An integrated scheduling problem of PCB components on sequential pick-and-place machines:mathematical models and heuristic solutions

This paper formulates several mathematical models for determining the optimal sequence of component placements and assignment of component types to feeders simultaneously or the integrated scheduling problem for a type of surface mount technology placement machines, called the sequential pick-andplace (PAP) machine. A PAP machine has multiple stationary feeders storing components, a stationary working table holding a printed circuit board (PCB), and a movable placement head to pick up components from feeders and place them to a board. The objective of integrated problem is to minimize the total distance traveled by the placement head. Two integer nonlinear programming models are formulated first. Then, each of them is equivalently converted into an integer linear type. The models for the integrated problem are verified by two commercial packages. In addition, a hybrid genetic algorithm previously developed by the authors is adopted to solve the models. The algorithm not only generates the optimal solutions quickly for small-sized problems, but also outperforms the genetic algorithms developed by other researchers in terms of total traveling distance.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.

Some aspects of the dynamic performance of machine tool structural joints

The work presented in this thesis is concerned with the dynamic behaviour of structural joints which are both loaded, and excited, normal to the joint interface. Since the forces on joints are transmitted through their interface, the surface texture of joints was carefully examined. A computerised surface measuring system was developed and computer programs were written. Surface flatness was functionally defined, measured and quantised into a form suitable for the theoretical calculation of the joint stiffness. Dynamic stiffness and damping were measured at various preloads for a range of joints with different surface textures. Dry clean and lubricated joints were tested and the results indicated an increase in damping for the lubricated joints of between 30 to 100 times. A theoretical model for the computation of the stiffness of dry clean joints was built. The model is based on the theory that the elastic recovery of joints is due to the recovery of the material behind the loaded asperities. It takes into account, in a quantitative manner, the flatness deviations present on the surfaces of the joint. The theoretical results were found to be in good agreement with those measured experimentally. It was also found that theoretical assessment of the joint stiffness could be carried out using a different model based on the recovery of loaded asperities into a spherical form. Stepwise procedures are given in order to design a joint having a particular stiffness. A theoretical model for the loss factor of dry clean joints was built. The theoretical results are in reasonable agreement with those experimentally measured. The theoretical models for the stiffness and loss factor were employed to evaluate the second natural frequency of the test rig. The results are in good agreement with the experimentally measured natural frequencies.

Control integrated finite state machine design for photovoltaic module integrated converter

The operation state of photovoltaic Module Integrated Converter (MIC) is subjected to change due to different source and load conditions, while state-swap is usually implemented with flow chart based sequential controller in the past research. In this paper, the signatures for different operational states are evaluated and investigated, which lead to an effective control integrated finite state machine (CIFSM), providing real-time state-swap as fast as the local control loop. The proposed CIFSM is implemented digitally for a boost type MIC prototype and tested under a variety of load and source conditions. The test results prove the effectiveness of the proposed CIFSM design.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.