Solutions of Fractional Diffusion-Wave Equations in Terms of H-functions

MSC 2010: 35R11, 42A38, 26A33, 33E12

Bayesian Prediction of Weibull Distribution Based on Fixed and Random Sample Size

2000 Mathematics Subject Classification: 62E16, 65C05, 65C20.

Fractional Korovkin Theory Based on Statistical Convergence

2000 Mathematics Subject Classification: 41A25, 41A36, 40G15.

Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics

2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.

Control of cascaded DC-DC converter based hybrid battery energy storage systems - Part II:Lyapunov approach

A cascaded DC-DC boost converter is one of the ways to integrate hybrid battery types within a grid-tie inverter. Due to the presence of different battery parameters within the system such as, state-of-charge and/or capacity, a module based distributed power sharing strategy may be used. To implement this sharing strategy, the desired control reference for each module voltage/current control loop needs to be dynamically varied according to these battery parameters. This can cause stability problem within the cascaded converters due to relative battery parameter variations when using the conventional PI control approach. This paper proposes a new control method based on Lyapunov Functions to eliminate this issue. The proposed solution provides a global asymptotic stability at a module level avoiding any instability issue due to parameter variations. A detailed analysis and design of the nonlinear control structure are presented under the distributed sharing control. At last thorough experimental investigations are shown to prove the effectiveness of the proposed control under grid-tie conditions.

Solar PV-powered SRM drive for EVs with flexible energy control functions

Electric vehicles (EVs) provide a feasible solution to reducing greenhouse gas emissions and thus become a hot topic for research and development. Switched reluctance motors (SRMs) are one of promised motors for EV applications. In order to extend the EVs’ driving miles, the use of photovoltaic (PV) panels on the vehicle helps decrease the reliance on vehicle batteries. Based on phase winding characteristics of SRMs, a tri-port converter is proposed in this paper to control the energy flow between the PV panel, battery and SRM. Six operating modes are presented, four of which are developed for driving and two for standstill on-board charging. In the driving modes, the energy decoupling control for maximum power point tracking (MPPT) of the PV panel and speed control of the SRM are realized. In the standstill charging modes, a grid-connected charging topology is developed without a need for external hardware. When the PV panel directly charges the battery, a multi-section charging control strategy is used to optimize energy utilization. Simulation results based on Matlab/Simulink and experiments prove the effectiveness of the proposed tri-port converter, which has potential economic implications to improve the market acceptance of EVs.

Improving energy efficiency in a wireless sensor network by combining cooperative MIMO with data aggregation

In wireless sensor networks where nodes are powered by batteries, it is critical to prolong the network lifetime by minimizing the energy consumption of each node. In this paper, the cooperative multiple-input-multiple-output (MIMO) and data-aggregation techniques are jointly adopted to reduce the energy consumption per bit in wireless sensor networks by reducing the amount of data for transmission and better using network resources through cooperative communication. For this purpose, we derive a new energy model that considers the correlation between data generated by nodes and the distance between them for a cluster-based sensor network by employing the combined techniques. Using this model, the effect of the cluster size on the average energy consumption per node can be analyzed. It is shown that the energy efficiency of the network can significantly be enhanced in cooperative MIMO systems with data aggregation, compared with either cooperative MIMO systems without data aggregation or data-aggregation systems without cooperative MIMO, if sensor nodes are properly clusterized. Both centralized and distributed data-aggregation schemes for the cooperating nodes to exchange and compress their data are also proposed and appraised, which lead to diverse impacts of data correlation on the energy performance of the integrated cooperative MIMO and data-aggregation systems.

Raman fibre laser based amplification in coherent transmission systems

The thesis presents a detailed study of different Raman fibre laser (RFL) based amplification techniques and their applications in long-haul/unrepeatered coherent transmission systems. RFL based amplifications techniques were characterised from different aspects, including signal/noise power distributions, relative intensity noise (RIN), mode structures of induced Raman fibre lasers, and so on. It was found for the first time that RFL based amplification techniques could be divided into three categories in terms of the fibre laser regime, which were Fabry-Perot fibre laser with two FBGs, weak Fabry-Perot fibre laser with one FBG and very low reflection near the input, and random distributed feedback (DFB) fibre laser with one FBG. It was also found that lowering the reflection near the input could mitigate the RIN of the signal significantly, thanks to the reduced efficiency of the Stokes shift from the FW-propagated pump. In order to evaluate the transmission performance, different RFL based amplifiers were evaluated and optimised in long-haul coherent transmission systems. The results showed that Fabry-Perot fibre laser based amplifier with two FBGs gave >4.15 dB Q factor penalty using symmetrical bidirectional pumping, as the RIN of the signal was increased significantly. However, random distributed feedback fibre laser based amplifier with one FBG could mitigate the RIN of the signal, which enabled the use of bidirectional second order pumping and consequently give the best transmission performance up to 7915 km. Furthermore, using random DFB fibre laser based amplifier was proved to be effective to combat the nonlinear impairment, and the maximum reach was enhanced by >28% in mid-link single/dual band optical phase conjugator (OPC) transmission systems. In addition, unrepeatered transmission over >350 km fibre length using RFL based amplification technique were presented experimentally using DP-QPSK and DP-16QAM transmitter.

A bizonyosságfüggvények elméletének alkalmazása a pénzügyi kimutatások ellenőrzésében (The application of the theory of belief functions in the audit of financial statements)

A könyvvizsgálati kockázat a téves auditjelentés kiadásának kockázata olyan esetekben, amikor a beszámoló lényeges hibás állítást tartalmaz. Ez a kockázat indirekt módon a hitelintézetek és pénzügyi vállalkozások működésében is megjelenik azokban az esetekben, amikor a lényeges hibás állítást a finanszírozott vállalkozás auditált beszámolója tartalmazza, amelynek az alapján finanszírozási döntést hoznak, vagy a finanszírozás folytatásáról a beszámolóban szereplő, hibás információkból számított hitelkovenánsok alapján döntenek. A könyvvizsgálat kockázatában a vizsgált gazdálkodó üzleti kockázatai tükröződnek vissza, ezért a kockázat felmérése és az ellenőrzés ennek alapján való megtervezése, majd végrehajtása kulcsfontosságú. Jelen tanulmány – kapcsolódva a Hitelintézeti Szemle 2011. évi 4. számához – szintén a kockázat és bizonytalanság témakörét tárgyalja, pontosabban ennek egy gyakorlati vetületét: a bizonyosságfüggvények (belief functions) alkalmazását a könyvvizsgálatban; mindezt a teljesség és a tankönyvszerű rendszerfelépítés igénye nélkül. A módszer ugyanis hazánkban szinte ismeretlen, nemzetközi viszonylatban viszont empirikus kutatásban is rámutattak már az alkalmazás lehetséges előnyeire a hagyományos valószínűségelméleten alapuló számszerű kockázatbecslésekkel szemben. Eszerint a bizonyosságfüggvények jobban reprezentálják a könyvvizsgálóknak a kockázatról alkotott képét, mint a valószínűségek, mert – szemben a hagyományos modellel – nem két, hanem három állapotot kezelnek: a pozitív bizonyíték létezését, a negatív bizonyíték létezését és a bizonyíték hiányának esetét. _______ Audit risk is the risk that the auditor expresses an inappropriate audit opinion when the fi nancial statements are materially misstated. This kind of risk indirectly appears in the fi nancial statements of fi nancial institutions, when the material misstatement is in the fi nanced entity’s statements that serve as a basis for lending decisions or when the decision is made based upon credit covenants calculated from misstated information. The risks of the audit process refl ect the business risks of the auditee, so the assessment of risks, and further the planning and performance of the audit based on it is of key importance. The current study – connecting to No 4 2011 of Hitelintézeti Szemle – also discusses the topic of risk and uncertainty, or to be more precise a practical implementation of the aforementioned: the application of belief functions in the fi eld of external audit. All this without the aim of achieving completeness or textbook-like scrutiny in building up the theory. While the formalism is virtually unknown in Hungary, on the international scene empirical studies pointed out the possible advantages of the application of the method in contrast to risk assessments based on the traditional theory of probability. Accordingly, belief functions provide a better representation of auditors’ perception of risk, as in contrast to the traditional model, belief functions deal with three rather than two states: the existence of supportive evidence, that of negative evidence and the lack of evidence.

Indication strength of coenological similarity patterns based on genus-level taxon lists and prevalence distribution

Several methods and indicators can be used to evaluate the coenological state of a given habitat, the ones which can be created simply, quickly, standardizably and reliably and which can be used to exactly quantify the state of a given habitat in point of numbers can be of outstanding practical importance in ecology. One possible method is the examination of the genera which can be found in a given habitat in great abundance and have little number of species and various ecological characteristics. For this purpose one of the most appropriate groups is that of ground-dwelling oribatid mites (Acari: Oribatida). In our research, joining the bioindication methodological project of the “Adaptation to Climate Change” Research Group of the Hungarian Academy of Sciences, the indication strength of genus-level taxon lists and the effects of the main pattern-generating factors creating similarity patterns were analysed with the help of data series on oribatid mites collected by us and originating from literature. Our aim was to develop a method with the help of which the difference expressed with distance functions between two oribatid mite genus lists originating from any sources can correspond to spatial and temporal scales. Our results prove that these genus lists are able to express the spatial distance of the habitats. With the help of this base of comparison changes in disturbed or transformed habitats can be expressed by means of oribatid mite communities, with spatial and temporal distances.

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

A methodological framework for modeling pavement maintenance costs for projects with performance-based contracts

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

A thermally wavelength-tunable photonic switch based on silicon microring resonator

Silicon photonics is a very promising technology for future low-cost high-bandwidth optical telecommunication applications down to the chip level. This is due to the high degree of integration, high optical bandwidth and large speed coupled with the development of a wide range of integrated optical functions. Silicon-based microring resonators are a key building block that can be used to realize many optical functions such as switching, multiplexing, demultiplaxing and detection of optical wave. The ability to tune the resonances of the microring resonators is highly desirable in many of their applications. In this work, the study and application of a thermally wavelength-tunable photonic switch based on silicon microring resonator is presented. Devices with 10μm diameter were systematically studied and used in the design. Its resonance wavelength was tuned by thermally induced refractive index change using a designed local micro-heater. While thermo-optic tuning has moderate speed compared with electro-optic and all-optic tuning, with silicon’s high thermo-optic coefficient, a much wider wavelength tunable range can be realized. The device design was verified and optimized by optical and thermal simulations. The fabrication and characterization of the device was also implemented. The microring resonator has a measured FSR of ∼18 nm, FWHM in the range 0.1-0.2 nm and Q around 10,000. A wide tunable range (>6.4 nm) was achieved with the switch, which enables dense wavelength division multiplexing (DWDM) with a channel space of 0.2nm. The time response of the switch was tested on the order of 10 μs with a low power consumption of ∼11.9mW/nm. The measured results are in agreement with the simulations. Important applications using the tunable photonic switch were demonstrated in this work. 1×4 and 4×4 reconfigurable photonic switch were implemented by using multiple switches with a common bus waveguide. The results suggest the feasibility of on-chip DWDM for the development of large-scale integrated photonics. Using the tunable switch for output wavelength control, a fiber laser was demonstrated with Erbium-doped fiber amplifier as the gain media. For the first time, this approach integrated on-chip silicon photonic wavelength control.

Reduced-parameter model of head-related transfer functions for synthesized spatial audio

Digital systems can generate left and right audio channels that create the effect of virtual sound source placement (spatialization) by processing an audio signal through pairs of Head-Related Transfer Functions (HRTFs) or, equivalently, Head-Related Impulse Responses (HRIRs). The spatialization effect is better when individually-measured HRTFs or HRIRs are used than when generic ones (e.g., from a mannequin) are used. However, the measurement process is not available to the majority of users. There is ongoing interest to find mechanisms to customize HRTFs or HRIRs to a specific user, in order to achieve an improved spatialization effect for that subject. Unfortunately, the current models used for HRTFs and HRIRs contain over a hundred parameters and none of those parameters can be easily related to the characteristics of the subject. This dissertation proposes an alternative model for the representation of HRTFs, which contains at most 30 parameters, all of which have a defined functional significance. It also presents methods to obtain the value of parameters in the model to make it approximately equivalent to an individually-measured HRTF. This conversion is achieved by the systematic deconstruction of HRIR sequences through an augmented version of the Hankel Total Least Squares (HTLS) decomposition approach. An average 95% match (fit) was observed between the original HRIRs and those re-constructed from the Damped and Delayed Sinusoids (DDSs) found by the decomposition process, for ipsilateral source locations. The dissertation also introduces and evaluates an HRIR customization procedure, based on a multilinear model implemented through a 3-mode tensor, for mapping of anatomical data from the subjects to the HRIR sequences at different sound source locations. This model uses the Higher-Order Singular Value Decomposition (HOSVD) method to represent the HRIRs and is capable of generating customized HRIRs from easily attainable anatomical measurements of a new intended user of the system. Listening tests were performed to compare the spatialization performance of customized, generic and individually-measured HRIRs when they are used for synthesized spatial audio. Statistical analysis of the results confirms that the type of HRIRs used for spatialization is a significant factor in the spatialization success, with the customized HRIRs yielding better results than generic HRIRs.

Mitigating congestion by integrating time forecasting and realtime information aggregation in cellular networks

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.