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14 pages, 11920 KiB

Open AccessArticle

EAR-Net: Efficient Atrous Residual Network for Semantic Segmentation of Street Scenes Based on Deep Learning

by Seokyong Shin, Sanghun Lee and Hyunho Han

Appl. Sci. 2021, 11(19), 9119; https://doi.org/10.3390/app11199119 - 30 Sep 2021

Cited by 9 | Viewed by 2660

Abstract

Segmentation of street scenes is a key technology in the field of autonomous vehicles. However, conventional segmentation methods achieve low accuracy because of the complexity of street landscapes. Therefore, we propose an efficient atrous residual network (EAR-Net) to improve accuracy while maintaining computation [...] Read more.

Segmentation of street scenes is a key technology in the field of autonomous vehicles. However, conventional segmentation methods achieve low accuracy because of the complexity of street landscapes. Therefore, we propose an efficient atrous residual network (EAR-Net) to improve accuracy while maintaining computation costs. First, we performed feature extraction and restoration, utilizing depthwise separable convolution (DSConv) and interpolation. Compared with conventional methods, DSConv and interpolation significantly reduce computation costs while minimizing performance degradation. Second, we utilized residual learning and atrous spatial pyramid pooling (ASPP) to achieve high accuracy. Residual learning increases the ability to extract context information by preventing the problem of feature and gradient losses. In addition, ASPP extracts additional context information while maintaining the resolution of the feature map. Finally, to alleviate the class imbalance between the image background and objects and to improve learning efficiency, we utilized focal loss. We evaluated EAR-Net on the Cityscapes dataset, which is commonly used for street scene segmentation studies. Experimental results showed that the EAR-Net had better segmentation results and similar computation costs as the conventional methods. We also conducted an ablation study to analyze the contributions of the ASPP and DSConv in the EAR-Net. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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21 pages, 8752 KiB

Open AccessArticle

Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks

by Cheng-Wei Lei, Li Zhang, Tsung-Ming Tai, Chen-Chieh Tsai, Wen-Jyi Hwang and Yun-Jie Jhang

Appl. Sci. 2021, 11(17), 7838; https://doi.org/10.3390/app11177838 - 25 Aug 2021

Cited by 3 | Viewed by 2671

Abstract

This study aims to develop a novel automated computer vision algorithm for quality inspection of surfaces with complex patterns. The proposed algorithm is based on both an autoencoder (AE) and a fully convolutional neural network (FCN). The AE is adopted for the self-generation [...] Read more.

This study aims to develop a novel automated computer vision algorithm for quality inspection of surfaces with complex patterns. The proposed algorithm is based on both an autoencoder (AE) and a fully convolutional neural network (FCN). The AE is adopted for the self-generation of templates from test targets for defect detection. Because the templates are produced from the test targets, the position alignment issues for the matching operations between templates and test targets can be alleviated. The FCN is employed for the segmentation of a template into a number of coherent regions. Because the AE has the limitation that its capacities for the regeneration of each coherent region in the template may be different, the segmentation of the template by FCN is beneficial for allowing the inspection of each region to be independently carried out. In this way, more accurate detection results can be achieved. Experimental results reveal that the proposed algorithm has the advantages of simplicity for training data collection, high accuracy for defect detection, and high flexibility for online inspection. The proposed algorithm is therefore an effective alternative for the automated inspection in smart factories with a growing demand for the reliability for high quality production. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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13 pages, 822 KiB

Open AccessArticle

Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox

by Alberto Blazquez-Herranz, Juan-Ignacio Caballero-Garzon, Albert Zilverberg, Christian Wolff, Alejandro Rodríguez-Gonzalez and Ernestina Menasalvas

Appl. Sci. 2021, 11(8), 3693; https://doi.org/10.3390/app11083693 - 20 Apr 2021

Viewed by 2315

Abstract

Mobile devices equipped with sensors are generating an amount of geo-spatial related data that, properly analyzed can be used for future applications. In particular, being able to establish similar trajectories is crucial to analyze events on common points in the trajectories. CROSS-CPP is [...] Read more.

Mobile devices equipped with sensors are generating an amount of geo-spatial related data that, properly analyzed can be used for future applications. In particular, being able to establish similar trajectories is crucial to analyze events on common points in the trajectories. CROSS-CPP is a European project whose main aim is to provide tools to store data in a data market and to have a toolbox to analyze the data. As part of these analytic tools, a set of functionalities has been developed to cluster trajectories. Based on previous work on clustering algorithms we present in this paper a Quickbundels algorithm adaptation to trajectory clustering . Experiments using different distance measures show that Quickbundles outperforms spectral clustering, with the WS84 geodesic distance being the one that provides the best results. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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11 pages, 3883 KiB

Open AccessArticle

Boosted Prediction of Antihypertensive Peptides Using Deep Learning

by Anum Rauf, Aqsa Kiran, Malik Tahir Hassan, Sajid Mahmood, Ghulam Mustafa and Moongu Jeon

Appl. Sci. 2021, 11(5), 2316; https://doi.org/10.3390/app11052316 - 5 Mar 2021

Cited by 10 | Viewed by 2749

Abstract

Heart attack and other heart-related diseases are among the main causes of fatalities in the world. These diseases and some other severe problems like kidney failure and paralysis are mainly caused by hypertension. Since bioactive peptides extracted from naturally existing food substances possess [...] Read more.

Heart attack and other heart-related diseases are among the main causes of fatalities in the world. These diseases and some other severe problems like kidney failure and paralysis are mainly caused by hypertension. Since bioactive peptides extracted from naturally existing food substances possess antihypertensive activity, these antihypertensive peptides (AHTP) can function as prospective replacements for existing pharmacological drugs with no or fewer side effects. Such naturally existing peptides can be identified using in-silico approaches. The in-silico methods have been proven to save huge amounts of time and money in the identification of effective peptides. The proposed methodology is a deep learning-based in-silico approach for the identification of antihypertensive peptides (AHTPs). An ensemble method is proposed that combines convolutional neural network (CNN) and support vector machine (SVM) classifiers. Amino acid composition (AAC) and g-gap dipeptide composition (DPC) techniques are used for feature extraction. The proposed methodology has been evaluated on two standard antihypertensive peptide sequence datasets. The model yields 95% accuracy on the benchmarking dataset and 88.9% accuracy on the independent dataset. Comparative analysis is provided to demonstrate that the proposed method outperforms existing state-of-the-art methods on both of the benchmarking and independent datasets. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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16 pages, 2308 KiB

Open AccessArticle

EDC-Net: Edge Detection Capsule Network for 3D Point Clouds

by Dena Bazazian and M. Eulàlia Parés

Appl. Sci. 2021, 11(4), 1833; https://doi.org/10.3390/app11041833 - 19 Feb 2021

Cited by 19 | Viewed by 4022

Abstract

Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point [...] Read more.

Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point clouds since they are capable of describing the shapes in down-sampled point clouds while maintaining the principal information. In this paper, we tackle challenges of edge detection tasks in 3D point clouds. To this end, we propose a novel technique to detect edges of point clouds based on a capsule network architecture. In this approach, we define the edge detection task of point clouds as a semantic segmentation problem. We built a classifier through the capsules to predict edge and non-edge points in 3D point clouds. We applied a weakly-supervised learning approach in order to improve the performance of our proposed method and built in the capability of testing the technique in wider range of shapes. We provide several quantitative and qualitative experimental results to demonstrate the robustness of our proposed EDC-Net for edge detection in 3D point clouds. We performed a statistical analysis over the ABC and ShapeNet datasets. Our numerical results demonstrate the robust and efficient performance of EDC-Net. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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21 pages, 1512 KiB

Open AccessArticle

Anomalous Event Recognition in Videos Based on Joint Learning of Motion and Appearance with Multiple Ranking Measures

by Shikha Dubey, Abhijeet Boragule, Jeonghwan Gwak and Moongu Jeon

Appl. Sci. 2021, 11(3), 1344; https://doi.org/10.3390/app11031344 - 2 Feb 2021

Cited by 33 | Viewed by 3955

Abstract

Given the scarcity of annotated datasets, learning the context-dependency of anomalous events as well as mitigating false alarms represent challenges in the task of anomalous activity detection. We propose a framework, Deep-network with Multiple Ranking Measures (DMRMs), which addresses context-dependency using a joint [...] Read more.

Given the scarcity of annotated datasets, learning the context-dependency of anomalous events as well as mitigating false alarms represent challenges in the task of anomalous activity detection. We propose a framework, Deep-network with Multiple Ranking Measures (DMRMs), which addresses context-dependency using a joint learning technique for motion and appearance features. In DMRMs, the spatial-time-dependent features are extracted from a video using a 3D residual network (ResNet), and deep motion features are extracted by integrating the motion flow maps’ information with the 3D ResNet. Afterward, the extracted features are fused for joint learning. This data fusion is then passed through a deep neural network for deep multiple instance learning (DMIL) to learn the context-dependency in a weakly-supervised manner using the proposed multiple ranking measures (MRMs). These MRMs consider multiple measures of false alarms, and the network is trained with both normal and anomalous events, thus lowering the false alarm rate. Meanwhile, in the inference phase, the network predicts each frame’s abnormality score along with the localization of moving objects using motion flow maps. A higher abnormality score indicates the presence of an anomalous event. Experimental results on two recent and challenging datasets demonstrate that our proposed framework improves the area under the curve (AUC) score by

6.5 %

compared to the state-of-the-art method on the UCF-Crime dataset and shows AUC of

68.5 %

on the ShanghaiTech dataset. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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14 pages, 7489 KiB

Open AccessArticle

Detecting Deformation on Pantograph Contact Strip of Railway Vehicle on Image Processing and Deep Learning

by Kyung-Min Na, Kiwon Lee, Seung-Kwon Shin and Hyungchul Kim

Appl. Sci. 2020, 10(23), 8509; https://doi.org/10.3390/app10238509 - 28 Nov 2020

Cited by 15 | Viewed by 4352

Abstract

An electric railway vehicle is supplied with electricity by an OCL (Overhead Contact Line) through the contact strip of its pantograph. This transmitted electricity is then used to power the electrical equipment of the railway vehicle. This contact strip wears out due to [...] Read more.

An electric railway vehicle is supplied with electricity by an OCL (Overhead Contact Line) through the contact strip of its pantograph. This transmitted electricity is then used to power the electrical equipment of the railway vehicle. This contact strip wears out due to contact with the OCL. In particular, deformations due to chipping and material loss occur because of friction with the fittings on the OCL. These deformations on the contact strip affect its power transmission quality because of contact loss with the OCL. However, it is difficult to monitor the contact strip during operation and judge its condition in order to implement accident prevention measures. Thus, in this study, we developed a contact strip monitoring method based on image processing for inspection. The proposed method measures the deformation in the contact strip based on an algorithm that determines the wear on the deformed contact strip using deep learning and image processing. The image of the contact strip is acquired by installing a camera and laser to capture the pantograph as it passes the setup. The proposed algorithm is able to determine the wear size by extracting the edges of the laser line using deep learning and estimating the fitted line of the deformations based on the least squares method. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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25 pages, 6683 KiB

Open AccessArticle

General Moving Object Localization from a Single Flying Camera

by Kin-Choong Yow and Insu Kim

Appl. Sci. 2020, 10(19), 6945; https://doi.org/10.3390/app10196945 - 4 Oct 2020

Cited by 8 | Viewed by 2957

Abstract

Object localization is an important task in the visual surveillance of scenes, and it has important applications in locating personnel and/or equipment in large open spaces such as a farm or a mine. Traditionally, object localization can be performed using the technique of [...] Read more.

Object localization is an important task in the visual surveillance of scenes, and it has important applications in locating personnel and/or equipment in large open spaces such as a farm or a mine. Traditionally, object localization can be performed using the technique of stereo vision: using two fixed cameras for a moving object, or using a single moving camera for a stationary object. This research addresses the problem of determining the location of a moving object using only a single moving camera, and it does not make use of any prior information on the type of object nor the size of the object. Our technique makes use of a single camera mounted on a quadrotor drone, which flies in a specific pattern relative to the object in order to remove the depth ambiguity associated with their relative motion. In our previous work, we showed that with three images, we can recover the location of an object moving parallel to the direction of motion of the camera. In this research, we find that with four images, we can recover the location of an object moving linearly in an arbitrary direction. We evaluated our algorithm on over 70 image sequences of objects moving in various directions, and the results showed a much smaller depth error rate (less than 8.0% typically) than other state-of-the-art algorithms. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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19 pages, 5093 KiB

Open AccessArticle

Pothole Classification Model Using Edge Detection in Road Image

by Ji-Won Baek and Kyungyong Chung

Appl. Sci. 2020, 10(19), 6662; https://doi.org/10.3390/app10196662 - 23 Sep 2020

Cited by 44 | Viewed by 7360

Abstract

Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road [...] Read more.

Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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15 pages, 1676 KiB

Open AccessArticle

DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT

by SungMin Yang, SoYeop Yoo and OkRan Jeong

Appl. Sci. 2020, 10(18), 6429; https://doi.org/10.3390/app10186429 - 15 Sep 2020

Cited by 23 | Viewed by 6720

Abstract

Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language. For computers to learn on their own, the skill of understanding [...] Read more.

Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language. For computers to learn on their own, the skill of understanding natural language is very important. There are a wide variety of tasks involved in the field of natural language processing, but we would like to focus on the named entity registration and relation extraction task, which is considered to be the most important in understanding sentences. We propose DeNERT-KG, a model that can extract subject, object, and relationships, to grasp the meaning inherent in a sentence. Based on the BERT language model and Deep Q-Network, the named entity recognition (NER) model for extracting subject and object is established, and a knowledge graph is applied for relation extraction. Using the DeNERT-KG model, it is possible to extract the subject, type of subject, object, type of object, and relationship from a sentence, and verify this model through experiments. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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15 pages, 7903 KiB

Open AccessArticle

Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image

by Hyun-Cheol Park, Sang-Woong Lee and Heon Jeong

Appl. Sci. 2020, 10(13), 4646; https://doi.org/10.3390/app10134646 - 5 Jul 2020

Cited by 5 | Viewed by 3868

Abstract

Recently, there have been many types of research applying drones with a thermal camera to detect deteriorations in photovoltaic (PV) modules. A thermal camera can measure temperatures on the surface of PV modules and find the deteriorated area. However, a thermal camera generally [...] Read more.

Recently, there have been many types of research applying drones with a thermal camera to detect deteriorations in photovoltaic (PV) modules. A thermal camera can measure temperatures on the surface of PV modules and find the deteriorated area. However, a thermal camera generally has a lower resolution than a visible camera because of the limitations of cost. Due to different resolutions between the visible and thermal cameras, there are often invalid frames from a thermal camera. In this paper, we describe a gimbal controller with a real-time image processing algorithm to control the angle of the camera to position the region of interest (ROI) in the center of target PV modules to solve this problem. We derived the horizontal angle and vertical position of ROI in visible images using image processing algorithms such as the Hough transform. These values are converted into a PID control signal for controlling the gimbal. This process makes the thermal camera capture the effective area of target PV modules. Finally, experimental results showed that the photovoltaic module’s control area was properly located at the center of the thermal image. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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16 pages, 21441 KiB

Open AccessArticle

Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera

by Chris Henry, Sahadev Poudel, Sang-Woong Lee and Heon Jeong

Appl. Sci. 2020, 10(11), 3802; https://doi.org/10.3390/app10113802 - 29 May 2020

Cited by 99 | Viewed by 12127

Abstract

In the last few decades, photovoltaic (PV) power station installations have surged across the globe. The output efficiency of these stations deteriorates with the passage of time due to multiple factors such as hotspots, shaded cell or module, short-circuited bypass diodes, etc. Traditionally, [...] Read more.

In the last few decades, photovoltaic (PV) power station installations have surged across the globe. The output efficiency of these stations deteriorates with the passage of time due to multiple factors such as hotspots, shaded cell or module, short-circuited bypass diodes, etc. Traditionally, technicians inspect each solar panel in a PV power station using infrared thermography to ensure consistent output efficiency. With the advancement of drone technology, researchers have proposed to use drones equipped with thermal cameras for PV power station monitoring. However, most of these drone-based approaches require technicians to manually control the drone which in itself is a cumbersome task in the case of large PV power stations. To tackle this issue, this study presents an autonomous drone-based solution. The drone is mounted with both RGB (Red, Green, Blue) and thermal cameras. The proposed system can automatically detect and estimate the exact location of faulty PV modules among hundreds or thousands of PV modules in the power station. In addition, we propose an automatic drone flight path planning algorithm which eliminates the requirement of manual drone control. The system also utilizes an image processing algorithm to process RGB and thermal images for fault detection. The system was evaluated on a 1-MW solar power plant located in Suncheon, South Korea. The experimental results demonstrate the effectiveness of our solution. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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17 pages, 4036 KiB

Open AccessArticle

Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network

by Lijuan Liu, Rung-Ching Chen and Shunzhi Zhu

Appl. Sci. 2020, 10(8), 2962; https://doi.org/10.3390/app10082962 - 25 Apr 2020

Cited by 24 | Viewed by 3707

Abstract

Metro systems play a key role in meeting urban transport demands in large cities. The close relationship between historical weather conditions and the corresponding passenger flow has been widely analyzed by researchers. However, few studies have explored the issue of how to use [...] Read more.

Metro systems play a key role in meeting urban transport demands in large cities. The close relationship between historical weather conditions and the corresponding passenger flow has been widely analyzed by researchers. However, few studies have explored the issue of how to use historical weather data to make passenger flow forecasting more accurate. To this end, an hourly metro passenger flow forecasting model using a deep long short-term memory neural network (LSTM_NN) was developed. The optimized traditional input variables, including the different temporal data and historical passenger flow data, were combined with weather variables for data modeling. A comprehensive analysis of the weather impacts on short-term metro passenger flow forecasting is discussed in this paper. The experimental results confirm that weather variables have a significant effect on passenger flow forecasting. It is interesting to find out that the previous variables of one-hour temperature and wind speed are the two most important weather variables to obtain more accurate forecasting results on rainy days at Taipei Main Station, which is a primary interchange station in Taipei. Compared to the four widely used algorithms, the deep LSTM_NN is an extremely powerful method, which has the capability of making more accurate forecasts when suitable weather variables are included. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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18 pages, 11937 KiB

Open AccessArticle

Body-Part-Aware and Multitask-Aware Single-Image-Based Action Recognition

by Bhishan Bhandari, Geonu Lee and Jungchan Cho

Appl. Sci. 2020, 10(4), 1531; https://doi.org/10.3390/app10041531 - 24 Feb 2020

Cited by 11 | Viewed by 4081

Abstract

Action recognition is an application that, ideally, requires real-time results. We focus on single-image-based action recognition instead of video-based because of improved speed and lower cost of computation. However, a single image contains limited information, which makes single-image-based action recognition a difficult problem. [...] Read more.

Action recognition is an application that, ideally, requires real-time results. We focus on single-image-based action recognition instead of video-based because of improved speed and lower cost of computation. However, a single image contains limited information, which makes single-image-based action recognition a difficult problem. To get an accurate representation of action classes, we propose three feature-stream-based shallow sub-networks (image-based, attention-image-based, and part-image-based feature networks) on the deep pose estimation network in a multitasking manner. Moreover, we design the multitask-aware loss function, so that the proposed method can be adaptively trained with heterogeneous datasets where only human pose annotations or action labels are included (instead of both pose and action information), which makes it easier to apply the proposed approach to new data on behavioral analysis on intelligent systems. In our extensive experiments, we showed that these streams represent complementary information and, hence, the fused representation is robust in distinguishing diverse fine-grained action classes. Unlike other methods, the human pose information was trained using heterogeneous datasets in a multitasking manner; nevertheless, it achieved 91.91% mean average precision on the Stanford 40 Actions Dataset. Moreover, we demonstrated the proposed method can be flexibly applied to multi-labels action recognition problem on the V-COCO Dataset. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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20 pages, 5118 KiB

Open AccessArticle

Multi-View Interactive Visual Exploration of Individual Association for Public Transportation Passengers

by Di Lv, Yong Zhang, Jiongbin Lin, Peiyuan Wan and Yongli Hu

Appl. Sci. 2020, 10(2), 628; https://doi.org/10.3390/app10020628 - 15 Jan 2020

Viewed by 2327

Abstract

More and more people in mega cities are choosing to travel by public transportation due to its convenience and punctuality. It is widely acknowledged that there may be some potential associations between passengers. Their travel behavior may be working together, shopping together, or [...] Read more.

More and more people in mega cities are choosing to travel by public transportation due to its convenience and punctuality. It is widely acknowledged that there may be some potential associations between passengers. Their travel behavior may be working together, shopping together, or even some abnormal behaviors, such as stealing or begging. Thus, analyzing association between passengers is very important for management departments. It is very helpful to make operational plans, provide better services to passengers and ensure public transport safety. In order to quickly explore the association between passengers, we propose a multi-view interactive exploration method that provides five interactive views: passenger 3D travel trajectory view, passenger travel time pixel matrix view, passenger origin-destination chord view, passenger travel vehicle bubble chart view and passenger 2D travel trajectory view. It can explore the associated passengers from multiple aspects such as travel trajectory, travel area, travel time, and vehicles used for travel. Using Beijing public transportation data, the experimental results verified that our method can effectively explore the association between passengers and deduce the relationship. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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Review

Jump to: Research

18 pages, 15955 KiB

Open AccessReview

A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires

by Daniel Cortés, David Gil, Jorge Azorín, Florian Vandecasteele and Steven Verstockt

Appl. Sci. 2020, 10(16), 5609; https://doi.org/10.3390/app10165609 - 13 Aug 2020

Cited by 12 | Viewed by 5475

Abstract

Confined space fires are common emergencies in our society. Enclosure size, ventilation, or type and quantity of fuel involved are factors that determine the fire evolution in these situations. In some cases, favourable conditions may give rise to a flashover phenomenon. However, the [...] Read more.

Confined space fires are common emergencies in our society. Enclosure size, ventilation, or type and quantity of fuel involved are factors that determine the fire evolution in these situations. In some cases, favourable conditions may give rise to a flashover phenomenon. However, the difficulty of handling this complicated emergency through fire services can have fatal consequences for their staff. Therefore, there is a huge demand for new methods and technologies to tackle this life-threatening emergency. Modelling and simulation techniques have been adopted to conduct research due to the complexity of obtaining a real cases database related to this phenomenon. In this paper, a review of the literature related to the modelling and simulation of enclosure fires with respect to the flashover phenomenon is carried out. Furthermore, the related literature for comparing images from thermal cameras with computed images is reviewed. Finally, the suitability of artificial intelligence (AI) techniques for flashover prediction in enclosed spaces is also surveyed. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)

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