INTELLIGENT VEHICLE PARKING USING FUZZY-NEURAL NETWORKS

This paper analyzes the performance and practical implementation of fuzzy-neural networks for the autonomous motion of mobile robots. The designed fuzzy-neural controller is a refined version of a conventional fuzzy controller, and was trained to optimize a given cost function minimizing positioning error. It was found that the mobile robot with fuzzyneural controller presents good positioning and tracking performance for different types of desired trajectories. It was verified by computer simulation as well as experimentally using a laboratory-scale car-like robot model

NationalConference on Construction, sustainable Infrastructure and Valorization of waste-2023

Gandhi Institute of Excellent Technocrats, Ghangapatna, BBSR is organizing a conference, “National Conference on Construction, sustainable Infrastructure and Valorization of waste-2023” on 6th& 7thOctober 2023, at GIET, Ghangapatna, BBSR. The conference provides a platform for deliberations on developing solutions that mitigate the impact of infrastructure on ecology and environment. Research and case studies on challenges, underlying opportunities and innovative ideas for the development of sustainable infrastructure will be presented and discussed.https://www.interscience.in/conf_proc_volumes/1089/thumbnail.jp

National Conference on COMPUTING 4.0 EMPOWERING THE NEXT GENERATION OF TECHNOLOGY (Era of Computing 4.0 and its impact on technology and intelligent systems)

As we enter the era of Computing 4.0, the landscape of technology and intelligent systems is rapidly evolving, with groundbreaking advancements in artificial intelligence, machine learning, data science, and beyond. The theme of this conference revolves around exploring and shaping the future of these intelligent systems that will revolutionize industries and transform the way we live, work, and interact with technology. Conference Topics Quantum Computing and Quantum Information Edge Computing and Fog Computing Artificial Intelligence and Machine Learning in Computing 4.0 Internet of Things (IOT) and Smart Cities Block chain and Distributed Ledger Technologies Cybersecurity and Privacy in the Computing 4.0 Era High-Performance Computing and Parallel Processing Augmented Reality (AR) and Virtual Reality (VR) Applications Cognitive Computing and Natural Language Processing Neuromorphic Computing and Brain-Inspired Architectures Autonomous Systems and Robotics Big Data Analytics and Data Science in Computing 4.0https://www.interscience.in/conf_proc_volumes/1088/thumbnail.jp

National Conference on ‘Renewable Energy, Smart Grid and Telecommunication-2023

Theme of the Conference: “The challenges and opportunities of integrating renewable energy into the grid” The National Conference on Renewable Energy, Smart Grid, and Telecommunication - 2023 is a platform for industry experts, researchers, and policymakers to come together and explore the latest advancements and challenges in the fields of renewable energy, smart grids, and telecommunication. Conference Highlights: In-depth discussions on renewable energy technologies and innovations. Smart grid integration for a sustainable future. The role of telecommunication in advancing renewable energy solutions. Networking opportunities with industry leaders and experts. Presentation of cutting-edge research papers and case studies. Conference topics: Renewable Energy Technologies and Innovations Smart Grid Development and Implementation Telecommunication for Energy Systems Energy Storage and Grid Balancing Policy, Regulation, and Market Dynamics Environmental and Social Impacts of Renewable Energy Energy Transition and Future Outlook Integration of renewable energy into the grid Microgrids and decentralized energy systems Grid cybersecurity and data analytics IoT and sensor technologies for energy monitoring Data management and analytics in energy sector Battery storage technologies and applicationshttps://www.interscience.in/conf_proc_volumes/1087/thumbnail.jp

Mechanical and Tribological Behaviour of Natural Fiber (Eulaliopsis Binata) Reinforced Polymer Composite

Environmental awareness today motivates the researchers, worldwide on the studies of natural fiber reinforced polymer composite and cost effective option to synthetic fiber reinforced composites. The availability of natural fibers and ease of manufacturing have tempted researchers to try locally available inexpensive fibers and to study their feasibility of reinforcement purposes and to what extent they satisfy the required specifications of good reinforced polymer composite for different applications. With low cost and high specific mechanical properties, natural fiber represents a good renewable and biodegradable alternative to the most common synthetic reinforcement, i.e. glass fiber. Despite the interest and environmental appeal of natural fibers, there use is limited to non-bearing applications, due to their lower strength compared with synthetic fiber reinforced polymer composite. The stiffness and strength shortcomings of bio composites can be overcomed by structural configurations and better arrangement in a sense of placing the fibers in specific locations for highest strength performance. Accordingly, extensive studies on preparation and properties of polymer matrix composite (PMC) replacing the synthetic fiber with natural fiber like Jute, Sisal, Pineapple, Bamboo, Kenaf and Bagasse were carried out. These plant fibers have many advantages over glass fiber or carbon fiber like renewable, environmental friendly, low cost, lightweight and high specific mechanical performance. There are many potential natural resources, which India has in abundance. Most of it comes from agriculture or forest. Eulaliopsis Binata (EB) fiber (locally known as “sabai fiber”) is one of such fiber whose potential as a reinforcement material in the PMCs has not been studied to an extent that comparable to jute, banana, bamboo, sugarcane etc. This fiber plant belongs to “poacceae” family of plant kingdom, which is known for its good fiber quality. These plants are grown in the eastern part of India along with some Asian countries such as China, Nepal, Pakistan, Myanmar, Thailand, Philippines, and Malaysia etc. The eulaliopsis binata (EB) fiber plant is generally grown on waste lands and also contributes to the soil conservation of the inclined and waste lands. The fiber quality of this plant could be recognized by its existing applications of the fibers as rope, mats, carpets, sofa sets, wall hangings and other sophisticated, fashionable articles, which have proved its worth as a structural material with reasonably good mechanical properties. A fiber to be used as reinforcement material must possess cellulose, hemicellulose and lignin along with other constituents like carbon, which makes it lighter. This fiber structure consists of 52% cellulose, 27% hemicellulose and 17% lignin along with other constituents such as ash and moisture etc. The constituents of the fiber material conform the requirements to be used as a reinforcement material in polymer based composites for structural and other applications. In India, the EB plants are cultivated in various parts of India such as Odisha, Uttar Pradesh, Bihar, West Bengal, Punjab, Haryana and Himachal Pradesh. The availability of EB fiber in India is about 3, 50,000 tons out of which only 82,000 tons are used in paper industry. The EB fiber is provides 25% of total fiber raw material requirement of India for various applications. These plants once grown, provide yield for 10-15 years and the cost of cultivation per acre decreases with progressing years from 47.89 dollar from the 1st year to 5.44 dollar in the 12th year due to lower maintenance requirements. From the economic point of view, EB plantation is very economical with an average net return 323 dollars per annum from the degraded lands whose opportunity cost is almost zero. The cost of EB long fibers per kilogram varies between 5-8 Indian rupees. The EB plants are considered to be the “money plants” due to cheap and profitable cultivation throughout the year. Against this background, the present research work has been undertaken with an objective to explore the use of natural fiber Eulaliopsis Binata as a reinforcement material in epoxy base. The work presented in this dissertation involves investigation of three distinct problems of natural fiber composites: i. A study of favorable mechanical properties of Eulaliopsis Binata fiber in thermosetting matrix composite. ii. Investigation of weathering behavior of Eulaliopsis Binata fiber composites and its influence on the mechanical performance iii. An experimental investigation of tribological properties (abrasive and erosive) of Eulaliopsis Binata reinforced epoxy composite. To study the mechanical properties of the composite, different weight fractions of fiber have been taken. Usual hand-lay-up technique has been adopted for manufacturing the composite. To have a good compatibility between the fiber and matrix, chemical modification of fibers such as acetone, alkali, and benzoyl-chloride and treatments has been carried out. It was found that benzoyl-chloride treated fiber composite exhibits favorable strength and stiffness in comparison to other treatments. Moisture absorption behavior of both treated and untreated fiber composite was also carried out. The moisture absorption kinetics of the composite has also been studied. The study confirms that the Fickian’s diffusion can be used to adequately describe the moisture absorption in the composite. The EB fiber epoxy composites exhibited better abrasive wear resistance properties as compared to neat epoxy. However, it is limited to twenty weight percent in comparison to thirty weight percent for strength and modulus under all tested conditions. The formation of fibrils and debonding between fiber and matrix material due to low load transfer is the main cause of reduction in fiber content for wear analysis. To study the tribo-potential of Eulaliopsis Binata fiber, solid particle erosion behavior by air jet erosion test rig have been carried out. All these tests have been carried out as per ASTM standard. The solid particle erosion test clearly indicates that the composite behavior is semi-ductile in nature. There are other fabrication techniques available like injection moulding, compression moulding and extrusion, where the volume fraction of reinforcement can be increased. In addition, there are other chemical methods by which the fiber surface modification could be carried out. This work can be further extended to those techniques. However, the results reported here can act as a starting point for both industrial designer and researchers to design and develop polymer matrix composite components using Eulaliopsis Binata fiber as reinforcement. The whole dissertation has been divided in to seven chapters to put the analysis independent of each other as far as possible. Major works on mechanical characterization, moisture absorption characteristics, abrasive and erosive wear characteristics of EB-epoxy composite is given in chapter 3, 4, 5, and 6 respectively

Moisture Absorption Behavior and its Effect on Mechanical Properties of Orange Peel Reinforced Polymer Composites

In recent years the natural fiber reinforced composites have attracted substantial importance as a potential material for numerous purposes. The attractive features of natural fibers like jute, sisal, coir and banana have been their low cost, light weights, high specific modulus, renewability and biodegradability. Natural fillers are lignocellulosic in nature. These composites are of much importance because of their non-carcinogenic and bio-degradable nature. The natural fiber composites can be very effective materials for building and construction industry and many more. However in many cases residues from traditional crops such as rice husk or sugarcane natural fibre or from the usual processing operations of timber industries do not meet the requisites of being long fibers. Hence these can be used in other forms such as particulate and short fiber reinforcements in composites. Natural fibers also attractive reinforcements for composites as these contain about 40% cellulose, 30% hemicellulose, and 15% lignin. Keeping this in view the present work has been undertaken to develop a polymer matrix composite (epoxy resin) using orange filler particulate as reinforcement and to study its mechanical properties and environmental effects on the mechanical properties of the composites . The composites are prepared with different weight fraction of orange filler particulate. Experiments have been conducted under laboratory conditions to assess the effect of different environment such as steam, saline and natural conditions on the mechanical properties of the composites. The change in weight, volume and dimensions are studied for different environments. Shear strength of the composites was also evaluated by three point bend test as per ASTM D2245-85. Micro structural analysis was carried out to have a clear understanding of the effect of exposure to different environments on the mechanical properties of the composite samples. Keeping this in view the present work has been under taken to develop a polymer matrix composite (epoxy resin) using orange peel particulate as reinforcement and to study its moisture absorption behavior and its effect on mechanical properties. The composites are prepared with different volume fraction (weight percent) of orange peel in particulate form

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Not AvailableIn plants, GIGANTEA (GI) protein plays different biological functions including carbon and sucrose metabolism, cell wall deposition, transpiration and hypocotyl elongation. This suggests that GI is an important class of proteins. So far, the resource-intensive experimental methods have been mostly utilized for identification of GI proteins. Thus, we made an attempt in this study to develop a computational model for fast and accurate prediction of GI proteins. Ten different supervised learning algorithms i.e., SVM, RF, JRIP, J48, LMT, IBK, NB, PART, BAGG and LGB were employed for prediction, where the amino acid composition (AAC), FASGAI features and physico-chemical (PHYC) properties were used as numerical inputs for the learning algorithms. Higher accuracies i.e., 96.75% of AUC-ROC and 86.7% of AUC-PR were observed for SVM coupled with AAC + PHYC feature combination, while evaluated with five-fold cross validation. With leave-one-out cross validation, 97.29% of AUC-ROC and 87.89% of AUC-PR were respectively achieved. While the performance of the model was evaluated with an independent dataset of 18 GI sequences, 17 were observed as correctly predicted. We have also performed proteome-wide identification of GI proteins in wheat, followed by functional annotation using Gene Ontology terms. A prediction server “GIpred” is freely accessible at http://cabgrid.res.in:8080/gipred/ for proteome-wide recognition of GI proteins.Not Availabl

Adaptive Motion Detection for Image Deblurring in RTS Controller

Abstract: An Adaptive method for Image Deblurring is presented here. Processing of image data collected from both surveillance camera and on road traffic control motor vehicle camera is a big issue because often the objects are in motion and sometimes both the objects and camera are not steady. This leads to Blurring of the image and further image processing is not possible due to the degradation of received image. So Image Deblurring techniques are applied before enhancement or further processing. But it needs proper data for Deblurring like the frequency characteristics (Point Spread Function (PSF)) and Noise characteristics (Noise-to-Signal Power Ratio(NSR)). The method presented here gives the above information along with the motion information. The information about motion detection is very important because in the Deblurring process the noise estimation cannot be done without knowing actual pixels of the sensor noise present in the image. So to get a deblurred image with proper noise reduction that can be further processed in the RTS (Road Traffic & Safety) controller required information are provided sequentially according to the motion detection and Deblurring algorithm. This method uses some good Deblurring methods like Blind Deconvolution and Regularization filtering along with proper motion detections and characteristics estimations to get an image close to the true image which is sufficient for further processing

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Not AvailableCircadian rhythms regulate several physiological and developmental processes of plants. Hence, the identification of genes with the underlying circadian rhythmic features is pivotal. Though computational methods have been developed for the identification of circadian genes, all these methods are based on gene expression datasets. In other words, we failed to search any sequence-based model, and that motivated us to deploy the present computational method to identify the proteins encoded by the circadian genes. Support vector machine (SVM) with seven kernels, i.e., linear, polynomial, radial, sigmoid, hyperbolic, Bessel and Laplace was utilized for prediction by employing compositional, transitional and physico-chemical features. Higher accuracy of 62.48% was achieved with the Laplace kernel, following the fivefold cross- validation approach. The developed model further secured 62.96% accuracy with an independent dataset. The SVM also outperformed other state-of-art machine learning algorithms, i.e., Random Forest, Bagging, AdaBoost, XGBoost and LASSO. We also performed proteome-wide identification of circadian proteins in two cereal crops namely, Oryza sativa and Sorghum bicolor, followed by the functional annotation of the predicted circadian proteins with Gene Ontology (GO) terms. To the best of our knowledge, this is the first computational method to identify the circadian genes with the sequence data. Based on the proposed method, we have developed an R-package PredCRG (https:// cran.rproject. org/ web/ packa ges/ PredC RG/ index. html) for the scientific community for proteome-wide identification of circadian genes. The present study supplements the existing computational methods as well as wet-lab experiments for the recognition of circadian genes.Not Availabl