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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Frontiers of Chemical science and engineering 2023年第6期17卷 759-771页

作者： Jiannan Zhu Vladimir Mahalec Chen Fan Minglei Yang Feng Qian Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Department of Chemical Engineering McMaster UniversityHamiltonOntario L8S 4L8Canada Engineering Research Center of Process System Engineering Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

This work introduces a deep-learning network,i.e.,multi-input self-organizing-map ResNet(MISR),for modeling refining units comprised of two reactors and a separation *** model is comprised of self-organizing-map and the neural network *** self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network *** the neural network part,residual blocks enhance the convergence and accuracy,ensuring that the structure will not be overfitted *** of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the *** results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model,thus leading to more accurate optimization of the hydrocracker ***,the MISR model has smoother error convergence than the previous *** operating conditions have been determined via multi-round-particle-swarm and differential evolution *** experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.

关键词： hydrocracking convolutional neural networks self-organizing map deep learning data-driven optimization

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Coherence enhancement via a diamond-graphene hybrid for nanoscale quantum sensing

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National science Review 2025年第5期12卷 99-105页

作者： Yucheng Hao Zhiping Yang Zeyu Li Xi Kong Wenna Tang Tianyu Xie Shaoyi Xu Xiangyu Ye Pei Yu Pengfei Wang Ya Wang Zhenhua Qiao Libo Gao Jian-Hua Jiang Fazhan Shi Jiangfeng Du CAS Key Laboratory of Microscale Magnetic Resonance and School of Physical Sciences University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China School of Biomedical Engineering and Suzhou Institute for Advanced Research University of Science and Technology of China CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics and Department of Physics University of Science and Technology of China The International Center for Quantum Design of Functional Materials University of Science and Technology of China National Laboratory of Solid State Microstructures and Department of Physics Nanjing University Hefei National Laboratory University of Science and Technology of China School of Physical Science and Technology &Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Anhui Province Key Laboratory of Scientific Instrument Development and Application University of Science and Technology of China Institute of Quantum Sensing and School of Physics Zhejiang University

Quantum coherence serves as a crucial quantum resource for achieving high-sensitivity quantum *** of its long coherence time at room temperature, the nitrogen-vacancy(NV) center has emerged as a quantum sensor in various fields in recent years. While nanoscale quantum sensing at room temperature has been demonstrated for NV centers, noise on the diamond surface severely limits its further development at a higher sensitivity. Here, we utilize the hybridization between graphene and diamond surfaces to directly deplete surface unpaired electron spins, thereby achieving roughly two-fold enhancement in *** the combination of electron spin resonance spectra and first-principle calculations, we explain that this phenomenon arises from a significant reduction in electron spin density on the diamond surface due to interface electron orbital hybridization. Our research presents a new approach for solid-state quantum sensors to reach the desired sensitivity level and offers a new pathway for future studies on material interfaces.

关键词： quantum coherence nanoscale graphene hybridization NV center

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Variable stiffness design optimization of fiber-reinforced composite laminates with regular and irregular holes considering fiber continuity for additive manufacturing

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Chinese Journal of Aeronautics 2025年第3期38卷 334-354页

作者： Yi LIU Zunyi DUAN Chunping ZHOU Yuan SI Chenxi GUAN Yi XIONG Bin XU Jun YAN Jihong ZHU School of Mechanics Civil Engineering&ArchitectureNorthwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China Key Lab for Airborne Hi-performance Electro-magnetic Window RISACJinan 130012China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsInternational Research Center for Computational MechanicsDalian University of TechnologyDalian 116024China State IJR Center of Aerospace Design and Additive Manufacturing MIIT Lab of Metal Additive Manufacturing and Innovative DesignNorthwestern Polytechnical UniversityXi’an 710072China

Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel, and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization (NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples cons

关键词： Variable stiffness composite laminates Discrete material interpolation scheme Normal distribution fiber optimization Discrete fiber continuous filtering strategy Additive manufacturing of composite laminates

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Output Linearization of Single-Input Single-Output Fuzzy system to Improve Accuracy and Performance

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Computers, Materials & Continua 2023年第5期75卷 2413-2427页

作者： Salah-ud-din Khokhar QinKe Peng Muhammad Yasir Noor Department of Automation Science and Technology(System Engineering Institute) School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anShaanxi710049China Department of Computer Science G.C.UniversityLahore54000PunjabPakistan Department of Physics(Electronics) G.C.UniversityLahore54000PunjabPakistan

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s *** a variety of non-linear techniques,a SISO FS is *** results of FS experiments conducted in comparable conditions are then *** simulated results and the results of the experimental setup agree fairly *** findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control *** using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of *** proposed fuzzy system performed far better than other modern days approaches available in the literature.

关键词： Mean absolute percentage error membership functions relative error fuzzy system

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Bayesian Estimation of Multiple Covariate of Autoregressive (MC-AR) Model

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Annals of Data science 2024年第4期11卷 1291-1301页

作者： Kumar, Jitendra Kumar, Ashok Agiwal, Varun Department of Statistics Central University of Rajasthan NH-8 Bandersindri Rajasthan Ajmer India Department of Applied Science and Humanities School of Engineering & Science MIT Art Design & Technology University Loni-Kalbhor Pune India Indian Institute of Public Health Telangana Hyderabad India

In present scenario, handling covariate/explanatory variable with the model is one of most important factor to study with the models. The main advantages of covariate are it’s dependency on past observations. So, study variable is modelled after explaining both on own past and past and future observation of covariates. Present paper deals estimation of parameters of autoregressive model with multiple covariates under Bayesian approach. A simulation and empirical study is performed to check the applicability of the model and recorded the better results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Inference engines

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Forecasting the Indian Financial Markets with LSTM and Price Indicators 2nd

Forecasting the Indian Financial Markets with LSTM and Price...

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2nd International Conference on Signal and Data Processing, ICSDP 2022

作者： Kumaria, Aayush Rajkar, Ajinkya Raut, Aniket Nair, Rashmi S. Department of Computer Science and Engineering MIT School of Engineering MIT Arts Design and Technology University Pune412201 India

ISBN: (纸本)9789819914098

The stock market in India is exceedingly volatile, with an infinite series of aspects influencing the financial market's trajectory and fluctuations;therefore, forecasting the upswing and decline is a challenging task. Prediction, often known as forecasting, is a significant factor in buying and selling equity market. While a combination of fundamental and technical methodologies can answer the question of "in which direction would the stock price move," it cannot answer the question of "by what margin would the movement occur?" That is where prediction comes into the picture. Despite the numerous answers, this study may reliably conclude that forecasting the stock market continues to be a problematic task impacted by a plethora of variables. The purpose of this study is to illustrate how recurrent neural networks may be utilized in finance to anticipate a stock's closing price and assess sentiment in real-time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Financial markets

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Real-Time Facial Recognition system for Secure College Bus Transport using Deep Learning Techniques 3

Real-Time Facial Recognition System for Secure College Bus T...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Kavitha, M.N. Saranya, S.S. Pragatheeswari, E. Priya, N. Kaviya Ranjith, S.R. Vanchivel, N. Rahul Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Kongu Engineering College Department of Csd Tamil Nadu India

ISBN: (纸本)9798331509675

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability. © 2025 IEEE.

关键词： Students

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Enhancing tensile properties of pulsed CMT–MIG welded high strength AA2014-T6 alloy joints: Effect of post weld heat treatment

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International Journal of Lightweight Materials and Manufacture 2024年第2期7卷 344-352页

作者： Rajendran, C. Sonar, Tushar Ivanov, Mikhail Sandeep, Ch. Shanthi, C. Gurajala, Naveen Kumar Balachandar, K. Xu, Jinyang Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore641008 India Chelyabinsk454080 Russia Department of Mechanical Engineering Institute of Aeronautical Engineering Telangana Hyderabad500043 India Department of Physics Sona College of Technology Tamil Nadu Salem636005 India Mechanical Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Department of Mechatronics Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China

The main objective of this investigation is to study the effect of post weld heat treatments (PWHTs) on tensile properties, hardness, and microstructure of pulsed CMT–MIG (cold metal transfer arc–metal inert gas) welded AA20214-T6 aluminum alloy joints. The welded joints were subjected to PWHT of artificial aging (AA), solution annealing treatment (ST) and ST + aging (STA). The tensile properties and microhardness of joints were evaluated. The microstructure of joints was studied using optical microscope (OM) and transmission electron microscope (TEM). The fractured surface of tensile specimens was analyzed using scanning electron microscope (SEM). Results showed that the tensile properties and hardness of as welded and PWHT joints are inferior compared to base metal (BM). This mainly refers to the microstructural heterogeneity in different regions of joints and softening of heat affected zone (HAZ) induced by the weld thermal cycle. The PWHTs of AA and ST did not show significant effect on tensile strength and hardness of AW joints. However, the slight reduction in elongation was observed in AA and ST joints. The STA joints showed higher joint efficiency of 71.6%, than other joints by compromising on the elongation. This refers to the greater precipitation of hardening precipitates in STA joints compared to AA and ST joints. It exhibited the higher tensile and yield strength of 326 MPa and 266 MPa and the lowest elongation of 3.8%. The STA joints showed 28.35%, 38.28% and 57.77% reduction in tensile strength, yield strength and elongation compared to BM respectively. All the tensile specimens of joints failed in HAZ owing to the lower hardness. This refers to dissolution of precipitates in HAZ. However, the HAZ softening is less severe in STA joints than AW, AA, and ST joints. © 2023 The Authors

关键词： Microstructure

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Hybrid Machine Learning Model for Predicting Damage Scale in Sunflower Leaves affected by Aster Yellow Disease 2

Hybrid Machine Learning Model for Predicting Damage Scale in...

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2nd IEEE International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2024

作者： Jain, Anuj Kumar Pradeep Ghantasala, G.S. Bhandari, Rahul Kaur, Gaganpreet Rajesh Sharma, R. Sungheetha, Akey Chandigarh University Department of Computer Science and Engineering Punjab Mohali India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350354461

Through this work a thorough summary of a predictive model specifically designed to evaluate Aster Yellows infections in sunflower leaves is presented. Exhibiting remarkable accuracy are the model's performance measures, which span from 97.74% to 97.89% for various Damage Scale categories and include precision, recall, and F1-Score. These measurements demonstrate how well the model performs in accurately categorizing cases within each group and highlight how consistently it measures the severity of Aster Yellows disease. The support values clarify the significant representation of cases on every Damage Scale, demonstrating the model's resilience to a variety of situations. This work trains and evaluates the model using the 6880-picture dataset, yielding an astonishing 97.81% overall accuracy, recorded repeatedly at 97.82% on average due to the high averages (of macro, weighted, or micro numbers within each kind). It not only shows high performance across the dataset but also its consistency across Damaged Scales is further supported. And not just the model's reliability, modest variations in support rates also increase its dependability: highlighting its capacity to manage multiple diverse Aster Yellows infection scenarios. Through the model, they can clearly distinguish between different levels of damage with an accuracy of 99 percent overall, which betokens its potential application in precision farming. Now, thanks to these compelling test numbers, farmers and agricultural experts will no longer have to guess at how to distinguish and treat sunflower plants suffering from Aster Yellows infection. In summary: The abstract deftly depicts this model's quantitative performance, general versatility, and possible pragmatic utility, making it a bright spot in the history of precision farming. © 2024 IEEE.

关键词： Plant diseases

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Recession Forecasting: Domain-Specific Feature Selection Methodology 1

Recession Forecasting: Domain-Specific Feature Selection Met...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Katale, Tejaswini Hase, Jayashri Chotrani, Aakash Ozarde, Sakshi Vishwakarma Institute of Technology Department of Computer Engineering Pune India K. K Wagh Institute of Engineering and Research Department of Computer Science and Design Nashik India LDRP Institute of Technology and Research Department of Information Technology Gandhinagar India

ISBN: (纸本)9798331518943

Existing feature selection algorithms often choose features based on their individual importance and consider them as independent entities, which may overlook underlying interrelationships amongst the features. This may result in overfitting because only highly correlated features are selected without considering their collective contribution. Hence it is crucial to take into account both individual feature importance and feature interactions. To mitigate this issue, this research paper proposes a Novel Methodology for Domain Wise feature selection approach which is capable of identifying the interrelationships by focusing on Domain-Wise feature selection. It ensures that correlated and similar features are considered together. It is implemented on a Recession Dataset consisting of 293 observations and 45 features. It groups similar features in 8 domains and further arranges them in descending order of Average Correlation values. For each mini dataset it considers only those data points which are nearest to the mean of that domain and merges all unique observations from these domains and creates a new dataset. Further applying Preprocessing steps - Outlier Elimination, Up sampling and Feature Selection. This paper also discusses how the order of preprocessing can impact the overall result of the evaluation metrics. The efficacy of the algorithm is checked by the accuracy scores of Static Probit Model-98% and Vector Auto Regression-88%. Therefore, by considering both the significance of individual features and the relationships between them the suggested approach successfully addresses the shortcomings of traditional feature selection algorithms. © 2024 IEEE.

关键词： Feature Selection

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