The all-terrain vehicle (ATV) has been popular due to its simplicity in controlling, high recreational value, and sports utility, Furthermore, seating comfort and control aptness are affected mainly by the suspension and steering mechanism. Therefore, this research is directed towards the ATV¡¦s front suspension and steering mechanism, bearing in mind a set of systematized design procedures as the design and development basis of the front suspension and steering mechanism. First, we will investigate the papers on the front suspension and steering mechanism in order to make an induction on its characteristics and demand. Then, the theories of kinematic analysis of the front suspension and steering mechanism are established. The aided analyzing computer program we edit using the theories, moreover could be the design basis. And, to carry out the creative design of the front suspension and steering mechanism using the systematized procedure of creative mechanism design. In this investigation, we have successfully constructed atlas of the creative mechanism, and the result supplies the following design to select; Finally, we complete the kinematic design of the front suspension and steering mechanism based on the Genetic algorithm (GA), and we can obtain the better mechanism dimensions than Yamaha 660 ATV.

In recent years, microstrip antennas are suitable for applications in wireless communication systems because they have the characteristics of compact size, light weight, low cost and easy to manufacture. So, they play an important role in the navigation equipment of the rocket, space shutter, personal communication, etc. However, in the design and synthesis of antennas, there are a large number of design variables that affect the antenna performance. In early stages, some researchers did not use any optimization tool in parameter tuning of antennas design. The one utilized most is the ¡§trial and error¡¨ method, which is very time-consuming in order to find a suitable solution to verify the possibilities of the antenna structure. Genetic algorithms have been shown to be effective in the design of broadband microstrip antenna. However, their effectiveness with various degrees depends on the skills of the different genetic algorithms. In this dissertation, we propose a Genetic Algorithm (GA)-based refined method to enhance the effectiveness and to solve the gap-coupled microstrip antenna design problem (largest impedance bandwidth). The refined method with optimization process improves the computing performance comparing with the conventional genetic algorithm. By the refined GA method, bandwidth can be widened up to 3.84 times that of a single excited patch. Furthermore, we present a new design for Ultra Wideband (UWB) antenna. In the new research topic, it is expected that the genetic algorithm can find out a range of feasible (range-based) solutions instead of a few of solutions. As a result, the manufacturing process will have more convenience and practicability. Finally, we propose a new method to overcome the problem of signal interference with the UWB system operations. A band notched characteristic is achieved for the antenna to restrain the interference bandwidth. The disclosed antenna and the circuitry for the antenna system are easily integrated. With the simple structure, the fabrication cost for the antenna is also reduced.

Intersymbol interference(ISI) is an important factor which affects the performance of communication systems. To achieve highspeed digital transmission and reliable communication, an equalizer can effectively eliminate ISI caused by band-limited channel or multipath. In this paper, we introduce an adaptive H¡Û equalizing algorithm. Different form conventional recursive least square filtering algorithm, the adaptive H¡Û filtering algorithm is a worst case optimization. It can minimize the effect of the worst disturbances (including input noise and modeling error) on the equalization error. It has been proven that the adaptive H¡Û filtering algorithm has the advantage of reduction of sensitivity to modeling error and suitability for arbitrary ambient noise over RLS algorithm. However, the computational burden of the H¡Û algorithm is enormous. To reduce the computational complexity, the subweight partition technique is employed to the H¡Û algorithm. Computer simulation also shows that the H¡Û algorithm can reduce the dynamic estimation error resulting from subweight partition. Finally, in order to overcome multiple access interference, interchip interference, time-varying effects from the channel and computational complexity, the H¡Û algorithm with subweight partition (termed H¡Û-based) is then further extended to the multiuser detection in code division multiple access (CDMA) system. Simulation results are presented to demonstrate the advantage of the H¡Û-based equalizing algorithm.

As a result of ¡§the X-ray Phase Problem¡¨, traditional direct methods can¡¦t solve the structures of the large molecules. For exploring alternate methods, Wu-Pei Su applied simulated annealing to solve the structure of the large molecules and obtained success. Adopting his concept, we wrote a program for solving the structure of the molecules by C Program Language. And for decreasing the running time of the program, we introduced the concept of genetic algorithm into simulated annealing method.

Many methods can be used to predict the secondary structure of an RNA sequence. One of the methods is the dynamic programming approach. However, the dynamic programming approach takes too much time. Thus, it is not practical to solve the problem of long sequences with dynamic programming. RAGA (RNA Sequence Alignment by the Genetic Algorithm) is a genetic algorithm to align two similar sequences that the structure of one of them (master sequence) is known and another (slave sequence) is unknown. We can predict an RNA sequence by analyzing several homologous sequence alignment. In this thesis, we add an operator to mutate the residues of the base pairs in the master sequence and realign two sequences again. We compare our operator with other traditional operators, such as crossover and mutation. The experiment results show that our new operator gets a big improvement.

This thesis would solve the control problem of the contiguous tape which using Reel-to-Reel winder mechanism to load and unload. In the past, some theses solve the speed matching between motors by using specific motor controller or specific mechanism. These designs are too complex and inflexible to suit the variety requirement. In this thesis, I have only used simple vibration mechanism. I hope that the variety requirement could be suited by turning the software. I have compared those results by using different algorithms and different type of sensors. According those results, I wish to find a better control method. During the experimenting, I found that the discontinuous sensor, like Photo-interrupter can not provide enough information about the vibration mechanism. The controller can not turn the motor speed in a short time. In opposition, when using the continuous sensor, like potentiometer can provide more information including the speed and the location of the vibration mechanism. It even provides the moving direction and acceleration. The controller can tune the motor speed by using typical PID control algorithm. I tuned the P¡BI and D parameter by the method provided by Ziegler & Nichols. When working in the factory, there is no problem!

The objective of this project is to determine whether utilizing an EM Algorithm to fit a Gaussian mixed model distribution model provides needed accuracy in identifying the number of defective parts per million when the overall population is made up of multiple independent runs or lots. The other option is approximating using standard software tools and common known techniques available to a process, industrial or quality engineer. These tools and techniques provide methods utilizing familiar distributions and statistical process control methods widely understood. This paper compares these common methods with an EM Algorithm programmed in R using a dataset of actual measurements for length of manufactured product. / text

A distributed system consists of a set of N processor nodes and a finite set of communication channels. It is frequently described as a directed graph in which each vertex represents a processor node and the edges represent the communication channels. A global snapshot of a distributed system consists of the local states of all the processor nodes and all of the in-transit messages of a distributed computation. This is meaningful as it corresponds to the global state where all the local states and communication channels of all the processor nodes in the system are recorded simultaneously. A classic example where snapshots are utilized is in the scenario of some failure where the system can restart from the last global snapshot. This is an important application of global snapshot algorithms as it forms the basis for fault-tolerance in distributed programs and aids in serviceability as a distributed program debugging mechanism. Another important application includes checkpointing and monitoring systems where a set of continuous global snapshots are employed to detect when a certain number of triggers have been received by the system. When the distributed system is scaled in terms of an increase in the number of processor nodes and an increase in the number of expected triggers the message complexity increases and impacts the total overhead for the communication and computation of the global snapshot algorithm. In such a large distributed system, an optimal algorithm is vital so that the distributed application program that is employing the snapshots does not suffer from performance degradation as the size of the distributed system continues to grow over time. We are interested in global snapshot algorithms that offer lower bound message complexity and lower bound MaxLoad messages for large values of N processor nodes and large values of W expected triggers. In this report we study and simulate the Centralized, Grid based, Tree Based, and LayeredRand global snapshot algorithms then evaluate the algorithms for total number of messages (sent and received) and MaxLoad messages (sent and received) for the trigger counting problem in distributed computing. The report concludes with simulation results that compare the performance of the algorithms with respect to the total number of messages and MaxLoad messages required by each algorithm to detect when the number of W triggers have been delivered to the distributed system. / text

Natural gas plants can have multiple owners for raw natural gas streams and processing facilities as well as for multiple products. Therefore, a proper cost allocation method is necessary for taxation of the profits from natural gas and crude oil as well as for cost sharing among gas producers. However, cost allocation methods most often used in accounting, such as the sales value method and the physical units method, may produce unacceptable or even illogical results when applied to natural gas processes. Wright and Hall (1998) proposed a new approach called the design benefit method (DBM), based upon engineering principles, and Wright et al. (2001) illustrated the potential of the DBM for reliable cost allocation for natural gas processes by applying it to a natural gas process. In the present research, a rigorous modeling technique for the DBM has been developed based upon a Taylor series approximation. Also, we have investigated a cost allocation framework that determines the virtual flows, models the equipment, and evaluates cost allocation for applying the design benefit method to other scenarios, particularly those found in the petroleum and gas industries. By implementing these individual procedures on a computer, the proposed framework easily can be developed as a software package, and its application can be extended to large-scale processes. To implement the proposed cost allocation framework, we have investigated an optimization methodology specifically geared toward economic optimization problems encountered in natural gas plants. Optimization framework can provide co-producers who share raw natural gas streams and processing plants not only with optimal operating conditions but also with valuable information that can help evaluate their contracts. This information can be a reasonable source for deciding new contracts for co-producers. For the optimization framework, we have developed a genetic-quadratic search algorithm (GQSA) consisting of a general genetic algorithm and a quadratic search that is a suitable technique for solving optimization problems including process flowsheet optimization. The GQSA inherits the advantages of both genetic algorithms and quadratic search techniques, and it can find the global optimum with high probability for discontinuous as well as non-convex optimization problems much faster than general genetic algorithms.

In this thesis two case studies are performed about solving two design problems we face during the design phase of new Volvo truck. One is to solve the frame packing problem on CAN bus. The other is to solve the LDC allocation problem. Both solutions are targeted to meet as many end-to-end latency requirements as possible. Now the solution is obtained through manually approach and based on the designer experience. But it is still not satisfactory enough. With the development of artificial intelligence method we propose two methods based on genetic algorithm to solve our design problem we face today. In first case study about frame packing we perform one single genetic algorithm process to find the optimal solution. In second case study about LDC allocation we proposed how to handle two genetic algorithm processes together to reach the optimal solution. In this thesis we show the feasibility of adopting artificial intelligence concept in some activities of the truck design phases like we do in both case studies.