scholarly journals Research on the Peripheral Sound Visualization Using the Improved Ripple Mode

2011 ◽  
Vol 2-3 ◽  
pp. 123-126
Author(s):  
Bin Xu ◽  
Dan Yang ◽  
Yun Yi Zhang ◽  
Xu Wang
Keyword(s):  
Neural Network ◽  
Power Spectrum ◽  
Real Time ◽  
Sound Source ◽  
Sound Intensity ◽  
Visualization Method ◽  
Audio Signals ◽  
Sound Sources ◽  
The Real ◽  
Spectrum Function

In this paper, we proposed a peripheral sound visualization method based on improved ripple mode for the deaf. In proposed mode, we designed the processes of transforming sound intensity and exterminating the locations of sound sources. We used power spectrum function to determine the sound intensity. ARTI neural network was subtly applied to identify which kind of the real-time input sound signals and to display the locations of the sound sources. We present the software that aids the development of peripheral displays and four sample peripheral displays are used to demonstrate our toolkit’s capabilities. The results show that the proposed ripple mode correctly showed the information of combination of the sound intensity and location of the sound source and ART1 neural network made accurate identifications for input audio signals. Moreover, we found that participants in the research were more likely to achieve more information of locations of sound sources.

scholarly journals Pedestrian detection based on improved LeNet-5 convolutional neural network

2019 ◽  
Vol 13 ◽  
pp. 174830261987360 ◽  
Author(s):  
Chuan-Wei Zhang ◽  
Meng-Yue Yang ◽  
Hong-Jun Zeng ◽  
Jian-Ping Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Network Model ◽  
Pedestrian Detection ◽  
The Real ◽  
Better Than

In this article, according to the real-time and accuracy requirements of advanced vehicle-assisted driving in pedestrian detection, an improved LeNet-5 convolutional neural network is proposed. Firstly, the structure of LeNet-5 network model is analyzed, and the structure and parameters of the network are improved and optimized on the basis of this network to get a new LeNet network model, and then it is used to detect pedestrians. Finally, the miss rate of the improved LeNet convolutional neural network is found to be 25% by contrast and analysis. The experiment proves that this method is better than SA-Fast R-CNN and classical LeNet-5 CNN algorithm.

scholarly journals A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jianjun Ni ◽  
Liuying Wu ◽  
Pengfei Shi ◽  
Simon X. Yang
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Planning Method ◽  
Planning Problem ◽  
Detection Range ◽  
The Real ◽  
Time Path

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

scholarly journals Sentinel-2 Cloud Removal Considering Ground Changes by Fusing Multitemporal SAR and Optical Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3998
Author(s):  
Jianhao Gao ◽  
Yang Yi ◽  
Tang Wei ◽  
Haoguan Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Sar Images ◽  
The Real ◽  
Removal Process ◽  
Optical Images ◽  
Cloud Removal ◽  
Real Time Information ◽  
Time Information ◽  
Sentinel 2

Publicly available optical remote sensing images from platforms such as Sentinel-2 satellites contribute much to the Earth observation and research tasks. However, information loss caused by clouds largely decreases the availability of usable optical images so reconstructing the missing information is important. Existing reconstruction methods can hardly reflect the real-time information because they mainly make use of multitemporal optical images as reference. To capture the real-time information in the cloud removal process, Synthetic Aperture Radar (SAR) images can serve as the reference images due to the cloud penetrability of SAR imaging. Nevertheless, large datasets are necessary because existing SAR-based cloud removal methods depend on network training. In this paper, we integrate the merits of multitemporal optical images and SAR images to the cloud removal process, the results of which can reflect the ground information change, in a simple convolution neural network. Although the proposed method is based on deep neural network, it can directly operate on the target image without training datasets. We conduct several simulation and real data experiments of cloud removal in Sentinel-2 images with multitemporal Sentinel-1 SAR images and Sentinel-2 optical images. Experiment results show that the proposed method outperforms those state-of-the-art multitemporal-based methods and overcomes the constraint of datasets of those SAR-based methods.

Closed Loop Dynamic Fuzzy Neural Network for Real-Time Lifetime Forecasting

2013 ◽  
Vol 834-836 ◽  
pp. 1074-1080
Author(s):  
Wen Wang Li ◽  
Gao Feng Zheng ◽  
Jian Yi Zheng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Real Time ◽  
Fuzzy Neural Network ◽  
Closed Loop ◽  
Failure Time ◽  
Membrane Function ◽  
The Real ◽  
Fuzzy Neural ◽  
Monitoring Information

Real-time lifetime forecasting has extensive application in the fields of machine system manufacturing and integration, which is a good way to promote the dependability and operation stability. In this paper, a closed loop adaptive forecasting model with feedback channel of state monitoring information is built up for the real-time lifetime forecasting. The difference of working state between prediction and monitoring information is used to evaluate the prediction performance. The dynamic fuzzy neural network introduced into the prediction model, in which the fuzzy rule, membrane function and structure parameters can be adjusted according to the evaluate results. A service lifetime testing experiment of gear case is utilized to validate the prediction model. The proposed model achieved reasonable precision with an error of less than 1 hour between the failure time of experimental results and the forecasting remaining lifetime. The adaptive prediction method can deal with the real-time lifetime forecasting for multiple factors and nonlinear system without specific parameters structure.

Mix-Subdivision Dynamic Terrain Visualization Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3372-3377 ◽  
Author(s):  
Xiao Hui Zhao ◽  
Bao Di Xie ◽  
De Peng Wan ◽  
Qing Yun Wang
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Physical Model ◽  
Linear Interpolation ◽  
Basic Data ◽  
Visualization Method ◽  
The Real ◽  
Terrain Visualization ◽  
Simulation Systems ◽  
Dynamic Terrain

Dynamic Terrain is becoming more and more important in ground-based simulation systems. In military simulation systems, craters and ruts can improve the reality. In this paper, a dynamic terrain visualization method based on quadtree and multi-resolution voxel is presented in order to realize the real-time rendering for realistic craters in battlefield. Quadtree is selected as our basic data structure and mix-subdivided according to the size of the terrain. Scene tree is recursive subdivided according to both the distance between the node and camera and error criterion. Vertex is removed to solve the cracks and linear interpolation to solve popping in the algorithm. We also implement the visualization of craters through combining our algorithm with the physical model of craters based on multi-resolution voxel. The implementation results prove that the method are feasible and efficient.

Data Center Cooling Prediction Using Artificial Neural Network

2007 ◽  
Author(s):  
Saurabh K. Shrivastava ◽  
James W. VanGilder ◽  
Bahgat G. Sammakia
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Data Center ◽  
Cooling Performance ◽  
The Real ◽  
Time Prediction ◽  
Linear Relationships ◽  
The Neural Network ◽  
Data Center Cooling ◽  
Computational Fluid Dynamics Cfd

An analytical approach using artificial intelligence has been developed for assessing the cooling performance of data centers. This paper discusses the use of a Neural Network (NN) model in the real-time prediction of the cooling performance of a cluster of equipment in a data center environment. The NN model is used to predict the Capture Index (CI) [1] as a function of rack power, cooler airflow and physical/geometric arrangement for a cluster located in a simple room environment. The Neural Network is “trained” on thousands of hypothetical but realistic cluster variations for which CI values have been computed using either PDA [2] or full Computational Fluid Dynamics (CFD). The great value of the NN approach lies in its ability to capture the non-linear relationships between input parameters and corresponding capture indices. The accuracy of the NN approach is 3.8% (Root Mean Square Error) for a set of example scenarios discussed here. Because of the real-time nature of the calculations, the NN approach readily facilitates optimization studies. Example cases are discussed which show the integration of the NN approach and a genetic algorithm used for optimization.

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