Mathematical Problems in Engineering
 Journal metrics
Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
CiteScore1.800
Impact Factor1.009

Estimation of Constant Stress Partially Accelerated Life Test for Fréchet Distribution with Type-I Censoring

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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.

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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.

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Research Article

Technological Innovation and Value Creation of Enterprise Innovation Ecosystem Based on System Dynamics Modeling

Based on the analysis of the dynamic interrelationships of enterprise innovation factors according to system dynamics, we build a dynamic causality diagram and a flow graph model of the enterprise innovation ecosystem to study the potential business value creation paths focusing on technological innovation. The system model is simulated using data from high-tech enterprises. Our results show that the model can reasonably simulate the operation of the enterprise innovation ecosystem. Two paths to value creation are identified: (1) input-technological innovation-commercialization of results-value creation; (2) external acquisition of technology-digestion and absorption-value creation as a complementary path. Also, the technological innovation path expands and extends the industrial chain and supply chain of enterprises and better promotes the value creation of enterprises in the same supply chain. Furthermore, our results show that R&D investment and technical cooperation investment should be allocated rationally in order to improve the utility of value creation investment.

Research Article

Simulation and Analysis of Extended Spatial Channel Model in Vehicle-to-Vehicle Communication Environments

In this paper, an extension spatial channel model (SCM) for vehicle-to-vehicle (V2V) communications is proposed. To efficiently illustrate the real-world scenarios and reflect nonstationary properties of V2V channels, all effective scattering objects are subdivided into three categories of clusters according to the relative position of clusters. Besides, a birth-death process is introduced to model the appearance and disappearance of clusters on both the array and time axes. Their impacts on V2V channels are investigated via statistical properties including correlation functions. Additionally, a closed-form expression of channel impulse response (CIR) is derived from an extension SCM and cluster-based models. Furthermore, the spatial and frequency statistical properties of the reference model are thoroughly investigated. Finally, simulation results show that the proposed SCM V2V model is in close agreement with previously reported results, thereby validating the accuracy and effectiveness of the proposed model.

Research Article

Weapon Detection Using YOLO V3 for Smart Surveillance System

Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.

Research Article

Design of Neutrosophic Self-Tuning PID Controller for AC Permanent Magnet Synchronous Motor Based on Neutrosophic Theory

In practical control applications, AC permanent magnet synchronous motors need to work in different response characteristics. In order to meet this demand, a controller which can independently realize the different response characteristics of the motor is designed based on neutrosophic theory and genetic algorithm. According to different response characteristics, neutrosophic membership functions are constructed. Then, combined with the cosine measure theorem and genetic algorithm, the neutrosophic self-tuning PID controller is designed. It can adjust the parameters of the controller according to response requirements. Finally, three kinds of controllers with typical system response characteristics are designed by using Simulink. The effectiveness of the designed controller is verified by simulation results.

Research Article

Optimization of CNC Milling Parameters for Complex 3D Surfaces of SIMOLD 2083 Alloy Mold Core Utilizing Multiobjective Water Cycle Algorithm

In this article, an effective multiobjective optimization approach is exploited to search for the best milling parameters for CNC for complex 3d surfaces of SIMOLD 2083 alloy mold core. To improve the quality responses, the cutting factors are optimized by a combination of Taguchi method (TM), response surface method (RSM), and multiobjective water cycle algorithm (MWCA). Firstly, the design for initial series experiments of the cutting factors was generated via the TM. Thereafter, the regression models between the cutting factors and the surface roughness of the machined workpiece surface as well as milling time are formed via applying the RSM. Moreover, analysis of variance and sensitivity analysis are also executed to define the influences and crucial contributions of cutting parameters on the surface roughness and milling time. The results of analysis of variance showed that the factors which have main effects on surface roughness were spindle speed (42.42%), feed rate (29.40%), and cutting depth (6.59%), respectively. Meanwhile, the feed rate with the influence of 92.6% was the most significant factor in controlling the milling time. Ultimately, based on mathematical models, the MWCA is performed to define the optimal factors. The optimal results indicated that the optimized surface roughness was about 0.260 μm and the milling time was roughly 1012.767 (s). In addition, the errors between forecasted results and experimental verifications for the surface roughness and milling time are 2.04% and 5.39%, respectively. Therefore, the results of experimental verifications are suitable with the forecasted results from the proposed optimization method. These results depicted that the proposed integration approach can define effectively the optimal cutting factors for CNC milling and expand to apply for complex multiobjective optimization problems.

Research Article

Predictive Maintenance and Sensitivity Analysis for Equipment with Multiple Quality States

This paper discusses the predictive maintenance (PM) problem of a single equipment system. It is assumed that the equipment has deteriorating quality states as it operates, resulting in multiple yield levels represented as system observation states. We cast the equipment deterioration as discrete-state and continuous-time semi-Markov decision process (SMDP) model and solve the SMDP problem in reinforcement learning (RL) framework using the strategy-based method. In doing so, the goal is to maximize the system average reward rate (SARR) and generate the optimal maintenance strategy for given observation states. Further, the PM time is capable of being produced by a simulation method. In order to prove the advantage of our proposed method, we introduce the standard sequential preventive maintenance algorithm with unequal time interval. Our proposed method is compared with the sequential preventive maintenance algorithm in a test objective of SARR, and the results tell us that our proposed method can outperform the sequential preventive maintenance algorithm. In the end, the sensitivity analysis of some parameters on the PM time is given.

Mathematical Problems in Engineering
 Journal metrics
Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
CiteScore1.800
Impact Factor1.009
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