bluuebunny/crossref_metadata_2025 huggingface dataset:
Internet of Things Enabled Smart Animal Farm Prototype
Authors: Easwaran Arjun , Arvindan P , Dhanyasree E , Surya R , Selvakumar S
Published: November 2021

Livestock plays very important economic, social and cultural roles in the well being of rural communities across the world. Quality environmental conditions, automation and monitoring are the key necessities of running a good and profitable livestock farm. Air quality, temperature of the surroundings and humidity play a major role while deciding the fan speeds of the exhaust System used in all aspects of livestock farming. Another important part of livestock production is increasing incubation speeds of eggs by performing artificial incubation. It is a requirement to maintain the temperature at a constant value in this system. This paper describes two mutually exclusive Fuzzy Logic algorithm-based systems to automate the exhaust system and an artificial egg incubator. The other important part of a livestock farm is production of milk and milk products. It is required to monitor the health of cows by overseeing their activities at any point of time. This can be done by determining and monitoring the activities performed by the cow. This paper describes a simple Deep Learning Model to classify the activities of a cow broadly as standing, walking or grazing. The Exhaust and the Incubator system are controlled and monitored using Internet of Things (IOT) System using a native web application developed using the Flask framework.
Metamaterial Based Multiband Microstrip Patch Antenna for 5G Wireless Technology-enabled IoT Devices and its applications
Authors: Colaco John , Lohani R.B.
Published: November 2021

In the present era of the digital world, demand for IoT based smart devices has seen tremendous growth. These devices involve real-time human-to-machine communication and interaction. Communication of uninterrupted quality depends on the high bandwidth and speed of the internet. The development of 5G wireless network technology is the response to the crucial factors that lead to this demand, because of its ability to provide extremely fast internet speed, high bandwidth, high performance, reduced latency, and high reliability. In this research work, the authors have developed a metamaterial-based multi-band microstrip rectangular shape patch antenna with a wide high-performance bandwidth because of the demand. The proposed design has a low dielectric constant of 2.2, which is of Rogers RT/Duroid substrate, and a dielectric loss tangent of 0.0010. The design has a resonant frequency of 26 GHz. The simulations carried out using FEKO software has been analyzed for performance. The simulation and analysis reveal a good return loss of -34.4 dB at 26 GHz, -13.49 dB at 40 GHz, -13.63 dB at 53.5 GHz, high bandwidth of 5.368 GHz at 26 GHz, 3.76 GHz at 40 GHz, 2.88 GHz at 53.5 GHz, desirable voltage standing wave ratio, 1⩽VSWR⩽ 2, high gain of 10 dBi at 26 GHz, 5 dBi at 40 GHz, and high antenna radiation efficiency of 99.7 % at 26 GHz, and 61% at 40 GHz, 50% at 53.5 GHz. The bandwidth, return loss, antenna radiation efficiency and power density indicate an improvement of 5.368 GHz to 5.630 GHz, -34.82 dB to -57.10 dB, 99.7 % to 99.8 % and 2208 kW/m2 to 2800 kW/m2 respectively after loading and incorporating artificial magnetic split-ring resonator-based metamaterial on the patch. Further improvement is also seen at other frequencies. The proposed design has immense benefits for humanity due to its improved capacity to manage larger connected IoT devices in the fields of Industrial 4.0, Healthcare 4.0, Autonomous Vehicles, Agriculture 4.0, Education, Climate Change, Sustainability, and Oceanography.
Comparative analysis of image processing techniques for obstacle avoidance and path deduction
Authors: Rajavarshini R , Shruthi S , Mahanth P , Kumar Boddu Chaitanya , Suyampulingam A
Published: November 2021

The growing need for automation has a significant impact on our daily lives. Automating the essentials of our society like transportation system has plenty of applications like unmanned ground vehicles in military, wheel chair for disabled, domestic robots, etc., There are driving, braking, obstacle tackling etc., to a transportation system that can be automated. This paper particularly focuses on automating the obstacle avoidance which provides intelligence to the vehicle and ensures a high degree of safety and is performed using image processing algorithms. Edge based detection, image segmentation, and Machine Learning based method are the three image processing techniques used to detect and avoid obstacles. Haar cascade classifier is the machine learning method where Haar cascade analysis is performed for better accurate results with justifying graphs and parametric values obtained. A comparison of the three image processing algorithms is also tabulated considering obstacle size, colour, familiarities and environmental lightings and the best image processing algorithm is inferred.
RETRACTED: Vulnerability Recognition and Resurgence in Network based on Prediction Model and Cognitive based Elucidation
Authors: Jagannathan Jayaganesh , Mohamed Parvees M.Y.
Published: November 2021

The challengeable factor in current network issue is identification of vulnerability and the same can be prevented before it occurs. Many traditional measures applied to keep track of assessing the system in terms of misconduct calculation. There are two different kind application running via network interface which are known process application and unknown process application. Known applications can be managed with the help of existing approaches whereas resolving problems of unknown applications are questionable. The proposed solution addresses this issue by applying cognitive based solutions and supervised learning model. Traffic parameters considered here as major concern and feature extraction is done against parameters flow of information does after pre-process the data. Training, Automation and detection is a sequence of process is used to find vulnerability misconduct in network and simulated with the help of python.
Applications of Autoencoders along with Deep Learning Techniques to generate valid molecules
Authors: Sesha Sai Aparna T , Anuradha T
Published: November 2021

From the moment of identifying the fundamental cause of an illness to its availability in the marketplace, it takes an average of 10 years and almost $2.6 billion dollars to develop a medication. We’re actually hunting for a needle in a haystack, which takes a lot of time, effort, and money. In a solution space of between 1030 and 10100 synthetically viable compounds, we’re seeking for the one molecule that can turn off a disease at the molecular level. The chemical solution space is just too large to adequately screen for the desired molecule. Only a small percentage of the synthetically viable compounds for wet lab research are stored in pharmaceutical chemical repositories. Computational de novo drug design can be used to explore this vast chemical space and develop previously undesigned compounds. Computational drug design can cut the amount of time spent in the discovery phase in half, resulting in a shorter time to market and lower drug prices. Deep learning and artificial intelligence (AI) have opened up new perspectives in cheminformatics, especially in molecules generative models. Recurrent neural networks (RNNs) trained with molecules in the SMILES text format, in particular, are very good at exploring the chemical space. Two baseline models were created for generating molecules, one of the model includes an encoder that takes SMILES as input and then develops a deep generative LSTM model which acts as a hidden layer and the output from layers acts as an input to the decoder. The other baseline model acts the same as the above-mentioned model but it includes latent space, it is simply a representation of compressed data that bring related data points closer together physically. To learn data properties and find simpler data representations for analysis, and weights which are obtained from the previous model to generate more efficient molecules. Then created a custom function to play with the temperature of the softmax activation function which creates a threshold value for the valid molecules to generate. This model enables us to produce new molecules through successful exploration.
Modelling the minislump spread of superplasticized PPC paste using Random forest, Decision tree and Multiple linear regression
Authors: Mrithula M , Bhavana Modepu , Janani S , Narayanan Vaishnavi , Rajesh C.B , Sathyan Dhanya
Published: November 2021

Workability is one of the key property of concrete which is governed by water cement ratio. In order to improve the workability of concrete without any variations in water cement ratio Superplasticizers(SPs) are added. Cement paste helps us to analyze the property of fresh concrete where the dispersion of cement particle is taken into account. SP’s Cement dispersive properties are governed by dosage and the family. Various dosages and families of SP are considered for estimating workability feature of cement paste which is picked for investigating on rheological properties through Mini slump spread diameter. The prime motive of this analysis includes measuring the workability of different superplasticizers by conducting a minislump test and hence modelling the flow rate of the superplasticized Portland Pozzolona Cement (PPC)paste using the application of random forest(RF), decision tree(DT) and multiple regression algorithms. Testing and training data for a model were 287 unique mixture compositions at a water by cement ratio was 0.37. This mixture was tested experimentally in a laboratory using four types of locally available PPC’s and of SP which can be broadly categorised in to four families. Amount of seven types of SP brands, water content, cement weight were the input parameters for the model and flow rate was the output parameter. The model’s predicted and experimentally measured values of flow speed were compared and the amount of deviation was recorded.
Optimal setting of interline power flow controller in deregulated power systems congestion management by using artificial intelligent controllers
Authors: Chaithanya K Krishna , Kumar G V Nagesh , Rafi Vempalle , Kumar B Sravan
Published: November 2021

In electricity market with a deregulation withmany times will become difficult for dispatchingpower, whichis obtained with thepower congestion in transmission lines due to flow of power in buses. To reduce the flow of power in buses and system loss in lines which are heavily loaded, toimproveloadability and system stability for which an Interline Power Flow Controller used. In this article, Disparity Utilization Factor for Line is utilized with the optimal tuning and placement with Gravitational Search Algorithm IPFC hinged transmission lines for power congestion. The rankingof transmission lines by line congestion in terms of relative DLUF. According to the IPFC placementwith the essentialminimum congested, weakline connected to the same bus in the congested distribution network. IPFC optimal Sizing is carried with Gravitational Search Algorithm. The multiobjective function is chosen for parameters tuning of IPFC. A suggested IEEE-30 bus test network is implemented by a graphical representation whichis considered in this study toreduce LUF in transmission lines with IPFC optimal placement. Bothactive power losses and reactive power losses in power systemare necessary to be minimized after every iterationof IPFC optimal tuning. The proposed tuning in this articlegives effectivenesswith the reduction in the values of power system with the objective functions.
Smart Villages: IoT Technology Based Transformation
Authors: Mudholkar Pankaj , Mudholkar Megha , Puneeth Kumar B S , Dankan Gowda V , Raju S. Srinivasulu
Published: November 2021

In recent years, large-scale urbanisation has been on the rise with cities being the hubs for growth, but rural business continues to play an important role in any country’s overall development. A recent report reveals that almost 69 percent of India’s population resides in their villages, accounting for almost 50 percent of the nation’s GDP. This agricultural region varies from small towns with less than 500 residents to small towns. There are also similar situations in western nations. Despite their economic contribution, smaller villages seem to earn fewer in terms of infrastructural expenditure. The major problems addressed in these small towns include shortage of adequate public transit, emergency care and limited knowledge on federal subsidies for rural areas. Our vision is to expand the Smart city to Smart village by allowing use of recent technical advances and giving more attention to the problems in rural areas. The approach in this chapter is to bring IoT technology to Villages by literally showing a network of linked sensors and knowledge dissemination devices, controlling energy use and ensuring infrastructure protection. It gives a wide-ranging vision of enhancing the standard of living in villages and encourages them to meet the essential needs of domestic villagers.
Design of MoS2/graphene heterostructure thin film sensors for high performance NO2 gas sensor applications
Authors: Sakthivel R , Geetha A , Anandh B A , Jagadeesan V , Ganesh A Shankar , Dineshkumar J
Published: November 2021

In this paper, we fabricate a large-area chemiresitive type MoS2/graphene films sensor is grown by spray pyrolysis technique. The prepared sensor films were characterization by XRD, SEM, TEM Raman and BET analysis. The synergistic effect between MoS2 and graphene through the CVD method produces such a hierarchical layer-by-layer assembly of the thin film structure. MoS2/graphene hybrid films not only show enhanced NO2 sensitivity compared to NO2 sensitivity alone. Graphene or MoS2 films, but they also exhibit characteristics of rapid response and strong reproducibility. Selectiveness and stability findings demonstrate the outstanding sensing properties of the MoS2 thin film sensor. The MoS2/G showed higher sensitivity (81%) towards NO2 gas at the concentration of 1000 ppm followed by graphene (22 %) and MoS2 (45 %) based sensors in sequence. The MoS2/G sensor also exhibits fast response (12 s) and recovery time (17 s) than other sensor samples. The concept of operation and sensing mechanism behind their impressive results has also been studied in depth. The effect of humidity on the performance of gas sensing was also discussed in the point of practical device applications.
Artificial Neural Network Controller Strategy for Improving DC Link Voltage of Grid Connected Hybrid PV-Wind Generation Systems
Authors: Maheswara Rao B. , Nagesh Kumar G.V. , Rafi Vempalle
Published: November 2021

This paper presents a power system, consisting of photovoltaic (PV) station and wind farm integrated by ac bus, connected to the grid. The load gets power from both the sources and maximum power is tracked by maximum power point techniques (MPPT) during any changes in the environment. The paper explores how MPPT techniques help power system in tracking power from PV and wind in the conditions of different solar irradiances and different wind speeds. This paper’s objective is to show the improvement in step response of dc link voltage by artificial neural network (ANN) controller. The control method significantly maintains constant grid voltage ensuring unity power factor even during climatic conditions variation. The whole system is simulated using matlab/simulink software and the results compare the proposed system with existing controller i.e., Proportional Integral (PI). The results show the efficient performance of ANN controller than PI controller.
A Reliable Low Power Multiplier Using Fixed Width Scalable Approximation
Authors: Stella K , Vinith T , Sriram K , Vignesh P
Published: November 2021

Recent Approximate computing is a change in perspective in energy-effective frameworks plan and activity, in light of the possibility that we are upsetting PC frameworks effectiveness by requesting a lot of precision from them. Curiously, enormous number of utilization areas, like DSP, insights, and AI. Surmised figuring is appropriate for proficient information handling and mistake strong applications, for example, sign and picture preparing, PC vision, AI, information mining and so forth Inexact registering circuits are considered as a promising answer for lessen the force utilization in inserted information preparing. This paper proposes a FPGA execution for a rough multiplier dependent on specific partial part-based truncation multiplier circuits. The presentation of the proposed multiplier is assessed by contrasting the force utilization, the precision of calculation, and the time delay with those of a rough multiplier dependent on definite calculation introduced. The estimated configuration acquired energy effective mode with satisfactory precision. When contrasted with ordinary direct truncation proposed model fundamentally impacts the presentation. Thusly, this novel energy proficient adjusting based inexact multiplier design outflanked another cutthroat model.
Texture Feature Extraction of Lumbar Spine Trabecular Bone Radiograph Image using Laplacian of Gaussian Filter with KNN Classification to Diagnose Osteoporosis
Authors: Patil Kavita Avinash , Mahendra Prashanth K V , Ramalingaiah A
Published: November 2021

The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy whereas the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. The detection of Osteoporosis in Lumbar Spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. The paper is systematized in two different sections to classify normal (non-osteoporosis) and abnormal(osteoporosis)Lumbar spine trabecular bone. In this method, the first section is based on discriminating the lumbar spine trabecular bone micro-architecture predisposing by means of first and second order directional derivative of Laplacian of Gaussian filter with different standard deviation to acquire the minimum and maximum responses. The dimension reduction of texture features, quantization and adjacent scale coding with weighted multipliers are used to lessen the intensity variations of texture features. The second section is based on the reduction of histogram features as a training data set for classification of normal and osteoporotic images of lumbar spine (L1-L4) using K-Nearest Neighborhood (KNN) classifier. The tested dataset result gives effective classification accuracy of 97.22% with lesser texture feature dimension. The usage of weight multiplier as well as quantization technique plays a major role for the improvement of accuracy to diagnose osteoporosis for an input noisy and noiseless image.
Optimal Design of SRM for EV Application
Authors: Srinivasulu B. , Nagesh Kumar G.V. , Rafi Vempalle , Nageswara Rao P.
Published: November 2021

Electric vehicles (EVs) need a wide speed and torque range for their reliable operation and working. Switched reluctance motors (SRMs) offer several advantages like high life cycle, meager cost, simple construction, robustness, good speed characteristics, and fault-tolerance, making it a suitable motor drive for EV application. The selection of independent variables like dimensions, winding turns, make materials, the number of slots, and the shape of rotor and stator teeth is a cumbersome task as SRM performance is mainly dependent on these parameters. This paper describes the methodology for selecting these independent variables by evaluating the SRM performance for different shapes of rotor and stator teeth, with the different stator and rotor materials, by taking all the design constraints like winding resistance, turns, and the number of slots. The EV drive performance is evaluated for the chosen independent variables and analyzed using JMAG software for determining the efficiency, Torque, and speed responses for comparison and analysis to get the optimal design constraints of the independent variables of SRM.
Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults
Authors: Sharma Pavan , Amhia Hemant , Sharma Sunil Datt
Published: November 2021

Nowadays, artificial intelligence techniques are getting popular in modern industry to diagnose the rolling bearing faults (RBFs). The RBFs occur in rotating machinery and these are common in every manufacturing industry. The diagnosis of the RBFs is highly needed to reduce the financial and production losses. Therefore, various artificial intelligence techniques such as machine and deep learning have been developed to diagnose the RBFs in the rotating machines. But, the performance of these techniques has suffered due the size of the dataset. Because, Machine learning and deep learning methods based methods are suitable for the small and large datasets respectively. Deep learning methods have also been limited to large training time. In this paper, performance of the different pre-trained models for the RBFs classification has been analysed. CWRU Dataset has been used for the performance comparison.
Development of Test Automation Framework for Printed Circuit Board Assembly
Authors: Aravind Balaji B , Sasikumar S , Ramesh K
Published: November 2021

A Test Automation Framework is a bunch of rules or coding guidelines for test-process handling that is followed during coding. These are simple strategies that produce helpful results such as improved code re-usability, higher portability, diminished script support cost, reduced interdependency, higher interchangeability, and so on. This paper presents the development of a test automation framework for Printed Circuit Board Assembly (PCBA) to test the functionality and fault identification of the electronic circuit in the PCBA. It provides an integrated array of test and measurement Instruments which is automated using SCPI (Standard Command for programming Instrument) based commands to control test and measurement processes, making it simpler to configure and query. These test instruments imitate the environment in which the PCBA is to be deployed, which helps to observe its characteristics to determine whether it meets the expectation for which it was designed. Any possible variations in its characteristics due to fault are observed during testing and those faults are identified automatically using the data-driven fault analysis method.
Millimeter Wave MIMO out Door Channel Estimation and Precoding
Authors: Murali Krishna P V , Ramana T.V.
Published: November 2021

Millimeter wave (mm Wave) communications is one of the technologies for 5G cellular systems. In the mm Wave communication, there is a lot of path loss can be reduced by Precoding. The channel state information (CSI) should be known at the transmitting station, in the design of precoding matrices and to get good accuracy in estimating sparse channels a Compressive sensing (CS) based recovery algorithms was used. Not only for good accuracy the algorithm is also used for mm Wave channel estimation for exploiting the mm Wave channel’s sparse in multi-path construction. Hence, in this paper, for mm Wave outdoor channel estimation, the CS recovery methods orthogonal matching pursuit (OMP) and compressive sampling matching pursuit (CoSaMP) are used. The singular value decomposition (SVD) precoding is developed using the estimated channel. By (MSE) mean square error and spectral efficiency which were the performance metrics in channel estimation and precoding were done by using MATLAB simulations to get the efficacy of the OMP and CoSaMP algorithm.
Study on piezo-electric flapping wing mechanism for bio-inspired micro aerial vehicles
Authors: Shakya N K , Padhee S S
Published: November 2021

The Micro Aerial Vehicle (MAV) with a flapping wing configuration is much more efficient and capable of generating substantial lift at low flight speeds and has excellent maneuverability. Different motor-driven mechanisms have been developed to mimic this flapping motion, but these mechanisms introduced mechanical complexity and heavy weight to the system. Piezo-electric based mechanisms have been used to solve these problems, but provide very small flapping amplitudes within the size limitation of MAVs. So some kind of amplification mechanism is needed. In this paper, a flexible wing is created by attaching a polymer skin to a pair of carbon fiber reinforced plastic spars. This wing is connected by means of an elastic-element (EE) to a pair of piezoelectric unimorphs (piezofan). The motion from the piezofan to the wing is transferred through this EE. Simulation has been done by applying sinusoidal voltages of varying frequency to this piezofan and observations have been made for the flapping amplitude of the wing for different stiffness of the EE. It is observed that the amplitude of the peak flapping amplitude initially increases, attains a maximum value, then decreases again with an increase in the stiffness of the EE. It is also observed that as the EE stiffness increases, the corresponding peak of the flapping amplitude shifts towards higher frequency.
Detection of Epilepsy based on EEG Signals using PCA with ANN Model
Authors: Shiva Shankar R , Raminaidu CH , Sivarama Raju VV , Rajanikanth J
Published: November 2021

Epilepsy is a chronic neurological illness that affects millions of people throughout the world. Epilepsy affects around 50 million people globally. It is estimated that if epilepsy is correctly diagnosed and treated, up to 70% of people with the condition will be seizure-free. There is a need to detect epilepsy at the initial stages to reduce symptoms by medications and other strategies. We use Epileptic Seizure Recognition dataset to train the model which is provided by UCI Machine Learning Repository. There are 179 attributes and 11,500 unique values in this dataset. MLP, PCA with RF, QDA, LDA, and PCA with ANN were applied among them; PCA with ANN provided the better metrics. For the metrics, we received the following findings. It is 97.55% Accuracy, 94.24% Precision, 91.48% recall, 83.38% hinge loss, and 2.32% mean squared error.
Techno-economic analysis by homer-pro approach of solar on-grid system for Fatehpur-Village, India
Authors: Kapoor Sarang , Sharma Arvind Kumar
Published: November 2021

There are numerous locations available in rural regions of India, where essential electrical energy for load demand is not feasible. Individuals of those particular areas are facing problems, related to their social and economic development. To eliminate the energy crisis, uses of renewable sources are increasing day by day. In this study, we are analysis an On-grid solar energy harvesting system, which will provide necessary power requirement for the whole community of Fatehpur village located at 27°59′15″N and 77°44′07″E, to fulfil energy demand of the community load without much affecting environment. Here, the economic analysis using Homer-pro programme version 3.14.4, are examined, with Homer pro cycle charging as dispatch strategy. Load calculation and components pricing details are concluded through a local survey. Homer pro provides optimized results for selected site, through total present cost (NPC), the least cost of energy (LCOE) and operating cost etc. Based on simulation results by Homer pro, we obtained 1.77 RS/kWh as levelized cost of energy, which is much less than grid power price i.e. 5.6 Rs/kWh and 9,291,770.00 Rs as NPC. Main criteria of this software-package are NPC and LCOE to find out the best possible outcomes for the suited site.
Corrosion inhibition and statistical data of low carbon steel in HCl media by admixed organic compounds
Authors: Loto Roland Tolulope , Loto Cleophas Akintoye , Leramo Richard , Oyebade Babatunde
Published: November 2021

Corrosion inhibition of carbon steels with organic compounds exhibiting effective corrosion inhibition is an on-going research. The corrosion inhibition of low carbon steel in 1M HCl solution by the combined admixture of vanillin and benzonitrile (VBN), and salvia officinalis with lavendulan officinalis (SLV) was studied by weight loss method. Results obtained show that VBN and SLV performed effectively with maximum inhibition efficiency 91.03% at 1.5% VBN concentration and 97.89% at 5% SLV concentration. The inhibition efficiency of VBN increased with concentration but decreased with exposure time compared to the values obtained for SLV which showed non-dependence on concentration and exposure time after 1% SLV concentration. Correlation plots of inhibition efficiency versus inhibitor concentration show VBN to be more concentration dependent in performance. Calculated data for standard deviation shows the degree of variation from mean values for both compounds is significant at low inhibitor concentration due to time dependence action. Statistical analysis through ANOVA shows inhibitor concentration overwhelmingly influences the inhibition performance of the despite even though exposure time is statistically relevant to minimal degree.
An experimental study of De-Icing property using Reduced Graphene Oxide - Boron Nitride Nano Powder in CFRP
Authors: Akash Raja A , Nisha M S , Jatin J , Kiren Joseph Noel
Published: November 2021

Icing is one of the critical factors that affects the flight of the aircraft in ways of drag. In this study, the rate of de-icing was experimented with the composite material using Boron Nitride Nano Powder (BNNP) and Reduced Graphene Oxide (rGO). Boron Nitride are highly thermal conductive and lesser toxic, and Graphene allows to be a good conductor of heat and electricity and thereby, these nanomaterials are used as a filler for de-icing application. The filler is mixed in Epoxy Resin using ultrasonicator. In order to get better specimen surface of composite, ultrasonic mixing method is used instead of hand-mixing. To fabricate Carbon Fiber Reinforced Polymer (CFRP) Hand Lay-up Method is carried out. The material characterization is done by X-ray Diffraction (XRD), and Scanning Electron Microscope (SEM). Mechanical properties are studied with Dynamic Mechanical Analysis (DMA), Thermo-Gravimetric Analysis (TGA), and Water Droplet test. Moreover, the Carbon Fiber Reinforced Polymer surface shows extraordinary de-icing effect contrasted with the non-functionalized surface. This study has proved that the de-icing property is very much effective with Carbon Fiber Reinforced Polymer where Graphene and Boron Nitride as the filler.
Influence of injection pressure on performance and emission characteristics of single cylinder RCCI engine fuelled with ethanol gasoline and diesel biodiesel blends
Authors: Kumbhar V S , Shahare A S , Awari G K
Published: November 2021

Reactivity controlled compression ignition (RCCI) has great potential for a simultaneous reduction in Nitrogen oxides (NOx) and particulate matter (PM) with increase in thermal efficiency. In this experimentation, an attempt is made to investigate the effect of injection pressure on the performance emission and combustion characteristics of single cylinder RCCI engine. Literature reveals that injection pressure has a great influence on the quality of charge preparation, fuel stratification, and incylinder reactivity. Suitably modified engine was operated for 0 to 12 kg loads, for 400 to 700 injection pressure. The blend of ethanol gasoline E20 used as a low reactivity fuel and blend of diesel jatropha biodiesel B20 used as a high reactivity fuel. Experimental results showed that increase in injection pressure enhances the degree of charge homogeneity, reduces the combustion duration, and provides higher rate of energy release. For 12 kg load and 700 bar injection pressure, it is observed that 5% rise in thermal efficiency, 27% reduction in smoke opacity, 2% reduction in HC, 4% reduction in CO and 20% rise in NOx as compared to 400 bar injection pressure.
Contravariant tensor algebra for anisotropic hyperelasticity
Authors: Basak Arthesh , Rajagopal Amirtham , Basappa Umesh
Published: November 2021

Analysis of tensors in oblique Cartesian coordinate systems always requires the definition of a set of orthogonal covariant basis vectors called the Reciprocal basis. This increases the complexity of the analysis and hence makes the method cumbersome. In this work a novel method is presented to effectively carry out the various transformations of tensors to and between oblique coordinate system/s without the need to create the covariant reciprocal basis. This will simplify the procedure of transformations involving problems where tensors are required to be defined in the oblique coordinate system. This work also demonstrates how the analysis of contravariant tensors can be applied to hyperelasticity. Continuum material and damage models can integrate this approach to model anisotropy and non linearity using a much simpler approach. The accuracy of the models was illustrated by matching the predictions to experimental results. A finite element analysis of material and damage model based on contravariant tensors was also carried out on a simple geometry with a re-entrant corner.
A Critical Study on Computation of Cutting Forces in Metal Cutting
Authors: Mohanta Dillip Kumar , Pani Biswajyoti , Sahoo Bidyadhar , Mohanty A. M.
Published: November 2021

Tool behavior in metal cutting is inevitable since they are typically required to produce components with high precision. This would have a profound impact on efficiency and costs of machining. The cutting forces involved at chip tool interface and the surface finish of the machined surfaces are the two major facets to gauge the performance of tool. The prediction by a statistical model, and the experimental values recorded using various sensors especially dynamometers are different approaches to critically analyze the cutting forces. Many researchers use to extensively practice these methodologies for their research activity. The aim of current research is to critically analyze & summarize approaches i.e., experimental/predictive available for gauging the cutting forces with user suggestion.
Experimental Investigation on Grades of Cement in the Nominal and Design Concrete Mixes
Authors: Maruthachalam D , Boobalan S C , Kaarthik M
Published: November 2021

In India, the experience in the use of concrete in housing is more than seven decades old. Concrete mix is a combination of cement, water and aggregates of sand and stone. The relative merits of using 33, 43 & 53 grades of cement in the nominal and design concrete mixes are studied, by testing to destruction hundreds of cubes, cylinders and prisms made using these three grades of cement, the concrete mix having been designed as per the relevant Indian Standard code of practice. The objective of this paper is to make awareness among researchers, engineers and the public about the latest scientific and technical developments in cement, and how to achieve economy in concrete. The foremost objective of concrete mix design is to hand-pick the optimum proportions of various ingredients of the concrete to satisfy the required properties in its fresh and hardened state. As per the investigation, if concrete mixes are designed for different grades adopting separately 33, 43, & 53 grades of cements, grade 53 gives the highest 28 days cube strength, whereas 33 grade cement gives the lowest value. The relative cost of using these three grades is also discussed in the paper.
Foot trajectory analysis of a robotic leg
Authors: Koushik M J , Krishna M S Aravind , Rahul R , Sreedharan Pramod
Published: November 2021

When it comes to walking robots, foot trajectory is a crucial element that can significantly influence the efficiency of the walking robot. This paper analyses the various foot return trajectories, which can provide higher step length while consuming less power. It is done through mathematical analysis and verified using simulations in software such as MSC Adams and Solidworks. This paper also discusses the kinematic and dynamic analysis of the two degrees of freedom leg using theoretical approaches in MATLAB and verifies the results using the simulation in MSC Adams.
Experimental analysis of microstructure and mechanical characteristics of LM6 & fly ash composite through stir casting method
Authors: Dhar A K , Sutradhar G
Published: November 2021

Fly ash has received a lot of attention as a possible support for aluminium alloy composites (AMCs) to improve characteristics while lowering production costs. Al alloy MMC was made with different amounts of fly ash (2, 5, and 8%). The semi-solid state mould was gradually filled with reinforced particles. The optical microscope investigation of the microstructure of AMCs reveals homogenous fly ash dispensation. With the increase in fly ash particulate, the microstructure with refined composition displays reducing Si-needle structure and extending space of eutectic-Al grid space. While hardness increases by 88 percent, tensile strength increases by 57 percent and yield strength by 17 % of the AMCs with the increment of fly ash. The addition of fly ash particles improved the AMCs’ physical and mechanical qualities. As a result, it leads to upgrade the energy utilisation.
Numerical Investigation of Precast Grouted Sleeve Connection under Cyclic Loading Using ABAQUS
Authors: Hemamathi A. , Murugavel Sanjith , Sukumar Binu , Usha Rani M.
Published: November 2021

This paper deals with numerical investigation using finite element software, ABAQUS CAE, for the analysis of precast reinforced concrete (RC) column connected to the foundation containing protruding bar in grouted sleeve subjected to axial, and lateral loads, including the cyclic loading response. The monolithic connection of column and foundation is designed and the same has been used for the design of precast concrete column and foundation. The model focusses on the length of the protruding bar provided in the foundation that is connected to the column through the grouted sleeve. As there is lack of Indian standard codes in the design of precast concrete connections, an attempt has been made by varying the length of protruding bars. The model was subjected to quasi-static loading condition and parameters such as ultimate load carrying capacity, load-displacement hysteresis, ductility and plastic strain were studied and were compared with that of monolithic connection. The results proved that when the length of protruding bars provided was equal to development length of the bar provided, the results were comparable with that of monolithic connection.
Experimental Study on Strengthening of Concrete Cylinders Using GFRP Sheets with Boron Carbide-Epoxy Composite
Authors: Joyson Silva P , Sukumar Binu , Periyasamy R , Siva Rahul M , Salman Ahmed A , Santhosh R , Sajin Raj S
Published: November 2021

Usage of composite materials as a concrete strengthening agent had increased evidently in recent years. One of those materials is the Glass Fibre Reinforced Polymer (GFRP) which is used in various fields for strengthening and retrofitting of concrete structures. Various studies have shown that, the wrapping of concrete specimens with Glass Fibre Reinforced Polymer (GFRP) resulted in increase in the Compressive Strength as well as the ductility of the concrete members. The main Objective of this project is to enhance the axial compressive strength of concrete block wrapped by Glass Fibre Reinforced Polymer sheets tested with various compositions of Boron Carbide (B4C) mixed with epoxy resin to find out the increase in the compressive strength. Cylindrical Concrete specimen of standard size 150mm diameter and 300mm height were casted of M30 Grade Concrete. Totally 6 batches were casted which consists of 18 specimens composing of different compositions of Boron Carbide varying 1.5%, 3.0%, 4.5% and 6.0% of boron carbide (B4C) were added and mixed with epoxy resin. Finally, Glass fibre Reinforced Polymer is wrapped around the Concrete specimen with a single wrap and the results obtained from Compressive strength of the specimens were studied.
Feasibility Study on Application of Natural Coagulants
Authors: Premkumar R , Rajesh S. , Prasanna venkadesh M
Published: November 2021

Creating nations are confronting consumable water gracefully issues because of lacking money-related assets. The expense of water treatment is expanding, and the nature of waterway water isn’t steady because of a suspended and colloidal molecule load brought about via land advancement and high tempest overflow during the stormy season. Because of numerous issues made by utilizing engineered coagulants a popularity to locate an elective characteristic coagulant emerges. In this exertion, an endeavor is made to examine the likelihood of normal coagulants like Cassia Alata, Calotropis Procera, Hyacinth bean, Banana leaves, Carcia Papaya, Acacia Mearnsii, Jatropha Curcas, Cactus, and Tamarind seeds on the reduction of turbidity of water. The clump coagulation test was done to decide the ideal coagulant amount required for the evacuation of turbidity of 100 NTU and to recognize the successful coagulant out of the nine chose normal coagulants. From this study, it may be accomplished that banana leaf can be used as an effectual coagulant for low and medium turbid water, whereas for high turbid water cactus can be used as an effective coagulant. Further examinations were completed utilizing the recognized coagulant to streamline the parameters like coagulant measurements, pH, introductory turbid focus, blending time, blending rate, and settling time. The higher percentage removal of turbidity was observed when pH was maintained at 6.5, initial turbid concentration was 500NTU, rapid mixing time given was 1 minute, slow mixing time with 25 minutes, and settling time of 25 minutes.
Influence of hybrid pin profile on microstructural and mechanical properties of Al/SiC graded composites produced by friction stir processing
Authors: Bikkina Venkatesh , Talasila Sadasiva Rao , Adepu Kumar
Published: November 2021

Friction stir processing (FSP) was employed to fabricate a surface graded composite by embedding SiC reinforcement particles in an AA6082-T6 matrix. Conical blind holes were drilled on the surface of the plate with varying inter-hole distances. The processing was performed with the different number of passes by keeping rotational and traverse speed constant. A new hybrid tool with a combination of conventional conical threaded tool and the triangular cross-sectioned tool was used in processing. The microstructural features of the processed samples were examined by a 3D microscope and scanning electron microscope (SEM). Mechanical properties such as microhardness, tensile strength was thoroughly evaluated. It is reported that the number of passes played an essential role in the distribution of reinforcement particles and grain refinement. The hardness value improved by applying multiple passes. The fractured tensile samples showed ductile failure. The sample treated with double passes gave better results with the homogenous distribution of reinforcement particles compared to samples processed with a single pass.
Numerical Analysis of Soil Reinforcement using Geocell infilled with Quarry Dust Powder
Authors: Adithan K , Neethi Chandra AC , Reddy Tiyyagura Laxmi Gayatri , Vaishnao Vignesh G , Sharma Animesh , Ramkrishnan R.
Published: November 2021

Massive urbanisation and infrastructural development caused by the growing population have taken place during the last decades. This means that in a rapidly expanding world economy we are running out of land. This problem has led to the use of ground improvement techniques to enhance the usability of land masses that were once not considered suitable for the development of infrastructure. Geocell is an innovative soil stabilisation product for civil engineering and development of infrastructures. They are cell containment systems which have been produced as an easy and durable material to stabilise and protect the compaction of the soil. The environmental concern regarding the disposability of quarry dust powder (QDP), produced from production units of M-sand (Manufactured sand), is of concern to the environment. The statement that is “waste of one industry should become the raw material for another” and that drawback can be addressed effectively by using it to improve the geotechnical characteristics of the weak soil. The purpose of this study is to find the optimal geocell-layer geometry and optimum combination of QDP infills to produce less settlement at a particular load using PLAXIS 2D software. The characteristics that have been varied are quarry dust powder in infill, geocell material and frequency of loading. These parameters were used for simulations to study the response of load vs settlements and the FOS of the slope. The FOS on the slope on the terrain was found to be 4.5, a steady slope. An optimised reinforcing mattress was ultimately found out.
Flexural Strength of Fly ash Brick Masonry Wall with four different bond
Authors: Shenbagavalli S , Chokkalingam Ramesh Babu
Published: November 2021

The strength of the masonry mainly depends on type of bond, types of bricks, compressive strength of the bricks and mortar used. The types of bonds play a major role in the properties of brick masonry wall. The most common types of bond used in practice are English bond, Flemish bond, Stretcher bond and Header bond. A lot of study has been performed on the load-carrying capacity of masonry walls. In this paper, effort has been taken to study the influence of different bonds on the flexural strength of the flyash brick masonry wall. For this wall of size 1m × 0.76m × 0.22m has been casted, cured for 28 days and tested in a loading frame. From the results, it was found the English bond gave higher flexural strength compared to other bonds such as Flemish, Stretcher and Header bond. The flexural strength of English bond was around 45 to 50% higher than the other bonds. The crack pattern at failure was also noted for all the masonry walls.
Sintering sensitivity of aluminium metal matrix composites developed through powder metallurgy proposed technique-a review
Authors: Choudhury Anup , Nanda Jajneswar , Das Sankar Narayan
Published: November 2021

This paper interprets the effect of sintering parameters like sintering time and sintering temperature as well as various sintering methods on distinct properties of the material. The variation of Physical, mechanical, and Tribological behaviour depending on sintering temperature, time and method based on various aluminium metal matrix composites have been investigated. The advantages of aluminium metal matrix composites are high strength to weight ratio, high wear resistance, and erosion resistance, etc. Aluminium Metal matrix composites have vast applications in various fields like structural, automobile, and aviation industries. The optimum value of sintering parameters and choice of sintering methods has a major role in getting these required properties of aluminium metal matrix composites prepared by the powder metallurgy process.
Experimental investigation on Ferrocement slab using partial replacement of cement by ceramic powder
Authors: Dhilip Kumar R G , Sukumar Binu , Hemamathi A , Ram Shankar P , Balki Sharanya
Published: November 2021

In this study, flexural behaviour of the Ferrocement panels of size 500 mm x 500 mm x 50 mm with partial replacement of ceramic powder under monotonic loading is observed. All the Ferrocement panels were casted with cement mortar of mix proportion 1:4 with w/c ratio of 0.45 and all the specimens were casted with single layer of galvanized welded wire mesh of 2mm diameter with mesh opening of 12.5 mm x 12.5 mm. The ceramic powder is administered at various percentage replacement of cement by weight and the adopted variations are 0%, 5%, 10% and 15%. Compressive strength of the mortar mix for all the different ratios of ceramic powder was carried out and the results were discussed. It can be concluded that the Panel with 10% replacement of ceramic powder offers appreciable results than the other panels.