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This paper introduces a fitness data management system based on the Android platform for the strength fitness equipment, which can display, storage and mange fitness data. The server for this system is Tomcat and the mobile terminal's operating system is Android. App installed on the terminal interact with the database through HTTP transport protocol, which enable the visual management for fitness...
The present status of heart sound recognition is introduced in the paper. In order to improve the performance of heart sound recognition, a new model based on SVM is proposed. Firstly, the wavelet transform is used to reduce the noise of the heart sound, and then MFCC feature is extracted from heart sound. On this basis, the Support Vector Machine is used to build the classification model. In the...
How to develop an intelligent ventilator and control it well to provide a better experience and treatment effect for respiratory patients is still a difficult task needed to be solved. The existing problems focus on the control algorithm and the mechanical structure. Dedicated to these two problems, the paper proposes a design of CPAP ventilator based on the ANN algorithm. Firstly, the paper introduces...
Most researchers believe that multi-view images taken from the same object lie on a low dimensional manifold. Based on this observation, this paper mainly focuses on answering the question: how accurately rigid object pose can be estimated by manifold embedding? Firstly, a new manifold embedding method was proposed which owns the property of preserving relative position on the low-dimensional manifold...
With the developing of modern training, advanced requirements of training process such as creative, economical, realistic, safety have been proposed. It is hard for tradition training method to satisfy the training requirement of new technology and equipment in the background of modern industry. Traditional training methods were facing severe challenges. In order to solve the complex technical training...
In this paper, a multi — factor prediction model based on Radical Basis Function(RBF) neural network is proposed to accurately predict the temperature of rolling bearing. According to the factors that affect the rolling bearing, including load, speed, vibration, displacement, bearing temperature and ambient temperature, the working temperature of the rolling bearing is predicted by combining the historical...
This paper presents a new solution for SAR image target recognition by designing a light level convolutional neural network (CNN), and raises an unsupervised detection method by taking the advantages of convolutional features. Firstly, we train a shallow convolutional network by using Moving and Stationary Target Recognition (MSTAR) dataset to classify SAR targets. Then, we extract the outputs of...
When we use binary tree support vector machine (SVM) to work the multi-classification problems out, we always find that the structure of the binary tree has a large chance and it has a great influence on the classification efficiency of the classification model. To solve this problem, according to the idea of separating the most widely distributed class first, an improved binary tree SVM multiple...
In this paper, we propose a novel face recognition method that embeds the locality-constrained sparse representation in the dictionary learning framework. The shared-specific dictionary learning is employed to explicitly learn class-specific dictionary for each class that captures the most discriminative features of this class, and simultaneously learn a shared dictionary, whose atoms are shared by...
Word2Vec (Word to Vector) processes natural language by calculating the cosine similarity. However, the serial algorithm of original Word2Vec fails to satisfy the demands of training of corpus text because of the explosive growth of information. It has become the bottleneck owing to its comparatively low processing efficiency. The High Performance Computing (HPC) specializes in improving the calculation...
In order to improve the accuracy and stability of industrial fault detection and diagnosis, this paper introduces the deep learning theory and proposes an improved Deep Belief Networks (DBNs). In the first, this paper introduces the “centering trick” in the pre-training process of network. This method is done by subtracting offset values from visible and hidden variables. Then, in the process of network...
In view of the emotional polarity classification problem, the deep learning has the disadvantages of incomplete information extraction and low precision, a model combining bi-directional gated recurrent unit with multiple convolution neural network is proposed. The unit is used to extract the history and future information of the sentence, then use the multi-convolution neural network for system training,...
Storage reliability of the ammunition dominates the efforts in achieving the mission reliability goal. Prediction of storage reliability is important in practice to monitor the ammunition quality. In this paper we provided an integrated method where particle swarm optimization (PSO) algorithm is applied to adjust and optimize the BP neural network global parameters (weights and thresholds). The experiment...
Low-resolution (LR) is a challenging problem in the real world. In order to obtain better performance for low-resolution face recognition (LRFR), this paper employs a novel approach for matching low-resolution images with high resolution (HR) images based on two-dimensional linear discriminant analysis (2D-LDA) and metric learning method. The LR and HR images are transformed into a common space via...
At present, the detection of mixing uniformity in glass furnace batching system is mainly realized by artificial detection. However, this method is time-consuming and laborious, and there are some risks. For the problem of mixing uniformity detection, the nonlinear relation between the actual weight value and the mixing uniformity is established by the BP neural network, which can predict the mixing...
The level of automated unmanned surface vehicle is always dependent on human interactions. An automated collision avoidance approach is proposed which is based on the visual system in order to improve it. Deep convolutional neural network (CNN) is a popular deep neural network for pattern recognition. Three types of encounter scenes are created and recorded which are used as the CNN training samples...
Moving object tracking with discriminative model is very popular in recent years, which focuses on online selecting highly informative features to maximize the separability between object and background. An adapted particle filter tracker with online learning and inheriting discriminative model is proposed in this paper. Top-ranked discriminative features are selected into appearance model by Online...
Despite great progress has been made in recent years, efficient and robust people detection continues to be a challenging problem in the filed of computer vision. In this paper, we propose a highly efficient indoor people detect method based on RGB-D sensor. First, two RGB and depth feature fusing strategies are proposed and compared. Secondly, an improved non-maximum suppression algorithm is proposed...
Support vector machine (SVM) algorithm received much attention in the research of voiceprint recognition, especially for small sample datasets. However, with the increase of recognition number and speech features number, the rate of model training and recognition is significantly reduced. In order to solve the problem, a new weighted clustering algorithm is proposed, which use “one to one” SVM model...
The inverse kinematics solution of the Six-DOF Serial Robots is a highly complex nonlinear problem, and the existence of the solution is not unique, therefore, the inverse kinematics method has attracted extensive attention. In this paper, BP neural network is applied to the inverse kinematics of Six-DOF Serial Robots, and the process of inverse solution is transformed into training of network weights...
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