scholarly journals An Implementation of Yolo-family Algorithms in Classifying the Product Quality for the ABS Metallization

2021 ◽  
Author(s):  
YUH-WEN CHEN ◽  
Jing Mau Shiu
Keyword(s):  
Visual Inspection ◽  
Acrylonitrile Butadiene Styrene ◽  
Labor Cost ◽  
Classification Performance ◽  
Video Data ◽  
Production Lines ◽  
Learning Framework ◽  
Optical Inspection ◽  
The Neural Network ◽  
Product Surface

Abstract In the traditional electroplating industry of Acrylonitrile Butadiene Styrene (ABS), quality control inspection of the product surface is usually performed with the naked eye. However, these defects on the surface of electroplated products are minor and easily ignored under reflective conditions. If the number of defectiveness and samples is too large, manual inspection will be challenging and time-consuming. We innovatively applied Additive Manufacturing (AM) to design and assemble an automatic optical inspection (AOI) system. The system can identify defects on the reflective surface of the plated product. Based on the deep learning framework from YOLO, we successfully started the neural network model on GPU using the family of YOLO algorithms: from v2 to v5. Finally, our efforts showed an accuracy rate over an average of 70 percentage for detecting real-time video data in production lines. We also compare the classification performance among various YOLO algorithms. Our efforts of visual inspection significantly reduce the labor cost of visual inspection in the electroplating industry.

scholarly journals Study on process of chemical fiber filament automatic doffing system based on simulation platform and machine learning

2021 ◽  
Vol 16 ◽  
pp. 155892502110548
Author(s):  
Hongxin Zhu ◽  
Kun Zou ◽  
Wenlan Bao
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Production Line ◽  
Simulation Platform ◽  
Chemical Fiber ◽  
Production Lines ◽  
The Neural Network ◽  
Spss Software ◽  
The Relationship

In recent years, a large number of automatic equipment has been introduced into the chemical fiber filament doffing production line, but the related research on the fully automatic production line technology is not yet mature. At present, it is difficult to collect data due to test costs and confidentiality. This paper proposes to develop a simulation platform for a chemical fiber filament doffing production line, which enables us to effectively obtain data and quantitatively study the relationship between the number of manual interventions and other process parameters of the production line. Considering that the parameter research is a multi-factor problem, an orthogonal test was designed by using SPSS software and was carried out by using a simulation platform. The multiple linear regression (MLR) and the neural network optimized by genetic algorithm were adopted to fit the relationship between the number of manual interventions and other parameters of the production line. The SPSS software was applied to obtain the standardized coefficients of the multiple linear regression fitting and the neural network mean impact value (MIV) algorithm was applied to obtain the magnitude and direction of the impact of different parameters on the number of manual interventions. The above results provide important reference for the design of similar new production lines and for the improvement of old production lines.

scholarly journals Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0188996 ◽  
Author(s):  
Muhammad Ahmad ◽  
Stanislav Protasov ◽  
Adil Mehmood Khan ◽  
Rasheed Hussain ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Hyperspectral Image Classification ◽  
Learning Framework

scholarly journals A Developed Siamese CNN with 3D Adaptive Spatial-Spectral Pyramid Pooling for Hyperspectral Image Classification

2020 ◽  
Vol 12 (12) ◽  
pp. 1964 ◽  
Author(s):  
Mengbin Rao ◽  
Ping Tang ◽  
Zheng Zhang
Keyword(s):  
Transfer Learning ◽  
Hyperspectral Image ◽  
Three Dimensional ◽  
Classification Performance ◽  
Generalization Capability ◽  
Training Strategy ◽  
Learning Framework ◽  
Spectral Bands ◽  
Learning Tasks ◽  
3D Information

Since hyperspectral images (HSI) captured by different sensors often contain different number of bands, but most of the convolutional neural networks (CNN) require a fixed-size input, the generalization capability of deep CNNs to use heterogeneous input to achieve better classification performance has become a research focus. For classification tasks with limited labeled samples, the training strategy of feeding CNNs with sample-pairs instead of single sample has proven to be an efficient approach. Following this strategy, we propose a Siamese CNN with three-dimensional (3D) adaptive spatial-spectral pyramid pooling (ASSP) layer, called ASSP-SCNN, that takes as input 3D sample-pair with varying size and can easily be transferred to another HSI dataset regardless of the number of spectral bands. The 3D ASSP layer can also extract different levels of 3D information to improve the classification performance of the equipped CNN. To evaluate the classification and generalization performance of ASSP-SCNN, our experiments consist of two parts: the experiments of ASSP-SCNN without pre-training and the experiments of ASSP-SCNN-based transfer learning framework. Experimental results on three HSI datasets demonstrate that both ASSP-SCNN without pre-training and transfer learning based on ASSP-SCNN achieve higher classification accuracies than several state-of-the-art CNN-based methods. Moreover, we also compare the performance of ASSP-SCNN on different transfer learning tasks, which further verifies that ASSP-SCNN has a strong generalization capability.

scholarly journals DLI-IT: A Deep Learning Approach to Drug Label Identification Through Image and Text Embedding

2020 ◽  
Author(s):  
Xiangwen Liu ◽  
Joe Meehan ◽  
Weida Tong ◽  
Leihong Wu ◽  
Xiaowei Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Package Insert ◽  
Drug Distribution ◽  
Labor Cost ◽  
Drug Label ◽  
Accurate Identification ◽  
Learning Framework ◽  
Universal Sentence ◽  
Reference Images ◽  
Text Information

Abstract [Background] Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and potentially dangerous drugs. Due to the time-consuming process and high labor cost of investigation, an artificial intelligence-based deep learning model is necessary for fast and accurate identification of the drugs. [Methods] In addition to image-based identification technology, we take advantages of rich text information on the pharmaceutical package insert of drug label images. In this study, we developed the Drug Label Identification through Image and Text embedding model (DLI-IT) to model text-based patterns of historical data for detection of suspicious drugs. In DLI-IT, we first trained a Connectionist Text Proposal Network (CTPN) to crop the raw image into sub-images based on the text. The texts from the cropped sub-images are recognized independently through the Tesseract OCR Engine and combined as one document for each raw image. Finally, we applied universal sentence embedding to transform these documents into vectors and find the most similar reference images to the test image through the cosine similarity. [Results] We trained the DLI-IT model on 1749 opioid and 2365 non-opioid drug label images. The model was then tested on 300 external opioid drug label images, the result demonstrated our model achieves up-to 88% of the precision in drug label identification, which outperforms previous image-based or text-based identification method by up-to 35% improvement. [Conclusion] To conclude, by combining Image and Text embedding analysis under deep learning framework, our DLI-IT approach achieved a competitive performance in advancing drug label identification.

scholarly journals Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach

2021 ◽  
Vol 2 ◽  
Author(s):  
Yongliang Qiao ◽  
Cameron Clark ◽  
Sabrina Lomax ◽  
He Kong ◽  
Daobilige Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Video Data ◽  
Identification Accuracy ◽  
Sequence Length ◽  
Livestock Farming ◽  
Precision Livestock Farming ◽  
Learning Framework ◽  
Spatio Temporal

Individual cattle identification is a prerequisite and foundation for precision livestock farming. Existing methods for cattle identification require radio frequency or visual ear tags, all of which are prone to loss or damage. Here, we propose and implement a new unified deep learning approach to cattle identification using video analysis. The proposed deep learning framework is composed of a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a self-attention mechanism. More specifically, the Inception-V3 CNN was used to extract features from a cattle video dataset taken in a feedlot with rear-view. Extracted features were then fed to a BiLSTM layer to capture spatio-temporal information. Then, self-attention was employed to provide a different focus on the features captured by BiLSTM for the final step of cattle identification. We used a total of 363 rear-view videos from 50 cattle at three different times with an interval of 1 month between data collection periods. The proposed method achieved 93.3% identification accuracy using a 30-frame video length, which outperformed current state-of-the-art methods (Inception-V3, MLP, SimpleRNN, LSTM, and BiLSTM). Furthermore, two different attention schemes, namely, additive and multiplicative attention mechanisms were compared. Our results show that the additive attention mechanism achieved 93.3% accuracy and 91.0% recall, greater than multiplicative attention mechanism with 90.7% accuracy and 87.0% recall. Video length also impacted accuracy, with video sequence length up to 30-frames enhancing identification performance. Overall, our approach can capture key spatio-temporal features to improve cattle identification accuracy, enabling automated cattle identification for precision livestock farming.

scholarly journals Towards Better Forecasting by Fusing Near and Distant Future Visions

2020 ◽  
Vol 34 (04) ◽  
pp. 3593-3600
Author(s):  
Jiezhu Cheng ◽  
Kaizhu Huang ◽  
Zibin Zheng
Keyword(s):  
Neural Network ◽  
Multivariate Time Series ◽  
Predictive Performance ◽  
Distant Future ◽  
Forecasting Accuracy ◽  
Learning Framework ◽  
The Neural Network ◽  
Multi Level ◽  
Real World Datasets ◽  
Future Visions

Multivariate time series forecasting is an important yet challenging problem in machine learning. Most existing approaches only forecast the series value of one future moment, ignoring the interactions between predictions of future moments with different temporal distance. Such a deficiency probably prevents the model from getting enough information about the future, thus limiting the forecasting accuracy. To address this problem, we propose Multi-Level Construal Neural Network (MLCNN), a novel multi-task deep learning framework. Inspired by the Construal Level Theory of psychology, this model aims to improve the predictive performance by fusing forecasting information (i.e., future visions) of different future time. We first use the Convolution Neural Network to extract multi-level abstract representations of the raw data for near and distant future predictions. We then model the interplay between multiple predictive tasks and fuse their future visions through a modified Encoder-Decoder architecture. Finally, we combine traditional Autoregression model with the neural network to solve the scale insensitive problem. Experiments on three real-world datasets show that our method achieves statistically significant improvements compared to the most state-of-the-art baseline methods, with average 4.59% reduction on RMSE metric and average 6.87% reduction on MAE metric.

scholarly journals The data conundrum: compression of automotive imaging data and deep neural network based perception

2021 ◽  
Vol 2021 (1) ◽  
pp. 78-82
Author(s):  
Pak Hung Chan ◽  
Georgina Souvalioti ◽  
Anthony Huggett ◽  
Graham Kirsch ◽  
Valentina Donzella
Keyword(s):  
Neural Network ◽  
Video Compression ◽  
Situational Awareness ◽  
Video Camera ◽  
Video Data ◽  
Compressed Video ◽  
Imaging Data ◽  
The Neural Network ◽  
Perception System ◽  
Driving Assistance

Video compression in automated vehicles and advanced driving assistance systems is of utmost importance to deal with the challenge of transmitting and processing the vast amount of video data generated per second by the sensor suite which is needed to support robust situational awareness. The objective of this paper is to demonstrate that video compression can be optimised based on the perception system that will utilise the data. We have considered the deployment of deep neural networks to implement object (i.e. vehicle) detection based on compressed video camera data extracted from the KITTI MoSeg dataset. Preliminary results indicate that re-training the neural network with M-JPEG compressed videos can improve the detection performance with compressed and uncompressed transmitted data, improving recalls and precision by up to 4% with respect to re-training with uncompressed data.

scholarly journals A Deep Learning Framework for Coronavirus Disease (COVID-19) Detection in X-Ray Images

2020 ◽  
Author(s):  
Tayyip Ozcan
Keyword(s):  
Transfer Learning ◽  
Performance Metrics ◽  
Experimental Studies ◽  
Large Family ◽  
Classification Performance ◽  
The Novel ◽  
X Ray ◽  
Learning Framework ◽  
Classification Feature ◽  
Novel Coronavirus

Abstract Coronavirus, a large family of viruses, causes illness in both humans and animals. The novel coronavirus (COVID-19) came up in Wuhan in December 2019. This deadly COVID-19 pandemic has become very fast-spreading and currently present in several countries worldwide. The timely detection of patients who have COVID-19 is vitally important. To this end, scientists are working on different detection methods.In this paper, a grid search (GS) and pre-trained model aided convolutional neural network (CNN) model is proposed to detect COVID-19 in X-Ray images. In the proposed method, the GS method is employed to optimize the hyperparameters of CNN, which directly affects classification performance. Three pre-trained CNN models (GoogleNet, ResNet18 and ResNet50), which can be used for classification, feature extraction and transfer learning purposes were used for transfer learning in this study. The proposed method was trained using the training and validation subdatasets of the collected dataset and detail evaluations are presented according to different performance metrics. According to the experimental studies, the best results were obtained with the GS and ResNet50 aided model.

scholarly journals Random Forests Highlight the Combined Effect of Environmental Heavy Metals Exposure and Genetic Damages for Cardiovascular Diseases

2021 ◽  
Vol 11 (18) ◽  
pp. 8405
Author(s):  
Alfonso Monaco ◽  
Antonio Lacalamita ◽  
Nicola Amoroso ◽  
Armando D’Orta ◽  
Andrea Del Buono ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heavy Metals ◽  
Cardiovascular Diseases ◽  
Random Forests ◽  
Classification Problem ◽  
Classification Performance ◽  
Cvd Risk ◽  
Learning Framework ◽  
Clinic Foundation ◽  
Highly Correlated

Heavy metals are a dangerous source of pollution due to their toxicity, permanence in the environment and chemical nature. It is well known that long-term exposure to heavy metals is related to several chronic degenerative diseases (cardiovascular diseases, neoplasms, neurodegenerative syndromes, etc.). In this work, we propose a machine learning framework to evaluate the severity of cardiovascular diseases (CVD) from Human scalp hair analysis (HSHA) tests and genetic analysis and identify a small group of these clinical features mostly associated with the CVD risk. Using a private dataset provided by the DD Clinic foundation in Caserta, Italy, we cross-validated the classification performance of a Random Forests model with 90 subjects affected by CVD. The proposed model reached an AUC of 0.78 ± 0.01 on a three class classification problem. The robustness of the predictions was assessed by comparison with different cross-validation schemes and two state-of-the-art classifiers, such as Artificial Neural Network and General Linear Model. Thus, is the first work that studies, through a machine learning approach, the tight link between CVD severity, heavy metal concentrations and SNPs. Then, the selected features appear highly correlated with the CVD phenotype, and they could represent targets for future CVD therapies.

Recycled Plastic Beads Sorting Machine for Polypropylene and Acrylonitrile Butadiene Styrene Type with Difference of Density

2017 ◽  
Vol 871 ◽  
pp. 230-236
Author(s):  
Ubolrat Wangrakdiskul ◽  
Pongsathorn Teammoke ◽  
Weerapat Laoharatanahirun
Keyword(s):  
Processing Time ◽  
Acrylonitrile Butadiene Styrene ◽  
Labor Cost ◽  
Payback Period ◽  
Previous Method ◽  
Fatigue Effect ◽  
Sorting Machine ◽  
Previous Cycle ◽  
Plastic Materials ◽  
Butadiene Styrene

This study aims to design and develop the recycled plastic beads sorting machine for 2 types of plastic beads, which are PP (Polypropylene) and ABS (Acrylonitrile butadiene styrene). Due to the process of preparing plastic materials for producing fan tube and base parts of electric fan, plastic beads scraps has been generated. Presently, the company has separated these scraps with manual method. The workers have stirred them with water for separating scrap types by difference of density. They have operated this process in the overtime period. In addition consuming processing time, this process makes fatigue effect in operators and also consume amount of water for each cycle of separating. Therefore, sorting machine for replacement workers has been developed. It can work for more efficiency than the previous method by reducing processing time by 41.8%. The proposed machine consists of 2 tanks for separating two types of plastic beads at capacity 50 Kg/hr. At the end of each cycle, the water consumed in the previous cycle can be reused. As this method, the proposed machine can reduce consuming water approximately 88.43%. This machine can reduce the number of operators from 2 to 1 person. Finally, payback period for recovering investment of machine comparing with saving labor cost is 1.48 months.

Sign in / Sign up

Export Citation Format

Share Document