Research

15 pages, 1632 KiB

Open AccessArticle

APSO-SL: An Adaptive Particle Swarm Optimization with State-Based Learning Strategy

by Mingqiang Gao and Xu Yang

Processes 2024, 12(2), 400; https://doi.org/10.3390/pr12020400 - 17 Feb 2024

Viewed by 470

Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization [...] Read more.

Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization with state-based learning strategy (APSO-SL) is put forward. In APSO-SL, the population distribution evaluation mechanism (PDEM) is used to evaluate the state of the whole population. In contrast to using iterations to just the population state, using the population spatial distribution is more intuitive and accurate. In PDEM, the population center position and best position for calculation are used for calculation, greatly reducing the algorithm’s computational complexity. In addition, an adaptive learning strategy (ALS) has been proposed to avoid the whole population’s premature convergence. In ALS, different learning strategies are adopted according to the population state to ensure the population diversity. The performance of APSO-SL is evaluated on the CEC2013 and CEC2017 test suites, and one engineering problem. Experimental results show that APSO-SL has the best performance compared with other competitive PSO variants. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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15 pages, 1947 KiB

Open AccessArticle

An Approach to Data Modeling via Temporal and Spatial Alignment

by Dapeng Zhang, Kaixuan Sun and Shumei Zhang

Processes 2024, 12(1), 62; https://doi.org/10.3390/pr12010062 - 27 Dec 2023

Cited by 1 | Viewed by 602

Abstract

It is important for data modeling to comply with a data observation window of physical variables behind the data. In this paper, a multivariate data alignment method is proposed to follow different time scales and different role effects. First, the length of the [...] Read more.

It is important for data modeling to comply with a data observation window of physical variables behind the data. In this paper, a multivariate data alignment method is proposed to follow different time scales and different role effects. First, the length of the sliding windows is determined by the frequency characteristics of the time-series reconstruction. Then, the time series is aligned to the length of the window by a sequence-to-sequence neural network. This neural network is trained by replacing the loss function with dynamic time warping (DTW) in order to prevent the losses of the time series. Finally, the attention mechanism is introduced to adjust the effect of different variables, which ensures that the data model of the matrix is in accord with the intrinsic relation of the actual system. The effectiveness of the approach is demonstrated and validated by the Tennessee Eastman (TE) model. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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12 pages, 4491 KiB

Open AccessArticle

From Segmentation to Classification: A Deep Learning Scheme for Sintered Surface Images Processing

by Yi Yang, Tengtuo Chen and Liang Zhao

Processes 2024, 12(1), 53; https://doi.org/10.3390/pr12010053 - 25 Dec 2023

Viewed by 691

Abstract

Effectively managing the quality of iron ore is critical to iron and steel metallurgy. Although quality inspection is crucial, the perspective of sintered surface identification remains largely unexplored. To bridge this gap, we propose a deep learning scheme for mining the necessary information [...] Read more.

Effectively managing the quality of iron ore is critical to iron and steel metallurgy. Although quality inspection is crucial, the perspective of sintered surface identification remains largely unexplored. To bridge this gap, we propose a deep learning scheme for mining the necessary information in sintered images processing to replace manual labor and realize intelligent inspection, consisting of segmentation and classification. Specifically, we first employ a DeepLabv3+ semantic segmentation algorithm to extract the effective material surface features. Unlike the original model, which includes a high number of computational parameters, we use SqueezeNet as the backbone to improve model efficiency. Based on the initial annotation of the processed images, the sintered surface dataset is constructed. Then, considering the scarcity of labeled data, a semi-supervised deep learning scheme for sintered surface classification is developed, which is based on pseudo-labels. Experiments show that the improved semantic segmentation model can effectively segment the sintered surface, achieving 98.01% segmentation accuracy with only a 5.71 MB size. In addition, the effectiveness of the adopted semi-supervised learning classification method based on pseudo-labels is validated in six state-of-the-art models. Among them, the ResNet-101 model has the best classification performance, with 94.73% accuracy for the semi-supervised strategy while only using 30% labeled data, which is an improvement of 1.66% compared with the fully supervised strategy. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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22 pages, 7345 KiB

Open AccessArticle

Research on Contact Anchoring Theory and Contact Optimization of Underwater Pipeline Recovery Tools

by Haixia Gong, Zhuoran Ping, Tong Zhao, Shuping Hou, Fuqiang Zu, Pengyue Qiu and Jianguo Qin

Processes 2023, 11(11), 3166; https://doi.org/10.3390/pr11113166 - 06 Nov 2023

Viewed by 793

Abstract

Technology for recycling abandoned undersea pipelines is crucial for lowering the cost of installing new submarine pipelines, polluting the ocean less, and improving recycling efficiency. A popular area of study is how to lessen the harm that underwater pipeline recycling instruments do to [...] Read more.

Technology for recycling abandoned undersea pipelines is crucial for lowering the cost of installing new submarine pipelines, polluting the ocean less, and improving recycling efficiency. A popular area of study is how to lessen the harm that underwater pipeline recycling instruments do to the inner wall of the pipeline during recycling. In order to recover pipelines, this study examines the anchoring theory and damage process of submerged pipeline recovery equipment. The effect of the contact body’s diameter and radius of the rounded corner on the depth of the pressed-in pipeline and the slip distance is examined using the contact body structure optimization design approach of the underwater pipeline recovery tool, which is based on a multi-objective genetic algorithm. Dynamic simulations of the insertion mechanism as a whole are performed using the Adams simulation program to make sure that the optimized contact body can exert enough contact force on the pipeline’s inner wall. According to the optimization results, the ideal design parameters are D = 57 mm and R = 11.5 mm. While still satisfying the criteria, the improved contact body has higher stability. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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11 pages, 276 KiB

Open AccessArticle

Scheduling Jobs with a Limited Waiting Time Constraint on a Hybrid Flowshop

by Sang-Oh Shim, BongJoo Jeong, June-Yong Bang and JeongMin Park

Processes 2023, 11(6), 1846; https://doi.org/10.3390/pr11061846 - 19 Jun 2023

Cited by 1 | Viewed by 848

Abstract

In this paper, we address a two-stage hybrid flowshop scheduling problem with identical parallel machines in each stage. The problem assumes that the queue (Q)-time for each job, which represents the waiting time to be processed in the current stage, must be limited [...] Read more.

In this paper, we address a two-stage hybrid flowshop scheduling problem with identical parallel machines in each stage. The problem assumes that the queue (Q)-time for each job, which represents the waiting time to be processed in the current stage, must be limited to a predetermined threshold due to quality concerns for the final product. This problem is motivated by one that occurs in the real field, especially in the diffusion workstation of a semiconductor fabrication. Our objective is to minimize the makespan of the jobs while considering product quality. To achieve this goal, we formulated mathematical programming, developed two dominance properties for this problem, and proposed three heuristics with the suggested dominance properties to solve the considered problem. We conducted simulation experiments to evaluate the performance of the proposed approaches using randomly generated problem instances that are created to closely resemble real production scenarios, and the results demonstrate their superiority over existing methods. Furthermore, we applied the proposed methods in a real-world setting within the semiconductor fabrication industry, where they have exhibited better performance compared to the dispatching rules commonly used in practical applications. These findings validate the effectiveness and applicability of our proposed methodologies in real-world scenarios. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

19 pages, 9487 KiB

Open AccessArticle

Improved Active Islanding Detection Technique with Different Current Injection Waveform

by Shaoru Zhang, Lijun Wang, Xiuju Du, Ruiye Zhang, Zhanping Huang, Shuchun Duan, Wenxiu Yang, Pingjun Wang and Jielu Zhang

Processes 2023, 11(6), 1838; https://doi.org/10.3390/pr11061838 - 18 Jun 2023

Cited by 3 | Viewed by 982

Abstract

The active frequency drift (AFD) method is an effective method to detect islanding in grid-connected photovoltaic systems. However, it has some inherent drawbacks, such as generating higher harmonics. In order to reduce the harmonics and non-detection zone (NDZ), various improved AFD methods have [...] Read more.

The active frequency drift (AFD) method is an effective method to detect islanding in grid-connected photovoltaic systems. However, it has some inherent drawbacks, such as generating higher harmonics. In order to reduce the harmonics and non-detection zone (NDZ), various improved AFD methods have been proposed, but they still suffer from high harmonics and reduced detection speed. To overcome these limitations, this paper proposes an innovative islanding detection technique based on AFD. Analysis reveals that the proposed method reduces harmonics by 68% compared to conventional AFD and has a larger chopping factor. Therefore, this technique offers several distinct advantages, including accelerated detection speed, reduced NDZ and harm caused by disturbances, and improved power quality. Furthermore, to verify the harmonic impact of this proposed islanding detection method, simulations and analyses are conducted using simulation software of Matlab/Simulink. An experimental prototype is set up in Laboratory. The simulation and experimental results demonstrate the superiority of the proposed method. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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19 pages, 7065 KiB

Open AccessArticle

Composite Fault Diagnosis of Aviation Generator Based on EnFWA-DBN

by Zhangang Yang, Xingwang Bao, Qingyu Zhou and Juan Yang

Processes 2023, 11(5), 1577; https://doi.org/10.3390/pr11051577 - 22 May 2023

Viewed by 903

Abstract

Because of the existence of composite faults, which consist of both short-out and eccentricity faults, the characteristics of the output voltage and internal magnetic field of aviation generators are less different than those of single short-out faults; this causes the eccentricity fault to [...] Read more.

Because of the existence of composite faults, which consist of both short-out and eccentricity faults, the characteristics of the output voltage and internal magnetic field of aviation generators are less different than those of single short-out faults; this causes the eccentricity fault to be difficult to identify. In order to solve this problem, this paper proposes a fault diagnosis method using an enhanced fireworks algorithm (EnFWA) to optimize a deep belief network (DBN). The aviation generator model is built according to the finite element method (FEM), whereas the output of different combinations of composite faults are obtained using simulations. The EnFWA algorithm is used to train and optimize the DBN network to obtain the best structure. Meanwhile, an extreme learning machine (ELM) classifier performs fault diagnosis and classification on the test data. The diagnosis results show that a pinpoint accuracy can be achieved using the proposed method in the diagnosis of composite faults in aviation generators. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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24 pages, 3883 KiB

Open AccessArticle

Some Logarithmic Intuitionistic Fuzzy Einstein Aggregation Operators under Confidence Level

by Khaista Rahman, Ibrahim M. Hezam, Darko Božanić, Adis Puška and Miloš Milovančević

Processes 2023, 11(4), 1298; https://doi.org/10.3390/pr11041298 - 21 Apr 2023

Cited by 1 | Viewed by 1017

Abstract

The objective of this paper is to introduce some new logarithm operational laws for intuitionistic fuzzy sets. Some structure properties have been developed and based on these, various aggregation operators, namely confidence logarithmic intuitionistic fuzzy Einstein weighted geometric (CLIFEWG) operator, confidence logarithmic intuitionistic [...] Read more.

The objective of this paper is to introduce some new logarithm operational laws for intuitionistic fuzzy sets. Some structure properties have been developed and based on these, various aggregation operators, namely confidence logarithmic intuitionistic fuzzy Einstein weighted geometric (CLIFEWG) operator, confidence logarithmic intuitionistic fuzzy Einstein ordered weighted geometric (CLIFEOWG) operator, confidence logarithmic intuitionistic fuzzy Einstein hybrid geometric (CLIFEHG) operator, confidence logarithmic intuitionistic fuzzy Einstein weighted averaging (CLIFEWA) operator, confidence logarithmic intuitionistic fuzzy Einstein ordered weighted averaging (CLIFEOWA) operator, confidence logarithmic intuitionistic fuzzy Einstein hybrid averaging (CLIFEHA) operator have been presented. To show the validity and the superiority of the proposed operators, we compared these methods with the existing methods and concluded from the comparison and sensitivity analysis our proposed techniques are more effective. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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19 pages, 2879 KiB

Open AccessArticle

Energy-Efficient Bi-Objective Optimization Based on the Moth–Flame Algorithm for Cluster Head Selection in a Wireless Sensor Network

by Mahmoud Z. Mistarihi, Haythem A. Bany Salameh, Mohammad Adnan Alsaadi, Omer F. Beyca, Laila Heilat and Raya Al-Shobaki

Processes 2023, 11(2), 534; https://doi.org/10.3390/pr11020534 - 09 Feb 2023

Cited by 6 | Viewed by 1298

Abstract

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of [...] Read more.

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth–Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes’ residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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21 pages, 3829 KiB

Open AccessArticle

Improving Process Efficiency at Pediatric Hospital Emergency Department Using an Integrated Six-Sigma Simulation Methodology

by Mahmoud Z. Mistarihi, Mohammad D. AL-Tahat and Saif H. AL-Nimer

Processes 2023, 11(2), 399; https://doi.org/10.3390/pr11020399 - 28 Jan 2023

Viewed by 2488

Abstract

Inadequate staffing and long waiting times in hospital emergency rooms are key concerns that can have a negative impact on patient safety and health, as well as the hospital’s overall performance. The purpose of this paper is to investigate the scope of combining [...] Read more.

Inadequate staffing and long waiting times in hospital emergency rooms are key concerns that can have a negative impact on patient safety and health, as well as the hospital’s overall performance. The purpose of this paper is to investigate the scope of combining the DMAIC (define, measure, analyze, improve, and control) methodology with discrete event simulation and to explore its successful deployment in the Jordanian healthcare sector. The study discussed in this paper is based on a case study conducted utilizing the DMAIC and simulation technique and its application in reducing waiting time and enhancing overall system efficiency in Jordan’s Princess Rahma hospital’s pediatric emergency department. The study shows improvements in the performance of the process and thus productivity in the emergency department through adapting the combined Six Sigma DES methodology. The cycle time of the process was reduced by 73% of the present value, while simultaneously enhancing the total performance of the emergency department by 83%. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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17 pages, 2484 KiB

Open AccessArticle

Multi-Time Scale Optimal Scheduling Model of Wind and Hydrogen Integrated Energy System Based on Carbon Trading

by Xuan Wen, Bo Sun, Bing Gu and Yan Lv

Processes 2023, 11(2), 344; https://doi.org/10.3390/pr11020344 - 20 Jan 2023

Viewed by 1186

Abstract

In the context of carbon trading, energy conservation and emissions reduction are the development directions of integrated energy systems. In order to meet the development requirements of energy conservation and emissions reduction in the power grid, considering the different responses of the system [...] Read more.

In the context of carbon trading, energy conservation and emissions reduction are the development directions of integrated energy systems. In order to meet the development requirements of energy conservation and emissions reduction in the power grid, considering the different responses of the system in different time periods, a wind-hydrogen integrated multi-time scale energy scheduling model was established to optimize the energy-consumption scheduling problem of the system. As the scheduling model is a multiobjective nonlinear problem, the artificial fish swarm algorithm–shuffled frog leaping algorithm (AFS-SFLA) was used to solve the scheduling model to achieve system optimization. In the experimental test process, the Griewank benchmark function and the Rosenbrock function were selected to test the performance of the proposed AFS-SFL algorithm. In the Griewank environment, compared to the SFLA algorithm, the AFS-SFL algorithm was able to find a feasible solution at an early stage, and tended to converge after 110 iterations. The optimal solution was −4.83. In the test of total electric power deviation results at different time scales, the maximum deviation of early dispatching was 14.58 MW, and the minimum deviation was 0.56 MW. The overall deviation of real-time scheduling was the minimum, and the minimum deviation was 0 and the maximum deviation was 1.89 WM. The integrated energy system adopted real-time scale dispatching, with good system stability and low-energy consumption. Power system dispatching optimization belongs to the objective optimization problem. The artificial fish swarm algorithm and frog algorithm were innovatively combined to solve the dispatching model, which improved the accuracy of power grid dispatching. The research content provides an effective reference for the efficient use of clean and renewable energy. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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15 pages, 446 KiB

Open AccessArticle

Can Direct Subsidies or Tax Incentives Improve the R&D Efficiency of the Manufacturing Industry in China?

by Zhen Liu and Xijun Zhou

Processes 2023, 11(1), 181; https://doi.org/10.3390/pr11010181 - 06 Jan 2023

Cited by 3 | Viewed by 1822

Abstract

The understanding of the impact of different government support methods on R&D efficiency is of great significance for evaluating the performance of innovation policies in various countries. We selected 31 manufacturing industries in China from 2009 to 2015, used the stochastic frontier analysis [...] Read more.

The understanding of the impact of different government support methods on R&D efficiency is of great significance for evaluating the performance of innovation policies in various countries. We selected 31 manufacturing industries in China from 2009 to 2015, used the stochastic frontier analysis (SFA) method to measure R&D efficiency, and used tobit regression method to examine the relationship between direct government subsidies and preferential tax policies and manufacturing R&D efficiency. The results reveal that the overall R&D efficiency of China’s manufacturing industry was low, but it has been steadily increasing, and the R&D efficiency of emerging industries was significantly higher than that of traditional industries. Tax incentives played a stable and significant role in promoting R&D efficiency in manufacturing. Affected by factors such as the government’s long-term preference and information asymmetry, direct subsidies had no significant impact on the current R&D efficiency of the manufacturing industry, and began to play a positive role after a two-year lag. Based on the above research findings, this paper suggests that progressive preferential tax rates can be designed according to the “base + increment” approach for tax preferential policies. At the same time, different proportions of tax cuts should be set for enterprises of different sizes and levels of innovation, and the focus should be on small and medium-sized enterprises and emerging industries. In terms of direct funding subsidies, the government should not only increase the support for basic research, but also give more preference to enterprises that receive tax incentives for research and development, so as to enhance the complementary effect of the two types of subsidy policies. The marginal contribution of this paper mainly includes three aspects: First, based on the Chinese situation, the impact of direct government subsidies and tax incentives on the R&D efficiency of the manufacturing industry is tested. Second, we present the evidence that direct government funding subsidies “crowd out” enterprise R&D funds. Thirdly, we describe the influence of enterprise scale, innovation level, ownership, and management ability on R&D efficiency of the manufacturing industry, and put forward the possible influence mechanism. Full article

(This article belongs to the Special Issue Design, Modeling, Optimization and Control in Manufacturing Industries and Energy System)

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