scholarly journals A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5725
Author(s):  
Mingliang Zhou ◽  
Wen Cheng ◽  
Hongwei Huang ◽  
Jiayao Chen
Keyword(s):  
Laser Scanning ◽  
Laser Intensity ◽  
Integrated Approach ◽  
Detection Methods ◽  
Depth Information ◽  
Railway Tunnel ◽  
Depth Images ◽  
Novel Approach ◽  
Proposed Model ◽  
Spalling Defect

The detection of concrete spalling is critical for tunnel inspectors to assess structural risks and guarantee the daily operation of the railway tunnel. However, traditional spalling detection methods mostly rely on visual inspection or camera images taken manually, which are inefficient and unreliable. In this study, an integrated approach based on laser intensity and depth features is proposed for the automated detection and quantification of concrete spalling. The Railway Tunnel Spalling Defects (RTSD) database, containing intensity images and depth images of the tunnel linings, is established via mobile laser scanning (MLS), and the Spalling Intensity Depurator Network (SIDNet) model is proposed for automatic extraction of the concrete spalling features. The proposed model is trained, validated and tested on the established RSTD dataset with impressive results. Comparison with several other spalling detection models shows that the proposed model performs better in terms of various indicators such as MPA (0.985) and MIoU (0.925). The extra depth information obtained from MLS allows for the accurate evaluation of the volume of detected spalling defects, which is beyond the reach of traditional methods. In addition, a triangulation mesh method is implemented to reconstruct the 3D tunnel lining model and visualize the 3D inspection results. As a result, a 3D inspection report can be outputted automatically containing quantified spalling defect information along with relevant spatial coordinates. The proposed approach has been conducted on several railway tunnels in Yunnan province, China and the experimental results have proved its validity and feasibility.

scholarly journals A novel approach for Identification of pills based on the method of Depth from Focus

2019 ◽  
Vol 69 (06) ◽  
pp. 466-471
Author(s):  
YU LING JIE ◽  
WANG RONG WU ◽  
ZHOU JIN FENG
Keyword(s):  
Laser Scanning ◽  
Optical Microscope ◽  
Depth Image ◽  
Depth Information ◽  
Scanning System ◽  
Depth From Focus ◽  
Depth Images ◽  
Novel Approach ◽  
Fabric Analysis ◽  
Low Efficiency

For automatic pilling evaluation of textiles, the depth information is one of the most critical and effective features in extracting pills from fabric image. Laser-scanning techniques are often used for acquiring 3D depth images. However, due to the high-cost and low-efficiency of Laser-scanning system, researchers have found it unsuitable for fabric analysis. This paper illustrates a new approach for acquiring the depth image used to extract pills by introducing the method of Depth From Focus (DFF). This approach firstly captures a sequence of images of the same view at different focal positions under the automatic optical microscope. Then the best-focused position (z) of each pixel(x, y) was determined by choosing the layer of image declaring the max sharpness and formed the depth image. This paper proposed a new sharpness-evaluation criterion which was based on the variance of gradients. Afterwards, a few basic points indicating the background area was selected from the depth image, and then the depth coordinates (x, y, z) at these basic points were used to calculate a predicted background plane. Via the background plane, pills above the background were extracted. A fabric sample with a single fiber upon it was presented to illustrate the process and result of the approach.

scholarly journals Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

2019 ◽  
Vol 11 ◽  
pp. 175682931882232
Author(s):  
Navid Dorudian ◽  
Stanislao Lauria ◽  
Stephen Swift
Keyword(s):  
Background Subtraction ◽  
High Speed ◽  
Micro Air Vehicles ◽  
Background Model ◽  
Depth Information ◽  
Subtraction Technique ◽  
Depth Images ◽  
Novel Approach ◽  
Model Update ◽  
Air Vehicles

A novel approach to detect micro air vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving micro air vehicles by combining colour and depth information. The proposed method stores several colour and depth images as models and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background model update presented uses different criteria from existing methods. Additionally, a blind update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different micro air vehicles and publicly available datasets. Results showing some improvements over existing methods are discussed.

A Novel Approach in the Detection of Chipless RFID

2012 ◽  
pp. 218-233 ◽  
Author(s):  
Prasanna Kalansuriya ◽  
Nemai Chandra Karmakar ◽  
Emanuele Viterbo
Keyword(s):  
Mathematical Model ◽  
Digital Communication ◽  
Point Of View ◽  
Detection Methods ◽  
Signal Space ◽  
Rfid Tag ◽  
Robust Detection ◽  
Novel Approach ◽  
Chipless Rfid ◽  
Proposed Model

This chapter presents a different perspective on the chipless RFID system where the chipless RFID detection problem is viewed in terms of a digital communication point of view. A novel mathematical model is presented, and a novel approach to detection is formulated based on the model. The chipless RFID tag frequency signatures are visualized as points in a signal space. Although data bits are stored in the tags using unconventional techniques, the proposed model enables the detection of these data bits through conventional robust detection methods. Through simulations it is shown that the proposed detection method has better performance compared to contemporary detection approaches.

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

Food waste treatment option selection through spherical fuzzy AHP

2021 ◽  
pp. 1-11
Author(s):  
Aysu Melis Buyuk ◽  
Gul T. Temur
Keyword(s):  
Treatment Option ◽  
Food Waste ◽  
Waste Treatment ◽  
Fuzzy Ahp ◽  
Treatment Options ◽  
Analytic Hierarchy ◽  
Food Treatment ◽  
Novel Approach ◽  
Proposed Model ◽  
Option Selection

In line with the increase in consciousness on sustainability in today’s global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions.

scholarly journals Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

2021 ◽  
Vol 10 (6) ◽  
pp. 367
Author(s):  
Simoni Alexiou ◽  
Georgios Deligiannakis ◽  
Aggelos Pallikarakis ◽  
Ioannis Papanikolaou ◽  
Emmanouil Psomiadis ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Change Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Value ◽  
Detection Methods ◽  
Mountainous Catchments ◽  
Aerial Vehicle ◽  
Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

scholarly journals Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

2021 ◽  
Vol 11 (12) ◽  
pp. 5685
Author(s):  
Hosam Aljihani ◽  
Fathy Eassa ◽  
Khalid Almarhabi ◽  
Abdullah Algarni ◽  
Abdulaziz Attaallah
Keyword(s):  
Attack Detection ◽  
Detection Methods ◽  
Software Systems ◽  
Distributed Software ◽  
Detection Techniques ◽  
Detection Systems ◽  
Novel Approach ◽  
Validation Experiment ◽  
Critical Issues ◽  
Concrete Solution

With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

scholarly journals Realtime Motion Assessment For Rehabilitation Exercises: Integration Of Kinematic Modeling With Fuzzy Inference

2014 ◽  
Vol 4 (4) ◽  
pp. 267-285 ◽  
Author(s):  
Wenbing Zhao ◽  
Roanna Lun ◽  
Deborah D. Espy ◽  
M. Ann Reinthal
Keyword(s):  
Fuzzy Inference ◽  
Kinematic Model ◽  
Integrated Approach ◽  
Kinematic Modeling ◽  
Motion Data ◽  
Fuzzy Interference System ◽  
Novel Approach ◽  
Motion Assessment ◽  
Rehabilitation Exercises ◽  
Rehabilitation Exercise

Abstract This article describes a novel approach to realtime motion assessment for rehabilitation exercises based on the integration of comprehensive kinematic modeling with fuzzy inference. To facilitate the assessment of all important aspects of a rehabilitation exercise, a kinematic model is developed to capture the essential requirements for static poses, dynamic movements, as well as the invariance that must be observed during an exercise. The kinematic model is expressed in terms of a set of kinematic rules. During the actual execution of a rehabilitation exercise, the similarity between the measured motion data and the model is computed in terms of their distances, which are then used as inputs to a fuzzy interference system to derive the overall quality of the execution. The integrated approach provides both a detailed categorical assessment of the overall execution of the exercise and the degree of adherence to individual kinematic rules.

scholarly journals An integrated approach for the selection of operation strategy using fuzzy TOPSIS, sensitivity analysis, and MOLP

2021 ◽  
pp. 1-16
Author(s):  
Tran Thi Tham ◽  
Linh Thi Truc Doan ◽  
Yousef Amer ◽  
Sang Heon Lee
Keyword(s):  
Sensitivity Analysis ◽  
Integrated Approach ◽  
Fuzzy Topsis ◽  
Joint Stock Company ◽  
Operation Strategy ◽  
Stock Company ◽  
Research Attention ◽  
Operational Strategies ◽  
Novel Approach ◽  
Conflicting Objectives

Operation strategy plays an important role in business improvement and calls for many research attention in recent years. This study aims to propose an integrated approach to determine the most appropriate operational strategies in their companies under multi-conflicting objectives with a limited budget. The novel approach is developed by using the combination of Fuzzy Technique for Order Preference by Similarity to Ideal Situation (Fuzzy TOPSIS), Sensitivity Analysis (SA) and Multi-Objective Linear Programming (MOLP) model. The operation strategies are evaluated through five objectives such as Productivity, Quality, Cost, Time and Importance score. The importance scores of all strategies are firstly obtained from the Fuzzy TOPSIS method. The sets of the weight of criteria are then established by using SA while MOLP approach is used to select appropriate strategies under multi-conflicting objectives with limited resources. A case study with 110 possible scenarios of operational strategies from An Giang Fisheries Import Export Joint Stock Company in Vietnam is considered to illustrate the practicability of the proposed approach. The results found that the proposed approach is suitable to make a decision on operation strategy.

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