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203 results on '"Khayyam, Hamid"'

3. Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach

Author

Rezaei, Ahmad Asgharian, Munoz, Justin, Jalili, Mahdi, and Khayyam, Hamid

Subjects
Computer Science - Social and Information Networks, Computer Science - Machine Learning
Abstract

Vital node identification is the problem of finding nodes of highest importance in complex networks. This problem has crucial applications in various contexts such as viral marketing or controlling the propagation of virus or rumours in real-world networks. Existing approaches for vital node identification mainly focus on capturing the importance of a node through a mathematical expression which directly relates structural properties of the node to its vitality. Although these heuristic approaches have achieved good performance in practice, they have weak adaptability, and their performance is limited to specific settings and certain dynamics. Inspired by the power of machine learning models for efficiently capturing different types of patterns and relations, we propose a machine learning-based, data driven approach for vital node identification. The main idea is to train the model with a small portion of the graph, say 0.5% of the nodes, and do the prediction on the rest of the nodes. The ground-truth vitality for the train data is computed by simulating the SIR diffusion method starting from the train nodes. We use collective feature engineering where each node in the network is represented by incorporating elements of its connectivity, degree and extended coreness. Several machine learning models are trained on the node representations, but the best results are achieved by a Support Vector Regression machine with RBF kernel. The empirical results confirms that the proposed model outperforms state-of-the-art models on a selection of datasets, while it also shows more adaptability to changes in the dynamics parameters., Comment: 20 Pages, 5 figures, 6 tables. Submitted to Expert Systems with Applications

Published
2022

4. Influential Node Ranking in Complex Information Networks Using A Randomized Dynamics-Sensitive Approach

Author

Rezaei, Ahmad Asgharian, Munoz, Justin, Jalili, Mahdi, and Khayyam, Hamid

Subjects
Computer Science - Social and Information Networks, Computer Science - Data Structures and Algorithms
Abstract

Identifying the most influential nodes in information networks has been the focus of many research studies. This problem has crucial applications in various contexts, such as controlling the propagation of viruses or rumours in real-world networks. While existing approaches mostly rely on the structural properties of networks and generate static rankings, in this work we propose a novel method that is responsive to any change in the diffusion dynamics. The main idea is to approximate the influential ability (influentiality) of a node with the reachability of other nodes from that node in a set of random sub-graphs. To this end, several random sub-graphs are sampled from the original network and then a hyper-graph is created in which each sub-graph is represented with a hyper-edge. From a theoretical standpoint, one can argue that a factor of the degree of nodes in the hyper-graph approximates influentiality. From an empirical perspective, the proposed model not only achieves the highest correlation with the ground-truth ranking, but also the ranking generated by this method hits the highest level of uniqueness and uniformity. Theoretical and practical analysis of the running time of this method also confirms a competitive running time compared with state-of-the-art methods., Comment: 18 Pages, 4 Figures, 5 Tables

Published
2021

45. Intelligent Robust Disturbance Rejection Control of a Ballbot Drive Micromobility Vehicle

Author

Xuan-Mung, Nguyen, Pham, Dinh Ba, Duong, Xuan Quang, Van, Mien, Sareh, Sina, Shojaei, Khoshnam, Asadi, Ehsan, Lee, Soon-Geul, and Khayyam, Hamid

Abstract

We propose a hybrid adaptive control method with robust disturbance rejection for a novel personal micromobility vehicle that uses ballbot drive mechanics with a single spherical wheel. Intelligent control of such a micromobility vehicle is significantly sensitive to discrete events of short duration and high-intensity road disturbances and shocks. Sudden bumps, potholes, pavement or kicks typically cause these events. Our approach combines adaptive disturbance observer (DOB) with two other techniques to achieve better and more robust nonlinear control of a ballbot drive vehicle (BDV) against small to large external disturbances. The proposed hybrid DOB-Based combined control system (DOB-CCS) stabilizes the body position via a DOB-based partial feedback linearization (PFL) strategy while enabling ball movement on the floor by introducing a DOB-based sliding mode control (SMC). The control scheme consists of linearly combined components responsible for maintaining an upright posture about unstable equilibrium points and tracking the motion under disturbances. Simulations, real-platform experiments and respective comparative studies show the efficacy of the proposed method for a BDV to reject external disturbances. The practical performance of the proposed DOB-CCS strategy is experimentally validated and compared with a CCS method. The experimental results indicate less than 0.7 and 1.5 degrees of ballbot's orientation error and robust rejection of disturbances under kicks during balancing and transferring modes, respectively.

Published
2024
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46. A multi-objective Gaussian process approach for optimization and prediction of carbonization process in carbon fiber production under uncertainty

Author

Ramezankhani, Milad, Crawford, Bryn, Khayyam, Hamid, Naebe, Minoo, Seethaler, Rudolf, and Milani, Abbas

Abstract

During composite fiber production, carbon fibers are normally derived from polyacrylonitrile precursor. Carbonization, as a key step of this process, is significantly energy-consuming and costly, owing to its high temperature requirement. A cost-effective approach to optimize energy consumption during the carbonization is implementing predictive modeling techniques. In this article, a Gaussian process approach has been developed to predict the mechanical properties of carbon fibers in the presence of manufacturing uncertainties. The model is also utilized to optimize the fiber mechanical properties under a minimum energy consumption criterion and a range of process constraints. Finally, as the Young’s modulus and ultimate tensile strength of the fibers did not show an evident correlation, a multi-objective optimization approach was introduced to acquire the overall optimum condition of the process parameters. To estimate the trade-off between these material properties, the standard as well as an adaptive weighted sum method were applied. Results were summarized as design chart for potential applications by manufacturing process designers. Graphical abstract

Published
2024
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