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Polyphenylene sulfide (PPS) is a semicrystalline thermoplastic which can pass UL94V-0 standard without adding flame retardant. In the present study, effect of alumina (Al 2 O 3 ) addition on the mechanical, tribological, and acoustical behavior of PPS polymer were investigated. Experimental findings indicate that elastic modulus, tensile strength, and flexural strength were enhanced by 21.4%, 7.7%, and 6.9%, respectively. The highest tensile strength was obtained when PPS was reinforced with 5 wt% Al 2 O 3 while highest bending strength and toughness were obtained with 20 wt% Al 2 O 3 . Reinforced composites encountered <5% and <14% mass loss when heated to 420 and 500 C, respectively. Lower filler concentrations (<5 wt%) had insignificant effect on the glass transition temperature (T g ) of the composite. Experimental results agreed well to Hashin-Shtrikman and Suquet predictions at lower filler concentrations since filler particles could be easily dispersed in the interstitial sites of PPS matrix without significant agglomeration. Besides, significant acoustic emission (AE) characteristic such as higher absolute energy rate was witnessed in Al 2 O 3 reinforced PPS composites. The insights derived from this study are expected to open a new avenue of producing composites with high stiffness, strength, and significant AE events by using zero-dimensional fillers.

No abstract

Detecting road lane is one of the key processes in vision-based driving assistance system and autonomous vehicle system. The main purpose of the lane detection process is to estimate car position relative to the lane so that it can provide a warning to the driver if the car starts departing the lane. This process is useful not only to enhance safe driving but also in self-driving car system. A novel approach to lane detection method using image processing techniques is presented in this research. The method minimizes the complexity of computation by the use of prior knowledge of color, intensity and the shape of the lane marks. By using prior knowledge, the detection process requires only two different analyses which are pixel intensity analysis and color component analysis. The method starts with searching a strong pair of edges along the horizontal line of road image. Once the strong edge is detected the process continues with color analysis on pixels that lie between the edges to check whether the pixels belong to a lane or not. The process is repeated for different positions of horizontal lines covering the road image. The method was successfully tested on selected 20 road images collected from internet.