The Middle to Upper Paleolithic transition in Siberia

Thesis (Ph.D.) University of Alaska Fairbanks, 1994This dissertation presents results of recent research in Siberia directed at (1) developing an accurate archaeological chronology for the mid-Upper Pleistocene of Siberia (chiefly through accelerator radiocarbon methods), and (2) defining and characterizing the region's Middle to Upper Paleolithic transition. Eleven Middle Paleolithic sites are now known from southwest Siberia. Relative age estimates of these cultural occupations range from the Last Interglacial (oxygen-isotope substage 5e, 128,000-118,000 years ago) to the mid-Middle Pleniglacial (oxygen-isotope stage 3,50,000-40,000 years ago). Associated lithic industries are Levallois and Mousterian. Middle Paleolithic interassemblage lithic variability is hinged on the differential production of Levallois points and Levallois flakes, and the intensity of side scraper reduction. Hominid remains from two sites, Denisova Peshchera and Peshchera Okladnikov, appear pre-modern and exhibit affinities to Neanderthals from southwest Asia. At least 15 sites have been assigned to the Siberian early Upper Paleolithic. Radiocarbon dates range from 42,000 to 30,000 years ago. Occupations at Kara-Bom (component IIa), Makarovo-4, and Varvarina Gora predate the effective range of radiocarbon dating (40,000 years ago), and may be considerably older than radiocarbon dates suggest. Initial Upper Paleolithic industries are characterized by the detachment of blades from "flat-faced" parallel blade cores, the absence of Levallois techniques, the presence of bifacial and burin secondary reduction technologies, and tool kits with end scrapers, angle burins, wedges, gravers, bifacial knives, and slender retouched points on blades. Also occurring for the first time are worked bone, ivory, and antler points, awls, and needles, pendants and other items of personal adornment, and rare examples of mobiliary art. Diagnostic hominid fossils are absent. The Middle to Upper Paleolithic transition involved dramatic and multi-faceted changes in tool technologies and tool forms. Patterns of change are discrete rather than discontinuous; no transitional industries have been identified. Stratigraphic evidence indicates rapid succession from one technocomplex to the other. This evidence supports population replacement rather than continuity for the origins of the Siberian Upper Paleolithic. Whether this event also signals the appearance of modern humans in this and surrounding areas of inner Asia must await additional hominid fossil discoveries

Multiple Damage Progression Paths in Model-Based Prognostics

Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are activ

Model Adaptation for Prognostics in a Particle Filtering Framework

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter

Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy