期刊文献+
共找到11篇文章
< 1 >
每页显示 20 50 100
An improved pulse coupled neural networks model for semantic IoT
1
作者 Rong Ma Zhen Zhang +3 位作者 Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung 《Digital Communications and Networks》 SCIE CSCD 2024年第3期557-567,共11页
In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data traffic.Meanwhile,with the... In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data traffic.Meanwhile,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication paradigm.However,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic information.With the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to increase.In contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image transmission.However,the parameters of PCNN are determined by trial and error,which limits its application.To overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this work.The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images.Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets.The IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information. 展开更多
关键词 Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation
下载PDF
Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network 被引量:6
2
作者 孙玉山 李岳明 +2 位作者 张国成 张英浩 吴海波 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第4期808-816,共9页
Autonomous underwater vehicles(AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corr... Autonomous underwater vehicles(AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model(estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective. 展开更多
关键词 autonomous underwater vehicle fault diagnosis THRUSTER improved Elman neural network
下载PDF
Optimization of processing parameters for microwave drying of selenium-rich slag using incremental improved back-propagation neural network and response surface methodology 被引量:4
3
作者 李英伟 彭金辉 +2 位作者 梁贵安 李玮 张世敏 《Journal of Central South University》 SCIE EI CAS 2011年第5期1441-1447,共7页
In the non-linear microwave drying process, the incremental improved back-propagation (BP) neural network and response surface methodology (RSM) were used to build a predictive model of the combined effects of ind... In the non-linear microwave drying process, the incremental improved back-propagation (BP) neural network and response surface methodology (RSM) were used to build a predictive model of the combined effects of independent variables (the microwave power, the acting time and the rotational frequency) for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are: the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process. 展开更多
关键词 microwave drying response surface methodology optimization incremental improved back-propagation neural network PREDICTION
下载PDF
Coal mine safety production forewarning based on improved BP neural network 被引量:38
4
作者 Wang Ying Lu Cuijie Zuo Cuiping 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2015年第2期319-324,共6页
Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method... Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production. 展开更多
关键词 Improved PSO algorithm BP neural network Coal mine safety production Early warning
下载PDF
Prediction of Parkinson’s Disease Using Improved Radial Basis Function Neural Network 被引量:1
5
作者 Rajalakshmi Shenbaga Moorthy P.Pabitha 《Computers, Materials & Continua》 SCIE EI 2021年第9期3101-3119,共19页
Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mecha... Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network(IRBFNN).Particle swarm optimization(PSO)with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN.The performance of RBFNN is seriously affected by the centers of hidden neurons.Conventionally K-means was used to find the centers of hidden neurons.The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.Thus,a metaheuristic algorithm called PSO integrated with K-means alleviates initial random centroid and computes optimal centers for hidden neurons in IRBFNN.The IRBFNN uses Particle swarm optimization K-means to find the centers of hidden neurons and the PSO K-means was designed to evaluate the fitness measures such as Intracluster distance and Intercluster distance.Experimentation have been performed on three Parkinson’s datasets obtained from the UCI repository.The proposed IRBFNN is compared with other variations of RBFNN,conventional machine learning algorithms and other Parkinson’s Disease prediction algorithms.The proposed IRBFNN achieves an accuracy of 98.73%,98.47%and 99.03%for three Parkinson’s datasets taken for experimentation.The experimental results show that IRBFNN maximizes the accuracy in predicting Parkinson’s disease with minimum root mean square error. 展开更多
关键词 Improved radial basis function neural network K-MEANS particle swarm optimization
下载PDF
Prediction of the Slope Solute Loss Based on BP Neural Network
6
作者 Xiaona Zhang Jie Feng +2 位作者 Zhiguo Yu Zhen Hong Xinge Yun 《Computers, Materials & Continua》 SCIE EI 2021年第12期3871-3888,共18页
The existence of soil macropores is a common phenomenon.Due to the existence of soil macropores,the amount of solute loss carried by water is deeply modified,which affects watershed hydrologic response.In this study,a... The existence of soil macropores is a common phenomenon.Due to the existence of soil macropores,the amount of solute loss carried by water is deeply modified,which affects watershed hydrologic response.In this study,a new improved BP(Back Propagation)neural network method,using Levenberg–Marquand training algorithm,was used to analyze the solute loss on slopes taking into account the soil macropores.The rainfall intensity,duration,the slope,the characteristic scale of macropores and the adsorption coefficient of ions,are used as the variables of network input layer.The network middle layer is used as hidden layer,the number of hidden nodes is five,and a tangent transfer function is used as its neurons transfer function.The cumulative solute loss on the slope is used as the variable of network output layer.A linear transfer function is used as its neurons transfer function.Artificial rainfall simulation experiments are conducted in indoor experimental tanks in order to verify this model.The error analysis and the performance comparison between the proposed method and traditional gradient descent method are done.The results show that the convergence rate and the prediction accuracy of the proposed method are obviously higher than that of traditional gradient descent method.In addition,using the experimental data,the influence of soil macropores on slope solute loss has been further confirmed before the simulation. 展开更多
关键词 Solute loss soil macropores improved BP neural network SLOPE
下载PDF
Object Recognition Algorithm Based on an Improved Convolutional Neural Network
7
作者 Zheyi Fan Yu Song Wei Li 《Journal of Beijing Institute of Technology》 EI CAS 2020年第2期139-145,共7页
In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted... In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition. 展开更多
关键词 object recognition selective search algorithm improved convolutional neural network(CNN)
下载PDF
GPS Spoofing Attack Detection in Smart Grids Based on Improved CapsNet 被引量:1
8
作者 Yuancheng Li Shanshan Yang 《China Communications》 SCIE CSCD 2021年第3期174-186,共13页
This paper analyzes the influence of the global positionong system(GPS)spoofing attack(GSA)on phasor measurement units(PMU)measurements.We propose a detection method based on improved Capsule Neural Network(CapsNet)to... This paper analyzes the influence of the global positionong system(GPS)spoofing attack(GSA)on phasor measurement units(PMU)measurements.We propose a detection method based on improved Capsule Neural Network(CapsNet)to handle this attack.In the improved CapsNet,the gated recurrent unit(GRU)is added to the front of the full connection layer of the CapsNet.The improved CapsNet trains and updates the network parameters according to the historical measurements of the smart grid.The detection method uses different structures to extract the temporal and spatial features of the measurements simultaneously,which can accurately distinguish the attacked data from the normal data,to improve the detection accuracy.Finally,simulation experiments are carried out on IEEE 14-,IEEE 118-bus systems.The experimental results show that compared with other detection methods,our method is proved to be more efficient. 展开更多
关键词 smart grid detection method improved capsule neural network phasor measurement units global positioning system spoofing attack
下载PDF
Identification of crack in a structural member using improved radial basis function(IRBF)neural networks 被引量:1
9
作者 Rajendra Machavaram Shankar Krishnapillai 《International Journal of Intelligent Computing and Cybernetics》 EI 2013年第2期182-211,共30页
Purpose–The purpose of this paper is to provide an effective and simple technique to structural damage identification,particularly to identify a crack in a structure.Artificial neural networks approach is an alternat... Purpose–The purpose of this paper is to provide an effective and simple technique to structural damage identification,particularly to identify a crack in a structure.Artificial neural networks approach is an alternative to identify the extent and location of the damage over the classical methods.Radial basis function(RBF)networks are good at function mapping and generalization ability among the various neural network approaches.RBF neural networks are chosen for the present study of crack identification.Design/methodology/approach–Analyzing the vibration response of a structure is an effective way to monitor its health and even to detect the damage.A novel two-stage improved radial basis function(IRBF)neural network methodology with conventional RBF in the first stage and a reduced search space moving technique in the second stage is proposed to identify the crack in a cantilever beam structure in the frequency domain.Latin hypercube sampling(LHS)technique is used in both stages to sample the frequency modal patterns to train the proposed network.Study is also conducted with and without addition of 5%white noise to the input patterns to simulate the experimental errors.Findings–The results show a significant improvement in identifying the location and magnitude of a crack by the proposed IRBF method,in comparison with conventional RBF method and other classical methods.In case of crack location in a beam,the average identification error over 12 test cases was 0.69 per cent by IRBF network compared to 4.88 per cent by conventional RBF.Similar improvements are reported when compared to hybrid CPN BPN networks.It also requires much less computational effort as compared to other hybrid neural network approaches and classical methods.Originality/value–The proposed novel IRBF crack identification technique is unique in originality and not reported elsewhere.It can identify the crack location and crack depth with very good accuracy,less computational effort and ease of implementation. 展开更多
关键词 Structures Stress(materials) Mechanical behaviour of materials BEAMS Structural members Crack identification Structural damage Frequency domain Latin hypercube sampling Improved radial basis function neural networks Reduced search space moving technique
原文传递
How to accurately extract large-scale urban land?Establishment of an improved fully convolutional neural network model
10
作者 Boling YIN Dongjie GUAN +4 位作者 Yuxiang ZHANG He XIAO Lidan CHENG Jiameng CAO Xiangyuan SU 《Frontiers of Earth Science》 SCIE CSCD 2022年第4期1061-1076,共16页
Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neur... Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neural network was provided for perceiving large-scale urban change,by modifying network structure and updating network strategy to extract richer feature information,and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution image.This paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network,the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88,which is better than traditional fully convolutional neural networks,it performs well in the construction land extraction at the scale of small and medium-sized cities. 展开更多
关键词 improved fully convolutional neural network remote sensing image classification city boundary precision evaluation
原文传递
An Algorithm to Recognize the Target Object Contour Based on 2D Point Clouds by Laser-CCD-Scanning 被引量:1
11
作者 MAO Hongyong SHI Duanwei +4 位作者 ZHOU Ji XU Pan CHEN Shiyu XU Yuxiang FENG Fan 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2015年第4期355-361,共7页
For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional(2D) point cloud that is output by th... For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional(2D) point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects. 展开更多
关键词 laser-CCD scanning sensor 2D point cloud contour recognition improved Hu invariant moments BP neural network
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部