Electric Properties of Graphene-Based Conductive Layers from DC Up To Terahertz Range

Kopyt, P.; Salski, Bartłomiej; Zagrajek, Przemysław; Janczak, Daniel; Słoma, Marcin; Jakubowska, М.; Olszewska-Placha, Marzena; Gwarek, W.

doi:10.1109/tthz.2016.2544142

Cited by 49 publications

(22 citation statements)

References 20 publications

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“…The idea of knowledge transfer is similar to that of transfer learning [1], [36], [37]. According to the definition of transfer learning, it aims to help improve the learning of the target predictive function f T (•) in target domain with the knowledge in resource domain and resource task [1]. Our knowledge transfer method is also intended for this purpose.…”

Section: Methodsmentioning

confidence: 99%

“…In the formula, W N = (w (1) , w (2) , w (3) , w (4) .w (5) ) is the integrated representation of the parameters of those layers in each branch, and w fuse is the fusion weight obtained through adaptive training of the network. The model structure shows that the loss value of fusion layer has relationship both with the network parameters in each branch and with what in fusion layer.…”

Section: B Knowledge Transferring From Training Progress and Tricks mentioning

confidence: 99%

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Multi-Resolution CNN and Knowledge Transfer for Candidate Classification in Lung Nodule Detection

Wang

Zhou

et al. 2019

IEEE Access

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The automatic lung nodule detection system can facilitate the early screening of lung cancer and timely medical interventions. However, there still exist multiple nodule candidates produced by initial rough detection in this system, and how to determine authenticity is a key problem. As this work is often challenged by the radiological heterogeneity of the computed tomography scans and the variable sizes of lung nodules, we put forward a multi-resolution convolutional neural network (CNN) to extract features of various levels and resolutions from different depth layers in the network for classification of lung nodule candidates. Through the use of knowledge transfer, the method can be divided into three steps. First, we transfer knowledge from the source CNN model which has been applied to edge detection and improve the model to a new multi-resolution model which is suitable for the image classification task. Then, the knowledge is transformed from source training progress so that all of the side-output branches in the model will be considered in the calculation. Moreover, the loss function and objective equation are improved to be imagewise calculation rather than pixel-wise. Finally, samples production and data enhancement are performed to train and test a classifier tailored for classification of lung nodule candidates. The experimental results on the LUNA16 data set show that our method gets an accuracy of 0.9733, a precision of 0.9673, and an AUC of 0.9954 while being used for lung nodule candidate classification, which is higher than the scores obtained by most of the state-of-the-art approach. In addition, when the test samples with three different sizes of 26 * 26, 36 * 36, and 48 * 48 are used to test the multi-resolution CNN, the accuracy rate of all three experiments exceed 92.81%, which demonstrates that the proposed model is insensitive to input scales. INDEX TERMS Convolutional neural network, lung nodule candidate classification, multi-resolution model, knowledge transfer.

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Section: Methodsmentioning

confidence: 99%

Section: B Knowledge Transferring From Training Progress and Tricks mentioning

confidence: 99%

Multi-Resolution CNN and Knowledge Transfer for Candidate Classification in Lung Nodule Detection

Wang

Zhou

et al. 2019

IEEE Access

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“…The sparse estimator solver only requires a small number of inner products, vector-scalar multiplications and vector Algorithm 3 Adaptive Feature Detection Based on the Bi-Level Nested Sparse Optimization Framework (BiNSOF) Require: Observation signal y, dictionary . Ensure: set θ 0 L by the formula (30) and θ 0 U = | T y| ∞ and = 10 −3 ,…”

Section: E Computational Complexitymentioning

confidence: 99%

“…Wang proposed a nonconvex sparse regularization method based on the generalized minimax convex penalty [29]. Zhang proposed a collaborative sparse classification method based on the low-rank property of two-dimensional fault feature signal and applied to the gear-hub crack fault detection [30].…”

Section: Introductionmentioning

confidence: 99%

A Bi-Level Nested Sparse Optimization for Adaptive Mechanical Fault Feature Detection

et al. 2020

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Denoising is a permanent topic and there are various denoisers proposed in the fault diagnosis of industrial systems. However, it is still ambiguous to evaluate their performance quantitatively in terms of mean square error (MSE) and further achieve their maximum gains, because it is always infeasible to obtain the MSE metric without real feature signals in the engineering practices. Therefore, leveraging Stein Unbiased Risk Estimator (SURE) theory, a bi-level nested sparse optimization framework (BiNSOF) is proposed to jointly optimize a parameterized sparse denoiser as well as its regularization parameter, further obtaining the near-optimal fault features with a minimum MSE. The inner level of BiNSOF utilizes a 1 regularized sparse denoiser to describe the intrinsic sparse structure of feature information, which can be effectively addressed by popular primal-dual splitting schemes. The core of the outer optimization level is a SURE-based unbiased estimator for MSE, and the minimum MSE search problem is transformed into a quadratic optimization problem which could be fast solved by classic golden section search schemes. The proposed BiNOSP can perfectly approximate the oracle MSE without any real feature information, and further provides a reliable way to obtain the optimal hyper-parameter sets for the maximum performance gains of the sparse denoiser. The computational complexity of the advocated approach is also investigated. Moreover, its feasibility and performances are profoundly evaluated by a set of comprehensive numerical studies. Lastly, two bearing fault detection cases confirm the applicability and superiority of the proposed framework. INDEX TERMS Sparse optimization, Stein unbiased risk estimator (SURE), fault feature detection, primaldual splitting, bi-level nested optimization, adaptive parameter selection.

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“…Head and neck (oral, larynx, and pharynx) [1] cancers and esophageal cancers are the 6th and 7th most common cancers worldwide, respectively [2]. They appear superficially in the mucous membrane and gradually infiltrate the underlying tissue [3], [4]. If cancers on the tissue surface can be treated at the early stage, the prognosis of patients is improved [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Design of Multiband Switching Illumination With Low-Concentration Lugol Stain for Esophageal Cancer Detection

et al. 2020

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Narrowband imaging (NBI), flexible spectral imaging color enhancement (FICE), and Lugol chromoendoscopy (LCE) are used in endoscopy of head and neck cancers and esophageal cancers. The NBI and FICE techniques enhance the capillary pattern in cancerous areas, whereas in LCE, normal and cancerous areas are differentiated using Lugol solution. Although invasive, LCE is more sensitive than NBI and FICE for this purpose. In this study, we propose a relatively less invasive and highly sensitive technique for detecting cancerous areas that combines low-concentration Lugol stain and narrowband illumination. This combined technique requires the use of an endoscopic system that can acquire RGB images synchronized with narrowband illumination switching. This technique produces a large number of images that can potentially discriminate smaller differences in color than is possible with conventional RGB imaging. Experiments to predict the performance of the proposed method were performed using four resected specimens, including esophageal cancer, and promising results were obtained. INDEX TERMS Endoscope, image color analysis, spectroscopy.

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Electric Properties of Graphene-Based Conductive Layers from DC Up To Terahertz Range

Cited by 49 publications

References 20 publications

Multi-Resolution CNN and Knowledge Transfer for Candidate Classification in Lung Nodule Detection

Multi-Resolution CNN and Knowledge Transfer for Candidate Classification in Lung Nodule Detection

A Bi-Level Nested Sparse Optimization for Adaptive Mechanical Fault Feature Detection

Design of Multiband Switching Illumination With Low-Concentration Lugol Stain for Esophageal Cancer Detection

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