Amrutvahini College of Engineering

Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. IA can completely replace humans with automation with better safety and intelligent movement of vehicles. This work surveys those recent methodologies and their comparative analysis, which use artificial intelligence, machine learning, and IoT in autonomous vehicles. With the shift from manual to automation, there is a need to understand risk mitigation technologies. Thus, this work surveys the safety standards and challenges associated with autonomous vehicles in context of object detection, cybersecurity, and V2X privacy. Additionally, the conceptual autonomous technology risks and benefits are listed to study the consideration of artificial intelligence as an essential factor in handling futuristic vehicles. Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. Furthermore, autonomous electric vehicle functionality is also covered with its applications. The real-life applications of autonomous truck, bus, car, shuttle, helicopter, rover, and underground vehicles in various countries and organizations are elaborated. Furthermore, the applications of autonomous vehicles in the supply chain management and manufacturing industry are included in this survey. The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. The important future directions are offered in order to indicate areas of potential study that may be carried out in order to enhance autonomous cars in the future.

This paper develops an effective encryption and steganography-based text extraction in IoT using deep learning method. Initially, the input text and cover images are separately pre-processed. DCT (discrete cosine transform) is utilized to transfer the image from spatial domain to frequency domain. Then, the original text is encrypted using new optimized equilibrium-based homomorphic encryption (OEHE) approach. Next, the extended wavelet convolutional transient search (EWCTS) optimizer with quotient multi-pixel value differencing (QMPVD) is developed to embed the secret text in cover images. Then, at receiver side, the reverse process for encryption and steganography is executed with secret key provided by the sender. Finally, the accurate text is extracted at receiver side using steganalysis process. The developed approach is executed in MATLAB software. The various evaluation metrics are used to authorize the effectiveness of suggested approach. Simulation outcomes proved that the suggested technique provides better outcomes than other existing approaches.

In this paper we described a method for moving object detection and tracking using Kalman filter. Basically, estimation process is very important in the surveillance system. This process is for finding out the location of the target. The decomposition is also helpful for the estimation process, in this process first step is the tracking the video, and then the video is converted into frames in the initialization period and every frame is made up of a piece of picture. In further step, the targets in each frame are identified by means of color recognition; next position is the moving target and to identify the center coordinates and next another last step the coordinate of the previous and current frames is inputted and find out the location of the moving target which is present frame. And this frame is estimated by filter. The tracking is very important for different object. The objects are tracked with the help of Kalman filter. This filter is used for the pixel wise subtraction of current frame. As well as also used to be find out the error between actual position of the ball and estimated position value with the help of this filter.

The present investigation is directed towards synthesis of zinc oxide (ZnO) nanoparticles and steady blending with soybean biodiesel (SBME25) to improve the fuel properties of SBME25 and enhance the overall characteristics of a variable compression ratio diesel engine. The soybean biodiesel (SBME) was prepared using the transesterification reaction. Numerous characterization tests were carried out to ascertain the shape and size of zinc oxide nanoparticles. The synthesized asymmetric ZnO nanoparticles were dispersed in SBME25 at three dosage levels (25, 50, and 75 ppm) with sodium dodecyl benzene sulphonate (SDBS) surfactant using the ultrasonication process. The quantified physicochemical properties of all the fuels blends were in symmetry with the American society for testing and materials (ASTM) standards. Nanofuel blends demonstrated enhanced fuel properties compared with SBME25. The engine was operated at two different compression ratios (18.5 and 21.5) and a comparison was made, and best fuel blend and compression ratio (CR) were selected. Fuel blend SBME25ZnO50 and compression ratio (CR) of 21.5 illustrated an overall enhancement in engine characteristics. For SBME25ZnO50 and CR 21.5 fuel blend, brake thermal efficiency (BTE) increased by 23.2%, brake specific fuel consumption (BSFC) were reduced by 26.66%, and hydrocarbon (HC), CO, smoke, and CO2 emissions were reduced by 32.234%, 28.21% 22.55% and 21.66%, respectively; in addition, the heat release rate (HRR) and mean gas temperature (MGT) improved, and ignition delay (ID) was reduced. In contrast, the NOx emissions increased for all the nanofuel blends due to greater supply of oxygen and increase in the temperature of the combustion chamber. At a CR of 18.5, a similar trend was observed, while the values of engine characteristics were lower compared with CR of 21.5. The properties of nanofuel blend SBME25ZnO50 were in symmetry and comparable to the diesel fuel.

Modeling offers great potential for reducing experimental effort in development of welding parameters, tool design and many other areas and at the same time reduce cost and time. An analytical model for heat generation for friction stir welding using taper cylindrical pin profile was developed. The proposed analytical expression is the modification of previous analytical models known from the literature which is verified and well matches with the model developed by previous researchers. The results of the proposed model were validated with the data from previous researchers. From the obtained results, it was observed that less temperature is generated using taper cylindrical pin profile than straight cylindrical pin profile under given set of working conditions. Furthermore, numerical simulation result shows that increasing the taper pin angle leads to decrease in peak temperature.

For Remaining useful life (RUL) prediction, this article presents a paradigm that separates the whole bearing life into many health states and then builds unique local regression models for each of those states, rather than searching for an overall regression model with multiple health state assessments. A method that utilised both unsupervised learnings and supervised learning to estimate a bearing’s real-time health status is presented without previous information. The primary technology used to perform health status assessment and RUL prediction is the support vector machine. The efficacy of the suggested framework has been shown via experiments, including accelerated deterioration testing on rolling element bearings.

In this paper, application of techniques for order preference by similarity to ideal solution (TOPSIS) method is applied for solving multiple criteria (objective) optimization problem in wire electrical discharge machining (WEDM) process. Three examples are included to illustrate the approach. In all the cases, it is found that, the results obtained using the TOPSIS method almost match with those derived by the past researchers which prove the applicability of this method while solving various complex decision-making problems in present day manufacturing environment.

The present study examines the effect of silicon dioxide (SiO2) nano-additives on the performance and emission characteristics of a diesel engine fuelled with soybean biodiesel. Soybean biofuel was prepared using the transesterification process. The morphology of nano-additives was studied using scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDS). The Ultrasonication process was used for the homogeneous blending of nano-additives with biodiesel, while surfactant was used for the stabilisation of nano-additives. The physicochemical properties of pure and blended fuel samples were measured as per ASTM standards. The performance and emissions characteristics of different fuel samples were measured at different loading conditions. It was found that the brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) increased by 3.48–6.39% and 5.81–9.88%, respectively, with the addition of SiO2 nano-additives. The carbon monoxide (CO), hydrocarbon (HC) and smoke emissions for nano-additive added blends were decreased by 1.9–17.5%, 20.56–27.5% and 10.16–23.54% compared to SBME25 fuel blends.

Commercially affordable titanium dioxide nanoparticles (TiO2 NPs) catalyst with low toxicity and high efficiency, were prepared by scalable Flame pyrolysis method. For this, titanium iso-propoxide solution was directly burned in air, the product in the form of white soot was collected over water cooled conical flask. The prepared nanoparticles were characterized by various sophisticated techniques viz. UV–Visible, FTIR, XRD, SEM, EDS and TEM. The SEM, TEM and Zeta Potential Analyzer illustrated spherical morphology of the synthesized TiO2 NPs with an average size of about 150 nm. The XRD confirmed formation of prominently dominated anatase phase co-existed with traces of rutile phase. The combination of anatase and rutile phase exhibited increased catalytic activity for removal of dyes. The flame synthesized TiO2 nanoparticles exhibited strong dye removal efficiency of 97.64 % for Methylene Blue and 92.39 % for Congo Red dyes respectively, making it vitally important for the waste water treatment and industrial dyes removal applications. Probable dye degradations mechanism on the surface of TiO2 NPs also proposed based on the LCMS results.

The dissimilar material joining of aluminum and titanium alloys is recognized as a challenge due to the significant differences in the physical, chemical, and metallurgical properties of these alloys, where the increasing demands for high strength and lightweight alloys in aerospace, defense, and automotive industries. Joining these two alloys using the conventional fusion techniques produces commercially unacceptable sound joints due to irregular, complex weld pool shapes, cracking and low strength, high residual stresses, cracks, and microporosity, and the brittle intermetallic compounds formation leads to poor formability or inferior mechanical properties. The formation of intermetallic compounds is inevitable but it is less severe in solid-state than in the fusion welding process. Hence, this article reviews on aluminum–titanium joining using different solid-state and hybrid joining processes with emphasis on the effect of process parameters of the different processes on the weld microstructure, mechanical properties along with the type of intermetallic compounds and defects formed at the weld interface. Among the various solid-state welding processes for aluminum–titanium joining, the following grades of aluminum and titanium alloys were employed such as cp Ti, Ti6Al4V, cp Al, AA1xxx, AA 2xxx, AA5xxx, AA6xxx, AA7xxx, out of which Ti6Al4V and AA6xxx alloys are the most common combination.

Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.

In this study, friction stir welding window for AA6061-T6 aluminium alloy based on tool rotational speed and weld speed was developed. The formation of friction stir welding/processing zone has been analysed macroscopically and microscopically. Fracture locations of the joints were also analysed using scanning electron microscope. It has been experimentally found that the joint fabricated using tool rotational speed of 1000 r/min and weld speed of 40 mm/min (obtained from friction stir welding window), tool shoulder diameter of 24 mm with tapered cylindrical pin profile and the ratio of shoulder to pin diameter with value 3 showed better mechanical properties compared to other joints. The developed welding window will be used as ready reckoner to select appropriate rotational and welding speed to fabricate defect-free joints.

A Cu-doped Fe 2 O 3 /g-C 3 N 4 composite, synthesized via a straightforward hydrothermal process with controlled morphologies, represents a significant advancement in supercapacitor electrode materials.

The performance evaluation of watermarking scheme is done by two performance metrics: perceptual transparency and robustness. Perceptual transparency refers quality of image which is measured by `Peak Signal to Noise Ratio' (PSNR). Robustness is measured in terms of `Correlation Factor' (ρ). A good quality watermarking scheme should have maximum PSNR, ideally Correlation Factor equals to 1 and should have maximum watermark information hiding capacity. In this paper, we have presented strongly robust digital image watermarking scheme based on `Discrete Wavelet Transform', by embedding scrambled watermark in middle frequency sub band. The security levels are increased by generating PN sequence depending on periodicity of watermark image. The image scrambling is applied by Arnold Transform. The decomposition is done with `Haar' which is simple, symmetric and orthogonal wavelet and the direct weighting factor is used in watermark embedding and extraction process. The scheme results in exact recovery of watermark with standard database images of size 512 × 512, giving Correlation Factor equals to 1. The Correlation Factor for different attacks like Noise addition, Filtering, Rotation and Compression ranges from 0.90 to 0.95. The PSNR with weighting factor 0.02 is up to 48.53 dBs. The presented scheme is nonblind and embeds binary watermark of 64 × 64 size.

In this work, a multiple user deep neural network-based non-orthogonal multiple access (NOMA) receiver is investigated considering channel estimation error. The decoding of the symbol in the case of the NOMA system follows the sequential order and decoding accuracy depends on the detection of the previous user. Without estimating the throughput, a deep neural network-based NOMA orthogonal frequency division multiplexing (OFDM) system is proposed to decode the symbols from the users. Firstly, the deep neural network is trained. Secondly, the data are trained and lastly, the data are tested for various users. In this work, for various values of signal to noise ratio, the performance of the deep neural network is investigated, and the bit error rate (BER) is calculated on a per subcarrier basis. The simulation results show that the deep neural network is more robust to symbol distortion due to inter-symbol information and will obtain knowledge of the channel state information using data testing.

The objective of this paper is to develop the irrigation planning model and to apply the same in the form of Multi Objective Fuzzy Linear Programming (MOFLP) approach for crop planning in command area of Jayakwadi Project Stage I, Maharashtra State, India. To formulate MOFLP model various Linear Programming (LP) models are developed to optimize the Net Benefits (NB), Crop/Yield Production (YP), Employment Generation (EG) and Manure Utilization (MU) for which the objective function and constraints are crisp in nature. From the results of these LP models the linear membership function for each individual objective function has been developed. Considering the decision makers satisfaction level (λ), all the four objectives are maximized simultaneously. The results of the MOFLP and LP are compared. The MOFLP model concentrates on satisfying four objectives simultaneously. The present model will be helpful for the decision maker to take decision under conflicting situation when planning for different objectives simultaneously. The degree of satisfaction λ, works out to be 0.58. Compromised solution provides Net Benefits 1503.73 Million Rupees, Crop Production 319563.50 Tons, Employment Generation/Labour Requirement 29.74 Million Man days and Manure Utilization 154506.50 Tons respectively.

For the better utilization of the enormous amount of data available to us on the Internet and in different archives, summarization is a valuable method. Manual summarization by experts is an almost impossible and time-consuming activity. People could not access, read, or use such a big pile of information for their needs. Therefore, summary generation is essential and beneficial in the current scenario. This paper presents an efficient qualitative analysis of the different algorithms used for text summarization. We implemented five different algorithms, namely, term frequency-inverse document frequency (TF-IDF), LexRank, TextRank, BertSum, and PEGASUS, for a summary generation. These algorithms are chosen based on various factors. After reviewing the state-of-the-art literature, it generates good summaries results. The performance of these algorithms is compared on two different datasets, i.e., Reddit-TIFU and MultiNews, and their results are measured using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure to perform analysis to decide the best algorithm among these and generate the summary. After performing a qualitative analysis of the above algorithms, we observe that for both the datasets, i.e., Reddit-TIFU and MultiNews, PEGASUS had the best average F-score for abstractive text summarization and TextRank algorithms for extractive text summarization, with a better average F-score.

The information gathered within the structure health monitored (SHM) device would display a range of irregularities mainly a result of sensing defeat, noise disturbance, and different causes. This will greatly impair the structure's security evaluation. This research presents a multipurpose deeper neural network-based data-driven abnormality diagnostic system called SHM. The multipurpose deeper neural networks fuse single-dimensional as well as two-dimensional properties regarding the sensory signals to increase the detecting efficiency. Two separate Convolutional Neural Network, streams within the system are used for obtaining time-frequency characteristics from information collected by sensors (also referred to as two-dimensional-CNN medium) as well as unprocessed one-dimensional characteristics (also referred to as one-dimensional-CNN medium). Following the 2D as well as 1D streams' individual clustering and filtering processes using the sensing information, the two categories of recovered properties have been distorted through single-dimensional matrices that combined within the fusion level. The ideal framework shows the efficacy as well as potential of the suggested approach having a precision percentage of 95.10%. Considering an accurate AI-assisted electronic instrument for evaluating security in structured health management networks, the suggested approach has an exciting period ahead of it.

In the present research, the influence of metallic copper-coated zinc oxide (Cu-ZnO) nanoparticles (NPs) and soybean biodiesel on the improvement in efficiency and emission characteristics of a VCR engine are examined. The soybean methyl ester (SBME) was produced utilizing the transesterification reaction. Several characterization experiments were performed to determine the shape, scale, and contents of the synthesized Cu-ZnO NPs. The Cu-ZnO NPs and SDBS surfactant were steadily distributed utilizing the ultrasonic vibration in SBME25-diesel at three stages (25, 50, and 75 ppm). The prepared physicochemical properties of fuels are comparable with ASTM requirements. In comparison to SBME25, nanofuel mixtures displayed better fuel properties. A compression ratio of 21.5 was used and a comparison was made with the SBME25. The SBME25Cu-ZnO50 combination and the CR 21.5 have illustrated an increase in overall engine characteristics. For the SBME25Cu-ZnO50 mixture, BTE and HRR raised by 16.1% and 19.2%, BSFC and ID dropped by 18.9% and 14.6%, and hydrocarbon, carbon monoxide, and smoke emissions lowered by 24.1%, 34.5%, and 16.8%. In all nanofuel blends, the oxide of nitrogen raised owing to a higher oxygen supply to the CC.

The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes.