No abstract

No abstract

Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. There are many challenges in developing a good background subtraction algorithm. First, it must be robust against changes in illumination. Second, it should avoid detecting non-stationary background objects such as swinging leaves, rain, snow, and shadow cast by moving objects. Finally, its internal background model should react quickly to changes in background such as starting and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.

Selective laser melting is a powder-based, additive-manufacturing process where a threedimensional part is produced, layer by layer, by using a high-energy laser beam to fuse the metallic powder particles. A particular challenge in this process is the selection of appropriate process parameters that result in parts with desired properties. In this study, we describe an approach to selecting parameters for high density (>99%) parts using 316L stainless steel. Though there has been significant success in achieving near-full density for 316L parts, this work has been limited to laser powers <225W. We discuss how we can exploit prior knowledge, design of computational experiments using a simple model of laser melting, and single-track experiments to determine the process parameters for use at laser powers up to 400W. Our results show that, at higher power values, there is a large range of scan speeds over which the relative density remains >99%, with the density reducing rapidly at high speeds due to insufficient melting, and less rapidly at low speeds due to the effect of voids created as the process enters keyhole mode.

The metal laser powder bed fusion additive manufacturing process uses high power lasers to build parts layer upon layer by melting fine metal powders. Qualification of parts produced using this technology is broadly recognised as a significant challenge. Physics based process models have been identified as being foundational to qualification of additively manufactured metal parts. In the present article, a multiscale modelling strategy is described that will serve as the foundation upon which process control and part qualification can be built. This includes a model at the scale of the powder that simulates single track/single multilayer builds and provides powder bed and melt pool thermal data. A second model computationally builds a complete part and predicts manufactured properties (residual stress, dimensional accuracy) in three dimensions. Modelling is tied to experiment through data mining.