Automated optimum visualization system for construction drawing reading

The task of reading drawings on construction sites has significant efficiency and cost problems. Recent products utilising laser projectors attempt to address the issue of drawing comprehension by projecting full scale versions of the drawings onto 3D surfaces, giving an in-place representation of the steps required to complete a task. However, they only allow projection in red or green at a single brightness level due to the inherent constraints of using a laser-based system, which could cause problems depending on the surface to be projected on and the ambient conditions. Thus, there is a need for a solution that is able to adjust the visualisation parameters of the displayed information based on the surface being projected onto. This study presents a system that automatically changes the visualisation parameters based on the colour and texture of the current surface to make drawings visible under any planar-like surfaces. The proposed system consists of software and hardware, and the software algorithm contains of two parts 1) the optimisation run that computes and updates the visualisation parameters and 2) the detection loop which runs continually and checks if the optimisation run needs to be triggered or not. In order to verify the proposed system, tests on 8 subjects with 4 background surfaces commonly found on site were performed. The test subjects were timed to find 10 bolt holes projected onto the surface using the optimisation system, which was then compared to a control case of black lines projected onto a white background. The system allowed users to complete the task on the real-world backgrounds in the same time as the control case, with the system resulting in up to a 600% decrease in recognition time on some backgrounds.

Download Full-text

Improving the robustness of a service robot for continuous indoor monitoring: An incremental approach

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211012181 ◽

2021 ◽

Vol 18 (3) ◽

pp. 172988142110121

Author(s):

David Portugal ◽

André G Araújo ◽

Micael S Couceiro

Keyword(s):

Real World ◽

Service Robots ◽

Service Robot ◽

Technology Readiness ◽

Incremental Approach ◽

Technology Readiness Level ◽

Readiness Level ◽

Software And Hardware ◽

Indoor Monitoring

To move out of the lab, service robots must reveal a proven robustness so they can be deployed in operational environments. This means that they should function steadily for long periods of time in real-world areas under uncertainty, without any human intervention, and exhibiting a mature technology readiness level. In this work, we describe an incremental methodology for the implementation of an innovative service robot, entirely developed from the outset, to monitor large indoor areas shared by humans and other obstacles. Focusing especially on the reliability of the fundamental localization system of the robot in the long term, we discuss all the incremental software and hardware features, design choices, and adjustments conducted, and show their impact on the performance of the robot in the real world, in three distinct 24-h long trials, with the ultimate goal of validating the proposed mobile robot solution for indoor monitoring.

Download Full-text

Characteristics of Preheated Bio-Oil Sprays Produced by an Air-Blast Injector

Volume 1: Aircraft Engine; Ceramics; Coal, Biomass and Alternative Fuels; Education; Electric Power; Manufacturing Materials and Metallurgy ◽

10.1115/gt2010-23397 ◽

2010 ◽

Cited By ~ 2

Author(s):

Heena V. Panchasara ◽

Ajay K. Agrawal

Keyword(s):

Laser Sheet ◽

Inlet Temperature ◽

Mass Ratio ◽

Ambient Conditions ◽

Air Blast ◽

Visualization System ◽

Radial Profiles ◽

Significant Difference ◽

Bio Oil ◽

Temperature And Pressure

In this study the vegetable oil (VO) is preheated to reduce the kinematic viscosity, and thus, improve atomization. A commercial air-blast atomizer is used to produce the VO spray at ambient conditions of temperature and pressure. Characteristics of the resulting spray are measured using a laser sheet visualization system and a Phase Doppler Particle Analyzer system. Experiments are conducted for VO temperatures varying from 40 C to 100 C and air to liquid mass ratio (ALR) of 2.0 and 4.0. Results show a decrease in Sauter Mean Diameter with an increase in VO temperature, regardless of the ALR. Radial profiles show larger droplets migrating towards the edge of the spray and smaller droplets in the interior spray region. Results show a significant difference in distributions of mean and root mean square axial velocity profiles as the VO inlet temperature is increased for a fixed ALR. Higher VO inlet temperature and higher ALR produced a narrower spray with smaller diameter droplets and higher peak axial velocities. Overall, this study has shown that preheating VO improves atomization by producing spray with smaller diameter droplets.

Download Full-text

Using Synthetic Data to Improve and Evaluate the Tracking Performance of Construction Workers on Site

Applied Sciences ◽

10.3390/app10144948 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4948

Author(s):

Marcel Neuhausen ◽

Patrick Herbers ◽

Markus König

Keyword(s):

Real World ◽

Safety Management ◽

Tracking System ◽

Synthetic Data ◽

Tracking Systems ◽

Construction Sites ◽

Virtual Construction ◽

Percentage Points ◽

Video Frames ◽

Graphics Software

Vision-based tracking systems enable the optimization of the productivity and safety management on construction sites by monitoring the workers’ movements. However, training and evaluation of such a system requires a vast amount of data. Sufficient datasets rarely exist for this purpose. We investigate the use of synthetic data to overcome this issue. Using 3D computer graphics software, we model virtual construction site scenarios. These are rendered for the use as a synthetic dataset which augments a self-recorded real world dataset. Our approach is verified by means of a tracking system. For this, we train a YOLOv3 detector identifying pedestrian workers. Kalman filtering is applied to the detections to track them over consecutive video frames. First, the detector’s performance is examined when using synthetic data of various environmental conditions for training. Second, we compare the evaluation results of our tracking system on real world and synthetic scenarios. With an increase of about 7.5 percentage points in mean average precision, our findings show that a synthetic extension is beneficial for otherwise small datasets. The similarity of synthetic and real world results allow for the conclusion that 3D scenes are an alternative to evaluate vision-based tracking systems on hazardous scenes without exposing workers to risks.

Download Full-text

Extended storage of SARS-CoV-2 nasopharyngeal swabs does not negatively impact results of molecular-based testing across three clinical platforms

Journal of Clinical Pathology ◽

10.1136/jclinpath-2020-206738 ◽

2020 ◽

pp. jclinpath-2020-206738

Author(s):

Karin A Skalina ◽

D Y Goldstein ◽

Jaffar Sulail ◽

Eunkyu Hahm ◽

Momka Narlieva ◽

...

Keyword(s):

Real World ◽

Nasopharyngeal Swab ◽

Molecular Testing ◽

Clinical Tests ◽

Ambient Conditions ◽

Long Term Stability ◽

The Us ◽

Us Fda ◽

World Environment

With the global outbreak of COVID-19, the demand for testing rapidly increased and quickly exceeded the testing capacities of many laboratories. Clinical tests which receive CE (Conformité Européenne) and Food and Drug Administration (FDA) authorisations cannot always be tested thoroughly in a real-world environment. Here we demonstrate the long-term stability of nasopharyngeal swab specimens for SARS-CoV-2 molecular testing across three assays recently approved by the US FDA under Emergency Use Authorization. This study demonstrates that nasopharyngeal swab specimens can be stored under refrigeration or even ambient conditions for 21 days without clinically impacting the results of the real-time reverse transcriptase-PCR testing.

Download Full-text

Sixth Sense Technology

International Journal of Virtual and Augmented Reality ◽

10.4018/ijvar.2017070102 ◽

2017 ◽

Vol 1 (2) ◽

pp. 18-41

Author(s):

Zeenat AlKassim ◽

Nader Mohamed

Keyword(s):

Virtual Reality ◽

Augmented Reality ◽

Human Computer Interaction ◽

Real World ◽

The Real ◽

Virtual Objects ◽

Unique Manner ◽

Software And Hardware ◽

Hardware Designs ◽

Computer Interaction

This paper discusses recent and unique inventions in Human Computer Interaction (HCI). To that end, firstly the authors discuss the Sixth Sense Technology. This technology allows users to interact with virtual objects in the real world in a unique manner. It has a number of applications which are further discussed. Then the opportunities and challenges are discussed. Most importantly, a list of inventions in fields of Augmented Reality (AR) and Virtual Reality (VR) in the recent years are discussed, grouped and compared. These include the smart eye glasses, VR headsets, smart watches, and more. Future implications of all those technologies are brought into light considering the new advancements in software and hardware designs. Recommendations are highlighted for future inventions.

Download Full-text

Building Multi-Modal Relational Graphs for Multimedia Retrieval

Multimedia Data Engineering Applications and Processing ◽

10.4018/978-1-4666-2940-0.ch009 ◽

2013 ◽

pp. 171-192

Author(s):

Jyh-Ren Shieh ◽

Ching-Yung Lin ◽

Shun-Xuan Wang ◽

Ja-Ling Wu

Keyword(s):

Social Media ◽

Web 2.0 ◽

Real World ◽

Ad Hoc ◽

User Studies ◽

Multimedia Retrieval ◽

New Approach ◽

Visualization System ◽

Relational Graphs ◽

Media Formats

The abundance of Web 2.0 social media in various media formats calls for integration that takes into account tags associated with these resources. The authors present a new approach to multi-modal media search, based on novel related-tag graphs, in which a query is a resource in one modality, such as an image, and the results are semantically similar resources in various modalities, for instance text and video. Thus the use of resource tagging enables the use of multi-modal results and multi-modal queries, a marked departure from the traditional text-based search paradigm. Tag relation graphs are built based on multi-partite networks of existing Web 2.0 social media such as Flickr and Wikipedia. These multi-partite linkage networks (contributor-tag, tag-category, and tag-tag) are extracted from Wikipedia to construct relational tag graphs. In fusing these networks, the authors propose incorporating contributor-category networks to model contributor’s specialization; it is shown that this step significantly enhances the accuracy of the inferred relatedness of the term-semantic graphs. Experiments based on 200 TREC-5 ad-hoc topics show that the algorithms outperform existing approaches. In addition, user studies demonstrate the superiority of this visualization system and its usefulness in the real world.

Download Full-text

Machine learning using synthetic images for detecting dust emissions on construction sites

Smart and Sustainable Built Environment ◽

10.1108/sasbe-04-2021-0066 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ruoxin Xiong ◽

Pingbo Tang

Keyword(s):

Object Detection ◽

Real World ◽

State Of The Art ◽

Synthetic Image ◽

Single Shot ◽

Construction Sites ◽

Content Type ◽

Detection Algorithms ◽

Dust Detection ◽

Synthetic Images

PurposeAutomated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust.Design/methodology/approachThis study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency.FindingsThis study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images.Originality/valueThis study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.

Download Full-text

Effects of Background Color, Flash and Exposure Value on the Accuracy of Smartphone-Based Pill Recognition System Using Deep Convolutional Neural Network (Preprint)

10.2196/preprints.26000 ◽

2020 ◽

Author(s):

KyeongMin Cha

Keyword(s):

Real World ◽

Color Difference ◽

Recognition System ◽

Reference Image ◽

White Background ◽

Factors Affecting ◽

The Real ◽

Black Background ◽

The Difference ◽

World Image

BACKGROUND It is difficult to develop a drug image recognition system due to the difference of the pill color influenced by external environmental factors such as the illumination or presence of flash. OBJECTIVE In this study, we wanted to see how the difference in color between the reference image and the real-world image affects the accuracy in pill recognition under 12 real-world conditions according to the background colors, presence of flash, and exposure values (EV). METHODS We used 19 medications with different features of colors, shapes, and dosages. The average color difference was calculated based on the color distance between the reference image and the real-world image. RESULTS In the case of the black background, as the exposure value lowered, the accuracy of top-1 and top-5 increased independently of the presence of flash. The top-5 accuracy in black background increased from 26.8% to 72.6% with the flash on and from 29.5% to 76.8% with the flash off as EV decreased as well. On the other hand, top-5 accuracy was 62.1% to 78.4% in white background with the flash on. The best top-1 accuracy was 51.1 % in the white background, flash on, and EV+2.0. The best top-5 accuracy was 78.4% in the white background, flash on, and EV0. CONCLUSIONS The accuracy generally increased as the color difference decreased except in the case of black background and EV-2.0. This study reveals that the background colors, presence of flash, and exposure values in real-world conditions are important factors affecting the performance of a pill recognition model.

Download Full-text

LabView and Connections with Third-Party Hardware

10.5772/intechopen.96056 ◽

2021 ◽

Author(s):

Giuseppe Porzio

Keyword(s):

Data Acquisition ◽

Real World ◽

Data Acquisition System ◽

Third Party ◽

System Control ◽

System Software ◽

Discrete Events ◽

Software And Hardware ◽

Open Source Hardware ◽

Physical Phenomena

Data acquisition is a function that plays a fundamental role in the automatic supervision and system control, it combine the system (software and hardware) to the process to be controlled (real world). The field of application starts from research to automation, from industry to home automation, in practice everything that in some way must be performed without human supervision. Data acquisition systems are mainly used to measure physical phenomena such as: temperature, voltage, current, distance and pressure, shock and vibration, and displacement, RPM, angle and discrete events, weight. In order to measure it we need a DAQ, Data AcQuisition System, in this chapter we propose to use a cheap open source hardware: Arduino.

Download Full-text