstrate that myelination metrics link indirectly to action switching by influencing local primary motor cortex dynamics. These findings suggest that myelination levels in white matter tracts may influence millisecond-level cortico-cortical interactions during tasks. They also unveil a link between the physiology of the motor network and the myelination of tracts connecting its components, and provide a putative mechanism mediating the relationship between brain myelination and human behaviour.

Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion

It is envisioned that healthcare systems of the future will be revolutionized with the development and integration of body-centric networks into future generations of communication systems, giving rise to the so-called “Internet of Bio-nano things”. Molecular communications (MC) emerge as the most promising way of transmitting information for in-body communications. One of the biggest challenges is how to minimize the effects of environmental noise and reduce the inter-symbol interference (ISI) which in an MC via diffusion scenario can be very high. To address this problem, channel coding is one of the most promising techniques. In this paper, we study the effects of different channel codes integrated into MC systems. We provide a study of Tomlinson, Cercas, Hughes (TCH) codes as a new attractive approach for the MC environment due to the codeword properties which enable simplified detection. Simulation results show that TCH codes are more effective for these scenarios when compared to other existing alternatives, without introducing too much complexity or processing power into the system. Furthermore, an experimental proof-of-concept macroscale test bed is described, which uses pH as the information carrier, and which demonstrates that the proposed TCH codes can improve the reliability in this type of communication channel.