LAMOST Fiber Positioning Unit Detection Based on Deep Learning

2021 ◽  
Vol 133 (1029) ◽  
pp. 115001
Author(s):  
Ming Zhou ◽  
Guanru Lv ◽  
Jian Li ◽  
Zengxiang Zhou ◽  
Zhigang Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Preliminary Test ◽  
Detection Accuracy ◽  
Test Results ◽  
Initial Angle ◽  
Spectral Flux ◽  
Robot Accuracy ◽  
Supervision Mode ◽  
Fiber Position ◽  
Better Than

Abstract The double revolving fiber positioning unit (FPU) is one of the key technologies of The Large Sky Area Multi-Object Fiber Spectroscope Telescope (LAMOST). The positioning accuracy of the computer controlled FPU depends on robot accuracy as well as the initial parameters of FPU. These initial parameters may deteriorate with time when FPU is running in non-supervision mode, which would lead to bad fiber position accuracy and further efficiency degradation in the subsequent surveys. In this paper, we present an algorithm based on deep learning to detect the FPU’s initial angle using the front illuminated image of LAMOST focal plane. Preliminary test results show that the detection accuracy of the FPU initial angle is better than 2.°5, which is good enough to distinguish those obvious bad FPUs. Our results are further well verified by direct measurement of fiber position from the back illuminated image and the correlation analysis of the spectral flux in LAMOST survey data.

scholarly journals EnsemblePigDet: Ensemble Deep Learning for Accurate Pig Detection

2021 ◽  
Vol 11 (12) ◽  
pp. 5577
Author(s):  
Hanse Ahn ◽  
Seungwook Son ◽  
Heegon Kim ◽  
Sungju Lee ◽  
Yongwha Chung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Test Data ◽  
Execution Time ◽  
Detection Accuracy ◽  
Practical Issue ◽  
Test Results ◽  
Monitoring Techniques ◽  
Training Images ◽  
Pig Farms ◽  
Monitoring Application

Automated pig monitoring is important for smart pig farms; thus, several deep-learning-based pig monitoring techniques have been proposed recently. In applying automated pig monitoring techniques to real pig farms, however, practical issues such as detecting pigs from overexposed regions, caused by strong sunlight through a window, should be considered. Another practical issue in applying deep-learning-based techniques to a specific pig monitoring application is the annotation cost for pig data. In this study, we propose a method for managing these two practical issues. Using annotated data obtained from training images without overexposed regions, we first generated augmented data to reduce the effect of overexposure. Then, we trained YOLOv4 with both the annotated and augmented data and combined the test results from two YOLOv4 models in a bounding box level to further improve the detection accuracy. We propose accuracy metrics for pig detection in a closed pig pen to evaluate the accuracy of the detection without box-level annotation. Our experimental results with 216,000 “unseen” test data from overexposed regions in the same pig pen show that the proposed ensemble method can significantly improve the detection accuracy of the baseline YOLOv4, from 79.93% to 94.33%, with additional execution time.

scholarly journals Multi-label topic classification for COVID-19 literature annotation using an ensemble model based on PubMedBERT

2021 ◽  
Author(s):  
Shubo Tian ◽  
Jinfeng Zhang
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Language Model ◽  
Ensemble Model ◽  
Test Results ◽  
Learning Models ◽  
Baseline Model ◽  
Satisfactory Performance ◽  
The Mean ◽  
Better Than

The BioCreative VII Track 5 calls for participants to tackle the multi-label classification task for automated topic annotation of COVID-19 literature. In our participation, we evaluated several deep learning models built on PubMedBERT, a pre-trained language model, with different strategies addressing the challenges of the task. Specifically, multi-instance learning was used to deal with the large variation in the lengths of the articles, and focal loss function was used to address the imbalance in the distribution of different topics. We found that the ensemble model performed the best among all the models we have tested. Test results of our submissions showed that our approach was able to achieve satisfactory performance with an F1 score of 0.9247, which is significantly better than the baseline model (F1 score: 0.8678) and the mean of all the submissions (F1 score: 0.8931).

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.

Kemampuan Komunikasi Matematika: Studi Komparasi Pembelajaran Number Head Together (NHT) dan Media Power Point (PPT)

2020 ◽  
Vol 3 (1) ◽  
pp. 39-50
Author(s):  
Bernadeta Ritawati ◽  
Sri Wahyuni
Keyword(s):  
Communication Skills ◽  
Hypothesis Test ◽  
Test Results ◽  
Mathematical Communication ◽  
Cluster Technique ◽  
Significance Level ◽  
Quasi Experimental ◽  
Power Point ◽  
Media Power ◽  
Better Than

This research is a quasi-experimental study that aims to determine the comparison of students' mathematical communication abilities with the cooperative learning model of the NHT and PPT media in class VII SMP Negeri 02 Ngabang. The population in this study were all students of class VII SMP 02 Ngabang consisting of 3 classes. The sample in this study was taken by using the Random Sampling Cluster technique. Class VII A as class I experimental class with 24 students and class VII B as a experimental class II with 24 students. The instruments used were pretest and posttest in the form of description. Data analysis uses the t test with a significance level of 5%. The results showed the average posttest for the NHT class was 72.5 and the average posttest for the PP class was 66.666. From the posttest hypothesis test results obtained t hitung> t tabel (1.9522> 1,667). Because t_hitung> t_tabel, H_0 is rejected and H_a is accepted. This means that students' mathematical communication skills taught with the NHT are better than using Power point media.

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

scholarly journals Evaluation of the Feasibility of NaCaPO4-Blended Zirconia as a New CAD/CAM Material for Dental Restoration

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3819
Author(s):  
Ting-Hsun Lan ◽  
Yu-Feng Chen ◽  
Yen-Yun Wang ◽  
Mitch M. C. Chou
Keyword(s):  
Computer Aided Design ◽  
Optical Microscope ◽  
Dental Restoration ◽  
Numerical Control ◽  
Test Results ◽  
Cnc Machine ◽  
Computer Aided ◽  
Cad Cam ◽  
Aided Design ◽  
Better Than

The computer-aided design/computer-aided manufacturing (CAD/CAM) fabrication technique has become one of the hottest topics in the dental field. This technology can be applied to fixed partial dentures, removable dentures, and implant prostheses. This study aimed to evaluate the feasibility of NaCaPO4-blended zirconia as a new CAD/CAM material. Eleven different proportional samples of zirconia and NaCaPO4 (xZyN) were prepared and characterized by X-ray diffractometry (XRD) and Vickers microhardness, and the milling property of these new samples was tested via a digital optical microscope. After calcination at 950 °C for 4 h, XRD results showed that the intensity of tetragonal ZrO2 gradually decreased with an increase in the content of NaCaPO4. Furthermore, with the increase in NaCaPO4 content, the sintering became more obvious, which improved the densification of the sintered body and reduced its porosity. Specimens went through milling by a computer numerical control (CNC) machine, and the marginal integrity revealed that being sintered at 1350 °C was better than being sintered at 950 °C. Moreover, 7Z3N showed better marginal fit than that of 6Z4N among thirty-six samples when sintered at 1350 °C (p < 0.05). The milling test results revealed that 7Z3N could be a new CAD/CAM material for dental restoration use in the future.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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.

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