Finite element analysis applied to redesign of submerged entry nozzles for steelmaking

The production of steel by continuous casting is facilitated by the use of refractory hollow-ware components. A critical component in this process is the submerged entry nozzle (SEN). The normal operating conditions of the SEN are arduous, involving large temperature gradients and exposure to mechanical forces arising from the flow of molten steel; experimental development of the components is challenging in so hazardous an environment. The effects of the thermal stress conditions in relation to a well-tried design were therefore simulated using a finite element analysis approach. It was concluded from analyses that failures of the type being experienced are caused by the large temperature gradient within the nozzle. The analyses pointed towards a supported shoulder area of the nozzle being most vulnerable to failure and practical in-service experience confirmed this. As a direct consequence of the investigation, design modifications, incorporating changes to both the internal geometry and to the nature of the intermediate support material, were implemented, thereby substantially reducing the stresses within the Al2O3/graphite ceramic liner. Industrial trials of this modified design established that the component reliability would be significantly improved and the design has now been implemented in series production

A cognitive framework for object recognition with application to autonomous vehicles

Autonomous vehicles or self-driving cars are capable of sensing the surrounding environment so they can navigate roads without human input. Decisions are constantly made on sensing, mapping and driving policy using machine learning techniques. Deep Learning – massive neural networks that utilize the power of parallel processing – has become a popular choice for addressing the complexities of real time decision making. This method of machine learning has been shown to outperform alternative solutions in multiple domains, and has an architecture that can be adapted to new problems with relative ease. To harness the power of Deep Learning, it is necessary to have large amounts of training data that are representative of all possible situations the system will face. To successfully implement situational awareness in driverless vehicles, it is not possible to exhaust all possible training examples. An alternative method is to apply cognitive approaches to perception, for situations the autonomous vehicles will face. Cognitive approaches to perception work by mimicking the process of human intelligence – thereby permitting a machine to react to situations it has not previously experienced. This paper proposes a novel cognitive approach for object recognition. The proposed cognitive object recognition algorithm, referred to as Recognition by Components, is inspired by the psychological studies pertaining to early childhood development. The algorithm works by breaking down images into a series of primitive forms such as square, triangle, circle or rectangle and memory based aggregation to identify objects. Experimental results suggest that Recognition by Component algorithm performs significantly better than algorithms that require large amounts of training data

Improving the rigor of quantitative HRD research: Four recommendations in support of the general hierarchy of evidence

Editorial for the Human Resource Development Quarterly journal

The taxonomic position of Rhizopogon melanogastroides (Boletales)

Volume: 84Start Page: 221End Page: 22