scholarly journals Web App Analysis and Identification of Macro Nutrients of Leaf

2021 ◽  
Vol 9 (VI) ◽  
pp. 1151-1156
Author(s):  
Bruffen R B K
Keyword(s):  
Plant Nutrition ◽  
Clustering Algorithm ◽  
Nutrient Deficiency ◽  
Tool Support ◽  
End User ◽  
Leaf Characteristics ◽  
Changing Trends ◽  
Web App ◽  
Non Destructive ◽  
Macro Nutrients

India is an agricultural country. Farmers are experiencing great difficulties in managing the fertilizer usage and decease rectification. Non-destructive nutrient deficiency analysis provides effective tool support for precise farming. According to the plant nutrition mechanism, leaf characteristics displays different changing trends under nitrogen (N) deficiency. In this paper the technique presented is for detection of macro nutrients and diseases identification in leaf. In this study, the dynamic capture of leaf by scanning was used to research the changing regulation of leaf characteristics under nutrient deficiency. The samples must be taken under the shadow of farmer. The work begins with capturing the images. From the captured images RGB components are extracted by segmentation process using K-Means clustering algorithm. In this process are host them using MATLAB web app serve, here end user can access and run this web app using browser without installing additional software.

scholarly journals A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound

2019 ◽  
Vol 9 (17) ◽  
pp. 3484
Author(s):  
Shuai Han ◽  
Heng Li ◽  
Mingchao Li ◽  
Timothy Rose
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Rock Strength ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Strength Measurement ◽  
Regression Algorithms ◽  
Almost All ◽  
The Relationship ◽  
Non Destructive

Hammering rocks of different strengths can make different sounds. Geological engineers often use this method to approximate the strengths of rocks in geology surveys. This method is quick and convenient but subjective. Inspired by this problem, we present a new, non-destructive method for measuring the surface strengths of rocks based on deep neural network (DNN) and spectrogram analysis. All the hammering sounds are transformed into spectrograms firstly, and a clustering algorithm is presented to filter out the outliers of the spectrograms automatically. One of the most advanced image classification DNN, the Inception-ResNet-v2, is then re-trained with the spectrograms. The results show that the training accurate is up to 94.5%. Following this, three regression algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) are adopted to fit the relationship between the outputs of the DNN and the strength values. The tests show that KNN has the highest fitting accuracy, and SVM has the strongest generalization ability. The strengths (represented by rebound values) of almost all the samples can be predicted within an error of [−5, 5]. Overall, the proposed method has great potential in supporting the implementation of efficient rock strength measurement methods in the field.

Survey and Analysis of Current End-user Data Analytics Tool Support

2019 ◽  
pp. 1-1
Author(s):  
Hourieh Khalajzadeh ◽  
Mohamed Abdelrazek ◽  
John Grundy ◽  
John G. Hosking ◽  
Qiang He
Keyword(s):  
Data Analytics ◽  
Tool Support ◽  
End User ◽  
User Data

End-User-Oriented Tool Support for Modeling Data Analytics Requirements

2020 ◽  
Author(s):  
Hourieh Khalajzadeh ◽  
Andrew J. Simmons ◽  
Mohamed Abdelrazek ◽  
John Grundy ◽  
John Hosking ◽  
...  
Keyword(s):  
Data Analytics ◽  
Tool Support ◽  
End User ◽  
Modeling Data

A Survey of Current End-User Data Analytics Tool Support

2018 ◽  
Author(s):  
Hourieh Khalajzadeh ◽  
Mohamed Abdelrazek ◽  
John Grundy ◽  
John Hosking ◽  
Qiang He
Keyword(s):  
Data Analytics ◽  
Tool Support ◽  
End User ◽  
User Data

scholarly journals Extending the Life of F-Class Gas Turbine Rotors

2018 ◽  
Author(s):  
Scott Keller ◽  
David Day
Keyword(s):  
Gas Turbine ◽  
Service Providers ◽  
Non Destructive Testing ◽  
End User ◽  
Destructive Testing ◽  
Rotor Systems ◽  
Operations And Maintenance ◽  
Turbine Rotors ◽  
Heavy Duty Gas Turbine ◽  
Non Destructive

Unlike more mature E-Class heavy duty gas turbine rotors, F-Class gas turbine rotors have exhibited a variety of failure mechanisms over the past 20 years. From the liberation of nickel turbine posts to large (600 mm) cracks in marriage components, F-Class rotors have failed to achieve the reliability of older units. Now as the F-Class units are approaching the OEM-recommended end of life (EOL), operators are struggling to repair and/or replace as operations and maintenance (O&M) budgets are dwindling. As such, end users are routinely forced to turn to other service providers to provide targeted (limited) inspections aimed at extending the life of these capital parts. While suitable for more mature rotor systems, recent EOL investigations into multiple OEM F-Class rotors have revealed significant issues with limited inspections. Utilizing comprehensive non-destructive testing (NDT), forging defects and surface cracks have been discovered throughout compressor and turbine rotors. However, inspection alone cannot determine if adequate life remains when an indication is found. In addition to the inspections, recommended analytical modeling and requisite material test data for CrMoV, NiCrMoV, and IN706 rotor materials will be overviewed. In some cases, the NDT indications have resulted in the retirement of individual components, as analytical predictions could not provide a suitable extension for those particular components. The concern is highlighted that a significant amount of these findings were in the cold end of the compressor, which would have been missed with more traditional, limited inspections. The goal of this paper is to provide the end user the information to reliably and safely extend the life of their rotor beyond the original OEM recommendation.

scholarly journals PWA-EU: uma abordagem para o desenvolvimento de aplicações PWA baseadas em EUD

2018 ◽  
Author(s):  
Giulia De Andrade Cardieri ◽  
Luciana Martinez Zaina
Keyword(s):  
End User ◽  
Web App

Interfaces adaptativas propõem a mudança de seu comportamento de acordo com determinadas características de uso, como preferências do usuário e contextos de uso. Progressive Web App (PWA) é uma nova técnica de desenvolvimento de aplicações móveis proposta pela Google, que mistura tecnologia de recursos de desenvolvimento web e nativo. Essas aplicações são inicialmente apresentadas como web e progressivamente, com o crescimento da relação do usuário e a aplicação, tornam-se mais completas e parecidas com aplicações nativas. Contudo, essa abordagem não dá a liberdade para o usuário atuar como um agente ativo e participar de escolhas na adaptação da interface durante a interação. Este projeto de mestrado tem como objetivo propor a abordagem PWA-EU, uma extensão da arquitetura de uma aplicação PWA, considerando a perspectiva do usuário final utilizando meta-design, uma abordagem de End-user development. Como contribuição deste trabalho, pode-se destacar a inclusão do usuário como agente ativo no processo de adaptação e explorar esse novo modelo de desenvolvimento que são as PWAs.

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