A Deep Learning-based approach for Fault Detection of Power Lines

Master's thesis in Information- and communication technology (IKT590)A transmission network is the most crucial part of modern infrastructure. However, it requires an extensive amount of power line inspection each year to maintain, and with an increased interest in replacing large helicopters with drones for this process, the possibility of including AI is equally compelling. This thesis goes into the second part by taking a deep learning-based approach in the interest of fault detection. A literature review illustrates that earlier research has some to none understanding of the complexity re-quired for inspection. Due to the advancement in object detection and classification, this thesis has identified and implemented an applicable model capable of giving state-of-the-art accuracy in electrical pole and component detection by dividing the process into multiple layers. This thesis takes as well and proposes a new method that presented great result in assuring more reliable fault detection and is a way to improve the quality of images taken by drones. The pole detection layer gave 97.7 mAP, the component detection layer reached 95.6mAP, the fault classifier delivered an accuracy of 93%, and the proposed quality classifier had an accuracy of 93% as well. The presented approach illustrates the possibility of phasing the physical inspection out. The amount of component labeled that must be available for algorithmic training to surpass a human expert is not readily available. Nevertheless, the presented approach is a sufficient tool for assisting the inspector

Molecular anatomy of adult mouse leptomeninges.

Leptomeninges, consisting of the pia mater and arachnoid, form a connective tissue investment and barrier enclosure of the brain. The exact nature of leptomeningeal cells has long been debated. In this study, we identify five molecularly distinct fibroblast-like transcriptomes in cerebral leptomeninges; link them to anatomically distinct cell types of the pia, inner arachnoid, outer arachnoid barrier, and dural border layer; and contrast them to a sixth fibroblast-like transcriptome present in the choroid plexus and median eminence. Newly identified transcriptional markers enabled molecular characterization of cell types responsible for adherence of arachnoid layers to one another and for the arachnoid barrier. These markers also proved useful in identifying the molecular features of leptomeningeal development, injury, and repair that were preserved or changed after traumatic brain injury. Together, the findings highlight the value of identifying fibroblast transcriptional subsets and their cellular locations toward advancing the understanding of leptomeningeal physiology and pathology

A Deep Learning-based approach for Fault Detection of Power Lines

A transmission network is the most crucial part of modern infrastructure. However, it requires an extensive amount of power line inspection each year to maintain, and with an increased interest in replacing large helicopters with drones for this process, the possibility of including AI is equally compelling. This thesis goes into the second part by taking a deep learning-based approach in the interest of fault detection. A literature review illustrates that earlier research has some to none understanding of the complexity re-quired for inspection. Due to the advancement in object detection and classification, this thesis has identified and implemented an applicable model capable of giving state-of-the-art accuracy in electrical pole and component detection by dividing the process into multiple layers. This thesis takes as well and proposes a new method that presented great result in assuring more reliable fault detection and is a way to improve the quality of images taken by drones. The pole detection layer gave 97.7 mAP, the component detection layer reached 95.6mAP, the fault classifier delivered an accuracy of 93%, and the proposed quality classifier had an accuracy of 93% as well. The presented approach illustrates the possibility of phasing the physical inspection out. The amount of component labeled that must be available for algorithmic training to surpass a human expert is not readily available. Nevertheless, the presented approach is a sufficient tool for assisting the inspector

The time required to reach different points along the 15-km classical race course at the Norwegian cross-country skiing championship for men in Tromsø, 2016.

<p>(A) Individual slow (n = 18, solid line) and fast (n = 17, medium dash) skiers cluster in the upper and middle of the figure, respectively. The reference line at the bottom (dotted line, black circles) represents the winner. (B). Mean (SD) differences between the slow (n = 18, solid line, white squares) and fast skiers (n = 17, medium dash, black triangles). The reference line at the bottom (dotted line, black circles) represents the winner. *** p < 0.001, for comparison of the two groups.</p

Profile of the 15-km classical cross-country skiing race course.

<p>The skiers covered three 5-km laps, two on track A (the first and final laps) and one on track B (the second lap). S1 (flat terrain, 0° incline), S2 (intermediate, 3.5°) and S3 (uphill, 7.1°) indicate the sections on which the skiers were filmed. See the text for further details.</p

Flowchart illustrating our procedure for selection of participants.

<p>Flowchart illustrating our procedure for selection of participants.</p

The sections video-recorded, showing the four red poles creating a perfect rectangle, the fifth pole set exactly in the middle of this rectangle, and the two orange lines drawn on the snow between the poles perpendicular to the ski track.

<p>This arrangement allowed appropriate calibration of the track chosen by each skier individually. This image was taken by the camera on the uphill section.</p

Relative usage of techniques (%), skiing velocity and kinematic variables for 36 world (fast) and national (slow) class Norwegian cross-country skiers on the intermediate section (S2, 3.5° incline) of the 15-km classical race at the Norwegian cross-country skiing championships for men in Tromsø, 2016.

<p>Relative usage of techniques (%), skiing velocity and kinematic variables for 36 world (fast) and national (slow) class Norwegian cross-country skiers on the intermediate section (S2, 3.5° incline) of the 15-km classical race at the Norwegian cross-country skiing championships for men in Tromsø, 2016.</p

Overview of the techniques (gears) employed in classical cross-country skiing: DP (double poling), a symmetrical technique utilized on flat terrain, involves a pole push phase, which begins just before the skier plants his poles in the snow, and ends when the poles are lifted off the snow behind him.

<p>DP_KICK (double poling with a kick), employed on flat and slightly uphill terrain, involves a symmetrical poling action followed by a single step or kick with the left or right leg for propulsion. DIA (diagonal skiing), employed uphill, involves a kicking action followed by a weight shift to the gliding ski, after which the skier quickly performs a poling action with the arm opposite the kicking leg.</p

A novel intragastric balloon for treatment of obesity and type 2 diabetes. A two-center pilot trial

Background and aims: Obesity with type-2 diabetes is a global challenge. Lifestyle interventions have limited effect for most patients. Bariatric surgery is highly effective, but resource-demanding, invasive and associated with serious complications. Recently, a new intragastric balloon was introduced, not requiring endoscopy for placement or removal (Elipse™, Allurion Inc., Natick, MA). The balloon is swallowed in a capsule and filled with water once in the stomach. The balloon self-deflates after 4 months and is naturally excreted. The present trial investigated balloon feasibility, safety and efficacy in patients with obesity and type-2 diabetes. Patients and methods: We treated 19 patients, with type-2 diabetes and body mass index (BMI) of 30.0-39.9 kg/m2 at two Norwegian centers with the Elipse balloon. Patient follow-up during balloon treatment mimicked real-world clinical practice, including dietary plan and outpatient visits. The primary efficacy endpoints were total body weight loss (TBWL) and HbA1c at weeks 16 and 52. Results: All patients underwent balloon insertion uneventfully as out-patients. Mean TBWL and HbA1c reduction after 16 and 52 weeks of balloon insertion was 3.9% (95%CI 2.1-5.7) and 0.8% (95%CI 1.9-3.5); and 7 (95%CI 4-10), and 1 (95%CI -6 to 9) mmol/mol, respectively. Adverse events occurred in two patients (10.5%): one developed gastric outlet obstruction, managed by endoscopic balloon removal; the other excessive vomiting and dehydration, managed conservatively. Conclusions: This first Scandinavian real-world clinical trial with a new minimally invasive intragastric balloon system demonstrated good feasibility, but did not confirm expected efficacy for weight loss and diabetes control