These slides are based on the following two research questions:
1) What are the possible areas where Deep Learning can be applied in Construction Industry?
2) What are the problems associated with the application of Deep Learning in Construction Industry?
Damage Assessment System for Aircraft Structural Defects using Wavelet Transformijtsrd
Defects are often arises on the inner surface of aircraft structures, but most of conventional techniques can determine only the damages and cannot determine the degree of damage. Digital Image Processing and Wavelet Transform are used in this project. Characteristics parameters such as the length of the crack can be exactly detected to measure the faults of aircraft structure. Generally failures of different aircraft components and parts are revealed and examined by the use of non destructive examination methods. In further detailed explanation and interpretation of failures optical and scanning electron microscopy are used. This paper presents a new approach in automation for crack detection on pavement surface images. The method is based on the continuous wavelet transform. Then, wavelet coefficients maximal values are searched and their propagation through scales is analyzed. Finally, a post processing gives a binary image which indicates the presence or not of cracks on the pavement surface image. V. Akilan | S. Rajkumar "Damage Assessment System for Aircraft Structural Defects using Wavelet Transform" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd43630.pdf Paper URL: https://www.ijtsrd.comengineering/aeronautical-engineering/43630/damage-assessment-system-for-aircraft-structural-defects-using-wavelet-transform/v-akilan
This document discusses the development of firefighting drones. It proposes attaching fire extinguisher balls and cameras with heat/temperature sensors to drones to help control the spread of fires. The drones could quickly be sent to fire-affected areas and also be used for surveillance. An alert would be sent to authorities informing them about detected fires using IOT. The document describes the hardware used to build the drones, including propellers, motors, ESC, flight controller and frame. It outlines the planned work and provides references for further research on drone applications.
ROAD POTHOLE DETECTION USING YOLOV4 DARKNETIRJET Journal
This document presents research on detecting potholes using the YOLOv4 object detection model in the Darknet framework. It begins with an introduction to the importance of road maintenance and automated pothole detection. It then describes the implementation process, which involves storing and preprocessing the dataset, training the YOLOv4 model, generating weights, and allowing users to upload images for detection. Case studies demonstrate the model successfully detecting potholes in test images. The document concludes that this method provides a cost-effective solution for government agencies to identify and repair potholes, improving road safety.
A Video Processing based System for Counting VehiclesIRJET Journal
This document describes a video processing system for counting vehicles. The system processes video frames using discrete wavelet transform (DWT) features and a neural network. In the first phase, vehicle images are extracted from videos and used to train a backpropagation neural network to detect vehicles based on DWT features. In the testing phase, video frames are extracted and the DWT features of frames showing the detection point are input to the neural network to detect vehicles. The system was tested on videos and achieved satisfactory counting accuracy ranging from 97.9-100%. The system provides an effective way to count vehicles for applications like traffic analysis.
Dr Dev Kambhampati | Electric Utilities Situational AwarenessDr Dev Kambhampati
This document is a draft of a NIST special publication providing guidance on situational awareness solutions for electric utilities. It includes an executive summary, approach, architecture, and security characteristics for implementing situational awareness. The publication describes a challenge electric utilities face in gaining comprehensive visibility across separate IT, operational technology, and physical security systems. It then outlines a solution developed by NIST to integrate these systems using commercial and open source tools to improve detection of cybersecurity incidents and support regulatory compliance. The benefits of the solution include improved cybersecurity, faster incident response, and more effective risk management.
NIST Guide- Situational Awareness for Electric UtilitiesDr Dev Kambhampati
This document is a draft of a NIST special publication providing guidance on situational awareness solutions for electric utilities. It includes an executive summary, approach, architecture, and security characteristics for implementing situational awareness. The publication describes a NCCoE project that developed an example solution to converge monitoring across IT, operational technology, and physical access systems in order to improve utilities' ability to detect cyberattacks and security incidents. The solution is presented as a modular guide to help utilities implement standards-based technologies in a risk-based manner to gain efficiencies in monitoring, identification, and response to cyber incidents.
IRJET- Intruder Detection System using Camera with Alert ManagementIRJET Journal
This document describes a proposed intruder detection system using a camera. The system would work as follows:
1. A camera would continuously capture video frames and an image processing processor would compare the latest frame to a static threshold frame to detect differences.
2. If a difference is detected above a certain threshold using a sum of absolute differences (SAD) algorithm, it would indicate a potential intruder.
3. The system would then raise an alert if an intruder is confirmed, such as sending an email or message with the captured photo of the intruder.
The goal is to create an affordable intruder detection system that can detect intruders and raise alerts, providing security for places when unattended.
CROP PROTECTION AGAINST BIRDS USING DEEP LEARNING AND IOTIRJET Journal
1) The document discusses developing a system to detect birds in high-definition video to help protect crops from damage by birds.
2) It explores using convolutional neural networks and background subtraction techniques to identify and classify birds.
3) The methodology section describes taking video input, preprocessing frames, performing background subtraction using mixtures of Gaussians modeling, and evaluating the system's performance using a confusion matrix.
Damage Assessment System for Aircraft Structural Defects using Wavelet Transformijtsrd
Defects are often arises on the inner surface of aircraft structures, but most of conventional techniques can determine only the damages and cannot determine the degree of damage. Digital Image Processing and Wavelet Transform are used in this project. Characteristics parameters such as the length of the crack can be exactly detected to measure the faults of aircraft structure. Generally failures of different aircraft components and parts are revealed and examined by the use of non destructive examination methods. In further detailed explanation and interpretation of failures optical and scanning electron microscopy are used. This paper presents a new approach in automation for crack detection on pavement surface images. The method is based on the continuous wavelet transform. Then, wavelet coefficients maximal values are searched and their propagation through scales is analyzed. Finally, a post processing gives a binary image which indicates the presence or not of cracks on the pavement surface image. V. Akilan | S. Rajkumar "Damage Assessment System for Aircraft Structural Defects using Wavelet Transform" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd43630.pdf Paper URL: https://www.ijtsrd.comengineering/aeronautical-engineering/43630/damage-assessment-system-for-aircraft-structural-defects-using-wavelet-transform/v-akilan
This document discusses the development of firefighting drones. It proposes attaching fire extinguisher balls and cameras with heat/temperature sensors to drones to help control the spread of fires. The drones could quickly be sent to fire-affected areas and also be used for surveillance. An alert would be sent to authorities informing them about detected fires using IOT. The document describes the hardware used to build the drones, including propellers, motors, ESC, flight controller and frame. It outlines the planned work and provides references for further research on drone applications.
ROAD POTHOLE DETECTION USING YOLOV4 DARKNETIRJET Journal
This document presents research on detecting potholes using the YOLOv4 object detection model in the Darknet framework. It begins with an introduction to the importance of road maintenance and automated pothole detection. It then describes the implementation process, which involves storing and preprocessing the dataset, training the YOLOv4 model, generating weights, and allowing users to upload images for detection. Case studies demonstrate the model successfully detecting potholes in test images. The document concludes that this method provides a cost-effective solution for government agencies to identify and repair potholes, improving road safety.
A Video Processing based System for Counting VehiclesIRJET Journal
This document describes a video processing system for counting vehicles. The system processes video frames using discrete wavelet transform (DWT) features and a neural network. In the first phase, vehicle images are extracted from videos and used to train a backpropagation neural network to detect vehicles based on DWT features. In the testing phase, video frames are extracted and the DWT features of frames showing the detection point are input to the neural network to detect vehicles. The system was tested on videos and achieved satisfactory counting accuracy ranging from 97.9-100%. The system provides an effective way to count vehicles for applications like traffic analysis.
Dr Dev Kambhampati | Electric Utilities Situational AwarenessDr Dev Kambhampati
This document is a draft of a NIST special publication providing guidance on situational awareness solutions for electric utilities. It includes an executive summary, approach, architecture, and security characteristics for implementing situational awareness. The publication describes a challenge electric utilities face in gaining comprehensive visibility across separate IT, operational technology, and physical security systems. It then outlines a solution developed by NIST to integrate these systems using commercial and open source tools to improve detection of cybersecurity incidents and support regulatory compliance. The benefits of the solution include improved cybersecurity, faster incident response, and more effective risk management.
NIST Guide- Situational Awareness for Electric UtilitiesDr Dev Kambhampati
This document is a draft of a NIST special publication providing guidance on situational awareness solutions for electric utilities. It includes an executive summary, approach, architecture, and security characteristics for implementing situational awareness. The publication describes a NCCoE project that developed an example solution to converge monitoring across IT, operational technology, and physical access systems in order to improve utilities' ability to detect cyberattacks and security incidents. The solution is presented as a modular guide to help utilities implement standards-based technologies in a risk-based manner to gain efficiencies in monitoring, identification, and response to cyber incidents.
IRJET- Intruder Detection System using Camera with Alert ManagementIRJET Journal
This document describes a proposed intruder detection system using a camera. The system would work as follows:
1. A camera would continuously capture video frames and an image processing processor would compare the latest frame to a static threshold frame to detect differences.
2. If a difference is detected above a certain threshold using a sum of absolute differences (SAD) algorithm, it would indicate a potential intruder.
3. The system would then raise an alert if an intruder is confirmed, such as sending an email or message with the captured photo of the intruder.
The goal is to create an affordable intruder detection system that can detect intruders and raise alerts, providing security for places when unattended.
CROP PROTECTION AGAINST BIRDS USING DEEP LEARNING AND IOTIRJET Journal
1) The document discusses developing a system to detect birds in high-definition video to help protect crops from damage by birds.
2) It explores using convolutional neural networks and background subtraction techniques to identify and classify birds.
3) The methodology section describes taking video input, preprocessing frames, performing background subtraction using mixtures of Gaussians modeling, and evaluating the system's performance using a confusion matrix.
Survey on deep learning applied to predictive maintenance IJECEIAES
Prognosis health monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0.
The document summarizes a research paper that proposes a method to summarize parking surveillance footage. The method first pre-processes the raw footage to extract only frames containing vehicles. These frames are then classified using a CNN model to detect vehicles and recognize license plates. The classified objects and license plate numbers are used to generate a textual summary of the vehicles in the footage, making it easier for users to review large amounts of surveillance video. The paper discusses related work on video summarization techniques and provides details of the proposed methodology, which includes preprocessing footage, extracting features from frames containing vehicles, using CNNs for object detection and license plate recognition, and generating a summarized video and text report.
IRJET - Cryptanalysis of a Text Encryption Scheme based on Bit Plane ExtractionIRJET Journal
This document summarizes a research paper that analyzes the security of an image text encryption scheme based on bit-plane extraction. The paper identifies security defects in the existing scheme and proposes improved known-plaintext and chosen-plaintext attacks. It then outlines an enhanced encryption scheme that adopts statistical values from the plain image during diffusion and builds relationships between bit planes to reduce pixel correlations in the encrypted image. The proposed scheme is evaluated experimentally and compared to other methods, showing improvements in security and robustness against attacks while maintaining encrypted image quality.
Detecting anomalies in security cameras with 3D-convolutional neural network ...IJECEIAES
This paper presents a novel deep learning-based approach for anomaly detec- tion in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested ap- proach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and con- volutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is em- ployed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XD- Violence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with Con- vLSTM can increase precision and reduce false positives, achieving a high ac- curacy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cutting- edge techniques mentioned in the comparison.
IRJET - Autonomous Navigation System using Deep LearningIRJET Journal
The document describes a proposed methodology for creating an autonomous navigation system using deep learning. Key aspects of the proposed methodology include:
1. Using the Unity real-time 3D rendering platform to build a simulated virtual environment for training an autonomous vehicle.
2. Employing artificial intelligence techniques like convolutional neural networks to create the "brain" of the autonomous system and train a model using data collected from the simulated environment.
3. Training the model to perform tasks like identifying lane lines, classifying images, and replicating human driving behaviors to enable the autonomous vehicle to navigate the virtual environment.
INTRODUCTION OF A NOVEL ANOMALOUS SOUND DETECTION METHODOLOGYIJCI JOURNAL
This paper is to introduce a novel semi-supervised methodology, the enhanced incremental principal
component analysis (“IPCA”) based deep convolutional neural network autoencoder (“DCNN-AE) for
Anomalous Sound Detection (“ASD”) with high accuracy and computing efficiency. This hybrid
methodology is to adopt Enhanced IPCA to reduce the dimensionality and then to use DCNN-AE to extract
the features of the sample sound and detect the anomality. In this project, 228 sets of normal sounds and
100 sets of anomaly sounds of same machine are used for the experiments. And the sound files of machines
(stepper motors) for the experiments are collected from a plant site. 50 random test cases are executed to
evaluate the performance of the algorithm with AUC, PAUC, F measure and Accuracy Score. IPCA Based
DCNN-AE shows high accuracy with the average AUC of 0.815793282, comparing with that of Kmeans++
of 0.499545351, of Incremental PCA based DBSCAN clustering of 0.636348073, of Incremental based
PCA based One-class SVM of 0.506749433 and of DCGAN of 0.716528104. From the perspective of
computing efficiency, because of the dimensions-reduction by the IPCA layer, the average execution time
of the new methodology is 15 minutes in the CPU computing module of 2.3 GHz quad-core processors,
comparing with that of DCGAN with 90 minutes in GPU computing module of 4 to 8 kernels.
Face Mask Detection utilizing Tensorflow, OpenCV and KerasIRJET Journal
This document describes a face mask detection system created using computer vision and deep learning techniques. The system uses OpenCV for image preprocessing, TensorFlow for creating and training a convolutional neural network (CNN) model, and Keras as the API for model definition and training. The CNN is trained on datasets containing images of faces with and without masks. It achieves 95.77% accuracy on one dataset and 94.58% accuracy on a more challenging dataset. When deployed, the trained model is able to detect and label faces in real-time video frames as wearing a mask or not wearing a mask, helping to monitor mask compliance and reduce disease spread.
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
CAR DAMAGE DETECTION USING DEEP LEARNINGIRJET Journal
This document discusses using deep learning techniques for car damage detection and classification. Specifically, it proposes using a Mask R-CNN model with transfer learning. As there was no publicly available car damage dataset, the authors created their own dataset of 970 images with various damage types labeled. They experimented with different deep learning approaches, finding that transfer learning combined with Mask R-CNN performed best for this task. The proposed methodology involves collecting and labeling images, applying the Mask R-CNN model with transferred features from pre-trained networks, and predicting the damage results.
This document proposes using the YOLOv5 object detection framework for real-time ship detection in satellite images. It reviews existing ship detection methods including machine learning and deep learning approaches. The methodology uses a dataset of satellite images with ship annotations to train and evaluate YOLOv5 models of different sizes (nano, small, medium, large, extra-large). Experimental results show the performance of each model on metrics like mAP, precision, and recall for real-time ship detection.
IRJET- A Real Time Yolo Human Detection in Flood Affected Areas based on Vide...IRJET Journal
This document proposes a method for real-time human detection in flood-affected areas using video content analysis and the YOLO object detection algorithm. It trains YOLO on the COCO Human dataset to detect and localize humans in video frames from surveillance cameras. The results show that YOLO can accurately detect multiple humans, even with occlusion, and single humans under varying illumination. This approach aims to help rescue operations quickly identify affected areas and prioritize aid.
Intelligent System For Face Mask DetectionIRJET Journal
This document presents research on developing an intelligent system to detect whether people are wearing face masks or not using deep learning techniques. The system uses a convolutional neural network called MobileNetV2 trained on a dataset of 480 masked and unmasked face images. Data augmentation is used to increase the size of the dataset. OpenCV is used for face detection. The system achieves 99% accuracy on the test dataset and can classify images and video frames in real-time. Applications discussed include use in airports, hospitals, offices and by law enforcement to monitor compliance with mask mandates and prevent the spread of COVID-19.
1) IEC 61508 is an international standard for functional safety of electrical, electronic, and programmable electronic safety-related systems. It standardizes safety requirements and assessment methodologies that can be applied across industries.
2) The nuclear industry could benefit from using components certified to IEC 61508, as it offers advantages in technical rigor and economics. Components certified as SIL 2 or higher have undergone reliability and correctness assessments that align with nuclear industry needs.
3) IEC 61508 certification of individual components, like sensors, controllers, and actuators, remains compatible with existing nuclear safety system requirements and could facilitate commercial-grade dedication or suitability evaluations for digital equipment.
ARRL: A Criterion for Composable Safety and Systems EngineeringVincenzo De Florio
While safety engineering standards define rigorous and controllable
processes for system development, safety standards’ differences in distinct
domains are non-negligible. This paper focuses in particular on the aviation,
automotive, and railway standards, all related to the transportation market.
Many are the reasons for the said differences, ranging from historical reasons,
heuristic and established practices, and legal frameworks, but also from the
psychological perception of the safety risks. In particular we argue that the
Safety Integrity Levels are not sufficient to be used as a top level requirement
for developing a safety-critical system. We argue that Quality of Service is a
more generic criterion that takes the trustworthiness as perceived by users better
into account. In addition, safety engineering standards provide very little
guidance on how to compose safe systems from components, while this is the
established engineering practice. In this paper we develop a novel concept
called Assured Reliability and Resilience Level as a criterion that takes the
industrial practice into account and show how it complements the Safety
Integrity Level concept.
IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET Journal
This document summarizes research on object detection and recognition using the Single Shot Multi-Box Detector (SSD) deep learning model. SSD improves on existing object detection systems by eliminating the need for generating object proposals and resampling pixels or features, thereby making detection faster and encapsulating all computation in a single neural network. The researchers applied SSD to standard datasets like PASCAL VOC, COCO, and ILSVRC and achieved competitive accuracy compared to methods using additional proposal steps, with SSD running significantly faster at 59 FPS. Experimental results on PASCAL VOC using SSD achieved a mean average precision of 74.3%, outperforming a comparable Faster R-CNN model.
IRJET - An Robust and Dynamic Fire Detection Method using Convolutional N...IRJET Journal
This document proposes a new fire detection method using convolutional neural networks (CNNs). Specifically, it uses the YOLOv3 object detection algorithm, which can detect objects like fire in images or videos quickly and accurately. The proposed method aims to reduce computational time and costs compared to other CNN-based approaches, while also improving detection accuracy and reducing false alarms. It discusses implementing the method using four main modules: data exploration, pre-processing, feature engineering, and model selection. The workflow involves exploring data, pre-processing images, extracting features, and selecting the YOLOv3 CNN model for fire detection. The goal is to develop a robust and dynamic fire detection system using computer vision techniques to help prevent accidents.
Design and Development of a Weather Drone Using IoTIRJET Journal
This document describes the design and development of a weather drone using IoT technology. A team of students and professors from S.G. Balekundri Institute of Technology in Belgaum, India worked on this project. They equipped a drone with sensors to measure temperature and humidity and transmit the data via WiFi to a cloud server. An Android app and website were created to visualize the weather data in real-time. The drone was tested on an agricultural site and successfully recorded temperature and humidity levels that followed typical daily weather patterns. The project demonstrated how IoT-enabled drones can help farmers access real-time weather data to make informed decisions about crop management.
Object Detection and Localization for Visually Impaired People using CNNIRJET Journal
The document presents a system to help visually impaired people by using CNNs (Convolutional Neural Networks) for object detection and localization based on camera images. The system aims to achieve over 95% accuracy in identifying objects captured by the camera and conveying this information to users through voice messages. It discusses existing assistive systems that use techniques like CNNs, speech recognition and custom object detection. The proposed system is intended to enable real-time object recognition and localization using a CNN model to improve awareness of the indoor environment for visually impaired individuals. Test results showed the CNN program for object recognition was implemented effectively.
Experiences evaluating cloud services and productsJavier Tallón
The market for IT products is constantly evolving. More and more vendors are developing products and services deployed only in the cloud (Cloud Native). This implies a paradigm shift in the way assessments are carried out, in the methodology to be followed and in the tests to be performed.
Today, it is NOT possible to use Common Criteria to evaluate cloud services, despite many administrations are migrating to cloud solutions.
This talk will not talk about Cloud programs such as FedRamp, ENS, C5, SecNumCloud or ENISA EUCS scheme. All these schemes, evaluate the clod infrastructure and the controls specified in the respective standards.
But in those standards, we cannot find assurance requirements related to the product/service itself. e.g. If your WAF (Web Application Firewall) is cloud native and deployed in the cloud, you could obtain those cloud certifications but it would be NOT possible to obtain a CC certification using NIAP PPs.
To solve this problematic, a practical approach has been followed in Spain, evaluating the cloud services using the LINCE methodology but obtaining a qualification mark (instead of a certification). Several vendors such as AWS, Google or Microsoft have already undergone this kind of processes.
In this talk, we want to show jtsec’s hands-on experience evaluating cloud services and discuss the main issues that have been faced and the solutions that have been found (TOE definition, Test environment, TOE identification, permission to test, etc…).
We would like also to discuss how the experience obtained using the LINCE methodology could be extrapolated (or NOT) to the CC World.
Correct time and timing is one of the foundational elements in enabling the communication and orchestration of technologies for accurate and optimal wide area monitoring, protection and control (WAMPAC) in the power industry. The National Institute of Standards and Technology (NIST) and the International Electrical and Electronic Engineer-Standard Association (IEEE-SA) conducted a workshop to gather inputs from stakeholders to identify, analyze, and provide guidance on technologies, standards and methodologies for addressing the practical timing challenges that are currently being experienced in wide area time synchronization. This paper summarizes the NIST “Timing Challenges in the Smart Grid,” workshop in January 2017.
Survey on deep learning applied to predictive maintenance IJECEIAES
Prognosis health monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0.
The document summarizes a research paper that proposes a method to summarize parking surveillance footage. The method first pre-processes the raw footage to extract only frames containing vehicles. These frames are then classified using a CNN model to detect vehicles and recognize license plates. The classified objects and license plate numbers are used to generate a textual summary of the vehicles in the footage, making it easier for users to review large amounts of surveillance video. The paper discusses related work on video summarization techniques and provides details of the proposed methodology, which includes preprocessing footage, extracting features from frames containing vehicles, using CNNs for object detection and license plate recognition, and generating a summarized video and text report.
IRJET - Cryptanalysis of a Text Encryption Scheme based on Bit Plane ExtractionIRJET Journal
This document summarizes a research paper that analyzes the security of an image text encryption scheme based on bit-plane extraction. The paper identifies security defects in the existing scheme and proposes improved known-plaintext and chosen-plaintext attacks. It then outlines an enhanced encryption scheme that adopts statistical values from the plain image during diffusion and builds relationships between bit planes to reduce pixel correlations in the encrypted image. The proposed scheme is evaluated experimentally and compared to other methods, showing improvements in security and robustness against attacks while maintaining encrypted image quality.
Detecting anomalies in security cameras with 3D-convolutional neural network ...IJECEIAES
This paper presents a novel deep learning-based approach for anomaly detec- tion in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested ap- proach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and con- volutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is em- ployed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XD- Violence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with Con- vLSTM can increase precision and reduce false positives, achieving a high ac- curacy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cutting- edge techniques mentioned in the comparison.
IRJET - Autonomous Navigation System using Deep LearningIRJET Journal
The document describes a proposed methodology for creating an autonomous navigation system using deep learning. Key aspects of the proposed methodology include:
1. Using the Unity real-time 3D rendering platform to build a simulated virtual environment for training an autonomous vehicle.
2. Employing artificial intelligence techniques like convolutional neural networks to create the "brain" of the autonomous system and train a model using data collected from the simulated environment.
3. Training the model to perform tasks like identifying lane lines, classifying images, and replicating human driving behaviors to enable the autonomous vehicle to navigate the virtual environment.
INTRODUCTION OF A NOVEL ANOMALOUS SOUND DETECTION METHODOLOGYIJCI JOURNAL
This paper is to introduce a novel semi-supervised methodology, the enhanced incremental principal
component analysis (“IPCA”) based deep convolutional neural network autoencoder (“DCNN-AE) for
Anomalous Sound Detection (“ASD”) with high accuracy and computing efficiency. This hybrid
methodology is to adopt Enhanced IPCA to reduce the dimensionality and then to use DCNN-AE to extract
the features of the sample sound and detect the anomality. In this project, 228 sets of normal sounds and
100 sets of anomaly sounds of same machine are used for the experiments. And the sound files of machines
(stepper motors) for the experiments are collected from a plant site. 50 random test cases are executed to
evaluate the performance of the algorithm with AUC, PAUC, F measure and Accuracy Score. IPCA Based
DCNN-AE shows high accuracy with the average AUC of 0.815793282, comparing with that of Kmeans++
of 0.499545351, of Incremental PCA based DBSCAN clustering of 0.636348073, of Incremental based
PCA based One-class SVM of 0.506749433 and of DCGAN of 0.716528104. From the perspective of
computing efficiency, because of the dimensions-reduction by the IPCA layer, the average execution time
of the new methodology is 15 minutes in the CPU computing module of 2.3 GHz quad-core processors,
comparing with that of DCGAN with 90 minutes in GPU computing module of 4 to 8 kernels.
Face Mask Detection utilizing Tensorflow, OpenCV and KerasIRJET Journal
This document describes a face mask detection system created using computer vision and deep learning techniques. The system uses OpenCV for image preprocessing, TensorFlow for creating and training a convolutional neural network (CNN) model, and Keras as the API for model definition and training. The CNN is trained on datasets containing images of faces with and without masks. It achieves 95.77% accuracy on one dataset and 94.58% accuracy on a more challenging dataset. When deployed, the trained model is able to detect and label faces in real-time video frames as wearing a mask or not wearing a mask, helping to monitor mask compliance and reduce disease spread.
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
CAR DAMAGE DETECTION USING DEEP LEARNINGIRJET Journal
This document discusses using deep learning techniques for car damage detection and classification. Specifically, it proposes using a Mask R-CNN model with transfer learning. As there was no publicly available car damage dataset, the authors created their own dataset of 970 images with various damage types labeled. They experimented with different deep learning approaches, finding that transfer learning combined with Mask R-CNN performed best for this task. The proposed methodology involves collecting and labeling images, applying the Mask R-CNN model with transferred features from pre-trained networks, and predicting the damage results.
This document proposes using the YOLOv5 object detection framework for real-time ship detection in satellite images. It reviews existing ship detection methods including machine learning and deep learning approaches. The methodology uses a dataset of satellite images with ship annotations to train and evaluate YOLOv5 models of different sizes (nano, small, medium, large, extra-large). Experimental results show the performance of each model on metrics like mAP, precision, and recall for real-time ship detection.
IRJET- A Real Time Yolo Human Detection in Flood Affected Areas based on Vide...IRJET Journal
This document proposes a method for real-time human detection in flood-affected areas using video content analysis and the YOLO object detection algorithm. It trains YOLO on the COCO Human dataset to detect and localize humans in video frames from surveillance cameras. The results show that YOLO can accurately detect multiple humans, even with occlusion, and single humans under varying illumination. This approach aims to help rescue operations quickly identify affected areas and prioritize aid.
Intelligent System For Face Mask DetectionIRJET Journal
This document presents research on developing an intelligent system to detect whether people are wearing face masks or not using deep learning techniques. The system uses a convolutional neural network called MobileNetV2 trained on a dataset of 480 masked and unmasked face images. Data augmentation is used to increase the size of the dataset. OpenCV is used for face detection. The system achieves 99% accuracy on the test dataset and can classify images and video frames in real-time. Applications discussed include use in airports, hospitals, offices and by law enforcement to monitor compliance with mask mandates and prevent the spread of COVID-19.
1) IEC 61508 is an international standard for functional safety of electrical, electronic, and programmable electronic safety-related systems. It standardizes safety requirements and assessment methodologies that can be applied across industries.
2) The nuclear industry could benefit from using components certified to IEC 61508, as it offers advantages in technical rigor and economics. Components certified as SIL 2 or higher have undergone reliability and correctness assessments that align with nuclear industry needs.
3) IEC 61508 certification of individual components, like sensors, controllers, and actuators, remains compatible with existing nuclear safety system requirements and could facilitate commercial-grade dedication or suitability evaluations for digital equipment.
ARRL: A Criterion for Composable Safety and Systems EngineeringVincenzo De Florio
While safety engineering standards define rigorous and controllable
processes for system development, safety standards’ differences in distinct
domains are non-negligible. This paper focuses in particular on the aviation,
automotive, and railway standards, all related to the transportation market.
Many are the reasons for the said differences, ranging from historical reasons,
heuristic and established practices, and legal frameworks, but also from the
psychological perception of the safety risks. In particular we argue that the
Safety Integrity Levels are not sufficient to be used as a top level requirement
for developing a safety-critical system. We argue that Quality of Service is a
more generic criterion that takes the trustworthiness as perceived by users better
into account. In addition, safety engineering standards provide very little
guidance on how to compose safe systems from components, while this is the
established engineering practice. In this paper we develop a novel concept
called Assured Reliability and Resilience Level as a criterion that takes the
industrial practice into account and show how it complements the Safety
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IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET Journal
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IRJET - An Robust and Dynamic Fire Detection Method using Convolutional N...IRJET Journal
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The document presents a system to help visually impaired people by using CNNs (Convolutional Neural Networks) for object detection and localization based on camera images. The system aims to achieve over 95% accuracy in identifying objects captured by the camera and conveying this information to users through voice messages. It discusses existing assistive systems that use techniques like CNNs, speech recognition and custom object detection. The proposed system is intended to enable real-time object recognition and localization using a CNN model to improve awareness of the indoor environment for visually impaired individuals. Test results showed the CNN program for object recognition was implemented effectively.
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But in those standards, we cannot find assurance requirements related to the product/service itself. e.g. If your WAF (Web Application Firewall) is cloud native and deployed in the cloud, you could obtain those cloud certifications but it would be NOT possible to obtain a CC certification using NIAP PPs.
To solve this problematic, a practical approach has been followed in Spain, evaluating the cloud services using the LINCE methodology but obtaining a qualification mark (instead of a certification). Several vendors such as AWS, Google or Microsoft have already undergone this kind of processes.
In this talk, we want to show jtsec’s hands-on experience evaluating cloud services and discuss the main issues that have been faced and the solutions that have been found (TOE definition, Test environment, TOE identification, permission to test, etc…).
We would like also to discuss how the experience obtained using the LINCE methodology could be extrapolated (or NOT) to the CC World.
Correct time and timing is one of the foundational elements in enabling the communication and orchestration of technologies for accurate and optimal wide area monitoring, protection and control (WAMPAC) in the power industry. The National Institute of Standards and Technology (NIST) and the International Electrical and Electronic Engineer-Standard Association (IEEE-SA) conducted a workshop to gather inputs from stakeholders to identify, analyze, and provide guidance on technologies, standards and methodologies for addressing the practical timing challenges that are currently being experienced in wide area time synchronization. This paper summarizes the NIST “Timing Challenges in the Smart Grid,” workshop in January 2017.
Similar to Applications of Deep Learning in Construction Industry (20)
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Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
2. Presentation by:
Gaurav Verma
M.Tech [IEM]
21103038
Submitted to:
Prof. Sudhir Misra
Dept. of Civil Engineering
IIT Kanpur
Literature Review on Different
Topics
3. OUTLINES
1. Research Questions
2. Classification of DLApplications in Construction Industry
3. Paper 1: A deep hybrid learning model to detect unsafe behaviour
4. Paper 2: Detecting Non-hardhat use
5. Paper 3: Automated text classification of near misses
6. Paper 4: Deep Learning for site safety
7. Paper 5: CNN for Pavement Roughness Assessment
8. Paper 6: Construction Vehicles Tracking
9. What else I have done?
4. RQ1.What are the
applications of Deep
Learning in Construction
Industry?
RQ2. What are the
challenges in the
applications of Deep
Learning in Construction
Industry?
RESEARCH QUESTIONS
5. CLASSIFICATION OF DL APPLICATIONS
Classificatio
n of DL
Applications
in
Construction
Industry
Construction
Safety &
Management
Equipment
Tracking
Sewer
Assessment
Crack
Detection
3D Point
Cloud
Enhancement
Miscellaneou
s
Applications
7. A deep hybrid learning model to
detect unsafe behavior: Integrating
convolution neural networks and long
short-term memory
8. INTRODUCTION
• Approximately 88% of all accidents that occur during construction materialize as a
consequence of unsafe behavior of workers.
• Conventional methods to determine workers' behavior have been predominately based
upon observational methods. While such methods may provide useful information, they
are time-consuming, labor-intensive and are subjective in nature. Due to these
limitations, computer vision technology, which has been used for object recognition, can
be applied to identify workers' unsafe actions on-site.
Reference: https://doi.org/10.1016/j.autcon.2017.11.002
9. DEEP LEARNING MODEL
• The deep models are trained to compute feature representations from the action videos,
which are structured using a combination of CNNs and LSTM models.
• 1 video stream =
25 video clips
• 2048 dimension
feature vector.
• 25 feature
vectors.
• The LSTM has an
advanced RNN
architecture, which
can learn long-range
dependencies due to
its memory cell.
Reference: https://doi.org/10.1016/j.autcon.2017.11.002
10. EXPERIMENT DETAILS
• Occupational Safety and Health Administration (OSHA) accident statistics were used.
• Falls are one of leading causes of accidents in construction, accounting for 34% fatalities
and 24% non-fatalities. Notably, falls from ladders account for 9% of deaths and 6% of
injuries.
• Video recordings of a person climbing and dismounting from a ladder were collected.
• Each video is on average 8 s in length, and has a resolution of 1920 ∗ 1080.
• For each class of actions, 50 samples (i.e., the number of cycles) were collected.
Reference: https://doi.org/10.1016/j.autcon.2017.11.002
11. IMPLEMENTATION DETAILS
Total of 200
videos
160 for
Training sets
40 for Testing
sets
Reference: https://doi.org/10.1016/j.autcon.2017.11.002
2 Types of Labels Used
Label Used Label Code Actions
Accuracy in
DL Model
Two types of
labels
(0,1)
0: Normal
ladder
climbing
1: Abnormal
ladder
climbing
97 %
Four types of
labels
(0,1,2,3)
0: Normal
climbing
1: With an
object
2: Backward
Facing
3: Reaching
Far
92 %
13. LIMITATION & FUTURE SCOPE
Limitations Future Scopes
Unable to identify unsafe behaviours
in case of multiple workers in a single
image.
Model that simultaneously
accommodate multiple pieces of
equipment/workers contained within
video frames.
Reference: https://doi.org/10.1016/j.autcon.2017.11.002
15. INTRODUCTION
• According to the United States' Bureau of Labor Statistics, the number of fatalities in the
US has gradually increased from 849 to 985 between 2012 and 2015.
• According to the UK Health and Safety Executive (HSE), 38 construction workers
suffered fatal injuries in Great Britain between April 2014 and March 2015, while this
figure rose to 45 during the same period the following year.
• From 2003 to 2010, 2210 construction workers in the United States died as a result of
traumatic brain injuries, accounting for 24% of the total number of deaths from
construction accidents.
• A survey conducted by the US Bureau of Labor Statistics (BLS) suggests that 84% of
workers who had suffered impact injuries to the head were not wearing head protection
equipment.
Reference: https://doi.org/10.1016/j.autcon.2017.09.018
16. DEEP LEARNING MODEL
• Compared with speed, high recognition precision and recall rate are more important for
NHU detection. Therefore, in this paper, Faster R-CNN is proposed for the detection of
construction NHU worker.
Advantages of Faster R-CNN:
1. Robust in dealing with complex construction site environments.
2. High precision of Faster R-CNN can fulfill the needs of practical engineering
applications.
3. Coupled with the short processing time of Faster R-CNN, real-time monitoring of NHU
can be achieved.
Reference: https://doi.org/10.1016/j.autcon.2017.09.018
17. EXPERIMENT DETAILS
• More than 100,000 image frames of surveillance videos from 25 different construction
projects. In order to create a comprehensive dataset (of assorted situations), the videos
were collected for more than one year.
• A total of 81,000 images from this dataset were randomly selected to comprise the
training dataset.
• All the images in the testing dataset were classified into several categories based on
weather, illumination, individuals' posture, visual range and occlusions.
Reference: https://doi.org/10.1016/j.autcon.2017.09.018
Evaluation Performance Metrices
Precision True Positive / (True Positive + False Positive)
Recall True Positive / (True Positive + False Negative)
Miss Rate 1 - Recall
18. RESULTS
Results under different Visual Range
Value TP FP FN Precision (%) Recall (%) Miss Rate (%) Speed (s)
Large 3374 226 280 93.7 92.3 7.7 0.212
Middle 2065 91 102 95.8 95.3 4.7 0.207
Small 1089 18 47 98.4 95.9 4.1 0.204
Results under different Weather Conditions
Value TP FP FN Precision (%) Recall (%) Miss Rate (%) Speed (s)
Sunny 2459 83 123 96.7 95.2 4.8 0.204
Cloudy 2155 98 94 95.7 95.8 4.2 0.202
Misty 1586 107 98 93.7 94.2 5.8 0.209
Rainy 2186 123 164 94.7 93.0 7.0 0.210
• Similarly, for other categories like illumination levels, individual postures, & occlusions
results have been described.
Reference: https://doi.org/10.1016/j.autcon.2017.09.018
19. LIMITATION & FUTURE SCOPE
Limitations Future Scopes
Currently, this algorithm is able to
detect NHU workers but not identify
the workers involved.
It is recommended that future
research focus on the identification
and integration of worker information
into real-time safety monitoring
systems as this will then enable
disciplinary action and targeted safety
training to be carried out.
Reference: https://doi.org/10.1016/j.autcon.2017.09.018
21. INTRODUCTION
• A near miss has been defined as an unplanned event that has the potential to cause but
does not result in personal injury, environmental or equipment damage, or interruption to
regular operation.
• Approximately 91% of accidents produced no injuries, while 9% were minor and less
than 1% major.
• The analysis of near-miss data can be labour-intensive and time-consuming, and it requires
an understanding of safety to be able to derive meaningful insights.
• As a result of classifying text using Deep Learning models, this can provide site managers
with an ability to identify work-areas and instances where the likelihood of an accident
may occur.
Reference: https://doi.org/10.1016/j.aei.2020.101060
22. DEEP LEARNING MODEL
• This paper utilized deep learning and Bidirectional Transformers for Language
Understanding (BERT) to develop a robust automatic text classification model of near-
misses.
• The BERT’s model architecture is a multi-layer bidirectional transformer encoder-decoder
structure.
• The encoder consists of six identical layers. Each layer has two sublayers: (1) a multi-head
self-attention mechanism; and a fully connected feed-forward network with simple and
position-wise.
• These two sub-layers are connected by a residual connection followed by layer
normalization, and then output a 768-dimension vectors.
Reference: https://doi.org/10.1016/j.aei.2020.101060
23. SOURCE OF DATA
• Approximately 3280 near-miss events are stored. Each near-miss contains its location,
time, name, description, safety level, categories, and images.
• These 3280 near-misses have been classified into 170 categories, such as quality of main
concrete structure, template installation, monitoring data overrun.
• The database is randomly divided into a training and testing database with a ratio of 8:2.
In other words, 2624 near-miss are used for training the BERT model, and 657 for testing
its performance.
Reference: https://doi.org/10.1016/j.aei.2020.101060
24. EXPERIMENT
Data Cleaning
• Punctuation
are removed.
• All words to
lowercase.
• Each sentence
is intercepted
by first N (64)
words.
Word-piece
Tokenization
• Completely
data-driven.
• Greedy
longest match
first algorithm
is used.
• “unaffable”
“un”,
“##aff”,
“##ble”.
Text-feature
Construction
• All the different tokens from
the previous step is arranged
and numbered from 1 to k.
• If the length of sentence is
less than N, it will be filled
with o.
• Each sentence will be
converted to an input feature
of length N.
Reference: https://doi.org/10.1016/j.aei.2020.101060
26. LIMITATION & FUTURE SCOPE
Limitations Future Scopes
• The developed model was unable to
100% accurately classify near-miss
reports due to sheer number of
categories (L = 170), which contained
too few events.
• The data source is in Chinese. In this
experiment, we translated the data into
English. Thus, the quality of the
translation may have affected the
experimental results.
• Further research is required to
improve the accuracy of classifying
safety data, particularly in the context
of annotating training text.
• Also, future research needs to focus
on creating larger datasets and using
unsupervised learning to improve the
accuracy of text classification.
Reference: https://doi.org/10.1016/j.aei.2020.101060
27. Deep learning for site safety: Real-
time detection of personal protective
equipment
28. INTRODUCTION
• The U.S. Occupational Safety and Health Administration (OSHA) and similar agencies in other
countries require that all personnel, working in close proximity of site hazards, wear proper PPE to
minimize the risk of being exposed to or injured by hazards.
• According to a report by the National Institute
for Occupational Safety and Health (NIOSH),
between 2003 and 2010, a total of 2,210
construction fatalities occurred because of
traumatic brain injury (TBI) which
represented 25% of all construction fatalities
during that period.
Percentage of fatal injuries caused by the “fatal four” in
construction industry in 2017.
• Three deep learning models are introduced for real time
detection of Personal Protective Equipment (PPE).
Reference: https://doi.org/10.1016/j.autcon.2020.103085
29. DEEP LEARNING MODELS
First Approach The algorithm detects workers, hats, and
vests and then, a machine learning model
(e.g., neural network and decision tree)
verifies if each detected worker is properly
wearing hat or vest.
Second Approach The algorithm simultaneously detects
individual workers and verifies PPE
compliance with a single convolutional
neural network (CNN) framework.
Third Approach The algorithm first detects only the
workers in the input image which are then
cropped and classified by CNN-based
classifiers (i.e., VGG-16, ResNet-50, and
Xception) according to the presence of PPE
attire.
Reference: https://doi.org/10.1016/j.autcon.2020.103085
33. FUTURE SCOPE
• The dataset can be expanded to detect other common PPE components, e.g., safety glass
and gloves, etc.
• The DL model can also be made to detect the identity of the faulty worker who has not
wore any of the PPE.
Reference: https://doi.org/10.1016/j.autcon.2020.103085
36. INTRODUCTION
• Various technologies, including vehicle-mounted laser profiling systems, have been developed and
adopted for road roughness (e.g., IRI—International Roughness Index) measurement; however,
their high cost limits their use.
• Yearly based inspections and limited coverage using the vehicle-mounted laser profiling systems
may not effectively reflect the overall health conditions of our extensive road networks in a timely
manner.
• These IRI estimation approaches that use vehicle dynamics, no matter which sensors are used,
intrinsically require a precise calibration of the vehicle model. This is typically done with known
road profiles or bump-induced vehicle responses at controlled vehicle speeds.
Reference: https://doi.org/10.1111/mice.12546
So, What?
37. INTRODUCTION
• No matter the calibration method, such a way of existing IRI estimations require precisely
calibrated vehicle models, which is not practically applicable for usual passenger vehicles.
• For example, the number and locations of passengers in the vehicle may change every day, and the
vehicle speeds vary all the time, and suspension characteristics change by aging over time.
Furthermore, the dynamic properties of vehicle mechanics may also change over time. The
location, direction, and way of smartphone mounting may also be different every time. These
ambient variations are closely related to the vehicle dynamics (i.e., vehicle mass, damping, pitch
inertia, etc.) and corresponding sensor measurements change significantly.
• Therefore, previous calibrations of the vehicle model become quickly invalid for the IRI estimation
under altered vehicle dynamics and sensor installations.
• This study develops a CNN-based road roughness (i.e., discrete IRI) estimation method
that utilizes anonymous passenger vehicles and their dynamic responses to compensate
for the drawbacks of current profiling-based technologies.
Reference: https://doi.org/10.1111/mice.12546
42. What Else?
1.Data Structures & Algorithms: Will be used in
the preparation of Deep Learning Models.
2.Various terminologies regarding DL.
3.Object Oriented Programming.