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- 저자 : Abdelkader Belkacem (검색결과 3 건)
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A Deep Recurrent Neural Network Framework for Swarm Motion Speed Prediction
Khaldi Belkacem,Harrou Fouzi,Dairi Abdelkader,Sun Ying 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.5
Controlling and maintaining swarm robotic systems executing daily collective actions and accomplishing tasks more successfully in groups requires a timely and accurate forecast of swarm motion speed, which becomes a challenging task owing to swarm motion’s high dynamic feature. In this work, six potent forecasting recurrent deep neural networks, including RNN, LSTM, GRU, ConvLSTM, Bidirectional LSTM (BiLSTM), and BiGRU, are explored and compared in forecasting the motion speed of miniature swarm mobile robots engaged in a simple aggregation formation task. Essentially, the introduced forecasting models take advantage of the viscoelastic control model in flexibly controlling swarm robots and the capabilities of DL models to capture patterns in time series data. To this end, sensor measurements from a simulated swarm of foot bots conducting a circle formation task through the viscoelastic controller are recorded every 0.1 s and used as input vectors for forecasting purposes. The results show the promising performance of DL for swarm motion forecasting. Moreover, obtained results report that BiGRU reaches the highest swarm motion speed forecasting performance with the no/with obstacles scenarios considered in this study.
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Free vibration of functionally graded carbon nanotubes reinforced composite nanobeams
Miloud Ladmek,Abdelkader Belkacem,Ahmed Amine Daikh,Aicha Bessaim,Aman Garg,Mohammed Sid Ahmed Houari,Mohamed-Ouejdi Belarbi,Abdelhak Ouldyerou Techno-Press 2023 Advances in materials research Vol.12 No.2
This paper proposes an analytical method to investigate the free vibration behaviour of new functionally graded (FG) carbon nanotubes reinforced composite beams based on a higher-order shear deformation theory. Cosine functions represent the material gradation and material properties via the thickness. The kinematic relations of the beam are proposed according to trigonometric functions. The equilibrium equations are obtained using the virtual work principle and solved using Navier's method. A comparative evaluation of results against predictions from literature demonstrates the accuracy of the proposed analytical model. Moreover, a detailed parametric analysis checks for the sensitivity of the vibration response of FG nanobeams to nonlocal length scale, strain gradient microstructure-scale, material distribution and geometry.
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The Mixed Kernel Function SVM-based Point Cloud Classification
Chao Chen,Xiaomin Li,Abdelkader Nasreddine Belkacem,Zhifeng Qiao,Enzeng Dong,Wenjun Tan,Duk Shin 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.5
Measurement and detection of ground information by airborne Lidar are one of the hot topics in the field of intelligent sensing in recent years. This study proposes a new point cloud classification algorithm of Mixed Kernel Function SVM to distinguish different types of ground objects. Firstly, the combined features including the coordinate values, the RGB value, normalized elevation, standard deviation of elevation, and elevation difference of point cloud data were extracted. A mixed kernel function of Gauss and Polynomial was designed. Then, one-versus-rest SVM multiple classifiers was constructed. Finally, the feature of 3D point cloud data was employed to train the SVM classifiers. The overall classification accuracies of test data were 97.69% and 99.13% for two data sets, I and II respectively. In addition, the experimental results have showed that the performance of the proposed method with mixed kernel function SVM was better than standard SVM method with Gaussian kernel function and polynomial kernel function only, which demonstrates the effectiveness of the proposed method.
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