<p>The complexity of industrial systems and the mathematical models to describe them increases. In many cases, point sensors are no longer sufficient to provide controllers and monitoring instruments with the information necessary for operation. The need for other types of information, such as audio and video, has grown. These are examples of information-rich signals for which suitable applications range in a broad spectrum from micro-electromechanical systems and bio-medical engineering to paper making and steel production.</p><p>Recursive parameter estimation algorithms are employed to identify parameters in a mathematical model from measurements of input and output signals. For accurate parameter estimation, the input signal must be <i>persistently exciting, i.e.</i> such that important features of the modelled system are reflected in the output over a sufficient period of time.</p><p>The Stenlund-Gustafsson (SG) algorithm, a Kalman filter based method for recursive parameter estimation in linear regression models, that does not diverge under lack of excitation, is studied. The stationary properties of the algorithm and the corresponding Riccati equation are formulated in terms of the excitation space spanned by the regressor vectors.</p><p>Furthermore, it is shown that the Riccati equation of the studied algorithm can be solved element-wise. Convergence estimates for the elements of the solution to the Riccati equation are provided, directly relating convergence rate to the signal-to-noise ratio in the regression model. An element-wise form of the parameter update equation is also given, where the connection to specific elements of the solution to the Riccati equation is apparent.</p><p>The SG-algorithm is employed for two applications with audio signals. One is in an acoustic echo cancellation setting where its performance is shown to match that of other well-known estimation techniques, such as the normalized least mean squares and the Kalman filter. When the input is not sufficiently exciting, the studied method performs best of all considered schemes.</p><p>The other application is the Linz-Donawitz (LD) steel converter. The converter consists of a vessel with molten metal and foam is produced to facilitate chemical reactions. A common problem, usually referred to as slopping, arises when the foam rises above the limits of the vessel and overflows. A warning system is designed, based on the SG-algorithm and change detection methods, to give alarms before slopping occurs. A black-box model relates different sensor values of which one is the microphone signal picked up in the area above the converter. The system detected slopping correctly in 80% of the blows in field studies at SSAB Oxelösund.</p><p>A practical example of a vision-based system is provided by cavity form estimation in a water model of the steel bath. The water model is captured from the side by a video camera. The images together with a non-linear model are used to estimate important process parameters, related to the heat and mass transport in the LD-converter.</p>

The complexity of industrial systems and the mathematical models to describe them increases. In many cases, point sensors are no longer sufficient to provide controllers and monitoring instruments with the information necessary for operation. The need for other types of information, such as audio and video, has grown. These are examples of information-rich signals for which suitable applications range in a broad spectrum from micro-electromechanical systems and bio-medical engineering to paper making and steel production. Recursive parameter estimation algorithms are employed to identify parameters in a mathematical model from measurements of input and output signals. For accurate parameter estimation, the input signal must be persistently exciting, i.e. such that important features of the modelled system are reflected in the output over a sufficient period of time. The Stenlund-Gustafsson (SG) algorithm, a Kalman filter based method for recursive parameter estimation in linear regression models, that does not diverge under lack of excitation, is studied. The stationary properties of the algorithm and the corresponding Riccati equation are formulated in terms of the excitation space spanned by the regressor vectors. Furthermore, it is shown that the Riccati equation of the studied algorithm can be solved element-wise. Convergence estimates for the elements of the solution to the Riccati equation are provided, directly relating convergence rate to the signal-to-noise ratio in the regression model. An element-wise form of the parameter update equation is also given, where the connection to specific elements of the solution to the Riccati equation is apparent. The SG-algorithm is employed for two applications with audio signals. One is in an acoustic echo cancellation setting where its performance is shown to match that of other well-known estimation techniques, such as the normalized least mean squares and the Kalman filter. When the input is not sufficiently exciting, the studied method performs best of all considered schemes. The other application is the Linz-Donawitz (LD) steel converter. The converter consists of a vessel with molten metal and foam is produced to facilitate chemical reactions. A common problem, usually referred to as slopping, arises when the foam rises above the limits of the vessel and overflows. A warning system is designed, based on the SG-algorithm and change detection methods, to give alarms before slopping occurs. A black-box model relates different sensor values of which one is the microphone signal picked up in the area above the converter. The system detected slopping correctly in 80% of the blows in field studies at SSAB Oxelösund. A practical example of a vision-based system is provided by cavity form estimation in a water model of the steel bath. The water model is captured from the side by a video camera. The images together with a non-linear model are used to estimate important process parameters, related to the heat and mass transport in the LD-converter.

全球化時代與資訊化社會的來臨，電腦與網際網路成為生活中不可或缺的要素，儘管至2008年為止，我國有將近七成的民眾透過網路科技享受到更多的便利性，但社會上仍存在著數位落差（digital divide）的問題，數位落差除了使得資訊窮人（information-poor）不易取得資訊，亦將對其經濟、人權等各方面造成影響。故研究目的在利用資料採礦的應用，配合SPSS Clementine 12.0的軟體，探討數位落差的現象，並嘗試找出形成數位落差的影響原因。 　　本研究主要投入人口統計變數以及生活型態變數，並藉由C5.0決策樹、C&RT分類樹，以及CHAID分類樹建立模型，透過這三個分類迴歸樹的模型，發現到「年齡」、「教育程度」、「地理區域」、「個人資產狀況」、「經濟主要來源：子女」、「個人每月可支配所得」以及「收入來源：薪資」共七項變數同時對民眾是否成為數位落差中的資訊富人（information-rich）有著較重要的影響性，因此，研究最後依據此七項進行政策建議，以提供相關單位之參考。 / In this globalized and informational society, computers and internet networks are essential elements in our daily lives. Until the year 2008, almost 70% of population in Taiwan has enjoyed greater conveniences through networking technologies. However, the issue of “digital divide” remains, where information-poor cannot obtain information easily, and the issue affects the society in terms of economies and human rights. Consequently, the purpose of this research is aimed to find the reasons behind “digital divide” using data-mining techniques with SPSS Clementine 12.0 statistical software. 　　The research will input demographic variables and life-style variables. Using C5.0 decision tree, C&RT tree, and CHAID methodologies to build model, and subsequently discovers that whether the 7 variables - “age”, “level of education”, “location”, “personal asset status”, “main source of income: children”, “monthly personal disposal income” and “source of income: salary” will have significant impacts on information-rich population within “digital divide”. Therefore, the research recommendations will be provided according to the results from these 7 variables.