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Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

International Journal of Minerals,Metallurgy and Materials 2024年第1期31卷 106-117页

作者： Xin Shao Qing Liu Zicheng Xin Jiangshan Zhang Tao Zhou Shaoshuai Li State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China Engineering Research Center of MES Technology for Iron&Steel Production Ministry of EducationBeijing 100083China Laiwu Iron and Steel Group Yinshan Section Steel Co. Ltd.Jinan 271104China

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

关键词： basic oxygen furnace oxygen consumption oxygen blowing time oxygen balance mechanism deep neural network hybrid model

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Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

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Journal of Systems Engineering and Electronics 2024年第2期35卷 495-508页

作者： YANG Luyi LI Haiyang ZHANG Jin ZHU Yuehe College of Aerospace Science and Engineering National University of Defense TechnologyChangsha 410073China China Astronauts Research and Training Center Beijing 100094China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions Changsha 410073China

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

关键词： manned lunar mission free return orbit reachable domain(RD) deep neural network computation efficiency

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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The Short-Term Prediction ofWind Power Based on the Convolutional Graph Attention deep neural network

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Energy Engineering 2024年第2期121卷 359-376页

作者： Fan Xiao Xiong Ping Yeyang Li Yusen Xu Yiqun Kang Dan Liu Nianming Zhang State Grid Hubei Electric Power Research Institute Wuhan430077China College of Energy and Electrical Engineering Hohai UniversityNanjing210098China

The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large ***,wind power forecasting plays a key role in improving the safety and economic benefits of the power *** paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm *** on the graph attention network and attention mechanism,the method extracts spatial-temporal characteristics from the data of multiple wind ***,combined with a deep neural network,a convolutional graph attention deep neural network model is ***,the model is trained with the quantile regression loss function to achieve the wind power deterministic and probabilistic prediction based on multi-wind farm spatial-temporal data.A wind power dataset in the *** taken as an example to demonstrate the efficacy of the proposed *** with the selected baseline methods,the proposed model achieves the best prediction *** point prediction errors(i.e.,root mean square error(RMSE)and normalized mean absolute percentage error(NMAPE))are 0.304 MW and 1.177%,*** the comprehensive performance of probabilistic prediction(i.e.,con-tinuously ranked probability score(CRPS))is ***,the significance of multi-wind farm data and spatial-temporal feature extraction module is self-evident.

关键词： Format wind power prediction deep neural network graph attention network attention mechanism quantile regression

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network Security Enhanced with deep neural network-Based Intrusion Detection System

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Computers, Materials & Continua 2024年第7期80卷 1457-1490页

作者： Fatma S.Alrayes Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan Jehad Saad Alqurni Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning deep-Learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

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Nuclear mass predictions based on a deep neural network and finite-range droplet model(2012)

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Chinese Physics C 2024年第2期48卷 112-123页

作者：姚道驄梁豪兆李曉菁 Department of Physics The University of Hong KongHong Kong 999077China Department of Physics Graduate School of ScienceThe University of TokyoTokyo 113-0033Japan Interdisciplinary Theoretical and Mathematical Sciences Program(iTHEMS) RIKENWako 351-0198Japan

A neural network with two hidden layers is developed for nuclear mass prediction,based on the finiterange droplet model(FRDM12).Different hyperparameters,including the number of hidden units,choice of activation functions,initializers,and learning rates,are adjusted explicitly and *** resulting mass predictions are achieved by averaging the predictions given by several different sets of hyperparameters with different regularizers and seed *** can provide not only the average values of mass predictions but also reliable estimations in the mass prediction *** overall root-mean-square deviations of nuclear mass are reduced from 0.603 MeV for the FRDM12 model to 0.200 MeV and 0.232 MeV for the training and validation sets,respectively.

关键词： nuclear mass machine learning deep neural network

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deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis

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Journal of Systems Engineering and Electronics 2022年第1期33卷 233-246页

作者： KULEVOME Delanyo Kwame Bensah WANG Hong WANG Xuegang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of *** likelihood of failure has the propensity of increasing under prolonged operation and varying working ***, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating *** variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault *** this paper, the efficacy and the leverage of a pre-trained convolutional neural network(CNN) is harnessed in the implementation of a robust fault classification *** the absence of sufficient data, this method has a high-performance ***, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a *** raw vibration data are strategically segmented and transformed into two representations which are trained separately and *** proposed approach is carried out on bearing vibration data and shows high-performance *** addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator(HI) under varying operating conditions for a given fault condition.

关键词： bearing failure deep neural network fault classification health indicator prognostics and health management

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deep neural network with Strip Pooling for Image Classification of Yarn-Dyed Plaid Fabrics

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Computer Modeling in Engineering & Sciences 2022年第3期130卷 1533-1546页

作者： Xiaoting Zhang Weidong Gao Ruru Pan School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China College of Textile and Science Engineering Jiangnan UniversityWuxi214122China

Historically,yarn-dyed plaid fabrics(YDPFs)have enjoyed enduring popularity with many rich plaid patterns,but production data are still classified and searched only according to production *** process does not satisfy the visual needs of sample order production,fabric design,and stock *** study produced an image dataset for YDPFs,collected from 10,661 fabric *** authors believe that the dataset will have significant utility in further research into *** neural networks,such as VGG,ResNet,and DenseNet,with different hyperparameter groups,seemed themost promising tools for the *** paper reports on the authors’exhaustive evaluation of the YDPF *** an overall accuracy of 88.78%,CNNs proved to be effective in YDPF image *** was true even for the low accuracy of Windowpane fabrics,which often mistakenly includes the Prince ofWales *** classification of traditional patterns is also improved by utilizing the strip pooling model to extract local detail features and horizontal and vertical *** strip pooling model characterizes the horizontal and vertical crisscross patterns of YDPFs with considerable *** proposed method using the strip pooling model(SPM)improves the classification performance on the YDPF dataset by 2.64%for ResNet18,by 3.66%for VGG16,and by 3.54%for *** results reveal that the SPM significantly improves YDPF classification accuracy and reduces the error rate of Windowpane patterns as well.

关键词： Yarn-dyed plaid fabric image classification image dataset deep neural network strip pooling model

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deep neural network and Pseudo Relevance Feedback Based Query Expansion

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Computers, Materials & Continua 2022年第5期71卷 3557-3570页

作者： Abhishek Kumar Shukla Sujoy Das Department of Mathematics Bio-Informatics and Computer ApplicationsMaulana Azad National Institute of Technology BhopalBhopalMadhya Pradesh462003India

The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining,Natural language processing,Image processing,and Information retrieval *** embedding has been applied by many researchers for Information retrieval *** this paper word embedding-based skip-gram model has been developed for the query expansion *** terms are obtained from the top“k”initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user *** performance of the model based on mean average precision is *** proposed model compares with other existing *** improvement of 6.61%,6.93%,and 9.07%on MAP value is observed compare to the Original query,BM25 model,and query expansion with the Chi-Square model *** proposed model also retrieves 84,25,and 81 additional relevant documents compare to the original query,query expansion with Chi-Square model,and BM25 model respectively and thus improves the recall value *** per query analysis reveals that the proposed model performs well in 30,36,and 30 queries compare to the original query,query expansion with Chi-square model,and BM25 model respectively.

关键词： Information retrieval query expansion word embedding neural network deep neural network

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deep neural network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

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Intelligent Automation & Soft Computing 2023年第5期36卷 1863-1880页

作者： S.Amutha J.Raja Sekar Department of Computer Science and Engineering Mepco Schlenk Engineering College(Autonmous)SivakasiIndia

In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing cause of death which has led to the mortality of 17.9 million lives each *** Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent *** becomes inevitable to pro-pose a solution to predict the CVD with high accuracy.A system for predicting Cardio Vascular Disease using deep neural network with Binarized Butterﬂy Optimization Algorithm(DNN–BBoA)is *** BBoA is incorporated to select the best *** optimal features are fed to the deep neural network classiﬁer and it improves prediction accuracy and reduces the time *** usage of a deep neural network further helps to improve the prediction accu-racy with minimal *** proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kag-gle *** proposed work is compared with different machine learning classiﬁers such as Support Vector Machine,Random Forest,and Decision Tree Classiﬁ*** accuracy of the proposed DNN–BBoA is 99.35%for the heart dis-ease data set from UCI repository yielding an accuracy of 80.98%for Kaggle repository for cardiovascular disease dataset.

关键词： deep neural network cardio vascular disease binarized butterﬂy optimization algorithm feature selection

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