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The extreme motion and load of ships have been assessed using a linear frequency domain method or a linear energy spectral method and RAOs, which may be too approximate to be used for estimation of ship motion in severest seas. The new technology uses simulation in the time domain to deal with the non-linear responses to the random seas. However, the current simulation technique has been successful only up to the sea state of 7 (“high seas”), defined by the significant wave height of 9 meters. The above cannot provide the extreme wave loads and motions for seas higher than the sea state 7. The ultimate goal of this work would be to develop a new technique that can simulate responses to the seas of states 8 and 9. The objective of the present study is to simulate the vertical relative motion and wave topping of a moored ship in the time domain by varying the significant wave heights. The analysis was able to predict with a fair accuracy the relative motion characteristics of a freely floating body in the head and beam sea conditions. The resonance aspects and its significance in the overall response are also analyzed.

CMOS active pixel sensors (APS) have shown competitive performance with charge-coupled device (CCD) and offer many advantages in cost, system power reduction and on-chip integration of VLSI electronics. Among CMOS image sensors, sensors with logarithmic pixels are particularly applicable for outdoor environment where the light intensity varies over a wide range. They are also randomly accessible in both time and space. A major drawback comes from process variations during fabrication. This gives rise to a considerable fixed pattern noise (FPN) which deteriorates the image quality. In this thesis, a technique that greatly reduces FPN using on-chip calibration is introduced. An image sensor that consists of 64x64 active pixels has been designed, fabricated and tested. Pixel pitch is 18um x 19.2um? and is fabricated in a 0.5-um? CMOS process. The proposed pixel circuit considerably reduces the FPN as predicted in theoretical analysis. The measured FPN value is 2.29% of output voltage swing and column-wise FPN is 1.49% of mean output voltage over each column.

Historically, the analysis of ship motion and loading responses has been performed in the frequency domain with both linear response amplitude operators and wave energy density spectra. This method, therefore, did not account for the nonlinear nature of waves. A more precise method is to obtain the response in the time domain, processing non-linear wave data with a linear response amplitude operator. Since the input is non-linear, even though the system is linear, the output will also be non-linear. This resultant data can then be used to generate a more accurate design of seaworthy vessels. Furthermore the linear frequency domain method does not account for the presence or effects of wave groups. This study shows the improved accuracy in the response obtained by accounting for non-linearities and furthermore indicates that wave groups affect the vertical relative motion of a moored ship (zero-speed).

In this thesis we use greedy randomize adaptive search procedure (GRASP) to solve the traveling salesman problem (TSP). Starting with nearest neighbor method to construct the initial TSP tour, we apply the 2-opt and the path-relinking method for the initial tour improvement. To increase 2-opt search speed, fixed-radius near neighbor search and don0t − look bit techniques are introduced. For the same reason a new efficient data structure, the reverse array, is proposed to represent the TSP tour. Computational results show that GRASP gives fairly good solutions in a short time.

Many people have readily suggested different types of hyperelastic models for high strain materials and biotissues since the 1940’s without validating them. But, there is no agreement for those models and no model is better than the other because of the ambiguity. The existence of ambiguity is because the error analysis has not been done yet (Criscione, 2003). The error analysis is motivated by the fact that no physical quantity can be measured without having some degree of uncertainties. Inelastic behavior is inevitable for the high strain materials and biotissues, and validity of the model should be justified by understanding the uncertainty due to it. We applied the fundamental statistical theory to the data obtained by randomized uniaxial stretch-controlled tests. The goodness-of-fit test (2R) and test of significance (t-test) were also employed. We initially presumed the factors that give rise to the inelastic deviation are time spent testing, stretch-rate, and stretch history. We found that these factors characterize the inelastic deviation in a systematic way. A huge amount of inelastic deviation was found at the stretch ratio of 1.1 for both specimens. The significance of this fact is that the inelastic uncertainties in the low stretch ranges of the rubber-like materials and biotissues are primarily related to the entropy. This is why the strain energy can hardly be determined by the experimentation at low strain ranges and there has been a deficiency in the understanding of the exclusive nature of the strain energy function at low strain ranges of the rubber-like materials and biotissues (Criscione, 2003). We also found the answers for the significance, effectiveness, and differences of the presumed factors above. Lastly, we checked the predictive capability by comparing the unused deviation data to the predicted deviation. To check if we have missed any variables for the prediction, we newly defined the prediction deviation which is the difference between the observed deviation and the point forecasting deviation. We found that the prediction deviation is off in a random way and what we have missed is random which means we didn’t miss any factors to predict the degree of inelastic deviation in our fitting.

While lean manufacturing has greatly improved the efficiency of production operations, it has left US enterprises in an increasingly risky environment. Causes of manufacturing disruptions continue to multiply, and today, seemingly minor disruptions can cause cascading sequences of capacity losses. Historically, enterprises have lacked viable tools for addressing operational volatility. As a result, each year US companies forfeit billions of dollars to unpredictable capacity disruptions and insurance premiums. In this dissertation we develop a number of stochastic models that capture the dynamics of capacity disruptions in complex multi-tier flow-matching feed-forward networks (FFN). In particular, we relax basic structural assumptions of FFN, introduce random propagation times, study the impact of inventory buffers on propagation times, and make initial efforts to model random network topology. These stochastic models are central to future methodologies supporting strategic risk management and enterprise network design.