Mathematical Problems in Engineering
 Journal metrics
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Acceptance rate48%
Submission to final decision53 days
Acceptance to publication27 days
CiteScore2.100
Journal Citation Indicator0.420
Impact Factor1.430

Article of the Year 2021

Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

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 Journal profile

Mathematical Problems in Engineering is a broad-based journal publishing results of rigorous engineering research across all disciplines, carried out using mathematical tools.

 Editor spotlight

Chief Editor, Professor Guangming Xie, is currently a full professor of dynamics and control with the College of Engineering, Peking University. His research interests include complex system dynamics and control and intelligent and biomimetic robots.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

Research on the Relationship between Motion Performance and User Experience of Golf Virtual Simulation Putting Simulator

This paper designs and develops a virtual golf simulation putting simulator based on the existing computer technology and conducts in-depth research and analysis on the relationship between its motion performance and user experience. The network architecture of the distributed virtual golf simulation system and the scene data management model are established, based on which the server-side system design and the client-side network communication module design of the distributed virtual golf simulation system are carried out. In the requirement analysis, the functional requirements such as building VR scenes, data communication and recognition models, and the non-functional requirements such as system security and ease of use are analyzed; in the outline design, the hardware equipment and logical architecture of the automatic user experience optimization system are described; in the detailed design, the functional modules of the system are designed in detail, including VR induction experience, physiological signal dataset user experience identification, data communication, optimization strategy, and so on, and important class diagrams and flowcharts are given. The intervention effects of positive thinking training on sports performance and improving athletes’ attention and receptivity have been verified and recognized by coaches and athletes. The putting simulator in the experimental class had higher hole-in-hole parameters than the control class, a highly significant difference; the putting simulator in the experimental class had higher hole-in-hole parameters than the control class, with a highly significant difference. These 3D models may contain more detailed information. In a virtual scene, the more detailed information a model contains, the more polygons the model needs, so that the computer needs to draw many polygons per frame, which has a great impact on the real-time performance of scene drawing. The parameters of the 5-yard chip-and-shoot in the experimental class were higher than those in the control class, and there was a very significant difference between the parameters of the 15-yard chip-and-shoot in the experimental class and those in the control class. The experimental results show that the model optimization processing method and rendering acceleration technology proposed in this paper can largely improve the rendering efficiency of 3D virtual scenes.

Research Article

Hybrid Deep Learning Approaches for sEMG Signal-Based Lower Limb Activity Recognition

Lower limb activity recognition utilizing body sensor data has attracted researchers due to its practical applications, such as neuromuscular disease detection and kinesiological investigations. The employment of wearable sensors including accelerometers, gyroscopes, and surface electromyography has grown due to their low cost and broad applicability. Electromyography (EMG) sensors are preferable for automated control of a lower limb exoskeleton or prosthesis since they detect the signal beforehand and allow faster movement detection. The study presents hybrid deep learning models for lower limb activity recognition. Noise is suppressed using discrete wavelet transform, and then the signal is segmented using overlapping windowing. Convolutional neural network is used for temporal learning, whereas long short-term memory or gated recurrent unit is used for sequence learning. After that, performance indices of the models such as accuracy, sensitivity, specificity, and F-score are calculated. The findings indicate that the suggested hybrid model outperforms the individual models.

Research Article

Judging the True Health of Finance Institutions Based on Risk Behavior and Operation Performance

As the core of the financial system, financial institutions are playing a significant role in financial stability in the process of development; traditional analysis mainly discusses the institution’s revenue of assets. However, the current financial stability system pays more attention to financial institution risk behavior and operational efficiency; to solve the previous two issues, we propose the two-stage model. Firstly, we measure the dynamic financial institution’s risk behavior coefficient based on the volatility and return principle for different institutions. Secondly, according to the cross-efficiency principle, different financial institution operation efficiencies that assimilate risk behavior will be obtained, and the institution risk behavior valve is also given. Finally, we analyze the 31 banks listed in China to verify the validity and applicability of the two-stage model; the model has made a certain theoretical contribution to the financial institution analysis model, especially when we consider the risk behavior and multiple indexes. Therefore, the two-stage model that we built can help investors make a portfolio in banking enterprises; it also can help financial institutions evaluate their risk behavior for making an optimal decision and help government agencies to supervise banks based on their risk behavior.

Research Article

Container Ship Carbon and Fuel Estimation in Voyages Utilizing Meteorological Data with Data Fusion and Machine Learning Techniques

The International Maritime Organization (IMO) had made effort to reduce the ship’s energy consumption and carbon emission by optimizing the ship’s operational measures such as speed and weather routing. However, existing fuel consumption models were relatively simple without considering the quantified effect of weather conditions. In this paper, a knowledge-based ridge regression-based algorithm is presented for enabling automated fuel consumption estimation under varying weather conditions during voyages. Wind speed, wave height, ship speed, draught, AIS segment distance, and ship’s heading (HDG) are used as input to predict the fuel consumption value from the MRV report. In this work, 3 types of models are tested: AIS-based model, MRV-based model, and MRV-based normalized model. In AIS based model, weather conditions are divided into nine categories based on wind speed, wave height, and wind directions then trained separately. In MRV-based mode, the daily weather condition was used, and the MRV-normalized model used the normalized daily weather data. The proposed ridge regression models (11 models total) were tested with 4 container ships for a period of one year, and the result shows that compared to real fuel consumption, MRV-based model could achieve the best result with an average error less than 3% comparing to real MRV report.

Research Article

Research on Intelligent Customization of Cross-Border E-Commerce Based on Deep Learning

Cross-border e-commerce has become an important way of “New Infrastructure for Foreign Trade” and “Online Silk Road construction.” Utilizing intelligent technology to energize cross-border e-commerce can improve the quality and efficiency of the whole industrial chain. Cross-border e-commerce enterprises generally face the problem of product customization and development due to the large difference in international market demand. This study first analyzes the research status and technical underpinnings of intelligent customization of cross-border e-commerce, then establishes the technical framework of intelligent customization of cross-border e-commerce based on in-depth learning, and subsequently trains, tests, and analyzes the model, and the intelligent customization model has achieved a higher learning level. Finally, it puts forward the promotion and application strategy of cross-border e-commerce intelligent customization based on deep learning. It has theoretical significance and practical value for intelligently identifying changes in international market demand, helping cross-border e-commerce enterprises select products, and optimizing cross-border e-commerce product development.

Research Article

Design of Interconnected Warehouse and Routing Optimization by BP Genetic Neural Network Algorithm

With the continuous progress of the chemical industry, warehouse design needs to be diversified on account of the increasing complex and multitudinous perilous chemicals. In this situation, this study projects the conception of the interconnected warehouse. By taking the storage points as the quantity and the path as the variable, this study establishes a quadratic allocation model on the operations of this novel kind of warehouse. Then, an improved neural network algorithm is proposed to ascertain the optimal solution. The innovation of this study is that it releases the space resources of the classic dangerous goods warehouse and improves the operational efficiency of the dangerous goods warehouse under the premise of ensuring safety. Finally, the proposed model and algorithm is tested and verified with a data of Shanghai Lingang dangerous Material Warehouse. The empirical research demonstrates that the interconnected warehouse has ideal performance for lifting the handling efficiency on the basis of ensuring safety.

Mathematical Problems in Engineering
 Journal metrics
See full report
Acceptance rate48%
Submission to final decision53 days
Acceptance to publication27 days
CiteScore2.100
Journal Citation Indicator0.420
Impact Factor1.430
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.