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Parallel computing architectures like GPUs have traditionally been used to accelerate applications with dense and highly-structured workloads; however, many important applications in science and engineering are irregular and dynamic in nature, making their effective parallel implementation a daunting task. Numerical simulation of charged particle beam dynamics is one such application where the distribution...
Direct methanol fuel cell (DMFC) uses liquid methanol as fuel to generate electricity at low operating temperatures as well as to mainly produce water and carbon dioxide. Since DMFC performance characteristics are inherently complex, it can be postulated that artificial neural networks (ANN) represent a marked improvement in prediction capabilities. However, very little investigation has been done...
In order to accomplish the fault prediction of complicated and enormous mechanical equipment, this paper proposed a fault prediction model for complicated mechanical equipment that based on rough sets theory and BP neural network . Firstly,the discretization of continuous data was implemented by the discretization algorithm based on dynamic hierarchical clustering in rough set theory;secondly, an...
The combination of observed data and dynamical models of mean-field type over networked systems is a challenging problem because of non-linearity, high dimensionality and partial observations. In many networked systems, the effective extraction and utilization of the information contained in observed data should be accomplished by exploiting the availability of accurate predictive, proactive tools...
We propose a dynamic Bayesian network approach to forecast the short-term passenger flows of the urban rail network of Paris. This approach can deal with the incompleteness of the data caused by failures or lack of collection systems. The structure of the model is based on the causal relationships between the adjacent flows and is designed to take into account the transport service. To reduce the...
Occupant presence and behaviour have a large impact on building energy performance. With the availability of low cost and affordable sensors, accurate occupancy detection by combining sensor stream data with machine learning approaches becomes possible. In this paper, we propose a novel dynamical hidden semi-Markov model (D-HSMM) which can accurately detect occupancy pattern from sensor data stream...
With the rapid development of electronic commerce (E-commerce), information overload has become an issue in people's daily lives. Personalized products and services have thus drawn wide attentions, and personalized recommendation techniques provide effective tools to capture the user's interests and find out most relevant information to the user. In this paper, a new personalized recommendation algorithm...
In order to improve the prediction accuracy of cognitive radio spectrum and providing more reliable spectrum access for the subsequent spectrum detection, the dynamic fuzzy neural network is applied to predict the cognitive radio spectrum, and prove its feasibility. Simulation results show that the algorithm has higher accuracy than the general spectral prediction algorithm.
This paper presents an algorithm to improve sensor fusion results in outdoor WSNs using environmental models to redefine periodically which sensors to use and the amount of weight for each particular sensor in the fusion solution. Using daily forecast simulations of the monitored environment dynamics, clusters of sensor nodes sharing data correlation can be defined, for priority sensor selection in...
This paper discusses the possibility of using a Jordan neural network as a model of dynamic systems and it presents a Model Predictive Control (MPC) algorithm in which such a network is used for prediction. The Jordan network is a simple recurrent neural structure in which only one value of the process input signal (from the previous sampling instant) and only one value of the delayed output signal...
A novel nonlinear time series prediction method based on the dynamic clustering neural network is proposed. This method selects prediction samples which are similar with training samples according to the cluster analysis based on the dynamic characteristics of samples and then a new subset of the samples is obtained. All of the samples in this subset have the similar dynamic characteristics. By training...
A dynamic control policy with optimized dynamics is explored for its use in a model predictive control (MPC) algorithm for a nonlinear system modeled with a feedforward neural network. The nonlinear system is expressed as a polytopic quasi-linear-parameter-varying (quasi-LPV) system over a region of the state-input space and the dynamics of the policy are allowed to depend on the time-varying parameter...
In this paper, in order to overcome the deficiency of the traditional SVM, a positive mapping between price volatilities and sample periods of underlying financial time series has been assumed according to the theorems of behavioral finance. By embedding this mapping into the constraint equations of the classic SVM algorithm, an improved SVM model named DHC-SVM (Dynamic Heteroskedasticity Convertible...
The problem of recommending items to users is relevant to many applications and the problem has often been solved using methods developed from Collaborative Filtering (CF). Collaborative Filtering model-based methods such as Matrix Factorization have been shown to produce good results for static rating-type data, but have not been applied to time-stamped item adoption data. In this paper, we adopted...
Aiming at increasing the precision of tunnel settlement prediction, a modified support vector machine (SVM) based on the dynamic on-line sliding window (Dolsw) technique is proposed. In the prediction model, the historically observational settlement data act as the learning samples. The nonlinear relationship between settlement data and influencing variables is established on the basis of on-line...
An integrated pattern mining technique for query answering is proposed for marine sensor data. In pattern query, we adopt the dynamic time warping (DTW) method and propose the use of a query relaxation approach in finding similar patterns. We further calculate prediction from discovered similar patterns in marine sensor data. The predictive values are then compared with the forecast from hydrodynamic...
Several approaches have been introduced for modeling and prediction of nonlinear dynamics which have chaotic characteristics. Among these methods, data driven approaches such as Auto Regressive (AR) models, Nonlinear Auto Regressive (NAR) models, Radial Basis Function (RBF) networks, and Multi Layered Perceptron (MLP) neural networks have proven themselves to be powerful approaches in modeling and...
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