Mathematical Problems in Engineering最新文献

{"title":"Few More Series of Reciprocals with Binomial Coefficients and Their Evaluations","authors":"Shruthi C. Bhat, M. Krithi, B. R. Srivatsa Kumar","doi":"10.1155/2024/6640697","DOIUrl":"https://doi.org/10.1155/2024/6640697","url":null,"abstract":"In the present work, utilizing the known series, new series involving reciprocals of binomial coefficients, alternating positive, and negative binomial coefficients are constructed. Further, several new series of reciprocals of binomial coefficients with two odd terms in the denominator are obtained. In the end, we use these to establish the closed form evaluations of hypergeometric functions for the argument 1/16.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"68 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139476044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Splitting Long Event Sequences Drawn from Cyclic Processes for Discovering Workflow Nets","authors":"Yolanda Alvarez-Pérez, Ernesto López-Mellado","doi":"10.1155/2024/7033048","DOIUrl":"https://doi.org/10.1155/2024/7033048","url":null,"abstract":"This paper addresses the preprocessing of event sequences issued from cyclic discrete event processes, which perform activities continuously whose delimitation of jobs or cases is not explicit. The sequences include several occurrences of the same events due to the iterative behaviour, such that discovery methods conceived for workflow nets (WFN) cannot process such sequences. In order to handle this issue, a novel technique for splitting a set of long event traces <i>S</i> = {<i>S</i>_k} (|<i>S</i>| ≥ 1) exhibiting the behaviour of cyclic processes is presented. The aim of this technique is to obtain from <i>S</i> a log <i>λ</i> = {<i>σ</i>_i} of event traces representing the same behaviour, which can be processed by methods that discover WFN. The procedures derived from this technique have polynomial-time complexity.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139082886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Study of Wax Deposition from Multiphase Flow in Oil Pipelines with Heat and Mass Transfer","authors":"Francis Oketch Ochieng, Mathew Ngugi Kinyanjui, Phineas Roy Kiogora, Jeconia Okelo Abonyo","doi":"10.1155/2023/1173505","DOIUrl":"https://doi.org/10.1155/2023/1173505","url":null,"abstract":"Wax deposition in field-scale crude oil pipelines poses a significant challenge to the oil and gas industry, leading to reduced flow rates, increased pressure drops, and potential blockages. Understanding the mechanisms governing wax deposition is crucial for developing effective mitigation strategies. This study investigates the impact of multiphase flow conditions, including water-in-oil emulsion, wax precipitation kinetics, shear dispersion, and molecular diffusion, on wax deposition in field-scale crude oil pipelines. A numerical model is developed that employs second-order semi-implicit temporal discretization schemes, such as Crank–Nicolson and Adams–Bashforth methods, in conjunction with a bivariate spectral collocation scheme using Chebyshev–Gauss–Lobatto grid points. The impact of various flow parameters, including Reynolds number (Re), mass Grashof number (Gr), Schmidt number (Sc), and Weber number (We), on the flow variables, wall shear stress, and heat and mass fluxes are investigated. The numerical simulations demonstrate that flow parameters significantly influence the flow behavior, wall shear stress, wall heat flux, and wall mass flux in waxy crude oil pipelines. Specifically, the aggregation of wax crystals in the pipeline decreases by at most 2.5% with increasing Reynolds number from 2.2361 to 3.1361. Conversely, it increases by at most 3.4% with increasing mass Grashof number from 5 to 11 and by at most 4.8% with increasing Weber number from 1.0 to 2.5. Furthermore, the Nusselt number increases from 1.9907 to 4.9834 with increasing Reynolds number from 2.2361 to 5.2361 and from 1.9907 to 2.0225 with increasing mass Grashof number from 5 to 20. It also increases from 1.9907 to 2.0434 with increasing Weber number from 1.0 to 2.5. The insights gained from this study can be applied to optimize pipeline design, operational parameters, and wax deposition mitigation strategies, leading to enhanced pipeline performance and reduced operational costs. The numerical model developed in this work serves as a valuable tool for simulating and predicting wax deposition behavior under various operating conditions.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139071646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stabilization and Discretization of the Coupled Heat and Wave Equations","authors":"Kun-Yi Yang, Xu Zhang","doi":"10.1155/2023/8901825","DOIUrl":"https://doi.org/10.1155/2023/8901825","url":null,"abstract":"In this paper, we consider the stabilization of the coupled heat and wave equations under the static feedback or the dynamic feedback. Moreover, we make the coupled systems discretized by using the finite-volume approach, and then we consider the stabilized properties of the discrete systems. First, for the coupled system under the static feedback, it is shown that the system is exponentially stable by using the Lyapunov method, and then the corresponding discrete system can be shown to be exponentially stable by constucting the discretized Lyapunov function. Second, for the coupled system under the dynamic feedback, we also show that both of the system and its discrete scheme are exponentially stable. Third, numerical simulations are given to show the effectiveness of the stable controllers.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"92 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139053423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Two-Archive Algorithm with Interactive Mechanism for Many-Objective Optimization","authors":"Shuren Liu, Shuping Li, Changchun Li, Changning Cai, Guojian Cheng","doi":"10.1155/2023/2005465","DOIUrl":"https://doi.org/10.1155/2023/2005465","url":null,"abstract":"This paper proposes an interactive two-archive method to solve many-objective optimization problems. Two updating strategies based on the aggregation-based framework are presented and incorporated into a two-archive framework. In addition, we further extend this method by introducing an interactive mechanism in which evolutionary information is passed from the diversity archive to the convergence archive. Given the requirement to balance convergence and diversity, a mating selection method is proposed to regulate the evolutionary speed of these two archives collaboratively. The proposed algorithm has been tested extensively on several problems with different peer algorithms to validate its effectiveness. The results show that the proposed method can outperform several state-of-the-art evolutionary algorithms for handling many-objective optimization.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"477 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138744305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals","authors":"Narmadha G, Deivasigamani S, Muthukumar Vellaisamy, Lídio Inácio Freitas, Badlishah Ahmad R, Sakthivel B","doi":"10.1155/2023/3356347","DOIUrl":"https://doi.org/10.1155/2023/3356347","url":null,"abstract":"An autonomic nervous system (ANS) of humans is majorly affected by psychological stress. The changes in ANS may cause several chronic diseases in humans. The electrocardiogram (ECG) signal is used to observe the variation in ANS. Numerous techniques are presented for an ECG stress signal handling feature extraction and classification. This work managed a heart rate variability feature acquired from smaller peak waveforms such as P, Q, S, and T waves. Also, the R peak is detected, which is a significant part of the ECG waveform. In this work, the proposed stress classification work has been categorized into two main processes: feature selection (FS) and classification. The main aim of the proposed work is to propose an optimized FS and classifier model for the detection of stress in ECG signals. The Metaheuristics model of the African vulture optimization (AVO) technique is presented to perform an FS. This selection is made to choose the required features and minimize the data for classification. The AVO-based modified Elman recurrent neural network (MERNN) technique is proposed to perform an efficient classification. The AVO is used for fine-tuning the weight of the MERNN technique. The experimental result of this technique is evaluated in terms of Recall (91.56%), Accuracy (92.43%), Precision (92.78%), and <i>F</i>1 score (95.86%). Thus, the proposed performance achieved a superior result than the conventional techniques.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138580761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting Credit Risk of SMEs in Supply Chain Finance Using Bayesian Optimization and XGBoost","authors":"Chen Zhang, Xinmiao Zhou","doi":"10.1155/2023/5609996","DOIUrl":"https://doi.org/10.1155/2023/5609996","url":null,"abstract":"Supply chain finance plays a crucial role as a financing channel for small- and medium-sized enterprises (SMEs). However, issues such as financial problems and credit defaults have led to disruptions in this channel. To address credit risk control in SME financing within the field of supply chain finance, this paper focuses on a sample of 506 equipment manufacturing companies listed on the SME board of the Shenzhen Stock Exchange from 2016 to 2020. Taking into consideration, the overall risks faced by these enterprises, the study establishes seven first-level indicators and identifies 84 candidate second-level indicators. Partial correlation and variance analysis are then used for the first round of indicator screening, followed by the use of a BP neural network for the second round of selection. As a result, a system of 26 indicators for supply chain financial risk is constructed. The XGBoost model is employed to evaluate the constructed risk index system, while SVM and random forest models are used as comparison models. Bayesian optimization is utilized for parameter tuning of the three models. Empirical results demonstrate that the BO-XGBoost model reduces prediction errors in comparison to the control models. Furthermore, statistical tests reveal that the predicted values of the BO-XGBoost model significantly differ from those of the other control models. Compared to other individual models, the BO-XGBoost model exhibits increased accuracy in credit risk prediction and a significant discriminative effect. These findings highlight the effectiveness of constructing an efficient risk indicator system and utilizing Bayesian optimization for parameter tuning in XGBoost to better differentiate between risky and normal enterprises, thereby minimizing default losses. The research results underscore the advantages of employing Bayesian optimization in XGBoost, which can be applied in credit default prediction for SMEs and serves as a valuable tool in financial risk management and control.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138568281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Aggregation with Two-Layer Ensemble Framework for Multilingual Speech Emotion Recognition","authors":"Sangho Ough, Sejong Pyo, Taeyong Kim","doi":"10.1155/2023/8837465","DOIUrl":"https://doi.org/10.1155/2023/8837465","url":null,"abstract":"In this study, we present a framework for improving the accuracy of speech emotion recognition in a multilingual environment. In our prior experiments, where machine learning (ML) models were trained to predict emotions in Korean and then tested in English, as well as vice versa, we observed a dependency on language in emotion recognition, resulting in poor accuracy. We suspect that this may be related to the spectral differences in certain emotions between Korean and English and to the tendency for different formant values to have different acoustic frequencies. For this study, we investigated several different methods, including models with mixed databases, a single database, and bagging, boosting, and voting ML algorithms. Finally, we developed a framework consisting of two branches: one for the aggregation of high-dimensional features from multilingual data and one for a two-layered ensemble framework for emotion classification. In the ensemble framework for Korean and English (EF-KEN), features are extracted and ensemble models are trained, boosted, and evaluated by applying them to different spoken languages (English and Korean). The final experimental result demonstrates a meaningful improvement in an environment with two different languages.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138568038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Energy Efficient Evolutionary Approach for Smart City-Based IoT Applications","authors":"Rashmi Prava Das, Tushar Kanta Samal, Ashish Kr. Luhach","doi":"10.1155/2023/9937949","DOIUrl":"https://doi.org/10.1155/2023/9937949","url":null,"abstract":"Internet of Things (IoT) has been used in smart cities, agriculture, weather forecasting, smart grids, and waste management. The IoT has huge potential but needs refinement. The paper focuses on lowering IoT sensor power consumption to improve network life. This work selects the best IoT cluster header (CH) to maximize energy consumption. The suggested technique uses particle swarm optimization (PSO) with artificial neural networks (ANNs). The optimal CH in an IoT network cluster was identified by taking into account the number of active nodes, the load, the residual energy, and the cost function. This work compares the suggested method with artificial bee colony, genetic, and adaptive gravity search algorithms. The hybrid solution beats conventional methods.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138580835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Chaotic Multi-Objective Runge–Kutta Optimization Algorithm for Optimized Circuit Design","authors":"Owen M. Nyandieka, Davies R. Segera","doi":"10.1155/2023/6691214","DOIUrl":"https://doi.org/10.1155/2023/6691214","url":null,"abstract":"Circuit design plays a pivotal role in engineering, ensuring the creation of efficient, reliable, and cost-effective electronic devices. The complexity of modern circuit design problems has led to the exploration of multi-objective optimization techniques for circuit design optimization, as traditional optimization tools fall short in handling such problems. While metaheuristic algorithms, especially genetic algorithms, have demonstrated promise, their susceptibility to premature convergence poses challenges. This paper proposes a pioneering approach, the chaotic multi-objective Runge–Kutta algorithm (CMRUN), for circuit design optimization, building upon the Runge–Kutta optimization algorithm. By infusing chaos into the core RUN structure, a refined balance between exploration and exploitation is obtained, critical for addressing complex optimization landscapes, enabling the algorithm to navigate nonlinear and nonconvex optimization challenges effectively. This approach is extended to accommodate multiple objectives, ultimately generating Pareto Fronts for the multiple circuit design goals. The performance of CMRUN is rigorously evaluated against 11 multiobjective algorithms, encompassing 15 benchmark test functions and practical circuit design scenarios. The findings of this study underscore the efficiency and real-world applicability of CMRUN, offering valuable insights for tailoring optimization algorithms to the real-world circuit design challenges.","PeriodicalId":18319,"journal":{"name":"Mathematical Problems in Engineering","volume":"284 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138580756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}