COMPREHENSIVE ANALYSIS OF SEISMIC SIGNALS FROM PACAYA VOLCANO USING DEEP LEARNING EVENT DETECTION

Pacaya volcano located 30 km SW of Guatemala City, Guatemala, has been erupting intermittently since 1961. Monitoring of seismicity is crucial to understanding current activity levels within Pacaya. Traditional methods of picking these small earthquakes in this noisy environment are imprecise. Pacaya produces many small events that can easily blend in with the background noise. A possible solution for this problem is a machine learning program to pick first arrivals for these earthquakes. We tested a deep learning algorithm (Mousavi et al., 2020) for fast and reliable seismic signal detection within a volcanic system. Data from multiple deployments were used, including permanent and temporary arrays from 2015 to 2022. Initially over 12,000 independent events were detected although most were unlocatable. A predetermined 1D velocity model calculated by Lanza & Waite (2018) was initially used to locate the earthquakes. This velocity model was updated using VELEST and the locations were calculated using new 1D P-wave and S-wave velocity models. This resulted in 512 events after a quality control filtering process. These events ranged in depths from -2.5 km (summit of Pacaya) to 0 km (sea level) all located directly beneath the vent. The detection process took about 2-3 hours per 15 days on each 3-component broadband seismometer. The method shows promise in providing an efficient and effective method to pick volcano tectonic seismic events, and it did well identifying the emergent arrivals in these datasets; however, it has shortcomings in detecting some low-frequency event types. This could be addressed through additional training of the algorithm. The very low speeds in our new P-wave and S-wave velocity models highlight the poor consolidation of the young MacKenney cone. Further study is encouraged to better understand the accuracy and type of earthquakes picked, especially the increased level of activity during or leading up to an eruption at Pacaya volcano

Neural Adaptive Control of a Robot Joint Using Secondary Encoders

Using industrial robots for machining applications in flexible manufacturing processes lacks a high accuracy. The main reason for the deviation is the flexibility of the gearbox. Secondary Encoders (SE) as an additional, high precision angle sensor offer a huge potential of detecting gearbox deviations. This paper aims to use SE to reduce gearbox compliances with a feed forward, adaptive neural control. The control network is trained with a second network for system identification. The presented algorithm is capable of online application and optimizes the robot accuracy in a nonlinear simulation

Improved vision based pose estimation for industrial robots via sparse regression

In this work amonocular machine vision based pose estimation system is developed for industrial robots and the accuracy of the estimated pose is im-proved via sparse regression. The proposed sparse regressionbased methodis usedimprove the accuracy obtained from the Levenberg-Marquardt (LM) based pose estimation algorithmduring the trajectory tracking of an industrial robot’s end effector. The proposed method utilizes a set of basis functions to sparsely identify the nonlinear relationship between the estimated pose and the true pose provided by a laser tracker.Moreover,a camera target was designed and fitted with fiducial markers,andto prevent ambiguities in pose estimation, the markers are placed in such a way to guarantee the detection of at least two distinct nonparallel markers from a single camera within ± 90° in all directions of the cam-era’s view. The effectiveness of the proposed method is validated by an experi-mental study performed using a KUKA KR240 R2900 ultra robot while follow-ing sixteen distinct trajectories based on ISO 9238. The obtained results show that the proposed method provides parsimonious models which improve the pose estimation accuracy and precision of the vision based system during trajectory tracking of industrial robots' end effector

Lightweight Handheld EMR with Spring-Damper Handle

Early versions of handheld (HH) electromagnetic riveters (EMR), while effective, were heavy. With the proven effectiveness of the EMR, the next step was to make the HH riveting system light and portable. To maintain the required output force in a small package, the upper limit of the voltage range was increased to 1000V, twice that of conventional 500V LVER systems. The 0-1000V range of the HH50s allows for the formation of rivets up to 3/16 " diameter. Due to the lower mass in the HH50s, the riveting actuator was developed to strategically maximize output force and minimize recoil. Recent developments have been made to drastically reduce recoil by incorporating a spring-damper system integral to the HH50 handle