Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1941
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Article Citation - WoS: 41Citation - Scopus: 49Adaptive Human Force Scaling Via Admittance Control for Physical Human-Robot Interaction(IEEE, 2021) Başdoğan, Çağatay; Aydın, Yusuf; Hamad, Yahya M.The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts’ reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.Article Citation - WoS: 22Audio Source Separation Using Variational Autoencoders and Weak Class Supervision(Institute of Electrical and Electronics Engineers (IEEE), 2019) Kırbız, Serap; Karamatlı, Ertuğ; Cemgil, Ali TaylanIn this letter, we propose a source separation method that is trained by observing the mixtures and the class labels of the sources present in the mixture without any access to isolated sources. Since our method does not require source class labels for every time-frequency bin but only a single label for each source constituting the mixture signal, we call this scenario as weak class supervision. We associate a variational autoencoder (VAE) with each source class within a non negative (compositional) model. Each VAE provides a prior model to identify the signal from its associated class in a sound mixture. After training the model on mixtures, we obtain a generative model for each source class and demonstrate our method on one-second mixtures of utterances of digits from 0 to 9. We show that the separation performance obtained by source class supervision is as good as the performance obtained by source signal supervision.Article Citation - WoS: 4Citation - Scopus: 5Monitoring of Intracerebral Hemorrhage With a Linear Microwave Imaging Algorithm(Springer, 2022) Dilman, Ismail; Dogu, Semih; Bilgin, Egemen; Akinci, Mehmet Nuri; Cosgun, Sema; Çayören, Mehmet; Akduman, IbrahimIntracerebral hemorrhage is a life-threatening condition where conventional imaging modalities such as CT and MRI are indispensable in diagnosing. Nevertheless, monitoring the evolution of intracerebral hemorrhage still poses a technological challenge. We consider continuous monitoring of intracerebral hemorrhage in this context and present a differential microwave imaging scheme based on a linearized inverse scattering. Our aim is to reconstruct non-anatomical maps that reveal the volumetric evolution of hemorrhage by using the differences between consecutive electric field measurements. This approach can potentially allow the monitoring of intracerebral hemorrhage in a real-time and cost-effective manner. Here, we devise an indicator function, which reveals the position, volumetric growth, and shrinkage of hemorrhage. Later, the method is numerically tested via a 3D anthropomorphic dielectric head model. Through several simulations performed for different locations of intracerebral hemorrhage, the indicator function-based technique is demonstrated to be capable of detecting the changes accurately. Finally, the robustness under noisy conditions is analyzed to assess the feasibility of the method. This analysis suggests that the method can be used to monitor the evolution of intracerebral hemorrhage in real-world scenarios. Graphical abstract: [Figure not available: see fulltext.]. © 2022, International Federation for Medical and Biological Engineering.Article Citation - WoS: 21Citation - Scopus: 24An Adaptive Admittance Controller for Collaborative Drilling With a Robot Based on Subtask Classification Via Deep Learning(Elsevier, 2022) Başdoğan, Çağatay; Niaz, P. Pouya; Aydın, Yusuf; Güler, Berk; Madani, AlirezaIn this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human–robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance adaptation based on the proposed subtask classifier leads to 20% lower human effort (i.e. higher transparency) in the Driving phase and 25% lower oscillation amplitude (i.e. higher stability) during drilling in the Contact phase compared to an admittance controller with fixed parameters.Article A Bayesian Allocation Model Based Approach To Mixed Membership Stochastic Blockmodels(Taylor and Francis Ltd., 2022) Kırbız, Serap; Hızlı, ÇağlarAlthough detecting communities in networks has attracted considerable recent attention, estimating the number of communities is still an open problem. In this paper, we propose a model, which replicates the generative process of the mixed-membership stochastic block model (MMSB) within the generic allocation framework of Bayesian allocation model (BAM) and BAM-MMSB. In contrast to traditional blockmodels, BAM-MMSB considers the observations as Poisson counts generated by a base Poisson process and marks according to the generative process of MMSB. Moreover, the optimal number of communities for BAM-MMSB is estimated by computing the variational approximations of the marginal likelihood for each model order. Experiments on synthetic and real data sets show that the proposed approach promises a generalized model selection solution that can choose not only the model size but also the most appropriate decomposition.Article Citation - WoS: 23Citation - Scopus: 23Experimental Observation of Temperature and Pressure Induced Frequency Fluctuations in Silicon Mems Resonators(IEEE, 2021) Zhao, Chun; Mustafazade, Arif; Pandit, Milind; Seshia A, Ashwin; Sobreviela, Guillermo; Zou, XudongSilicon MEMS resonators are increasingly being adopted for applications in timing and frequency control, as well as precision sensing. It is well established that a key limitation to performance is associated with sensitivity to environmental variables such as temperature and pressure. As a result, technical approaches to address these factors such as vacuum sealing and ovenization of the resonators in a temperature controlled system have been introduced. However, residual sensitivity to such effects can still serve as a significant source of frequency fluctuations and drift in precision devices. This is experimentally demonstrated in this paper for a precision oven-controlled and vacuum-sealed silicon resonators. The frequency fluctuations of oscillators constructed using two separate nearly-identical co-located resonators on the same chip are analysed and differential frequency fluctuations are examined as a means of reducing the impact of common-mode effects such as temperature and pressure. For this configuration, our results show that the mismatch of temperature and pressure coefficients between the resonators ultimately limits the frequency stability.