Axioms

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37 pages, 5937 KiB

Open AccessArticle

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

by You-Shyang Chen, Ying-Hsun Hung, Mike Yau-Jung Lee, Chien-Jung Lai, Jieh-Ren Chang and Chih-Yao Chien

Axioms 2024, 13(1), 54; https://doi.org/10.3390/axioms13010054 - 16 Jan 2024

Cited by 1 | Viewed by 1659

Abstract

With the vigorous development of emerging technology and the advent of the Internet generation, high-speed Internet and fast transmission 5G wireless networks contribute to interpersonal communication. Now, the Internet has become popular and widely available, and human life is inseparable from various experiences [...] Read more.

With the vigorous development of emerging technology and the advent of the Internet generation, high-speed Internet and fast transmission 5G wireless networks contribute to interpersonal communication. Now, the Internet has become popular and widely available, and human life is inseparable from various experiences on the Internet. Many base stations and data centers have been established to convert and switch from electrical transmission to optical transmission; thus, it is entering the new era of optical fiber networks and optical communication technologies. For optical communication, the manufacturing of components for the purpose of high-speed networks is a key process, and the requirement for the stability of its production conditions is very strict. In particular, product yields are always low due to the restriction of high-precision specifications associated with the limitations of too many factors. Given these reasons, this study proposes a hybrid fuzzy control-based model for industry data applications to organize advanced techniques of box-and-whisker plot method, association rule, and decision trees to find out the determinants that affect the yield rate of products and then use the fuzzy control Proportional-Integral-Derivative (PID) method to manage the determinants. Since it is unrealistic to test the real machine online operation at the manufacturing stage, the simulation software supersedes this for improved results, and a mathematical neural network is used to verify the given data to confirm whether its result is similar to that of the simulation. The study suggests that excessive temperature differentials between substrate and cavity can lead to low yields. It suggests using fuzzy control technology for temperature management, which could increase yield, reduce labor costs, and accelerate the transition to high-speed networks by mass-producing high-precision optical filters. Full article

(This article belongs to the Special Issue Application of Machine Learning and Optimization Methods in Engineering Mathematics)

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19 pages, 6282 KiB

Open AccessArticle

Enhanced Remora Optimization with Deep Learning Model for Intelligent PMSM Drives Temperature Prediction in Electric Vehicles

by Abdul Latif, Ibrahim M. Mehedi, Mahendiran T. Vellingiri, Rahtul Jannat Meem and Thangam Palaniswamy

Axioms 2023, 12(9), 852; https://doi.org/10.3390/axioms12090852 - 31 Aug 2023

Cited by 5 | Viewed by 1557

Abstract

One of the widespread electric motors for electric vehicles (EVs) is permanent magnet synchronous machine (PMSM) drives. It is because of the power density and high energy of the PMSM with moderate assembly cost. The widely adopted PMSM as the motor of choice [...] Read more.

One of the widespread electric motors for electric vehicles (EVs) is permanent magnet synchronous machine (PMSM) drives. It is because of the power density and high energy of the PMSM with moderate assembly cost. The widely adopted PMSM as the motor of choice for EVs, together with variety of applications urges stringent monitoring of temperature to ignore high temperatures. Temperature monitoring of the PMSM is highly complex to accomplish because of complex measurement device for internal components of the PMSM. Temperature values beyond a certain range might result in additional maintenance costs together with major operational problems in PMSM. The latest developments in artificial intelligence (AI) and deep learning (DL) methods pave a way for accurate temperature prediction in PMSM drivers. With this motivation, this article introduces an enhanced remora optimization algorithm with stacked bidirectional long short-term memory (EROA-SBiLSTM) approach for temperature prediction of the PMSM drives. The presented EROA-SBiLSTM technique mainly focuses on effectual temperature prediction using DL and hyperparameter tuning schemes. To accomplish this, the EROA-SBiLSTM technique applies Pearson correlation coefficient analysis for observing the correlation among various features, and the p-value is utilized for determining the relevant level. Next, the SBiLSTM model is used to predict the level of temperature that exists in the PMSM drivers. Finally, the EROA based hyperparameter tuning process is carried out to adjust the SBiLSTM parameters optimally. The experimental outcome of the EROA-SBiLSTM technique is tested using electric motor temperature dataset from the Kaggle dataset. The comprehensive study specifies the betterment of the EROA-SBiLSTM technique. Full article

(This article belongs to the Special Issue Application of Machine Learning and Optimization Methods in Engineering Mathematics)

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24 pages, 3233 KiB

Open AccessArticle

Optimal Reinsurance–Investment Strategy Based on Stochastic Volatility and the Stochastic Interest Rate Model

by Honghan Bei, Qian Wang, Yajie Wang, Wenyang Wang and Roberto Murcio

Axioms 2023, 12(8), 736; https://doi.org/10.3390/axioms12080736 - 27 Jul 2023

Cited by 1 | Viewed by 1435

Abstract

This paper studies insurance companies’ optimal reinsurance–investment strategy under the stochastic interest rate and stochastic volatility model, taking the HARA utility function as the optimal criterion. It uses arithmetic Brownian motion as a diffusion approximation of the insurer’s surplus process and the variance [...] Read more.

This paper studies insurance companies’ optimal reinsurance–investment strategy under the stochastic interest rate and stochastic volatility model, taking the HARA utility function as the optimal criterion. It uses arithmetic Brownian motion as a diffusion approximation of the insurer’s surplus process and the variance premium principle to calculate premiums. In this paper, we assume that insurance companies can invest in risk-free assets, risky assets, and zero-coupon bonds, where the Cox–Ingersoll–Ross model describes the dynamic change in stochastic interest rates and the Heston model describes the price process of risky assets. The analytic solution of the optimal reinsurance–investment strategy is deduced by employing related methods from the stochastic optimal control theory, the stochastic analysis theory, and the dynamic programming principle. Finally, the influence of model parameters on the optimal reinsurance–investment strategy is illustrated using numerical examples. Full article

(This article belongs to the Special Issue Application of Machine Learning and Optimization Methods in Engineering Mathematics)

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24 pages, 830 KiB

Open AccessArticle

Robust Fisher-Regularized Twin Extreme Learning Machine with Capped L₁-Norm for Classification

by Zhenxia Xue and Linchao Cai

Axioms 2023, 12(7), 717; https://doi.org/10.3390/axioms12070717 - 24 Jul 2023

Cited by 3 | Viewed by 1251

Abstract

Twin extreme learning machine (TELM) is a classical and high-efficiency classifier. However, it neglects the statistical knowledge hidden inside the data. In this paper, in order to make full use of statistical information from sample data, we first come up with a Fisher-regularized [...] Read more.

Twin extreme learning machine (TELM) is a classical and high-efficiency classifier. However, it neglects the statistical knowledge hidden inside the data. In this paper, in order to make full use of statistical information from sample data, we first come up with a Fisher-regularized twin extreme learning machine (FTELM) by applying Fisher regularization into TELM learning framework. This strategy not only inherits the advantages of TELM, but also minimizes the within-class divergence of samples. Further, in an effort to further boost the anti-noise ability of FTELM method, we propose a new capped

L_{1}

-norm FTELM (C

L_{1}

-FTELM) by introducing capped

L_{1}

-norm in FTELM to dwindle the influence of abnormal points, and C

L_{1}

-FTELM improves the robust performance of our FTELM. Then, for the proposed FTELM method, we utilize an efficient successive overrelaxation algorithm to solve the corresponding optimization problem. For the proposed C

L_{1}

-FTELM, an iterative method is designed to solve the corresponding optimization based on re-weighted technique. Meanwhile, the convergence and local optimality of C

L_{1}

-FTELM are proved theoretically. Finally, numerical experiments on manual and UCI datasets show that the proposed methods achieve better classification effects than the state-of-the-art methods in most cases, which demonstrates the effectiveness and stability of the proposed methods. Full article

(This article belongs to the Special Issue Application of Machine Learning and Optimization Methods in Engineering Mathematics)

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13 pages, 829 KiB

Open AccessArticle

Analysis of Water Infiltration under Impermeable Dams by Analytical and Boundary Element Methods in Complex

by Elena Corina Cipu

Axioms 2023, 12(7), 654; https://doi.org/10.3390/axioms12070654 - 30 Jun 2023

Viewed by 1089

Abstract

The boundary element method (BEM) is used by applying Cauchy’s formula to the boundary of the water movement domain under a dam. By approximating the border with a polygon through linear interpolation, the relationships between the complex velocities on each edge of the [...] Read more.

The boundary element method (BEM) is used by applying Cauchy’s formula to the boundary of the water movement domain under a dam. By approximating the border with a polygon through linear interpolation, the relationships between the complex velocities on each edge of the polygon are analytically deduced. For the case of the flow domain described by a semi- circular closed contours, the numerical values of the velocity are computed and compared with those obtained only analytically. Conclusions on the analytical and numerical context are drawn. Full article

(This article belongs to the Special Issue Application of Machine Learning and Optimization Methods in Engineering Mathematics)

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25 pages, 4641 KiB

Open AccessArticle

Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data

by Eloi Garcia, Mohammad Peyman, Carles Serrat and Fatos Xhafa

Axioms 2023, 12(4), 349; https://doi.org/10.3390/axioms12040349 - 1 Apr 2023

Cited by 5 | Viewed by 1915

Abstract

In this paper, we present a novel framework for enriching time series data in smart cities by supplementing it with information from external sources via semantic data enrichment. Our methodology effectively merges multiple data sources into a uniform time series, while addressing difficulties [...] Read more.

In this paper, we present a novel framework for enriching time series data in smart cities by supplementing it with information from external sources via semantic data enrichment. Our methodology effectively merges multiple data sources into a uniform time series, while addressing difficulties such as data quality, contextual information, and time lapses. We demonstrate the efficacy of our method through a case study in Barcelona, which permitted the use of advanced analysis methods such as windowed cross-correlation and peak picking. The resulting time series data can be used to determine traffic patterns and has potential uses in other smart city sectors, such as air quality, energy efficiency, and public safety. Interactive dashboards enable stakeholders to visualize and summarize key insights and patterns. Full article

(This article belongs to the Special Issue Application of Machine Learning and Optimization Methods in Engineering Mathematics)

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23 pages, 3387 KiB

Open AccessArticle

Interval Valued Pythagorean Fuzzy AHP Integrated Model in a Smartness Assessment Framework of Buildings

by Mimica R. Milošević, Dušan M. Milošević, Dragan M. Stević and Miljan Kovačević

Axioms 2023, 12(3), 286; https://doi.org/10.3390/axioms12030286 - 9 Mar 2023

Cited by 5 | Viewed by 2409

Abstract

Buildings can be made more user-friendly and secure by putting “smart” design strategies and technology processes in place. Such strategies and processes increase energy efficiency, make it possible to use resources rationally, and lower maintenance and construction costs. In addition to using wireless [...] Read more.

Buildings can be made more user-friendly and secure by putting “smart” design strategies and technology processes in place. Such strategies and processes increase energy efficiency, make it possible to use resources rationally, and lower maintenance and construction costs. In addition to using wireless technologies and sensors to improve thermal, visual, and acoustic comfort, “smart” buildings are known for their energy, materials, water, and land management systems. Smart buildings use wireless technologies and sensors to improve thermal, visual, and acoustic comfort. These systems are known for managing energy, materials, water, and land. The task of the study is to consider the indicators that form the basis of the framework for evaluating intelligent buildings. The indicators for the development of “smart” buildings are classified into six categories in this paper: green building construction, energy management systems, safety and security management systems, occupant comfort and health, building automation and control management systems, and communication and data sharing. The paper aims to develop a scoring model for the smartness of public buildings. In developing the scoring system, the decision-making process requires an appropriate selection of the optimal solution. The contents of the research are the methods known as the Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP), Interval Valued Pythagorean Fuzzy AHP with differences (IVPF-AHP d), and the proposed method Interval Valued Pythagorean Fuzzy AHP (IVPF-AHP p). The research focuses on the IVPF-AHP as one of the methods of Multi-Criteria Decision-Making (MCDM) and its implementation. The comparative analysis of the three presented methods indicates a significant degree of similarity in the ranking, which confirms the ranking similarity. The results highlight the importance of bioclimatic design, smart metering, ecological materials, and renewable energy systems. Full article

(This article belongs to the Special Issue Application of Machine Learning and Optimization Methods in Engineering Mathematics)

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19 pages, 6133 KiB

Open AccessArticle

Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique

by Musa Adamu, Andaç Batur Çolak, Yasser E. Ibrahim, Sadi I. Haruna and Mukhtar Fatihu Hamza

Axioms 2023, 12(1), 81; https://doi.org/10.3390/axioms12010081 - 12 Jan 2023

Cited by 20 | Viewed by 2169

Abstract

The use of enormous amounts of material is required for production. Due to the current emphasis on the environment and sustainability of materials, waste products and by-products, including silica fume and fly ash (FA), are incorporated into concrete as a substitute partially for [...] Read more.

The use of enormous amounts of material is required for production. Due to the current emphasis on the environment and sustainability of materials, waste products and by-products, including silica fume and fly ash (FA), are incorporated into concrete as a substitute partially for cement. Additionally, concrete fine aggregate has indeed been largely replaced by waste materials like crumb rubber (CR), thus it reduces the mechanical properties but improved some other properties of the concrete. To decrease the detrimental effects of the CR, concrete is therefore enhanced with nanomaterials such nano silica (NS). The concrete mechanical properties are essential for the designing and constRuction of concrete structures. Concrete with several variables can have its mechanical characteristics predicted by an artificial neural network (ANN) technique. Using ANN approaches, this paper predict the mechanical characteristics of concrete constructed with FA as a partial substitute for cement, CR as a partial replacement for fine aggregate, and NS as an addition. Using an artificial neural network (ANN) technique, the mechanical characteristics investigated comprise splitting tensile strength (Fs), compressive strength (Fc), modulus of elasticity (Ec) and flexural strength (Ff). The ANN model was used to train and test the dataset obtained from the experimental program. Fc, Fs, F_f and Ec were predicted from added admixtures such as CR, NS, FA and curing age (P). The modelling result indicated that ANN predicted the strength with high accuracy. The proportional deviation mean (MoD) values calculated for F_c, F_s, F_f and E_c values were −0.28%, 0.14%, 0.87% and 1.17%, respectively, which are closed to zero line. The resulting ANN model’s mean square error (MSE) values and coefficient of determination (R²) are 6.45 × 10⁻² and 0.99496, respectively. Full article

(This article belongs to the Special Issue Application of Machine Learning and Optimization Methods in Engineering Mathematics)

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