..., the acetabular index, center-edge angle, Sharp’s angle, and migration percentage metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify physical metrics on radiographs and improve traditional methods...

... and efficient MD simulations. machine learning potential feature importance feature selection neural network atomistic simulation Proceedings of the ASME 2023 International Mechanical Engineering Congress and Exposition IMECE2023 October 29-November 2, 2023, New Orleans, Louisiana IMECE2023-112990...

... Abstract This paper introduces an innovative approach that utilizes neural networks to incorporate stress constraints into topology optimization. In this method, a neural network is trained to generate the topology density field using a loss function, while Fourier space projection is employed...

... neural network methodology is proposed to avoid these problems. Neural networks can recognize features very quickly and the trained features can be interpreted directly from the geometric information instead of relying on expert-defined rules. A common difficulty encountered when using neural networks...

...) for machine learning method. This study employed neural network (NN) for machine learning method, which connects cellular geometric pattern with mechanical performance (force - displacement curve and peak force - work of energy absorption relationship). Our results showed that the proposed framework...

... Abstract In this present work, a neural network (NN) is trained to deal with the optimization process of topology optimization and generate optimized structures. The NN’s activation functions are used to represent the popular Solid Isotropic Material with Penalization (SIMP) density field...

...-images level, 5 neural networks are trained with the input of different extracted features, respectively. Then, to overcome the representation limitation of one single extracted feature, a weighted combination of 5 neural networks is developed. Thirdly, a search algorithm is developed to extend...

... network model was established to optimize the positions of multiple flexoelectric actuators on a cantilever beam. It was proved that the neural network can recognize the relationship between actuator position and tip displacement and forecast the tip displacement of the beam accurately with reduced...

... single shot multibox detector (DSSD) under a naturalistic driving environment. An 80-classes COCO dataset trains each neural network at first, then fine-tuned by the BDD100k dataset. The detection results are compared by True/False Positive Results, Precision-Recall Curve, and average precision...

... consists of 2 main parts: a filter bank for discrete wavelet transform (DWT) to obtain time-frequency signal, and a deep neural network (DNN) for nonlinear adaptive control. A 4-DOF AMB-rotor system is analyzed and its model is established. The rotor dynamics are simulated and the results are compared...

... Abstract This paper presents the use of neural networks and a genetic algorithm in time-optimal control of a closed-loop 3-dof robotic system. Extended Kohonen networks which contain an additional lattice of output neurons are used in conjunction with PID controllers in position control...

... Abstract In this research, an advanced hybrid neural network ( AHNN ) friction component model is integrated with an automotive drivetrain model for system simulations. The AHNN model accurately predicts the dynamic behaviors of transmission friction components over a broad operating range...

... Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks adequate learning algorithm especially when it is used in a hybrid-type controller. Part I of this work was devoted to introduce a new CMAC...

... Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study...

.... semi-supervised learning detection system additive manufacturing cyber-physical attack layered image machine learning neural network INFILL DEFECTIVE DETECTION SYSTEM AUGMENTED BY SEMI-SUPERVISED LEARNING Jinwoo Song, Young B. Moon Syracuse University, Syracuse, NY ABSTRACT In an effort...

.... The maximum, minimum and standard deviation of acceleration data were also selected as neural network inputs. To improve the generalization ability of the neural network, a Bayesian regularization method was adopted. To validate the performance of the proposed method, experiments were conducted under seven...

... alleviate misdetection and improves accuracies. Employing neural networks, intelligent detection of bubble sizes and their distribution was developed and provides a robust alternative to traditional techniques. Human intervention was employed to label in-focus and out-of-focus bubbles in the set of training...

... of 25%. The neural network is then employed which yield an interesting overall classification accuracy of 95.7 %. This paper will pave the way for better rehabilitative programs, understanding of gait biomechanics and fall prevention among the elderly. ensemble empirical mode decomposition...

...CENTRIFUGAL COMPRESSOR PERFORMANCE PREDICTION USING GAUSSIAN PROCESS REGRESSION AND ARTIFICIAL NEURAL NETWORKS Pau Cutrina Vilalta Department of Computer Science University of Colorado Colorado Springs, CO 80918 paucutrina@gmail.com Hui Wan Department of Mechanical and Aerospace Engineering...

... Abstract In this study a Neural Network based fault tolerant control is proposed to accommodate oil leakages in a magnetorheological suspension system based in a half car dynamic model. This model consists of vehicle body (spring mass) connected by the MR suspension system to two lateral wheels...