Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    A Hybrid Actuation System for Enhancing the Performance Metrics Related to Kinesthetic-Type Haptic Devices
    (IEEE-Inst Electrical Electronics Engineers Inc, 2025) Kucukoglu, Sefa Furkan; Can Dede, Mehmet ismet
    High torque to volume ratio, fast response, and high dynamic range are some of the desired performance metrics for kinesthetic-type haptic device actuation systems. In this article, we present a hybrid actuation system consisting of an active actuator and a magnetorheological fluid-based brake (MRF brake). MRF brake's tradeoffs, namely, off-state torque and slow response (compared to an electric motor), are investigated and resolved by this hybrid actuation system. First, the transient behavior of the MRF brake is investigated and an mathematical model is proposed to mimic its transient response behavior. It is found that the performance of the proposed model performs better than the conventionally used first-order transfer function. Second, hybrid actuation system is constructed. The active actuator is used for compensating for the speed of the response and the off-state torque based on the proposed mathematical model of the MRF brake. It is measured that the off-state torque is largely eliminated from 0.178 to 0.008 N center dot m, the dynamic range is enlarged from 15 to 42.4 dB, and its time constant is improved from 69.6 to 4.4 ms when the hybrid actuation system is used instead of just an MRF brake.
  • Article
    FW-S3KIFCM: Feature Weighted Safe-Semi Kernel-Based Intuitionistic Fuzzy C-Means Clustering Method
    (Tsinghua Univ Press, 2025) Khezri, Shirin; Aghazadeh, Nasser; Hashemzadeh, Mahdi; Oskouei, Amin Golzari
    Semi-supervised clustering (SSC) methods have emerged as a notable research area in machine learning. These methods integrate prior knowledge of class distribution into their clustering process. Despite their efficiency and straightforwardness, SSCs encounter some fundamental issues. Generally, the proportion of unlabeled data surpasses that of labeled data. Consequently, handling the uncertainty of unlabeled data becomes difficult. This issue is frequently related to numerous real-world problems. On the other hand, existing SSC techniques fail to differentiate between the varied attributes within the feature space. When forming clusters, they presume uniform significance for all attributes, disregarding potential variations in feature importance. This presumption hinders the creation of optimal clusters. Furthermore, all existing approaches employ the Euclidean distance metric, susceptible to noise and outliers. This paper proposes a robust safe-semi-supervised clustering algorithm to mitigate these shortcomings. For the first time, this approach combines two concepts of Intuitionistic Fuzzy C-Means (IFCM) clustering and Safe-Semi-Supervised Fuzzy C-Means (S3FCM) clustering to address the uncertainty problem in unlabeled data. Also, it uses a kernel function as a distance metric to tackle noise and outliers. Additionally, incorporating a feature weighting parameter in the objective function highlights the importance of significant features in creating optimal clusters. The effectiveness of the proposed method is thoroughly evaluated on various benchmark datasets, and its performance is compared with state-of-the-art methods. The results show the superiority of the proposed method over its competitors.