Browsing by Author "Reddy, H."

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    Body-in-White Joint Stiffness Sensitivity Analysis
    (2019) Ramachandran, A.; Reddy, H.; Chavali, T.; Hukar, A.; Somasekharan, J.
    The major objectives of a car design are reducing costs, maximizing performance, and improving fuel economy. The total mass of a car has a direct effect on all these objectives. Around 25% of the total mass of a car is typically accumulated in its body in white (BIW). Thus, reducing the total mass of the BIW while satisfying the target stiffness is of utmost importance in the early stages of design. Reducing the total mass of the BIW involves the identification of potential locations for mass reduction and stiffness improvement. The joints of the BIW are often the most critical locations that decide the overall stiffness of the BIW. Understanding the contribution of each joint toward the overall stiffness is thus of paramount importance toward improving the stiffness of the BIW.This paper describes a new approach of identifying the contribution of each joint in a BIW toward the overall stiffness of the BIW. The joints in the BIW are parametrized and Altair Optistruct [1] tool is used to find optimum value of each parameter. The contribution of different joints to the overall stiffness is different for different loads. This methodology was applied to a simplified BIW model for a combination of two load cases-static torsion and bending. The relative joint sensitivities of the joints in BIW have been identified and verified based on the results. The entire methodology has also been automated in a preprocessor to reduce the total time involved. � 2019 SAE International. All rights reserved.
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    Body-in-White Joint Stiffness Sensitivity Analysis
    (SAE International, 2019) Ramachandran, A.; Reddy, H.; Chavali, T.; Hukar, A.; Somasekharan, J.
    The major objectives of a car design are reducing costs, maximizing performance, and improving fuel economy. The total mass of a car has a direct effect on all these objectives. Around 25% of the total mass of a car is typically accumulated in its body in white (BIW). Thus, reducing the total mass of the BIW while satisfying the target stiffness is of utmost importance in the early stages of design. Reducing the total mass of the BIW involves the identification of potential locations for mass reduction and stiffness improvement. The joints of the BIW are often the most critical locations that decide the overall stiffness of the BIW. Understanding the contribution of each joint toward the overall stiffness is thus of paramount importance toward improving the stiffness of the BIW.This paper describes a new approach of identifying the contribution of each joint in a BIW toward the overall stiffness of the BIW. The joints in the BIW are parametrized and Altair Optistruct [1] tool is used to find optimum value of each parameter. The contribution of different joints to the overall stiffness is different for different loads. This methodology was applied to a simplified BIW model for a combination of two load cases-static torsion and bending. The relative joint sensitivities of the joints in BIW have been identified and verified based on the results. The entire methodology has also been automated in a preprocessor to reduce the total time involved. © 2019 SAE International. All rights reserved.
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    Robustness Analysis of EV Charging System using Random Forest Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2023) Barre, U.P.V.; Satyanarayan, S.; Reddy, H.; Pulikala, A.; Bajaj, A.
    Electric cars offer numerous benefits and are considered the future of the automobile industry. However, their worldwide adoption still needs to grow. One of the primary reasons for this delay in electrification is charge anxiety, which refers to the uncertainty customers feel when connecting the charging cable to the car. To address this issue, this study analyses the performance of the charging system using a machine learning model to identify sensitive signals that influence the charging process and can cause successful charging or charge termination. The analysis will also help to define robust operating regions where the charging component can reliably function, regardless of external conditions. This study's findings will provide insights into electric vehicle charging behavior with the supply station. © 2023 IEEE.
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    Text-mining-based Fake News Detection Using Ensemble Methods
    (Chinese Academy of Sciences, 2020) Reddy, H.; Raj, N.; Gala, M.; Annappa, A.
    Social media is a platform to express one’s views and opinions freely and has made communication easier than it was before. This also opens up an opportunity for people to spread fake news intentionally. The ease of access to a variety of news sources on the web also brings the problem of people being exposed to fake news and possibly believing such news. This makes it important for us to detect and flag such content on social media. With the current rate of news generated on social media, it is difficult to differentiate between genuine news and hoaxes without knowing the source of the news. This paper discusses approaches to detection of fake news using only the features of the text of the news, without using any other related metadata. We observe that a combination of stylometric features and text-based word vector representations through ensemble methods can predict fake news with an accuracy of up to 95.49%. © 2020, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature.
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    Users roles identification on online crowdsourced Q&A platforms and encyclopedias: a survey
    (Springer, 2022) Saxena, A.; Reddy, H.
    Online informal learning and knowledge-sharing platforms, such as Stack Exchange, Reddit, and Wikipedia have been a great source of learning. Millions of people access these websites to ask questions, answer the questions, view answers, or check facts. However, one interesting question that has always attracted the researchers is if all the users share equally on these portals, and if not then how the contribution varies across users, and how it is distributed? Do different users focus on different kinds of activities and play specific roles? In this work, we present a survey of users’ social roles that have been identified on online discussion and Q&A platforms including Usenet newsgroups, Reddit, Stack Exchange, and MOOC forums, as well as on crowdsourced encyclopedias, such as Wikipedia, and Baidu Baike, where users interact with each other through talk pages. We discuss the state of the art on capturing the variety of users roles through different methods including the construction of user network, analysis of content posted by users, temporal analysis of user activity, posting frequency, and so on. We also discuss the available datasets and APIs to collect the data from these platforms for further research. The survey is concluded with open research questions. © 2021, The Author(s).
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    WideCaps: a wide attention-based capsule network for image classification
    (Springer Science and Business Media Deutschland GmbH, 2023) Pawan, S.J.; Sharma, R.; Reddy, H.; Vani, M.; Rajan, J.
    The capsule network is a distinct and promising segment of the neural network family that has drawn attention due to its unique ability to maintain equivariance by preserving spatial relationships among the features. The capsule network has attained unprecedented success in image classification with datasets such as MNIST and affNIST by encoding the characteristic features into capsules and building a parse-tree structure. However, on datasets involving complex foreground and background regions, such as CIFAR-10 and CIFAR-100, the performance of the capsule network is suboptimal due to its naive data routing policy and incompetence in extracting complex features. This paper proposes a new design strategy for capsule network architectures for efficiently dealing with complex images. The proposed method incorporates the optimal placement of the novel wide bottleneck residual block and squeeze and excitation Attention Blocks into the capsule network upheld by the modified factorized machines routing algorithm to address the defined problem. This setup allows channel interdependencies at almost no computational cost, thereby enhancing the representation ability of capsules on complex images. We extensively evaluate the performance of the proposed model on the five publicly available datasets, namely the CIFAR-10, Fashion MNIST, Brain Tumor, SVHN, and the CIFAR-100 datasets. The proposed method outperformed the top-5 capsule network-based methods on Fashion MNIST, CIFAR-10, SVHN, Brain Tumor, and gave a highly competitive performance on the CIFAR-100 datasets. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.