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.

The dynamic flammability and toxicity of magnesium hydroxide filled intumescent fire retardant polypropylene

Abstract: The flammability properties of an intumescent fire retardant polypropylene added with magnesium hydroxide was discussed in this study. To evaluate the flammability of the material, limit oxygen index, smoke emission, tensile strength, and our exploitation dynamic flammability evaluation system, tests were assessed in experiments. The results showed that the intumescent flame retardant ammonium polyphosphate-filled polypropylene has superior flammability properties but higher carbon oxide (CO) concentration and smoke density. By adding some magnesium hydroxide additives in intumescent fire retarded polypropylene, the smoke density and CO concentration decrease; and the compound also has superior fire properties. It is concluded that intumescent system and magnesium hydroxide additives are effective on improving combustion properties for polypropylene. © 1998 John Wiley & Sons, Inc. J Appl Polym Sci 67:989–995, 1998

A self-organizing HCMAC neural-network classifier

Abstract: This paper presents a self-organizing hierarchical cerebellar model arithmetic computer (HCMAC) neural-network classifier, which contains a self-organizing input space module and an HCMAC neural network. The conventional CMAC can be viewed as a basis function network (BFN) with supervised learning, and performs well in terms of its fast learning speed and local generalization capability for approximating nonlinear functions. However, the conventional CMAC has an enormous memory requirement for resolving high-dimensional classification problems, and its performance heavily depends on the approach of input space quantization. To solve these problems, this paper presents a novel supervised HCMAC neural network capable of resolving high-dimensional classification problems well. Also, in order to reduce what is often trial-and-error parameter searching for constructing memory allocation automatically, proposed herein is a self-organizing input space module that uses Shannon's entropy measure and the golden-section search method to appropriately determine the input space quantization according to the various distributions of training data sets. Experimental results indicate that the self-organizing HCMAC indeed has a fast learning ability and low memory requirement. It is a better performing network than the conventional CMAC for resolving high-dimensional classification problems. Furthermore, the self-organizing HCMAC classifier has a better classification ability than other compared classifiers.

A reputation system for traffic safety event on vehicular ad hoc networks

Abstract: Traffic safety applications on vehicular ad hoc networks (VANETs) have drawn a lot of attention in recent years with their promising functions on car accident reduction, real-time traffic information support, and enhancement of comfortable driving experience on roadways. However, an inaccurate traffic warning message will impact drivers' decisions, waste drivers' time and fuel in their vehicles, and even invoke serious car accidents. To enable eco-friendly driving VANET environments, that is, to save fuel and time in this context, we proposed an event-based reputation system to prevent the spread of false traffic warning messages. In this system, a dynamic reputation evaluation mechanism is introduced to determine whether an incoming traffic message is significant and trustworthy to the driver. The proposed system is characterized and evaluated through experimental simulations. The simulation results show that, with a proper reputation adaptation mechanism and appropriate threshold settings, our proposed system can effectively prevent false messages spread on various VANET environments.

Melt Electrospun Reduced Tungsten Oxide /Polylactic Acid Fiber Membranes as a Photothermal Material for Light-Driven Interfacial Water Evaporation

Abstract: The development of efficient photothermal materials is the most important issue in solar water evaporation. In this work, melt electrospun reduced tungsten oxide/polylactic acid (WO2.72/PLA) fiber membranes were successfully prepared with improved near-infrared (NIR) photothermal conversion properties owing to strong NIR photoabsorption by the metal oxide. WO2.72 powder nanoparticles were incorporated into PLA matrix by melt processing, following which the composites were extruded into wires using a single screw extruder. Subsequently, fiber membranes were prepared from the extruded wire of the WO2.72/PLA composite by melt electrospinning, which is a cost-effective technique that can produce fiber membranes without the addition of environmentally unfriendly chemicals. The melt electrospun WO2.72/PLA fiber membranes, floatable on water due to surface hydrophobicity, were systematically designed for, and applied to, vapor generation based on the interfacial concept of solar heating. With the photothermal WO2.72/PLA fiber membrane containing 7 wt % WO2.72 nanoparticles, the water evaporation efficiency was reached 81.39%, which is higher than that for the pure PLA fiber membrane and bulk water. Thus, this work contributes to the development of novel photothermal fiber membranes in order to enhance light-driven water evaporation performance for potential applications in the fields of water treatment and desalination.