National Taiwan University of Science and Technology

About: National Taiwan University of Science and Technology is a education organization based out in Taipei, Taipei, Taiwan. It is known for research contribution in the topics: Fuzzy logic & Control theory. The organization has 16288 authors who have published 21577 publications receiving 426294 citations. The organization is also known as: Taiwan Tech & Taiwantech.

Adaptive Sliding-Mode With Hysteresis Control Strategy for Simple Multimode Hybrid Energy Storage System in Electric Vehicles

Abstract: In this paper, a simple multimode hybrid energy storage system (HESS) is proposed for electric vehicles (EVs). Compared to the improved semiactive HESS, only two switches are added in the main circuit topology of the multimode HESS, thereby achieving the operating modes can be actively switched. The mode switch strategy is designed according to the driving modes of the EV and the status of the power sources. To improve the overall system efficiency of the multimode HESS, the boost converter will operate at the peak efficiency to convey the energy from the battery to the supercapacitor (SC). An adaptive sliding-mode control (ASMC) with hysteresis control (HC) strategy is also developed by combining practical application of the multimode HESS. Simulations and experiments are presented to verify the effectiveness of the proposed multimode HESS and its ASMC strategy. Simulated results show that the multimode HESS can select suitable operating modes in corresponding conditions. Compared to the total sliding-mode control strategy, experimental results demonstrate that the ASMC with HC strategy can improve the operating stability of the multimode HESS under different operating modes. The multimode HESS can not only switch to suitable operating modes, but also avoid the excessive output power of the battery to meet different power demands of the load. In addition, the SC absorbs all the braking energy such that the battery safety can be effectively ensured.

A High-Density Seismic Network for Earthquake Early Warning in Taiwan Based on Low Cost Sensors

Abstract: Because of persistent collisions between the Philippine Sea plate and the Eurasian plate, Taiwan has been constantly threatened by large and devastating earthquakes that often cause large losses of life and property. To reduce losses caused by future damaging earthquakes, it is crucial for Taiwan to seek solutions through scientific research. The Earthquake Early Warning System (EEWS) is one of the most promising tools for alleviating threats caused by large earthquakes, and has been tested and operated in many countries (Allen et al. , 2009; Lee and Wu, 2009; Satriano et al. , 2011). Taiwan has been developing an EEWS and is one of the leading countries in EEWS practices (Wu et al. , 1998, 1999, 2011; Wu and Teng, 2002; Hsiao et al. , 2009, 2011). The present EEWS in Taiwan has been operated by the Central Weather Bureau (CWB) since 1995 and consists of 109 telemetered seismic stations that span the entire region of Taiwan. The EEWS can provide earthquake information within 20 s following an earthquake occurrence (Hsiao et al. , 2009, 2011; Wu and Teng, 2002). Although a 20 s reporting time is short, if the number of seismic stations operating within the network is increased, this time period can still be reduced. However, the cost of building such a high‐density seismic network by traditional, force‐balance seismometers is extremely high. Since the 1990s, the Micro Electro Mechanical Systems (MEMS) accelerometers introduced in seismic applications (Holland, 2003) have been cost‐saving miniature devices and ideal for recording strong ground motions. The Earthquake Early Warning (EEW) research group at National Taiwan University (NTU) worked with a technology corporation to develop a P ‐wave alert device named P alert (Fig. 1) that uses MEMS accelerometers for onsite …

Trends in the research design and application of mobile language learning: a review of 2007–2016 publications in selected SSCI journals

Abstract: This study reviewed the mobile technology-assisted language learning studies published from 2007 to 2016 in selected journals. Several critical issues were investigated, such as research methods, r...

Efficient Map/Reduce-Based DBSCAN Algorithm with Optimized Data Partition

Abstract: DBSCAN is a well-known algorithm for density-based clustering because it can identify the groups of arbitrary shapes and deal with noisy datasets. However, with the increasing amount of data, DBSCAN algorithm running on a single machine has to face the scalability problem. In this paper, we propose a Map/Reduce-based DBSCAN algorithm called DBSCAN-MR to solve the scalability problem. In DBSCAN-MR, the input dataset is partitioned into smaller parts and then parallel processed on the Hadoop platform. However, choosing different partition mechanisms will affect the execution efficiency and load balance of each node. Therefore, we propose a method, partition with reduce boundary points (PRBP), to select partition boundaries based on the distribution of data points. Our experimental results show that DBSCAN-MR with the design of PRBP has higher efficiency and scalability than competitors.