When the penetrator collides with the target, the penetrator has different penetrating characteristics and residual velocity after penetration, according to the geometry of the penetrator. In this study, we optimized the geometry of the penetrator using the artificial neural network and the genetic algorithm to derive the best penetration performance. The Latin hypercube sampling method was used to collect the sample data, Simulation for predicting the behavior of the penetrator was conducted with the finite cavity pressure method to generate the training data for the artificial neural network. Also, the optimal hyper parameter was derived by using the Latin hypercube sampling method and the artificial neural network was used as the fitness function of the genetic algorithm to optimize the geometry of the penetrator. The optimized geometry presented the deepest penetration depth.
Citations
Citations to this article as recorded by
A Study on 3D Printing Conditions Prediction Model of Bone Plates Using Machine Learning Song Yeon Lee, Yong Jeong Huh Journal of the Korean Society for Precision Engineering.2022; 39(4): 291. CrossRef
In this paper, a prosthetic robot hand was designed and fabricated and experimental evaluation of the realization of basic gripping motions was performed. As a first step, a robot finger was designed with same structural configuration of the human hand and the movement of the finger was evaluated via kinematic analysis. Electromyogram (EMG) signals for hand motions were measured using commercial wearable EMG sensors and classification of hand motions was achieved by applying the artificial neural network (ANN) algorithm. After training and testing for three kinds of gripping motions via ANN, it was observed that high classification accuracy can be obtained. A prototype of the proposed robot hand is manufactured through 3D printing and servomotors are included for position control of fingers. It was demonstrated that effective realization of gripping motions of the proposed prosthetic robot hand can be achieved by using EMG measurement and machine learning-based classification under a real-time environment.
Citations
Citations to this article as recorded by
Development of a Caterpillar-Type Walker for the Elderly People Yeon-Kyun Lee, Chang-Min Yang, Sol Kim, Ji-Yong Jung, Jung-Ja Kim Applied Sciences.2021; 12(1): 383. CrossRef
Remote Control of Mobile Robot Using Electromyogram-based Hand Gesture Recognition Daun Lee, Jung Woo Sohn Transactions of the Korean Society for Noise and Vibration Engineering.2020; 30(5): 497. CrossRef
The objective of this study is to investigate a novel temperature and humidity prediction algorithm for smart greenhouse based on the machine learning method. The smart greenhouse is known to increase farm production by automatically controlling temperature and humidity and other factors. However, maintaining constant inside temperature and humidity in the conventional smart greenhouse system is still a problem because of the multiple time delay elements. To solve the problems, prediction control scheme is required. But, since the system is highly nonlinear with the lack of sensory data, predicting accurate temperature and humidity is very challenging. In this paper, the multi-dimensional Long Short-Term Memory networks (LSTMs) is being applied to deal with the unstructured greenhouse environmental data. The designed LSTMs learning model is trained with the 27 dimensional data which comprises of all the greenhouse control parameter and environmental sensory data. The prediction performance was evaluated using the short, mid and long term experiments. Also, the comparison with the conventional recurrent neural networks (RNNs) based prediction algorithm was done using the experimental results and later on discussions.
Citations
Citations to this article as recorded by
Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction Model Kwang Cheol Oh, Sunyong Park, Seok Jun Kim, La Hoon Cho, Chung Geon Lee, Dae Hyun Kim Agronomy.2024; 14(11): 2545. CrossRef
Development and Verification of Smart Greenhouse Internal Temperature Prediction Model Using Machine Learning Algorithm Kwang Cheol Oh, Seok Jun Kim, Sun Yong Park, Chung Geon Lee, La Hoon Cho, Young Kwang Jeon, Dae Hyun Kim Journal of Bio-Environment Control.2022; 31(3): 152. CrossRef
Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse Xue-Bo Jin, Wei-Zhen Zheng, Jian-Lei Kong, Xiao-Yi Wang, Min Zuo, Qing-Chuan Zhang, Seng Lin Agriculture.2021; 11(8): 802. CrossRef
First
Prev
