... deviation of the residuals where MAD = median(| r |). The proposed deep learning method was trained using run-to-fail (offline) monitoring data where the EOD sequence of each discharge cycle expressed a decreasing trend that ends at 0 s. Therefore, the proposed method will output a sequence of EOD...

...Anna Ghidotti; Andrea Vitali; Daniele Regazzoni; Miri Weiss Cohen; Caterina Rizzi Segmentation of anatomical components is a major step in creating accurate and realistic 3D models of the human body, which are used in many clinical applications, including orthopedics. Recently, many deep learning...

...Salah A. Faroughi; Nikhil M. Pawar; Célio Fernandes; Maziar Raissi; Subasish Das; Nima K. Kalantari; Seyed Kourosh Mahjour Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines...

... by stepLR; the process is shown in Fig. 13 , and the model loss gradually converges after 20 epochs of training. The experimental environment is described as follows: deep learning open-source framework Pytorch, Windows 10, python 3.9, Intel Xeon Gold 6230R processor, and NVIDIA Quadro RTX5000 Graphic...

...@stonybrook.edu 1 Corresponding author. Email: anurag.purwar@stonybrook.edu 19 02 2023 01 11 2023 02 11 2023 30 11 2023 planar linkage mechanisms simulation machine learning deep learning object detection Directorate for Engineering 10.13039/100000084 1563413...

... in severe cases when there are defects in the riveted parts. A deep learning detection model is proposed that integrates atrous convolution and dynamic convolution to identify defects of self-piercing riveting parts efficiently to overcome the problem in quality inspection after the body self-piercing...

... 1 Corresponding author. Email: anurag.purwar@stonybrook.edu 11 02 2023 06 10 2023 06 10 2023 27 10 2023 planar four-bar linkage path synthesis Fourier descriptors wavelets variational autoencoder neural networks machine learning deep learning Division...

... the deployment of this technology in practice. This research proposes to estimate the six degrees-of-freedom pose of a human foot from a color image using deep learning models to solve the problem. We construct a training dataset consisting of synthetic and real foot images that are automatically annotated...

...Rishi Malhan; Satyandra K. Gupta There is a growing interest in using deep learning technologies within the manufacturing industry to improve quality, productivity, safety, and efficiency, while also reducing costs and cycle time. This position paper discusses the applications of deep learning...

... a deep learning (DL) algorithm that predicts UUVs’ resistance with different hull shapes under different velocities and attack angles with the particle swarm optimization (PSO) algorithm for UUV hull design. We tested the proposed methodology in a path energy consumption-based experiment, where...

... the given task on, say, location of joints, which could enable users to directly synthesize mechanisms with specific properties. The basic premise of the deep learning-driven robot mechanisms is that we want to create a probabilistic distribution of output motions of a variety of mechanisms including...

...Omey M. Manyar; Junyan Cheng; Reuben Levine; Vihan Krishnan; Jernej Barbič; Satyandra K. Gupta Deep learning-based image segmentation methods have showcased tremendous potential in defect detection applications for several manufacturing processes. Currently, majority of deep learning research...

... ferrograph (OLVF) apparatus is used to obtain online measurements of wear particle quantities, and monitor the wearing of a four-ball tribometer under different lubrication conditions, and several popular deep learning algorithms are evaluated for their effectiveness in providing maintenance decisions...

... Separable Convolutions ,” 29th British Machine Vision Conference (BMVC 2018) , Newcastle, UK , Sept. 2–6 , https://arxiv.org/abs/1808.05517 [18] Chollet , F. , 2016 , “ Xception: Deep Learning With Depthwise Separable Convolutions ,” CoRR abs/1610.02357 . [19] Wu , H. , Zhang...

... a new solution. Compared with traditional machine learning models, deep learning models possess more powerful nonlinear expression capabilities and feature extraction capabilities. Therefore, this study focuses on studying the RUL prediction algorithm for turbofan engines based on the fused deep...

... in the remaining useful life (RUL) estimation of critical industrial systems. In this paper, long short-term memory (LSTM) and bidirectional-LSTM (bi-LSTM) deep neural architecture-based predictive algorithms are proposed for the RUL estimation of the lathe spindle unit. The deep learning algorithm is embedded...

... a specific class of problems. However, these techniques do not handle noise, variations in lighting conditions, and backgrounds with complex textures. In recent times, deep learning has been widely explored for use in automation of defect detection. This survey article presents three different ways...

... . 10.1214/aoms/1177729694 deep generative models path synthesis planar mechanisms machine learning deep learning Kinematic synthesis of mechanisms dates back to centuries. However, a systematic treatment of the synthesis process has been largely missing from the literature. True...

... this problem, different supervised machine learning algorithms for object recognition are analyzed. smart logistics supervised machine learning deep learning container routing automated labeling applied artificial intelligence artificial intelligence computer-aided manufacturing engineering...

... to handle the features extracted from the vibration signal because of the large data quantity, complex feature relations, and limited degeneration mechanisms. In this paper, a deep learning-based approach is proposed to predict the failure of the complex equipment. To supply plenty of features, three...