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.
In this study, we investigate the control performance of an adaptive-type feedforward feedback controller using multilayer hypercomplex-valued neural network. The control system consists of a neural network and a feedback controller, whereby the control input of a plant is synthesised online by using the sum of the multilayer hypercomplex-valued neural network and the feedback controller to track...
Speaker recognition has been developed over many years and it comes with many different methods. MFCC is one of more the successful methods due to it being generally modeled on the human auditory system. It represents high success rate of recognition and strong robustness against noise in the lower frequency regions. However, in the higher frequency regions, it captures speaker characteristics information...
Automatic image description generation is a challenging task in computer vision and computational linguistics. It helps people to get access to social media images easily. Content generation and surface realization are two important phases of this task. Deep learning techniques have a major role in content generation phase. The important scenario in content generation is object recognition. Deep learning...
In order to avoid the serious loss caused by the fire, to achieve the initial fire alarm, multi-sensor system is widely used in fire prediction. In the processing method, it is essentially different from the traditional classical signal. The multi-sensor information fusion system can be merged at different levels. It can be abstracted distributed into three levels: information fusion layer, feature...
Atmospheric aerosol is one of the most important factors that cause the random variation of solar radiation intensity. In view of the problem that the atmospheric aerosol optical depth (AOD) is difficult to obtain real-timely and conveniently with high accuracy, estimation model of AOD using PM concentration is proposed in this paper. Two kinds of modeling methods BP neural network and support vector...
Neural network is a kind of machine learning algorithm, applied in many ways. The traditional predictive guidance of aerocraft is hard to resolve the contradiction among robustness, real-time and the guidance of precision. The paper provides a predictive guidance algorithm for aerocraft, by combining neural network with predictive guidance to solve this problem. This research about the new style guidance...
Word2vec is a novel technique for the study and application of natural language processing(NLP). It trains a word embedding neural network model with a large training corpus. After the model is trained, each word is represented by a vector in the specified vector space. The vectors obtained possess many interesting and useful characteristics that are implicitly embedded with the original words. The...
This paper examines the application of a deep learning approach to converting night-time images to day-time images. In particular, we show that a convolutional neural network enables the simulation of artificial and ambient light on images. In this paper, we illustrate the design of the deep neural network and some preliminary results on a real indoor environment and two virtual environments rendered...
The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. A key issue is that the neural network training problem is nonconvex, hence optimization algorithms may not return a global minima. This paper provides sufficient conditions...
Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic...
This paper proposes an improved method for DBN, by means of introducing the detachment rate. The introduction of detachment rate can play a similar average role, and can make the complex relationship between the neurons weakened, so that DBN learning has stronger robustness. Three kinds of data (corresponding to healthy, faulted and deteriorating) were classified by the improved depth belief network...
With the development of network technology and e-commerce, online-purchasing has become a fashion which takes a significant ratio of the whole market. Product reviews in e-market platform have a lot of information, and buyers tend to rely on the product'information and the reviews to determine the exactly quality of the product. However, the existence of fake reviews will mislead the consumers and...
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...
Pedestrian detection is one of the key technologies in automotive safety, robotic and intelligent video surveillance. Recently, deep convolutional neural networks have achieved significant effect in image classification and retrieval tasks. In this paper, we propose a novel deep convolutional neural networks model for pedestrian detection to simultaneously extract and classify pedestrian features...
As a kind of deep learning model, convolutional neural networks (CNNs) have greatly boosted the state-of-the-art performance and have found their successful applications in many fields, such as computer version, pattern recognition, natural language processing, etc. Many distinguished CNN models, for example, AlexNet, Google inception net, VGGNet, and so on, have been developed for various tasks....
How to operate a BFG/coal co-firing boiler in high efficiency is challenging for a gas/solid multi-fuel combustion system. Taking operation data from a real boiler, this study proposes an operation optimization strategy of BFG/coal co-firing boiler based on deep learning. Firstly, the thermal efficiency model is constructed based on deep learning with all the actual sampling data, which outperform...
To solve the problem of gradient descent (GD) method which has low accuracy and easily falling into local optimum, the radial basis function (RBF) based on immune algorithm system (IAS-RBF) is proposed. In this method, each antibody is a RBF neural network and the optimal affinity is calculated by immune algorithm system (IAS) to get the best antibody, then the optimal parameter of RBF neural network...
In this paper, an algorithm that based on pca-bp-bagging model is developed for the prediction of pathological data. This algorithm aims at improving the characteristics of bp neural network that the prediction accuracy of pathological data is low, the generalization ability of single bp neural network model is poor, and the anti-interference ability is weak. To enhance the performance of the whole...
Spare parts are indispensable resources to ensure equipment the normal operation and continuous production, especially for urban raü vehicles. When the spare parts storage is insufficient, the equipment can't be replaced or repair ed in time, which can cause serious loss. Therefore, it is important to forecast the demand of the urban rail vehicle spare parts. A combination forecasting method based...
In industrial process, some important variables such as quality index, efficiency index and concentration of product components are difficult or even impossible to be measured directly due to the limitation of technology. This phenomenon leads to few labeled data and plenty of unlabeled data. Traditional identification method for controlled auto regressive (CAR) model usually cannot deal with unlabeled...
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.