Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Directional synthesis of aviation-, diesel-, and gasoline range hydrocarbon fuels by catalytic transformations of biomass components: An overview(Elsevier Ltd, 2023) Dutta, S.; Madav, V.; Joshi, G.; Naik, N.; Kumar, S.Selective conversion of heavily oxygenated biomolecules into hydrocarbon-based liquid transportation fuels with stipulated structural traits is of academic and industrial significance. This work provides an overview of producing fuel precursors from biomass components and their catalytic transformation into aviation-, diesel-, and gasoline-range hydrocarbon fuels (HCFs). Strategic applications of various organic transformations for the molecular design of targeted products have been rationalized. Construction and alteration of the carbon skeletal system in the fuel candidates via chemical-catalytic transformations have been highlighted. Emphasis has also been given to the process conditions and details of the catalysts employed in these processes. Critical analysis of the literature data presented in this review will assist the researchers in developing more proficient processes for the biorenewable production of drop-in HCFs. © 2023 Elsevier LtdItem Application of Modeling and Control Approaches of Piezoelectric Actuators: A Review(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Kanchan, M.; Mohith, M.; Bhat, R.; Naik, N.Piezoelectric actuators find extensive application in delivering precision motion in the micrometer to nanometer range. The advantages of a broader range of motion, rapid response, higher stiffness, and large actuation force from piezoelectric actuators make them suitable for precision positioning applications. However, the inherent nonlinearity in the piezoelectric actuators under dynamic working conditions severely affects the accuracy of the generated motion. The nonlinearity in the piezoelectric actuators arises from hysteresis, creep, and vibration, which affect the performance of the piezoelectric actuator. Thus, there is a need for appropriate modeling and control approaches for piezoelectric actuators, which can model the nonlinearity phenomenon and provide adequate compensation to achieve higher motion accuracy. The present review covers different methods adopted for overcoming the nonlinearity issues in piezoelectric actuators. This review highlights the charge-based and voltage-based control methods that drive the piezoelectric actuators. The survey also includes different modeling approaches for the creep and hysteresis phenomenon of the piezoelectric actuators. In addition, the present review also highlights different control strategies and their applications in various types of piezoelectric actuators. An attempt is also made to compare the piezoelectric actuator’s different modeling and control approaches and highlight prospects. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.Item Intraday Stock Prediction Based on Deep Neural Network(Springer, 2020) Naik, N.; Mohan, B.R.Predicting stock price movements is difficult due to the speculative nature of the stock market.Accurate predictions of stock prices allow traders to increase their profits. Stock prices react when receiving new information.During the trading day, it is difficult to understand the up and down movements signaled by stock prices. This paper addresses the problem of fluctuations in stock prices. We proposed the method to identify stock movement trend in data, and this method considered the combination of candlestick data and technical indicator values. The outcome of this method is given as inputs to a deep neural network (DNN) to classify a stock price’s up and down movements. National Stock Exchange, India, datasets are considered for an experiment from the years 2008 to 2018. The work is carried out using H2O deep learning on an RStudio platform. Experimental results are compared with a three-layer artificial neural network (ANN) model. The proposed five-layer DNN model outperforms state-of-the-art methods by 8–11% in predicting up and down movements of a given stock. © 2019, The National Academy of Sciences, India.Item Novel Stock Crisis Prediction Technique - A Study on Indian Stock Market(Institute of Electrical and Electronics Engineers Inc., 2021) Naik, N.; Mohan, B.R.A stock market crash is a drop in stock prices more than 10% across the major indices. Stock crisis prediction is a difficult task due to more volatility in the stock market. Stock price sell-offs are due to various reasons such as company earnings, geopolitical tension, financial crisis, and pandemic situations. Crisis prediction is a challenging task for researchers and investors. We proposed a stock crisis prediction model based on the Hybrid Feature Selection (HFS) technique. First, we proposed the HFS algorithm to removes the irrelevant financial parameters features of stock. The second is the Naive Bayes method is considered to classify the strong fundamental stock. The third is we have used the Relative Strength Index (RSI) method to find a bubble in stock price. The fourth is we have used moving average statistics to identify the crisis point in stock prices. The fifth is stock crisis prediction based on Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) regression method. The performance of the model is evaluated based on Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error(RMSE). HFS based XGBoost method was performed better than HFS based DNN method for predicting the stock crisis. The experiments considered the Indian datasets to carry out the task. In the future, the researchers can explore other technical indicators to predict the crisis point. There is more scope to improve and fine-tune the XGBoost method with a different optimizer. © 2013 IEEE.Item Dynamic force modelling and experimental analysis of reaming(Engineered Science Publisher, 2021) Kamath, C.R.; Bekinal, S.I.; Bhat, R.; Naik, N.; Kuttan, A.The production Reaming process plays a vital role in several applications, ranging from automotive to medical sectors. It is performed to enlarge the pre-drilled hole to obtain its required diameter within the specified tolerance limits. The typical operational faults found in the reaming process significantly contribute to damage in the final hole quality. Thus, a dynamic force model is developed in the present work to predict the cutting forces developed during the reaming process. The inputs to the model are broadly classified into tool geometry and vibration system elements. The cutting forces acting in all three directions during the reaming are predicted. The double modulation principle is applied to develop the dynamic force model for computing the cutting forces in the reaming process. The dynamic force model thus developed and simulated using MATLAB® R2019b is examined and validated through actual experiments for no fault conditions. The results obtained infer a high degree of fitness between the values obtained from the developed mechanistic model and the experimental values with a prediction error of less than 5%. © 1999. The American Astronomical Society. All rights reserved.Item Stock price volatility estimation using regime switching technique-empirical study on the indian stock market(MDPI AG, 2021) Naik, N.; Mohan, B.R.Volatility is the degree of variation in the stock price over time. The stock price is volatile due to many factors, such as demand, supply, economic policy, and company earnings. Investing in a volatile market is riskier for stock traders. Most of the existing work considered Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) models to capture volatility, but this model fails to capture when the volatility is very high. This paper aims to estimate the stock price volatility using the Markov regime-switching GARCH (MSGARCH) and SETAR model. The model selection was carried out using the Akaike-Informations-Criteria (AIC) and Bayesian-Information Criteria (BIC) metric. The performance of the model is evaluated using the Root mean square error (RMSE) and mean absolute percentage error (MAPE) metric. We have found that volatility estimation using the MSGARCH model performed better than the SETAR model. The experiments considered the Indian stock market data. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Item Processing, Mechanical Characterization, and Electric Discharge Machining of Stir Cast and Spray Forming-Based Al-Si Alloy Reinforced with ZrO2 Particulate Composites(MDPI, 2022) Shetty, R.; Gurupur, P.R.; Hindi, J.; Hegde, A.; Naik, N.; Ali, M.S.S.; Patil, I.S.; Nayak, M.High performance lightweight structures made of metal matrix composites (MMCs) are in demand for application in variety of industries such as aircraft, spacecraft, automobile, marine, sports equipment, etc. However, uniform distribution of the reinforcement phase to improve the mechanical properties and quality of MMCs has been the challenge for the manufacturing industries. Hence, researchers are focusing on the development of traditional low-cost method of producing metal matrix composites. In the view of above facts, an attempt is made to study the processing and characterization of Si-Al alloy reinforced with zirconium dioxide particulate composites in this paper. Hence, this paper concentrates on experimentally identifying the effect of stir cast and spray forming processing techniques followed by hot pressing on micro hardness, compressive strength, and tensile strength using Taguchi’s design of experiments for aluminum silicon matrix alloy reinforced with zirconium dioxide particulates. From the extensive experimentation on aluminum and silicon reinforced with the ZrO2 powder particulates, it was observed that there was an improvement in selected mechanical properties as the percentage of ZrO2 increased with 13 wt.% of silicon under spray forming processing technique compared to stir cast composites. This may be due to uniform distribution homogenous dispersion, larger work hardening rate, and structure of dislocation tangles around the ZrO2 particulates that occurred during spray forming processing technique. Further, results obtained from the interaction plot, contour plot, main effects plot, and analysis of variance (ANOVA) proved to be successful for identifying the optimum processing parameters for Si-Al alloy reinforced with zirconium dioxide particulate composites. Further, this paper also discusses wear study using pin on disc wear testing apparatus on spray forming processed aluminum and silicon (13.0 wt.%) alloy reinforced with the ZrO2 powder particulates based on Taguchi’s design of experiments followed by second order model generation for wear using response surface methodology. Finally, electrode wear study of spray forming processed aluminum and silicon alloy reinforced with the ZrO2 powder particulates using electric discharge machining by varying peak current (A), pulse on time (μs), and pulse off time (μs) using brass, copper, and graphite as electrode material based on L27orthogonal array. The understanding gained from the design of experiments in this paper can be used to develop future guidelines for processing and characterization of Si-Al alloy reinforced with zirconium dioxide particulate composites. © 2022 by the authors.Item Dual attention guided deep encoder-decoder network for change analysis in land use/land cover for Dakshina Kannada District, Karnataka, India(Springer Science and Business Media Deutschland GmbH, 2023) Naik, N.; Chandrasekaran, K.; Sundaram, V.M.; Prabhavathy, P.The Earth is frequently changed by natural occurrences and human actions that have threatened our environment to a certain extent. Therefore, accurate and timely monitoring of transformations at the surface of the Earth is crucial for precisely facing their harmful effects and consequences. This paper aims to perform a change detection (CD) analysis and assessment of the Dakshina Kannada region, being one of the coastal districts of Karnataka, India. The spatial and temporal variations in land use and land cover (LULC) are being monitored and examined from the data received as LULC maps from the National Remote Sensing Agency, Indian Space Research Organization, India. The time-series data from advanced wide-field sensor (AWiFS) Resourcesat2 satellite as LULC maps (1:250k) are analyzed using a deep learning approach with an encoder–decoder architecture with dual-attention modules for the change analysis. The model provides an overall accuracy and meanIOU(intersection over union) of 94.11% and 74.1%. The LULC maps from 2005 to 2018 (13 years) are utilized to decide the variations in the LULC, including urban development, agricultural variations, vegetation dynamics, forest areas, barren land, littoral swamp, and water bodies, current fallow, etc. The multiclass area-wise changes in terms of percentage show a decline in most LULC classes, which raises a point of concern for the environmental safety of the considered area, which is highly exposed to coastal flooding due to increased urbanization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Spatio-temporal analysis of land use/land cover change detection in small regions using self-supervised lightweight deep learning(Springer Science and Business Media Deutschland GmbH, 2023) Naik, N.; Chandrasekaran, K.; Sundaram, V.; Prabhavathy, P.Change detection (CD) has sparked a lot of scientific interest in recent decades as one of the core concerns in Earth observation. The enhancement of the CD source data with the availability of multitemporal images with varying resolutions provides ample change indicators due to the rapid improvement of satellite sensors. However, precisely detecting real changed locations continues to be a complicated task. CD from remote sensing images (RSI) becomes challenging when the labeled data for supervised learning is unavailable. This article proposes a novel CD framework using a self-supervised learning (SSL) approach to overcome these limitations. First, the superpixel segmentation method of simple linear iterative clustering (SLIC) using a structural similarity index is incorporated to produce a difference image (DI). The change features are extracted to represent the difference information using spatial features between the corresponding superpixels. Second, a parallel clustering algorithm called fuzzy C-means (FCM) separates the DI into three clusters of changed, unchanged, and intermediate classes. The image patches of changed, unchanged and intermediate classes are constructed as training and testing samples. A lightweight deep convolutional neural network (LWDCNN) is trained with the training samples to detect the semantic difference and classify the testing samples into the changed or unchanged class. Finally, merging intermediate and change class labels generates a robust and high-contrast CD map. Numerical experiments were performed on two small regions like the Alappuzha, Kerala, India, and Paris building dataset to demonstrate the usefulness of the proposed approach, achieving an overall accuracy of 98.28% and 96.43% for determining changes effectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Forecasting Land-Use and Land-Cover Change Using Hybrid CNN-LSTM Model(Institute of Electrical and Electronics Engineers Inc., 2024) Varma, B.; Naik, N.; Chandrasekaran, K.; Venkatesan, M.; Rajan, J.Land-use and land-cover (LULC) information helps analyze future trends and is essential for environmental management and sustainable planning. Time-series satellite images are employed in this study to forecast changes in LULC. Deep-learning (DL) frameworks have been widely used for modeling dynamic LULC changes at the regional level. However, improving the accuracy of the existing prediction models is necessary. This letter proposes an integrated convolutional neural network (CNN) and long short-term memory network (LSTM) known as a hybrid CNN-LSTM model to address the fine-scale LULC prediction requirement. The efficiency of the proposed approach was examined using LULC data for the Dakshina Kannada District of Karnataka State, India. The proposed model achieved an overall accuracy of 95.11% and a kappa coefficient of 0.92, based on the ground-truth data for 2014. The model's predictions for 2035, based on data from 2005 to 2014, revealed the following trends: Urbanization exhibited a pattern of rapid expansion and increased growth. The integrated CNN-LSTM model extracted spatial and temporal features for effectively predicting LULC changes. Infrastructure development, population density, and enhanced economic activities were the major driving factors of changes in LULC for the study region. Robust LULC change forecasting will strengthen LULC evaluations, aid in understanding complex land-use systems, and empower decision-makers to formulate effective land management strategies in the coming years. © 2004-2012 IEEE.