VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T14:57:56ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2023-02-10T07:29:09Z2023-02-10T07:29:09Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4790This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47902023-02-10T07:29:09ZCloud-Based Simulation of Precision Feeding System for Pig Health ManagementXuan Truong Nguyentruongnx91@vnu.edu.vnManh Linh Phamlinhmp@vnu.edu.vn2023-02-10T07:29:04Z2023-02-10T07:29:04Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4789This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47892023-02-10T07:29:04ZDetecting Multiple Perturbations on Swine using Data from Simulation of Precision Feeding SystemsXuan Truong Nguyentruongnx91@vnu.edu.vnManh Linh Phamlinhmp@vnu.edu.vn2022-08-22T04:06:08Z2022-08-22T04:06:08Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4765This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47652022-08-22T04:06:08ZAutomatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian modelsMultiphase CT scanning of the liver is performed for several clinical applications; however, radiation exposure from CT scanning poses a nontrivial cancer risk to the patients. The radiation dose may be reduced by determining the scan range of the subsequent scans by the location of the target of interest in the first scan phase. The purpose of this study is to present and assess an automatic method for determining the scan range for multiphase CT scans. Our strategy is to first apply a CNN-based method for detecting the liver in 2D slices, and to use a liver range search algorithm for detecting the liver range in the scout volume. The target liver scan range for subsequent scans can be obtained by adding safety margins achieved from Gaussian liver motion models to the scan range determined from the scout. Experiments were performed on 657 multiphase CT volumes obtained from multiple hospitals. The experiment shows that the proposed liver detection method can detect the liver in 223 out of a total of 224 3D volumes on average within one second, with mean intersection of union, wall distance and centroid distance of 85.5%, 5.7 mm and 9.7 mm, respectively. In addition, the performance of the proposed liver detection method is comparable to the best of the state-of-the-art 3D liver detectors in the liver detection accuracy while it requires less processing time. Furthermore, we apply the liver scan range generation method on the liver CT images acquired from radiofrequency ablation and Y-90 transarterial radioembolization (selective internal radiation therapy) interventions of 46 patients from two hospitals. The result shows that the automatic scan range generation can significantly reduce the effective radiation dose by an average of 14.5% (2.56 mSv) compared to manual performance by the radiographer from Y-90 transarterial radioembolization, while no statistically significant difference in performance was found with the CT images from intra RFA intervention ( = 0.81). Finally, three radiologists assess both the original and the range-reduced images for evaluating the effect of the range reduction method on their clinical decisions. We conclude that the automatic liver scan range generation method is able to reduce excess radiation compared to the manual performance with a high accuracy and without penalizing the clinical decision.Manh Ha Luuhalm@vnu.edu.vnTheo van WalsumHong Son MaiDaniel FranklinThi Thu Thao NguyenThi My LeAdraan MoelkerVan Khang LeDang Luu VuNgoc Ha LeQuoc Long Trantqlong@vnu.edu.vnDuc Trinh Chutrinhcd@vnu.edu.vnLinh Trung Nguyenlinhtrung@vnu.edu.vn2022-08-22T03:57:22Z2022-08-22T03:57:22Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4743This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47432022-08-22T03:57:22ZMulti-level just-enough elasticity for MQTT brokers of Internet of Things applicationsApplications for the Internet of Things (IoT) are rapidly having an impact on all areas of daily life. Every day, its embedded devices generate loads of data that requires efficient network infrastructure. The integration of lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT) is to send millions of IoT messages back and forth with as few errors as possible. In practice, IoT big data analytic systems are often deployed with highly regarded MQTT solutions to handle huge amounts of dynamic data and achieve scalability. However, these solutions do not adapt well to fluctuations in workload, so they are not elastic yet. This article introduces a novel framework that provides just-enough elasticity for MQTT brokers with multiple levels of virtualization and its implementation using EMQX MQTT broker, Kubernetes container-orchestration system and OpenStack cloud environment. Various experiments based on a real life IoT application are conducted to validate our proposed framework and its elastic functionality.Manh Linh Phamlinhmp@vnu.edu.vnNguyen Tuan Thanh Lethanhlnt@tlu.edu.vnXuan Truong Nguyennguyenxuantruong@hpu2.edu.vn2022-08-19T05:34:26Z2022-08-19T05:35:07Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4742This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47422022-08-19T05:34:26ZSupport Vector Machine-based Phase Prediction of Multi-principal Element AlloysDesigning new materials with desired properties is a complex and time-consuming process. One of the most challenging factors of the design process is the huge search space of possible materials. Machine learning methods such as k-nearest neighbours, support vector machine (SVM) and artificial neural network (ANN) can contribute to this process by predicting materials properties accurately. Properties of multi-principal element alloys (MPEAs) highly depend on alloys’ phase. Thus, accurate prediction of the alloy’s phase is important to narrow down the search space. In this paper, we propose a solution of employing support vector machine method with hyperparameters tuning and the use of weighted values for prediction of the alloy’s phase. Using the dataset consisting of the experimental results of 118 MPEAs, our solution achieves a cross-validation accuracy of 90.2%. We confirm the superiority of this score over the performance of ANN statistically. On the other dataset containing 401 MPEAs, our SVM model is comparable to ANN
and exhibits 70.6% cross-validation accuracy. We also found that additional variables, including average melting temperature and standard deviation of melting temperature, increase prediction accuracy by 3.34% in the best case.Hai Chau Nguyenchaunh@vnu.edu.vnMasatoshi Kubomasa104k@ruri.waseda.jpViet Hai Le16020936@vnu.edu.vnTomoyuki Yamamototymmt@waseda.jp