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Challenging Boundaries: Life and Material, Self and Environment
Published in Harry F. Tibbals, Medical Nanotechnology and Nanomedicine, 2017
These issues are faced with brain-machine interfaces and neuroprosthet-ics. Typically, neuroprosthetics are resorted to only after pharmacologic and neurosurgical options have been exhausted. Bioengineers and other designers of systems to augment human capabilities are cognizant that their role should be to assist the body’s adaptation and compensation for a deficit, rather than replace any remaining function. Systems that surpass natural capabilities can be intimately interfaced to the human body, in “bionic human” or cyborg scenarios. Nanotechnology is making such capabilities more feasible and affordable, obliging us to confront the social, medical, and ethical consequences [57-59].
User Adaptation in Automotive Environments
Published in Gavriel Salvendy, Advances in Human Aspects of Road and Rail Transportation, 2012
Ming Sun, Alexander Rudnicky, Ute Winter
We find that the techniques that we evaluated may promote efficiency in a user's interaction with a geographic information system, as might be used in an automobile. Adaptation can provide a benefit beyond just familiarity with a system and the adaptive system may produce improvement in efficiency than the non-adaptive system (whose performance reflects only user experience). Our preliminary evaluation does not show a clear effect; however this is an initial effort on our part and several improvements are possible, for example the design of the initial language may have a significant effect. We also suspect that the length of the adaptation period may have been too short. We are continuing our work on developing adaptation techniques and on developing a better understanding of the process.
Underground mine planning: stope layout optimisation under grade uncertainty using genetic algorithms
Published in International Journal of Mining, Reclamation and Environment, 2019
Martha E. Villalba Matamoros, Mustafa Kumral
The GA improves the fitness value iteratively by creating a new solution through operators, using a set of solutions called a population. Increasingly better solutions are generated from the best partial solutions of the past generation, instead of trying every possible combination. GA is an intelligent search technique and if it is well-designed, it can avoid local optima [17]. Initially proposed by John Holland in the 1960s, the GA mimics biological evolution and suggests a theoretical framework for adaptation, where the population of chromosomes moves to a new population by using the concepts of natural selection and operators, such as crossover, mutation, and inversion [18]. The parents are randomly chosen to produce children each generation; however, the selection operator selects the parents or chromosomes in the population that will be able to survive over time. Over multiple generations, the population trends toward an optimal solution.