Browsing by Subject "Contamination"
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Item Open Access Investigation of native oxide removing from HCPA ALD grown GaN thin films surface utilizing HF solutions(IEEE, 2016) Deminskyi, Petro; Haider, Ali; Bıyıklı, Necmi; Ovsianitsky, A.; Tsymbalenko, A.; Kotov, D.; Matkivskyi, V.; Liakhova, N.; Osinsky, V.The paper consider oxygen contamination of HCPA ALD grown GaN films under an air conditioning and during different time duration. High resolution XPS analysis of HCPA ALD grown GaN films after diluted 1:10 HF(41 %) : H2O and undiluted HF (41 %) influence on oxygen impurities was investigated. Lesser oxygen impurities have been observed. Better resistivity to oxygen atoms of GaN thin films after diluted HF solution treatment was achieved compared to undiluted HF treatment and without treatment.Item Open Access Open-set object recognition(Bilkent University, 2022-07) Mohammad, SalmanDespite significant advances in object recognition and classification over the past couple of decades, there are various situations where collecting representative training samples from all classes in real-world scenarios is quite expensive, or the system may be exposed to unpredictable novel samples at the test time. The pattern classification problem is commonly referred to as an open-set recognition task in such cases where limited and incomplete knowledge of the entire data distribution is provided to the model during the training time. During test phase, unknown classes can be faced which requires the classifier to accurately classify the previously seen classes while effectively rejecting unseen ones. Among others, one-class classification serves as a plausible solution to the open-set recognition problem. Nevertheless, current one-class classifiers have their limitations. Classical kernel-based approaches require carefully designed features to obtain reasonable performance but rest on a solid basis in statistical learning theory, providing good robustness against training set impurities. More recent deep learning-based methods, on the other hand, focus on learning relevant features directly from the data but typically rely on ad hoc one-class loss functions, which very often do not generalize well and are not robust against the omnipresent noise and contamination in the training set. In this thesis, we introduce a novel approach which leverages the advantages of both kernel-based and deep-learning approaches by bringing the two learning formalisms under a common umbrella. In particular, the proposed method learns deep convolutional features to optimize a kernel Fisher null-space loss subject to a Tikhonov regularisation on the discriminant in the Hilbert space. As such, it can be trained in a deep end-to-end fashion while being robust against training set contamination. Through extensive experiments conducted on different image datasets in various evaluation settings, the proposed approach is shown to be quite robust and more effective than the current state-of-the-art methods for anomaly detection in the scenario where the training set is corrupted and contains noisy samples. At the same time, the proposed approaches can be effectively utilized in an unsupervised scenario to rank the data points based on their conformity with the majority of samples.Item Open Access Robust one-class kernel spectral regression(IEEE, 2021-03) Arashloo, Shervin Rahimzadeh; Kittler, J.The kernel null-space technique is known to be an effective one-class classification (OCC) technique. Nevertheless, the applicability of this method is limited due to its susceptibility to possible training data corruption and the inability to rank training observations according to their conformity with the model. This article addresses these shortcomings by regularizing the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation. In this respect, first, the effect of the Tikhonov regularization in the Hilbert space is analyzed, where the one-class learning problem in the presence of contamination in the training set is posed as a sensitivity analysis problem. Next, the effect of the sparsity of the solution is studied. For both alternative regularization schemes, iterative algorithms are proposed which recursively update label confidences. Through extensive experiments, the proposed methodology is found to enhance robustness against contamination in the training set compared with the baseline kernel null-space method, as well as other existing approaches in the OCC paradigm, while providing the functionality to rank training samples effectively.Item Open Access Size effect in optical activation of TiO2 nanoparticles in photocatalytic process(IEEE, 2007) Soğancı, İbrahim Murat; Mutlugün, Evren; Tek, Sümeyra; Demir, Hilmi Volkan; Yücel, D.; Çeliker, G.In this work, we optically investigate and characterize the photocatalytic recovery of contaminated TiO 2 nanoparticles of different sizes that are incorporated in solgel films to study the size effect. We demonstrate significant improvement in the optical efficiency of the photocatalytic nanoparticles as we reduce the particle size.