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4th International Conference on advanced Materials and Modern Manufacturing, ICAMMM 2021

作者： Shaisundaram, V.S. Chandrasekaran, M. Hariharan, C. Shanmugam, Mohanraj Sujith, S. Praveen Kumar, K.J. Department of Automobile and Mechanical Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Automobile Engineering Easwari Engineering College Chennai India Department of Materials Engineering and Nanotechnology Politecnico di Milano Milan Italy Engineering and Industrial Services Tata Consultancy Services Chennai India

ISBN: (纸本)9789811902437

Since the commercialization of automobiles in the 1960s, vehicles have been running on conventional fuels such as gasoline or diesel. Major automobile accidents, especially passenger vehicles that lead to explosions and fatalities are due to frontal impact collisions that cause the breaking of fuel lines. This leads to chemical reactions of the fuel in the fuel lines with the heated atmospheric air. The result is an intense explosion that causes, in most cases, the death of the passengers, if not major injuries. In this work, the Automatic Flame Suppression System for Four Wheeler Passenger Vehicles reduces the chances of fatalities during such explosions by the high-pressure application of commonly used coolant LN2. The application of LN2 using a sensor-based actuator mechanism will prevent the reaction between fuel and the high-temperature air of the surroundings that triggers the explosion. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Actuator mechanism Fuel lines Fuel reactions Sensors

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Prediction of Uterine Fibroids from Ultrasound Images

Prediction of Uterine Fibroids from Ultrasound Images

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Chandrasekaran. R Parthasarathy. K Gowtham. R U. Mutheeswaran Department of Biomedical Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai Department of EEE Indian Naval Academy Kannur

Uterine fibroids are generally benign in nature and are present in the muscular walls of female reproductive system. They affect approximately 20-25% of women during their childbearing years when estrogen hormone secretion is high. The fibroids which are present in the muscular walls can create infertility and serious health issues in females, making it a significant health concern. Ultrasonic imaging, which uses non-ionizing radiation, is the preferred method for diagnosing and monitoring pregnant women. This imaging technique has been successfully used for detecting liver cancer, prostate cancer, and other medical conditions. A new study proposes an automated detection method for uterine fibroids using a neural network classifier and different image features. The proposed method uses a convolutional neural network (CNN) classifier to diagnose between fibroid uterus images and normal images. The input image passes through multiple layers of the CNN, which identifies features and recognizes the image to produce a classification result. The accuracy of the method is validated by normality, abnormality, and accuracy. However, it is important to note that automated detection methods should not be used as the sole diagnostic tool for uterine fibroids. Medical expertise and additional diagnostic tools are necessary for proper diagnosis and treatment. Further research is needed to improve the accuracy and reliability of the proposed method.

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A novel Approach for optimizing the crop yield by considering climatic and soil features through fusion of Machine Learning and Deep Learning algorithms

A novel Approach for optimizing the crop yield by considerin...

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International Conference on Computing and Networking technology (ICCNT)

作者： K. Suganthavalli C. Meenakshi K Parvathy Department of Computer Science Vels Institute of Science Technology and Advanced Studies(VISTAS) Pallavaram Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The agricultural sector is the scientific and artistic cultivation of plants and animals. Agriculture has an important role in India’s economy, which covers a significant 60.45% of the country’s land and stands as the world’s second-largest agricultural sector, driving economic growth through its diverse produce and associated industries. Soil composition really matters for growing stuff. Elements like Nitrogen, Phosphorous, & Potassium play a big role. The way you rotate crops, soil moisture, and even temperature all affect how well things grow. Rainfall is important too. Research has shown that using machine learning can help figure out the best crops to grow. They utilize methods that include Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) for this. The study also looks into deep learning methods to make the model even better. In addition to predicting crop yields, it also provides detailed information on the required amounts of soil elements and their associated costs. The new model offers superior precision compared to the current one. The system examines the provided data and assists farmers in forecasting a crop, which subsequently aids in achieving financial gains. The prediction of a suitable yield takes into account the meteorological and soil parameters of the area. This Python-based system utilizes advanced algorithms to optimize decision-making, forecasting the most lucrative outcomes in various situations while streamlining costs and boosting productivity. The study employs Support Vector Machines (SVM) as an example of Machine Learning (ML) method, while Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used as Deep Learning (DL) techniques.

关键词： Deep learning Support vector machines Recurrent neural networks Machine learning algorithms Costs Soil moisture Prediction algorithms Forecasting Long short term memory Crop yield

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Minimal prime ideals in seminearrings 2

Minimal prime ideals in seminearrings

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2nd International Conference on Mathematical Modeling and Computational Methods in Science and engineering, ICMMCMSE 2020

作者： Senthil, S. Perumal, R. Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai-117 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

This paper deals with the properties of ideals in seminearrings with special reference to right duo seminearrings. We formulate the concept of prime ideal and minimal prime ideal in a seminearring. In the case of Noetherian right duo seminearring, we derive some additional properties. © Published under licence by IOP Publishing Ltd.

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Decoding Respiratory Signals from LFP in Olfactory Bulb based on ConvLSTM Framework 7

Decoding Respiratory Signals from LFP in Olfactory Bulb base...

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7th International Conference on Biological Information and Biomedical engineering, BIBE 2024

作者： Xu, Haoze Yu, Bowen Yu, Chaonan Xu, Kedi Qiushi Academy for Advanced Studies Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory for Biomedical Engineering of Ministry of Education School of Biomedical Engineering and Instrument Science Zhejiang University China Nanhu Brain-computer interface institute Hangzhou311100 China State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9783800762910

Respiratory signals are crucial for decoding olfactory neural circuits and understanding respiration-entrained local field potential (LFP) rhythms. Acquiring high-precision respiratory signals often requires additional equipment, increasing the experimental burden, particularly in free-moving paradigms. However, within the current neuroscience paradigm, we have not yet found a non-invasive method for obtaining the respiratory signals of laboratory animals. To minimize surgical trauma to experimental animals, this paper introduces a ConvLSTM-based decoding method, which uses the LFP from the olfactory bulb (OB) region of animals to accurately decode respiratory signals. After validating the effectiveness of the ConvLSTM method through comparative experiments, we further investigated the factors influencing the model's decoding performance. The Gamma frequency band exhibited the best decoding results, with a mean absolute error (MAE) of 0.16±0.09, which may be related to the causal direction from respiratory signals to Gamma rhythms. Additionally, the ConvLSTM model demonstrated strong trend prediction capabilities in multi-sequence point predictions and forward predictions, despite a slight decline in single-point prediction accuracy. Extensive validation on publicly available datasets indicates that the ConvLSTM-based respiratory decoding approach is highly applicable, offering a non-invasive alternative for respiratory signal acquisition in animal studies. © VDE VERLAG GMBH.

关键词： Noninvasive medical procedures

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Simultaneous Fault Diagnosis for Sensor and Railway Point Machine for Autonomous Rail System 27

Simultaneous Fault Diagnosis for Sensor and Railway Point Ma...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Hu, Xiaoxi Zhang, Xiaohan Chen, Fei Liu, Zongyang Liu, Jin Tan, Lei Tang, Tao Polytechnic Institute Zhejiang University Hangzhou310015 China Wuhan University State Key Laboratory of Water Resources Engineering and Management Wuhan430072 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China Institute for Transport Studies University of Leeds LeedsLS2 9JT United Kingdom Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Municipal Engineering Research Institute Beijing100037 China

ISBN: (纸本)9798331505929

Since the safe and reliable behavior of a Railway Point Machine (RPM) is definitely pivotal for rail transportation, scholars and engineers have studied a considerable number of algorithms for RPM fault diagnosis via many kinds of sensor. However, few scholars have considered the possibility that sensors can also experience faults, which cannot be ignored in practical railway applications. To fill the gap, we propose an end-to-end deep learning network named Simultaneous Fault Diagnosis Network (SFDNet) for the simultaneous fault diagnosis for sensor and RPM. Our approach includes Information Aggregate and Update Module (IAUM) and Fault Diagnosis Module (FDM). The IAUM module leverages the concept of Spatial Attention Mechanism (SAM) and Channel Attention Mechanism (CAM) to effectively filter and aggregate information from diverse sensors. It enhances the salience of normal sensor observations while attenuating the influence of anomalous signals, thereby enabling the resultant features to more accurately capture the operational state of the RPMs. The features yielded by SAM are directly employed for sensor fault diagnosis. The FDM integrates Bidirectional Gated Recurrent Units (BiGRUs) and Time-series Feature Pyramid Network (TFPN) to respectively extract and integrate long-term dependencies and multi-scale features, enhancing fault diagnosis in RPM by capturing comprehensive temporal patterns and correlations. Lastly, ablation experiments and comparison studies shows the superiority of our SFDNet. © 2024 IEEE.

关键词： Railroads

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Analysis of Improving Sales Process Efficiency with Salesforce Industries CPQ in CRM 7th

Analysis of Improving Sales Process Efficiency with Salesfor...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Pathak, Pritesh Pal, Souvik Maity, Saikat Jeyalaksshmi, S. Adhikari, Saurabh Akila, D. Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Management Information Systems Saveetha College of Liberal Arts And Sciences Saveetha Institute of Medical And Technical Sciences Chennai India Kolkata India Department of Information Technology Vels Institute of Science Technology and Advanced Studies Chennai India School of Engineering Swami Vivekananda University Kolkata India Department of Computer Applications Saveetha College of Liberal Arts And Sciences Saveetha Institute of Medical And Technical Sciences Chennai India

ISBN: (纸本)9789819995615

The successful execution of Configure, Price, Quote (CPQ) protocols is of utmost importance for businesses that handle intricate product portfolios within their sales processes. The objective of these approaches is to optimise the generation of competitive quotations by leveraging data from diverse business systems, leading to a decrease in processing time and enhancement of operational efficiency. Salesforce's Industries CPQ for Communications Cloud, previously referred to as Vlocity CPQ, assumes a crucial function within the Salesforce platform by facilitating the automation of the quotation creation process for sales teams. Designed specifically for the communications industry, this solution enables communications service providers (CSPs) to effectively provide a wide array of products and services to their customers in a streamlined manner. By implementing Industries CPQ, Communication Service Providers (CSPs) have the potential to save expenses associated with customisation and maintenance, while also accelerating their time-to-market. In order to assess the practical ramifications of Salesforce Industries CPQ, the researcher intends to deploy the aforementioned system within ABC Insurance Company. The objective of the study is to evaluate the program's influence on sales efficiency and profitability by conducting interviews with a targeted cohort of employees who have employed the software in their sales endeavours. Furthermore, the study will include hypothesis testing to analyse the timing of quotation production across various policy configurations utilising Salesforce Industries CPQ, aiming to provide further validation of its benefits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

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Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry

arXiv

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arXiv 2024年

作者： Gan, Mengjie Lian, Penglong Su, Zhiheng Zhang, Jiyang Huang, Jialong Wang, Benhao Zou, Jianxiao Fan, Shicai Shenzhen Institute of Advanced Studies University of Electronic Science and Technology of China Guangdong Shenzhen518000 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu China China

Industrial equipment fault diagnosis often encounter challenges such as the scarcity of fault data, complex operating conditions, and varied types of failures. Signal analysis, data statistical learning, and conventional deep learning techniques face constraints under these conditions due to their substantial data requirements and the necessity for transfer learning to accommodate new failure modes. To effectively leverage information and extract the intrinsic characteristics of faults across different domains under limited sample conditions, this paper introduces a fault diagnosis approach employing Multi-Scale Graph Convolution Filtering (MSGCF). MSGCF enhances the traditional Graph Neural Network (GNN) framework by integrating both local and global information fusion modules within the graph convolution filter block. This advancement effectively mitigates the over-smoothing issue associated with excessive layering of graph convolutional layers while preserving a broad receptive field. It also reduces the risk of overfitting in few-shot diagnosis, thereby augmenting the model's representational capacity. Experiments on the University of Paderborn bearing dataset (PU) demonstrate that the MSGCF method proposed herein surpasses alternative approaches in accuracy, thereby offering valuable insights for industrial fault diagnosis in few-shot learning scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Failure analysis

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A novel fault localization with data refinement for hydroelectric units

arXiv

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arXiv 2024年

作者： Huang, Jialong Song, Junlin Lian, Penglong Gan, Mengjie Su, Zhiheng Wang, Benhao Zhu, Wenji Pu, Xiaomin Zou, Jianxiao Fan, Shicai Shenzhen Institute of Advanced Studies University of Electronic Science and Technology of China Guangdong Shenzhen518000 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Due to the scarcity of fault samples and the complexity of non-linear and non-smooth characteristics data in hydroelectric units, most of the traditional hydroelectric unit fault localization methods are difficult to carry out accurate localization. To address these problems, a sparse autoencoder (SAE)-generative adversarial network (GAN)-wavelet noise reduction (WNR)manifold-boosted deep learning (SG-WMBDL) based fault localization method for hydroelectric units is proposed. To overcome the data scarcity, a SAE is embedded into the GAN to generate more high-quality samples in the data generation module. Considering the signals involving non-linear and non-smooth characteristics, the improved WNR which combining both soft and hard thresholding and local linear embedding (LLE) are utilized to the data preprocessing module in order to reduce the noise and effectively capture the local features. In addition, to seek higher performance, the novel Adaptive Boost (AdaBoost) combined with multi deep learning is proposed to achieve accurate fault localization. The experimental results show that the SG-WMBDL can locate faults for hydroelectric units under a small number of fault samples with non-linear and non-smooth characteristics on higher precision and accuracy compared to other frontier methods, which verifies the effectiveness and practicality of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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The missing base molecules in atmospheric acid-base nucleation

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National Science Review 2022年第10期9卷 196-208页

作者： Runlong Cai Rujing Yin Chao Yan Dongsen Yang Chenjuan Deng Lubna Dada Juha Kangasluoma Jenni Kontkanen Roope Halonen Yan Ma Xiuhui Zhang Pauli Paasonen Tuukka Pet?j? Veli-Matti Kerminen Yongchun Liu Federico Bianchi Jun Zheng Lin Wang Jiming Hao James N.Smith Neil M. Donahue Markku Kulmala Douglas R.Worsnop Jingkun Jiang State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Institute for Atmospheric and Earth System Research/Physics Faculty of Science University of Helsinki Aerosol and Haze Laboratory Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical Technology Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Laboratory of Atmospheric Chemistry Paul Scherrer Institute Center for Joint Quantum Studies and Department of Physics School of Science Tianjin University Key Laboratory of Cluster Science Ministry of Education of China School of Chemistry and Chemical Engineering Beijing Institute of Technology Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) Department of Environmental Science and Engineering Fudan University Chemistry Department University of California Center for Atmospheric Particle Studies Carnegie Mellon University Department of Chemistry Carnegie Mellon University Aerodyne Research Inc.

Transformation of low-volatility gaseous precursors to new particles affects aerosol number concentration,cloud formation and hence the *** clustering of acid and base molecules is a major mechanism driving fast nucleation and initial growth of new particles in the ***,the acid-base cluster composition,measured using state-of-the-art mass spectrometers,cannot explain the measured high formation rate of new *** we present strong evidence for the existence of base molecules such as amines in the smallest atmospheric sulfuric acid clusters prior to their detection by mass *** demonstrate that forming（H2SO4）1（amine）1is the rate-limiting step in atmospheric H2SO4-amine nucleation and the uptake of（H2SO4）1（amine）1is a major pathway for the initial growth of *** proposed mechanism is very consistent with measured new particle formation in urban Beijing,in which dimethylamine is the key base for H2SO4nucleation while other bases such as ammonia may contribute to the growth of larger *** findings further underline the fact that strong amines,even at low concentrations and when undetected in the smallest clusters,can be crucial to particle formation in the planetary boundary layer.

关键词： new particle formation acid-base nucleation aerosol polluted urban environment

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