Short-term wind power forecasting is crucial for the efficient operation of power systems with high wind power penetration. Many forecasting approaches have been developed in the past to forecast short-term wind power. In recent years, artificial neural network-based approaches (ANNs) have been one of the most effective and popular approaches for short-term wind power forecasting because of the availability of large amounts of historical data and strong computational power. Although ANNs usually perform well for short-term wind power forecasting, further improvement can be obtained by selecting suitable input features, model parameters, and using forecasting techniques like spatial correlation and ensemble for ANNs. In this research, the effect of input features, model parameters, spatial correlation and ensemble techniques on short-term wind power forecasting performance of the ANNs models was evaluated. Pearson correlation coefficients between wind speed and other meteorological variables, together with a basic ANN model, were used to determine the impact of different input features on the forecasting performance of the ANNs. The effect of training sample resolution and training sample size on the forecasting performance was also investigated. To separately investigate the impact of the number of hidden layers and the number of hidden neurons on short-term wind power forecasting and to keep a single variable for each experiment, the same number of hidden neurons was used in each hidden layer. The ANNs with a total of 20 hidden neurons are shown to be sufficient for the nonlinear multivariate wind power forecasting problems faced in this dissertation. The ANNs with two hidden layers performed better than the one with a single hidden layer because additional hidden layer adds nonlinearity to the model. However, the ANNs with more than two hidden layers have the same or worse forecasting performance than the one with two hidden layers. ANNs with too many hidden layers and hidden neurons can overfit the training data. Spatial correlation technique was used to include meteorological variables from highly correlated neighbouring stations as input features to provide more surrounding information to the ANNs. The advantages of input features, model parameters, and spatial correlation and ensemble techniques were combined to form an ANN-based ensemble model to further enhance the forecasting performance from an individual ANN model. The simulation results show that all the available meteorological variables have different levels of impact on forecasting performance. Wind speed has the most significant impact on both short-term wind speed and wind power forecasting, whereas air temperature, barometric pressure, and air density have the smallest effects. The ANNs perform better with a higher data resolution and a significantly larger training sample size. However, one requires more computational power and a longer training time to train the model with a higher data resolution and a larger training sample size. Using the meteorological variables from highly related neighbouring stations do significantly improve the forecasting accuracy of target stations. It is shown that an ANNs-based ensemble model can further enhance the forecasting performance of an individual ANN by obtaining a large amount of surrounding meteorological information in parallel without encountering the overfitting issue faced by a single ANN model.

Yes / A confocal microscope provides a sequence of images of the corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patient’s cornea. Hybrid model based on snake and particle swarm optimisation (S-PSO) is proposed in this paper to analyse the confocal endothelium images. The proposed system is able to pre-process (quality enhancement, noise reduction), detect the cells, measure the cell density and identify abnormalities in the analysed data sets. Three normal corneal data sets acquired using confocal microscope, and two abnormal endothelium images associated with diseases have been investigated in the proposed system. Promising results are achieved and the performance of this system are compared with the performance of two morphological based approaches. The developed system can be deployed as clinical tool to underpin the expertise of ophthalmologists in analysing confocal corneal images.

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With the growing importance of software on which human lives increasingly depend, the correctness requirement of the underlying software becomes especially critical. However, the increasing complexities and sizes of modern software systems pose special challenges on the effectiveness as well as efficiency of software verification. Two major obstacles include the quality of test generation in terms of error detection in software testing and the state space explosion problem in software formal verification (model checking). In this dissertation, we investigate several hybrid techniques that explore dynamic (with program execution), static (without program execution) as well as the synergies of multiple approaches in software verification from the perspectives of testing and model checking. For software testing, a new simulation-based internal variable range coverage metric is proposed with the goal of enhancing the error detection capability of the generated test data when applied as the target metric. For software model checking, we utilize various dynamic analysis methods, such as data mining, swarm intelligence (ant colony optimization), to extract useful high-level information from program execution data. Despite being incomplete, dynamic program execution can still help to uncover important program structure features and variable correlations. The extracted knowledge, such as invariants in different forms, promising control flows, etc., is then used to facilitate code-level program abstraction (under-approximation/over-approximation), and/or state space partition, which in turn improve the performance of property verification. In order to validate the effectiveness of the proposed hybrid approaches, a wide range of experiments on academic and real-world programs were designed and conducted, with results compared against the original as well as the relevant verification methods. Experimental results demonstrated the effectiveness of our methods in improving the quality as well as performance of software verification. For software testing, the newly proposed coverage metric constructed based on dynamic program execution data is able to improve the quality of test cases generated in terms of mutation killing — a widely applied measurement for error detection. For software model checking, the proposed hybrid techniques greatly take advantage of the complementary benefits from both dynamic and static approaches: the lightweight dynamic techniques provide flexibility in extracting valuable high-level information that can be used to guide the scope and the direction of static reasoning process. It consequently results in significant performance improvement in software model checking. On the other hand, the static techniques guarantee the completeness of the verification results, compensating the weakness of dynamic methods. / Ph. D.

Integrated circuits, from general purpose microprocessors to application specific designs (ASICs), have become ubiquitous in modern technology. As our applications have become more complex, so too have the circuits used to drive them. Moore's law predicts that the number of transistors on a chip doubles every 18-24 months. This explosion in circuit size has also lead to significant growth in testing effort required to verify the design. In order to cope with the required effort, the testing problem must be approached from several different design levels. In particular, exploiting the Register Transfer Level for test generation allows for the use of relational information unavailable at the structural level. This dissertation demonstrates several novel methods for generating tests applicable for both structural and functional tests. These testing methods allow for significantly faster test generation for functional tests as well as providing high levels of fault coverage during structural test, typically outperforming previous state of the art methods. First, a semi-formal method for functional verification is presented. The approach utilizes a SMT-based bounded model checker in combination with an ant colony optimization based search engine to generate tests with high branch coverage. Additionally, the method is utilized to identify unreachable code paths within the RTL. Compared to previous methods, the experimental results show increased levels of coverage and improved performance. Then, an ant colony optimization algorithm is used to generate high quality tests for fault coverage. By utilizing co-simulation at the RTL and gate level, tests are generated for both levels simultaneously. This method is shown to reach previously unseen levels of fault coverage with significantly lower computational effort. Additionally, the engine was also shown to be effective for behavioral level test generation. Next, an abstraction method for functional test generation is presented utilizing program slicing and data mining. The abstraction allows us to generate high quality test vectors that navigate extremely narrow paths in the state space. The method reaches previously unseen levels of coverage and is able to justify very difficult to reach control states within the circuit. Then, a new method of fault grading test vectors is introduced based on the concept of operator coverage. Operator coverage measures the behavioral coverage in each synthesizable statement in the RTL by creating a set of coverage points for each arithmetic and logical operator. The metric shows a strong relationship with fault coverage for coverage forecasting and vector comparison. Additionally, it provides significant reductions in computation time compared to other vector grading methods. Finally, the prior metric is utilized for creating a framework of automatic test pattern generation for defect coverage at the RTL. This framework provides the unique ability to automatically generate high quality test vectors for functional and defect level testing at the RTL without the need for synthesis. In summary, We present a set of tools for the analysis and test of circuits at the RTL. By leveraging information available at HDL, we can generate tests to exercise particular properties that are extremely difficult to extract at the gate level. / Ph. D.

The PMU has been found to carry great deal of value for applications in the wide area monitoring of power systems. Historically, deployment of these devices has been limited by the prohibitive cost of the device itself. Therefore, the objective of the conventional optimal PMU placement problem is to find the minimum number devices, which if carefully placed throughout the network, either maximize observability or completely observe subject to different constraints. Now due to improved technology and digital relays serving a dual use as relay & PMU, the cost of the PMU device itself is not the largest portion of the deployment cost, but rather the substation installation. In a recently completed large-scale deployment of PMUs on the EHV network, Virginia Electric & Power Company (VEPCO) has found this to be so. The assumption then becomes that if construction work is done in a substation, enough PMU devices will be placed such that everything at that substation is measured. This thesis presents a technique proposed to minimize the number of substation installations thus indirectly minimizing the synchrophasor deployment costs. Also presented is a brief history of the PMU and its applications along with the conventional Optimal PMU placement problem and the scope for expanding this work. / Master of Science

Many species of bats primarily use echolocation, a type of active sensing wherein bats emit ultrasonic pulses and listen to echoes, for guidance and navigation. Swarms of such bats are a unique type of multi-agent systems that feature bats's echolocation and flight behaviors. In the work of this dissertation, we used bat swarming as an inspiration for multi-agent systems to study various topics which include predation success, active sensing, and collision avoidance. To investigate the predation success, we modeled a group of bats hunting a number of collectively behaving prey. The modeling results demonstrated the benefit of localized grouping of prey in avoiding predation by bats. In the topics regarding active sensing and collision avoidance, we studied individual behavior in swarms as bats could potentially benefit from information sharing while suffering from frequency jamming, i.e., bats having difficulty in distinguishing between self and peers's information. We conducted field experiments in a cave and found that individual bat increased biosonar output as swarm size increased. The experimental finding indicated that individual bat acquired more sensory information in larger swarms even though there could be frequency jamming risk. In a simulation wherein we modeled bats flying through a tunnel, we showed the increasing collision risk in larger swarms for bats either sharing information or flying independently. Thus, we hypothesized that individual bat increased pulse emissions for more sensory information for collision avoidance while possibly taking advantage of information sharing and coping with frequency jamming during swarming. / Ph. D.

Over the years, phased array antennas have provided electronic scanning with high gain and low sidelobe levels for many radar and satellite applications. The need for higher bandwidth as well as greater scanning ability has led to research in the area of aperiodically spaced antenna arrays. Aperiodic arrays use variable spacing between antenna elements and generally require fewer elements than periodically spaced arrays to achieve similar far field pattern performance. This reduction in elements allows the array to be built at much lower cost than traditional phased arrays. This thesis introduces the concept of aperiodic phased arrays and their design via optimization algorithms, specifically Particle Swarm Optimization. An axial mode helix is designed as the antenna array element to obtain the required half power beamwidth and bandwidth. The final optimized aperiodic array is compared to a traditional periodic array and conclusions are made. / Master of Science