Knowledge of the mass of the manipulated load (i.e. payload) in off-highway machines is useful information for a variety of reasons ranging from knowledge of machine stability to ensuring compliance with transportion regulations. This knowledge is difficult to ascertain however. This dissertation concerns itself with delineating the motivations for, and difficulties in development of a dynamic payload weighing algorithm. The dissertation will describe how the new type of dynamic payload weighing algorithm was developed and progressively overcame some of these difficulties.<p> The payload mass estimate is dependent upon many different variables within the off-highway vehicle. These variables include static variability such as machining tolerances of the revolute joints in the linkage, mass of the linkage members, etc as well as dynamic variability such as whole-machine accelerations, hydraulic cylinder friction, pin joint friction, etc. Some initial effort was undertaken to understand the static variables in this problem first by studying the effects of machining tolerances on the working linkage kinematics in a four-wheel-drive loader. This effort showed that if the linkage members were machined within the tolerances prescribed by the design of the linkage components, the tolerance stack-up of the machining variability had very little impact on overall linkage kinematics.<p> Once some of the static dependent variables were understood in greater detail significant effort was undertaken to understand and compensate for the dynamic dependent variables of the estimation problem. The first algorithm took a simple approach of using the kinematic linkage model coupled with hydraulic cylinder pressure information to calculate a payload estimate directly. This algorithm did not account for many of the aforementioned dynamic variables (joint friction, machine acceleration, etc) but was computationally expedient. This work however produced payload estimates with error far greater than the 1% full scale value being targeted. Since this initial simplistic effort met with failure, a second algorithm was needed. The second algorithm was developed upon the information known about the limitations of the first algorithm. A suitable method of compensating for the non-linear dependent dynamic variables was needed. To address this dilemma, an artificial neural network approach was taken for the second algorithm. The second algorithms construction was to utilise an artificial neural network to capture the kinematic linkage characteristics and all other dynamic dependent variable behaviour and estimate the payload information based upon the linkage position and hydraulic cylinder pressures. This algorithm was trained using emperically collected data and then subjected to actual use in the field. This experiment showed that that the dynamic complexity of the estimation problem was too large for a small (and computationally feasible) artificial neural network to characterize such that the error estimate was less than the 1% full scale requirement.<p> A third algorithm was required due to the failures of the first two. The third algorithm was constructed to ii take advantage of the kinematic model developed and utilise the artificial neural networks ability to perform nonlinear mapping. As such, the third algorithm developed uses the kinematic model output as an input to the artificial neural network. This change from the second algorithm keeps the network from having to characterize the linkage kinematics and only forces the network to compensate for the dependent dynamic variables excluded by the kinematic linkage model. This algorithm showed significant improvement over the previous two but still did not meet the required 1% full scale requirement. The promise shown by this algorithm however was convincing enough that further effort was spent in trying to refine it to improve the accuracy.<p> The fourth algorithm developed proceeded with improving the third algorithm. This was accomplished by adding additional inputs to the artificial neural network that allowed the network to better compensate for the variables present in the problem. This effort produced an algorithm that, when subjected to actual field use, produced results very near the 1% full scale accuracy requirement. This algorithm could be improved upon slightly with better input data filtering and possibly adding additional network inputs.<p> The final algorithm produced results very near the desired accuracy. This algorithm was also novel in that for this estimation, the artificial neural network was not used soley as the means to characterize the problem for estimation purposes. Instead, much of the responsibility for the mathematical characterization of the problem was placed upon a kinematic linkage model that then fed its own payload estimate into the neural network where the estimate was further refined during network training with calibration data and additional inputs. This method of nonlinear state estimation (i.e. utilising a neural network to compensate for nonlinear effects in conjunction with a first principles model) has not been seen previously in the literature.

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Security  is  the second step after  that a successful network has been deployed. There are many  types  of  attacks  that  could  potentially  harm  the  network  and  an  administrator should  carefully  document  and  plan  the  weak  areas,  where  the  network  could  be compromised. Attackers use special tools and techniques to find out all the possible ways of defeating the network security.  This  thesis  addresses  all  the  possible  tools  and  techniques  that  attackers  use  to compromise the network. The purpose for exploring these tools will help an administrator to find the security holes before an attacker can. All of these tools in this thesis are only for the forensic purpose. Securing routers and switches in the best possible way is another goal. We in this part try to identify important ways of securing these devices, along with their limitations, and then determine the best possible way. The solution will be checked with network vulnerable  tools  to get  the  results.  It  is  important  to note  that most  of  the attention  in  network  security  is  given  to  the  router,  but  far  less  attention  is  given  to securing a switch. This  thesis will also address some more ways of securing a switch, if there is no router in the network. / The opponent for the thesis was Yan Wang and the presentation time was 60 minutes.

Taiwan is often threaten by typhoon during summer and autumn. The surges brought by theses typhoons not only cause human lives in danger, but also cause severe floods in coastal area. Storm surge prediction remains still a complex coastal engineering problem to solve since lots of parameters may affect the predictions. The purpose of this study is to predict storm surges using an Artificial Neural Network (ANN). A non-linear hidden-layer forward feeding neural network using back-propagation learning algorithms was developed. The study included a detailed analysis the factors may affect the predictions. The factors were obtained from the formulation of storm surge discrepancies after Horikawa (1987). Storm surge behaviors may vary from different geographical locations and weather conditions. A correlation analysis of the parameters was carried out first to pick up those factors shown high correlations as input parameters for establishing the typhoon surge predictions. The applications started with collecting tide and meteorological data (wind speed, wind direction and pressure) of Dapeng Bay and Kaohsiung harbor. A harmonic analysis was utilized to identify surge deviations. The surge deviation recorded at Dapeng Bay was found higher then Kaohsiung harbor for the same typhoon events. Correlation analysis has shown positive correlations between wind field, both wind speed and direction, and the associated storm surge deviations at Dapeng Bay. Correlation coefficients (CC) 0.6702 and 0.58 were found respectively. The variation of atmospheric pressure during typhoons is found with positive correlation too (i.e. CC=0.3626). Whereas the analysis has shown that the surges at Kaohsiung harbor were only sensitive to wind speed (CC=0.3723), while the correlation coefficients of the wind direction (CC=-0.1559) and atmospheric pressure (CC= -0.0337) are low. The wind direction, wind speed and atmospheric pressure variation were then used as input parameters for the training and predictions. An optimum network structure was defined using the Dapeng Bay data. The best results were obtained by using wind speed, wind direction and pressure variation as input parameters. The ANN model can predict the surge deviation better if the empirical mode decomposition (EMD) method was used for training.

Several demands relied on high-performance computing come up with the advanced technologies, like Satellite Imaging, Genetic Engineering, Global Weather Forecast, Nuclear Explosion Emulation, and in the meantime, the amount of data usually approaches the rank of Tera-Bytes, even Peta-Bytes. Besides, we need practical image application in our daily life, such as Game, 3D Display, High-Definition Video, etc. These requirements of high-performance computing are rigorous challenge to current devices. The performance of GPU (Graphic Processing Unit) is growing up rapidly in recent years. GPU doubles its computing power every year, which is far superior to CPU (Central Processing Unit) performance based on Moore¡¦s Law. Nowadays, the computing power of GPU on the single-precision floating-point operations is ten times than that of CPU. Furthermore, CUDA (Compute Unified Device Architecture) is a parallel computing architecture proposed by NVIDIA at 2007, and it is the first C-like language software development environment without Graphics API. In this research, we use GPU to assist network devices in filtering packets of the network flow, whose quantity is becoming more and more large. Due to the popularization of network, people pay attention to different types of network attacks or safety problems. Therefore, it is important to remove malicious packets from normal ones without degrading the network performance.

Logistics and supply chain problems arising in the context of intermodal transportation and less-than-truckload (LTL) network design typically require commodities to be consolidated and shipped via the most economical route to their destinations. Traditionally, these problems have been modelled using network design or hub-and- spoke approaches. In a network design problem, one is given the network and flow requirements between the origin and destination pairs (commodities), and the objective is to route the flows over the network so as to minimize the sum of the fixed charge incurred in using arcs and routing costs. However, there are possible benefits, due to economies-of-scale in transportation, that are not addressed in standard network design models. On the other hand, hub location problems are motivated by potential economies-of-scale in transportation costs when loads are consolidated and shipped together over a completely connected hub network. However, in a hub location problem, the assignment of a node to a hub is independent of the commodities originating at, or destined to, this node. Such an indiscriminate assignment may not be suitable for all commodities originating at a particular node because of their different destinations. Problems arising in the area of LTL transportation, intermodal transportation and package routing generally have characteristics such as economies- of-scale in transportation costs in addition to the requirement of commodity-based routing. Obviously, the existing network design and hub location-based models are not directly suitable for these applications. In this dissertation, we investigate the development of models and solution algorithms for problems in the areas of LTL and intermodal transportation as well as in the freight forwarders industry. We develop models and solution methods to address strategic, tactical and operational level decision issues and show computational results. This research provides new insights into these application areas and new solution methods therein. The solution algorithms developed here also contribute to the general area of discrete optimization, particularly for problems with similar characteristics.

This thesis studies the applications of the network coding technique for intercon- nect optimization and improving the routability of Very-large-scale integration (VLSI) designs. The goal of the routing process is to connect the required sets of sources and sinks while minimizing the total wirelength and reducing congestion. Typically, chip interconnects include multiple sinks and are routed through intermediate nodes. The main idea of the network coding technique is to enable the intermediate nodes to generate new signals by combining the signals received over their incoming wires. This is in contrast to the traditional approaches, in which an intermediate node can only forward the incoming signals. This thesis attempts to explore the possible ben- efits of the network coding technique for reducing the total wirelengh and mitigating congestion in VLSI designs. The contribution of the thesis is three-fold. First, we extend the Hanan’s theo- rem for multi-net rectilinear coding networks. Second, we propose several exact and heuristic solutions for finding near-optimal routing topologies that utilize network coding techniques. Next, we perform extensive simulation study to evaluate the ad- vantage of network coding over the traditional approaches. The simulations help to identify routing instances where the network coding techniques are expected to be beneficial. Finally, we evaluate the potential benefits from network coding in practical settings by analyzing its performance on the International Symposium on Physical Design (ISPD) benchmarks. Our results show that while network coding shows upto 2.43% improvement on unconstrained rectilinear grids, it shows upto 4.34% improvement in cases with con- straints along the grid. In addition, it shows an improvement upto 8.4% in cases involving congestion reduction and also improves routing performance on ISPD rout- ing benchmarks.

Biological phenomena in the cells can be explained in terms of the interactions among biological macro-molecules, e.g., DNAs, RNAs and proteins. These interactions can be modeled by genetic regulatory networks (GRNs). This dissertation proposes to reverse engineering the GRNs based on heterogeneous biological data sets, including time-series and time-independent gene expressions, Chromatin ImmunoPrecipatation (ChIP) data, gene sequence and motifs and other possible sources of knowledge. The objective of this research is to propose novel computational methods to catch pace with the fast evolving biological databases. Signal processing techniques are exploited to develop computationally efficient, accurate and robust algorithms, which deal individually or collectively with various data sets. Methods of power spectral density estimation are discussed to identify genes participating in various biological processes. Information theoretic methods are applied for non-parametric inference. Bayesian methods are adopted to incorporate several sources with prior knowledge. This work aims to construct an inference system which takes into account different sources of information such that the absence of some components will not interfere with the rest of the system. It has been verified that the proposed algorithms achieve better inference accuracy and higher computational efficiency compared with other state-of-the-art schemes, e.g. REVEAL, ARACNE, Bayesian Networks and Relevance Networks, at presence of artificial time series and steady state microarray measurements. The proposed algorithms are especially appealing when the the sample size is small. Besides, they are able to integrate multiple heterogeneous data sources, e.g. ChIP and sequence data, so that a unified GRN can be inferred. The analysis of biological literature and in silico experiments on real data sets for fruit fly, yeast and human have corroborated part of the inferred GRN. The research has also produced a set of potential control targets for designing gene therapy strategies.

SAS has become an increasingly important public-health problem in recent years. It can adversely affect neurocognitive, cardiovascular, respiratory diseases and can also cause behavior disorder. Moreover, up to 90% of these cases are obstructive sleep apnea (OSA). Therefore, the study of how to diagnose, detect and treat OSA is becoming a significant issue, both academically and medically. Polysomnography can monitor the OSA with relatively fewer invasive techniques. However, polysomnography-based sleep studies are expensive and time-consuming because they require overnight evaluation in sleep laboratories with dedicated systems and attending personnel. Therefore, to improve such inconveniences, one needs to develop a simplified method to diagnose the OSA, so that the OSA can be detected with less time and reduced financial costs. Since currently there seems to be no OSA detection technique available in Taiwan, the goal of this work is to develop a reliable OSA diagnostic algorithm. In particular, via signal processing, feature extraction and artificial intelligence, this thesis describes an on-line ECG-based OSA diagnostic system. It is hoped that with such a system the OSA can be detected efficiently and accurately.

There are more and more Internet services such as video on demand, voice over IP,Blog, and so on. The network quality is important for providing good services. P2P technology can decentralize the usage of bandwidth, so a server can provide services with lower bandwidth. The bandwidth is filled by P2P traffic if we don¡¦t limit the usage of P2P applications, so we need a service controller that can limit the P2P traffic to provide better quality for other applications. The traditional network systems use software solutions or hardware solutions. The software solutions offer flexibility but have low performance; The hardware solutions offer highest speed but are inflexible and expensive to modify or upgrade. there is another solution known as network processors. A network processor can be programmed and has been optimizede for packet procecssing. We need a good service classifier to classify P2P traffic, then we can limit it. The performance of a signature based service classifier is dominated by the speed of its pattern matching algorithm. In this paper, we proposed a fast ulti-pattern matching algorithm by improving WM algorithm. Serveral algorithms are implemented on IXP2400 network processor for performance evaluation, and our proposed algorithm outperforms other algorithms if its parameters are properly set.