scholarly journals Real‐time comprehensive glass container inspection system based on deep learning framework

2019 ◽  
Vol 55 (3) ◽  
pp. 131-132 ◽  
Author(s):  
Qiaokang Liang ◽  
Shao Xiang ◽  
Jianyong Long ◽  
Wei Sun ◽  
Yaonan Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Glass Container ◽  
Inspection System ◽  
Learning Framework ◽  
Container Inspection

scholarly journals Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 649
Author(s):  
Yifeng Liu ◽  
Wei Zhang ◽  
Wenhao Du
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Quality Data ◽  
Time Data ◽  
High Quality ◽  
Real Time System ◽  
High Quality Data ◽  
Learning Framework ◽  
Data Accumulation

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

scholarly journals Skin Diseases Prediction using Deep Learning Framework

2020 ◽  
Vol 8 (6) ◽  
pp. 4781-4784
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Skin Diseases ◽  
Dermatological Diseases ◽  
Learning Framework ◽  
Computer Aided ◽  
High Level ◽  
Almost All

Dermatological diseases are found to induce a serious impact on the health of millions of people as everyone is affected by almost all types of skin disorders every year. Since the human analysis of such diseases takes some time and effort, and current methods are only used to analyse singular types of skin diseases, there is a need for a more high-level computer-aided expertise in the analysis and diagnosis of multi-type skin diseases. This paper proposes an approach to use computer-aided techniques in deep learning neural networks such as Convolutional neural networks (CNN) and Residual Neural Networks (ResNet) to predict skin diseases real-time and thus provides more accuracy than other neural networks.

Fog Computing Based Hybrid Deep Learning Framework in effective inspection system for smart manufacturing

2020 ◽  
Vol 160 ◽  
pp. 636-642
Author(s):  
Shih-Yang Lin ◽  
Yun Du ◽  
Po-Chang Ko ◽  
Tzu-Jung Wu ◽  
Ping-Tsan Ho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fog Computing ◽  
Smart Manufacturing ◽  
Inspection System ◽  
Learning Framework

scholarly journals Real-Time Inspection System for Ballast Railway Fasteners Based on Point Cloud Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 61604-61614
Author(s):  
Hao Cui ◽  
Jian Li ◽  
Qingwu Hu ◽  
Qingzhou Mao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Point Cloud ◽  
Inspection System

Real-time monitoring of high-power disk laser welding statuses based on deep learning framework

2019 ◽  
Vol 31 (4) ◽  
pp. 799-814 ◽  
Author(s):  
Yanxi Zhang ◽  
Deyong You ◽  
Xiangdong Gao ◽  
Congyi Wang ◽  
Yangjin Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Laser Welding ◽  
Real Time ◽  
High Power ◽  
Real Time Monitoring ◽  
Disk Laser ◽  
Learning Framework

scholarly journals Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Sixian You ◽  
Yi Sun ◽  
Lin Yang ◽  
Jaena Park ◽  
Haohua Tu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Receiver Operating Curve ◽  
Label Free ◽  
New Era ◽  
Virtual Histology ◽  
Learning Framework ◽  
Normal Human Breast ◽  
Normal Human ◽  
The Rich

AbstractRecent advances in label-free virtual histology promise a new era for real-time molecular diagnosis in the operating room and during biopsy procedures. To take full advantage of the rich, multidimensional information provided by these technologies, reproducible and reliable computational tools that could facilitate the diagnosis are in great demand. In this study, we developed a deep-learning-based framework to recognize cancer versus normal human breast tissue from real-time label-free virtual histology images, with a tile-level AUC (area under receiver operating curve) of 95% and slide-level AUC of 100% on unseen samples. Furthermore, models trained on a high-quality laboratory-generated dataset can generalize to independent datasets acquired from a portable intraoperative version of the imaging technology with a physics-based adapted design. Classification activation maps and final feature visualization revealed discriminative patterns, such as tumor cells and tumor-associated vesicles, that are highly associated with cancer status. These results demonstrate that through the combination of real-time virtual histopathology and a deep-learning framework, accurate real-time diagnosis could be achieved in point-of-procedure clinical applications.

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