scholarly journals Evaluation and Modelling of a Low Budget Hall Effect Based Flow-Rate Sensor using Adaptive Calibration Paradigm

2020 ◽  
Vol 5 (9) ◽  
Author(s):  
Emmanuel M. Eronu ◽  
Moses Odiagbe
Keyword(s):  
Hall Effect ◽  
Research Work ◽  
Rate Of Change ◽  
Adaptive Calibration ◽  
Volumetric Rate ◽  
Efficient Data ◽  
Rate Sensor ◽  
Data Collections ◽  
Associative Analysis ◽  
Pulse Count

The research work demonstrated the use of adaptative and comparative paradigm to calibrate and validate Hall Effect flowrate sensor’s related performance data. The experimental testbed used for the research work is composed of an IoT based platform integrated into a water pipe network. The use of IoT largely assisted in facilitating a well-coordinated and flexible paradigm for efficient data collections and analysis. Correlated and Associative analysis on data obtained shows a strong significant relationship (R2=89%) between the rate of Pulse count and rate of change in differential volume leading to the derivation of a model that is helpful in determining of volumetric rate and quantity of liquid dispense as function of pulse count generated from a Hall Effect flowrate sensor.

DATA-DRIVEN DISCOVERY OF MATERIAL STATES IN COMPOSITES UNDER FATIGUE LOADS

2021 ◽  
Author(s):  
MUTHU RAM ELENCHEZHIAN ◽  
VAMSEE VADLAMUDI ◽  
RASSEL RAIHAN ◽  
KENNETH REIFSNIDER
Keyword(s):  
Domain Knowledge ◽  
Data Science ◽  
Damage Tolerance ◽  
Electrochemical Impedance ◽  
Research Work ◽  
Rate Of Change ◽  
Data Driven ◽  
Develop Model ◽  
Damage Development ◽  
Fatigue Loads

Our community has a widespread knowledge on the damage tolerance and durability of the composites, developed over the past few decades by various experimental and computational efforts. Several methods have been used to understand the damage behavior and henceforth predict the material states such as residual strength (damage tolerance) and life (durability) of these material systems. Electrochemical Impedance Spectroscopy (EIS) and Broadband Dielectric Spectroscopy (BbDS) are such methods, which have been proven to identify the damage states in composites. Our previous work using BbDS method has proven to serve as precursor to identify the damage levels, indicating the beginning of end of life of the material. As a change in the material state variable is triggered by damage development, the rate of change of these states indicates the rate of damage interaction and can effectively predict impending failure. The Data-Driven Discovery of Models (D3M) [1] aims to develop model discovery systems, enabling users with domain knowledge but no data science background to create empirical models of real, complex processes. These D3M methods have been developed severely over the years in various applications and their implementation on real-time prediction for complex parameters such as material states in composites need to be trusted based on physics and domain knowledge. In this research work, we propose the use of data-driven methods combined with BbDS and progressive damage analysis to identify and hence predict material states in composites, subjected to fatigue loads.

IMPROVING THE QUALITY OF EDUCATION BY STRENGTHENING THE RESEARCH COMPONENT OF TRAINING

Fisheries ◽  
2021 ◽  
Vol 2021 (3) ◽  
pp. 25-27
Author(s):  
Liliy Kucherenko
Keyword(s):  
Hall Effect ◽  
Educational Research ◽  
Experimental Research ◽  
Research Work ◽  
Educational Process ◽  
Quality Of Education ◽  
Electricity And Magnetism ◽  
The University

The paper deals with the issues of improving the quality of education at the university. The role of the material and technical support of the educational process is emphasized. The possibilities of using the modern laboratory complex "Electricity and Magnetism" are shown when conducting educational research work of students in physics. The author gave an example of experimental research on the topic "Study of the Hall effect in semiconductors." The contribution of students' educational and research work to the formation of general professional competencies is noted.

Monte Carlo Potts Model Simulation and Statistical Theory of 3D Grain Growth

2007 ◽  
Vol 558-559 ◽  
pp. 1219-1224 ◽  
Author(s):  
Dana Zöllner ◽  
Peter Streitenberger
Keyword(s):  
Grain Size ◽  
Monte Carlo ◽  
Grain Growth ◽  
Potts Model ◽  
Model Simulation ◽  
Mean Field Theory ◽  
Mean Field ◽  
Three Dimensional ◽  
Rate Of Change ◽  
Volumetric Rate

An improved Monte Carlo (MC) Potts model algorithm has been implemented allowing an extensive simulation of three-dimensional (3D) normal grain growth. It is shown that the simulated microstructure reaches a quasi-stationary state, where the growth of grains can be described by an average self-similar volumetric rate of change, which depends only on the relative grain size. Based on a quadratic approximation of the volumetric rate of change a generalized analytic mean-field theory yields a scaled grain size distribution function that is in excellent agreement with the simulation results.

scholarly journals An Efficient Data Mining Techniques - Multi-Objective KNN Algorithm to Predict Breast Cancer

2019 ◽  
Vol 8 (2S8) ◽  
pp. 986-990
Keyword(s):  
Breast Cancer ◽  
Learning Algorithm ◽  
Research Work ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Multi Objective ◽  
Related Information ◽  
Health Related ◽  
Efficient Data ◽  
Data Investigation

Breast cancer becomes most important foundation of mortality among women. The convenience of medical related dataset and data investigation support to extracting unidentified pattern in medical related or health related dataset. The objective of this research work is is to develop a health care prediction tool predicts the occurrence of the disease at near the beginning level of the criteria by analyzing the collected data set attributes to extract the disease exact level from the medical related information. The projected multi-objective KNN machine learning algorithm (classification) confirms that the highest accuracy (97.16%) is achieved compared to existing decision tree and Random Forest Techniques.

scholarly journals A Comparative Machine Learning Study on IT Sector Edge Nearer to Working From Home (WFH) Contract Category for Improving Productivity

2021 ◽  
Vol 2 (4) ◽  
pp. 217-225
Author(s):  
Akey Sungheetha ◽  
Rajesh Sharma R
Keyword(s):  
Research Work ◽  
Rate Of Change ◽  
Working Environment ◽  
City Region ◽  
Private Companies ◽  
Research Article ◽  
Working From Home ◽  
And Control ◽  
Method Analysis ◽  
Research Analysis

Many private companies in India offered working from home (WFH) for employees due to COVID’19 lockdown. The WFH has both merits and demerits for the employees as well as employer when it compared within office working environment. Many research works is showing many opinions about increases or decreases of productivity in the real time for any industries. This works talks about WFH impression is leads to edge nearer for the efficient productivity to any employer. In addition, the research article is providing survey of the benefits and demerits of WFH in India. In the view of the higher capacity, ultra very low level inactivity for better security is in the internetwork domain, there are lots of benefits in telework, and internet based work. The predicting development is done by Random Forest, Decision Tree, and Naïve Bayes for future with the help of three datasets. The datasets has taken from three types of general public such as city, town, and village for this research analysis. This research article is weighing up the rate of changes of productivity from the employees. Finally, this research work compares the learning method analysis includes prediction of rate of change of productivity from employees at city region. This prediction is computed by ML algorithm. Based on this prediction employers can improve and plan for their production and control the system in a better way.

scholarly journals Classification of Human Organ Using Image Processing

2017 ◽  
Vol 10 (2) ◽  
pp. 333-337
Author(s):  
Sindhu Sindhu ◽  
V Vaidhehi
Keyword(s):  
Medical Imaging ◽  
Imaging System ◽  
Research Work ◽  
Data Sets ◽  
Medical Field ◽  
Data Set ◽  
Human Organ ◽  
Efficient Data ◽  
Canny Edge

The collection of large database of digital image has been used for efficient and advanced way for classifying and intelligent retrieval of medical imaging. This research work is to classify human organs based on MRI images. The various MRI images of organ have been considered as the data set. The main objective of this research work is to automate the medical imaging system. Digital images retrieved based on its shape by Canny Edge Detection and is clustered together in one class using K-Means Algorithm. 2564 data sets related to brain and heart is considered for this research work. The system was trained to classify the image which results in faster execution in medical field, also helped in obtain noiseless and efficient data.

scholarly journals An Efficient Data Integrity using Attribute Based Encryption for Cloud Computing System

2019 ◽  
Vol 9 (2) ◽  
pp. 2186-2190
Keyword(s):  
Cloud Computing ◽  
Research Work ◽  
Computing System ◽  
Public Key ◽  
Work Effort ◽  
Attribute Based Encryption ◽  
Cloud Computing System ◽  
Efficient Data ◽  
User Revocation ◽  
System Calculation

The new and efficient method determinedly concentrates on the data processing, store and access the information which will be intended to make sure the users for legal powers should get equivalent information and also will confine the normal and unofficial legal users get admittance of the information which make suitable for those mobile cloud computing. There are various parameters with assess those execution of the active Attribute-Based encryption (ABE) method over cloud computing as takes after: cipher text measure (communication cost), private key span (storage cost), public key size (“Required storage on store public key in about powers in the ABE method”), re-keying extent (the size of the rekeying message that could make used to identify the user revocation for every attributes in the ABE system), calculation expense on the information manager (required time to encrypt the information by owner), calculation cost on the user (required run time to decrypt the information by a user). Our research work effort likewise analyses the vitality of the information security in the cloud. Purpose behind picking symmetric encryption algorithms are proficient to handle encryption and decryption to substantial measure about information and powerful speed about storing information and gaining access to those information in the cloud system.

scholarly journals Monitoring Vegetation Change in the Dryland Ecosystem of Sokoto, Northwestern Nigeria using Geoinformatics

2019 ◽  
Vol 51 (1) ◽  
pp. 9 ◽  
Author(s):  
Abubakar Magaji Jibrillah ◽  
Mokhtar Ja'afar ◽  
Lam Kuok Choy
Keyword(s):  
Vegetation Cover ◽  
Vegetation Change ◽  
Population Distribution ◽  
Research Work ◽  
Dry Season ◽  
Rate Of Change ◽  
Ecosystem Change ◽  
Gradual Change ◽  
North Western ◽  
Anthropogenic Drivers

The dryland ecosystem of Sokoto state, in the North-western part of Nigeria has been witnessing gradual loss of vegetation cover in the recent decades caused by natural and human induced drivers of ecosystem change. This negative trend poses great challenges to both the physical environment and the people of the area, particularly due to the fragile nature of the ecosystems in the region and the peoples’ over dependence on it for their livelihoods. This study tries to monitor and assess the rate of change in the spatial distribution of vegetation in the area over the time and identify the drivers responsible for changing the vegetation. This is with a view to providing evidence-based information to the policy makers that would guide them in making informed decisions that would assist in conserving the vegetation and the entire ecosystem of the area. Using multi-temporal MODIS-NDVI satellite data, image processing and GIS techniques, this research work tries to monitor and assess gradual change in vegetation cover in Sokoto state, North-western Nigeria. Correlation analysis was also used to measure the degree of relationship between vegetation change and some drivers of ecosystem change in the area. The findings of the research reveal a gradual but persistent decline in vegetation cover in the area, both during the rainy and dry seasons. This is also show a strong positive relationship with the rainfall distribution and a perfect negative relationship with the population distribution of the area. This indicate that, both climate change and anthropogenic drivers plays a significant role in changing vegetation distribution of the area. Anthropogenic drivers however, play a more significant influence. The degree of relationship is however, stronger during the dry season, making the ecosystem more vulnerable during the dry season due to increasing aridity. Although change in the vegetation cover of the area seems to be gradual and unnoticed, if left unchecked the long-term cumulative impacts could have serious negative impacts on both the structure and functions of the ecosystems of the area. This could in turn, affect the livelihoods and socio-economic development of the area.

scholarly journals An Efficient Data Sharing and Retrieval Method for Cloud Computing

2020 ◽  
Vol 9 (6) ◽  
pp. 458-460
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Sharing ◽  
Research Work ◽  
Data Access ◽  
Local Data ◽  
Sensitive Data ◽  
Retrieval Method ◽  
User Data ◽  
Efficient Data

now a days mobile devices can use cloud for data Access and manipulation without knowing overhead of local data management this may lead to leakage of sensitive data. Major disadvantage is to provide security for the user data , so this leads to concern for the user to access cloud computing. Lot of Research work carried out to provide security to user data over cloud computing, However these solutions are not resolve issues of mobile cloud computing due to constrained resource in mobile devices[2]. To overcome these issues, proposed methodology efficient data sharing and retrieval method, provide secure access control terminology using attribute based encryption in cloud platforms, as well as by using lazy-revocation technique will reduce user revocation cost

Research on Routing Protocols for Wireless Sensor Network

2013 ◽  
Vol 303-306 ◽  
pp. 92-95
Author(s):  
Yue Bin Wang ◽  
Li Qiang Liu ◽  
Quan Feng Yan
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Research Work ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Directed Diffusion ◽  
Node Location ◽  
Efficient Data

This paper introduces the architecture of wireless sensor networks, compares several typical routing protocols of wireless sensor network, and focuses on the research of directed diffusion(DD) protocol and geograohic and energy aware routing(GEAR) protocol. GEAR selects the next hop nodes based on the neighbor node location information, limiting the information diffused in the network in the appropriate location of region, which can effectively reduce the maintaining routing information needed for overhead, and form the energy efficient data transmission path, to achieve the purpose of extending the life cycle of the network. Related research work provides the reference for the future network construction.

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