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This paper presents a knee torque estimation in non-pathological gait cycle at stance phase. Comparative modelling by using dynamics model and neural network model is discussed. Dynamics modelling is constructed by using simple two degree of freedom dynamics with Newtonian calculation approach and more complex four degree of freedom dynamics with Lagrangian calculation approach. Neural network based...
In the practice applications of defect detecting, large amounts of data need to be analyzed. In this paper, a new analysis method is developed based on adaboost algorithm. By using neural networks with a fixed structure, a series of models are built which may be not accurate. Error rates of the models are computed to gain and adjust the weights of every model. A higher accurate model is built by the...
In the present work a Cuckoo Search (CS) trained Neural Network (NN) or NN-CS based model has been proposed to detect Chronic Kidney Disease (CKD) which has become one of the newest threats to the developing and undeveloped countries. Studies and surveys in different parts of India have suggested that CKD is becoming a major concern day by day. The financial burden of the treatment and future consequences...
While the task of Optical Character Recognition is deemed to be a solved problem in many languages, it still requires certain improvements in some languages with more complex script structures such as Farsi. Furthermore, Deep Convolution Neural Networks have reached excellent results in various computer vision tasks, including character recognition. Although, these networks require a great amount...
Deep neural networks (DNNs), which show outstanding performance in various areas, consume considerable amounts of memory and time during training. Our research led us to propose a controlled dropout technique with the potential of reducing the memory space and training time of DNNs. Dropout is a popular algorithm that solves the overfitting problem of DNNs by randomly dropping units in the training...
With the cloud computing development, elastic scaling capability is an important factor to ensure the quality of cloud services. In this paper, the author designed resource requirement model about web system based on neural network under the certain quality of service on cloud platforms. According to the model, the method and mechanism for elastic scaling is realized by BP algorithm on cloud platforms.
Recently, more neuroscience researches focus on the role of dendritic structure during the neural computation. Inspired by the specified topologies of numerous dendritic trees, we proposed a single neural model with a particular dendritic structure. The dendrites are composed of several branches, and these branches correspond to three distributions in coordinate, which are used to classify the training...
Automatic Speech Recognition is an active field of research to identify speech patterns for providing the equivalent text. Many types of interactive software applications are available and the uses of these applications are limited due to language barriers. Therefore development of speech recognition systems in local languages will help anyone to make use of this technological advancement. This paper...
The power flow in distribution grids is becoming more complicated as reverse power flows and undesired voltage rises might occur under particular circumstances due to integration of renewable energy sources, increasing the occurrence of critical bus voltages. To identify these critical feeders the observability of distribution systems has to be improved. To increase the situational awareness of the...
Accurate individual anatomical joint models are becoming increasingly important for both realistic animation and diagnostic medical applications. A number of recent approaches have exploited unit quaternions to eliminate singularities when modelling orientations between limbs at a joint. This has resulted in the development of unit quaternion based joint constraint validation and correction methods...
On-Line Learning Behavior depend on learning subject self-control learning, collaborative learning, and obtaining of support and help. Based on learning subject, the On-Line learning behavior need the real-time monitoring and the effective instruction to through the evaluation in the learning process. The BP algorithm model of evaluating E-Learning behavior selects the learning behavior which affects...
In this work a neural network NARX model has been developed in order to predict availability of a heavy duty equipment of an important copper mining site in Chile. Four exogenous inputs have been considered (Number of Detentions, Mean Time to Repair, Mean Time between Failures and Use of Physical Availability) while Availability is the autoregressive variable. A 30 days moving average has been performed...
In this paper, we propose the Multi-Layer Perceptron (MLP) technique for Neighbor Selection in Peer-to-Peer (P2P) Computing to reduce the communication overhead. The selection of Neighbor is one of the challenging areas in P2P Computing. Root Mean Square Error and Testing time are two Parameters considered for neighbor selection in P2P network. The objective of the proposed technique is to minimize...
This paper proposes a novel kernel-based mixture of experts model for linear regression. The method is novel in that it formulates the mixture of experts model for linear regression so that kernel functions can be used. This allows the method to work directly in terms of kernels and avoids the explicit introduction of the feature vector, allowing one to use feature spaces of high, even infinite dimensionality...
Sequential Monte Carlo methods (Particle Filters) have been successfully applied to the online training of neural networks. However the generic Particle Filter requires the model noise to be known prior to training. Furthermore, the random walk assumption with which the network weights are modeled by may be problematic as a result of the insufficient knowledge of the model noise. In this paper, the...
At present, patients whose have suffered from stroke in Thailand are increasing every year. Stroke impairments relate to many functions such as sensory, motor function, communication, visual and emotional function which depend on brain's lesion. Physical examinations and assessments are important for planning the rehabilitation programs. For this reason, there are several information for medical decision...
The non-local feature always plays an important role in improving performance of SMT. Nonlinear neural network model can take better advantage of non-local features to improve the performance of translation through the introduction of the hidden layer. So this paper will build reranking models based on neural network to make use of non-local features to improve the translation performance. In this...
The accurate simulation of anatomical joint models is becoming increasingly important for both realistic animation and diagnostic medical applications. Recent models have exploited unit quaternions to eliminate singularities when modelling orientations between limbs at a joint. This has led to the development of unit quaternion based joint constraint validation and correction methods. This paper builds...
The reservoir is one of flood mitigation methods that aim to reduce the effect of flood at downstream flood prone areas. At the same time the reservoir also serves other purposes. Through modelling, how the reservoir operator made decisions in the past can be revealed. Consequently, the information can be used to guide reservoir operator making present decision especially during emergency situations...
Online nonlinear real-time simulation is carried out based on neural network in the turbine governing system. The interpolation fitting process of the nonlinear relation is performed on the PC and only the weights and thresholds of the non-linear relationship are transmitted to the control core DSC of the local computer, which effectively improved the nonlinear real-time simulation speed. The process...
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