Zugl.: Marburg, Universiẗat, Diss., 2009.

In this study, we propose to investigate the variation of impedance,energy transfer from the RF generator to the discharge is not perfect, and then to improve the sputtering efficiency. For the deposition of insulating film by sputtering technique, the external factors such as input RF power,gar pressure and gas flow rate, and the internal parts of sputtering system such as sputtering target, substrate, and the structure of internal wells of chamber, lead to deviate sputtering parameters such as the DC bias on substrate, ion density in the plasma, and the capacitance of the sheath of glow discharge. All of the factors introduce larger deviations of impedance matching into the sputtering system, that results in decrease the efficiency of film deposition and/or induce re-sputtering phenomena.Comparisons are made with various matching networks applied in real RF sputtering systems. The networks, L-type is choose as the impedance matching network of the RF sputtering system for investigation, is analytically studied in their interaction with the experimental device.From the characteristics of L-type studied, in case of numerical values are deduced and used for the optimizing control the impedance matching network. Finally, by using this technology of impedance matching network, the optimizing sputtering efficiency is achieved and that can enhance the stability of equipment and increase the sputtering rate.

Nowadays, ‘green’ communication is of great importance to save electric energy. In communication systems, power amplifiers (PAs) play an important role and consume large amount of power. As a consequence, the enhancement of amplifier efficiency is significantly important for saving energy.   This thesis describes a method to enhance the amplifier efficiency. The goal for this thesis is to find the matching impedances of harmonics for optimum efficiency performance of an amplifier. The idea is to control and change the load impedances at 2nd and 3rd harmonics for maximum efficiency performance of an amplifier at fundamental frequency and finally to build a matching network according to the matching impedances at harmonics.   The load pull technique is applied in this thesis to control the impedances with automatically controlled tuners. In this way, different impedances correspond to specific tuner positions. Then for different tuner positions, the corresponding load impedances of the harmonics are determined, the input, output as well as DC power of the amplifier are measured, and the corresponding efficiency is computed. Therefore, after appropriate efficiency sweep for specific tuner positions, the matching impedances with maximum efficiency performance can be found.   The efficiency of the amplifier with harmonic matching (the method implemented in this thesis) can be improved 2.13 percent which proves the feasibility of the method investigated in this thesis.

Conditioning reservoir models to production data and assessment of uncertainty can be done by Bayesian theorem. This inverse problem can be computationally intensive, generally requiring orders of magnitude more computation time compared to the forward flow simulation. This makes it not practical to assess the uncertainty by multiple realizations of history matching for field applications. We propose a robust adaptation of the Bayesian formulation, which overcomes the current limitations and is suitable for large-scale applications. It is based on a generalized travel time inversion and utilizes a streamline-based analytic approach to compute the sensitivity of the travel time with respect to reservoir parameters. Streamlines are computed from the velocity field that is available from finite-difference simulators. We use an iterative minimization algorithm based on efficient SVD (singular value decomposition) and a numerical ‘stencil’ for calculation of the square root of the inverse of the prior covariance matrix. This approach is computationally efficient. And the linear scaling property of CPU time with increasing model size makes it suitable for large-scale applications. Then it is feasible to assess uncertainty by sampling from the posterior probability distribution using Randomized Maximum Likelihood method, an approximate Markov Chain Monte Carlo algorithms. We apply this approach in a field case from the Goldsmith San Andres Unit (GSAU) in West Texas. In the application, we show the effect of prior modeling on posterior uncertainty by comparing the results from prior modeling by Cloud Transform and by generalized travel time inversion and utilizes a streamline-based analytic approach to compute the sensitivity of the travel time with respect to reservoir parameters. Streamlines are computed from the velocity field that is available from finite-difference simulators. We use an iterative minimization algorithm based on efficient SVD (singular value decomposition) and a numerical Collocated Sequential Gaussian Simulation. Exhausting prior information will reduce the prior uncertainty and posterior uncertainty after dynamic data integration and thus improve the accuracy of prediction of future performance.

Thesis (Ph. D.)--Ohio University, August, 1981. / Title from PDF t.p.

Thesis (Ph. D.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references (p. 90).

Köln, University, Diss., 2004.

We study the matching problem and some variants such as b-matching and (g, f)-factors. This thesis aims at polynomial algorithms which in addition have other properties. In particular, we develop a polynomial algorithm which can find optimal solutions of each possible size for weighted matching problem, and a strongly polynomial algorithm which can find a (g, f)-factor of fixed size. / Science, Faculty of / Mathematics, Department of / Graduate

Due to the rapid development of information technologies, especially the network technologies, business activities have never been as integrated as they are now. Business decision making often requires gathering information from different sources. This dissertation focuses on the problem of entity matching, associating corresponding information elements within or across information systems. It is devoted to providing complete and accurate information for business decision making. Three challenges have been identified that may affect entity matching performance: feature selection for entity representative, matching techniques, and searching strategy. This dissertation first provides a theoretical foundation for entity matching by connecting entity matching to the similarity and categorization theories developed in the field of cognitive science. The theories provide guidance for tackling the three challenges identified. First, based on the feature contrast similarity model, we propose a case-study-based methodology that identifies key features that uniquely identify an entity. Second, we propose a record comparison technique and a multi-layer naïve Bayes model that correspond respectively to the deterministic and the probability response selection models defined in the categorization theory. Experiments show that both techniques are effective in linking deceptive criminal identities. However, the probabilistic matching technique is preferable because it uses a semi-supervised learning method, which requires less human intervention during training. Third, based on the prototype access assumption proposed in the categorization theory, we apply an adaptive detection algorithm to entity matching so that efficiency can be greatly improved by the reduced search space. Experiments show that this technique significantly improves matching efficiency without significant accuracy loss. Based on the above findings we developed the Arizona IDMatcher, an identity matching system based on the multi-layer naïve Bayes model and the adaptive detection method. We compare the proposed system against the IBM Identity Resolution tool, a leading commercial product developed using heuristic decision rules. Experiments do not suggest a clear winner, but provide the pros and cons of each system. The Arizona IDMatcher is able to capture more true matches than IBM Identity Resolution (i.e., high recall). On the other hand, the matches identified by IBM Identity Resolution are mostly true matches (i.e., high precision).

Stuttgart, Univ., Diss., 2003.