Track Defects Recognition Based on Axle-Box Vibration Acceleration and Deep- Learning Techniques

As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail squats fas-tener defects,etc.Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit.In this paper,an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network,and the coexistence of the above-mentioned typical track defects in the track system is considered.Firstly,the dynamic relationship between the track defects(using the example of the fastening defects)and the axle-box vibration acceleration(ABVA)is investigated using the dynamic vehicle-track model.Then,a simulation model for the coupled dynamics of the vehicle and track with different track defects is established,and the wavelet power spectrum(WPS)analysis is performed for the vibra-tion acceleration signals of the axle box to extract the characteristic response.Lastly,using wavelet spectrum photos as input,an automatic detection technique based on the deep convolution neural network(DCNN)is sug-gested to realize the real-time intelligent detection and identification of various track problems.Thefindings demonstrate that the suggested approach achieves a 96.72%classification accuracy.

Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network

Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect lives.Operators monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one time.Consequently,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras.When weapons are identified,the cameras sound an alarm.In the proposed IADC system,CNN was used to detect firearms and ammunition.The CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data classification.Additionally,this system generates an early warning through detection of ammunition before conditions become critical.Hence the faster and earlier the prediction,the lower the response time,loses and potential victims.The proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3.

Intelligent Intrusion Detection System Model Using Rough Neural Network

A model of intelligent intrusion detection based on rough neural network (RNN), which combines the neural network and rough set, is presented. It works by capturing network packets to identify network intrusions or malicious attacks using RNN with sub-nets. The sub-net is constructed by detection-oriented signatures extracted using rough set theory to detect different intrusions. It is proved that RNN detection method has the merits of adaptive, high universality, high convergence speed, easy upgrading and management.

Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to establish an efficient intelligent identification model for pavement cracks.The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix.It has been widely used in geotechnical engineering,computer vision,medicine,and other fields.However,there are three major problems in the application of neural networks to crack identification.There are too few layers,extracted crack features are not complete,and the method lacks the efficiency to calculate the whole picture.In this study,a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions.This method,using a convolutional layer instead of a fully connected layer,realizes full convolution and accelerates calculation.The region proposals come from the feature map at the end of the base network,which avoids multiple computations of the same picture.Online hard example mining and data-augmentation techniques are adopted to improve the model’s recognition accuracy.We trained and tested Concrete Crack Images for Classification(CCIC),which is a public dataset collected using smartphones,and the Crack Image Database(CIDB),which was automatically collected using vehicle-mounted charge-coupled device cameras,with identification accuracy reaching 91.4%and 86.4%,respectively.The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models,and can extract more complete and accurate crack features in CIDB.We also analyzed translation processing,fuzzy,scaling,and distorted images.The proposed model shows a strong robustness and stability,and can automatically identify image cracks of different forms.It has broad application prospects in practical engineering problems.

Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network

In view of the uncertainty of the status of primary users in cognitive networks and the fact that the random detection strategy cannot guarantee cognitive users to accurately find available channels,this paper proposes a joint random detection strategy using the idle cognitive users in cognitive wireless networks.After adding idle cognitive users for detection,the compressed sensing model is employed to describe the number of available channels obtained by the cognitive base station to derive the detection performance of the cognitive network at this time.Both theoretical analysis and simulation results show that using idle cognitive users can reduce service delay and improve the throughput of cognitive networks.After considering the time occupied by cognitive users to report detection information,the optimal participation number of idle cognitive users in joint detection is obtained through the optimization algorithm.

New Lite YOLOv4-Tiny Algorithm and Application on Crack Intelligent Detection

Conforming to the rapidly increasing market demand of crack detection for tall buildings,the idea of integrating deep network technology into wall-climbing robot for crack detection is put forward in this paper.Taking the dependence and hardware requirements when deployed on such edge devices as Raspberry Pi into consideration,the Darknet neural network is selected as the basic framework for detection.In order to improve the inference efficiency on edge devices and avoid the possible premature over-fitting of deep networks,the lite YOLOv4-tiny algorithm is then improved from the original YOLOv4-tiny algorithm and its structure is illustrated using Netron accordingly.The images downloaded from Internet and taken from the buildings in campus are processed to form crack detection data sets,which are trained on personal computer with the AlexeyAB version of Darknet to generate weight files.Meanwhile,the AlexeyAB version of Darknet accelerated by NNpack package is deployed on Raspberry Pi 4B,and the crack detection experiments are carried out.Some characteristics,e.g.,fast speed and lower false detection rate of the lite YOLOv4-tiny algorithm,are confirmed by comparison with those of original YOLOv4-tiny algorithm.The innovations of this paper focus on the simple network structure,fewer network layers,and earlier forward transmission of features to prevent over-fitting,showing the new lite neural network exceeds the original YOLOv4-tiny network significantly.

Investigation of system structure and information processing mechanism for cognitive skywave over-the-horizon radar

Based on the cognitive radar concept and the basic connotation of cognitive skywave over-the-horizon radar（SWOTHR）, the system structure and information processingmechanism about cognitive SWOTHR are researched. Amongthem, the hybrid network system architecture which is thedistributed configuration combining with the centralized cognition and its soft/hardware framework with the sense-detectionintegration are proposed, and the information processing framebased on the lens principle and its information processing flowwith receive-transmit joint adaption are designed, which buildand parse the work law for cognition and its self feedback adjustment with the lens focus model and five stages informationprocessing sequence. After that, the system simulation andthe performance analysis and comparison are provided, whichinitially proves the rationality and advantages of the proposedideas. Finally, four important development ideas of futureSWOTHR toward ＂high frequency intelligence information processing system＂ are discussed, which are scene information fusion, dynamic reconfigurable system, hierarchical and modulardesign, and sustainable development. Then the conclusion thatthe cognitive SWOTHR can cause the performance improvement is gotten.

Artificial intelligence in polyp detection-where are we and where are we headed?

The goal of artificial intelligence in colonoscopy is to improve adenoma detection rate and reduce interval colorectal cancer.Artificial intelligence in polyp detection during colonoscopy has evolved tremendously over the last decade mainly due to the implementation of neural networks.Computer aided detection(CADe)utilizing neural networks allows real time detection of polyps and adenomas.Current CADe systems are built in single centers by multidisciplinary teams and have only been utilized in limited clinical research studies.We review the most recent prospective randomized controlled trials here.These randomized control trials,both non-blinded and blinded,demonstrated increase in adenoma and polyp detection rates when endoscopists used CADe systems vs standard high definition colonoscopes.Increase of polyps and adenomas detected were mainly small and sessile in nature.CADe systems were found to be safe with little added time to the overall procedure.Results are promising as more CADe have shown to have ability to increase accuracy and improve quality of colonoscopy.Overall limitations included selection bias as all trials built and utilized different CADe developed at their own institutions,non-blinded arms,and question of external validity.

Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature

The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

Creation of Extension Knowledge Base System About Intelligent Detection in Dendrobium Huoshanense Photosynthesis Process

Aiming at the limitations of the existing knowledge representations in intelligent detection,a novel extension-based knowledge representation(EKR) is proposed.The definitions,grammar rules,and formal semantics of EKR are presented.A rhombus solving strategy(RSS) based on EKR is discussed in detail,including creation of the problem oriented model,extension operator,the solution formation of contradictions problem and extended inference of matter-element.A knowledge base system based on EKR and RSS is developed,which is applied in intelligent detection in the Dendrobium huoshanense photosynthesis process(DHPP).More reasonable results are obtained than traditional rule-based system.The EKR is feasible in intelligent detection to solve the limitations of traditional knowledge representations.

Computer vision technology in agricultural automation--A review

Computer vision is a field that involves making a machine “see”.This technology uses a camera and computer instead of the human eye to identify,track and measure targets for further image processing.With the development of computer vision,such technology has been widely used in the field of agricultural automation and plays a key role in its development.This review systematically summarizes and analyzes the technologies and challenges over the past three years and explores future opportunities and prospects to form the latest reference for researchers.Through the analyses,it is found that the existing technology can help the development of agricultural automation for small field farming to achieve the advantages of low cost,high efficiency and high precision.However,there are still major challenges.First,the technology will continue to expand into new application areas in the future,and there will be more technological issues that need to be overcome.It is essential to build large-scale data sets.Second,with the rapid development of agricultural automation,the demand for professionals will continue to grow.Finally,the robust performance of related technologies in various complex environments will also face challenges.Through analysis and discussion,we believe that in the future,computer vision technology will be combined with intelligent technology such as deep learning technology,be applied to every aspect of agricultural production management based on large-scale datasets,be more widely used to solve the current agricultural problems,and better improve the economic,general and robust performance of agricultural automation systems,thus promoting the development of agricultural automation equipment and systems in a more intelligent direction.