A Novel Autonomous Inspection System of USV for Submarine Buried Pipeline

2019 ◽  
Vol 53 (3) ◽  
pp. 90-95
Author(s):  
Lei Gao ◽  
Hai-Tao Gu ◽  
Hong-li Xu
Keyword(s):  
Conventional Method ◽  
Low Cost ◽  
Quadratic Functions ◽  
Inspection System ◽  
Submarine Pipeline ◽  
Data Set ◽  
Surface Vessels ◽  
Submarine Buried Pipeline ◽  
Embedded Algorithms ◽  
Subbottom Profiling

AbstractThe conventional method of surveying utilizing manned vessels requires a large investment of labor-intensive and time-consuming efforts. With the phenomenal progress of unmanned surface vessels (USVs), they have become a useful tool for surveyors and engineers who have been seeking a more productive and low-cost method as an alternative. This paper depicts a novel design of USVs for autonomous detection and recognition of buried submarine pipeline. The design adopted a parametric subbottom profiling system with embedded algorithms for path planning, autonomous obstacle avoidance, and autonomous pipeline recognition and navigation. The pipeline detection is based on the analysis of quadratic functions generated by the subbottom data set. Compared to the conventional method, the use of USVs equipped with subbottom profiling system turns out to be more useful and efficient for accurate detections of submarine pipeline.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals Mask R-CNN Based C. Elegans Detection with a DIY Microscope

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 257
Author(s):  
Sebastian Fudickar ◽  
Eike Jannik Nustede ◽  
Eike Dreyer ◽  
Julia Bornhorst
Keyword(s):  
Cell Biology ◽  
High Throughput Screening ◽  
Low Cost ◽  
Image Acquisition ◽  
Rapid Development ◽  
Model Organism ◽  
Large Data ◽  
Data Set ◽  
C Elegans ◽  
Do It Yourself

Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an [email protected] IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906.

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

scholarly journals A Cotton Module Feeder Plastic Contamination Inspection System

2020 ◽  
Vol 2 (2) ◽  
pp. 280-293
Author(s):  
Mathew G. Pelletier ◽  
Greg A. Holt ◽  
John D. Wanjura
Keyword(s):  
Mechanical Design ◽  
Low Cost ◽  
Processing System ◽  
Technical Note ◽  
Inspection System ◽  
Learning Program ◽  
Cotton Lint ◽  
Cotton Industry ◽  
Bill Of Materials ◽  
The U.S

The removal of plastic contamination in cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales, at the U.S. Department of Agriculture’s classing office, is plastic from the module wrap used to wrap cotton modules produced by the new John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during unwrapping of the seed cotton modules, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin; an inspection system was developed that utilized low-cost color cameras to see plastic on the module feeder’s dispersing cylinders, that are normally hidden from view by the incoming feed of cotton modules. This technical note presents the design of an automated intelligent machine-vision guided cotton module-feeder inspection system. The system includes a machine-learning program that automatically detects plastic contamination in order to alert the cotton gin personnel as to the presence of plastic contamination on the module feeder’s dispersing cylinders. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. This note describes the over-all system and mechanical design and provides an over-view and coverage of key relevant issues. Included as an attachment to this technical note are all the mechanical engineering design files as well as the bill-of-materials part source list. A discussion of the observational impact the system had on reduction of plastic contamination is also addressed.

scholarly journals Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket colposcope

2018 ◽  
Author(s):  
Mercy Nyamewaa Asiedu ◽  
Anish Simhal ◽  
Usamah Chaudhary ◽  
Jenna L. Mueller ◽  
Christopher T. Lam ◽  
...  
Keyword(s):  
Acetic Acid ◽  
Visual Inspection ◽  
Point Of Care ◽  
Low Cost ◽  
Intraepithelial Neoplasia ◽  
Support Vector ◽  
Machine Model ◽  
Data Set ◽  
Automatic Feature Extraction ◽  
Lugol’S Iodine

AbstractGoalIn this work, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol’s iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance.MethodsWe developed algorithms to pre-process pathology-labeled cervigrams and to extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol’s iodine, and a combination of the two contrasts.ResultsThe proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, 63% accuracy).ConclusionThe results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol’s iodine images may provide unbiased automation of cervigrams.SignificanceThis would enable automated, expert-level diagnosis of cervical pre-cancer at the point-of-care.

scholarly journals Identification of RBC and WBC Count in Human Blood Using ARM Based Instrumentation

2015 ◽  
Vol 11 (1) ◽  
pp. 145-150
Author(s):  
B. Basavanagoud ◽  
K. Priya
Keyword(s):  
Conventional Method ◽  
High Speed ◽  
Low Cost ◽  
Blood Analysis ◽  
Medical Sciences ◽  
Accurate Analysis ◽  
Human Errors ◽  
Arm Processor ◽  
Wbc Count ◽  
Human Fluids

The rapid growth in microelectronics and crunching RISC in the field of bio-medical sciences incorporated of soft tools to diagnose various parameters of human fluids. Conventional method of blood sample analysis makes use of laboratory technique of titration, which is operator-dependent and results in lot of errors depending on the skill of the technician. In order to eliminate the human errors involved in the conventional method, in this paper an attempt has been made to present a capillary centrifuge technique driven by high speed DC motor fed by Morgan chopper and controlled by powerful ARM processor. It results in accurate analysis of the blood samples. The various techniques involved in accurate sensing of speed using timer and generation of firing pulses to thyristor in the Morgan chopper is judiciously achieved. This paper clearly brings out the advantages of the proposed blood measurement technique which effectively gives blood analysis faster and at a low cost.

scholarly journals An Approach to Efficient Dictionary Utilization and Improved Data Compression Technique for LZW Algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 224-229
Keyword(s):  
Data Compression ◽  
Conventional Method ◽  
Large Data ◽  
Compression Algorithm ◽  
Case Scenario ◽  
Compression Technique ◽  
Data Set ◽  
New Methods ◽  
Lzw Algorithm ◽  
The Way

This paper proposes an improved data compression technique compared to existing Lempel-Ziv-Welch (LZW) algorithm. LZW is a dictionary-updation based compression technique which stores elements from the data in the form of codes and uses them when those strings recur again. When the dictionary gets full, every element in the dictionary are removed in order to update dictionary with new entry. Therefore, the conventional method doesn’t consider frequently used strings and removes all the entry. This method is not an effective compression when the data to be compressed are large and when there are more frequently occurring string. This paper presents two new methods which are an improvement for the existing LZW compression algorithm. In this method, when the dictionary gets full, the elements that haven’t been used earlier are removed rather than removing every element of the dictionary which happens in the existing LZW algorithm. This is achieved by adding a flag to every element of the dictionary. Whenever an element is used the flag is set high. Thus, when the dictionary gets full, the dictionary entries where the flag was set high are kept and others are discarded. In the first method, the entries are discarded abruptly, whereas in the second method the unused elements are removed once at a time. Therefore, the second method gives enough time for the nascent elements of the dictionary. These techniques all fetch similar results when data set is small. This happens due to the fact that difference in the way they handle the dictionary when it’s full. Thus these improvements fetch better results only when a relatively large data is used. When all the three techniques' models were used to compare a data set with yields best case scenario, the compression ratios of conventional LZW is small compared to improved LZW method-1 and which in turn is small compared to improved LZW method-2.

The Commercialization of University and Research Institutes’ Science-Based Innovations: The Four Successful Chinese Cases

2018 ◽  
Vol 03 (03) ◽  
pp. 1850013 ◽  
Author(s):  
Yacheng Yang ◽  
Hong Chen ◽  
Qingzhi Zhang ◽  
Jiasu Lei
Keyword(s):  
Development Strategy ◽  
Financial Incentives ◽  
High Performance ◽  
Low Cost ◽  
Global Market ◽  
Inspection System ◽  
Policy Enforcement ◽  
Innovative Strategy ◽  
Low Cost Strategy ◽  
Business Acumen

Science empowers as a nation’s toughest weapon in the future global competition and cooperation. A large number of countries listed in-house R&D for science-based innovations as their core development strategy in the next decade. This paper conducts multi-case analysis on four science-based innovations in China as a reference for how a new science-based venture superseded in global market and developed indigenous capability to generate much business value as well as scientific value. The four cases detailed are container inspection system, hot redundant JX-100 DCS, high-performance Dawning supercomputers and Chinese-character laser phototypesetting system. We concluded that the successful commercialization of a nationwide and grand scientific project requires the following: (1) visionary scientists’ solid authority, direct participation, business acumen and a strong sense of patriotism, without intermediaries, are the core for successful science-based innovation and commercialization during knowledge transformation; and (2) the powerful and direct support from the policymakers. Forms of support may vary from financial incentives, policy enforcement and endorsement. The consequences for the success of science-based innovations are the creation of highly-skilled manpower, new market as well as shifting away from low-cost strategy to innovative strategy.

Robotic inspection of ship hull surfaces using a magnetic crawler and a monocular camera

Sensor Review ◽  
2017 ◽  
Vol 37 (4) ◽  
pp. 425-435 ◽  
Author(s):  
Annalisa Milella ◽  
Rosalia Maglietta ◽  
Massimo Caccia ◽  
Gabriele Bruzzone
Keyword(s):  
Visual Inspection ◽  
Low Cost ◽  
New Technology ◽  
Experimental Tests ◽  
Ship Hull ◽  
Inspection System ◽  
Content Type ◽  
Monocular Camera ◽  
Mosaic Image ◽  
Automatic Visual Inspection

Purpose Periodic inspection of large tonnage vessels is critical to assess integrity and prevent structural failures that could have catastrophic consequences for people and the environment. Currently, inspection operations are undertaken by human surveyors, often in extreme conditions. This paper aims to present an innovative system for the automatic visual inspection of ship hull surfaces, using a magnetic autonomous robotic crawler (MARC) equipped with a low-cost monocular camera. Design/methodology/approach MARC is provided with magnetic tracks that make it able to climb along the vertical walls of a vessel while acquiring close-up images of the traversed surfaces. A homography-based structure-from-motion algorithm is developed to build a mosaic image and also produce a metric representation of the inspected areas. To overcome low resolution and perspective distortion problems in far field due to the tilted and low camera position, a “near to far” strategy is implemented, which incrementally generates an overhead view of the surface, as long as it is traversed by the robot. Findings This paper demonstrates the use of an innovative robotic inspection system for automatic visual inspection of vessels. It presents and validates through experimental tests a mosaicking strategy to build a global view of the structure under inspection. The use of the mosaic image as input to an automatic corrosion detector is also demonstrated. Practical implications This paper may help to automate the inspection process, making it feasible to collect images from places otherwise difficult or impossible to reach for humans and automatically detect defects, such as corroded areas. Originality/value This paper provides a useful step towards the development of a new technology for automatic visual inspection of large tonnage ships.

Factors Impacting Physical Therapy Utilization for Patients With Nonspecific Low Back Pain: Retrospective Analysis of a Clinical Data Set

2020 ◽  
Vol 100 (9) ◽  
pp. 1502-1515
Author(s):  
Janet Dolot ◽  
Matthew Hyland ◽  
Qiuhu Shi ◽  
Hae-Young Kim ◽  
Deborah Viola ◽  
...  
Keyword(s):  
Back Pain ◽  
Physical Therapy ◽  
Physical Therapist ◽  
Low Cost ◽  
Care Organization ◽  
Data Set ◽  
Individual Patient Level ◽  
Episode Of Care ◽  
Out Of Pocket Payments ◽  
Provider Organization

Abstract Objective Factors predicting physical therapy utilization for lower back pain (LBP) remain unclear, limiting the development of value-based initiatives. The purpose of this study was to identify important factors that impact the number of physical therapist visits per episode of care for US adults with nonspecific LBP. Methods This study was a retrospective observational cohort study of a clinical dataset derived from 80 clinics of a single physical therapy provider organization. Research variables were categorized at the individual (patient) level and the organization (therapist, clinic) level. A hierarchical regression model was designed to identify factors influencing the number of physical therapist visits per episode of care. Results Higher out-of-pocket payments per visit, receipt of “active” physical therapy, longer average visit length, earlier use of physical therapy, and sex of the therapist (male) were found to predict fewer visits per episode of care. Percent change of function, prior receipt of physical therapy by the same provider organization, self-discharge from physical therapy, level of starting function, and therapist certification were found to predict more visits. Of the variance in number of visits, 8.0% was attributable to the health care organization. Conclusions Individual factors, such as higher out-of-pocket payment, have a significant impact on reducing visits per episode of care and should be considered when developing value-based initiatives to optimize clinical and utilization outcomes. Impact Payers use consumer-directed healthcare to reduce costs by discouraging utilization of low value services and encouraging use of low-cost providers. LBP is a costly condition for which physical therapy is a high-value treatment. This study shows that non-need factors predict the number of physical therapy visits per episode of care for patients with nonspecific LBP. Insurance benefit plans with lower out-of-pocket payments for physical therapy and higher reimbursement for active physical therapist interventions may facilitate appropriate utilization of high-value treatment for LBP.

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