An improved pulse coupled neural networks model for semantic IoT

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.

2-median location improvement problems under weighted l_1 norm and l_∞ norm on trees

This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices to the respective closest one of two prescribed vertices in the modified network is upper bounded by a given value.l1 norm and l∞norm are used to measure the total modification cost. These two problems have a strong practical application background and important theoretical research value. It is shown that such problems can be transformed into a series of sum-type and bottleneck-type continuous knapsack problems respectively.Based on the property of the optimal solution two O n2 algorithms for solving the two problems are proposed where n is the number of vertices on the tree.

Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

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.

Optimization of processing parameters for microwave drying of selenium-rich slag using incremental improved back-propagation neural network and response surface methodology

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.

Smart Rewiring:Improving Network Robustness Faster

Previous work puts forward a random edge rewiring method which is capable of improving the network robustness noticeably, while it lacks further discussions about how to improve the robustness faster. In this study, the detailed analysis of the structures of improved networks show that regenerating the edges between high-degree nodes can enhance the robustness against a targeted attack. Therefore, we propose a novel rewiring strategy based on regenerating more edges between high-degree nodes, called smart rewiring, which could speed up the increase of the robustness index effectively. The smart rewiring method also explains why positive degree-degree correlation could enhance network robustness.

Coal mine safety production forewarning based on improved BP neural network

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.

Classification of Infrared Monitor Images of Coal Using an Feature Texture Statistics and Improved BP Network

It is very important to accurately recognize and locate pulverized and block coal seen in a coal mine's infrared image monitoring system. Infrared monitor images of pulverized and block coal were sampled in the roadway of a coal mine. Texture statistics from the grey level dependence matrix were selected as the criterion for classification. The distributions of the texture statistics were calculated and analysed. A normalizing function was added to the front end of the BP network with one hidden layer. An additional classification layer is joined behind the linear layer. The recognition of pulverized from block coal images was tested using the improved BP network. The results of the experiment show that texture variables from the grey level dependence matrix can act as recognizable features of the image. The innovative improved BP network can then recognize the pulverized and block coal images.

GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems

Owing to the expansion of the grid interconnection scale,the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important.These characteristics can provide effective support in coordinated security control.However,traditional model-based frequencyprediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models.Therefore,this study presents a rolling frequency-prediction model based on a graph convolutional network(GCN)and a long short-term memory(LSTM)spatiotemporal network and named as STGCN-LSTM.In the proposed method,the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input.An improved GCN embedded with topology information is used to extract the spatial features,while the LSTM network is used to extract the temporal features.The spatiotemporal-network-regression model is further trained,and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information.The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response.The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems.

Prediction of Parkinson’s Disease Using Improved Radial Basis Function Neural Network

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.

Prediction of the Slope Solute Loss Based on BP Neural Network

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.

Object Recognition Algorithm Based on an Improved Convolutional Neural Network

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.

Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network

Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment,a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network(IADRSN)is proposed.Firstly,the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets.Then,a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established.To improve the ability of fault feature extraction of the model,the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer.To further improve the anti-noise ability of the model,the first layer of the model was set as the Drop-block layer.Finally,the labeled data samples were used for training model and the trained model was applied to the test set to output the fault diagnosis results.The results showed that the proposed method could achieve the fault diagnosis of wind turbine bearing more accurately in the high noise environment through comparison and verification.

A New Searching Strategy for the Lost Plane Based on RBF Neural Network Model and Global Optimization Model

In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power.

Improved deep residual shrinkage network for a multi-cylinder heavy-duty engine fault detection with single channel surface vibration

The health monitoring and fault diagnosis of heavy-duty engines are increasingly important for energy storage ecosystem. During operation, vibration characters corresponding to the specific fault need to be extracted from the overall system vibration. Faulty characteristics emanating from one single cylinder are also mixed with those from other cylinders. Besides, the change of working condition brings strong nonlinearities in surface vibration. To solve these problems, an improved deep residual shrinkage network (IDRSN) is developed for detecting diverse engine faults at various degrees using single channel surface vibration signal. Within IDRSN, a wide convolution kernel is utilized in first convolution layer to capture the long-term fault-related impacts and eliminate the short-time random impact. The residual network module is adopted to enhance the focus the relevant components of vibration signals. Mini-batch training strategy is used to improve the model stability. Meanwhile, Gradient-weighted class activation map is adopted to assess the consistency between the learned knowledge and the fault-related information. The IDRSN is implemented to diagnosing a diesel engine under various faults, faulty degrees and operating speeds. Comparisons with existing models are analyzed in terms of hyper-parameters, training samples, noise resistance, and visualization. Results demonstrate the proposed IDRSN's superior performance on fault diagnosis accuracy, stability, anti-noise performance, and anti-interference performance. An average accuracy rate of 98.38 % was achieved by the proposed IDRSN, in comparison to 96.64 % and 93.56 % achieved by the DRSN and the wide-kernel deep convolutional neural network respectively. These results highlight the proposed IDRSN's superiority in diagnosing multiple faults under various working conditions, offering a low-cost, highly effective, and applicable approach for complex fault diagnosis tasks.

Real-Time Recognition and Location of Indoor Objects

Object recognition and location has always been one of the research hotspots in machine vision.It is of great value and significance to the development and application of current service robots,industrial automation,unmanned driving and other fields.In order to realize the real-time recognition and location of indoor scene objects,this article proposes an improved YOLOv3 neural network model,which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network,which is applied to the detection and recognition of objects in indoor scenes.In this article,RealSense D415 RGB-D camera is used to obtain the RGB map and depth map,the actual distance value is calculated after each pixel in the scene image is mapped to the real scene.Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene.More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement.The running time of objects detection and recognition is reduced to less than half of the original.This improved network has a certain reference value for practical engineering application.

GPS Spoofing Attack Detection in Smart Grids Based on Improved CapsNet

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.

Network Modeling and Operation Optimization of Electricity-HCNG-Integrated Energy System

Hydrogen-enriched compressed natural gas(HCNG)has great potential for renewable energy and hydrogen utilization.However,injecting hydrogen into the natural gas network will change original fluid dynamics and complicate compressed gas's physical properties,threatening operational safety of the electricity-HCNG-integrated energy system(E-HCNG-IES).To resolve such problem,this paper investigates effect of HCNG on gas network dynamics and presents an improved HCNG network model,which embodies the influence of blending hydrogen on the pressure drop equation and line pack equation.In addition,an optimal dispatch model for the E-HCNG-IES,considering the“production-storage-blending-transportation-utilization”link of the HCNG supply chain,is also proposed.The dispatch model is converted into a mixed-integer second-order conic programming(MISOCP)problem using the second-order cone(SOC)relaxation and piecewise linearization techniques.An iterative algorithm is proposed based on the convex-concave procedure and bound-tightening method to obtain a tight solution.Finally,the proposed methodology is evaluated through two E-HCNGIES numerical testbeds with different hydrogen volume fractions.Detailed operation analysis reveals that E-HCNG-IES can benefit from economic and environmental improvement with increased hydrogen volume fraction,despite declining energy delivery capacityand line pack flexibility.

Fuzzy and IRLNC-based routing approach to improve data storage and system reliability in IoT

Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high possibility for data corruption in the mid of transmission.On the other hand,the network performance is also affected due to various attacks.To address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is proposed.Initially,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert system.Improved Random Linear Network Coding(IRLNC)is used to create an encoded packet.This encoded packet from the source and neighboring nodes is transmitted to the storage node.Finally,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)algorithm.Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics.Average residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 s.Based on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.

Identification of crack in a structural member using improved radial basis function(IRBF)neural networks

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.

Improving the Reliability of Computer Communication Networks

For networks that are directed or can be represented by a directed network,reversing one or more of the uni-directional links may provide the ability to reconnect a network that has been disconnected by link failure, In this paper,a new approach to reconfigure such networks is proposed.We develop a linear time algorithm which,when reachability has been destroyed by the removal of a single link,optimally restores teachability through the reversal of selected links.Multi-link failure reconnectability is discussed and an algorithm with polynomial complexity is given which provides a nearly optimum solution to reconnect the network.We show that the reliability of a network that allows reversals is at least twice more than that in which reversals are not permitted.Unfortunately,the reconnection of some networks cannot be established.Therefore,we discuss the maximization of reachability of such networks so that each node can reach maximum number of the other nodes.