Process Selection Using Variation and Cost Relations

Variations are inherent in all manufacturing processes and can significantly affect the quality of a final assembly, particularly in multistage assembly systems. Existing research in variation management has primarily focused on incorporating GD&T factors into variation propagation models in order to predict product quality and allocate tolerances. However, process induced variation, which has a key influence on process planning, has not been fully studied. Furthermore, the link between variation and cost has not been well established, in particular the effect that assembly process selection has on the final quality and cost of a product. To overcome these barriers, this paper proposes a novel method utilizing process capabilities to establish the relationship between variation and cost. The methodology is discussed using a real industrial case study. The benefits include determining the optimum configuration of an assembly system and facilitating rapid introduction of novel assembly techniques to achieve a competitive edge.

Attribute exclusion strategies in airline choice : accounting for exogenous information on decision maker processing strategies in models of discrete choice

We examine the impact of individual-specific information processing strategies (IPSs) on the inclusion/exclusion of attributes on the parameter estimates and behavioural outputs of models of discrete choice. Current practice assumes that individuals employ a homogenous IPS with regards to how they process attributes of stated choice (SC) experiments. We show how information collected exogenous of the SC experiment on whether respondents either ignored or considered each attribute may be used in the estimation process, and how such information provides outputs that are IPS segment specific. We contend that accounting the inclusion/exclusion of attributes will result in behaviourally richer population parameter estimates.

Is the unigram relevance model term independent? Classifying term dependencies in query expansion

This paper develops a framework for classifying term dependencies in query expansion with respect to the role terms play in structural linguistic associations. The framework is used to classify and compare the query expansion terms produced by the unigram and positional relevance models. As the unigram relevance model does not explicitly model term dependencies in its estimation process it is often thought to ignore dependencies that exist between words in natural language. The framework presented in this paper is underpinned by two types of linguistic association, namely syntagmatic and paradigmatic associations. It was found that syntagmatic associations were a more prevalent form of linguistic association used in query expansion. Paradoxically, it was the unigram model that exhibited this association more than the positional relevance model. This surprising finding has two potential implications for information retrieval models: (1) if linguistic associations underpin query expansion, then a probabilistic term dependence assumption based on position is inadequate for capturing them; (2) the unigram relevance model captures more term dependency information than its underlying theoretical model suggests, so its normative position as a baseline that ignores term dependencies should perhaps be reviewed.

Probabilistic power system contingency analysis considering wind

This thesis was a step forward in developing probabilistic assessment of power system response to faults subject to intermittent generation by renewable energy. It has investigated the wind power fluctuation effect on power system stability, and the developed fast estimation process has demonstrated the feasibility for real-time implementation. A better balance between power network security and efficiency can be achieved based on this research outcome.

Blind frequency synchronisation for OFDM systems with pulse shaping

In multi-carrier systems, small carrier frequency offsets result in significant degradation of performance and this offset should be compensated before demodulation can be performed. In this paper, we consider a generic multi-carrier system with pulse shaping and estimate the frequency offset by exploiting the cyclostationarity of the received signal. By transforming the time domain signal to the cyclic correlation domain we are able to estimate the frequency offset without the aid of pilot symbols or the cyclic prefix. The Bayesian framework is used to obtain the estimate and we show how we can simplify the estimation process. © 1999 IEEE.

三维环境中基于ESMF的多机器人协作观测方法

提出了一种基于扩展集员估计（ESMF）的多机器人协作观测方法，该方法将多机器人之间的观测数据融合过程嵌入到估计过程当中，从而减少了数据处理的过程，增强了算法的快速性。同时，这种方法在实现协作观测时只需要协作机器人传送观测信息而不是整个的估计信息，因此可以减轻多机器人系统的通信负担。除此之外，该方法在融合多机器人的观测数据过程中避免了多余的近似过程，增加了观测的准确性。最后，给出了三维环境下的仿真结果，验证了方法的可行性。

3D Trajectory Recovery for Tracking Multiple Objects and Trajectory Guided Recognition of Actions

A mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture of Gaussian classifier. The system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling. Experiments with synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.

Trajectory Guided Tracking and Recognition of Actions

A combined 2D, 3D approach is presented that allows for robust tracking of moving people and recognition of actions. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object that are then used as input to action recognition modules. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture-of-Gaussians classifier. The system was tested in recognizing various dynamic human outdoor activities: running, walking, roller blading, and cycling. Experiments with real and synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.

Prediction of reinforcement corrosion in concrete and its effects on concrete cracking and strength reduction

Based on extensive research on reinforcing steel corrosion in concrete in the past decades, it is now possible to estimate the effect of the progression of reinforcement corrosion in concrete infrastructure on its structural performance. There are still areas of considerable uncertainty in the models and in the data available, however This paper uses a recently developed model for reinforcement corrosion in concrete to improve the estimation process and to indicate the practical implications. In particular stochastic models are used to estimate the time likely to elapse for each phase of the whole corrosion process: initiation, corrosion-induced concrete cracking, and structural strength reduction. It was found that, for practical flexural structures subject to chloride attacks, corrosion initiation may start quite early in their service life. It was also found that, once the structure is considered to be unserviceable due to corrosion-induced cracking, there is considerable remaining service life before the structure can be considered to have become unsafe. The procedure proposed in the paper has the potential to serve as a rational tool for practitioners, operators, and asset managers to make decisions about the optimal timing of repairs, strengthening, and/or rehabilitation of corrosion-affected concrete infrastructure. Timely intervention has the potential to prolong the service life of infrastructure.

Parameter Optimisation for Woodwind Single-Reed Models

Time-domain modelling of single-reed woodwind instruments usually involves a lumped model of the excitation mechanism. The parameters of this lumped model have to be estimated for use in numerical simulations. Several attempts have been made to estimate these parameters, including observations of the mechanics of isolated reeds, measurements under artificial or real playing conditions and estimations based on numerical simulations. In this study an optimisation routine is presented, that can estimate reed-model parameters, given the pressure and flow signals in the mouthpiece. The method is validated, tested on a series of numerically synthesised data. In order to incorporate the actions of the player in the parameter estimation process, the optimisation routine has to be applied to signals obtained under real playing conditions. The estimated parameters can then be used to resynthesise the pressure and flow signals in the mouthpiece. In the case of measured data, as opposed to numerically synthesised data, special care needs to be taken while modelling the bore of the instrument. In fact, a careful study of various experimental datasets revealed that for resynthesis to work, the bore termination impedance should be known very precisely from theory. An example is given, where the above requirement is satisfied, and the resynthesised signals closely match the original signals generated by the player.

How to improve supply chain performance of a specialized retailer: Managing landed costs of Zippy’s global supply chain

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

How do domestic policies affect the integration of Ethiopian fertiliser markets with world markets?

Frequent shifts in policy on fertiliser markets have occurred in Ethiopia with the aim of facilitating both physical and economic access of farmers to fertiliser. The last shift was the introduction of a monopoly on each stage of the supply chain in 2008. Furthermore, government control of prices and margins as well as stockholding programmes are also present on the markets. This paper evaluates the effect of these policies on the integration of domestic with world markets of fertiliser, using cointegration methods. Time series data of diammonium phosphate (DAP) and urea prices on world, import and retail markets between 1971 and 2012 are used. The findings show high transmission of price signals from world markets to import prices for both DAP and urea. However, between import and retail prices there is no evidence of cointegration for urea, while for DAP full price transmission is concluded. In the retail market, domestic transaction costs associated with storing large volumes of fertiliser act as a buffer between import and retail prices, especially for urea. Therefore, economic benefits could be achieved by reducing the size of stocks and revising the demand estimation process.

System identification using partitioned least squares

A novel partitioned least squares (PLS) algorithm is presented, in which estimates from several simple system models are combined by means of a Bayesian methodology of pooling partial knowledge. The method has the added advantage that, when the simple models are of a similar structure, it lends itself directly to parallel processing procedures, thereby speeding up the entire parameter estimation process by several factors.

Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. Specifically, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalisation of Hammerstein channels.

Current account and capital mobility hypothesis: Evidence from the G-7

This paper investigates an intertemporal optimization model in order to analyze the current account of the G-7 countries, measured as the present value of the future changes in net output. The study compares observed and forecasted series, generated by the model, using Campbell & Shiller’s (1987) methodology. In the estimation process, the countries are considered separately (with OLS technique) as well as jointly (SURE approach), to capture contemporaneous correlations of the shocks in net output. The paper also proposes a note on Granger causality and its implications to the optimal current account. The empirical results are sensitive to the technique adopted in the estimation process and suggest a rejection of the model in the G-7 countries, except for the USA and Japan, according to some papers presented in the literature.