This paper deals with the application of speed variable pumps in industrial hydraulic systems. The benefit of the natural feedback of the load torque is investigated for the issue of condition monitoring as the development of losses can be taken as evidence of faults. A new approach is proposed to improve the fault detection capabilities by tracking the changes via machine learning techniques. The presented algorithm is an art of adaptive modeling of the torque balance over a range of steady operation in fault free behavior. The aim thereby is to form a numeric reference with acceptable accuracy of the unit used in particular, taking into consideration the manufacturing tolerances and other operation conditions differences. The learned model gives baseline for identification of major possible abnormalities and offers a fundament for fault isolation by continuously estimating and analyzing the deviations.

Speed and displacement variable pumps offer a degree of freedom for process control. As a certain operation point can be supplied by different combinations of drive speed and pump displacement intelligent control strategies can address major issues like energy efficiency, process dynamics and noise level in industrial applications. This paper will provide an overview of recent research and development activities to evaluate the named potentials.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Compiling a PCM data frame with super commutation poses problems of maintaining constant sample intervals for the parameters whilst keeping within channel bandwidth limitations. Add an extra requirement of using variable word lengths to optimise the use of available bit rate and the problem becomes more challenging. The available telemetry or tape recorder channel bandwidth rather than the capabilities of the data acquisition system normally govern the amount of data that can be acquired by the aircraft instrumentation system. The amount of data demanded usually expands to fill all available bandwidth and the bit rates are operated at the maximum for the particular channel. The use of variable word lengths can, in some circumstances, increase the utilisation of a channel bandwidth. In order to visualise if a particular requirement can be accommodated within a given data structure a method of sketching PCM data frames containing a wide mixture of sample rates using an intermediate matrix has been devised. The method is described in three stages. 1. Compiling a simple PCM frame. 2. Sketching the intermediate matrix to assist in visualising super commutation limits. 3. Mixing variable word lengths and super commutation in the same PCM format. The method is not guaranteed to be the most efficient but does give a relatively simple, non mathematical, way to visualise if the required sample rates can be accommodated in a given data structure. If the requirement will not fit into the data structure then the method allows the impact of the necessary changes to the structure to be rapidly assessed. The paper includes comments on the relevant characteristics needed in the aircraft data acquisition system. These include variable word lengths, frame lengths, incremental bit rates and coherency of multiple data bus word parameters

In this thesis we provide statistical and model-based data mining methods for pattern detection with applications to biomedical and healthcare data sets. In particular, we examine applications in costly acute or chronic disease management. In Chapter II, we consider nuclear magnetic resonance experiments in which we seek to locate and demix smooth, yet highly localized components in a noisy two-dimensional signal. By using wavelet-based methods we are able to separate components from the noisy background, as well as from other neighboring components. In Chapter III, we pilot methods for identifying profiles of patient utilization of the healthcare system from large, highly-sensitive, patient-level data. We combine model-based data mining methods with clustering analysis in order to extract longitudinal utilization profiles. We transform these profiles into simple visual displays that can inform policy decisions and quantify the potential cost savings of interventions that improve adherence to recommended care guidelines. In Chapter IV, we propose new methods integrating survival analysis models and clustering analysis to profile patient-level utilization behaviors while controlling for variations in the population’s demographic and healthcare characteristics and explaining variations in utilization due to different state-based Medicaid programs, as well as access and urbanicity measures.

Structure is one of the fundamentals of music, yet the complexity arising from the vast number of possible variations of musical elements such as rhythm, melody, harmony, key, texture and form, along with their combinations, makes music modelling a particularly challenging task for machine learning. The research presented in this thesis focuses on the problem of learning a generative model for melody directly from musical sequences belonging to the same genre. Our goal is to develop probabilistic models that can automatically capture the complex statistical dependencies evident in music without the need to incorporate significant domain-specifc knowledge. At all stages we avoid making assumptions explicit to music and consider models that can can be readily applied in different music genres and can easily be adapted for other sequential data domains. We develop the Dirichlet Variable-Length Markov Model (Dirichlet-VMM), a Bayesian formulation of the Variable-Length Markov Model (VMM), where smoothing is performed in a systematic probabilistic manner. The model is a general-purpose, dictionary-based predictor with a formal smoothing technique and is shown to perform significantly better than the standard VMM in melody modelling. Motivated by the ability of the Restricted Boltzmann Machine (RBM) to extract high quality latent features in an unsupervised manner, we next develop the Time-Convolutional Restricted Boltzmann Machine (TC-RBM), a novel adaptation of the Convolutional RBM for modelling sequential data. We show that the TC-RBM learns descriptive musical features such as chords, octaves and typical melody movement patterns. To deal with the non-stationarity of music, we develop the Variable-gram Topic model, which employs the Dirichlet-VMM for the parametrisation of the topic distributions. The Dirichlet-VMM models the local temporal structure, while the latent topics represent di erent music regimes. The model does not make any assumptions explicit to music, but it is particularly suitable in this context, as it couples the latent topic formalism with an expressive model of contextual information.

Thesis (PhD (Statistics and Actuarial Science))—University of Stellenbosch, 2008. / The problem of variable selection in binary kernel classification is addressed in this thesis. Kernel methods are fairly recent additions to the statistical toolbox, having originated approximately two decades ago in machine learning and artificial intelligence. These methods are growing in popularity and are already frequently applied in regression and classification problems. Variable selection is an important step in many statistical applications. Thereby a better understanding of the problem being investigated is achieved, and subsequent analyses of the data frequently yield more accurate results if irrelevant variables have been eliminated. It is therefore obviously important to investigate aspects of variable selection for kernel methods. Chapter 2 of the thesis is an introduction to the main part presented in Chapters 3 to 6. In Chapter 2 some general background material on kernel methods is firstly provided, along with an introduction to variable selection. Empirical evidence is presented substantiating the claim that variable selection is a worthwhile enterprise in kernel classification problems. Several aspects which complicate variable selection in kernel methods are discussed. An important property of kernel methods is that the original data are effectively transformed before a classification algorithm is applied to it. The space in which the original data reside is called input space, while the transformed data occupy part of a feature space. In Chapter 3 we investigate whether variable selection should be performed in input space or rather in feature space. A new approach to selection, so-called feature-toinput space selection, is also proposed. This approach has the attractive property of combining information generated in feature space with easy interpretation in input space. An empirical study reveals that effective variable selection requires utilisation of at least some information from feature space. Having confirmed in Chapter 3 that variable selection should preferably be done in feature space, the focus in Chapter 4 is on two classes of selecion criteria operating in feature space: criteria which are independent of the specific kernel classification algorithm and criteria which depend on this algorithm. In this regard we concentrate on two kernel classifiers, viz. support vector machines and kernel Fisher discriminant analysis, both of which are described in some detail in Chapter 4. The chapter closes with a simulation study showing that two of the algorithm-independent criteria are very competitive with the more sophisticated algorithm-dependent ones. In Chapter 5 we incorporate a specific strategy for searching through the space of variable subsets into our investigation. Evidence in the literature strongly suggests that backward elimination is preferable to forward selection in this regard, and we therefore focus on recursive feature elimination. Zero- and first-order forms of the new selection criteria proposed earlier in the thesis are presented for use in recursive feature elimination and their properties are investigated in a numerical study. It is found that some of the simpler zeroorder criteria perform better than the more complicated first-order ones. Up to the end of Chapter 5 it is assumed that the number of variables to select is known. We do away with this restriction in Chapter 6 and propose a simple criterion which uses the data to identify this number when a support vector machine is used. The proposed criterion is investigated in a simulation study and compared to cross-validation, which can also be used for this purpose. We find that the proposed criterion performs well. The thesis concludes in Chapter 7 with a summary and several discussions for further research.

Dissertation (PhD)--Stellenbosch University, 2003. / ENGLISH ABSTRACT: In this dissertation we study the influence of data cases when the Cp criterion of Mallows (1973) is used for variable selection in multiple linear regression. The influence is investigated in terms of the predictive power and the predictor variables included in the resulting model when variable selection is applied. In particular, we focus on the importance of identifying and dealing with these so called selection influential data cases before model selection and fitting are performed. For this purpose we develop two new selection influence measures, both based on the Cp criterion. The first measure is specifically developed to identify individual selection influential data cases, whereas the second identifies subsets of selection influential data cases. The success with which these influence measures identify selection influential data cases, is evaluated in example data sets and in simulation. All results are derived in the coordinate free context, with special application in multiple linear regression. / AFRIKAANSE OPSOMMING: Invloedryke waarnemings as die C-p kriterium vir veranderlike seleksie in meervoudigelineêre regressie gebruik word: In hierdie proefskrif ondersoek ons die invloed van waarnemings as die Cp kriterium van Mallows (1973) vir veranderlike seleksie in meervoudige lineêre regressie gebruik word. Die invloed van waarnemings op die voorspellingskrag en die onafhanklike veranderlikes wat ingesluit word in die finale geselekteerde model, word ondersoek. In besonder fokus ons op die belangrikheid van identifisering van en handeling met sogenaamde seleksie invloedryke waarnemings voordat model seleksie en passing gedoen word. Vir hierdie doel word twee nuwe invloedsmaatstawwe, albei gebaseer op die Cp kriterium, ontwikkel. Die eerste maatstaf is spesifiek ontwikkelom die invloed van individuele waarnemings te meet, terwyl die tweede die invloed van deelversamelings van waarnemings op die seleksie proses meet. Die sukses waarmee hierdie invloedsmaatstawwe seleksie invloedryke waarnemings identifiseer word beoordeel in voorbeeld datastelle en in simulasie. Alle resultate word afgelei binne die koërdinaatvrye konteks, met spesiale toepassing in meervoudige lineêre regressie.

This paper examines the relationship between executive compensation – in the form ofvariable pay – and earnings management. Since most research is done on Americancompanies, and Swedish companies are adopting a more Anglo-American compensationstructure, we would like to study this in Sweden. We hypothesize that CEOs with highervariable pay are more likely to engage in earnings management. This study is done onSwedish companies listed on Large- and Mid-Cap. Using data from the companies’ annualreports we find no significant relationship between variable pay and discretionary accrualsusing our regression. / Denna studie undersöker relationen mellan kompensation till ledande befattningshavare –den del av lönen som är rörlig - och manipulering av intäkter. Då större delen av tidigareforskning har fokuserat på amerikanska företag, och svenska företag anammar en mer angloamerikanskstruktur av kompensation, vill vi undersöka detta i Sverige. Vi kommer fram tillen hypotes där vi antar att en VD med högre rörlig lön är mer trolig att manipulera intäkter.Studien är gjord på svenska företag listade på Large och Mid Cap. Genom att använda datafrån företagens årsredovisningar och Datastream hittar vi genom våra regressioner ingasamband mellan rörlig lön och diskretionära periodiseringar.

Scheduling large real world problems is a complex process and finding high quality solutions is not a trivial task. In cooperation with Trimble MRM Ltd., who provide scheduling solutions for many large companies, a problem is identified and modelled. It is a general model which encapsulates several important scheduling, routing and resource allocation problems in literature. Many of the state-of-the-art heuristics for solve scheduling problems and indeed other problems require specialised heuristics tailored for the problem they are to solve. While these provide good solutions a lot of expert time is needed to study the problem, and implement solutions. This research investigates methods to enhance existing search based methods. We study hyperheuristic techniques as a general search based heuristic. Hyperheuristics raise the generality of the solution method by using a set of tools (low level heuristics) to work on the solution. These tools are problem specific and usually make small changes to the problem. It is the task of the hyperheuristic to determine which tool to use and when. Low level heuristics using exact/heuristic hybrid method are used in this thesis along with a new Tabu based hyperheuristic which decreases the amount of CPU time required to produce good quality solutions. We also develop and investigate the Variable Fitness Function approach, which provides a new way of enhancing most search-based heuristics in terms of solution quality. If a fitness function is pushing hard in a certain direction, a heuristic may ultimately fail because it cannot escape local minima. The Variable Fitness Function allows the fitness function to change over the search and use objective measures not used in the fitness calculation. The Variable Fitness Function and its ability to generalise are extensively tested in this thesis. The two aims of the thesis are achieved and the methods are analysed in depth. General conclusions and areas of future work are also identified.

本論文旨在研究，績效良好的高技術密集度公司與低技術密集度公司，其組織結構是 否有所不同，以做為欲從低技術密集度產業進入高技術密集度產業者之參考。本研究 共壹冊，預計四萬至五萬字，論文綱要如左： 第一章緒論：研究動機，研究問題與研究架構。 第二章文獻探討：學者對於技術與組織的研究探討，技術密集度的定義。 第三章研究方法與分析：變數設計，資料來源，研究架構，研究設計，資料收集與樣 本描述，研究限制。 第四章資料分析與研究發現。 第五章結論與建議。