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The paper is devoted to a comprehensive modelling of the influence of the operation process and the climate-weather change process on the safety of a critical infrastructure. Particular models of critical infrastructure safety influenced by its inside among its components and subsystems dependences and by its outside operating environment threats and climate-weather hazards are created and a reasonable...
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...
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...
This paper has been written to identify the needs and competencies required for new graduate student intake in a small company dedicated to the design and build of process control systems for a variety of applications in a diverse range of industries. While emphasis has been placed on core technical competencies such as IT (Information Technology), electronics, control techniques, industrial process...
This paper deals with the design of a nonlinear model predictive control (NMPC) scheme for the regulation of the substrate concentration in a lipase production bioprocess that takes place inside a Fed-batch Bioreactor. The NMPC control structure is based on the nonlinear model of the process whose parameters are known and all the states are measurable. Minimization of the cost function is realized...
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...
In order to provide safety against high sea water levels, in many low-lying countries on the one hand dunes are maintained at a certain safety level and dikes are built, while on the other hand large control structures that can be controlled dynamically are constructed. Currently, these structures are often operated purely locally, without coordination on actions between different structures. Automatically...
The instability of the concentration of CO2 in the system of CO2 refining is controlled by the following means: recognizing the system by neural net work; building the prediction model and the technological parameters optimization model of the system; predicting the key producing parameters which affect the concentration of CO2 the most; optimizing and controlling the key producing parameters by the...
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...
Batch chemical processes have become significant in chemical manufacturing. Recently, economy globalization has resulted in growing worldwide competitions in traditional chemical process industry. In order to increase competition, reduce production costs and meet safety requirements, it is necessary to implement varies optimization methods and advanced control strategies. The goal of this paper is...
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...
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...
This work presents a SystemC-based simulation approach for fast performance analysis of parallel software components, using source code annotated with low-level timing properties. In contrast to other source-level approaches for performance analysis, timing attributes obtained from binary code can be annotated even if compiler optimizations are used without requiring changes in the compiler. To consider...
Design and optimization of microwave passive components is one of the most critical problems for RF IC designers. However, the state-of-the-art methods either have good efficiency but highly depend on the accuracy of the equivalent circuit models, which may fail the synthesis when the frequency is high; or fully depend on electromagnetic (EM) simulations, whose solution quality is high but are too...
By directly measuring human soluble blood samples by Fourier transform infrared spectroscopy (FTIR) and attenuated total reflection (ATR) technology, the rapid quantitative analysis method for human hemoglobin (HGB) was established. The optimization of Savitzky-Golay (SG) smoothing modes combined with partial least squares (PLS) factor was applied to optimize the model of FTIR/ATR spectroscopy analysis...
In this paper, an automatic network planner and optimizer are presented. Algorithms are developed to estimate a minimal number of access points needed to achieve a predefined throughput in the different rooms in a building, and to reduce the number of access points without reducing reception quality. The algorithms are applied to realistic building floor plans. Also, the concept of dynamic network...
The construction method of background value has been researched in this paper because it is one key to affect the precision and adaptability of GM(1,1) model. And a new calculation formula of background value has been reestablished based on the literatures. Besides, in order to further improve the precision, the initial value of GM (1,1) prediction formula has been developed and optimized by using...
This paper presents a method for complex product modeling based on approximation approach. Kriging models which are very popular for approximating deterministic computer models are used in this paper and different design of experiment (DOE) approaches are compared according to some assessment methods.
During construction of high buildings, subsidence increases as load increase, and safe or not and development trend are need to be known, so the method for predicating the deformation of high building is presented through the optimized method of grey system theory. The GM(1, 1) model establishing, the raw sequence's transformation and model optimized method are discussed, and the parameter optimized...
This paper studies the identification algorithm of parameters self adaptive SMO based on linear kernel function, and analyses its performance and advantages. For ARX model and long-term prediction model, the method is used to identify the model of main steam pressure of thermal system and dual-lane gas turbine engine of aero system. The simulation results show that the algorithm can effectively identify...
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