... for the products. A CNN is a deep learning algorithm, that is analogous to that the connectivity pattern of neurons in the human brain, has become popular and effective to image classification problems recently. It takes in the image of the object and assigns importance to various aspects/objects in the image so...

... processing is developed to identify bolt rotation angle in a steel multi-story frame structure. The experimental results show that the bolt target detection accuracy can reach 100% by using the Yolo-V5s deep learning model trained with a self-developed bolt object dataset. The dataset consists of 337 bolt...

... of deep learning in the area of feature extraction/description, pose/depth estimation, mapping, loop closure detection and global optimization as it concerns visual SLAM. This paper sets out to elucidate diverse applications of supervised and unsupervised deep learning methods in all aspects of visual...

.... topology optimization deep learning neural network length scale control thermomechanical problems Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition IMECE2022 October 30-November 3, 2022, Columbus, Ohio IMECE2022-94604 TOPOLOGY OPTIMIZATION THROUGH DEEP NEURAL...

... Abstract This paper presents an initial investigation on the feasibility of modeling structural dynamics of complex structures using the Long Short-Time Memory (LSTM) deep learning neural networks, and predicting the structures’ vibration responses due to random excitation. LSTM networks...

.... Bayesian Network is a graph layout that models accident scenarios and various real-world problems. This paper investigates the application of artificial intelligence (Deep Learning (DL)) to enhance FT analysis through the conversion of FT and ANN models. The potentiality of extending this technique...

... Abstract Errors and failures occur inevitably in modern Cyber-Physical Systems (CPS) due to their structural variability and internal heterogeneity. This can cause economic losses or even hazardous accidents. Currently, deep learning-based anomaly detection methods, e.g., Transformer or LSTM...

... and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. While convolutional neural network (CNN) has already been demonstrated to be an effective deep learning tool for automated detection...

... dataset which is hard to obtain. fracture mechanics photoelasticity deep learning deep regression convolutional neural network Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition IMECE2021 November 1-5, 2021, Virtual, Online IMECE2021-73114 DEEP LEARNING...

... to lessen vibration than the standard (symmetric) profile spur gears. Gearbox faults that cannot be detected early may lead the entire system to stop or serious damage to the machine. In this regard, Deep Learning (DL) algorithms have started to be utilized for gear early fault diagnosis. This study aims...

..., respectively. The Yolo-v4 precision is 90.81% better than the DSSD algorithm, proving that the Yolo-v4 is a better fit for real-world driving environment studies. intelligent vehicle neural network object detection deep learning Proceedings of the ASME 2021 International Mechanical Engineering...

... challenging. Recently, structural health monitoring (SHM) has benefitted from data-driven techniques, such as deep learning, that can overcome unclear system dynamics. This paper presents a hybrid damage classification method that fuses deep, shallow, unsupervised, and supervised machine learning (ML...

... controlled outcome can be achieved. social robots multi-axial control deep learning Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition IMECE2021 November 1-5, 2021, Virtual, Online IMECE2021-70510 TRACKING CONTROL DESIGN AND IMPLEMENTATION OF MULTIAXIAL...

... via machine learning tools is a well-established approach. In recent years, deep learning methods have received increasing attention from researchers and engineers because of their inherent capability of dealing with big data, mining complex representations, and overcoming the disadvantage...

... adequately compensate errors. In this paper, we proposed a new Deep Learning-based approach for system failure prevention. We employed the Long Short-Term Memory (LSTM) network for error detection and mitigation. Error detection is achieved using the prediction approach. The LSTM models are mitigating...

... loss or even hazardous events. Anomaly Detection (AD) techniques provide a potential solution to this problem, and conventional methods, e.g., Autoregressive Integrated Moving Average model (ARIMA), are no longer the best choice for anomaly detection for modern complex CPS. Recently, Deep Learning (DL...

... and gear design parameters. Therefore, catastrophic failures can be prevented, and maintenance costs can be optimized by early crack detection. gears machine learning fault diagnosis deep learning VIBRATION-BASED EARLY CRACK DIAGNOSIS WITH MACHINE LEARNING FOR SPUR GEARS Fatih Karpat1, Ahmet...

... cavitation mitigation bubble size deep learning neural network OpenCV object detection you only look once YOLO DEEP LEARNING FOR INTELLIGENT BUBBLE SIZE DETECTION IN THE SPALLATION NEUTRON SOURCE VISUAL TARGET Fayaz Rasheed 1, Elvis E. Dominguez-Ontiveros, Justin R. Weinmeister, Charlotte N...

... was 21.42%. The assumptions made for the initial version of the program affect the accuracy of the particle in wall detection, so new techniques that do not follow the assumptions will need to be investigated. More work will be completed to implement machine learning or deep learning to assist...

... engineering was first performed to explore the compressed feature space to be used to represent the data. An ensemble deep learning algorithm was then developed to learn the underlying pattern, and hyperparameter tuning techniques were employed to select the learning model that provides the best mapping...