Due to the complexity of electro-elastic coupling characteristics in piezoelectric material, some of the elastic, dielectric and piezoelectric parameters are difficult to be measured. Usually, these parameters are determined by assuming that all offer parameters are remained constant during the measurement. However, the interactive effect between material properties makes this assumption be not always true. In this study, the measured natural frequencies of the specified circular piezoelectric plate are used to extract these parameters simultaneously. In other words, all these parameters are determined with considering the interactive electro-elastic coupling effect. The analytic model of free-free circular piezoceramic plate was derived and solved to establish the relationship between natural frequencies and its material parameters, to cover most all the parameters, the out-of-plane(non-symmetric transverse) and in-plane(symmetric extensional) modes are considered. The genetic algorithm is employed to determine most all elastic, dielectric and piezoelectric parameters from a least square error between the calculated and measured natural frequencies. Numerical results derived from the parameters proposed in this work reveal a good agreement with the measured data. In other words, the proposed method to extract the piezoelectric parameters is feasible and effective.

In recent years, with the microarray technique widely adopted, a large amount of biomedical literatures are published to provide a lot of useful information. However, some relationships among disease, genes and drug are still to be explored, since the authors only focus on part of the significant genes to the disease or the significant genes to the drug but not connect them to obtain new relationships. There are several methods proposed for finding out the hidden relationships, however many of them requires manual involvements. The main objective of this dissertation is to discover the hidden connections between human diseases and genes and the connections between drugs and the same genes. In order achieve this goal, the intermediate nodes (signification genes) must be found first. When a gene has more significant difference in observed group (abnormal patients) than in control group (normal persons), this gene is called significant genes to the disease. These signification genes often play a crucial role in cancer diagnosis and treatment. Via classifying the microarray gene expression data to find these significant genes, doctors can obtain the feasible and appropriate information for treatments that can give to the patients according to their cancer symptoms. A variety of existing classifiers have been proposed for this problem. However, most of them often work inefficiently when attributes grow up over thousands. To further improve the accuracy and the speed of the existing classifiers, a novel microarray attribute reduction scheme (MARS) is proposed for selecting significant genes to the disease. Experimental results demonstrate that combining the proposed scheme with multiclass support vector machine (MCSVM) obtains better performance than other different gene selection methods with the same MCSVM. In addition, the proposed scheme with MCSVM performs better than the results listed in the existing literature.. Furthermore, 19 of 22 genes selected by the proposed scheme in acute lymphoblastic leukemia and acute myeloid leukemia (AML-ALL) dataset are related to the AML and ALL diseases that have been reported in the literatures. Thus the proposed scheme not only can significantly reduce large amount of attributes (genes) for gene expression classification problem, but also increase the classification accuracy. MARS finds related gene set according to a threshold determined by using receiver operating characteristic (ROC) curve. However, it requires repeating the experiment many times to determine the best threshold. Hence, we propose a novel disease-oriented feature selection algorithm (DOFA) to improve MARS. DOFA uses the Genetic Algorithm (GA) in the selection method for automatic picking up the related genes and Support Vector Machine (SVM) and K-nearest-neighborhood (KNN) as the classifier. DOFA is tested on picking up related genes for AML-ALL and Colon datasets. For AML-ALL and Colon datasets, it selects 21 genes and 25 genes, respectively. Based on the literatures, it shows that 20 of 21 genes are related to the disease or cancers related for AML-ALL dataset and one of these genes is still uncertain. And 20 of 25 genes are directly related to the disease colon cancer or cancers related and 5 of these genes are still uncertain. Three more experiments are conducted to verify the discriminability of the genes selected by DOFA. Experimental results all indicate that DOFA obtains better performance than other competing methods. Thus DOFA not only can select the genes related to the diseases, but also increase the classification accuracy. After obtaining the significant gene group, we can further use these genes to obtain the hidden connections. We propose a high specificity literature mining method based on microarray expression profile for discovering hidden connections among disease, drug, and genes. The proposed method can automatically select related genes from the disease or drug microarray expression profiles, and use the disease names or the drug names and gene names or aliases of the selected genes to obtain the related abstract collections. An alias expansion scheme and a weight function are used to eliminate the unrelated literatures. We perform three scenarios to verify the proposed method. Experimental results show that using the proposed method can obtain the hidden connections among diseases, genes and drugs. The (ROC) curve shows that the proposed method can not only find the hidden connections between diseases and drugs but also have high specificity. Concluding this dissertation, our goal is to discover the hidden connections between the diseases and the drugs. In order to achieve this goal, we first proposed MARS to select the significant genes to the diseases. And then, we proposed DOFA to improve the ability of MARS. We proposed a high specificity literature mining method based on microarray expression profile for discovering the hidden connections among diseases, genes, and drugs. The proposed method combines the power of searching significant genes to the disease of DOFA to further obtain the hidden connections. Experimental results show that the proposed method not only can obtain the hidden connections among diseases, genes, and drugs, but also has high specificity.

In this thesis, security measures and vulnerability mitigation are mainly addressed. How to improve the system vulnerability is one of the main issues for power system operation and planning. Recent research revealed that Energy Storage Systems (ESSs) have a great potential to be used to improve system vulnerability. A vulnerability assessment is proposed in this thesis to identify the impact factors in the power systems due to generation outage and line outage. A Bus Impact Severity (BIS) analysis is then proposed and used to find the vulnerable buses in the system. The buses with the larger BIS value defined in this thesis are the better locations for ESSs placement. Formulations for optimal locations and capacities of ESSs placement are derived and then solved by Genetic Algorithm (GA). Test results show that the proposed method can be used to find the optimal locations and capacities for ESSs for system vulnerability improvement.

IEEE 802.16e ¡]also known as Mobile WiMAX¡^ is currently the international MAC ¡]medium access control¡^ standard for wireless metropolitan area networks. To enhance the network throughput and extend the coverage of base station, IEEE then defined the 802.16j standard. Clearly, one of the popular applications for WiMAX is the multicast service. On the other hand, the design of power saving technologies is important since mobile stations are often powered by batteries. In this thesis, we study the maximum energy-efficient multicast scheduling ¡]MEMS¡^ problem for an IEEE 802.16j network with transparent mode. Specifically, the base station should determine how to schedule the multicast data in a multicast superframe such that the multicast energy efficiency of network is maximal. We first prove that the MEMS problem is NP-complete. Then on the basis of SMBC-AMC, we propose its variant, called SMBC-relay, to solve this problem. However, in SMBC-relay, the base station may send the same multicast data several times, wasting the scarce bandwidth. Hence we we propose a genetic algorithm-based multicast scheduling algorithm, called GAMS. One of the key features of GAMS is that the base station can control when to terminate the algorithm by stopping the evolution at any time. Simulation results show that GAMS significantly outperforms SMBC-relay in terms of multicast energy efficiency.

Utility mining algorithms have recently been proposed to discover high utility itemsets from a quantitative database. Factors such as profits or prices are concerned in measuring the utility values of purchased items for revealing more useful knowledge to managers. Nearly all the existing algorithms are performed in a batch way to extract high utility itemsets. In real-world applications, transactions may, however, be inserted, deleted or modified in a database. The batch mining procedure requires more computational time for rescanning the whole updated database to maintain the up-to-date knowledge. In the first part of this thesis, two algorithms for data insertion and data deletion are respectively proposed for efficiently updating the discovered high utility itemsets based on pre-large concepts. The proposed algorithms firstly partition itemsets into three parts with nine cases according to whether they are large (high), pre-large or small transaction-weighted utilization in the original database. Each part is then performed by its own procedure to maintain and update the discovered high utility itemsets. Based on the pre-large concepts, the original database only need to be rescanned for much fewer itemsets in the maintenance process of high utility itemsets. Besides, the risk of privacy threats usually exists in the process of data collection and data dissemination. Sensitive or personal information are required to be kept as private information before they are shared or published. Privacy-preserving utility mining (PPUM) has thus become an important issue in recent years. In the second part of this thesis, two evolutionary privacy-preserving utility mining algorithms to hide sensitive high utility itemsets in data sanitization for inserting dummy transactions and deleting transactions are respectively proposed. The two evolutionary privacy-preserving utility mining algorithms find appropriate transactions for insertion and deletion in the data-sanitization process. They adopt a flexible evaluation function with three factors. Different weights are assigned to the three factors depending on users¡¦ preference. The maintenance algorithms proposed in the first part of this thesis are also used in the GA-based approach to reduce the cost of rescanning databases, thus speeding up the evaluation process of chromosomes. Experiments are conducted as well to evaluate the performance of the proposed algorithms.

Abstract In this dissertation, person identification systems based on Karhunen-Loeve transform (KLT) are investigated. Both speaker and face recognition are considered in our design. Among many aspects of the system design issues, three important problems: how to improve the correct classification rate, how to reduce the computational cost and how to increase the robustness property of the system, are addressed in this thesis. Improvement of the correct classification rate and reduction of the computational cost for the person identification system can be accomplished by appropriate feature design methodology. KLT and hard-limited KLT (HLKLT) are proposed here to extract class related features. Theoretically, KLT is the optimal transform in minimum mean square error and maximal energy packing sense. The transformed data is totally uncorrelated and it contains most of the classification information in the first few coordinates. Therefore, satisfactory correct classification rate can be achieved by using only the first few KLT derived eigenfeatures. In the above data transformation process, the transformed data is calculated from the inner products of the original samples and the selected eigenvectors. The computation is of course floating point arithmetic. If this linear transformation process can be further reduced to integer arithmetic, the time used for both person feature training and person classification will be greatly reduced. The hard-limiting process (HLKLT) here is used to extract the zero-crossing information in the eigenvectors, which is hypothesized to contain important information that can be used for classification. This kind of feature tremendously simplifies the linear transformation process since the computation is merely integer arithmetic. In this thesis, it is demonstrated that the hard-limited KL transform has much simpler structure than that of the KL transform and it possess approximately the same excellent performances for both speaker identification system and face recognition system. Moreover, a hybrid KLT/GMM speaker identification system is proposed in this thesis to improve classification rate and to save computational time. The increase of the correct rate comes from the fact that two different sets of speech features, one from the KLT features, the other from the MFCC features of the Gaussian mixture speaker model (GMM), are applied in the hybrid system. Furthermore, this hybrid system performs classification in a sequential manner. In the first stage, the relatively faster KLT features are used as the initial candidate selection tool to discard those speakers with larger separability. Then in the second stage, the GMM is utilized as the final speaker recognition means to make the ultimate decision. Therefore, only a small portion of the speakers needed to be discriminated in the time-consuming GMM stage. Our results show that the combination is beneficial to both classification accuracy and computational cost. The above hybrid KLT/GMM design is also applied to a robust speaker identification system. Under both additive white Gaussian noise (AWGN) and car noise environments, it is demonstrated that accuracy improvement and computational saving compared to the conventional GMM model can be achieved. Genetic algorithm (GA) is proposed in this thesis to improve the speaker identification performance of the vector quantizer (VQ) by avoiding typical local minima incurred in the LBG process. The results indicates that this scheme is useful for our application on recognition and practice.

Object recognition is of much interest in recent industrial automation. Although a variety of approaches have been proposed to tackle the recognition problem, some cases such as overlapping objects, articulated objects, and low-resolution images, are still not easy for the existing schemes. Coping with these more complex images has remained a challenging task in the field. This dissertation, aiming to recognize objects from such images, proposes a new integrated method. For images with overlapping or articulated objects, graph matching methods are often used, seeing them as solving a combinatorial optimization problem. Both Hopfield network and the genetic algorithm are decent tools for the combinatorial optimization problems. Unfortunately, they both have intolerable drawbacks. The Hopfield network is sensitive to its initial state and stops at a local minimum if it is not properly given. The GA, on the other hand, only finds a near-global solution, and it is time-consuming for large-scale tasks. This dissertation proposes to combine these two methods, while eliminating their bad and keeping their good, to solve some complex recognition problems. Before the integration, some arrangements are required. For instance, specialized 2-D GA operators are used to accelerate the convergence. Also, the ¡§seeds¡¨ of the solution of the GA is extracted as the initial state of the Hopfield network. By doing so the efficiency of the system is greatly improved. Additionally, several fine-tuning post matching algorithms are also needed. In order to solve the homomorphic graph matching problem, i.e., multiple occurrences in a single scene image, the Hopfield network has to repeat itself until the stopping criteria are met. The method can not only be used to obtain the homomorphic mapping between the model and the scene graphs, but it can also be applied to articulated object recognition. Here we do not need to know in advance if the model is really an articulated object. The proposed method has been applied to measure some kinematic properties, such as the positions of the joints, relative linear and angular displacements, of some simple machines. The subject about articulated object recognition has rarely been mentioned in the literature, particularly under affine transformations. Another unique application of the proposed method is also included in the dissertation. It is about using low-resolution images, where the contour of an object is easily affected by noise. To increase the performance, we use the hexagonal grid in dealing with such low-resolution images. A hexagonal FFT simulation is first presented to pre-process the hexagonal images for recognition. A feature vector matching scheme and a similarity matching scheme are also devised to recognize simpler images with only isolated objects. For complex low-resolution images with occluded objects, the integrated method has to be tailored to go with the hexagonal grid. The low-resolution, hexagonal version of the integrated scheme has also been shown to be suitable and robust.

Exons are expression DNA sequences. A DNA sequence which includes gene has exons and introns. During transcription and translation, introns will be removed, and exons will remain to become protein. Many researchers need exon primers for PCR experiments. However, it is a difficult to find that many exon primers satisfy all primer design constraints at the same time. Here, we proposed an efficient exon primer design algorithm. The algorithm applies multiobjective genetic algorithm (MGA) instead of the single objective algorithm which can easily lend to unsuitable solutions. And a hash-index algorithm is applied to make specificity checking in a reasonable time. The algorithm has tested by a variety of mRNA sequences. These dry dock experiments show that our proposed algorithm can find primers which satisfy all exon primer design constraints.

To determine the maximum rectangular block (MRB) from a rare material as larger as possible indicates to increase of the rate of material usage. The cutting problem has been addressed since 1984. But its applications were strongly restricted due to simple definition of the cutting problem. In order to expand the area of applications, in this dissertation, a general cutting problem will be considered. At first, the rectangular boundary of the original material is replaced by an arbitrary closed region. Due to the general material profile, many other materials can be involved. When the maximum rectangular block has been obtained, the remaining closed space (RCS) of the material can be divided again. A blind search algorithm (BSA), which globally searches the MRB point-by-point from the boundary points of the contour, will be developed. The BSA is able to acquire the MRB from mother material continuously from larger areas to smaller ones until a predefined threshold value is reached. Although the MRB in an arbitrary closed region can be successfully resolved, two problems are still unsolved. The first limitation is that both edges of the MRB must be parallel with image axes. The second limitation is that the mother material needs to be uniform, i.e., no defects inside the material. In order to release these two assumptions, some algorithms will be presented. Applications of those techniques to the leather material will be demonstrated. In spite of resolving the cutting problem by the presented algorithms, a possible improvement is needed for larger MRBs. The challenge about larger MRBs is that how to make the searching process more efficiently. Therefore, two new methods of GA to obtain the MRB are proposed. By comparing the results using the BSA, the GA approaches are verified to be able to reach the near-optimal performance. Even though only leather material is focused in this research, the proposed methods can be easily extended to other industrial materials, especially for those expensive materials.

This study aims to develop a computer program to optimize the performance of loader mechanisms in backhoe-loaders. The complexity and the constraints imposed on the loader mechanism does not permit the use of classical optimization techniques used in the synthesis of mechanisms. Genetic algorithm is used to determine the values of the design parameters of the mechanism while satisfying the constraints and trying to maximize breakout forces that the machine can generate.