scholarly journals Content Estimation of Foreign Fibers in Cotton Based on Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1795
Author(s):  
Wei Wei ◽  
Chen Zhang ◽  
Dexiang Deng
Keyword(s):  
Deep Learning ◽  
Evaluation Method ◽  
Test Results ◽  
Production Lines ◽  
Prototype Test ◽  
Textile Product ◽  
Time Segmentation ◽  
Image Segmentation Algorithm ◽  
Actual Size

Cotton foreign fibers directly affect the quality of a textile product; the less foreign fibers in raw cotton, the higher the quality grade of the textile product. Based on the foreign fiber clean machine, this paper proposed an evaluation method of foreign fiber content using deep learning. First of all, a large number of images of foreign fibers were collected from different production lines and annotated to obtain the mask image dataset of foreign fibers. Secondly, by comparing the image segmentation algorithm based on deep learning, tests showed that U-Net has a better performance on different segment metrics evaluations, and U-Net is improved to realize the real-time segmentation of foreign fiber images. The actual size of the foreign fiber could be calculated through the combination of the segment result and the mechanical parameters of the machine. Finally, the test results showed that the relative error between the estimated size and the actual size was less than 4%. After the prototype test, the algorithm was deployed on the actual production line and, by comparing the algorithm data in a random time with the actual foreign fiber statistical data, the overall error was less than 2%. The test showed that the new evaluation method can fully reflect the content of foreign fiber in raw cotton.

scholarly journals A Double-Branch Surface Detection System for Armatures in Vibration Motors with Miniature Volume Based on ResNet-101 and FPN

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2360
Author(s):  
Tao Feng ◽  
Jiange Liu ◽  
Xia Fang ◽  
Jie Wang ◽  
Libin Zhou
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Complete System ◽  
Detection System ◽  
Discrimination Performance ◽  
Training Method ◽  
Production Lines ◽  
Training Set ◽  
Test Set

In this paper, a complete system based on computer vision and deep learning is proposed for surface inspection of the armatures in a vibration motor with miniature volume. A device for imaging and positioning was designed in order to obtain the images of the surface of the armatures. The images obtained by the device were divided into a training set and a test set. With continuous experimental exploration and improvement, the most efficient deep-network model was designed. The results show that the model leads to high accuracy on both the training set and the test set. In addition, we proposed a training method to make the network designed by us perform better. To guarantee the quality of the motor, a double-branch discrimination mechanism was also proposed. In order to verify the reliability of the system, experimental verification was conducted on the production line, and a satisfactory discrimination performance was reached. The results indicate that the proposed detection system for the armatures based on computer vision and deep learning is stable and reliable for armature production lines.

scholarly journals Rancang Bangun Smart Plug Untuk Sistem Monitoring Dan Proteksi Hubungsingkat Listrik

2019 ◽  
Vol 3 (2) ◽  
pp. 77-84
Author(s):  
Budi Arto ◽  
Basuki Winarno ◽  
Nur Asyik Hidayatullah
Keyword(s):  
Control Systems ◽  
Industrial Revolution ◽  
Electricity Sector ◽  
The Internet ◽  
Test Results ◽  
Production Lines ◽  
Protection And Control ◽  
And Control ◽  
The Web

The development of technology is expected to help humans in quality of life. The industrial world is making preparations for industrial revolution 4.0 which is designed to integrate the online world with production lines in industrial processes. All production processes in the 4.0 era and running with the internet as support. The internet has become one of the human's main needs. At present, the development of the internet has not yet developed with the development of protection and control systems in the electricity sector. A breakthrough is needed to create the necessary protection systems and also control the load. A Smart Plug is a breakthrough that can accommodate this. Relays available on Smart Plugs can be used as protection and control devices on household electrical appliances. To read current and load, the current sensor (ZHT 03) and voltage sensor (ZMPT101B) are used. All sensor and relay data is performed by Arduino Nano. Whereas NodeMCU 12-E is used to connect the Smart Plug with the web and database. Smart Plug test results with the web site show that Smart Plug can work well on protection and control systems. This is evidenced by the interrupted current flowing at the load, the load compilation current is more than the value of the current that has been determined as the set point. While the control system can also work optimally. Relays can be received according to orders given from the website.

scholarly journals Higher Education Curriculum Evaluation Method Based on Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mei Zuo ◽  
Jixiang Wang
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Evaluation Method ◽  
Curriculum Evaluation ◽  
Learning System ◽  
Education Curriculum ◽  
Higher Education Curriculum ◽  
Quality Of Higher Education ◽  
Deep Learning Network

Higher education plays an important role in the improvement of people’s quality and the development of our country. Therefore, it is necessary to evaluate the higher education curriculum. This paper analyzes and constructs the deep network learning system and self-encoder and evaluates the Chongqing higher education curriculum based on the deep learning network selected by 50 universities in Chongqing. It is found that the numbers of test objects, indicators, and hidden layers have an impact on the evaluation results. At the same time, a classroom teaching model is designed to improve the quality of higher education and solve the problem of insufficient curriculum quality of higher education.

Implementasi Voip Server Berbasis IPV6 Dengan Raspberry PI

2019 ◽  
Vol 9 (01) ◽  
pp. 47-54
Author(s):  
Rabbai San Arif ◽  
Yuli Fitrisia ◽  
Agus Urip Ari Wibowo
Keyword(s):  
Internet Protocol ◽  
Wireless Transmission ◽  
Raspberry Pi ◽  
Test Results ◽  
Average Delay ◽  
Ipv6 Network ◽  
Ip Network ◽  
Selection Of ◽  
Better Than

Voice over Internet Protocol (VoIP) is a telecommunications technology that is able to pass the communication service in Internet Protocol networks so as to allow communicating between users in an IP network. However VoIP technology still has weakness in the Quality of Service (QoS). VOPI weaknesses is affected by the selection of the physical servers used. In this research, VoIP is configured on Linux operating system with Asterisk as VoIP application server and integrated on a Raspberry Pi by using wired and wireless network as the transmission medium. Because of depletion of IPv4 capacity that can be used on the network, it needs to be applied to VoIP system using the IPv6 network protocol with supports devices. The test results by using a wired transmission medium that has obtained are the average delay is 117.851 ms, jitter is 5.796 ms, packet loss is 0.38%, throughput is 962.861 kbps, 8.33% of CPU usage and 59.33% of memory usage. The analysis shows that the wired transmission media is better than the wireless transmission media and wireless-wired.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

Sinonasal effects of external dacryocystorhinostomy

2021 ◽  
pp. 1-4
Author(s):  
H Eyigor ◽  
E A Cetinkaya ◽  
D T Coban ◽  
G Ozturk ◽  
Ö Erdem
Keyword(s):  
Mucociliary Clearance ◽  
Nasal Symptom ◽  
Ciliary Activity ◽  
Test Results ◽  
Saccharine Test ◽  
Significant Difference ◽  
Symptom Scores ◽  
Endoscopic Score ◽  
External Dacryocystorhinostomy

Abstract Objective External dacryocystorhinostomy is thought to cause mucociliary dysfunction by damaging the mucosa, in turn affecting ciliary activity and mucus quality. This study investigated the effect of external dacryocystorhinostomy on sinonasal function. Methods Patients scheduled for unilateral external dacryocystorhinostomy who underwent endoscopic nasal examination and paranasal sinus computed tomography were included in this study. A saccharine test was performed on the planned surgical side and the mucociliary clearance time was determined. The sinonasal quality of life was measured in all patients, pre-operatively and at six months post-operatively, using the Sino-Nasal Outcome Test-22. The Lund–Kennedy endoscopic score was also determined in all patients, both pre- and post-operatively. Results The study comprised 28 patients (22 females and 6 males). A statistically significant difference was found between the pre- and post-operative saccharine test results (p = 0.006), but not between the pre- and post-operative Sino-Nasal Outcome Test-22 scores (p > 0.05). Conclusion This study is one of only a few to investigate the effect of external dacryocystorhinostomy on sinonasal function. The results showed that external dacryocystorhinostomy impairs mucociliary clearance. The surgical procedure is well tolerated and does not significantly change nasal symptom scores.

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