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Benhabib, Wafaa,Fizazi, Hadria Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.2
In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MO-TRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.
( Wafaa Benhabib ),( Hadria Fizazi ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.2
In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MOTRIBES/ OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.
칼만필터를 이용한 군집로봇의 위치 및 위상 최적 추정 방법
윤현중,Kasra Eshaghi,Goldie Nejat,Beno Benhabib 제어·로봇·시스템학회 2022 제어·로봇·시스템학회 논문지 Vol.28 No.6
This paper addresses the localization and topology estimation for a robotic swarm. The concept of a swarm, a conceptual group of robots, has been used for more effective navigation of the robotic swarm. Localization is an optimization problem to determine the pose—or the position and orientation—of the swarm, while topology estimation is an optimization problem to determine the formation of the robots in the swarm. Optimization approaches have been proposed for the localization and topology estimation problems that combine sensor-based and movement command-based estimation using a Kalman filter. The simulation results are presented to demonstrate the effectiveness of the proposed method. .