bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematical model of wear volumes due to sliding speed using Buckingham Pi Model
Authors: Kaelani Yusuf , Syaifudin Achmad 
Published: March 2021

Abstract
The objective of this research is to determine a mathematical model of wear volumes due to sliding speed. Some researchers have studied the influence of sliding speed, roughness and coefficient of friction. However, mathematical model dealing with sliding speed has never been explicitly reported. Wear analysis due to various speed is offenly expressed experimently through charts concerning both wear volume and sliding speed instead of mathematics. This research conducts variables affecting wear. The study is started by modeling mathematical representation within Buckingham Pi Theory. The mathematical model includes wear volume, hardness,normal loads, sliding speed, sliding distance, and density of materials. Since Buckingham set requires constant of equality, the equation is solved by rectifications. The model, in turn, is verified by data gathering to determine the constant. This value is called Wear-Speed Coefficient. Experiment using pin-on-disk tribometer is conducted by varying sliding speed and normal loads. Further more, those parameters are applied to estimate wear volumes. Furthermore, It is compared with those measured data. Moreover, the mathematical model is verified by comparing graphically to results of previous researchers having evaluated wear volumes due to sliding speed. The materials included are 2014 Aluminum Alloy, NBR Rubber Nitril, Ultra High Moleculair Weight Poly Ethylene (UHMWPE), and Poly Tetra Fluoro Ethylene (PTFE). The results of discussion shows that the mathematical approach modelled using Bucking Pi Theory is mostly similar to patterns. This study yields several constants of wear-speed coefficient relating to the materials.
Influence of chamber pressure variations of plasma nitriding on surface hardness and micro structure of Al 6061
Authors: Oerbandono Tjuk 
Published: March 2021

Abstract
Aluminium 6061 is a material that is widely applied in industrial and automotive parts. The corrosion resistance and surface hardness of Al 6061 can be improved by using plasma nitriding. Plasma nitriding is the process of treating the surface of a material by inserting nitrogen in the material through the plasma, i.e. nitrogen gas was converted in the form of plasma before being inserted into the material. This research was conducted using experimental research methods by directly observing the plasma nitriding process. This research used aluminium alloy 6061 as a specimen, variations in chamber pressure of plasma nitriding 100 mTorr, 200 mTorr, and 300 mTorr, constant temperature of 150 °C, constant power input of 40 watts, and holding time for 2 hours. Hardness testing was carried out using a micro-Vickers hardness tester, whereas microstructure testing used SEM (Scanning Electron Microscope) and EDAX. Experimental results showed the highest surface hardness value of Al 6061 was 276.5 VHN. This condition occurred at a chamber pressure of 200 mTorr, temperature of 150 °C, the input power of 40 watts and a holding time of 2 hours. Based on composition and microstructure analysis using EDAX and Scanning Electron Microscope shown that the lowest surface hardness 179.4 VHN had a nitrogen content of 1.225% and this occurred at a chamber pressure of 300 mTorr.
ASTM A36 steel corrosion rate control in 1M HCl using Electrophoretic Deposition (EPD) with chitosan coating
Authors: Gapsari Femiana , Setyarini Putu Hadi , Hidayatullah Syarif , Puraditya Yohanes D. , Wijaya Hastono , Abidin Zainul 
Published: March 2021

Abstract
This study aims to investigate the effective coating time for ASTM A36 steel using chitosan in 1M HCl. In this study, ASTM A36 steel was used as a test specimen and the EPD technique was employed with coating time of 10, 20, 30, 40, and 50 minutes. The electrochemical technique was conducted using Autolab PGSTAT128N. In addition, this study also employed potentiodynamic polarization and Electrochemical Impedance Spectroscopy (EIS). It was found that longer chitosan coating using EPD will produce better coating results.
The effect of the welding methods on the corrosion rate and impact strength of AA5083 material
Authors: Suryo Dhanang , Sugiarto  , Setyarini Putu Hadi 
Published: March 2021

Abstract
The 5xx series aluminum alloy is currently widely used in marine construction applications for the fabrication of chemical tankers, offshore and others because it has good corrosion resistance. This study aims to determine the effect of the welding method on the corrosion rate and impact strength of 5083 aluminum. Four specimens of AA 5083 in this observation were preheated until reaching temperature 50 °C, 150 °C, 250°C and 300 oC as the selected variations. Then, welding result were tested by Charpy impact test with different load for determining amount of energy absorbed during the fracture process. After comparing the the impact toughness, the best specimen was conducting the corrosion rate test by using Corrosion Tester 3-Electrode system. The study result show that specimen with 250oC preheat was the highest impact toughness with 0.1593 mmPY corrosion rate result.
Failure analysis of pipeline elbow connecting high pressure heater to deaerator in a steam power plant
Authors: Yudanto Fajar Dwi , Ariyanto Dwi , Arfi Abdul Aziz , Suwarno  
Published: March 2021

Abstract
The coal-based steam power plant accounts for the largest electricity generation in Indonesia. Disruption in operation mostly caused by a failure of a component of the system and considered a serious problem and must be prevented. Failure can be originated from operations as well as the design. In the present work, a root cause study was performed on failure elbow in the steam line from the high-pressure heater (HPH) to the deaerator. The failure occurred only after 5 years of operation. The analysis was started with a visual examination, material verification, microstructural investigation, thickness measurement, and computational modelling. The analysis and simulation study showed that the failure of the elbow caused by two-phase flow where steam quality was compromised. The recommendation was to examine the HPH and to find possible leaks of the waterside to the steam side in the HPH system.
Dynamic Multi-agent Real Time Scheduling Framework for Production Management
Authors: Talmale Girish , Shrawankar Urmila 
Published: March 2021

Abstract
Production management is a complex problem. The problem becomes more complex in open, uncertain, dynamic environments and distributed productions. The existing static scheduling methods are not efficient to deal with this problem efficiently. This paper proposed multi-agent approach for real time scheduling in production management to efficiently manage production. The proposed scheduling optimizes the performance of production management in dynamic uncertain environment. Dynamic multi-agent approach in real time scheduling consist of the agent collect and analysis the production data in real time and ensure real time response to emergency events. Real time scheduling agents increase the resource utilization and success rate in production management by using real time feedback.
One Hop Neighbor based Broadcast Method for Reactive Routing in Mobile Ad-hoc Network
Authors: Mittal Nimit , Pahadiya Pallavi , Agrawal Amit 
Published: March 2021

Abstract
To overcome redundant issues of routing messages for the reactive routing protocol is become research challenge. In general, to discover route or path reactive routing protocols broadcast routing messages which may leads congestions and degrades battery power unnecessary. Though, to resolve the challenges several efficient broadcast methods have been suggested. Each method has its own procedure and results sort of advantage and disadvantages. The motive of the paper is to presents a method which is proposed to minimize redundant issue of routing messages resulted by broadcasting. Proposed method uses one hop neighbor’s knowledge to broadcast routing messages for the purpose of route discovery. Proposed method experimented through simulation software that named as NS-2. An experimental accomplish certain factors like number of nodes, routing protocol, MAC protocol etc. Proposed method evaluated on the basis of different criteria such as throughput, control overhead, routing efficiency and node remaining battery power.
Linear Regression based Power Optimization of Wireless Sensor Network in Smart City
Authors: Sahu Manish Kumar , Patil Sunil 
Published: March 2021

Abstract
The modern smart cities are highly dependent on the performance of the Internet of Things (IoT) based energy-efficient sensor networks. Energy efficiency is a critical and indispensable issue for wireless sensor networks (WSNs). In the collection of sensor nodes, one node is selected to collect data and forward it to the base station. The modern base stations in smart cities are unmanned aerial vehicle (UAV) based. This paper presents a linear regression based model, where the initial residual energy and its corresponding transmission power are submitted to the proposed system, then it generates a prediction model since transmission power depends on the residual energy of the sensor node. Based on this model the transmission power of the sensor node can be calculated for data transmission as a higher residual node is best suitable for data transmission to the base station. As presented in the simulation result, the regression based model gives better performance for energy efficiency in WSN.
Analysis of image inpainting and object removal methodologies
Authors: Thomas Shiney , Mathew Juby 
Published: March 2021

Abstract
Image inpainting is a process that tries to fill in missing parts of a degraded image or remove objects from an image, but maintain the realistic content in the image. Various methods can be employed to perform inpainting. Here three benchmark methods of which two are conventional and other GAN based are evaluated to check the effectives of the inpainting process, for different class of images. The visual quality evaluation as well as quantification parameter namely the SSIM score was computed. Based on the test score it was seen that the GAN based method was capable of providing visually good restored images with an average SSIM score of 0.9516, which is an indicator on the quality of the image. The GAN based methods can be further enhanced to provide more realistic images by incorporating functions to extract both local and global contextual content.
Robust Segmentation and Classification of Moving Objects from Surveillance Video
Authors: Chandrajit M , Shobha Rani N , Manohar N 
Published: March 2021

Abstract
Object segmentation and classification in the video sequence is a classical and critical problem that is constantly addressed due to vast real-time applications such as autonomous vehicles, smart surveillance, etc. Segmentation and classification of moving objects in video sequences captured from real-time non-constraint sequences is a challenging task. This paper presents a moving object segmentation and classification method for video sequences captured in a real-time environment. The key contributions of the paper include a method to detect and segment motion regions by applying the non-parametric Kolmogorov–Smirnov statistical test in the Spatio-temporal domain and a probabilistic neural network-based classification method to classify the moving objects into various classes. Promising results are obtained by experimentation using challenging PETS and Change Detection datasets. To corroborate the efficacy, a comparative analysis with contemporary method is also performed.
A recurrent neural network forecasting technique for daily PM2.5 concentration level in Southern Kerala
Authors: Babu Sherin , Thomas Binu 
Published: March 2021

Abstract
The natural environment and public health are seriously impacted by air pollution. One of the most dominant contributors to air pollution around the world is particulate matter PM2.5. Predicting air pollution in advance has considerable importance for the regulation of people’s health and to implement pollution control strategies for air quality management. The study was conducted for Thiruvananthapuram district, the southernmost region of Kerala. The data for the period from 1st July 2017 to 31st December 2019 were collected from the Central Pollution Control Board (CPCB) website. To predict the daily PM2.5 concentration, Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) was used here. LSTM was built on the inputs of four meteorological parameters, namely average temperature, wind speed, wind direction and relative humidity and air pollutant parameter PM2.5 values. Performance evaluation of the prediction model has conducted, and the results showed that the model attained considerable prediction accuracy.
Segmentational color map analysis to identify retinal detachment
Authors: Poongothai L. , Sharmila K. 
Published: March 2021

Abstract
The recent perilous times entails the importance of good health for humans. This can be pertaining the maintenance of good health in various organs of the human body. With the increase of digital platforms, and the necessity to stay longer hours with gadgets and electronic devices, the human eye takes a toll in terms of absorbing radiations that maybe harmful. An imperative check of the eye is essential from time to time, as the luminous membrane that aids in vision needs to hold firm to the various components within itself. Although there are various treatments associated with the detection of eye detriments, several gremlins tend to loom at large. These eye diseases can arise from numerous reasons ranging from genetic disorders to accidents and vision loss due to stimulated injuries, infections and dysfunctional development of tissues/cells/nerves within the eye. Retinal detachment (RD) is one of the profound problems that persist amongst many individuals scaling over a diverse range of ages. The risk in relevance to untreated detachment of the eye leads to purblind and sometimes complete loss of vision. The previous paper concerning retinal detachment of human eye has focused on various algorithmic and post-processing approaches, but failed to identify the origination and symptoms that could aid in meticulous diagnosis. The indagation in this paper pivots on the unsheathing of attributes that explicates the disassociation of the retina from the underlying layers of the eye using the fundus image with cost-effectiveness and swifter analysis. An important characteristic that is emphasized in this study is the segmentation of eye floaters in the vitreous fluid of the eye. These eye floaters are color mapped to identify the presence of detached tissues in the eye. The histogram analysis using the grayscale thresholding value further aids in providing a detailed distinction of the normal eye from the detached eye. The simulations are carried out in MATLAB, and the results have been obtained successfully.
ECG based Decision Support System for Clinical Management using Machine Learning Techniques
Authors: Moturi Sireesha , Vemuru Srikanth , Tirumala Rao S. N. 
Published: March 2021

Abstract
Heart disease prediction system using ECG is to predict heart disease using ECG signals. Heart is the next major organ comparing to brain, which has more priority in human body. Heart disease diagnosis is a complex task which requires much experience and knowledge. The huge amount of data generated for prediction of heart disease is too complex and voluminous to be processed by traditional methods. By using traditional methods doctors took lot of time to diagnosis the disease. So, an entropy based feature selection technique is used with classification algorithms in order to reduce the search space. The proposed model was tested on the real time dataset of NRI Hospital medical data. Using this system it is easier to predict the disease. It will also helpful for the doctors to take quick decisions.
An evaluation of deep learning approaches for detection of voice disorders.
Authors: Chinchu M S , Kirubagari B. 
Published: March 2021

AbstractThe human voice manufacturing system is a complicated natural device capable of modulating pitch and loudness Human sound frequency particularly. The part in which the folded is the primary source underlying internal and/or external factors often destroys vocal folds justification. Some changes consequences are reflected in functioning emotional state soul. Therefore it essential to identify variations at an early stage gives the patient an endangerment overcome any impact modernize their quality of life will-less detection disorders using depth study methods plays an important role as has been proven to facilitate the process. Many researchers have explored technologies for streamlined that can help clinics diagnose noise paper we present the conducted research activities.
Answer Book Valuation Using Semantic Similarity
Authors: Sunilkumar P. , Sunil K.S. 
Published: March 2021

Abstract
Conventional examination systems are a tedious task for the universities and the faculties. It takes a lot of time and human effort for the valuation of the answer books, updation of the marks and the declaration of the results. Automation of descriptive answer book assessment would be useful for universities and academic institutions to simplify the valuation system to a large extend. It ensures a uniform valuation and also helps to publish the results without much delay after the examination. We design a system for automatic assessment of descriptive answer books of technical subjects. Semantic similarity is a metric used to assess the similarity between documents and it gives the degree of similarity as a numeric value. It can be used to value the students’ answer books by checking the similarity with original answers given in the answer key and then award appropriate marks.
Analysis of distance measures in spatial trajectory data clustering
Authors: Sharmila S , Sabarish B A 
Published: March 2021

Abstract
Tremendous growth of Location-based technologies resulted in the generation of a huge volume of spatial data, which needs to be analyzed to get potentially important patterns. The spatial patterns extracted can be used to design a better infrastructure ensuring reliable service coverage. Trajectory data is one variant of spatial data that are generated by moving objects travelling across. It is represented as a sequence of spatial coordinates (latitude, longitude) of a location. Trajectory clustering tries to group similar spatial data points to extract the most common movement behaviors. Trajectory data poses major challenges including uncertainty, sampling rate, representation, relationships, spatial autocorrelation, serialization, redundancy, and triviality, which makes it hard to apply traditional clustering algorithms over trajectory data. In this paper, K-Means and DBSCAN (Density-based spatial clustering of applications with noise) clustering algorithms are analyzed using different similarity measures like Euclidean, Hausdorff and Haversine distances with the help of index measures say Adjusted Rand Index (ARI) and Fowlkes-Mallows scores (FMS). Experiment is carried out over two different trajectory datasets and it is proved that usage of Haversine distance for clustering is efficient than Euclidean and Hausdorff distances in terms of spatial trajectory data.
An Early Disease Prediction and Risk Analysis of Diabetic Mellitus using Electronic Medical Records
Authors: Gulhane Rutuja A , Gupta Sunil R 
Published: March 2021

Abstract
In the world today, the fourth leading disease is Diabetes that could lead to other serious complicating diseases. Diabetes is one of the most common chronic disease which can also be the cause of death in many cases. An efficient system for early disease prediction and risk analysis of diabetic mellitus is very much needed as it has the major adverse effects. The large amount of medical data is collected by healthcare industry in the form of Electronic Medical Records. The Electronic Medical Records is communal database for clinical disease and risk prediction that are useful in accurately predicting multiple medical events using machine learning approach. Therefore, this research presents an efficient technique for early prediction and risk analysis of diabetic mellitus disease to improve accuracy and precision using Electronic Medical Records.
Optimize Continuous Integration and Continuous Deployment in Azure DevOps for a controlled Microsoft .NET environment using different techniques and practices
Authors: Dileepkumar S R , Mathew Juby 
Published: March 2021

Abstract
Delivering a software is not a simple process, even if it is a completely new software created from the scratch, the enhancement of an existing software or fixing the bugs of an existing application. As per the new industrial standard most of the companies follow the DevOps methodologies to deliver the software products, which leverage the flexibilities of the software delivery with expected quality on time with minimal errors. One of the most important factors of the DevOps methodology is Continuous Integration and Continuous Deployment. As the entire software industry is moving towards Cloud Computing one of the most powerful, cost effective and easily maintainable environment to host the software applications. There are various Cloud computing providers available in the industry now. Most of them have its own advantage and dis-advantages. There are many researches going on various topics on these areas and the importance of these topics are getting more relevant in the industry now. There are lot of gaps on the Continuous Integration and Continuous Deployment concepts and more improvement options have to be identified and certified throughout the various researches. To understand the importance of this platform, we decided to concentrate the research on the Continuous Integration and Continuous Deployment in Azure DevOps for a controlled Microsoft .NET environment using different techniques and practices. Here our intention is to identify the various factors which helps the industry to do a better Continuous Integration and Continuous Deployment in the Azure DevOps for a controlled Microsoft .NET environment using different techniques and practices. Among the “n” number of factors identified on this subject, only couple of factors are considered as part of this research.
Real time supervision of faults in converter of PV system
Authors: Jadhav P R , Chavan S B 
Published: March 2021

Abstract
PV systems are remotely installed at unmanned places. They have sub-systems like converters, inverters etc. Electronic components in these circuits work under electrical stress conditions. PV systems occupy huge land area; therefore system supervision and fault diagnosis are critical in these applications. In this work internet connectivity based supervising system is presented for boost converter to monitor its working status. The system implemented at site monitors the converter output, voltage across the devices, compares it with the known standard values and records it. Whenever any remote node accesses it, the formatted information is displayed which shows the converter status. Due to this remote site supervision and electrical parameter observation is easy.