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The integration of solar Photovoltaic (PV) systems with the distribution network of Dubai Electricity & Water Authority has led to a situation that daily load curves of Medium Voltage (MV) feeders, with PV connections, will be changed to daily net load curves. Hence, peak load values will be determined based on net load curves that give lower values than original peaks. This in turn results in...
The use of pervasive systems and positioning technologies is increasing to track moving objects in several domains such as: health care, bioinformatic, natural diseases, etc. The modeling and the analysis of trajectory data resulting from moving objects activities have become a necessity in order to make the best decision. Tracking every movement leads to storing large volumes of data. The obtained...
The paper introduces a novel model-guided method for liver segmentation in CT and PET-CT images. Using a model liver volume as a template and a liver shape annotated in one of the patient slices, it automatically segments the whole liver volume in the patient dataset. The method is based on non-deformable registration of the model volume to the patient data and combination of components pre-segmented...
This paper aims to propose a two-stage clustering approach for calibration of traffic flow fundamental diagrams for dynamic traffic assignment (DTA) simulations. Unlike previous research efforts focusing on supervised grouping strategies that are largely dependent on roadway physical attributes, a data-driven perspective is explored using big traffic data. The two-regime modified Greenshields traffic...
Accelerated life testing (ALT) is one of the major methods of rapidly evaluating product field reliability and life. The topic of how to improve assessment precision using ALT and field observation data has attracted research interest recently. A new method of field reliability assessment is proposed in this paper. Firstly, in view of data fusion, a more accurate model is established by integrating...
There are numerous technologies and tools to acquire data related to the evolution of spatial phenomena over time. These data are typically organized as sequences of 2D geometric shapes obtained from observations taken at different times. The transformation of such sequences of 2D geometric shapes into spatiotemporal data representations, which can be easily processed and interpreted, has the potential...
Recently, segmentation-free methods for handwritten Chinese text were proposed. They do not require character-level annotations to be trained, and avoid character segmentation errors at decoding time. However, segmentation-free methods need to make at least as many predictions as there are characters in the image, and often a lot more. Combined with the fact that there are many characters in Chinese,...
Variability of low frequency noise (LFN) in MOSFETs is both geometry- and bias-dependent. RTS noise prevails in smaller devices where noise deviation is mostly area-dominated. As device dimensions increase, operating conditions determine noise variability maximizing it in weak inversion and increasing it with drain voltage. This dependence is shown to be directly related with fundamental carrier number...
A quantitative diagnostic method for liver fibrosis using ultrasound echo signals is highly required. A probability density function (PDF) of echo envelope from a normal liver can be approximated by a Rayleigh distribution; however, the PDF of echo envelope from liver fibrosis deviates from the Rayleigh distribution. To evaluate tissue characteristics in the ultrasound B-mode image, several amplitude...
This paper deals with clustering non-gaussian data with fixed bounds. It considers the problem using recursive mixture estimation algorithms under the Bayesian methodology. Such a solution is often desired in areas, where the assumption of normality of modeled data is rather questionable and brings a series of limitations (e.g., non-negative, bounded data, etc.). Here for modeling the data a mixture...
In this paper an approach to deal with Predictive Maintenance (PdM) problems with time-series data is discussed. PdM is a important approach to tackle maintenance and it is gaining an increasing attention in advanced manufacturing to minimize scrap materials, downtime, and associated costs. PdM approaches are generally based on Machine Learning tools that require the availability of historical process...
In this study, the parameters of different distributions such as the Weibull, Rayleigh, Lognormal, Gamma and Generalized Gamma, which are used for modelling wind speed, at the different heights 10 and 30 m, have been evaluated. The monthly and annual variations of the scale parameters of the distributions and performances of the distributions for the wind speed over these different heights have been...
The northwest of Turkey is an important region which is a rapidly developing industrial center. Thus, energy analysis of this region has a high importance for the region's geopolitical, economic and demographic structure. In this study, wind energy potential of three regions in northwest of Turkey is evaluated using wind speed data collected from three stations. The Weibull, Rayleigh and generalized...
Data stream is relatively new and emerging domain in the current era of Internet advancement. Clustering data streams is equally important and difficult because of the numerous hurdles attached to it. A number of algorithms have been proposed to offer solutions for efficient clustering. Grid-based clustering approach was adopted few years ago to overcome the limitations of conventional partition-based...
In this paper, a Constrained Mismatched Maximum Likelihood (CMML) estimator for the joint estimation of the scatter matrix and the power of Complex Elliptically Symmetric (CES) distributed vectors is derived under misspecified data models. Specifically, this estimator is obtained by assuming a Normal model while the data are sampled from a complex t-distribution. The convergence point of such CMML...
In this paper, we propose an automated preoperative planning method that estimates a plan fitted to the data of a new patient using a planned dataset of previous patients. Although mandibular reconstruction with fibular segments needs preoperative planning for the precise placement of segments, recent interactive planning software cannot secure objectivity of the planning and time-consuming trial-and-error...
Predictive maintenance task is of crucial role for any plant equipment supervision and scheduling of service activities. For this purpose it should be known what is current aging status of any equipment. Presented approach assumes that we know the nominal (starting) element curve and a damage one as well. It is also assumed that the aging course progresses according to some good practice aging Lorentz...
Predictive models for geometric shape deformation constitute an important component in geometric fidelity control for three-dimensional (3D) printing. However, model building is made difficult by the wide variety of possible process conditions and shapes. A methodology that can make full use of data collected on different shapes and conditions, and reduce the haphazard aspect of traditional statistical...
A new technique to perform fuzzification of Density based clusters of 2-dimensional data using regression models has been proposed here. Generally, for fuzzification in partition based clusters, one would compute the center of clusters and then assign memberships for the instances based on their relative distances from centers of all clusters, but the same approach cannot be used for density-based...
Tracking and characterizing both active and inactive Space Objects (SOs) is required for protecting space assets. Characterizing and classifying space debris is critical to understanding the threat they may pose to active satellites and manned missions. This work examines SO classification using brightness measurements derived from electrical-optical sensors. The classification approach discussed...
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