Abstract Recently, titanium dioxide¡]TiO2¡^ is one of the most extensively studied transition-metal oxides because of its remarkable photocatalyst efficiency and electronic properties. In this paper, thin films ware obtained by dual ion beam sputtering. By different processes, these samples can be classified into three categories. Firstly, thin films, deposited on 200 mash copper grids for 15 minutes, were investigated that many TiO grains is about 5 nm in size by transmission electron microscopy¡]TEM¡^. Next, TiO2 thin films, sputtered on si wafers and glass for 180 minutes in an O2 environment by using titanium target, were initially identified by X ray diffraction instrument¡]XRD¡^. The result shows that some thin films have good orientations. By TEM, TiO2 grains on bottom of films are about 20 nm. By scanning electron microscopy¡]SEM¡^, TiO2 grains on the surface are about 1~2 £gm in size and are oblong in shape. The last, TiO thin films were directly deposited on si wafer for 180 minutes in no O2 environment by using titanium target and then annealed to transform from TiO to TiO2. By XRD, the thin film, annealed at 600¢J for 1hr, has good orientation. By TEM, TiO2 grains, annealed at 1000¢J for 24hr, grow up to 1-2 £gm in size and are oblong in shap.

Complex urban scene rendering is not feasible without culling invisible geometry before the rendering actually takes place. Visibility culling can be performed on predefined regions of scene where for each region a potential visible set of scene geometry is computed. Rendering cost is reduced since instead of a bigger set only a single PVS which is associated with the region of the viewer is rendered. However, when the viewer leaves a region and enters one of its neighbors, disposing currently loaded PVS and loading the new PVS causes stalls. Prefetching policies are utilized to overcome stalls by loading PVS of a region before the viewer enters it. This study presents a prefetching method for interactive urban walkthroughs. Regions and transitions among them are represented as a graph where the regions are the nodes and transitions are the edges. Groups of nodes are formed according to statistical data of transitions and used as the prefetching policy. Some heuristics for constructing groups of nodes are developed and Simulated Annealing is utilized for constructing optimized groups based on developed heuristics. The proposed method and underlying application of Simulated Annealing are customized for minimizing average transition cost.

Multidisciplinary design optimization of aerospace vehicles has attracted interest of many researchers. Well known aerospace companies are developing tools for the mutlidisciplinary design optimization. However, the multiobjective optimization of the design is a new and important area investigated very little by the researchers. This thesis will examine the approaches to the multiobjective and mutlidisciplinary design optimization of rockets and missiles. In the study, multiobjective optimization method called MC-MOSA will be used.

Excessive pumping in coastal aquifers results in seawater intrusion where optimal and efficient planning is essential. In this study, numerical solution of single potential solution by Strack is combined with genetic algorithm (GA) to find the maximum extraction amount in a coastal aquifer. Seawater intrusion is tracked with the potential value at the extraction well locations. A code is developed by combining GA and a subroutine repeatedly calling MODFLOW as a numerical solver to calculate the potential distribution for different configurations of solution (trial solutions). Potential distributions are used to evaluate the fitness values for GA. The developed model is applied to a previous work by Mantoglou. Another heuristic method, simulated annealing (SA) is utilized to compare the results of GA. Different seawater prevention methods (i.e. injection wells, canals) and decision variables related to those methods (i.e. location of the injection wells or canals) are added to model to further prevent the seawater intrusion and improve the coastal aquifer benefit. A method called &ldquo / Alternating Constraints Method&rdquo / is introduced to improve the solution for the cases with variable location. The results show that both proposed method and the regular solution with GA or SA prove to be successful methods for the optimal management of coastal aquifers.

No description available.

The primary objectives of this study were: (1) to verify the effectiveness of ECAE to induce equal amounts of strain and grain refinement in bars of different cross-sectional areas, (2) to determine the effectiveness of ECAE in breaking down the as-cast macrostructure in CDA 101 Cu and in producing a homogeneous material containing micron-scale grains upon recrystallization, and (3) to determine a thermomechanical processing (TMP) schedule (from the ones examined) that produces the best microstructure in terms of grain size and uniformity. The effects of extrus ion route, levels of strain and intermediate heat treatment were investigated. To achieve the first objective, bars having square cross-sections of three different sizes, 19 mm, 25 mm and 50 mm, were processed up to eight ECAE passes through routes A, B, C and E. To achieve the second and third objectives, bars were processed up to eight ECAE passes with and without intermediate heat treatments through routes Bc, C, E and F. ECAE processing was carried out in a 90o extrusion die with sliding walls at an extrusion speed of 2.5 mm/s. Recrystallization studies were carried out on the processed material to evaluate the recrystallization behavior and thermal stability of the material. The as-worked and recrystallized materials were characterized by Vickers microhardness, optical microscopy (OM) and transmission electron microscopy (TEM). Results indicate that similar hardness values, sub-grain morphology and recrystallized grain size are generated in the three bars having different cross-sectional sizes processed through ECAE. ECAE is shown to induce uniform strain in all three billet sizes. ECAE is therefore shown to be effective in scale-up to a size of at least 50 mm, with larger billets giving better load efficiency. Results from the later parts of this study indicate that eight extrusion passes via route Bc produces the best microstructure in terms of grain size and microstructural uniformity. The routes can be arranged in the sequence Bc> E, F> C for their ability to produce a uniform recrystallized microstructure with small average grain size. Macroscopic shear bands are sometimes generated during extrusion depending upon the initial grain morphology and texture of the material.

This dissertation provides one methodology to determine potential energy savings of buildings with limited information. This methodology is based upon the simplified energy analysis procedure of HVAC systems and the control of the comfort conditions. Numerically, the algorithm is a tailored exhaustive search over all the independent variables that are commonly controlled for a specific type of HVAC system. The potential energy savings methodology has been applied in several buildings that have been retrofitted and/or commissioned previously. Results from the determined savings for the Zachry building at Texas A&M after being commissioned show a close agreement to the calculated potential energy savings (about 85%). Differences are mainly attributed to the use of simplified models. Due to the restriction of limited information about the building characteristics and operational control, the potential energy savings method requires the determination of parameters that characterize its thermal performance. Thus, a calibrated building is needed. A general procedure has been developed to carry out automated calibration of building energy use simulations. The methodology has been tested successfully on building simulations based on the simplified energy analysis procedure. The automated calibration is the minimization of the RMSE of the energy use over daily conditions. The minimization procedure is fulfilled with a non-canonical optimization algorithm, the Simulated Annealing, which mimics the Statistical Thermodynamic performance of the annealing process. That is to say, starting at a specified temperature the algorithm searches variable-space states that are steadier, while heuristically, by the Boltzmann distribution, the local minima is avoided. The process is repeated at a new lower temperature that is determined by a specific schedule until the global minimum is found. This methodology was applied to the most common air-handler units producing excellent results for ideal cases or for samples modified with a 1% white noise.

Recently, distributed detection has been intensively studied. The prevailing model for distributed detection (DD) is a system involving both distributed local sensors and a fusion center. In a DD system, multiple sensors work collaboratively to distinguish between two or more hypotheses, e.g., the presence or absence of a target. In this thesis, the classical DD problem is reexamined in the context of wireless sensor network applications. For minimize the error probability at the fusion center, we consider the conventional method that designs the optimal binary local sensor decision rule in a channel-aware system, i.e., it integrates the transmission channel characteristics for find the optimal binary local sensor decision threshold to minimize the error probability at the fusion center. And there have different optimal local sensor decision thresholds for different channel state information. Because of optimal multi-bit (soft) local sensor decision is more practical than optimal binary local sensor decision. Allowing for multi-bit local sensor output, we also consider another conventional method that designs the optimal multi-bit (soft) local sensor decision rule in a channel-aware system. However, to design the optimal local sensor decision rule, both of two conventional methods are easily trapped into local optimal thresholds, which are depended on the pre-selected initialization values. To overcome this difficulty, we consider several modified Simulated Annealing (SA) algorithms. Based on these modified SA algorithms and two conventional methods, we propose two novel SA algorithms for implementing the optimal local sensor decision rule. Computer simulation results show that the employments of two novel SA algorithms can avoid trapping into local optimal thresholds in both optimal binary local sensor decision problem and optimal multi-bit local sensor decision problem. And two novel SA algorithms offer superior performance with lower search points compared to conventional SA algorithm.

Annealed neural network combines the characteristics of both simulation annealing and Hopfield-Tank neural network, which are high quality solutions and fast convergence. Mobile agent planning is an important technique of information retrieval systems to provide the minimum cost of the location-aware services in mobile computing environment. By taking the time constraints of effective resources into account and the mobile agent to explore the cost optimization, we modify annealing neural network to design a new energy function and control the annealing temperature in order to deal with the dynamic temporal feature of computing environments. We not only consider the server performance and network latency when scheduling mobile agents, but also investigate the location-based constraints, such as the home site of routing sequence of the traveling mobile agent must be the start and end node. To guarantee the convergent stable state and existence of the valid solution, the energy function is reformulated into a Lyapunov function which is combined with the annealing temperature to form an activation function. The connection weights between the neurons and the activation function of state variables in the dynamic network are devised in searching for the valid solutions. Simulation of different coefficients assess the proposed model and algorithm. Furthermore, Taguchi method is used to obtain the optimal combination factors of annealing neural network. The results show that this research presents the feature of both simulated annealing and Hopfield neural network by providing fast convergence and highly quality. In addition with a larger number of sites, the experimental results demonstrate the benefits of the annealed neural network. This innovation would be applicable to improve the effectiveness of solving optimization problems.

Computational structural biology Even with rapid advances in structure determination methods, there is a long gap to be bridged between the number of proteins that have been sequenced and the number whose three-dimensional structures have been experimentally elucidated. Experimentally protein structures are determined by X-ray crystallography or by nuclear magnetic resonance spectroscopy (NMR). X-ray crystal structures give a time averaged picture but little information on conformational dynamics. Though NMR gives dynamical information, the technique cannot be applied to systems whose molecular weight is large. Only small proteins fall within the ken of NMR experiments. In most cases the three dimensional structure of the protein alone cannot give a complete picture of its mechanism. It is also essential to know the interactions of proteins with other proteins, with their ligands and substrates in order to have a better understanding of their functioning. Computer modeling and simulations are now indispensable supplements to experimental structural biology. The last word in protein structure prediction method is far from being said but the ever-improving homology and ab-initio modeling methods give rise to optimism that sometime in the near future these methods will become almost as reliable as experimental techniques. Ligand docking onto protein molecules is as challenging a problem as protein structure predicting itself. Computer modeling methods to dock ligands have to search a wide region of conformational space besides taking into consideration issues of charge and shape complementarities.