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The parameter extraction method of De Soto is implemented in the single diode model and evaluated against measured data at varying temperatures and irradiances. The simulated current-voltage-curves (IV-curves) of the method can be seen as acceptable at reference conditions, but they result in a different IV-curve, not matching the measured IV-curve, at conditions that differ from the reference conditions...
The South African industrial sector is under significant financial pressure and need to reduce production costs to remain competitive in the global and local market. Some steel plants use reheating furnaces to reheat materials before hot rolling. These furnaces use industrial fuel gases as an energy source. Process by-product and purchased gases can be used as fuel. Inefficiencies in gas distribution...
In this day and age, many universities in Thailand have specific targets for students to achieve academic success and earn honours degrees. Such targets are based in part on the unique identity of each university. This paper aimed to provide a modelling and recommender application, which gathers and utilises the characteristics and attitudes of students in order to accurately predict and select the...
The continuously growing wealth of data has radically changed the data science landscape. At the same time, Big Data tools have known important progress in terms of optimising performance and scalability. However, applying them into practical deployment settings is still a challenging task that is highly dependent on the particularities of the data. In this paper, we present our experiences with implementing...
We introduce the notion of Optimal Patterns (OPs), defined as the best patterns according to a given user preference, and show that OPs encompass many data mining problems. Then, we propose a generic method based on a Dynamic Constraint Satisfaction Problem to mine OPs, and we show that any OP is characterized by a basic constraint and a set of constraints to be dynamically added. Finally, we perform...
In the future there will be an increased uptake of solar and battery systems in the residential sector, driven by falling battery costs and increasing electricity tariffs. The increased uptake means we need new methods to forecast electricity demand when considering these technologies. This paper has achieved this goal using a two stage model. Stage 1: A machine learning demand model has been created...
The European Electronic Toll Service (EETS) allows different possibilities for the overall system design of interoperable toll systems. We study the effect of the overall system design on the operational costs using a model based, holistic, executable specification. The model is set up to give an accurate description of the interdependences as seen in the German toll system. Two different scenarios...
This paper is based on data analysis and literatures, land use system, demographic factors, and economic development situation, fiscal and financial policies which have influence on the price of the house are studied. In order to discuss housing price purely on the basis of statistical data, the main factors and their weights are calculated based on the survey of house price and usage of grey theory...
Accurate cardinality estimates are essential for a successful query optimization. This is not only true for relational DBMSs but also for RDF stores. An RDF database consists of a set of triples and, hence, can be seen as a relational database with a single table with three attributes. This makes RDF rather special in that queries typically contain many self joins. We show that relational DBMSs are...
This paper presents using bootstrap aggregated neural networks for the modelling and optimization control of reactive polymer composite moulding processes. Neural network models for the degree of cure are developed from process operational data. To improve model generalization capability, multiple neural networks are developed from bootstrap re-samples of the original data and are combined. Optimal...
The reliability of numerical control machine tool is one of the key problems to restrict the rapid development of numerical control machine too industry. Based on traditional reliability analysis methods, the degradation failure modes of numerical control system are investigated, the reliability modeling analysis method based on competing mode of hard and soft failure is presented. The relations between...
We present a Nonlinear Model Predictive Control (NMPC) algorithm for real-time control of large-scale river networks in delta areas. The algorithm consists of an iterative, finite-horizon optimization of the system over a short-term control horizon. The underlying set of nonlinear internal process models represents relevant physical phenomena such as flow routing in the river network, and the dynamics...
Anomaly diagnostics and fault classification with prognostics is an active research topic, and real-time detection of anomalies and their classification has remained a critical challenge to be overcome. We developed an innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems to mitigate catastrophic failures. The efficacy of the Model-based...
The monitoring system of the dam is introduced. To different dams and monitoring points at different locations of dams, because their geographical environment and geological environment are different, their deformation discipline is different, we can preplace some deformation models, and let the computer look for the deformation model whose forecast error is least. In order to improve the fitted accuracy...
The channelized Hotelling observer (CHO) has become a widely used approach for evaluating medical image quality, acting as a surrogate for human observers in early-stage research on assessment and optimization of imaging devices and algorithms. Its popularity stems from experiments showing that, when an internal-noise model is introduced, the CHO's detection performance can be tuned to correlate well...
Recent work has shown that resonate-and-fire model is both computationally efficient and suitable for large network simulations. In this paper, we examine the estimation problem of a resonate-and-fire model with random threshold. The model parameters are divided into two sets. The first set is associated with subthreshold behavior and can be optimized by a nonlinear least squares algorithm. The other...
A GPU-accelerated OpenCL implementation of a back-propagation artificial neural network for the creation of QSAR models for drug discovery and virtual high-throughput screening is presented. A QSAR model for HSD achieved an enrichment of 5.9 and area under the curve of 0.83 on an independent data set which signifies sufficient predictive ability for virtual high-throughput screening efforts. The speed-up...
In this paper we deal with modeling serum proteolysis process from tandem mass spectrometry data. The parameters of peptide degradation process inferred from LC-MS/MS data correspond directly to the activity of specific enzymes present in the serum samples of patients and healthy donors. Our approach integrate the existing knowledge about peptidases' activity stored in MEROPS database with the efficient...
In large organizations and small firms in transportation, there is a growing need to use and analyze spatial data. Transportation system analysis and planning as well as mobility studies frequently use Geographic Information Systems (GIS). In this paper we propose the development of a web services platform dedicated to transportation and logistics. Taking advantage of the web services development...
The aim of this paper is to develop a methodology that makes it possible to take advantage of power transformer operators' knowledge in order to reach a better control of process of preventive maintenance (PM). The main idea is to construct probabilistic models using a Bayesian approach along with an age reduction model in order to compensate for both the lack of failure data on the maintained system...
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