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Practical Applications of New Optimization Methods and Intelligent Control

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 57841

Special Issue Editors

Prof. Dr. Jin Guo

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: system identification; control theory and related applications
Dr. Wei Su

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China
Interests: complex systems; artificial intelligence; multiagent systems
Prof. Dr. Wendong Xiao

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: wireless localization and tracking; energy harvesting based network resource management; distributed machine learning for big data; wireless sensor networks; internet of things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, many new optimization and control methods have been developed rapidly. Theoretical results are often based on perfect assumptions, but the actual background is often inconsistent with them, and there may be more complicated problems that make it impossible for theoretical knowledge to be directly applied to actual projects. At this stage, there is an urgent need to develop new optimization methods and intelligent technologies oriented to actual industrial systems.

This Special Issue will publish high-quality and original research papers in the following fields, but not limited to them:

  • Methods of fault diagnosis and life prediction in the metallurgical industry.
  • Aircraft modeling and control.
  • Modeling and control of multiagent systems.
  • Pattern recognition and artificial intelligence in medicine.
  • Modeling and system identification for practical systems.
  • Optimization and control methods for practical systems.

Prof. Dr. Jin Guo
Dr. Wei Su
Prof. Dr. Wendong Xiao
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault diagnosis and life prediction
  • pattern recognition
  • artificial intelligence
  • modeling and system identification
  • optimization and control methods
  • practical systems

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Published Papers (33 papers)

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Research

17 pages, 3303 KiB  
Article
Research on the Design Method of Camellia oleifera Fruit Picking Machine
by Shan Hu, Jing Xin, Dong Zhang and Geqi Xing
Appl. Sci. 2024, 14(18), 8537; https://doi.org/10.3390/app14188537 - 23 Sep 2024
Viewed by 768
Abstract
Camellia oleifera fruit pickers are essential for improving picking efficiency and promoting the Camellia oleifera industry. However, it is challenging to develop pickers that meet user needs. Current design tools and methods have limitations, such as a single model, poor synergy between integrated [...] Read more.
Camellia oleifera fruit pickers are essential for improving picking efficiency and promoting the Camellia oleifera industry. However, it is challenging to develop pickers that meet user needs. Current design tools and methods have limitations, such as a single model, poor synergy between integrated models, and subjective bias when analysing user requirements and translating them into product attributes. To solve these problems, this study proposes a new design decision model based on the Fuzzy Analytic Hierarchy Process (FAHP), Function Analysis System Technique (FAST), Theory of Inventive Problem Solving (TRIZ Theory), and extension transformation theory. The model was developed and applied to design an Camellia oleifera fruit picker. In this paper, an empirical investigation of an Camellia oleifera base in Wuhan was carried out, and multi-level demand analysis was used to identify the design demands in the behavioural process; FAHP was used to calculate the demand weights to clarify the design focus; expert knowledge was used to convert the demands into specific product functional features, and FAST was used to decompose these features to find the contradictory conflicts; TRIZ theory was used to determine the principles of resolving the contradictions, and the extension transformation theory were used to generate the creative design solutions for the products. By integrating FAHP, FAST, TRIZ theory and the extension transformation theory, the subjective bias in product design is eliminated, the design decision-making process is improved, and new methods and ideas are provided for the design of oleaginous tea fruit pickers and similar products. Finally, the conceptual design of an Camellia oleifera fruit picking machine was produced. However, the conceptual design has yet to be subjected to exhaustive simulation experiments and prototype testing. Future research will focus on conducting the necessary simulations, prototypes, and field tests to fully assess the feasibility and effectiveness of the design and make the required iterative improvements accordingly to commercialize the product eventually. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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19 pages, 7233 KiB  
Article
Optimization of Mode-Switching Quality of Hybrid Tractor Equipped with HMCVT
by Zhen Zhu, Jie Sheng, Hongwei Zhang, Dehai Wang and Long Chen
Appl. Sci. 2024, 14(14), 6288; https://doi.org/10.3390/app14146288 - 19 Jul 2024
Cited by 1 | Viewed by 709
Abstract
During the mode-switching process of a hybrid tractor equipped with a hydraulic-mechanical continuously variable transmission (HMCVT) device, the separation and combination of the clutch will cause transient shocks, affecting the smoothness and driving comfort of the entire vehicle. This article conducts simulation and [...] Read more.
During the mode-switching process of a hybrid tractor equipped with a hydraulic-mechanical continuously variable transmission (HMCVT) device, the separation and combination of the clutch will cause transient shocks, affecting the smoothness and driving comfort of the entire vehicle. This article conducts simulation and experimental research on the impact problem when switching from pure electric drive mode to hybrid-power speed coupling mode. Firstly, establish a system dynamics model in SimulationX 3.5 software and build a hardware-in-the-loop (HIL) experimental platform. Secondly, a strategy of “clutch oil pressure fuzzy control + motor torque compensation” is proposed to solve the problem of the slow dynamic response of the engine. Finally, the orthogonal experiment range analysis method and variance analysis method are used to optimize the quality of mode switching, with six clutch-switching time sequences as experimental factors. The simulation results show that adopting the strategy of “clutch oil pressure fuzzy control + motor torque compensation” and optimizing the clutch-switching timing can effectively reduce the amplitude of output shaft speed reduction, dynamic load coefficient, and impact, and shorten the switching time. The comparison between the HIL test results and the simulation results shows that there is a certain difference between the two, but the overall trend is consistent, which verifies the effectiveness of the proposed control strategy and method. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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21 pages, 4793 KiB  
Article
Compensation Observer-Based Adaptive Output Feedback Control for Multi-Agent Systems
by Zhaoyuan Liu and Ye Liu
Appl. Sci. 2024, 14(13), 5406; https://doi.org/10.3390/app14135406 - 21 Jun 2024
Viewed by 719
Abstract
This study presents a modified compensating observer control strategy for nonlinear multi-agent systems affected by unknown hysteresis signal loops. Compared to conventional high-gain observers, this approach introduces a novel compensation signal, effectively reducing the tracking error of traditional observers. Then, by utilizing a [...] Read more.
This study presents a modified compensating observer control strategy for nonlinear multi-agent systems affected by unknown hysteresis signal loops. Compared to conventional high-gain observers, this approach introduces a novel compensation signal, effectively reducing the tracking error of traditional observers. Then, by utilizing a backstepping method, an adaptive output feedback controller is designed, such that the tracking error converges to the small neighborhood around the origin. Simulation experiments with and without the compensation term demonstrate that this control strategy can effectively reduce error, but it increases input chattering to some extent. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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19 pages, 6529 KiB  
Article
Optimization and Analysis of Clutch Switching Timing for Hybrid Tractors Equipped with Hydraulic Mechanical Combined Transmission
by Zhen Zhu, Jie Sheng, Hongwei Zhang, Dehai Wang and Long Chen
Appl. Sci. 2024, 14(11), 4914; https://doi.org/10.3390/app14114914 - 5 Jun 2024
Viewed by 664
Abstract
The working environment of tractors is harsh and the working conditions are very complicated. To cope with this complex and changeable working environment, this paper presents a hybrid tractor powertrain loaded with hydraulic mechanical combined transmission (HMCVT). The powertrain can switch modes according [...] Read more.
The working environment of tractors is harsh and the working conditions are very complicated. To cope with this complex and changeable working environment, this paper presents a hybrid tractor powertrain loaded with hydraulic mechanical combined transmission (HMCVT). The powertrain can switch modes according to different operating environments. During mode switching, impact loads can cause wear and even damage to the transmission components. Interrupting the power transmission will reduce the tractor’s operational efficiency. In this paper, the orthogonal experimental range analysis method was proposed to optimize the quality of mode switching. Mode switching involves interactions between clutches. Orthogonal table L16 (215) was selected to design the orthogonal table head. SimulationX was used for simulation. Employing MINITAB range analysis yielded the optimal results. The simulations indicated a reduction in the output shaft’s velocity drop amplitude from 17.647% to 6.591%. The dynamic load coefficient decreased from 2.743 to 1.857. The impact strength was reduced from 14.125 to 5.67 m/s3. The switching time was reduced from 2.13 to 1.71 s. We built a model-in-the-loop (MIL) test platform to validate the results. The MIL test results were compared with the simulation results. Our findings indicate a degree of discrepancy between the simulated and the experimental results, yet the overall trends remain largely consistent. The correctness of the optimal scheme obtained from the orthogonal experiment is verified. This study provides a theoretical basis for the optimization of powertrain mode switching in hybrid tractors equipped with HMCVT. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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19 pages, 3765 KiB  
Article
Optimization and Testing of the Technological Parameters for the Microwave Vacuum Drying of Mulberry Harvests
by Yuyang Cong, Yang Liu, Yurong Tang, Jiale Ma, Jiaxin Ma, Zhuoyang Liu, Xirui Yang and Hong Zhang
Appl. Sci. 2024, 14(10), 4137; https://doi.org/10.3390/app14104137 - 13 May 2024
Cited by 1 | Viewed by 867
Abstract
This study focuses on mitigating the decrease in the quality of mulberry after harvest and increasing the value of mulberry products through microwave vacuum drying. The effects of mulberry moisture content on texture properties were investigated, and the test method was optimized through [...] Read more.
This study focuses on mitigating the decrease in the quality of mulberry after harvest and increasing the value of mulberry products through microwave vacuum drying. The effects of mulberry moisture content on texture properties were investigated, and the test method was optimized through the membership function model and the central composite technique. The influences of the material surface temperature, vacuum degree, and microwave power on the quality of instant mulberry were analyzed comprehensively. A regression model was established to analyze the relationship between each test factor and quality index. The results show that, with increases in the moisture content, both the hardness and chewiness of mulberry present a decreasing trend; when elasticity increases gradually, adhesivity is presented in an inverted V-shaped variation trend, and the cohesiveness remains basically constant. Moreover, the moisture content of mulberry had significant correlations with elasticity, glueyness, chewiness, and hardness (R2 > 0.9). When the moisture content of the samples reached ≤44.07%, the total plate count reached a minimum and changed gradually; thus, the moisture content (44.07%) of mulberry was determined for the technological optimization test. The combination of optimal technological parameters was as follows: material surface temperature = 51.0 °C, vacuum degree = 0.07 MPa, and microwave power = 370 W. Under these optimal technological parameters, the soluble solid content was 42.37%, chewiness was 9.08, and the Vc content was 0.725 mg·(100 g)−1. The average error between the test results and software optimization parameters was 5.88%. The optimized microwave vacuum drying technological parameters improved the drying quality of mulberry significantly. The results can provide theoretical support for the microwave vacuum drying of berries. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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26 pages, 9167 KiB  
Article
Research on Brake Energy Recovery Strategy Based on Working Condition Identification
by Weiguang Zheng, Haiqiao Li and Jun Li
Appl. Sci. 2024, 14(8), 3235; https://doi.org/10.3390/app14083235 - 11 Apr 2024
Viewed by 955
Abstract
As a transitional vehicle between fuel and electric vehicles, hybrid vehicles achieve energy savings and emission reductions without range anxiety. Regenerative braking has a direct impact on the fuel consumption of the whole vehicle; however, the current regenerative braking strategy for commercial vehicles [...] Read more.
As a transitional vehicle between fuel and electric vehicles, hybrid vehicles achieve energy savings and emission reductions without range anxiety. Regenerative braking has a direct impact on the fuel consumption of the whole vehicle; however, the current regenerative braking strategy for commercial vehicles is not yet perfect and has a poor adaptability in terms of working conditions and whole-vehicle load changes. Therefore, this paper proposes a regenerative braking strategy based on the identification of working conditions, by considering the influence of the vehicle load state and driving conditions on braking. Firstly, historical driving data of commercial vehicles were obtained from GPS data, driving conditions were classified using principal component analysis (PCA) and K-means, and a working condition recogniser was constructed using a back propagation neural network (BPNN) optimised with the Coati optimisation algorithm (COA). The recognition accuracy of the COA-BPNN was 7.6% better than that of the BPNN. Secondly, front and rear axle braking force distribution strategies are proposed, according to the braking intensity magnitude and load state under empty-, half-, and full-load conditions. Finally, a genetic algorithm (GA) was used to find the optimal control parameters for each category of working conditions, and the COA-BPNN condition recogniser identified the current category of working conditions needed to retrieve the corresponding optimal control parameters in the offline parameter library. The simulation results under C-WTVC and synthetic conditions show that the energy recovery rate of the proposed strategy in this paper reached up to 69.65%, which is at most 206.3% higher than that of the fixed-ratio strategy and at most 37.4% higher than that of the fuzzy control strategy. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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17 pages, 4878 KiB  
Article
STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
by Kui Zeng, Shutan Xu, Daode Shu and Ming Chen
Appl. Sci. 2024, 14(3), 1239; https://doi.org/10.3390/app14031239 - 2 Feb 2024
Viewed by 1159
Abstract
Medaka (Oryzias latipes), as a crucial model organism in biomedical research, holds significant importance in fields such as cardiovascular diseases. Currently, the analysis of the medaka ventricle relies primarily on visual observation under a microscope, involving labor-intensive manual operations and visual [...] Read more.
Medaka (Oryzias latipes), as a crucial model organism in biomedical research, holds significant importance in fields such as cardiovascular diseases. Currently, the analysis of the medaka ventricle relies primarily on visual observation under a microscope, involving labor-intensive manual operations and visual assessments that are cumbersome and inefficient for biologists. Despite attempts by some scholars to employ machine learning methods, limited datasets and challenges posed by the blurred edges of the medaka ventricle have constrained research to relatively simple tasks such as ventricle localization and heart rate statistics, lacking precise segmentation of the medaka ventricle edges. To address these issues, we initially constructed a video object segmentation dataset comprising over 7000 microscopic images of medaka ventricles. Subsequently, we proposed a semi-supervised video object segmentation model named STAVOS, incorporating a spatial-temporal attention mechanism. Additionally, we developed an automated system capable of calculating various parameters and visualizing results for a medaka ventricle using the provided video. The experimental results demonstrate that STAVOS has successfully achieved precise segmentation of medaka ventricle contours. In comparison to the conventional U-Net model, where a mean accuracy improvement of 0.392 was achieved, our model demonstrates significant progress. Furthermore, when compared to the state-of-the-art Tackling Background Distraction (TBD) model, there is an additional enhancement of 0.038. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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23 pages, 5151 KiB  
Article
Research on an Energy Recovery Strategy for Fuel Cell Commercial Vehicles Based on Slope Estimation
by Weiguang Zheng, Jialei Chen and Shanchao Wang
Appl. Sci. 2024, 14(2), 748; https://doi.org/10.3390/app14020748 - 16 Jan 2024
Viewed by 969
Abstract
Road slope is an essential parameter in the study of vehicle driving processes. In future traffic development, constructing road segments with slopes is indispensable. Furthermore, road slope is a fundamental parameter for realizing energy recovery during braking. Hence, research on road slope estimation [...] Read more.
Road slope is an essential parameter in the study of vehicle driving processes. In future traffic development, constructing road segments with slopes is indispensable. Furthermore, road slope is a fundamental parameter for realizing energy recovery during braking. Hence, research on road slope estimation is extremely crucial. This article proposes a combination of adaptive filtering and strong tracking filter factors for road slope estimation, followed by establishing case settings for verification. It was found that the proposed slope estimation algorithm has a high degree of accuracy in estimating the slope angle, with a mean absolute error (MAE) and a root mean square error (RMSE) of 0.0254 and 0.0359, respectively, at fixed slopes, and a MAE and a RMSE of 0.2799 and 0.3710, respectively, at varying slopes. By combining the slope angle with a braking force distribution optimization algorithm, an optimized braking distribution coefficient is obtained. In the Cruise2019 software, slope angles of 0° and 5° are set and combined with the braking force distribution strategy built in Matlab2021/Simulink for verification under China Heavy-duty Commercial Vehicle Test Cycle (CHTC-HT) and Worldwide Transient Vehicle Cycle (C-WTVC) conditions. The recovered energy increased by 7.24% and 4.99%, respectively, under CHTC-HT conditions, and by 6.42% and 1.73%, respectively, under C-WTVC. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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22 pages, 6267 KiB  
Article
Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
by Zhen Wang, Shuaiyu Wang and Yiyang Cheng
Appl. Sci. 2024, 14(1), 289; https://doi.org/10.3390/app14010289 - 28 Dec 2023
Cited by 2 | Viewed by 1011
Abstract
For the problem that the fault states of parallel shaft gearboxes are difficult to identify, a diagnostic method is proposed to optimize variational modal decomposition (VMD) and t-distributed stochastic neighbor embedding (t-SNE) using an improved dung beetle optimization algorithm I have checked and [...] Read more.
For the problem that the fault states of parallel shaft gearboxes are difficult to identify, a diagnostic method is proposed to optimize variational modal decomposition (VMD) and t-distributed stochastic neighbor embedding (t-SNE) using an improved dung beetle optimization algorithm I have checked and revised all. (IDBO). IDBO is obtained by amplifying dung beetle optimization (DBO) using strategies such as chaos mapping, Levy flight policy, and dynamic adaptive weighting. IDBO is employed to optimize VMD, extracting decomposed eigenvalues restructured into high-dimensional feature vectors. Subsequently, we employ the t-SNE algorithm for dimensionality reduction to eliminate redundancy, obtaining two-dimensional vectors. Finally, these vectors are input into a support vector machine (SVM) for fault diagnosis. We apply IDBO, grey wolf optimization (GWO), DBO, and the sparrow search algorithm (SSA) to both benchmark functions and VMD, conducting a performance comparison. The results demonstrate that IDBO exhibits superior convergence speed and global search capability, effectively suppressing modal aliasing issues in VMD, thereby enhancing the algorithm’s robustness. Through experimental fault diagnosis on a gear transmission system, we compare our proposed method with EMD + t-SNE and traditional VMD + t-SNE feature extraction approaches. The experimental results indicate that the fault diagnosis accuracy reaches 100% after processing the fault signals with IDBO-VMD + t-SNE. This method proves to be an effective fault diagnosis approach specifically tailored for parallel-axis gearboxes, providing a reliable means to enhance diagnostic accuracy. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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17 pages, 11798 KiB  
Article
Study of a Small Robot for Mine Hole Detection
by Liang Ge, Ziyang Fang, Hao Li, Le Zhang, Wen Zeng and Xiaoting Xiao
Appl. Sci. 2023, 13(24), 13249; https://doi.org/10.3390/app132413249 - 14 Dec 2023
Cited by 3 | Viewed by 1167
Abstract
China is rich in coal resources, but complex hydrogeological conditions lead to difficulties in coal mining, including coal mine collapses, roof and water damage, and other accidents that occur frequently, resulting in many casualties and property losses. The use of coal mine hole [...] Read more.
China is rich in coal resources, but complex hydrogeological conditions lead to difficulties in coal mining, including coal mine collapses, roof and water damage, and other accidents that occur frequently, resulting in many casualties and property losses. The use of coal mine hole detection technology to detect and analyze the internal environment of the coal mines in advance helps to reduce safety hazards and prevent coal mine accidents; however, the operation of existing coal mine hole detection technology is cumbersome, difficult to control, and encounters problems due to an insufficient depth of jacking. This paper designs a new type of small robot for mine hole detection. Firstly, we analyzed the function and structural design of the mine hole detection robot, designed a variable diameter function according to the characteristics of narrow and uneven mine holes in coal mines, and analyzed the mechanics of the critical parts using theoretical calculations. Secondly, using three-dimensional modeling software (Solidworks 2019), we established a structural model of the small robot for mine hole detection. After that, we designed a hardware circuit and control program for the robot and emphasized the safety design of the circuit, considering the presence of water and gas inside the coal mine. Finally, to verify the feasibility of the design program, the basic parameters and function tests of the mine hole-detection small robot were carried out. The experimental results show that the developed mine hole-detection small robot can adapt to working hole diameters from 65 mm to 100 mm and has a maximum working power of only 12 W and a maximum crawling speed of 3.96 m/min. The maximum crawling slope reaches 90°, which can meet existing mine hole inspection needs. This research provides theoretical and design guidance for developing mine hole-detection robots with substantial engineering practical reference values. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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21 pages, 7993 KiB  
Article
Predictive Torque Control of the Vehicle’s Permanent-Magnet Synchronous-Motor Model Based on Multi-Objective Sorting
by Weiguang Zheng, Quanfu Geng, Xiaohong Xu and Zhixiang Liu
Appl. Sci. 2023, 13(20), 11572; https://doi.org/10.3390/app132011572 - 23 Oct 2023
Cited by 2 | Viewed by 1445
Abstract
The permanent-magnet synchronous motor (PMSM), with the advantages of low energy consumption and stable operation, is considered a green power source to replace gasoline engines. Motor control is the core problem of the electric-drive system, so it is important to study the high-performance [...] Read more.
The permanent-magnet synchronous motor (PMSM), with the advantages of low energy consumption and stable operation, is considered a green power source to replace gasoline engines. Motor control is the core problem of the electric-drive system, so it is important to study the high-performance motor control algorithm. The traditional PMSM control strategy has problems such as torque pulsation, large overshoot, and parameters which are not easy to adjust. This work proposes a new model-predictive torque control (MPTC) based on multi-objective ranking for these issues. The Romberg observer was utilized to accurately estimate motor flux and torque across a wide range of speeds and ensure optimal performance of the MPTC. The optional voltage vectors were classified using graph theory. The model’s cost function was optimized and the control delay caused by hardware processing was compensated by a modified Euler method. A multi-objective ranking method was used to avoid the offline selection of MPTC weight coefficients. Additionally, one ranking method was used to reduce the complexity of the algorithm for multiple objectives. Based on the simulation results, the newly proposed MPTC method, when compared with traditional approaches, reduced the total harmonic distortion from 2.78% to 2.26%. Torque ripple decreased by approximately 58.4%, and the switching frequency was reduced by 3.05%, lowering the inverter’s switching losses. Therefore, the newly proposed MPTC had faster torque response, reduced computation time, and less torque pulsation, which further improved the dynamic performance of the permanent-magnet synchronous motor. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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18 pages, 5857 KiB  
Article
Camellia oleifera Fruit Detection Algorithm in Natural Environment Based on Lightweight Convolutional Neural Network
by Zefeng Li, Lichun Kang, Honghui Rao, Ganggang Nie, Yuhan Tan and Muhua Liu
Appl. Sci. 2023, 13(18), 10394; https://doi.org/10.3390/app131810394 - 17 Sep 2023
Cited by 3 | Viewed by 1451
Abstract
At present, Camellia oleifera fruit harvesting relies on manual labor with low efficiency, while mechanized harvesting could result in bud damage because flowering and fruiting are synchronized. As a prerequisite, rapid detection and identification are urgently needed for high accuracy and efficiency with [...] Read more.
At present, Camellia oleifera fruit harvesting relies on manual labor with low efficiency, while mechanized harvesting could result in bud damage because flowering and fruiting are synchronized. As a prerequisite, rapid detection and identification are urgently needed for high accuracy and efficiency with simple models to realize selective and intelligent harvesting. In this paper, a lightweight detection algorithm YOLOv5s-Camellia based on YOLOv5s is proposed. First, the network unit of the lightweight network ShuffleNetv2 was used to reconstruct the backbone network, and thereby the number of computations and parameters of the model was reduced to increase the running speed for saving computational costs. Second, to mitigate the impact of the lightweight improvement on model detection accuracy, three efficient channel attention (ECA) modules were introduced into the backbone network to enhance the network’s attention to fruit features, and the Concat operation in the neck network was replaced by the Add operation with fewer parameters, which could increase the amount of information under features while maintaining the same number of channels. Third, the Gaussian Error Linear Units (GELU) activation function was introduced to improve the nonlinear characterization ability of the network. In addition, to improve the ability of the network to locate objects in the natural environment, the penalty index was redefined to optimize the bounding box loss function, which can improve the convergence speed and regression accuracy. Furthermore, the final experimental results showed that this model possesses 98.8% accuracy, 5.5 G FLOPs computation, and 6.3 MB size, and the detection speed reached 60.98 frame/s. Compared with the original algorithm, the calculation amount, size, and parameters were reduced by 65.18%, 56.55%, and 57.59%, respectively. The results can provide a technical reference for the development of a Camellia oleifera fruit-harvesting robot. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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19 pages, 6055 KiB  
Article
Adaptive Impedance Control for Force Tracking in Manipulators Based on Fractional-Order PID
by Longhao Gu and Qingjiu Huang
Appl. Sci. 2023, 13(18), 10267; https://doi.org/10.3390/app131810267 - 13 Sep 2023
Cited by 4 | Viewed by 1896
Abstract
Force tracking control in robot arms has been widely used in many industrial applications, particularly in tasks involving end effectors and environmental contact, such as grinding, polishing, and other similar operations. However, these environments are not always precisely known. In order to address [...] Read more.
Force tracking control in robot arms has been widely used in many industrial applications, particularly in tasks involving end effectors and environmental contact, such as grinding, polishing, and other similar operations. However, these environments are not always precisely known. In order to address the force tracking control problem in unknown environments, this paper proposes a fractional-order PID adaptive impedance control strategy based on traditional impedance control. The unknown environmental information is estimated online using the adaptive impedance control algorithm, and the estimated parameters are used to generate reference trajectories to reduce force tracking errors. Fractional-order PID control is then introduced into the system to improve the control performance of the system model, and the theoretical proof of strategy stability is conducted. Finally, a comparison of four strategies was conducted through simulations: traditional impedance control, adaptive hybrid impedance control, adaptive variable impedance control, and the fractional-order PID impedance control proposed in this paper. The simulation results demonstrate that the strategy proposed in this paper exhibits robustness, virtually eliminates overshoot, and enhances response speed. In contrast, both adaptive hybrid impedance control and adaptive variable impedance control exhibit approximately 30% to 45% overshoot during interactions with the environment. Furthermore, in terms of force tracking error, the proposed strategy in this paper outperforms the above two strategies by approximately 29% to 60%, achieving excellent force tracking control performance. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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17 pages, 1809 KiB  
Article
Accurate Segmentation of Tilapia Fish Body Parts Based on Deeplabv3+ for Advancing Phenotyping Applications
by Guofu Feng, Hao Wang, Ming Chen and Zhixiang Liu
Appl. Sci. 2023, 13(17), 9635; https://doi.org/10.3390/app13179635 - 25 Aug 2023
Cited by 1 | Viewed by 1835
Abstract
As an important economic fish resource, germplasm resources and phenotypic measurements of tilapia are of great importance in the direction of culture and genetic improvement. Furthermore, accurate identification and precise localization of tilapia body parts are crucial for enabling key technologies such as [...] Read more.
As an important economic fish resource, germplasm resources and phenotypic measurements of tilapia are of great importance in the direction of culture and genetic improvement. Furthermore, accurate identification and precise localization of tilapia body parts are crucial for enabling key technologies such as automated capture and precise cutting. However, there are some problems in the semantic segmentation of tilapia fish, including the accuracy of target edge segmentation and the ambiguity in segmenting small targets. To improve the accuracy of semantic segmentation of tilapia parts in real farming environments, an improved Deeplabv3+ network model method is proposed for implementing tilapia part segmentation to facilitate phenotypic measurements on tilapia in this paper. The CBAM module is embedded in the encoder, which can improve the accurate identification and localization of tilapia parts by adaptively adjusting the channel weights and spatial weights and better focus on the key features and spatial connections of tilapia body parts. Furthermore, the decoding part of the Deeplabv3+ model is optimized by using SENet, which greatly increases the segmentation accuracy of the network by establishing the interdependence between channels while suppressing useless features. Finally, model performance is tested and compared with the original network and other methods on the tilapia part segmentation dataset. The experimental results show that the segmentation performance of the improved network is better than other networks, such as PSPNet and U-Net, and the IoU values in the head, fins, trunk, and tail of the fish body are 9.78, 2.27, 6.27, and 6.58 percentage points higher than those of the Deeplabv3+ network, respectively. The results validate the effectiveness of our approach in solving the above problems encountered in the semantic segmentation of tilapia parts. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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17 pages, 2873 KiB  
Article
Dynamic Spatio-Temporal Graph Fusion Convolutional Network for Urban Traffic Prediction
by Haodong Ma, Xizhong Qin, Yuan Jia and Junwei Zhou
Appl. Sci. 2023, 13(16), 9304; https://doi.org/10.3390/app13169304 - 16 Aug 2023
Cited by 2 | Viewed by 1692
Abstract
Urban traffic prediction is essential for intelligent transportation systems. However, traffic data often exhibit highly complex spatio-temporal correlations, posing challenges for accurate forecasting. Graph neural networks have demonstrated an outstanding ability in capturing spatial correlations and are now extensively applied to traffic prediction. [...] Read more.
Urban traffic prediction is essential for intelligent transportation systems. However, traffic data often exhibit highly complex spatio-temporal correlations, posing challenges for accurate forecasting. Graph neural networks have demonstrated an outstanding ability in capturing spatial correlations and are now extensively applied to traffic prediction. However, many graph-based methods neglect the dynamic spatial features between road segments and the continuity of spatial features across adjacent time steps, leading to subpar predictive performance. This paper proposes a Dynamic Spatio-Temporal Graph Fusion Convolutional Network (DSTGFCN) to enhance the accuracy of traffic prediction. Specifically, we designed a dynamic graph fusion module without prior road spatial information, which extracts dynamic spatial information among roads from observed data. Subsequently, we fused the dynamic spatial features of the current time step and adjacent time steps to generate a dynamic graph for each time step. The graph convolutional gated recurrent network was employed to model the spatio-temporal correlations jointly. Additionally, residual connections were added to the model to enhance the ability to extract long-term temporal relationships. Finally, we conducted experiments on six publicly available traffic datasets, and the results demonstrated that DSTGFCN outperforms the baseline models with state-of-the-art predictive performance. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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19 pages, 2115 KiB  
Article
Solving Panel Block Assembly Line Scheduling Problem via a Novel Deep Reinforcement Learning Approach
by Tao Zhou, Liang Luo, Yuanxin He, Zhiwei Fan and Shengchen Ji
Appl. Sci. 2023, 13(14), 8483; https://doi.org/10.3390/app13148483 - 22 Jul 2023
Cited by 1 | Viewed by 1576
Abstract
The panel block is a quite important “intermediate product” in the shipbuilding process. However, the assembly efficiency of the panel block assembly line is not high. Therefore, rational scheduling optimization is of great significance for improving shipbuilding efficiency. Currently, the processing sequence of [...] Read more.
The panel block is a quite important “intermediate product” in the shipbuilding process. However, the assembly efficiency of the panel block assembly line is not high. Therefore, rational scheduling optimization is of great significance for improving shipbuilding efficiency. Currently, the processing sequence of the panel blocks in the panel block assembly line is mainly determined using heuristic and metaheuristic algorithms. However, these algorithms have limitations, such as small problem-solving capacity and low computational efficiency. To address these issues, this study proposes an end-to-end approach based on deep reinforcement learning to solve the scheduling problem of the ship’s panel block assembly line. First, a Markov decision model is established, and a disjunctive graph is creatively used to represent the current scheduling status of the panel block assembly line. Then, a policy function based on a graph isomorphism network is designed to extract information from the disjunctive graph’s state and train it using Proximal Policy Optimization algorithms. To validate the effectiveness of our method, tests on both real shipbuilding data and publicly available benchmark datasets are conducted. We compared our proposed end-to-end deep reinforcement learning algorithm with heuristic algorithms, metaheuristic algorithms, and the unimproved reinforcement learning algorithm. The experimental results demonstrate that our algorithm outperforms other baseline methods in terms of model performance and computation time. Moreover, our model exhibits strong generalization capabilities for larger instances. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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21 pages, 4644 KiB  
Article
Design and Optimization of Conformal Cooling Channels for Increasing Cooling Efficiency in Injection Molding
by Yuandi Wang and Changmyung Lee
Appl. Sci. 2023, 13(13), 7437; https://doi.org/10.3390/app13137437 - 23 Jun 2023
Cited by 11 | Viewed by 3922
Abstract
Injection molding is widely used in the manufacturing process of plastic products. The injection molding process aims to increase productivity, which is impacted by factors such as cooling time, the temperature distribution of plastic parts, thermal stress, warpage, etc. The cooling stage of [...] Read more.
Injection molding is widely used in the manufacturing process of plastic products. The injection molding process aims to increase productivity, which is impacted by factors such as cooling time, the temperature distribution of plastic parts, thermal stress, warpage, etc. The cooling stage of the forming process is a critical factor that significantly influences the quality and cost-effectiveness of the final product in injection molding. With the development of additive manufacturing, the fabrication of conformal cooling channels becomes easier and more affordable compared to traditional machining techniques. Well-designed conformal cooling channels can substantially reduce cooling time, resulting in increased productivity and improved efficiency of the injection molding process. While conformal cooling channels offer improved cooling efficiency and part quality, their design process is more intricate than that of conventional channels due to the demand to account for the complex geometry of the part and to accommodate manufacturing constraints. In this paper, the conformal cooling channels are designed, and the variables for the channels are optimized to increase the cooling efficiency, and 3D printing technology is applied to produce the conformal cooling channel. A simplified finite element cooling analysis was used to compare traditional and hybrid solutions for designing cooling channels to reduce injection cycle time for plastic parts, and the design and simulation of the cooling system were based on UG and Moldflow. After comparing the simulation results of the cooling performance, we found that conformal cooling channels can reduce cooling time by 30% compared to conventional cooling channels. Additionally, they result in a more uniform temperature distribution throughout the plastic products. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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14 pages, 295 KiB  
Article
Switching Control of Closed-Loop Supply Chain Systems with Markov Jump Parameters
by Huiming Wu and Sicong Guo
Appl. Sci. 2023, 13(11), 6798; https://doi.org/10.3390/app13116798 - 2 Jun 2023
Cited by 1 | Viewed by 1086
Abstract
The switching system model of a closed-loop supply chain with Markov jump parameters is established. The system is modeled as a switching system with Markov jump parameters, taking into account the uncertainties of the process and the inventory decay factors. The Markov switching [...] Read more.
The switching system model of a closed-loop supply chain with Markov jump parameters is established. The system is modeled as a switching system with Markov jump parameters, taking into account the uncertainties of the process and the inventory decay factors. The Markov switching idea is applied to the controller design and performance analysis of the system to effectively suppress the bullwhip effect while ensuring the stability of the closed-loop supply chain system. Simulation examples are presented to illustrate the validity of the results obtained. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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20 pages, 7792 KiB  
Article
Research on Three-Dimensional Scanning Path Planning of Casing Parts Based on Industrial Robot
by Jing Li, Minghai Wang, Ligang Qu and Guangming Lv
Appl. Sci. 2023, 13(10), 6162; https://doi.org/10.3390/app13106162 - 17 May 2023
Cited by 2 | Viewed by 1672
Abstract
According to the requirements for the rapid scanning and measurement of the geometric shape during the process of chemical milling of an aviation engine casing, the scanning path of the casing is planned and studied. This paper introduces the principle and method of [...] Read more.
According to the requirements for the rapid scanning and measurement of the geometric shape during the process of chemical milling of an aviation engine casing, the scanning path of the casing is planned and studied. This paper introduces the principle and method of the tracking scanner and automatic measuring system and analyzes the scanning area range, approach distance, and wide angle of the field. The casing process is modeled by applying part of the machine, obtaining a series of scanning path point and synthesizing the scanning trajectory. On this basis, the entire scanning process is divided into two alternating actions: scanning measurement and posture adjustment, and the mathematical model of the annular scanning path on the outer surface of the casing part is obtained. Adjusting the scan height was used to solve the repeated scan area problem, and the results show that the adjustment method effectively shortened the scan path’s length and time. The simulation method verifies the planned finite ring-scanning path, which verifies the correctness and feasibility of the mathematical model. Through the automatic scanning reconstruction process test, the reconstruction rate of the ring scanning trajectory reaches 85%, which is 80% higher than the manual detection efficiency. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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12 pages, 2917 KiB  
Article
A Cosine-Similarity-Based Deconvolution Method for Analyzing Data-Independent Acquisition Mass Spectrometry Data
by Xiang Zhang, Ruitao Wu and Zhijian Qu
Appl. Sci. 2023, 13(10), 5969; https://doi.org/10.3390/app13105969 - 12 May 2023
Viewed by 2425
Abstract
Although data-independent acquisition (DIA) has the ability to identify and quantify all peptides in a sample, highly complex mixed mass spectra present difficulties for accurate peptide and protein identification. Additionally, the correspondence between the precursor and its fragments is broken, making it challenging [...] Read more.
Although data-independent acquisition (DIA) has the ability to identify and quantify all peptides in a sample, highly complex mixed mass spectra present difficulties for accurate peptide and protein identification. Additionally, the correspondence between the precursor and its fragments is broken, making it challenging to perform peptide identification directly using conventional DDA search engines. In this paper, we propose a cosine-similarity-based deconvolution method: CorrDIA. This is achieved by reconstructing the correspondence between precursor and fragment ions based on the consistency of extracted ion chromatograms (XICs). A deisotope peak cluster operation is added and centered on the MS/MS spectrum to improve the accuracy of spectrum interpretation and increase the number of identified peptides. The resulting MS/MS spectra can be identified using any data-dependent acquisition (DDA) sequencing software. The experimental results demonstrate that the number of peptide results increased by 12 percent and 21 percent respectively, and the repetition rate decreased by 12 percent. This reduces mass spectra complexity and difficulties in mass spectra analysis without the need for any mass spectra libraries. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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13 pages, 1815 KiB  
Article
Gesture Detection and Recognition Based on Object Detection in Complex Background
by Renxiang Chen and Xia Tian
Appl. Sci. 2023, 13(7), 4480; https://doi.org/10.3390/app13074480 - 31 Mar 2023
Cited by 11 | Viewed by 3298
Abstract
In practical human–computer interaction, a hand gesture recognition method based on improved YOLOv5 is proposed to address the problem of low recognition accuracy and slow speed with complex backgrounds. By replacing the CSP1_x module in the YOLOv5 backbone network with an efficient layer [...] Read more.
In practical human–computer interaction, a hand gesture recognition method based on improved YOLOv5 is proposed to address the problem of low recognition accuracy and slow speed with complex backgrounds. By replacing the CSP1_x module in the YOLOv5 backbone network with an efficient layer aggregation network, a richer combination of gradient paths can be obtained to improve the network’s learning and expressive capabilities and enhance recognition speed. The CBAM attention mechanism is introduced to filtering gesture features in channel and spatial dimensions, reducing various types of interference in complex background gesture images and enhancing the network’s robustness against complex backgrounds. Experimental verification was conducted on two complex background gesture datasets, EgoHands and TinyHGR, with recognition accuracies of mAP0.5:0.95 at 75.6% and 66.8%, respectively, and a recognition speed of 64 FPS for 640 × 640 input images. The results show that the proposed method can recognize gestures quickly and accurately with complex backgrounds, and has higher recognition accuracy and stronger robustness compared to YOLOv5l, YOLOv7, and other comparative algorithms. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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11 pages, 407 KiB  
Communication
Frequency-Limited Model Reduction for Linear Positive Systems: A Successive Optimization Method
by Yingying Ren and Qian Wang
Appl. Sci. 2023, 13(6), 4039; https://doi.org/10.3390/app13064039 - 22 Mar 2023
Cited by 2 | Viewed by 1211
Abstract
This paper studies frequency-limited model reduction for linear positive systems. Specifically, the objective is to develop a reduced-order model for a high-order positive system that preserves the positivity, while minimizing the approximation error within a given H∞ upper bound over a limited [...] Read more.
This paper studies frequency-limited model reduction for linear positive systems. Specifically, the objective is to develop a reduced-order model for a high-order positive system that preserves the positivity, while minimizing the approximation error within a given H∞ upper bound over a limited frequency interval. To characterize the finite-frequency H∞ specification and stability, we first present the analysis conditions in the form of bilinear matrix inequalities. By leveraging these conditions, we derive convex surrogate constraints by means of an inner-approximation strategy. Based on this, we construct a novel iterative algorithm for calculating and optimizing the reduced-order model. Finally, the effectiveness of the proposed model reduction method is illustrated with a numerical example. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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17 pages, 3457 KiB  
Article
Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks
by Jinglin Li, Chengpeng Jiang, Jing Wang, Taian Xu and Wendong Xiao
Appl. Sci. 2023, 13(5), 2840; https://doi.org/10.3390/app13052840 - 22 Feb 2023
Cited by 10 | Viewed by 1633
Abstract
In wireless rechargeable sensor networks (WRSNs), a novel approach to energy replenishment is offered by the utilization of mobile chargers (MCs), which charge nodes via wireless energy transfer technology. However, previous research on mobile charging schemes has commonly prioritized charging efficiency as a [...] Read more.
In wireless rechargeable sensor networks (WRSNs), a novel approach to energy replenishment is offered by the utilization of mobile chargers (MCs), which charge nodes via wireless energy transfer technology. However, previous research on mobile charging schemes has commonly prioritized charging efficiency as a performance index, neglecting the importance of quality of sensing coverage (QSC). As the network scale increases, the MC’s charging power becomes unable to meet the energy needs of all nodes, leading to a decline in network QSC when nodes’ energy is depleted. To solve this problem, we study the problem of mobile charging sequence scheduling for optimal network QSC (MSSQ) and propose an improved quantum-behaved particle swarm optimization (IQPSO) algorithm. With the attraction of potential energy in quantum space, this algorithm will adaptively adjust the contraction expansion coefficient iteratively, leading to a global optimal solution for the mobile charging sequence. Extensive simulation results demonstrate the superiority of IQPSO over the widely used QPSO and Greedy algorithms in terms of network QSC, especially in large-scale networks. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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19 pages, 1489 KiB  
Article
Distributed Event-Triggered Synchronization for Complex Cyber–Physical Networks under DoS Attacks
by Xiaojie Huang, Yunxia Xia and Da-Wei Ding
Appl. Sci. 2023, 13(3), 1716; https://doi.org/10.3390/app13031716 - 29 Jan 2023
Cited by 2 | Viewed by 1444
Abstract
With the continuous development of the networked society, the ability of cyber attackers is becoming increasingly intelligent, posing a huge threat to complex cyber–physical networks (CCPNs). Therefore, how to design a security strategy for CCPNs under attack has become an urgent problem to [...] Read more.
With the continuous development of the networked society, the ability of cyber attackers is becoming increasingly intelligent, posing a huge threat to complex cyber–physical networks (CCPNs). Therefore, how to design a security strategy for CCPNs under attack has become an urgent problem to be solved, which promotes our work. The problem of the distributed event-triggered synchronization of CCPNs in the presence of denial-of-service (DoS) attacks is investigated in this paper. Firstly, a distributed event-triggered controller is designed such that all nodes of networks are synchronized without DoS attacks by relieving the communication occupancy rate of limited bandwidths. Meanwhile, Zeno and singular triggering behaviors are excluded to illustrate the effectiveness of the proposed event-triggered strategy. Secondly, in view of the continuous switching of CCPNs topologies caused by DoS attacks, an event-triggered control (ETC) strategy is proposed to ensure the synchronization of CCPNs under DoS attacks. Meanwhile, the frequency and duration of tolerable DoS attacks that can ensure the stability of the systems are calculated. Finally, two examples are given to illustrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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17 pages, 2637 KiB  
Article
A Novel Disturbance Rejection Control of Roll Channel for Small Air-to-Surface Missiles
by Xiaomiao Ding, Yanpeng Hu, Ruilong Jia and Jin Guo
Appl. Sci. 2023, 13(1), 389; https://doi.org/10.3390/app13010389 - 28 Dec 2022
Cited by 2 | Viewed by 1705
Abstract
In this paper, the issue of roll channel for attitude control for small air-to-surface missiles suffering from multiple disturbances is investigated. The dynamic model of roll channel is established and the roll controller of roll channel based on the active disturbance rejection control [...] Read more.
In this paper, the issue of roll channel for attitude control for small air-to-surface missiles suffering from multiple disturbances is investigated. The dynamic model of roll channel is established and the roll controller of roll channel based on the active disturbance rejection control (ADRC) is designed. Based on the extended state observer, the disturbance is observed and compensated to track the roll rate accurately. Then, simulations and verification are carried out for rudder efficiency, barycenter location deviation, steering gear stuck, wind disturbance, drift error of inertial measurement unit (IMU) and other disturbances. The control effect is also compared with proportional integral derivative (PID) control and other ADRC algorithms. The results demonstrate that the algorithm has a good ability to suppress multiple disturbances. It can meet the control performance requirements of small air-to-surface missiles. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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20 pages, 6544 KiB  
Article
Future Definition and Extraction of the Blast Furnace 3D Burden Surface Based on Intelligent Algorithms
by Shaolun Sun, Zejun Yu, Sen Zhang and Wendong Xiao
Appl. Sci. 2022, 12(24), 12860; https://doi.org/10.3390/app122412860 - 14 Dec 2022
Cited by 1 | Viewed by 1455
Abstract
The accurate identification of the shape of the blast furnace (BF) burden surface is a crucial factor in the fault diagnosis of the BF condition and guides the charge operation. Based on the BF 3D point cloud data of phased array radar, this [...] Read more.
The accurate identification of the shape of the blast furnace (BF) burden surface is a crucial factor in the fault diagnosis of the BF condition and guides the charge operation. Based on the BF 3D point cloud data of phased array radar, this paper proposes a 3D burden surface feature definition system. Based on expert experience, the feature parameters of the burden surface are extracted. The voxel feature was extracted based on improved BNVGG. The optimized PointCNN extracts the point cloud features. The features of the burden surface were defined from three perspectives: the surface shape, voxel, and point cloud. The research of the 2D burden line is extended to the 3D burden surface, and the assumption of the symmetry of the BF is eliminated. Finally, the accuracy of the burden surface classification under each feature was evaluated, and the effectiveness of each feature extraction algorithm was verified. The experimental results show that the shape feature defined based on expert experience affects the recognition of the burden surface. However, it is defined from the data perspective and cannot accurately identify a similar burden surface shape. Therefore, the voxel features and point cloud features of the burden surface were extracted, improving the identification accuracy. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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13 pages, 801 KiB  
Article
Research on the Effective Reduction of Accidents on Operating Vehicles with fsQCA Method—Case Studies
by Xue Zhang, Yi Lu, Xianwen Huang and Aizhao Zhou
Appl. Sci. 2022, 12(24), 12737; https://doi.org/10.3390/app122412737 - 12 Dec 2022
Cited by 4 | Viewed by 2244
Abstract
Traffic accidents are caused by man mainly, especially improper driving. The effective way to reduce safety accidents caused by improper driving is to find out the cause and path causing the accident, block the accident formation chain, and then reduce safety accidents. Therefore, [...] Read more.
Traffic accidents are caused by man mainly, especially improper driving. The effective way to reduce safety accidents caused by improper driving is to find out the cause and path causing the accident, block the accident formation chain, and then reduce safety accidents. Therefore, using data from 337 road transport safety accidents in operating vehicles caused by improper driving behavior as the initial research sample, this paper uses the fuzzy set qualitative comparative analysis method to conduct a group analysis of typical cases and identifies the cause and path of safety accidents. The research results show that there are mainly four types of paths leading to safety accidents. According to the distribution of their core conditions, safety accidents are highly correlated with passenger transport and the degree of individualization of business models on operating vehicles. The following measures can be taken to prevent safety accidents: strengthen the supervision of operating enterprises (especially individual operations and individual-affiliated operations), carry out detailed safety training, and fully use advanced technology such as big data and high-tech means. The research results will help traffic and road transport management departments to prevent road safety accidents more effectively, which is of great significance to promoting the healthy development of road transport. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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16 pages, 3211 KiB  
Article
A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
by Jiarui Cui, Zhijing Li, Xiangquan Li, Bo Liu, Qing Li, Qun Yan, Ruoyu Huang, Hui Lu and Bin Cao
Appl. Sci. 2022, 12(23), 12403; https://doi.org/10.3390/app122312403 - 4 Dec 2022
Cited by 2 | Viewed by 2125
Abstract
A method of local anode effect prediction is proposed for the problem that it is difficult to detect the local anode effect in large aluminum reduction cell in real time. Firstly, a fuzzy classification of local anode effect prediction in terms of fuzziness [...] Read more.
A method of local anode effect prediction is proposed for the problem that it is difficult to detect the local anode effect in large aluminum reduction cell in real time. Firstly, a fuzzy classification of local anode effect prediction in terms of fuzziness level is proposed considering various working conditions of anode current in the region. Secondly, a current volatility detection method based on time-sliding window density is designed from the problem of uneven current distribution in the region, and the anode currents in the region are classified and tracked for prediction according to the different current volatility. Thirdly, an improved Gated Recurrent Unit (GRU) neural network structure is proposed to improve the prediction accuracy of fluctuating currents. Finally, simulation experiments are conducted based on actual data, and compared with Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), the proposed method has certain advantages in both prediction time, training time, and the mean absolute error (MAE) and mean square error (MSE), which verifies the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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13 pages, 376 KiB  
Article
Positivity-Preserving H∞ Model Reduction for Discrete-Time Positive Systems via a Successive Convex Optimization Algorithm
by Yingying Ren, Yunxia Xia, Qian Wang and Da-Wei Ding
Appl. Sci. 2022, 12(23), 12277; https://doi.org/10.3390/app122312277 - 30 Nov 2022
Cited by 1 | Viewed by 1354
Abstract
This paper considers the positivity-preserving model reduction for discrete-time positive systems. Given a stable high-order positive system, we aim to find a reduced-order model such that the approximation error is minimized within a prescribed H∞ performance and positivity is preserved. Regarding the [...] Read more.
This paper considers the positivity-preserving model reduction for discrete-time positive systems. Given a stable high-order positive system, we aim to find a reduced-order model such that the approximation error is minimized within a prescribed H∞ performance and positivity is preserved. Regarding the bounded real lemma, the sufficient and necessary condition for the existence of a reduced-order model is established in terms of bilinear matrix inequality and convex semi-definite constraint, which ensures that the reduced-order system is positive and the resulted error system is stable and has an H∞ performance level. Based on the inner-approximation strategy, we approximate the bilinear constraints with convex ones, under which an iterative procedure is provided to calculate the desired reduced-order model. Finally, an example is provided to demonstrate the effectiveness and potential benefits of the presented results. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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18 pages, 2981 KiB  
Article
Fault-Tolerant Control of Multi-Joint Robot Based on Fractional-Order Sliding Mode
by Jinghui Pan, Lili Qu and Kaixiang Peng
Appl. Sci. 2022, 12(23), 11908; https://doi.org/10.3390/app122311908 - 22 Nov 2022
Cited by 3 | Viewed by 1511
Abstract
In this paper, the problem of fault-tolerant control of actuators for multi-joint robots is studied. Aiming at the jitter problem in the design of fault-tolerant control law for conventional sliding mode controllers (SMC), a controller design method based on fractional-order sliding mode (FSMC) [...] Read more.
In this paper, the problem of fault-tolerant control of actuators for multi-joint robots is studied. Aiming at the jitter problem in the design of fault-tolerant control law for conventional sliding mode controllers (SMC), a controller design method based on fractional-order sliding mode (FSMC) theory is proposed. At first, the mathematical model of the multi-joint robot is established and the fractional-order sliding mode surface is constructed according to the mathematical model. Then, the robust control law is designed based on the Lyapunov function. Finally, the experiments are carried out. Compared with the conventional sliding mode control, the experimental results show that the multi-joint robot is more stable under the control of fractional-order sliding mode, and it can achieve almost no jitter while tracking the reference. The steady-state error for joint1 and joint2 could reach 0.073 radians under the control of SMC, while it is 0.015 radians under the control of FSMC. The steady-state error data indicate that the fluctuation amplitude under FSMC is five times smaller than SMC for the end part of the multi-joint robot under actuator gain faults. The regulation time for joint1 and joint2 is about 0.11 s under the control of SMC, and it is around 0.04 s for FSMC. The regulation time is reduced to one of three or four. These data show the effectiveness of the FSMC proposed in this paper. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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16 pages, 1349 KiB  
Article
An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy
by Jian Hao, Xu Yang, Chen Wang, Rang Tu and Tao Zhang
Appl. Sci. 2022, 12(22), 11573; https://doi.org/10.3390/app122211573 - 14 Nov 2022
Cited by 7 | Viewed by 3490
Abstract
Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but it suffers from poor diversity and the tendency to fall into a local optimum. In this paper, we propose an improved non-dominated sorting genetic algorithm, which aims to address the issues [...] Read more.
Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but it suffers from poor diversity and the tendency to fall into a local optimum. In this paper, we propose an improved non-dominated sorting genetic algorithm, which aims to address the issues of poor global optimization ability and poor convergence ability. The improved NSGA-II algorithm not only uses Levy distribution for global search, which enables the algorithm to search a wider range, but also improves the local search capability by using the relatively concentrated search property of random walk. Moreover, an adaptive balance parameter is designed to adjust the respective contributions of the exploration and exploitation abilities, which lead to a faster search of the algorithm. It helps to expand the search area, which increases the diversity of the population and avoids getting trapped in a local optimum. The superiority of the improved NSGA-II algorithm is demonstrated through benchmark test functions and a practical application. It is shown that the improved strategy provides an effective improvement in the convergence and diversity of the traditional algorithm. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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13 pages, 625 KiB  
Article
A Novel Named Entity Recognition Algorithm for Hot Strip Rolling Based on BERT-Imseq2seq-CRF Model
by Fengwei Jing, Mengyang Zhang, Jie Li, Guozheng Xu and Jing Wang
Appl. Sci. 2022, 12(22), 11418; https://doi.org/10.3390/app122211418 - 10 Nov 2022
Cited by 3 | Viewed by 1521
Abstract
Named entity recognition is not only the first step of text information extraction, but also the key process of constructing domain knowledge graphs. In view of the large amount of text data, complex process flow and urgent application needs in the hot strip [...] Read more.
Named entity recognition is not only the first step of text information extraction, but also the key process of constructing domain knowledge graphs. In view of the large amount of text data, complex process flow and urgent application needs in the hot strip rolling process, a novel named entity recognition algorithm based on BERT-Imseq2seq-CRF model is proposed in this paper. Firstly, the algorithm uses the BERT preprocessing language model to mine the dependencies in the domain text and obtain the corresponding representation vector. Then, the representation vector is sent to the encoder layer, and the output vector is input to the decoder at the same time, on the premise that the original model only considers the semantic vector. The Teacher-Forcing mechanism is integrated into the decoder layer to randomly modify the labeling results, and error accumulation is avoided to guarantee the sequence recognition effect. Finally, the validity of the labeling results is checked according to the conditional random field constraints, and the overall labeling quality of the algorithm is improved. The experimental results show that this model can efficiently and accurately predict the physical label of hot strip rolling, and the model performance index is better than other models, with the F1-Score reaching 91.47%. This model further provides technical support for information extraction and domain knowledge graph construction of hot strip rolling. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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17 pages, 1454 KiB  
Article
Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling
by Fengwei Jing, Junliang Li, Shimeng Hao, Jie Li and Jing Wang
Appl. Sci. 2022, 12(16), 8208; https://doi.org/10.3390/app12168208 - 17 Aug 2022
Viewed by 1419
Abstract
Aiming at the problems of large rolling deviation and low stability in limit specification of hot strip rolling, the optimal rolling suggestions were obtained based on back propagation (BP) neural network and genetic algorithm. According to equipment state and strip specification to select [...] Read more.
Aiming at the problems of large rolling deviation and low stability in limit specification of hot strip rolling, the optimal rolling suggestions were obtained based on back propagation (BP) neural network and genetic algorithm. According to equipment state and strip specification to select excellent sample set, in the sample set based on the data of application of neural network to build the mapping relationship between process parameters and the rolling stability, limit specifications of the mapping model is set up, and then using the genetic algorithm for the search of this mapping model, the search model of rolling stability of ideal point, determine a set of process parameters optimal advice accordingly. Taking the rolling of MRTRG00201_1276_3 as an example, a set of optimal process parameters are obtained by simulating rolling of MRTRG00201_1276_3. Then the sample distribution and rolling stability of each process are analyzed in turn. The results show that the process parameters obtained by optimizing the model accord with the distribution law of rolling samples, can obtain high rolling stability, and can play a guiding role in limit specification rolling. Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
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