scholarly journals Methods to detect and measure scour around bridge foundations

2021 ◽  
Author(s):  
Muhanad Al-jubouri ◽  
Richard Ray
Keyword(s):  
Health Monitoring ◽  
Transportation Networks ◽  
Detection System ◽  
Cost Effective ◽  
Detection Methods ◽  
Bridge Scour ◽  
Measurement Equipment ◽  
Rail Transportation ◽  
Bridge Foundations ◽  
Human Effort

Bridges are indispensable structures vital to the operation of road and rail transportation networks. Crossing rivers and artificial waterways, however, presents a risk to their foundations due to scour actions. Scour is the number one cause for bridge failures and may occur beneath any bridge, large or small, with supports located within the waterway. This paper provides a summary of present scour detection and measurement equipment and associated assessment methodologies. In this regard, particular emphasis is placed on structural health monitoring better to evaluate the presence and influence of potential scour. A Sensitivity Analysis on a newly introduced monitoring system is also assumed. Furthermore, much research has been undertaken to create a technology that can instantly identify and detect bridge scour, improving survey reliability through prior inspection and prompt intervention. This research will explore and evaluate bridge scour detection methods employed and suggest a possible path for developing the detection system to identify scour depth effectively and efficiently. Finally, our key aim is to minimize human effort in identifying and bridge scour by using a quick, easy-to-use, cost-effective process, resulting in fewer injuries and economic savings.

scholarly journals Chromium Monitoring in Water by Colorimetry Using Optimised 1,5-Diphenylcarbazide Method

2019 ◽  
Vol 16 (10) ◽  
pp. 1803 ◽  
Author(s):  
Annija Lace ◽  
David Ryan ◽  
Mark Bowkett ◽  
John Cleary
Keyword(s):  
Detection System ◽  
Colorimetric Method ◽  
Cost Effective ◽  
Detection Methods ◽  
Detection Systems ◽  
Quantitation Limit ◽  
Portable Detection ◽  
Reagent Consumption ◽  
Trained Staff ◽  
Colour Development

Chromium contamination of drinking water has become a global problem due to its extensive use in industry. The most commonly used methods for chromium detection in water are laboratory-based methods, such as atomic absorption spectroscopy and mass spectroscopy. Although these methods are highly selective and sensitive, they require expensive maintenance and highly trained staff. Therefore, there is a growing demand for cost effective and portable detection methods that would meet the demand for mass monitoring. Microfluidic detection systems based on optical detection have great potential for onsite monitoring applications. Furthermore, their small size enables rapid sample throughput and minimises both reagent consumption and waste generation. In contrast to standard laboratory methods, there is also no requirement for sample transport and storage. The aim of this study is to optimise a colorimetric method based on 1,5-diphenylcarbazide dye for incorporation into a microfluidic detection system. Rapid colour development was observed after the addition of the dye and samples were measured at 543 nm. Beer’s law was obeyed in the range between 0.03–3 mg·L−1. The detection limit and quantitation limit were found to be 0.023 and 0.076 mg·L−1, respectively.

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

scholarly journals A Synthetic Biology Approach Using Engineered Bacteria to Detect Perfluoroalkyl Substance (PFAS) Contamination in Water

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 801-807
Author(s):  
Nathaniel A Young ◽  
Ryan L Lambert ◽  
Angela M Buch ◽  
Christen L Dahl ◽  
Jackson D Harris ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Dna Sequences ◽  
Detection System ◽  
The United States ◽  
Detection Methods ◽  
Health Concern ◽  
Contaminated Water ◽  
Engineered Bacteria ◽  
Synthetic Biology Approach ◽  
Rhodococcus Jostii

ABSTRACT Introduction Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic compounds used industrially for a wide variety of applications. These PFAS compounds are very stable and persist in the environment. The PFAS contamination is a growing health issue as these compounds have been reported to impact human health and have been detected in both domestic and global water sources. Contaminated water found on military bases poses a potentially serious health concern for active duty military, their families, and the surrounding communities. Previous detection methods for PFAS in contaminated water samples require expensive and time-consuming testing protocols that limit the ability to detect this important global pollutant. The main objective of this work was to develop a novel detection system that utilizes a biological reporter and engineered bacteria as a way to rapidly and efficiently detect PFAS contamination. Materials and Methods The United States Air Force Academy International Genetically Engineered Machine team is genetically engineering Rhodococcus jostii strain RHA1 to contain novel DNA sequences composed of a propane 2-monooxygenase alpha (prmA) promoter and monomeric red fluorescent protein (mRFP). The prmA promoter is activated in the presence of PFAS and transcribes the mRFP reporter. Results The recombinant R. jostii containing the prmA promoter and mRFP reporter respond to exposure of PFAS by activating gene expression of the mRFP. At 100 µM of perfluorooctanoic acid, the mRFP expression was increased 3-fold (qRT-PCR). Rhodococcus jostii without exposure to PFAS compounds had no mRFP expression. Conclusions This novel detection system represents a synthetic biology approach to more efficiently detect PFAS in contaminated samples. With further refinement and modifications, a similar system could be readily deployed in the field around the world to detect this critical pollutant.

scholarly journals Food Allergen Detection Methods: A Coordinated Approach

2004 ◽  
Vol 87 (6) ◽  
pp. 1383-1390 ◽  
Author(s):  
Philip R Goodwin
Keyword(s):  
Hazard Analysis ◽  
Adult Population ◽  
Control Point ◽  
Cost Effective ◽  
Test Methods ◽  
Detection Methods ◽  
Critical Control Point ◽  
Life Threatening ◽  
Monitoring And Surveillance ◽  
Analytical Approaches

Abstract The levels (1–2%) and increasing severity of allergic responses to food in the adult population are well documented, as is the phenomenon of even higher (3–8%) and apparently increasing incidence in children, albeit that susceptibility decreases with age. Problematic foods include peanut, milk, eggs, tree nuts, and sesame, but the list is growing as awareness continues to rise. The amounts of such foods that can cause allergic reactions is difficult to gauge; however, the general consensus is that ingestion of low parts per million is sufficient to cause severe reactions in badly affected individuals. Symptoms can rapidly—within minutes—progress from minor discomfort to severe, even life-threatening anaphylactic shock in those worst affected. Given the combination of high incidence of atopy, potential severity of response, and apparently widespread instances of “hidden” allergens in the food supply, it is not surprising that this issue is increasingly subject to legislative and regulatory scrutiny. In order to assist in the control of allergen levels in foods to acceptable levels, analysts require a combination of test methods, each designed to produce accurate, timely, and cost-effective analytical information. Such information contributes significantly to Hazard Analysis Critical Control Point programs to determine food manufacturers’ risk and improves the accuracy of monitoring and surveillance by food industry, commercial, and enforcement laboratories. Analysis thereby facilitates improvements in compliance with labeling laws with concomitant reductions in risks to atopic consumers. This article describes a combination of analytical approaches to fulfill the various needs of these 3 analytical communities.

scholarly journals CRISPR/Cas12a-Based Ultrasensitive and Rapid Detection of JAK2 V617F Somatic Mutation in Myeloproliferative Neoplasms

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 247
Author(s):  
Miaomiao Chen ◽  
Chunhua Zhang ◽  
Zhiqing Hu ◽  
Zhuo Li ◽  
Menglin Li ◽  
...  
Keyword(s):  
Detection System ◽  
Myeloproliferative Neoplasms ◽  
Cost Effective ◽  
Jak2 V617f ◽  
Jak2 V617f Mutation ◽  
Minimum Detectable Concentration ◽  
Lateral Flow Strip ◽  
Whole Process ◽  
Detectable Concentration ◽  
V617f Mutation

The JAK2 V617F mutation is a major diagnostic, therapeutic, and monitoring molecular target of Philadelphia-negative myeloproliferative neoplasms (MPNs). To date, numerous methods of detecting the JAK2 V617F mutation have been reported, but there is no gold-standard diagnostic method for clinical applications. Here, we developed and validated an efficient Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR associated protein 12a (Cas12a)-based assay to detect the JAK2 V617F mutation. Our results showed that the sensitivity of the JAK2 V617F/Cas12a fluorescence detection system was as high as 0.01%, and the JAK2 V617F/Cas12a lateral flow strip assay could unambiguously detect as low as 0.5% of the JAK2 V617F mutation, which was much higher than the sensitivity required for clinical application. The minimum detectable concentration of genomic DNA achieved was 0.01 ng/μL (~5 aM, ~3 copies/μL). In addition, the whole process only took about 1.5 h, and the cost of an individual test was much lower than that of the current assays. Thus, our methods can be applied to detect the JAK2 V617F mutation, and they are highly sensitive, rapid, cost-effective, and convenient.

Prediction of wind turbine blade trailing edge noise under various flow conditions for a passive damage detection system

2021 ◽  
Vol 263 (2) ◽  
pp. 4079-4087
Author(s):  
Murat Inalpolat ◽  
Caleb Traylor
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Detection System ◽  
Wind Turbine Blade ◽  
Flow Conditions ◽  
Trailing Edge ◽  
Structural Health ◽  
Acoustic Spectra

Noise generated by turbulent boundary layer over the trailing edge of a wind turbine blade under various flow conditions is predicted and analyzed for structural health monitoring purposes. Wind turbine blade monitoring presents a challenge to wind farm operators, and an in-blade structural health monitoring system would significantly reduce O&M costs. Previous studies into structural health monitoring of blades have demonstrated the feasibility of designing a passive detection system based on monitoring the flow-generated acoustic spectra. A beneficial next step is identifying the robustness of such a system to wind turbine blades under different flow conditions. To examine this, a range of free stream air velocities from 5 m/s to 20 m/s and a range of rotor speeds from 5 rpm to 20 rpm are used in a reduced-order model of the flow-generated sound in the trailing edge turbulent boundary layer. The equivalent lumped acoustics sources are predicted based on the turbulent flow simulations, and acoustic spectra are calculated using acoustic ray tracing. Each case is evaluated based on the changes detected when damage is present. These results can be used to identify wind farms that would most benefit from this monitoring system to increase efficiency in deployment of turbines.

scholarly journals Hybrid classification model to detect advanced intrusions using data mining techniques

2018 ◽  
Vol 7 (2.4) ◽  
pp. 10
Author(s):  
V Mala ◽  
K Meena
Keyword(s):  
Data Mining ◽  
Learning Algorithm ◽  
Detection System ◽  
Classification Model ◽  
Detection Methods ◽  
Data Mining Techniques ◽  
Detection Systems ◽  
Intruder Detection ◽  
Hybrid Classification ◽  
Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

scholarly journals Automated Quality Assessment of Crops Using CNN - Keras

2021 ◽  
pp. 641-645
Author(s):  
Meghashree ◽  
Alwyn Edison Mendonca ◽  
Ashika S Shetty
Keyword(s):  
Image Classification ◽  
Agricultural Production ◽  
Detection System ◽  
Cost Effective ◽  
Farming System ◽  
Modern Method ◽  
Innovative Technology ◽  
Disease Detection ◽  
Agricultural Field ◽  
Plant Leaf

Plant disease is an on-going challenge for the farmers and it has been one of the major threats to the income and the food security. This project is used to classify plant leaf into diseased and healthy leaf,to improve the quality and quantity of agricultural production in the country. The innovative technology that helps in improve the quality and quantity in the agricultural field is the smart farming system. It represented the modern method that provides cost-effective disease detection and deep learning with convolutional neural networks (CNNs) has achieved large successfulness in the categorisation of different plant leaf diseases. CNN reads a really very larger picture in a simple way. CNN nearly utilised to examine visual imagery and are frequently working behind the scenes in image classification. To extract the general features and then classify them under multiple based upon the features detected. This project will help the farmers financially in increasing the production of the crop yield as well as the overall agricultural production. The paper reviews the expected methods of plant leaf disease detection system that facilitates the advancement in agriculture. It includes various phases such as image preprocessing, image classification, feature extraction and detecting healthy or diseased.

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