Description:
In this paper we propose a novel technique of using a hybrid evolutionary method, which uses a combination of genetic algorithm and matrix based solution methods such as QR factorization. The training of the model is based on a layer based hierarchical structure for the architecture and the weights for the Artificial Neural Network classifier. The architecture for the classifier is found using a binary search type procedure. The hierarchical structured algorithm (EALS-BT) is also a hybrid, because it combines the Genetic Algorithm based method with the Matrix based solution method for finding weights. A heuristic segmentation algorithm is initially used to over segment each word. Then the segmentation points are passed through the rule-based module to discard the incorrect segmentation points and include any missing segmentation points. Following the segmentation the contour is extracted between two correct segmentation points. The contour is passed through the feature extraction module that extracts the angular features, after which the EALS-BT algorithm finds the architecture and the weights for the classifier network. These recognized characters are grouped into words and passed to a variable length lexicon that retrieves words that have the highest confidence value.
Description:
Theodolites represent a well-established three-dimensional-point-measuring technology. However, when used for robot applications they have to be properly calibrated to fulfil the necessary accuracy requirements. The theodolite calibration methods reported in the literature involve the use of costly sophisticated equipment not easily available to most users. Therefore, a new simplified calibration technique is presented based on the use of a graduated precision bar suspended freely to align with the vertical direction. To develop efficient mathematical models, the theodolites will be regarded as 2R open-ended mechanisms with the end-effector axis directed along the line of sight. The proposed models are then coded in a computer program designed to verify the validity of the technique presented. The simulation results will be presented at the end of the paper.
Description:
Theodolites represent a well-established three-dimensional-point-measuring technology. However, when used for robot applications they have to be properly calibrated to fulfil the necessary accuracy requirements. The theodolite calibration methods reported in the literature involve the use of costly sophisticated equipment not easily available to most users. Therefore, a new simplified calibration technique is presented based on the use of a graduated precision bar suspended freely to align with the vertical direction. To develop efficient mathematical models, the theodolites will be regarded as 2R open-ended mechanisms with the end-effector axis directed along the line of sight. The proposed models are then coded in a computer program designed to verify the validity of the technique presented. The simulation results will be presented at the end of the paper.
Description:
The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix.
Description:
This paper focuses on pricing Energy Storage as a Service (ESaaS) for Transmission congestion relief (TCR). We consider a merchant storage facility that competes in an electricity market to trade energy and ancillary services on a day-to-day basis. The facility also has the opportunity to provide a firm TCR service to a regional network operator under a long-term contract. Providing the additional TCR service would impose limitations on the facility's ability to fully harvest daily market trade opportunities. Thus, we model the opportunity costs associated with the TCR service and use it in a hybrid cost-value customized pricing technique to determine the risk-constrained optimal price of ESaaS for TCR. Given the long-term nature of the commitment to provide the TCR service, we use the Conditional Value at Risk (CVaR) metric to mitigate the long-term financial risks faced by the facility. The proposed pricing strategy enables the storage owner to estimate the additional financial gains and the associated risks that would likely result from adding the new service to its operation. Numerical simulations are provided to support the proposed methodology.
Description:
"The purpose of this thesis is to develop and study new techniques for the mathematical modeling of dynamical systems and to apply these techniques to data classification problems. This approach is based on the notion of a fuzzy derivative. The main aim of the thesis is to examine this notion in data classification."
Description:
"The purpose of this thesis is to develop and study new techniques for the mathematical modeling of dynamical systems and to apply these techniques to data classification problems. This approach is based on the notion of a fuzzy derivative. The main aim of the thesis is to examine this notion in data classification."
Description:
Due to green power generation, large solar plants with capacities in the order of several MWs are increasingly installed worldwide and plenty of these plants participate in electricity markets. However, the uncertain production of a solar plant poses a noticeable risk to the profit of a solar producer. To overcome the associated risk, storage systems have been recently paired with solar plants. Not only storage systems can increase the reliability of photovoltaic (PV) solar power, but also can increase economic benefits through energy arbitrage in an electricity market. Although paring a storage system with a solar plant is an economically promising combination, a proper bidding strategy model, which appropriately characterizes the uncertainties of both PV solar power productions and electricity prices, is essential. To do so, this paper proposes an affinely adjustable robust bidding strategy for a solar producer paired with a battery storage system. The uncertainties associated with solar power productions and electricity prices are characterized through bounded intervals in a controllable polyhedral uncertainty set. Affine functions are used to solve the proposed model directly without decomposition. Numerical results illustrate the effectiveness of the proposed method.
Description:
Load and price forecasts are necessary for optimal operation planning in competitive electricity markets. However, most of the load and price forecast methods suffer from lack of an efficient feature selection technique with the ability of modeling the nonlinearities and interacting features of the forecast processes. In this paper, a new feature selection method is presented. An important contribution of the proposed method is modeling interaction in addition to relevancy and redundancy, based on information-theoretic criteria, for feature selection. Another main contribution of the paper is proposing a hybrid filter-wrapper approach. The filter part selects a minimum subset of the most informative features by considering relevancy, redundancy, and interaction of the candidate inputs in a coordinated manner. The wrapper part fine-tunes the settings of the composite filter.
Description:
"In many cities around the world traffic congestion has been increasing faster than can be dealt with by new road construction. To resolve this problem traffic management devices and technology such as ramp meters are increasingly being utilized."--leaf 1.
Description:
"In many cities around the world traffic congestion has been increasing faster than can be dealt with by new road construction. To resolve this problem traffic management devices and technology such as ramp meters are increasingly being utilized."--leaf 1.
Description:
The Douglas–Rachford algorithm is a classical and very successful method for solving optimization and feasibility problems. In this paper, we provide novel conditions sufficient for finite convergence in the context of convex feasibility problems. Our analysis builds upon, and considerably extends, pioneering work by Spingarn. Specifically, we obtain finite convergence in the presence of Slater’s condition in the affine-polyhedral and in a hyperplanar-epigraphical case. Various examples illustrate our results. Numerical experiments demonstrate the competitiveness of the Douglas–Rachford algorithm for solving linear equations with a positivity constraint when compared to the method of alternating projections and the method of reflection–projection.
Description:
Inverse design techniques in the area of fluid mechanics are normally conducted for continuous flow turbomachines rather than positive displacement devices. However, the work in this article is concerned with a class of rotary positive displacement device referred to as the limaçon-to-limaçon machine. The rotors and housings of these machines are manufactured of limaçon profiles, and are likely to suffer from interference if the rotors are not carefully profiled. Published literature indicates that solutions proposed to tackle the interference problem in these machines will adversely affect their efficiency figures. This notion motivated the work presented in this article, which first introduces the relevant mathematical models of the limaçon-to-limaçon machine and then uses these models to construct an inverse geometric design problem formulation. The proposed model has been coded in a computer program that utilises a Marquardt-Levenberg technique to converge to the required geometric parameters. Case studies are presented at the end of the article to verify the validity of the proposed inverse design model.
Description:
The coast of Bangladesh is funnel shaped. The narrowing of the Meghna estuary along with its peculiar topography creates a funneling effect that has a large impact on surge response. In order to have an accurate estimation of surge levels, the impacts of the estuary should be treated with due importance. To represent in detail the real complexities of the estuary, a very high resolution is required, which in turn necessitates more computational cost. Considering the facts into account, a location specific vertically integrated shallow water model in cylindrical polar coordinates is developed in this study to foresee water levels associated with a storm. A one-way nested grid technique is used to incorporate coastal complicities with minimum cost. In specific, a fine mesh scheme (FMS) capable of incorporating coastal complexities with acceptable accuracy is nested into a coarse mesh scheme (CMS) covering up to 15°N latitude in the Bay of Bengal. The coastal and island boundaries are approximated through appropriate stair step representation and the model equations are solved by a conditionally stable semi-implicit finite difference technique using a structured C-grid. Numerical experiments are performed using the model to estimate water levels due to surge associated with the April 1991 and AILA, 2009 cyclones, which struck the coast of Bangladesh. Time series of tidal level is generated from an available tide table through a cubic spline interpolation method. The computed surge response is superimposed linearly with the generated time series of tidal oscillation to obtain the time series of total water levels. The model results exhibit a good agreement with observation and reported data.
Description:
This paper presents a hybrid estimation and identification (HEI) method for identifying the load model parameters of voltage-dependent loads along with estimating the system states. The parameters of voltage-dependent load models are identified using multiple snapshots consecutively gathered through supervisory control and data acquisition system. The proposed HEI method imposes no additional cost to the installed metering devices or communication network. Mathematical aspects of the proposed HEI method are analytically proved and a four-step approach is presented for implementing it. Comprehensive numerical experiments and statistical analysis are presented to demonstrate the effectiveness of the proposed method for both load model identification and state estimation.