Parameter-Free On-line Deep Learning

2017 ◽  
pp. 543-553
Author(s):  
Paweł Wawrzyński
Keyword(s):  
Deep Learning ◽  
On Line

scholarly journals Palmprint Recognition in Uncontrolled and Unco-operative Environment

2021 ◽  
Vol 9 (8) ◽  
pp. 1711-1718
Author(s):  
Jayakrishnan S Kumar
Keyword(s):  
Deep Learning ◽  
Learning Rule ◽  
Industrial Applications ◽  
Palmprint Recognition ◽  
User Cooperation ◽  
Victim Identification ◽  
Palmprint Identification ◽  
On Line ◽  
End To End ◽  
Area Unit

Abstract: On-line palmprint recognition and latent palmprint identification unit two branches of palmprint studies. The previous uses middle-resolution footage collected by a camera in an exceedingly} very well-controlled or contact-based surroundings with user cooperation for industrial applications and so the latter uses high resolution latent palmprints collected in crime scenes for rhetorical investigation. However, these two branches do not cowl some palmprint footage that have the potential for rhetorical investigation. Attributable to the prevalence of smartphone and shopper camera, further proof is at intervals the variability of digital footage taken in uncontrolled and uncooperative surroundings. However, their palms area unit typically noticeable. To visualize palmprint identification on footage collected in uncontrolled and uncooperative surroundings, a novel palmprint info is established Associate in nursing AN end-to-end deep learning rule is projected. The new data named NTU Palmprints from the net (NTU-PI-v1) contains 7881 footage from 2035 palms collected from the net. The projected rule consists of Associate in Nursing alignment network and a feature extraction network and is end-to-end trainable. The projected rule is compared with the progressive on-line palmprint recognition ways that and evaluated on three public contactless palmprint infos, IITD, CASIA, and PolyU and a couple of new databases, NTU-PI-v1 and NTU contactless palmprint info. The experimental results showed that the projected rule outperforms the current palmprint recognition ways that. Keywords: Biometrics, criminal and victim identification, forensics, palmprint recognition

scholarly journals On-line part deformation prediction based on deep learning

2019 ◽  
Vol 31 (3) ◽  
pp. 561-574 ◽  
Author(s):  
Zhiwei Zhao ◽  
Yingguang Li ◽  
Changqing Liu ◽  
James Gao
Keyword(s):  
Deep Learning ◽  
Deformation Prediction ◽  
Line Part ◽  
On Line

A Deep Learning Based On-Line Voltage Estimation Method for Distribution Station Area

2021 ◽  
Author(s):  
Yangjun Zhou ◽  
Xuemei Dong ◽  
Li Yu ◽  
Huimin Zhao ◽  
Liwen Qin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Estimation Method ◽  
Line Voltage ◽  
On Line ◽  
Distribution Station

scholarly journals Portable System for Box Volume Measurement Based on Line-Structured Light Vision and Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3921 ◽  
Author(s):  
Tao Peng ◽  
Zhijiang Zhang ◽  
Yingjie Song ◽  
Fansheng Chen ◽  
Dan Zeng
Keyword(s):  
Deep Learning ◽  
Structured Light ◽  
Calibration Method ◽  
Volume Measurement ◽  
Experimental Results ◽  
Measuring Range ◽  
Measuring Device ◽  
On Line ◽  
Portable System ◽  
Structured Light Vision

Portable box volume measurement has always been a popular issue in the intelligent logistic industry. This work presents a portable system for box volume measurement that is based on line-structured light vision and deep learning. This system consists of a novel 2 × 2 laser line grid projector, a sensor, and software modules, with which only two laser-modulated images of boxes are required for volume measurement. For laser-modulated images, a novel end-to-end deep learning model is proposed by using an improved holistically nested edge detection network to extract edges. Furthermore, an automatic one-step calibration method for the line-structured light projector is designed for fast calibration. The experimental results show that the measuring range of our proposed system is 100–1800 mm, with errors less than ±5.0 mm. Theoretical analysis indicates that within the measuring range of the system, the measurement uncertainty of the measuring device is ±0.52 mm to ±4.0 mm, which is consistent with the experimental results. The device size is 140 mm × 35 mm × 35 mm and the weight is 110 g, thus the system is suitable for portable automatic box volume measurement.

scholarly journals Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis

2019 ◽  
Vol 11 (12) ◽  
pp. 3489
Author(s):  
Hyungjin Ko ◽  
Jaewook Lee ◽  
Junyoung Byun ◽  
Bumho Son ◽  
Saerom Park
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Data Distribution ◽  
Series Data ◽  
Learning Models ◽  
Classification Problems ◽  
Ensemble Models ◽  
On Line

Developing a robust and sustainable system is an important problem in which deep learning models are used in real-world applications. Ensemble methods combine diverse models to improve performance and achieve robustness. The analysis of time series data requires dealing with continuously incoming instances; however, most ensemble models suffer when adapting to a change in data distribution. Therefore, we propose an on-line ensemble deep learning algorithm that aggregates deep learning models and adjusts the ensemble weight based on loss value in this study. We theoretically demonstrate that the ensemble weight converges to the limiting distribution, and, thus, minimizes the average total loss from a new regret measure based on adversarial assumption. We also present an overall framework that can be applied to analyze time series. In the experiments, we focused on the on-line phase, in which the ensemble models predict the binary class for the simulated data and the financial and non-financial real data. The proposed method outperformed other ensemble approaches. Moreover, our method was not only robust to the intentional attacks but also sustainable in data distribution changes. In the future, our algorithm can be extended to regression and multiclass classification problems.

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