Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery

This work presents a novel system utilizing previously developed convolutional neural network (CNN) architectures to aid in automating maintenance inspections of the dead-end body component (DEBC) from high-tension power lines. To maximize resolution of inspection images gathered via unmanned aerial systems (UAS), two different CNNs were developed. One to detect and crop the DEBC from an image. The second to classify the likelihood the component in question contains a crack. The DEBC detection CNN utilized a Python implementation of Faster R-CNN fine-tuned for three classes via 270 inspection photos collected during UAS inspection, alongside 111 images from provided simulated imagery. The data was augmented to develop 2,707 training images. The detection was tested with 111 UAS inspections images. The resulting CNN was capable of 97.8% accuracy in detecting and cropping DEBC welds. To train the classification CNN if the DEBC weld region cropped from the DEBC detection CNN was cracked, 1,149 manually cropped images from both the simulated images, as well images collected of components previously replaced both inside and outside a warehouse, were augmented to provide a training set of 4,632 images. The crack detection network was developed using the VGG16 model implemented with the Caffe framework. Training and testing of the crack detection CNNs performance was accomplished using a random 5-fold cross validation strategy resulting in an overall 98.8% accuracy. Testing the combined object detection and crack classification networks on the same 5-fold cross validation test images resulted in an average accuracy of 73.79%

The Role of Mouse Barrel Cortex in Tactile Trace Eye Blink Conditioning

Mouse whisker-related primary somatosensory cortex (also known as barrel cortex, BCx) is required to form an association between a behaviorally relevant tactile stimulus and its consequences, only if the first conditioned stimulus CS (here a single whisker deflection), and the latter unconditioned stimulus US (here a corneal air puff) are separated by a ‘trace’ (brief memory period). I investigated whether tactile trace eye blink conditioning (TTEBC) has a correlate in BCx activity and whether such BCx activity in the two periods, CS and trace are required for learning. I trained three head-fixed mice on TTEBC to assess learning related functional plasticity of BCx by recording LFPs and multi-unit (MU) spiking from 4-shank laminar silicone probes (8 electrodes per shank, inter-shank distance 200μm) spanning the depths of the principal barrel column and its neighbors. Current source density analysis (CSD) showed the known short latency sink (~8ms) in L4 and L5/6 during CS presentation, followed by a weaker current sink during ongoing tactile stimulation, spanning across the column. At the same depth, a novel current source was discovered during the trace period. The latter two currents were consistently attenuated during TTEBC acquisition. Onset MU spike response to the CS (at a latency of <15ms) was stable in most units, while steady state CS-response (50-250ms) typically decreased below the pre-learning level. Spiking during the trace period also depressed during learning. These plastic changes were observed in neighboring shanks at a horizontal distance of up to 400μm. These findings show that BCx is functionally involved in TTEBC acquisition. Matching the lateral spread of the neuronal signal into the neighboring column, I found mice to generalize the CS-US association only to adjacent, but not to near and far whiskers. I next asked whether the involvement of BCx during the trace period has any causal role in TTEBC. I employed the well-established VGAT-ChR2 mouse line that, due to expression of channelrhodopsin-2 in inhibitory neurons (Zhao et al., 2011), blocks virtually all spikes in a column with high temporal precision, using blue light. I found that BCx functionality was required during CS presentation. However, mice learned normally when blocking BCx during the trace period. After learning, BCx activity during CS & trace was entirely dispensable for task performance. In summary, I demonstrate that the barrel column is involved in acquiring the TTEBC association. Nevertheless, the plasticity of the neuronal response in the trace period is a non- causal reflection of learning, and after learning, in the early phase of retention BCx is not needed for task performance. Future research need to establish if BCx assumes a more critical role in late consolidation. Further, the nature and projection of the signals measured during the learning have to be explored on the microscopic network and cellular level

Polarity- and Intensity-Independent Modulation of Timing During Delay Eyeblink Conditioning Using Cerebellar Transcranial Direct Current Stimulation

Delay eyeblink conditioning (dEBC) is widely used to assess cerebellar-dependent associative motor learning, including precise timing processes. Transcranial direct current stimulation (tDCS), noninvasive brain stimulation used to indirectly excite and inhibit select brain regions, may be a promising tool for understanding how functional integrity of the cerebellum influences dEBC behavior. The aim of this study was to assess whether tDCS-induced inhibition (cathodal) and excitation (anodal) of the cerebellum differentially impact timing of dEBC. A standard 10-block dEBC paradigm was administered to 102 healthy participants. Participants were randomized to stimulation conditions in a double-blind, between-subjects sham-controlled design. Participants received 20-min active (anodal or cathodal) stimulation at 1.5 mA (n = 20 anodal, n = 22 cathodal) or 2 mA (n = 19 anodal, n = 21 cathodal) or sham stimulation (n = 20) concurrently with dEBC training. Stimulation intensity and polarity effects on percent conditioned responses (CRs) and CR peak and onset latency were examined using repeated-measures analyses of variance. Acquisition of CRs increased over time at a similar rate across sham and all active stimulation groups. CR peak and onset latencies were later, i.e., closer to air puff onset, in all active stimulation groups compared to the sham group. Thus, tDCS facilitated cerebellar-dependent timing of dEBC, irrespective of stimulation intensity and polarity. These findings highlight the feasibility of using tDCS to modify cerebellar-dependent functions and provide further support for cerebellar contributions to human eyeblink conditioning and for exploring therapeutic tDCS interventions for cerebellar dysfunction

Autonomous Localization Of A Uav In A 3d Cad Model

This thesis presents a novel method of indoor localization and autonomous navigation of Unmanned Aerial Vehicles(UAVs) within a building, given a prebuilt Computer Aided Design(CAD) model of the building. The proposed system is novel in that it leverages the support of machine learning and traditional computer vision techniques to provide a robust method of localizing and navigating a drone autonomously in indoor and GPS denied environments leveraging preexisting knowledge of the environment. The goal of this work is to devise a method to enable a UAV to deduce its current pose within a CAD model that is fast and accurate while also maintaining efficient use of resources. A 3-Dimensional CAD model of the building to be navigated through is provided as input to the system along with the required goal position. Initially, the UAV has no idea of its location within the building. The system, comprising a stereo camera system and an Inertial Measurement Unit(IMU) as its sensors, then generates a globally consistent map of its surroundings using a Simultaneous Localization and Mapping (SLAM) algorithm. In addition to the map, it also stores spatially correlated 3D features. These 3D features are then used to generate correspondences between the SLAM map and the 3D CAD model. The correspondences are then used to generate a transformation between the SLAM map and the 3D CAD model, thus effectively localizing the UAV in the 3D CAD model. Our method has been tested to successfully localize the UAV in the test building in an average of 15 seconds in the different scenarios tested contingent upon the abundance of target features in the observed data. Due to the absence of a motion capture system, the results have been verified by the placement of tags on the ground at strategic known locations in the building and measuring the error in the projection of the current UAV location on the ground with the tag

An advanced unmanned aerial vehicle (UAV) approach via learning-based control for overhead power line monitoring: a comprehensive review

Detection and prevention of faults in overhead electric lines is critical for the reliability and availability of electricity supply. The disadvantages of conventional methods range from cumbersome installations to costly maintenance and from lack of adaptability to hazards for human operators. Thus, transmission inspections based on unmanned aerial vehicles (UAV) have been attracting the attention of researchers since their inception. This article provides a comprehensive review for the development of UAV technologies in the overhead electric power lines patrol process for monitoring and identifying faults, explores its advantages, and realizes the potential of the aforementioned method and how it can be exploited to avoid obstacles, especially when compared with the state-of-the-art mechanical methods. The review focuses on the development of advanced Learning Control strategies for higher manoeuvrability of the quadrotor. It also explores suitable recharging strategies and motor control for improved mission autonomy

An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset

Predictive maintenance is increasingly advancing into the aerospace industry, and it comes with diverse prognostic health management solutions. This type of maintenance can unlock several benefits for aerospace organizations. Such as preventing unexpected equipment downtime and improving service quality. In developing data-driven predictive modelling, one of the challenges that cause model performance degradation is the data-imbalanced distribution. The extreme data imbalanced problem arises when the distribution of the classes present in the datasets is not uniform. Such that the total number of instances in a class far outnumber those of the other classes. Extremely skew data distribution can lead to irregular patterns and trends, which affects the learning of temporal features. This paper proposes a hybrid machine learning approach that blends natural language processing techniques and ensemble learning for predicting extremely rare aircraft component failure. The proposed approach is tested using a real aircraft central maintenance system log-based dataset. The dataset is characterized by extremely rare occurrences of known unscheduled component replacements. The results suggest that the proposed approach outperformed the existing imbalanced and ensemble learning methods in terms of precision, recall, and f1-score. The proposed approach is approximately 10% better than the synthetic minority oversampling technique. It was also found that by searching for patterns in the minority class exclusively, the class imbalance problem could be overcome. Hence, the model classification performance is improve

Hope College Abstracts: 10th Annual Celebration of Undergraduate Research and Creative Performance

The abstracts...are representative of student-faculty collaborative research and creative work that takes place throughout the year at Hope

Semper floreat

Title varies: Gamut; Time off: Semper; The press. Numbering system very erratic