The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The paper presents the two-stages adaptive approach for short-term forecast of parameters of expected operating conditions. The first stage involves decomposition of the time series into intrinsic modal functions and subsequent application of the Hilbert transform. During the second stage the computed modal functions and amplitudes are employed as input functions for artificial neural networks. Their...
This article reviews financial prediction models at home and abroad. On the basis of the actual situation in China, it introduces rough set theory and neural networks principles into the financial prediction model, builds the new prediction model, analysis the process of financial prediction model. Finally, it shows the science and rationality of the model using data in recent years of China's listed...
A high accurate wind speed forecasting can effectively reduce or avoid the adverse effect of wind farm on power grid, meanwhile enhances the competitive ability of wind power in electricity market. In this paper, a short-term wind speed forecasting method based on auto-regressive integrated moving average (ARIMA) and least square support vector machine (LS-SVM) is proposed. The weights are calculated...
Multi-Processor Systems-on-Chip (MPSoCs) are penetrating the electronics market as a powerful, yet commercially viable, solution to answer the strong and steadily growing demand for scalable and high performance systems, at limited design complexity. However, it is critical to develop dedicated system-level design methodologies for multi-core architectures that seamlessly address their thermal modeling,...
In most countries, especially in deltas, there is a long tradition in the management of water resource systems, in particular related to structural measures such as the construction of dikes and riverine/coastal hydraulic structures. We discuss how this infrastructure can be supported and managed in order to serve as non-structural measures. Therefore, we present a review about technology for the...
Efficient flood management requires accurate real time forecasts to allow early warnings, real time control of hydraulics structures or other actions. Commercially available computing tools typically use, for flow or level forecasting, hydraulic models derived from the numerical approximation of Saint-Venant equations. These tools need powerful computers, accurate knowledge of the riverbed topography...
Sludge recycling system is an important part of wastewater treatment plants. Because of the lack of control model and ensure water quality, the sludge recycle flow rate is controlled by high percentage of the influent to the wastewater treatment plants generally, which result in high energy consumption and decreasing of handling capacity. At present, the artificial intelligence modeling technique...
The aim of this work is to present the development of a computer aided multilevel modeling network for the systematic design and analysis of processes employing lipid technologies. This is achieved by decomposing the problem into four levels of modeling: i) pure component property modeling and a lipid-database of collected experimental data from industry and generated data from validated predictive...
The Leaky Integrate and Fire (LIF) model of a neuron is one of the best known models for a spiking neuron. A current limitation of the LIF model is that it may not accurately reproduce the dynamics of an action potential. There have recently been some studies suggesting that a LIF coupled with a multi-timescale adaptive threshold (MAT) may increase LIF's accuracy in predicting spikes in cortical neurons...
A new hybrid predictive coding scheme for lossy data compression is introduced and studied here. The proposed technique attempts to improve the compression performance of a conventional adaptive predictive coder through three levels of prediction. The first level estimates and removes a time-varying mean signal. The second level uses DFT and IDFT to estimate and remove the predictable high frequency...
In this paper, we propose the bitwise structured prediction model for lossless image coding, especially for the oscillatory regions. The learning-based model utilizes the regular features obtained from the predicted local data. At first, the pixel-wise prediction is decomposed into the bitwise ones. In each bit plane, the prediction of the current bit is simplified to the max margin estimation for...
This paper discusses on the adaptive neural network model for predicting the energy consumption at a metering station. The function of the metering system is to calculate the energy consumption of the outgoing gas flow. To ensure the robustness of the developed model, it is suggested to make the model an adaptive model that will periodically update the weights. This will ensure the reliability of...
Knowledge discovery and data analysis in resource constrained wireless sensor networks faces different challenges. One of the main challenges is to identify misbehaviors or anomalies with high accuracy while minimizing energy consumption in the network. In this paper, we extend a previous work of us and we present an algorithm for temporal anomalies detection in wireless sensor networks. Our experiments...
We present in this paper a framework for performance prediction of parallel programs on hierarchical clusters. This framework is mainly designed for the use by the switching functions in parallel adaptive applications. Indeed, the principal referred objectives by this framework are the accuracy of the prediction and the rapidity of the prediction process. To achieve these objectives, our framework...
In most real-life scenarios for Ambient Intelligence, the need arises for scalable simulations that provide reliable sensory data to be used in the preliminary design and test phases. This works present an approach to modeling data generated by a hybrid simulator for wireless sensor networks, where virtual nodes coexist with real ones. We apply our method to real data available from a public repository...
We present a video rate adaptation scheme for wireless video streaming which offers high implementation feasibility by requiring only loosely coupled system components. Simulation using the H.264 standard and 802.11n channel traces demonstrates the scheme's validity and shows performance improvements over non-adaptive schemes in similar environments.
This paper investigates the optimal model predictive control for the path tracking of an autonomous vehicle. For the control algorithm of an autonomous vehicle following pre-calculated path, two indices must be considered: 1. The performance index including the path tracking error and the energy consumed in control process. 2. The computational cost. In this paper, an optimal model predictive controller...
Rapidly and accurately estimating the impact of design decisions on performance metrics is critical to both the manual and automated design of wireless sensor networks. Estimating system-level performance metrics such as lifetime, data loss rate, and network connectivity is particularly challenging because they depend on many factors, including network design and structure, hardware characteristics,...
With ever increased needs for an improved product quality, production efficiency, and cost in today's globalized world market, advanced process control should not only realize the accuracy of each control loops, but also has the ability to achieve an optimization control of production indices that are closely related to the improved product quality, enhanced production efficiency and reduced consumption...
This work aims at the development of multi-rate adaptive model predictive control (MR-AMPC) based on the fast rate model, which is identified from irregularly sampled multi-rate data. The model is assumed to have output error structure and is parameterized using generalized orthonormal basis filters. The identified model is used to generate inter-sample estimates of the irregularly sampled outputs...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.