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Bui Le Anh Tuan,Mewael Gebregirogis Tesfamariam,Chao-Lung Hwang,Chun-Tsun Chen,Yuan-Yuan Chen,Kae-Long Lin 사단법인 한국계산역학회 2014 Computers and Concrete, An International Journal Vol.14 No.3
Effects of polypropylene (PP) fibers, steel fibers (SF) and hybrid on the properties of high-strength fiber reinforced self-consolidating concrete (HSFR-SCC) under different volume contents are investigated in this study. Comprehensive laboratory tests were conducted in order to evaluate both fresh and hardened properties of HSFR-SCC. Test results indicated that the fiber types and fiber contents greatly influenced concrete workability but it is possible to achieve self consolidating properties while adding the fiber types in concrete mixtures. Compressive strength, dynamic modulus of elasticity, and rigidity of concrete were affected by the addition as well as volume fraction of PP fibers. However, the properties of concrete were improved by the incorporation of SF. Splitting tensile and flexural strengths of concrete became increasingly less influenced by the inclusion of PP fibers and increasingly more influenced by the addition of SF. Besides, the inclusion of PP fibers resulted in the better efficiency in the improvement of toughness than SF. Furthermore, the inclusion of fibers did not have significant effect on the durability of the concrete. Results of electrical resistivity, chloride ion penetration and ultrasonic pulse velocity tests confirmed that HSFR-SCC had enough endurance against deterioration, lower chloride ion penetrability and minimum reinforcement corrosion rate.
Design of an Advanced Wearable Sensor Platform for Multi Applications
Anh-Tuan Nguyen,Tung Hoang,Quang-Vinh Thai,T.T. Quyen Bui 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
In the emerging Internet-of-Things (IoT), wireless sensors are the vital mediators between the physical world and the cyber space. In many telemonitoring and interactive applications, high-rate data streams up to MB/s may need to be transported from the wireless sensors to the cloud computing servers over the Internet and processed by the remote servers in quasi-real time fashion. These applications generally demand substantial reduction of communication bandwidth, response latency and power consumption of these Internet-based cyber-physical systems. To tackle these demands for efficient use of communication, computing and power resources, we developed an advanced wearable sensor platform (AWSP) which integrated a powerful system-on-chip (SoC) processor, a smart power management unit, a highly accurate real-time clock and multi-function peripherals into a miniature module. This AWSP designed to function is capable of performing sophisticated data pre-processing including real-time artifact and noise removal, data compression, and even feature extraction before uploading the data to other devices like mobile phone, computer, etc. Furthermore, with the installation of embedded real-time Linux operating system, this sensor provides a familiar and powerful software development environment for system developers to build their computation-intensive real-time applications. This paper presents the design and development process.
Quoc Tuan Hoang,Xuan Hien Pham,Anh Vu Le,Trung Thanh Bui 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.3
Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.