Analysis of four decades of high elevation climate data

Four decades of instrumented climate records at D1 on Niwot Ridge suggest that high elevation data are an important - and even unique - part of the full climate picture. High elevation data provide information on changing lapse rates as well as model verification for global warming, which is predicted to occur earliest in high latitudes and at high elevations. The D1 records show climatic trends that arguably support global warming, assuming that greater planetary wave amplitude is verification of warming. Lapse rates reflect conditions of air mass stability, atmospheric moisture, and could [sic] cover, which contribute to feedback processes involving temperature, precipitation, and snowpack. The D1 record show a period, 1981-1985, when the lapse rate increased significantly, and this change was not detected by other data.

Copula Processes

We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions. As an example, we develop a stochastic volatility model, Gaussian Copula Process Volatility (GCPV), to predict the latent standard deviations of a sequence of random variables. To make predictions we use Bayesian inference, with the Laplace approximation, and with Markov chain Monte Carlo as an alternative. We find both methods comparable. We also find our model can outperform GARCH on simulated and financial data. And unlike GARCH, GCPV can easily handle missing data, incorporate covariates other than time, and model a rich class of covariance structures.

Generalised Wishart Processes

We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary dependent variable. We use it to model dynamic (e.g. time varying) covariance matrices. Unlike existing models, it can capture a diverse class of covariance structures, it can easily handle missing data, the dependent variable can readily include covariates other than time, and it scales well with dimension; there is no need for free parameters, and optional parameters are easy to interpret. We describe how to construct the GWP, introduce general procedures for inference and predictions, and show that it outperforms its main competitor, multivariate GARCH, even on financial data that especially suits GARCH. We also show how to predict the mean of a multivariate process while accounting for dynamic correlations.

Analysis of basic processes inside the keyhole during deep penetration ND-YAG CW laser welding

During laser welding, the keyhole is generated by the recoil pressure induced by the evaporation processes occurring mainly on the front keyhole wall (KW). In order to characterize the evaporation process, we have measured this recoil pressure by using a plume deflection technique, where the plume generated for static conditions (i. e. with no sample displacement) is deflected by a transverse side gas jet. From the measurement of the plume deflection angle, the recoil pressure can be determined as a function of incident intensity and sample material. From these data one can estimate the pressure generated on the front KW, during laser welding. Therefore, the corresponding dynamic pressure exerted by the vapor plume expansion on the rear KW, in contact with the melt pool, can be also estimated. These pressures appear to be in close agreement with those generated by an additional side jet that has been used in previous experiments, for stabilizing the observed melt pool oscillations or fluctuations.

Multiple pitch estimation using non-homogeneous poisson processes

Novel statistical models are proposed and developed in this paper for automated multiple-pitch estimation problems. Point estimates of the parameters of partial frequencies of a musical note are modeled as realizations from a non-homogeneous Poisson process defined on the frequency axis. When several notes are combined, the processes for the individual notes combine to give a new Poisson process whose likelihood is easy to compute. This model avoids the data-association step of linking the harmonics of each note with the corresponding partials and is ideal for efficient Bayesian inference of unknown multiple fundamental frequencies in a signal. © 2011 IEEE.

An evaluation of a new product data model

Engineering companies face many challenges today such as increased competition, higher expectations from consumers and decreasing product lifecycle times. This means that product development times must be reduced to meet these challenges. Concurrent engineering, reuse of engineering knowledge and the use of advanced methods and tools are among the ways of reducing product development times. Concurrent engineering is crucial in making sure that the products are designed with all issues considered simultaneously. The reuse of engineering knowledge allows existing solutions to be reused. It can also help to avoid the mistakes made in previous designs. Computer-based tools are used to store information, automate tasks, distribute work, perform simulation and so forth. This research concerns the evaluation of tools that can be used to support the design process. These tools are evaluated in terms of the capture of information generated during the design process. This information is vital to allow the reuse of knowledge. Present CAD systems store only information on the final definition of the product such as geometry, materials and manufacturing processes. Product Data Management (PDM) systems can manage all this CAD information along with other product related information. The research includes the evaluation of two PDM systems, Windchill and Metaphase, using the design of a single-handed water tap as a case study. The two PDMs were then compared to PROSUS/DDM. PROSUS is the Process-Based Support System proposed by [Blessing 94] using the same case study. The Design Data Model is the product data model that includes PROSUS. The results look promising. PROSUS/DDM is able to capture most design information and structure and present it logically. The design process and product information is related and stored within the DDM structure. The PDMs can capture most design information, but information from early stages of design is stored only as unstructured documentation. Some problems were found with PROSUS/DDM. A proposal is made that may make it possible to resolve these problems, but this will require further research.

Computer-based selection of joining processes

The assembly of any manufactured product involves joining. This paper describes ways of selecting processes for joining. The method allows discrimination of the joint geometry, joint loading, material, and other attributes of the joint itself, identifying the subset of available processes capable of meeting a given set of design constraints. A relational database containing data-tables for joining processes, materials to be joined, and joint geometry and mode of loading, allows the attributes of each of these to be stored in an appropriate format, and permits links to be created between those that are related. A search engine isolates the processes that meet design requirements on material, joint geometry and loading. The method is illustrated in Part 2 by case studies, utilising software that embodies the method.

A Comparative Review of Existing Data and Methodologies for Calculating Embodied Energy and Carbon of Buildings

In the Climate Change Act of 2008 the UK Government pledged to reduce carbon emissions by 80% by 2050. As one step towards this, regulations are being introduced requiring all new buildings to be ‘zero carbon’ by 2019. These are defined as buildings which emit net zero carbon during their operational lifetime. However, in order to meet the 80% target it is necessary to reduce the carbon emitted during the whole life-cycle of buildings, including that emitted during the processes of construction. These elements make up the ‘embodied carbon’ of the building. While there are no regulations yet in place to restrict embodied carbon, a number of different approaches have been made. There are several existing databases of embodied carbon and embodied energy. Most provide data for the material extraction and manufacturing only, the ‘cradle to factory gate’ phase. In addition to the databases, various software tools have been developed to calculate embodied energy and carbon of individual buildings. A third source of data comes from the research literature, in which individual life cycle analyses of buildings are reported. This paper provides a comprehensive review, comparing and assessing data sources, boundaries and methodologies. The paper concludes that the wide variations in these aspects produce incomparable results. It highlights the areas where existing data is reliable, and where new data and more precise methods are needed. This comprehensive review will guide the future development of a consistent and transparent database and software tool to calculate the embodied energy and carbon of buildings.

Toward automated generation of parametric BIMs based on hybrid video and laser scanning data

Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing.

A Comparative Review of existing data and methodologies for calculating embodied energy and carbon of buildings

In the Climate Change Act of 2008 the UK Government pledged to reduce carbon emissions by 80% by 2050. As one step towards this, regulations are being introduced requiring all new buildings to be ‘zero carbon’ by 2019. These are defined as buildingswhichemitnetzerocarbonduringtheiroperationallifetime.However,inordertomeetthe80%targetitisnecessary to reduce the carbon emitted during the whole life-cycle of buildings, including that emitted during the processes of construction. These elements make up the ‘embodied carbon’ of the building. While there are no regulations yet in place to restrictembodiedcarbon,anumberofdifferentapproacheshavebeenmade.Thereareseveralexistingdatabasesofembodied carbonandembodiedenergy.Mostprovidedataforthematerialextractionandmanufacturingonly,the‘cradletofactorygate’ phase. In addition to the databases, various software tools have been developed to calculate embodied energy and carbon of individual buildings. A third source of data comes from the research literature, in which individual life cycle analyses of buildings are reported. This paper provides a comprehensive review, comparing and assessing data sources, boundaries and methodologies. The paper concludes that the wide variations in these aspects produce incomparable results. It highlights the areas where existing data is reliable, and where new data and more precise methods are needed. This comprehensive review will guide the future development of a consistent and transparent database and software tool to calculate the embodied energy and carbon of buildings.

Modeling natural sounds with modulation cascade processes

Natural sounds are structured on many time-scales. A typical segment of speech, for example, contains features that span four orders of magnitude: Sentences ($\sim1$s); phonemes ($\sim10$−$1$ s); glottal pulses ($\sim 10$−$2$s); and formants ($\sim 10$−$3$s). The auditory system uses information from each of these time-scales to solve complicated tasks such as auditory scene analysis [1]. One route toward understanding how auditory processing accomplishes this analysis is to build neuroscience-inspired algorithms which solve similar tasks and to compare the properties of these algorithms with properties of auditory processing. There is however a discord: Current machine-audition algorithms largely concentrate on the shorter time-scale structures in sounds, and the longer structures are ignored. The reason for this is two-fold. Firstly, it is a difficult technical problem to construct an algorithm that utilises both sorts of information. Secondly, it is computationally demanding to simultaneously process data both at high resolution (to extract short temporal information) and for long duration (to extract long temporal information). The contribution of this work is to develop a new statistical model for natural sounds that captures structure across a wide range of time-scales, and to provide efficient learning and inference algorithms. We demonstrate the success of this approach on a missing data task.

Pressure data analysis of formula one racing engines

The measured time-history of the cylinder pressure is the principal diagnostic in the analysis of processes within the combustion chamber. This paper defines, implements and tests a pressure analysis algorithm for a Formula One racing engine in MATLAB1. Evaluation of the software on real data is presented. The sensitivity of the model to the variability of burn parameter estimates is also discussed. Copyright © 1997 Society of Automotive Engineers, Inc.

Bayesian Learning of Noisy Markov Decision Processes

We consider the inverse reinforcement learning problem, that is, the problem of learning from, and then predicting or mimicking a controller based on state/action data. We propose a statistical model for such data, derived from the structure of a Markov decision process. Adopting a Bayesian approach to inference, we show how latent variables of the model can be estimated, and how predictions about actions can be made, in a unified framework. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from the posterior distribution. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.

Change prediction using interface data

Managing change can be challenging due to the high levels of interdependency in concurrent engineering processes. A key activity in engineering change management is propagation analysis, which can be supported using the change prediction method. In common with most other change prediction approaches, the change prediction method has three important limitations: L1: it depends on highly subjective input data; L2: it is capable of modelling 'generalised cases' only and cannot be; customised to assess specific changes; and L3: the input data are static, and thus, guidance does not reflect changes in the design. This article contributes to resolving these limitations by incorporating interface information into the change prediction method. The enhanced method is illustrated using an example based on a flight simulator. © The Author(s) 2013.

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets.