PGP College of Engineering and Technology

Purpose The purpose of this paper is to propose a structured model for the selection of a third‐party reverse logistics provider (3PRLP) under fuzzy environment for the battery industry, which establishes the relative weights for attributes and sub‐attributes. Design/methodology/approach This paper uses fuzzy extent analysis to solve the 3PRLP selection problem. Findings Owing to growing environmental legislations, reverse logistics (RL) has attained more importance among practitioners and academicians. The important decision related to RL is whether the company should maintain the separate RL system or whether it can be outsourced. RL takes 12 times as many steps to process returns as it does to manage outbound logistics (Accenture supply chain management practice), consequently many companies decided to outsource the RL activities or functions through 3PRLPs. The paper proposes a way of selecting the best 3PRLP using fuzzy extent analysis. Research limitations/implications Fuzzy extent analysis is a highly multi‐faceted methodology which requires more numerical calculations and increases the time to take a decision. A limitation of this work is that in this study only fuzzy extent analysis has been concentrated on and other multi‐criteria decision‐making methods such as VIKOR, TOPSIS and ELECTRE can be applied in a fuzzy environment for solving such problems. Originality/value In this research, seven attributes and 34 sub‐attributes are considered and the interpretation of RL attributes in terms of their pair‐wise comparisons has been carried out. Those attributes possessing lower priorities in the fuzzy extent analysis need to be taken care of on a selection of the best 3PRLP.

Internet of Things (IoT) can be defined as a thing or device, physical and virtual, connected and communicating together, and integrated to a network for a specific purpose. The IoT uses technologies and devices such as sensors, radio-frequency identification (RFID) and actuators to collect data. IoT is not only about collecting data generated from sensors, but also about analyzing it. IoT applications must, of necessity, keep out all attackers and intruders so as to thwart attacks. IoT must allow for information to be shared, with every assurance of confidentiality, and is about a connected environment where people and things interact to enhance the quality of life. IoT infrastructure must be an open source, without ownership, meaning that anyone can develop, deploy and use it. The objective of this paper is to discuss the various challenges, issues and applications confronting the Internet of Things.

To meet the ever increasing energy demand of the world needs an urgent research to find an alternate fuel for diesel. Biodiesel can be a promising alternate for diesel engine in the years to come. The objective of the present experimental work is to investigate the combustion characteristics of VCR engine using mixture of two biodiesel blend with diesel at 100% or rated load, at constant speed. Simarouba and Jatropha oil are used to prepare, respective biodiesel and mixed in the volume ratio of 75:25, and is designated as B100. The combustion characteristics investigated are cylinder gas pressure, net heat and cumulative heat release, rate of pressure rise, and the mass fraction burned. Investigation is carried out varying load from zero to 100% or rated load of engine with an increment of 20% each time. Influence of blends and compression ratio on the combustion characteristics of engine is investigated. The results reveal that blends results in higher cylinder gas pressure, lesser heat release, higher rate of pressure rise and increased combustion duration. Increasing CR improve the combustion characteristics of engine.

To transfer the medical image from one place to another place or to store a medical image in a particular place with secure manner has become a challenge. In order to solve those problems, the medical image is encrypting and compressing before sending or saving at a place. In this paper, a new block pixel sort algorithm has been proposed for compressing the encrypted medical image. The encrypted medical image acts as an input for this compression process. During the compression, encrypted secret image E12(;) is compressed by the pixel block sort encoding (PBSE). The image is divided into four identical blocks, similar to 2×2 matrix. The minimum occurrence pixel(s) are found out from every block and the positions of the minimum occurrence pixel(s) are found using the verdict occurrence process. The pixel positions are shortened with the help of a shortening process. The features (symbols and shortened pixel positions) are extracted from each block and the extracted features are stored in a particular place, and the values of these features put together as a compressed medical image. The next process of PBSE is pixel block short decoding (PBSD) process. In the decoding process, there are nine steps involved while decompressing the compressed encrypted medical image. The feature extraction value of compressed information is found out from the feature extraction, the symbols are split and the positions are shortened in a separate manner. The position is retrieved from the rescheduled process and the symbols and reconstructed positions of the minimum occurrence pixels are taken block wise. Every symbol is placed based on the position in each block: if the minimum occurrence pixel is ‘0’, then the rest of the places are automatically allocated as ‘1’ or if the minimum occurrence pixel is ‘1’ the remaining place is automatically allocated as ‘0’. Both the blocks are merged as per order 2×2. The final output is the reconstructed encrypted medical image. From this compression method, we can achieve the high compression ratio, minimum time, less compression size and lossless compression, which are the things experimented and proved.

Abstract The utilization of biodiesel‐diesel blends in compression ignition (CI) engines are a viable option for the current energy crises. But due to better combustion features of biodiesel‐diesel blends leading to high NO x release from engine exhaust hinders the use of such blends. Usually all of the harmful exhaust emissions like HC and CO reduces marginally, except nitrogen oxides at higher compression ratios with biodiesel blended fuel. The present paper focuses on the study of variation of compression ratio and use of NiO nanoparticles in neem biodiesel‐diesel mixture (NB25) at different braking loads. A total of four test fuels of NB25 blend were prepared having nickel oxides at different concentration levels of 25, 50, 75, and 100 ppm. The current findings reveal that the use of 75 ppm of NiO in NB25 blend reduces the amount of thermal NO x by 6.2% compared to the absence of nanoparticles. Also, the performance parameters such as brake thermal efficiency improved by 2.9% and brake specific fuel consumption reduced by 1.8%. The presence of 75 ppm of NiO in NB25 not only shows best performance and also lower harmful emission.

Purpose The purpose of this paper is to present a new method to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V). Design/methodology/approach The desirability function analysis (DFA), fuzzy set theory with trapezoidal membership function and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method are used to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V). In recent years, the utilization of titanium and its alloys, especially of Ti6Al4V materials, in many different engineering fields has undergone a tremendous increase. The ECM process has a potential in the machining of Ti6Al4V. The machining parameters such as electrolyte concentration, current, applied voltage and feed rate with multiple responses such as material removal rate (MRR) and surface roughness (SR) are considered. Experimental work is carried out on Ti6Al4V using second order central composite rotatable design. The two responses are converted into global knit quality index using DFA. Fuzzy set theory with trapezoidal membership function is used to convert all machining parameters and responses into fuzzy values. Then a TOPSIS approach which determines the optimal machining parameters in terms of higher closeness coefficient is proposed to optimize the machining parameters of ECM for titanium alloy. Finally, ANOVA is performed to investigate the significance of each machining parameter and to identify the most influencing factor which affects the process responses. Findings The optimal machining parameters for ECM process are determined using desirability function analysis, fuzzy set theory and TOPSIS. Originality/value A new method is proposed to optimize the electro chemical machining process parameters for titanium alloy.

Sign Language Detection has become crucial and effective for humans and research in this area is in progress and is one of the applications of Computer Vision. Earlier works included detection using static signs with the help of a simple deep learning-based Convolutional Neural Network. This proposal is based on continuous detection of image frames in real-time using action detection so as to detect the action performed by the user. The model uses LSTM neural network model after identifying keypoints using mediapipe holistic which includes face, pose and hand features. The proposed work is done by collecting key value points for training and testing, pre-processing the data, and creating labels and features. It saves the weights and evaluates the model using confusion matrix accuracy.

In this article, a theoretical study of the effect of surface roughness on the squeeze film characteristics of short porous partial journal bearing lubricated with micropolar fluids is made. The modified averaged Reynolds equation accounting for the randomized surface roughness structure is mathematically derived. The Christensen stochastic theory of hydrodynamic lubrication of a rough surface is used to study the effect of two types of one-dimensional (1D) surface roughness on the squeeze film characteristics of a short porous partial journal bearing with micropolar fluid. It is assumed that the roughness asperity heights are small compared to the film thickness. It is observed that the transverse surface roughness pattern improves the squeeze film characteristics, whereas the squeeze film bearing performance is affected due to the presence of 1D longitudinal surface roughness. These effects are more pronounced for the micropolar fluids.

ABSTRACT In this paper, a theoretical analysis of the problem of magneto‐hydrodynamic couple‐stress squeeze film lubrication between rough circular stepped plates is presented. The modified averaged Reynolds equation is derived for the two types of one‐dimensional roughness structures, namely the radial roughness pattern and the azimuthal roughness pattern. The closed‐form expressions are obtained for the mean squeeze film pressure, load‐carrying capacity and squeeze film time. The results are presented for different operating parameters. It is observed that the effect of azimuthal (radial) roughness pattern on the bearing surface is to increase (decrease) the mean load‐carrying capacity and squeeze film time. The applied magnetic field increases the load‐carrying capacity and lengthens the squeezing time. Copyright © 2012 John Wiley & Sons, Ltd.

Aluminum alloy Al 6061 alloy has a good combination of mechanical properties. It has wide applications in the aerospace and marine industries. However, welded part of the alloy differs in properties which intern depends on welding input parameters. The proper selection of welding parameters plays an important significance in improvement in weld bead geometry. This present research focused on the study of welding parameters of MIG welding of alloy Al 6061 by Taguchi’s GR analysis using L32 orthogonal array. The angle of torch, Wire feed rate, Standoff distance, Welding speed, and Welding current are different parameters considered for analysis. AN0VA method was used to obtain the importance of each parameter on the weld bead. From ANOVA it was found that welding current plays a significant role and followed by wire feed rate, welding speed, angle of torch, and least influenced by standoff distance.

The influence of different layering pattern on the thermal and mechanical properties of the hybrid lyocell/rayon woven fabric reinforced polyester composites is demonstrated in this research work. The effect of different layering pattern was studied and characterized with tensile, open hole tensile, flexural, and impact testing. Composites manufacture with rayon/rayon/lyocell pattern reinforcement has the best tensile and flexural properties. The tensile and flexural strength increased by 36.5% and 43.79%, respectively, with reference to neat resin. It is also noted that the tensile modulus reduced by 31.1% when compared to neat resin. This represents an increase of material ductility in comparison with neat resin. It is observed that placing of high strength lyocell fabric at the extreme layer as reinforcement would result in enhanced properties. In case of rayon/lyocell/rayon hybrid tensile composites it can be seen that there is only 8% increase in tensile properties in comparison with neat resin due to rayon fabric placed as a skin rather than core. A negative effect of 6–26% reduced tensile strength was observed for the open hole (6 mm diameter) samples of all pattern of textile composites. This was due to fiber and inter fiber (matrix) fracture. A more detailed microfractography analysis was used to determine the crack growth direction and initiation. While in impact properties the lyocell/lyocell/lyocell pattern reinforcement showed the best impact strength of 3.36 kJ/m 2 compared to rayon/rayon/rayon and other tri-layer composites. Thermal analysis showed higher thermal stability for the rayon/rayon/lyocell pattern reinforcement.

A solid-state planar reference electrode (SSPRE) was fabricated and evaluated using polymer membrane, comprising polyvinyl chloride-co-polyvinyl acetate, nitrophenyl octyl ether as plasticizer, and potassium tetraphenylborate as anion blocker, and was used for the fabrication of SSPRE. Screen-printed Ag/AgCl electrode was used as the base electrode for the fabrication of SSPRE and was evaluated in background electrolyte solutions containing KNO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> /KCl and was observed that there was no significant potential dependence on anions present in the background electrolyte. Furthermore, SSPRE was tested using a series of cations and anions in solution and varying pH of the background electrolyte with respect to commercial Ag/AgCl with 0.1 M KCl reference electrode. It was noted that the SSPRE provided stable potential by tuning the design of the screen-printed electrodes and the membrane potential. The reference potential does not differ in various background electrolyte solutions, except thiocyanate, which gave a negative shift at high concentration, ca. 0.1 M. Furthermore, the SSPRE was integrated with screen-printed potassium and sodium ion-selective electrodes, and was tested, and the performance was evaluated.

A microgrid typically maintains multiple voltage bus bars with AC or DC or both. The various subsystems participating in the microgrid are connected to the appropriate voltage bus bars. Renewable energy sources like the wind energy or the solar photovoltaic energy may also be integrated to the microgrid. A battery-based energy storage system is also usually required. In this work, a dual-output DC to DC converter derived from the double lift or the relift Luo converter is proposed and validated. The proposed system uses a solar photovoltaic source and delivers a high-voltage DC output to deliver power to the high-voltage DC bus bar. A battery of medium voltage is connected across the second output of the dual-output converter. The proposed idea is validated using modeling and simulations in the MATLAB Simulink environment and an experimental prototype.

This article presents a procedure for the development of a scaled-down laboratory model of a transmission line based on per unit. values of a true line. The X/R ratio of the model is kept same as that of the true line, as then only their performance can be compared. The current-carrying capacity of the scaled-down model is closer to 20 A to enhance its utility with different flexible AC transmission systems devices. A scaled-down model of a true line for which parameters are available in the open literature is fabricated, and a case study of its simulation and validation is presented. These studies show that the laboratory model is able to emulate closely the true line, and therefore, it can be fruitfully used for the dynamic study of the transmission line embedded with flexible AC transmission systems and hardware-in-loop simulation studies.

In this work we have proposed a fully automatic algorithm to detect brain tumors by using digital image processing techniques is proposed. Here we detect the tumor, segment the tumor and calculate the area of the tumor. The complex problem of segmenting tumor in MRI can be successfully addressed by considering multi-step approaches. The tumor detection is often an essential preliminary phase to solve the segmentation problem successfully. The experiments showed good results also in complex situations. Segmentation of images embraces a significant position in the region of image processing. Tumor segmentation and area calculation from MRI data is an essential but fatigue, boring and time unbearable task when it completed manually by medical professional when evaluate with present day's high speed computing machines which facilitate us to visual study the area and position of unnecessary tissues. We use SVM classifier to classify the type of tumor.

Supersonic flows associated with missiles, aircraft, missile engine intake and rocket nozzles are often steady. In this present work, the computational analysis was conducted on C-D (convergent –divergent) nozzle for understanding the flow regime with various flow properties such as velocity and various turbulent models (spalert almaras, K-ε and K-ω). The Scale down model of C-D nozzle was chosen for this study and it was modelled computationally with Gambit software package. In this integrated component model, the inlet flow is assumed a two-dimensional, steady, compressible, turbulent and supersonic. The physics based mathematical model of the considered flow consists of conservation of mass, momentum and energy equations subject to appropriate boundary conditions as defined by the physical problem stated above. The system of the governing equations with turbulent effects is solved numerically using different turbulence models to demonstrate their numerical accuracy in predicting the characteristics of turbulent gas flow in such complex geometry. Fluent software package was used for solving gas flow equations with turbulence models. The Mach number was chosen for different cases of analyses were 1.2, 1.5 and 2. For each case, different turbulence were engaged and solved and all the results were compared finally.

Wireless sensor networks is a distributed and non-transferable resource.Over time, variations in energy availability would likely to arise.Protocols will be similar to routing trees may concentrate energy handling at certain nodes.Differences in energy harvestshould arising from environmental variations, such as if one node is in the form of cluster and another is in the shade, can pro-duce variations in charging rates and battery levels.We propose a novel approach for mobile users to collect the network-wide large data.The routing node structure of data collection is additively updated with the group of the mobile user.With this approaches, we only perform a limited modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance.The proposed protocol is easy to implement.shows that the proposed scalable in maintenanceexpenses, performs efficiently in the routing performance, provides continuous data delivery during the user movement.We implement the proposed protocol an ns2 simulator to verify the efficiency of our proposed protocol

Abstract In this current study, high‐toughness epoxy bio‐composites were prepared and characterized for lightweight and low‐cost technological applications using novel palmyra sprout fiber (PSF) and red matta rice husk (RHA) biosilica. The principal aim of this research was to study how the alkali‐silane treatments on the fiber and particle influence the mechanical, wear, thermal and hydrophobic behavior of epoxy composites. The PSF was alkali‐silane treated whereas the biosilica was treated only with silane. The composites were prepared via a hand layup process and characterized. According to the results, the treated PSF with 3 vol.% RHA biosilica has the maximum tensile, impact, flexural strength, and hardness. The composite with the inclusion of 5 vol.% biosilica, and the palmyra sprout fiber has the lowest specific wear rate and COF of 0.007 mm 3 /Nm and 0.37. Moreover, these composites possess good thermal stability with the highest initial decomposition temperature of 342°C. Similarly, the composite designation ESP3 have the lowest contact angle of 75.7°, which is within the hydrophobic limit. These composites with improved mechanical, thermal and wear properties may be useful in a variety of engineering applications that can be used for high load‐bearing capacity and biodegradability, such as sporting goods, automobiles, home furniture, food packaging, transport, and aircrafts.

Dual-output DC to DC converters have drawn attention in the domestic, automobile, and industrial domains. A dual-output converter usually provides a voltage step-down channel and a voltage step-up channel. Typically, an automobile needs a battery charging unit, a traction motor drive, and several other applications. A typical application may require two channels of DC output with a low-voltage (LV) channel and a high-voltage (HV) channel. While the generic boost-derived and quadratic boost-derived dual-output converters are available in the literature, this article focuses on the control aspects of a relift type Luo converter-derived dual-output converter (LDDOC). A solar photovoltaic (SPV) source is the main power, and it charges a battery. The LV loads may be connected across the battery, and the relift stage delivers a regulated 48 V output. The regulation of the 48 V output using a PI controller, a fuzzy logic controller, an ANN-based controller, and a sliding mode controller (SMC) has been studied using simulations. The simulations reveal that the sliding mode controller is advantageous because of meeting out the required performance, easy implementation, and low cost. An experimental setup has also been developed to verify the performance of the sliding mode controller for the regulation of the HV channel output voltage at 48 V.

The non-stationarity of electroencephalograms (EEG) has a substantial effect on the performance of classifiers in brain-computer interface (BCI) systems. To tackle this challenge, an adaptable version of the linear discriminant analysis (LDA) classifier was proposed. Accuracy is crucial when using BCIs for neurorestorative tasks or memory improvement. The accurate comprehension of brain responses facilitates more focused interventions, which may improve neurorestorative outcomes. BCIs equipped with adaptive classifiers are useful for personalizing therapies to individual requirements and for improving neurorestorative processes. Notably, neurorestorative interventions that yield consistent, accurate, and reliable outcomes are more likely to inspire trust and elicit satisfaction in users. The proposed classifier continuously modified its parameters in accordance with EEG signals. The covariance matrix and mean values for each pair of classes were the updating parameters. The proposed classifier modified the model parameters by prioritizing current signal data over older signal history. The proposed classifier was tested using a hybrid SSVEP + P300 BCI system. The proposed classifier had an estimated classification accuracy of 97.4%, and was more effective than pooled mean LDA and conventional multiclass LDA classifiers. Increased classification accuracy may increase the responsiveness of neurorestorative interventions and increase the usefulness of BCIs in neurorestoration.