Current Trends and Perspectives of Detection and Location for Buried Non-Metallic Pipelines

Buried pipelines are an essential component of the urban infrastructure of modern cities.Traditional buried pipes are mainly made of metal materials.With the development of material science and technology in recent years,non-metallic pipes,such as plastic pipes,ceramic pipes,and concrete pipes,are increasingly taking the place of pipes made from metal in various pipeline networks such as water supply,drainage,heat,industry,oil,and gas.The location technologies for the location of the buried metal pipeline have become mature,but detection and location technologies for the non-metallic pipelines are still developing.In this paper,current trends and future perspectives of detection and location of buried non-metallic pipelines are summarized.Initially,this paper reviews and analyzes electromagnetic induction technologies,electromagnetic wave technologies,and other physics-based technologies.It then focuses on acoustic detection and location technologies,and finally introduces emerging technologies.Then the technical characteristics of each detection and location method have been compared,with their strengths and weaknesses identified.The current trends and future perspectives of each buried non-metallic pipeline detection and location technology have also been defined.Finally,some suggestions for the future development of buried non-metallic pipeline detection and location technologies are provided.

Detection and location algorithm against local-worm

The spread of the worm causes great harm to the computer network. It has recently become the focus of the network security research. This paper presents a local-worm detection algorithm by analyzing the characteristics of traffic generated by the TCP-based worm. Moreover, we adjust the worm location algorithm, aiming at the differences between the high-speed and the low-speed worm scanning methods. This adjustment can make the location algorithm detect and locate the worm based on different scanning rate. Finally, we verified the validity and efficiency of the proposed algorithm by simulating it under NS-2.

Optic flaws detection and location based on a plenoptic camera

In this Letter,we propose an on-line inspection method based on a plenoptic camera to detect and locate flaws of optics.Specifically,due to the extended depth of field of the plenoptic camera,a series of optics can be inspected efficiently and simultaneously.Moreover,the depth estimation capability of the plenoptic camera allows for locating flaws while detecting them.Besides,the detection and location can be implemented with a single snapshot of the plenoptic camera.Consequently,this method provides us with the opportunity to reduce the cost of time and labor of inspection and remove the flaw optics,which may lead to performance degradation of optical systems.

Intelligent representation method of image flatness for cold rolled strip

Real flatness images are the bases for flatness detection based on machine vision of cold rolled strip.The characteristics of a real flatness image are analyzed,and a lightweight strip location detection(SLD)model with deep semantic segmentation networks is established.The interference areas in the real flatness image can be eliminated by the SLD model,and valid information can be retained.On this basis,the concept of image flatness is proposed for the first time.An image flatness representation(IFAR)model is established on the basis of an autoencoder with a new structure.The optimal structure of the bottleneck layer is 16×16×4,and the IFAR model exhibits a good representation effect.Moreover,interpretability analysis of the representation factors is carried out,and the difference and physical meaning of the representation factors for image flatness with different categories are analyzed.Image flatness with new defect morphologies(bilateral quarter waves and large middle waves)that are not present in the original dataset are generated by modifying the representation factors of the no wave image.Lastly,the SLD and IFAR models are used to detect and represent all the real flatness images on the test set.The average processing time for a single image is 11.42 ms,which is suitable for industrial applications.The research results provide effective methods and ideas for intelligent flatness detection technology based on machine vision.

A comprehensive review of DC fault protection methods in HVDC transmission systems

High voltage direct current (HVDC) transmission is an economical option for transmitting a large amount of power over long distances. Initially, HVDC was developed using thyristor-based current source converters (CSC). With the development of semiconductor devices, a voltage source converter (VSC)-based HVDC system was introduced, and has been widely applied to integrate large-scale renewables and network interconnection. However, the VSC-based HVDC system is vulnerable to DC faults and its protection becomes ever more important with the fast growth in number of installations. In this paper, detailed characteristics of DC faults in the VSC-HVDC system are presented. The DC fault current has a large peak and steady values within a few milliseconds and thus high-speed fault detection and isolation methods are required in an HVDC grid. Therefore, development of the protection scheme for a multi-terminal VSC-based HVDC system is challenging. Various methods have been developed and this paper presents a comprehensive review of the different techniques for DC fault detection, location and isolation in both CSC and VSC-based HVDC transmission systems in two-terminal and multi-terminal network configurations.

Mapping near-real-time power outages from social media

Social media,including Twitter,has become an important source for disaster response.Yet most studies focus on a very limited amount of geotagged data(approximately 1%of all tweets)while discarding a rich body of data that contains location expressions in text.Location information is crucial to understanding the impact of disasters,including where damage has occurred and where the people who need help are situated.In this paper,we propose a novel two-stage machine learningand deep learning-based framework for power outage detection from Twitter.First,we apply a probabilistic classification model using bag-ofngrams features to find true power outage tweets.Second,we implement a new deep learning method-bidirectional long short-term memory networks-to extract outage locations from text.Results show a promising classification accuracy(86%)in identifying true power outage tweets,and approximately 20 times more usable tweets can be located compared with simply relying on geotagged tweets.The method of identifying location names used in this paper does not require language-or domain-specific external resources such as gazetteers or handcrafted features,so it can be extended to other situational awareness analyzes and new applications.

Detecting and Locating Failures in Communication Networks

The connectivity of a strongly connected network may be destroyed after link damage.Since many net- works are connected by directed links,the reachability may be restored by altering the direction of one or more of the links and thus reconfigoring the network.The location of the failed link must first be determined.In this paper,we examine new methods to determine the location of failed links and nodes in networks.A routing test approach is proposed and the conditions under which communication networks may be tested are discussed. Finally,an adaptive algorithm and a heuristic algorithm that can locate a single failed llnk or a single failed node are presented.