Mathematics

Research

Jump to: Review

14 pages, 2794 KiB

Open AccessArticle

Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network

by Xinying Zhang, Shanshan Kong, Yang Han, Baoshan Xie and Chunfeng Liu

Mathematics 2023, 11(6), 1363; https://doi.org/10.3390/math11061363 - 10 Mar 2023

Cited by 4 | Viewed by 2144

Abstract

To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature [...] Read more.

To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a local residual design was adopted in the upsampling process. Finally, the effectiveness of the proposed algorithm was evaluated using the LIDC-IDRI lung nodule public dataset. The results showed that the improved algorithm had 7.03%, 14.05%, and 10.43% higher performance than the U-Net segmentation algorithm under the three loss functions of DC, MIOU, and SE, and the accuracy was 2.45% higher compared with that of U-Net. Thus, the proposed method had an effective network structure. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

18 pages, 3112 KiB

Open AccessArticle

Prediction Model of Elderly Care Willingness Based on Machine Learning

by Yongchao Jin, Dongmei Liu, Kenan Wang, Renfang Wang and Xiaodie Zhuang

Mathematics 2023, 11(3), 606; https://doi.org/10.3390/math11030606 - 26 Jan 2023

Viewed by 2207

Abstract

At present, the problem of an aging population in China is severe. The integration of existing healthcare services with elderly care services is inefficient and cannot meet the needs of the elderly. As such, China urgently needs the concerted efforts of various social [...] Read more.

At present, the problem of an aging population in China is severe. The integration of existing healthcare services with elderly care services is inefficient and cannot meet the needs of the elderly. As such, China urgently needs the concerted efforts of various social forces to cope with the increasingly serious problem of aging. In accordance with Andersen’s behavioral model, a survey was conducted in Tangshan City among seniors 60 years of age and older. Using logistic regression models, decision tree models, and random forest models, we examined the factors impacting senior people’s desire to choose the integrated medical care and nursing care model. The results of the three models displayed that the elderly’s propensity to choose the combined medical care and nursing care model is significantly influenced by the amount of insurance, life care needs, and healthcare needs. Moreover, the study found that the willingness of the elderly in Tangshan to improve the combined medical and nursing care service system is low. The government should appeal to the community to participate in multiple developments to improve the integrated medical and nursing service system. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

21 pages, 2811 KiB

Open AccessArticle

Three-Branch Random Forest Intrusion Detection Model

by Chunying Zhang, Wenjie Wang, Lu Liu, Jing Ren and Liya Wang

Mathematics 2022, 10(23), 4460; https://doi.org/10.3390/math10234460 - 26 Nov 2022

Cited by 12 | Viewed by 2159

Abstract

Network intrusion detection has the problems of large amounts of data, numerous attributes, and different levels of importance for each attribute in detection. However, in random forests, the detection results have large deviations due to the random selection of attributes. Therefore, aiming at [...] Read more.

Network intrusion detection has the problems of large amounts of data, numerous attributes, and different levels of importance for each attribute in detection. However, in random forests, the detection results have large deviations due to the random selection of attributes. Therefore, aiming at the current problems, considering increasing the probability of essential features being selected, a network intrusion detection model based on three-way selected random forest (IDTSRF) is proposed, which integrates three decision branches and random forest. Firstly, according to the characteristics of attributes, it is proposed to evaluate the importance of attributes by combining decision boundary entropy, and using three decision rules to divide attributes; secondly, to keep the randomness of attributes, three attribute random selection rules based on attribute randomness are established, and a certain number of attributes are randomly selected from three candidate fields according to conditions; finally, the training sample set is formed by using autonomous sampling method to select samples and combining three randomly selected attribute sets randomly, and multiple decision trees are trained to form a random forest. The experimental results show that the model has high precision and recall. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

15 pages, 675 KiB

Open AccessArticle

Anti-Rumor Dissemination Model Based on Heat Influence and Evolution Game

by Jing Chen, Nana Wei, Chen Xin, Mingxin Liu, Zeren Yu and Miaomiao Liu

Mathematics 2022, 10(21), 4064; https://doi.org/10.3390/math10214064 - 1 Nov 2022

Cited by 3 | Viewed by 1691

Abstract

Aiming at the problem that the existing rumor dissemination models only focus on the characteristics of rumor dissemination and ignore anti-rumor dissemination, an evolution game model, SDIR, based on heat influence is proposed in this paper. Firstly, in order to solve the problem [...] Read more.

Aiming at the problem that the existing rumor dissemination models only focus on the characteristics of rumor dissemination and ignore anti-rumor dissemination, an evolution game model, SDIR, based on heat influence is proposed in this paper. Firstly, in order to solve the problem that rumor and anti-rumor information of emergency events disseminate simultaneously in social networks, the model extracts the factors that affect information dissemination: user behavior characteristics, user closeness and heat influence of participating topics. Secondly, anti-rumor information and an evolutionary game mechanism are introduced into the traditional SIR model, binary information is introduced to analyze the anti-rumor dissemination model SDIR, and the four state transitions and dissemination processes of SDIR are discussed. Finally, the SDIR model is experimentally validated in different datasets and dissemination models. The experimental results show that the SDIR model is in line with the actual dissemination law, and it can be proved that high self-identification ability plays a certain role in suppressing rumors; the anti-rumor information effectively inhibits the spread of rumor information to a certain extent. Compared with other models, the SDIR model is closer to the real diffusion range in the dataset. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

13 pages, 5776 KiB

Open AccessArticle

Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model

by Yongchao Jin, Renfang Wang, Xiaodie Zhuang, Kenan Wang, Honglian Wang, Chenxi Wang and Xiyin Wang

Mathematics 2022, 10(21), 4001; https://doi.org/10.3390/math10214001 - 28 Oct 2022

Cited by 19 | Viewed by 3738

Abstract

The purpose of this study is to study the spread of COVID-19, establish a predictive model, and provide guidance for its prevention and control. Considering the high complexity of epidemic data, we adopted an ARIMA-LSTM combined model to describe and predict future transmission. [...] Read more.

The purpose of this study is to study the spread of COVID-19, establish a predictive model, and provide guidance for its prevention and control. Considering the high complexity of epidemic data, we adopted an ARIMA-LSTM combined model to describe and predict future transmission. A new method of the ARIMA-LSTM model paralleling by weight of regression coefficient was proposed. Then, we used the ARIMA-LSTM model paralleling by weight of regression coefficient, ARIMA model, and ARIMA-LSTM series model to predict the epidemic data in China, and we found that the ARIMA-LSTM model paralleling by weight of regression coefficient had the best prediction accuracy. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 4049.913, RMSE = 63.639, MAPE = 0.205, R² = 0.837, MAE = 44.320. In order to verify the effectiveness of the ARIMA-LSTM model paralleling by weight of regression coefficient, we compared the ARIMA-LSTM model paralleling by weight of regression coefficient with the SVR model and found that ARIMA-LSTM model paralleling by weight of regression coefficient has better prediction accuracy. It was further verified with the epidemic data of India and found that the prediction accuracy of the ARIMA-LSTM model paralleling by weight of regression coefficient was still higher than that of the SVR model. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 744,904.6, RMSE = 863.079, MAPE = 0.107, R² = 0.983, MAE = 580.348. Finally, we used the ARIMA-LSTM model paralleling by weight of regression coefficient to predict the future epidemic situation in China. We found that in the next 60 days, the epidemic situation in China will become a steady downward trend. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

12 pages, 2204 KiB

Open AccessArticle

Preprocessing Enhancement Method for Spatial Domain Steganalysis

by Xueming Duan, Chunying Zhang, Yingshuo Ma and Shouyue Liu

Mathematics 2022, 10(21), 3936; https://doi.org/10.3390/math10213936 - 23 Oct 2022

Cited by 2 | Viewed by 1411

Abstract

In the field of steganalysis, in recent years, the research focus has mostly been on optimizing the structures of neural networks, while the application of high-pass filters is still limited to the simple selection of filters and simple adjustment of the number of [...] Read more.

In the field of steganalysis, in recent years, the research focus has mostly been on optimizing the structures of neural networks, while the application of high-pass filters is still limited to the simple selection of filters and simple adjustment of the number of filters. In this paper, we propose a method to enhance the assistance and contribution of high-pass filters to the detection capability of a spatial domain steganalysis model, which mainly contains the preprocessing enhancement of high-pass filters and cross-layer enhancement of high-pass filters, and we construct a preprocessing enhancement model, the HPF-Enhanced Model, for spatial domain steganalysis, based on Yedroudj-Net. In the experimental part, we find the best preprocessing enhancement method through various validations, and we compare the HPF-Enhanced Model with the classical models. The results show that the proposed enhancement method can bring a significant improvement, and they also show that the preprocessing enhancement method can help to reduce the model size, and it thus can be used to construct a lightweight spatial domain steganalysis model with strong performance. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

16 pages, 6900 KiB

Open AccessArticle

Community Evolution Prediction Based on Multivariate Feature Sets and Potential Structural Features

by Jing Chen, Haitong Zhao, Xinyu Yang, Mingxin Liu, Zeren Yu and Miaomiao Liu

Mathematics 2022, 10(20), 3802; https://doi.org/10.3390/math10203802 - 15 Oct 2022

Cited by 2 | Viewed by 1405

Abstract

The current study on community evolution prediction ignores the problem of internal community topology characteristics and does not take feature sets extraction into account. Therefore, the MF-PSF (Multivariate Feature sets and Potential Structural Features) method based on multivariate feature sets and potential structural [...] Read more.

The current study on community evolution prediction ignores the problem of internal community topology characteristics and does not take feature sets extraction into account. Therefore, the MF-PSF (Multivariate Feature sets and Potential Structural Features) method based on multivariate feature sets and potential structural features for community evolution prediction is proposed in this paper. Firstly, the multivariate feature sets are built from four aspects: community core node features, community structural features, community sequential features and community behavior features. Secondly, the community’s potential structural characteristics based on DeepWalk and spectral propagation theories are extracted, and the overall community’s internal structural characteristics and vertex distribution are analyzed. Finally, the community’s multivariate structural features and potential structural features are merged to predict community evolution events, and the importance of each feature in the process of evolutionary prediction is discussed. The experimental results show that compared with other community evolution prediction methods, the MF-PSF prediction method not only provides a foundation for analyzing the influence of various feature sets on predicted events, but it also effectively improves the accuracy of evolution prediction. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

16 pages, 7724 KiB

Open AccessArticle

Risk Evaluation Model of Coal Spontaneous Combustion Based on AEM-AHP-LSTM

by Xu Zhou, Shangsheng Ren, Shuo Zhang, Jiuling Zhang and Yibo Wang

Mathematics 2022, 10(20), 3796; https://doi.org/10.3390/math10203796 - 14 Oct 2022

Cited by 3 | Viewed by 1775

Abstract

Immediately and accurately assessing the risk of coal spontaneous combustion and taking targeted action are crucial steps in coal spontaneous combustion prevention and control. A new model, AEM-AHP-LSTM, was proposed to solve the weight calculation problem of multiobjective evaluation in the process of [...] Read more.

Immediately and accurately assessing the risk of coal spontaneous combustion and taking targeted action are crucial steps in coal spontaneous combustion prevention and control. A new model, AEM-AHP-LSTM, was proposed to solve the weight calculation problem of multiobjective evaluation in the process of coal spontaneous combustion. Firstly, the key indicators of coal spontaneous combustion were analyzed and used as risk factors to establish an evaluation system. Next, the objective and subjective weights were calculated using AEM and AHP, respectively. The objective and subjective weights were then combined, and TOPSIS was used to calculate the score of the evaluation sample. Finally, the obtained evaluation samples were trained with the BP, RBF, and LSTM model to resolve the problem of model overdependence on historical data and achieve the auto-adapt adjustment of weight with data change. Additionally, data from 15 typical Chinese coal mines were applied to the model. The results indicate that, compared with the BP and RBF neural networks, the LSTM model has higher prediction accuracy, stronger generalization ability, and stronger practicability. The modeling and application findings show that the AEM-AHP-LSTM model was better appropriate for the risk assessment of coal spontaneous combustion. This method can potentially be further applied as reliable approach for the assessment of mine disaster risk. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

16 pages, 4649 KiB

Open AccessArticle

Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine

by Shaohong Yan, Yanbo Zhang, Xiangxin Liu and Runze Liu

Mathematics 2022, 10(18), 3276; https://doi.org/10.3390/math10183276 - 9 Sep 2022

Cited by 7 | Viewed by 1675

Abstract

Rock burst disasters occurring in underground high-stress rock mass mining and excavation engineering seriously threaten the safety of workers and hinders the progress of engineering construction. Rock burst classification prediction is the basis of reducing and even eliminating rock burst hazards. Currently, most [...] Read more.

Rock burst disasters occurring in underground high-stress rock mass mining and excavation engineering seriously threaten the safety of workers and hinders the progress of engineering construction. Rock burst classification prediction is the basis of reducing and even eliminating rock burst hazards. Currently, most of mainstream discriminant models for rock burst grade prediction are based on small samples. Comprehensive selection according to many pieces of literature, the maximum tangential stress of surrounding rock and rock uniaxial compressive strength ratio coefficient (stress state parameter), rock uniaxial compressive strength and uniaxial tensile strength ratio (brittleness modulus), and the elastic energy index are used as a grading evaluation index of rock burst based on the collection of different construction engineering instances of rock burst in 114 groups of extensive sample data in different regions of the world, which are used to carry out the training study. The representativeness and accuracy of the index selection were verified by the indicator variance analysis and Spearman correlation coefficient hypothesis test. The Intelligent Rock burst Identification System (IRIS) based on an optimizable SVM model was established using data set samples. After extensive data cross-validation training, the accuracy of the SVM discriminant analysis model can reach 95.6%, which is significantly better than the prediction accuracy of the traditional SVM model of 71.9%. The model is used to classify and predict the rock burst intensity of 10 typical projects at home and abroad. The prediction results are consistent with the actual rock burst intensity, which is better than the discriminant model based on small sample data and other existing prediction models. The application of engineering examples shows that the results of the rock burst intensity classification prediction model based on extensive sample data processing analysis and the SVM discriminant method are in good agreement with the actual rock burst intensity, which can effectively provide a reference for the prediction of rock burst intensity grade in a construction area. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

23 pages, 8623 KiB

Open AccessArticle

SEIARN: Intelligent Early Warning Model of Epidemic Spread Based on LSTM Trajectory Prediction

by Liya Wang, Yaxun Dai, Renzhuo Wang, Yuwen Sun, Chunying Zhang, Zhiwei Yang and Yuqing Sun

Mathematics 2022, 10(17), 3046; https://doi.org/10.3390/math10173046 - 24 Aug 2022

Cited by 2 | Viewed by 1603

Abstract

A SEIARN compartment model with the asymptomatic infection and secondary infection is proposed to predict the trend of COVID-19 more accurately. The model is extended according to the propagation characteristics of the novel coronavirus, the concepts of the asymptomatic infected compartment and secondary [...] Read more.

A SEIARN compartment model with the asymptomatic infection and secondary infection is proposed to predict the trend of COVID-19 more accurately. The model is extended according to the propagation characteristics of the novel coronavirus, the concepts of the asymptomatic infected compartment and secondary infection are introduced, and the contact rate parameters of the improved model are updated in real time by using the LSTM trajectory, in order to make accurate predictions. This SEIARN model first builds on the traditional SEIR compartment model, taking into account the asymptomatic infection compartment and secondary infection. Secondly, it considers the disorder of the trajectory and uses the improved LSTM model to predict the future trajectory of the current patients and cross-track with the susceptible patients to obtain the contact rate. Then, we conduct real-time updating of exposure rates in the SEIARN model and simulation of epidemic trends in Tianjin, Xi’an, and Shijiazhuang. Finally, the comparison experiments show that the SEIARN model performs better in prediction accuracy, MSE, and RMSE. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

16 pages, 1739 KiB

Open AccessArticle

BiInfGCN: Bilateral Information Augmentation of Graph Convolutional Networks for Recommendation

by Jingfeng Guo, Chao Zheng, Shanshan Li, Yutong Jia and Bin Liu

Mathematics 2022, 10(17), 3042; https://doi.org/10.3390/math10173042 - 23 Aug 2022

Cited by 1 | Viewed by 1650

Abstract

The current graph-neural-network-based recommendation algorithm fully considers the interaction between users and items. It achieves better recommendation results, but due to a large amount of data, the interaction between users and items still suffers from the problem of data sparsity. To address this [...] Read more.

The current graph-neural-network-based recommendation algorithm fully considers the interaction between users and items. It achieves better recommendation results, but due to a large amount of data, the interaction between users and items still suffers from the problem of data sparsity. To address this problem, we propose a method to alleviate the data sparsity problem by retaining user–item interactions while fully exploiting the association relationships between items and using side-information enhancement. We constructed a “twin-tower” model by combining a user–item training model and an item–item training model inspired by the knowledge distillation technique; the two sides of the structure learn from each other during the model training process. Comparative experiments were carried out on three publicly available datasets, using the recall and the normalized discounted cumulative gain as evaluation metrics; the results outperform existing related base algorithms. We also carried out extensive parameter sensitivity and ablation experiments to analyze the influence of various factors on the model. The problem of user–item interaction data sparsity is effectively addressed. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

14 pages, 2415 KiB

Open AccessArticle

Research on Attack Detection of Cyber Physical Systems Based on Improved Support Vector Machine

by Fengchun Liu, Sen Zhang, Weining Ma and Jingguo Qu

Mathematics 2022, 10(15), 2713; https://doi.org/10.3390/math10152713 - 1 Aug 2022

Cited by 6 | Viewed by 1673

Abstract

Cyber physical systems (CPS), in the event of a cyber attack, can have a serious impact on the operating physical equipment. In order to improve the attack detection capability of CPS, an support vector machine (SVM) attacks detection model based on particle swarm [...] Read more.

Cyber physical systems (CPS), in the event of a cyber attack, can have a serious impact on the operating physical equipment. In order to improve the attack detection capability of CPS, an support vector machine (SVM) attacks detection model based on particle swarm optimization (PSO) is proposed. First, the box plot anomaly detection method is used to detect the characteristic variables, and the characteristic variables with abnormal distribution are discretized. Secondly, the number of attack samples was increased by the SMOTE method to solve the problem of data imbalance, and the linear combination of characteristic variables was performed on the high-dimensional CPS network traffic data using principal component analysis (PCA). Then, the penalty coefficient and the hyperparameter of the kernel function in the SVM model are optimized by the PSO algorithm. Finally, Experiments on attack detection of CPS network traffic data show that the proposed model can detect different types of attack data and has higher detection accuracy compared with general detection models. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

16 pages, 2223 KiB

Open AccessArticle

The Improvement of DV-Hop Model and Its Application in the Security Performance of Smart Campus

by Aimin Yang, Qunwei Zhang, Yikai Liu and Ji Zhao

Mathematics 2022, 10(15), 2663; https://doi.org/10.3390/math10152663 - 28 Jul 2022

Cited by 3 | Viewed by 1530

Abstract

In the smart campus, sensors are the basic units in the whole the Internet of Things structure, which play the role of collecting information and transmitting it. How to transmits more information at a certain power level has attracted the attention of many [...] Read more.

In the smart campus, sensors are the basic units in the whole the Internet of Things structure, which play the role of collecting information and transmitting it. How to transmits more information at a certain power level has attracted the attention of many researchers. In this paper, the DV-Hop algorithm is optimized by combining simulated annealing-interference particle swarm optimization algorithm to improve the node localization of wireless sensor networks and enhance the security performance of smart campus. To address the problem that particle swarm optimization easily falls into local optimum, a perturbation mechanism is introduced in the basic particle swarm optimization algorithm. Meanwhile, the acceptance probability P is introduced in the simulated annealing algorithm to determine whether a particle is accepted when it “flies” to a new position, which improves the probability of finding a global optimal solution. Comparing the average localization error and optimization rate of the DV-Hop algorithm, PSO-DV-Hop algorithm, and the optimized algorithm. The results show a greater advantage of the algorithm. This will greatly enhance the safety performance and efficiency of the smart campus. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

20 pages, 4814 KiB

Open AccessArticle

Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint

by Xiao Chen, Tong Hao, Li Han, Meng Leng, Jing Chen and Jingfeng Guo

Mathematics 2022, 10(15), 2623; https://doi.org/10.3390/math10152623 - 27 Jul 2022

Cited by 1 | Viewed by 1344

Abstract

In heterogeneous networks, random walks based on meta-paths require prior knowledge and lack flexibility. On the other hand, random walks based on non-meta-paths only consider the number of node types, but not the influence of schema and topology between node types in real [...] Read more.

In heterogeneous networks, random walks based on meta-paths require prior knowledge and lack flexibility. On the other hand, random walks based on non-meta-paths only consider the number of node types, but not the influence of schema and topology between node types in real networks. To solve these problems, this paper proposes a novel model HNE-RWTIC (Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint). Firstly, to realize flexible walks, we design a Type strategy, which is a node type selection strategy based on the co-occurrence probability of node types. Secondly, to achieve the uniformity of node sampling, we design an Inner strategy, which is a node selection strategy based on the adjacency relationship between nodes. The Type and Inner strategy can realize the random walks based on meta-paths, the flexibility of the walks, and can sample the node types and nodes uniformly in proportion. Thirdly, based on the above strategy, a transition probability model is constructed; then, we obtain the nodes’ embedding based on the random walks and Skip-Gram. Finally, in the classification and clustering tasks, we conducted a thorough empirical evaluation of our method on three real heterogeneous networks. Experimental results show that HNE-RWTIC outperforms state-of-the-art approaches. In the classification task, in DBLP, AMiner-Top, and Yelp, the values of Micro-F1 and Macro-F1 of HNE-RWTIC are the highest: 2.25% and 2.43%, 0.85% and 0.99%, 3.77% and 5.02% higher than those of five other algorithms, respectively. In the clustering task, in DBLP, AMiner-Top, and Yelp networks, the NMI value is increased by 19.12%, 6.91%, and 0.04% at most, respectively. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

16 pages, 3821 KiB

Open AccessArticle

Multiobjective Collaborative Optimization of Argon Bottom Blowing in a Ladle Furnace Using Response Surface Methodology

by Zicheng Xin, Jiankun Sun, Jiangshan Zhang, Bingchang He, Junguo Zhang and Qing Liu

Mathematics 2022, 10(15), 2610; https://doi.org/10.3390/math10152610 - 26 Jul 2022

Cited by 4 | Viewed by 1546

Abstract

In order to consider both the refining efficiency of the ladle furnace (LF) and the quality of molten steel, the water model experiment is carried out. In this study, the single factor analysis, central composite design principle, response surface methodology, visual analysis of [...] Read more.

In order to consider both the refining efficiency of the ladle furnace (LF) and the quality of molten steel, the water model experiment is carried out. In this study, the single factor analysis, central composite design principle, response surface methodology, visual analysis of response surface, and multiobjective optimization are used to obtain the optimal arrangement scheme of argon blowing of LF, design the experimental scheme, establish the prediction models of mixing time (MT) and slag eye area (SEA), analyze the comprehensive effects of different factors on MT and SEA, and obtain the optimal process parameters, respectively. The results show that when the identical porous plug radial position is 0.6R and the separation angle is 135°, the mixing behavior is the best. Moreover, the optimized parameter combination is obtained based on the response surface model to simultaneously meet the requirements of short MT and small SEA in the LF refining process. Meanwhile, compared with the predicted values, the errors of MT and SEA for different conditions from the experimental values are 1.3% and 2.1%, 1.3% and 4.2%, 2.5% and 3.4%, respectively, which is beneficial to realizing the modeling of argon bottom blowing in the LF refining process and reducing the interference of human factors. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

19 pages, 7650 KiB

Open AccessArticle

Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder

by Feng Yu, Jianchang Liu and Dongming Liu

Mathematics 2022, 10(14), 2526; https://doi.org/10.3390/math10142526 - 20 Jul 2022

Cited by 7 | Viewed by 2107

Abstract

Clustering algorithms and deep learning methods have been widely applied in the multimode process monitoring. However, for the process data with unknown mode, traditional clustering methods can hardly identify the number of modes automatically. Further, deep learning methods can learn effective features from [...] Read more.

Clustering algorithms and deep learning methods have been widely applied in the multimode process monitoring. However, for the process data with unknown mode, traditional clustering methods can hardly identify the number of modes automatically. Further, deep learning methods can learn effective features from nonlinear process data, while the extracted features cannot follow the Gaussian distribution, which may lead to incorrect control limit for fault detection. In this paper, a comprehensive monitoring method based on modified density peak clustering and parallel variational autoencoder (MDPC-PVAE) is proposed for multimode processes. Firstly, a novel clustering algorithm, named MDPC, is presented for the mode identification and division. MDPC can identify the number of modes without prior knowledge of mode information and divide the whole process data into multiple modes. Then, the PVAE is established based on distinguished multimode data to generate the deep nonlinear features, in which the generated features in each VAE follow the Gaussian distribution. Finally, the Gaussian feature representations obtained by PVAE are provided to construct the statistics

H^{2}

, and the control limits are determined by the kernel density estimation (KDE) method. The effectiveness of the proposed method is evaluated by the Tennessee Eastman process and semiconductor etching process. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

16 pages, 5745 KiB

Open AccessArticle

Mathematical Model and Numerical Simulation Study of the Mining Area with Multiple Air Leakage Paths

by Jiuling Zhang, Gaoyang Ruan, Yang Bai and Tao Ning

Mathematics 2022, 10(14), 2484; https://doi.org/10.3390/math10142484 - 16 Jul 2022

Cited by 2 | Viewed by 1577

Abstract

The natural fire in the mining area is the main source of mine fires, and the distribution of spontaneous combustion “three zones” is a key issue in mine fire prevention and suppression. In order to study the change law of spontaneous combustion “three [...] Read more.

The natural fire in the mining area is the main source of mine fires, and the distribution of spontaneous combustion “three zones” is a key issue in mine fire prevention and suppression. In order to study the change law of spontaneous combustion “three zones” in the mining area with multiple air leakage paths, a segmented numerical simulation method is proposed. In order to consider the common influence of various factors, we firstly establish the coupled model of oxygen consumption rate of coal relics, the regional fluidity model of the porous medium and the three-dimensional distribution model of void rate in the mining area. Then, based on this, the corresponding conditions of air leakage speed, air leakage location and oxygen concentration are set in each stage of numerical simulation. The mathematical model shows that: the oxygen consumption rate of coal shows an approximate exponential growth trend with the increase in temperature, which is proportional to the original oxygen concentration; the void rate of the mining area shows a logarithmic distribution with a tendency of “double hump” proportional coupling. The numerical simulation results show that: the width of the “oxidation zone” decreases gradually along the tendency when there is only air leakage from the working face; the smaller airflow and lower oxygen concentration in the overlying mining area will increase the width of the “oxidation zone” in the coverage area; air leakage from the shelf road will form an “oxidation zone” near the entrance of the shelf road. The leakage of air from the shelf road will form an “oxidized zone” near the entrance of the shelf road; the leakage of air from the adjacent mining area will increase the width of the overall “dispersal zone” and “oxidized zone” due to the larger air flow and higher oxygen concentration. The comparison with the monitoring data of the downhole bundle tube verifies the rationality of the mathematical model and the accuracy of the numerical simulation results. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

15 pages, 1742 KiB

Open AccessArticle

An Improved Model for Kernel Density Estimation Based on Quadtree and Quasi-Interpolation

by Jiecheng Wang, Yantong Liu and Jincai Chang

Mathematics 2022, 10(14), 2402; https://doi.org/10.3390/math10142402 - 8 Jul 2022

Cited by 4 | Viewed by 2425

Abstract

There are three main problems for classical kernel density estimation in its application: boundary problem, over-smoothing problem of high (low)-density region and low-efficiency problem of large samples. A new improved model of multivariate adaptive binned quasi-interpolation density estimation based on a quadtree algorithm [...] Read more.

There are three main problems for classical kernel density estimation in its application: boundary problem, over-smoothing problem of high (low)-density region and low-efficiency problem of large samples. A new improved model of multivariate adaptive binned quasi-interpolation density estimation based on a quadtree algorithm and quasi-interpolation is proposed, which can avoid the deficiency in the classical kernel density estimation model and improve the precision of the model. The model is constructed in three steps. Firstly, the binned threshold is set from the three dimensions of sample number, width of bins and kurtosis, and the bounded domain is adaptively partitioned into several non-intersecting bins (intervals) by using the iteration idea from the quadtree algorithm. Then, based on the good properties of the quasi-interpolation, the kernel functions of the density estimation model are constructed by introducing the theory of quasi-interpolation. Finally, the binned coefficients of the density estimation model are constructed by using the idea of frequency replacing probability. Simulation of the Monte Carlo method shows that the proposed non-parametric model can effectively solve the three shortcomings of the classical kernel density estimation model and significantly improve the prediction accuracy and calculation efficiency of the density function for large samples. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

13 pages, 2030 KiB

Open AccessArticle

Information Leakage Detection and Risk Assessment of Intelligent Mobile Devices

by Xiaolei Yang, Yongshan Liu and Jiabin Xie

Mathematics 2022, 10(12), 2011; https://doi.org/10.3390/math10122011 - 10 Jun 2022

Cited by 1 | Viewed by 2367

Abstract

(1) Background: Smart mobile devices provide conveniences to people’s life, work, and entertainment all the time. The basis of these conveniences is the data exchange across the entire cyberspace, and privacy data leakage has become the focus of attention. (2) Methods: First, we [...] Read more.

(1) Background: Smart mobile devices provide conveniences to people’s life, work, and entertainment all the time. The basis of these conveniences is the data exchange across the entire cyberspace, and privacy data leakage has become the focus of attention. (2) Methods: First, we used the method of directed information flow to conduct an API test for all applications in the application market, then obtained the application data transmission. Second, by using tablet computers, smart phones, and bracelets as the research objects, and taking the scores of senior users on the selected indicators as the original data, we used the fusion information entropy and Markov chain algorithm skillfully to build a data leakage risk assessment mode to obtain the steady-state probability values of different risk categories of each device, and then obtained the entropy values of three devices. (3) Results: Tablet computers have the largest entropy in the risk of data leakage, followed by bracelets and mobile phones. (4) Conclusions: This paper compares the risk situation of each risk category of each device, and puts forward simple avoidance opinions, which might lay a theoretical foundation for subsequent research on privacy protection strategies, image steganography, and device security improvements. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

Review

Jump to: Research

26 pages, 1856 KiB

Open AccessReview

Research on Medical Problems Based on Mathematical Models

by Yikai Liu, Ruozheng Wu and Aimin Yang

Mathematics 2023, 11(13), 2842; https://doi.org/10.3390/math11132842 - 24 Jun 2023

Cited by 12 | Viewed by 11130

Abstract

Mathematical modeling can help the medical community to more fully understand and explore the physiological and pathological processes within the human body and can provide more accurate and reliable medical predictions and diagnoses. Neural network models, machine learning models, and statistical models, among [...] Read more.

Mathematical modeling can help the medical community to more fully understand and explore the physiological and pathological processes within the human body and can provide more accurate and reliable medical predictions and diagnoses. Neural network models, machine learning models, and statistical models, among others, have become important tools. The paper details the applications of mathematical modeling in the medical field: by building differential equations to simulate the patient’s cardiovascular system, physicians can gain a deeper understanding of the pathogenesis and treatment of heart disease. With machine learning algorithms, medical images can be better quantified and analyzed, thus improving the precision and accuracy of diagnosis and treatment. In the drug development process, network models can help researchers more quickly screen for potentially active compounds and optimize them for eventual drug launch and application. By mining and analyzing a large number of medical data, more accurate and comprehensive disease risk assessment and prediction results can be obtained, providing the medical community with a more scientific and accurate basis for decision-making. In conclusion, research on medical problems based on mathematical models has become an important part of modern medical research, and great progress has been made in different fields. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

27 pages, 1887 KiB

Open AccessReview

Research Progress of Complex Network Modeling Methods Based on Uncertainty Theory

by Jing Wang, Jing Wang, Jingfeng Guo, Liya Wang, Chunying Zhang and Bin Liu

Mathematics 2023, 11(5), 1212; https://doi.org/10.3390/math11051212 - 1 Mar 2023

Cited by 1 | Viewed by 2868

Abstract

A complex network in reality contains a large amount of information, but some information cannot be obtained accurately or is missing due to various reasons. An uncertain complex network is an effective mathematical model to deal with this problem, but its related research [...] Read more.

A complex network in reality contains a large amount of information, but some information cannot be obtained accurately or is missing due to various reasons. An uncertain complex network is an effective mathematical model to deal with this problem, but its related research is still in its infancy. In order to facilitate the research into uncertainty theory in complex network modeling, this paper summarizes and analyzes the research hotspots of set pair analysis, rough set theory and fuzzy set theory in complex network modeling. This paper firstly introduces three kinds of uncertainty theories: the basic definition of set pair analysis, rough sets and fuzzy sets, as well as their basic theory of modeling in complex networks. Secondly, we aim at the three uncertainty theories and the establishment of specific models. The latest research progress in complex networks is reviewed, and the main application fields of the three uncertainty theories are discussed, respectively: community discovery, link prediction, influence maximization and decision-making problems. Finally, the prospect of the modeling and development of uncertain complex networks is put forward. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

22 pages, 5787 KiB

Open AccessReview

Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods

by Liyan Zhang, Jingfeng Guo, Jiazheng Wang, Jing Wang, Shanshan Li and Chunying Zhang

Mathematics 2022, 10(11), 1921; https://doi.org/10.3390/math10111921 - 3 Jun 2022

Cited by 11 | Viewed by 6060

Abstract

With the advent of big data and the information age, the data magnitude of various complex networks is growing rapidly. Many real-life situations cannot be portrayed by ordinary networks, while hypergraphs have the ability to describe and characterize higher order relationships, which have [...] Read more.

With the advent of big data and the information age, the data magnitude of various complex networks is growing rapidly. Many real-life situations cannot be portrayed by ordinary networks, while hypergraphs have the ability to describe and characterize higher order relationships, which have attracted extensive attention from academia and industry in recent years. Firstly, this paper described the development process, the application areas, and the existing review research of hypergraphs; secondly, introduced the theory of hypergraphs briefly; then, compared the learning methods of ordinary graphs and hypergraphs from three aspects: matrix decomposition, random walk, and deep learning; next, introduced the structural optimization of hypergraphs from three perspectives: dynamic hypergraphs, hyperedge weight optimization, and multimodal hypergraph generation; after that, the applicability of three uncertain hypergraph models were analyzed based on three uncertainty theories: probability theory, fuzzy set, and rough set; finally, the future research directions of hypergraphs and uncertain hypergraphs were prospected. Full article

(This article belongs to the Special Issue Engineering Calculation and Data Modeling)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Engineering Calculation and Data Modeling

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (22 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI