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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 52253

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Special Issue Editors

Prof. Dr. Jong-Jae Lee

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Guest Editor

Department of Civil and Environmental Engineering, Sejong University, Seoul, Korea
Interests: smart structures; structural health monitoring; artificial intelligence; signal/image processing; object detection; ground penetrating radar

Prof. Dr. Sung-Han Sim

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Guest Editor

School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Interests: structural health monitoring; structural dynamics; earthquake engineering; reliability analysis; image processing; damage detection
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Special Issue Information

Dear Colleagues,

The rapid development of sensor technologies accelerates the construction of smart cities and smart infrastructures. Sensors can transform cities and their infrastructure into truly smart systems by providing essential information for their intelligent functioning and decision making. Over the past decade, tremendous research efforts have been devoted to developing a wide variety of smart sensors and associated data processing strategies, showing great potential in realizing the concept of smart cities and smart infrastructure. For example, smart sensors not only provide measurements of structural and environmental responses, but also assess structural health to assist with infrastructure maintenance. Furthermore, sensor systems are the key components of smart cities, such as management systems for water, energy, waste, air quality, and transportation. In this regard, the Special Issue, entitled “Smart City and Smart Infrastructure” aims to provide relevant information on recent research, development, and application activities on advanced technologies applicable for smart cities and smart infrastructures.

The theme of this Special Issue includes but is not limited to sensor development, information processing, pattern recognition, artificial intelligence, augmented/virtual reality, sensor-based automation, robotics, etc. Sensor development and the advanced processing of sensor data, which are the fundamental enablers of smart cities and smart infrastructures, are of most interest for this Special Issue.

Prof. Dr. Jong-Jae Lee
Assoc. Prof. Dr. Sung-Han Sim
Guest Editors

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Keywords

smart sensors
signal, image, information processing
pattern recognition
city and infrastructure monitoring
artificial intelligence
computer vision applications for defect identification and monitoring
sensor applications for infrastructure safety monitoring
sensor-based management system for smart cities
energy harvesting for long-term monitoring using smart sensors
structural and geotechnical sensors
risk analysis of smart city and infrastructure
structural damage prognosis
big data-driven sensor technologies for smart cities

Published Papers (12 papers)

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Editorial

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4 pages, 188 KiB

Open AccessEditorial

Special Issue on “Smart City and Smart Infrastructure”

by Sung-Han Sim and Jong-Jae Lee

Sensors 2021, 21(21), 7064; https://doi.org/10.3390/s21217064 - 25 Oct 2021

Cited by 1 | Viewed by 1784

Abstract

Recent developments in sensor technologies and data-driven approaches have been recognized as the main enablers of smart cities [...] Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

Research

Jump to: Editorial

16 pages, 10896 KiB

Open AccessArticle

A Novel Method for Estimating Monocular Depth Using Cycle GAN and Segmentation

by Dong-hoon Kwak and Seung-ho Lee

Sensors 2020, 20(9), 2567; https://doi.org/10.3390/s20092567 - 30 Apr 2020

Cited by 17 | Viewed by 5295

Abstract

Modern image processing techniques use three-dimensional (3D) images, which contain spatial information such as depth and scale, in addition to visual information. These images are indispensable in virtual reality, augmented reality (AR), and autonomous driving applications. We propose a novel method to estimate monocular depth using a cycle generative adversarial network (GAN) and segmentation. In this paper, we propose a method for estimating depth information by combining segmentation. It uses three processes: segmentation and depth estimation, adversarial loss calculations, and cycle consistency loss calculations. The cycle consistency loss calculation process evaluates the similarity of two images when they are restored to their original forms after being estimated separately from two adversarial losses. To evaluate the objective reliability of the proposed method, we compared our proposed method with other monocular depth estimation (MDE) methods using the NYU Depth Dataset V2. Our results show that the benchmark value for our proposed method is better than other methods. Therefore, we demonstrated that our proposed method is more efficient in determining depth estimation. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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25 pages, 3807 KiB

Open AccessArticle

Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning

by Bartosz Szeląg, Jakub Drewnowski, Grzegorz Łagód, Dariusz Majerek, Ewa Dacewicz and Francesco Fatone

Sensors 2020, 20(7), 1941; https://doi.org/10.3390/s20071941 - 30 Mar 2020

Cited by 27 | Viewed by 2825

Abstract

The paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including: artificial neural networks, support vector machines, random forests, boosted trees, and logistic regression. The analysis conducted sought the combinations of independent variables for which the devised soft sensor is characterized with high accuracy and at a relatively low cost of determination. With the measurement results pertaining to the quantity and quality of wastewater as well as the temperature in the activated sludge chambers, a good fit can be achieved with the boosted trees method. In order to simplify the selection of an optimal method for the identification of activated sludge bulking depending on the model requirements and the data collected within the WWTP, an original system of weight estimation was proposed, enabling a reduction in the number of independent variables in a model—quantity and quality of wastewater, operational parameters, and the cost of conducting measurements. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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18 pages, 10259 KiB

Open AccessArticle

Novel Method of Semantic Segmentation Applicable to Augmented Reality

by Tae-young Ko and Seung-ho Lee

Sensors 2020, 20(6), 1737; https://doi.org/10.3390/s20061737 - 20 Mar 2020

Cited by 20 | Viewed by 3218

Abstract

This paper proposes a novel method of semantic segmentation, consisting of modified dilated residual network, atrous pyramid pooling module, and backpropagation, that is applicable to augmented reality (AR). In the proposed method, the modified dilated residual network extracts a feature map from the original images and maintains spatial information. The atrous pyramid pooling module places convolutions in parallel and layers feature maps in a pyramid shape to extract objects occupying small areas in the image; these are converted into one channel using a 1 × 1 convolution. Backpropagation compares the semantic segmentation obtained through convolution from the final feature map with the ground truth provided by a database. Losses can be reduced by applying backpropagation to the modified dilated residual network to change the weighting. The proposed method was compared with other methods on the Cityscapes and PASCAL VOC 2012 databases. The proposed method achieved accuracies of 82.8 and 89.8 mean intersection over union (mIOU) and frame rates of 61 and 64.3 frames per second (fps) for the Cityscapes and PASCAL VOC 2012 databases, respectively. These results prove the applicability of the proposed method for implementing natural AR applications at actual speeds because the frame rate is greater than 60 fps. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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18 pages, 1847 KiB

Open AccessArticle

Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities

by Yazan Qarout, Yordan P. Raykov and Max A. Little

Sensors 2020, 20(3), 784; https://doi.org/10.3390/s20030784 - 31 Jan 2020

Cited by 3 | Viewed by 2491

Abstract

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative, which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekend/weekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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10 pages, 97204 KiB

Open AccessArticle

Extreme Low-Light Image Enhancement for Surveillance Cameras Using Attention U-Net

by Sophy Ai and Jangwoo Kwon

Sensors 2020, 20(2), 495; https://doi.org/10.3390/s20020495 - 15 Jan 2020

Cited by 30 | Viewed by 8789

Abstract

Low-light image enhancement is one of the most challenging tasks in computer vision, and it is actively researched and used to solve various problems. Most of the time, image processing achieves significant performance under normal lighting conditions. However, under low-light conditions, an image turns out to be noisy and dark, which makes subsequent computer vision tasks difficult. To make buried details more visible, and reduce blur and noise in a low-light captured image, a low-light image enhancement task is necessary. A lot of research has been applied to many different techniques. However, most of these approaches require much effort or expensive equipment to perform low-light image enhancement. For example, the image has to be captured in a raw camera file in order to be processed, and the addressing method does not perform well under extreme low-light conditions. In this paper, we propose a new convolutional network, Attention U-net (the integration of an attention gate and a U-net network), which is able to work on common file types (.PNG, .JPEG, .JPG, etc.) with primary support from deep learning to solve the problem of surveillance camera security in smart city inducements without requiring the raw image file from the camera, and it can perform under the most extreme low-light conditions. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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15 pages, 23448 KiB

Open AccessArticle

Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning

by Chanjun Chun and Seung-Ki Ryu

Sensors 2019, 19(24), 5501; https://doi.org/10.3390/s19245501 - 12 Dec 2019

Cited by 55 | Viewed by 6578

Abstract

The various defects that occur on asphalt pavement are a direct cause car accidents, and countermeasures are required because they cause significantly dangerous situations. In this paper, we propose fully convolutional neural networks (CNN)-based road surface damage detection with semi-supervised learning. First, the training DB is collected through the camera installed in the vehicle while driving on the road. Moreover, the CNN model is trained in the form of a semantic segmentation using the deep convolutional autoencoder. Here, we augmented the training dataset depending on brightness, and finally generated a total of 40,536 training images. Furthermore, the CNN model is updated by using the pseudo-labeled images from the semi-supervised learning methods for improving the performance of road surface damage detection technique. To demonstrate the effectiveness of the proposed method, 450 evaluation datasets were created to verify the performance of the proposed road surface damage detection, and four experts evaluated each image. As a result, it is confirmed that the proposed method can properly segment the road surface damages. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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15 pages, 6728 KiB

Open AccessArticle

Bayesian Prediction of Pre-Stressed Concrete Bridge Deflection Using Finite Element Analysis

by Jaebeom Lee, Kyoung-Chan Lee, Sung-Han Sim, Junhwa Lee and Young-Joo Lee

Sensors 2019, 19(22), 4956; https://doi.org/10.3390/s19224956 - 14 Nov 2019

Cited by 7 | Viewed by 3353

Abstract

Vertical deflection has been emphasized as an important safety indicator in the management of railway bridges. Therefore, various standards and studies have suggested physics-based models for predicting the time-dependent deflection of railway bridges. However, these approaches may be limited by model errors caused by uncertainties in various factors, such as material properties, creep coefficient, and temperature. This study proposes a new Bayesian method that employs both a finite element model and actual measurement data. To overcome the limitations of an imperfect finite element model and a shortage of data, Gaussian process regression is introduced and modified to consider both, the finite element analysis results and actual measurement data. In addition, the probabilistic prediction model can be updated whenever additional measurement data is available. In this manner, a probabilistic prediction model, that is customized to a target bridge, can be obtained. The proposed method is applied to a pre-stressed concrete railway bridge in the construction stage in the Republic of Korea, as an example of a bridge for which accurate time-dependent deflection is difficult to predict, and measurement data are insufficient. Probabilistic prediction models are successfully derived by applying the proposed method, and the corresponding prediction results agree with the actual measurements, even though the bridge experienced large downward deflections during the construction stage. In addition, the practical uses of the prediction models are discussed. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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17 pages, 6763 KiB

Open AccessArticle

Design and Optimization of an MFL Coil Sensor Apparatus Based on Numerical Survey

by Ali Azad and Namgyu Kim

Sensors 2019, 19(22), 4869; https://doi.org/10.3390/s19224869 - 08 Nov 2019

Cited by 15 | Viewed by 3551

Abstract

In this study, we aimed to design a coil sensor prototype capable of detecting metallic area loss based on numerical simulations using the magnetic flux leakage (MFL) method. Unlike previous numerical simulation-based studies, which are only conducted to obtain the MFL itself, the main objectives of this study were (1) to acquire the induced current in the coil sensor and (2) to optimize the apparatus based on a time-dependent numerical analysis. As a result, the optimum values of parameters in magnetizing and sensing units were obtained numerically. A magnetic sensor prototype was then fabricated using the optimum parameters obtained by numerical parametric study. Finally, experimental validation tests were conducted on a solid steel rod specimen with a stepwise cross-sectional reduction flaw. It was observed that numerical simulation had approximately 91% precision compared to the experimental test. The results reveal that application of a realistic numerical simulation of an MFL coil sensor can probably provide essential information for MFL-sensor fabrication and allows for preventive measures to be taken before manufacturing failure or defect misdetection. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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17 pages, 6480 KiB

Open AccessArticle

Intelligent Image-Based Railway Inspection System Using Deep Learning-Based Object Detection and Weber Contrast-Based Image Comparison

by Jinbeum Jang, Minwoo Shin, Sohee Lim, Jonggook Park, Joungyeon Kim and Joonki Paik

Sensors 2019, 19(21), 4738; https://doi.org/10.3390/s19214738 - 31 Oct 2019

Cited by 27 | Viewed by 5790

Abstract

For sustainable operation and maintenance of urban railway infrastructure, intelligent visual inspection of the railway infrastructure attracts increasing attention to avoid unreliable, manual observation by humans at night, while trains do not operate. Although various automatic approaches were proposed using image processing and computer vision techniques, most of them are focused only on railway tracks. In this paper, we present a novel railway inspection system using facility detection based on deep convolutional neural network and computer vision-based image comparison approach. The proposed system aims to automatically detect wears and cracks by comparing a pair of corresponding image sets acquired at different times. We installed line scan camera on the roof of the train. Unlike an area-based camera, the line scan camera quickly acquires images with a wide field of view. The proposed system consists of three main modules: (i) image reconstruction for registration of facility positions, (ii) facility detection using an improved single shot detector, and (iii) deformed region detection using image processing and computer vision techniques. In experiments, we demonstrate that the proposed system accurately finds facilities and detects their potential defects. For that reason, the proposed system can provide various advantages such as cost reduction for maintenance and accident prevention. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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26 pages, 3353 KiB

Open AccessArticle

Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints

by Khuong An Nguyen, You Wang, Guang Li, Zhiyuan Luo and Chris Watkins

Sensors 2019, 19(19), 4184; https://doi.org/10.3390/s19194184 - 26 Sep 2019

Cited by 5 | Viewed by 4138

Abstract

Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This paper presents a novel yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network. Our rationales are that London tubes are self-driving transports with predictable accelerations, decelerations, and travelling time and that they always travel on the same fixed rail lines between stations with distinctive bumps and vibrations, which permit us to generate an accelerometer map of the tubes’ movements on each line. Given the passenger’s accelerometer data, we identify in realtime what line they are travelling on and what station they depart from, using a pattern-matching algorithm, with an accuracy of up to about 90% when the sampling length is equivalent to at least 3 station stops. We incorporate Principal Component Analysis to perform inertial tracking of passengers’ positions along the line when trains break away from scheduled movements during rush hours. Our proposal was painstakingly assessed on the entire London underground, covering approximately 940 km of travelling distance, spanning across 381 stations on 11 different lines. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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16 pages, 8024 KiB

Open AccessArticle

In-Situ Data-Driven Buffeting Response Analysis of a Cable-Stayed Bridge

by Sehoon Kim, Hyunjun Jung, Min Joon Kong, Deok Keun Lee and Yun-Kyu An

Sensors 2019, 19(14), 3048; https://doi.org/10.3390/s19143048 - 10 Jul 2019

Cited by 5 | Viewed by 2880

Abstract

To analytically evaluate buffeting responses, the analysis of wind characteristics such as turbulence intensity, turbulence length, gust, and roughness coefficient must be a priority. The analytical buffeting response is affected by the static aerodynamic force coefficient, flutter coefficient, structural damping ratio, aerodynamic damping ratio, and natural frequencies of the bridge. The cable-stayed bridge of interest in this study has been used for 32 years. In that time, the terrain conditions around the bridge have markedly changed from the conditions when the bridge was built. Further, the wind environments have varied considerably due to climate change. For these reasons, the turbulence intensity, length, spectrum coefficient, and roughness coefficient of the bridge site must be evaluated from full-scale measurements using a structural health monitoring system. Although the bridge is located on a coastal area, the evaluation results indicated that the wind characteristics of bridge site were analogous to those of open terrain. The buffeting response of the bridge was analyzed using the damping ratios, static aerodynamic force coefficients, and natural frequencies obtained from measured data. The analysis was performed for four cases. Two case analyses were performed by applying the variables obtained from measured data, while two other case analyses were performed based on the Korean Society of Civil Engineers (KSCE) Design Guidelines for Steel Cable Supported Bridges. The calculated responses of each analysis case were compared with the buffeting response measured at wind speeds of less than 25 m/s. The responses obtained by numerical analysis using estimated variables based on full-scale measurements agreed well with the measured buffeting responses measured at wind speeds of less than 25 m/s. Moreover, an extreme wind speed of 44 m/s, corresponding to a recurrence interval of 200 years, was derived from the Gumbel distribution. Therefore, the buffeting responses at wind speeds of 45 m/s were also determined by applying the estimated variables. From these results, management criteria based on measurement data for in-service bridge are determined and each level of management is proposed. Full article

(This article belongs to the Special Issue Smart City and Smart Infrastructure)

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