10 skills found
opasche / EQRNExtreme Quantile Regression Neural Networks for Conditionnal Risk Assessment
engineers-tools / XDGAAn implementation of Dissolved Gas Analysis (DGA) assessment algorithms and guidelines in the form of Excel Add-ins.
capepoint / SHMnetSHMnet: Condition Assessment of Bolted Connection with Beyond Human-level Performance
itschenyu / AECIF NetImplementation of the paper "AECIF-Net: An Attention-Enhanced Co-Interactive Fusion Network for Automated Structural Condition Assessment in Visual Inspection"
ArafatHabib / Concrete Detection Using AE Features K NN And BorutaThis study aims at characterizing crack types for reinforced concrete beams through the use of acoustic emission burst (AEB) features. The study includes developing a solid crack assessment indicator (CAI) accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal condition, micro-cracks, and macro-cracks (fractures) of concrete beam test specimens. Reinforced concrete (RC) beams undergo a three-point bending test, from which acoustic emission (AE) signals are recorded for further processing. From the recorded AE signals, crucial AEB features like the rise time, decay time, peak amplitude, AE energy, AE counts, etc. are extracted. The Boruta-Mahalanobis system (BMS) is utilized to fuse these features to provide us with a comprehensive and reliable CAI. The noise from the CAI is removed using the cumulative sum (CUMSUM) algorithm, and the final CAI plot is used to classify the three different conditions: normal, micro-cracks, and fractures using k-NN. The proposed method not only for the first time uses the entire time history to create a reliable CAI, but it can meticulously distinguish between micro-cracks and fractures, which previous works failed to deal with in a precise manner. Results obtained from the experiments display that the CAI built upon AEB features and BMS can detect cracks occurring in early stages, along with the gradually increasing damage in the beams. It also soundly outperforms the existing method by achieving an accuracy (classification) of 99.61%, which is 17.61% higher than the previously conducted research.
NidhiChopdekar / Pavement Condition AssessmentBuilt a CNN model which is trained on detecting the type of damage (potholes, alligator cracking, block cracking) on roads by feeding it with captured image of the road with different kinds of defect.
duplisea / CccaConditioning risk-based advice to climate change for single species stock assessment using empirical models
Sayan-Maity / Derma Prediction🤖 Dermify.AI is an ⚡ AI powered Web Application which harnesses the power of image processing to offer cost-effective and accessible skin condition assessments worldwide
NicoDeshler / IBM Disaster Response HackAid disaster response teams by identifying optimal terrestrial routes through calamity-stricken areas. Satellite image data informs road condition assessment and obstruction detection.
MighTy-Weaver / Inefficient AC DetectionCode for the Journal of Cleaner Production paper: Data-driven Assessment of Room Air Conditioner Efficiency for Saving Energy (https://doi.org/10.1016/j.jclepro.2022.130615).
