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Applied Computing and Artificial Intelligence
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Applied Computing and Artificial Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 45561

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Xiang Li

grade E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: industrial artificial intelligence; industrial big data; deep learning; fault diagnosis; prognosis; intelligent maintenance
Special Issues, Collections and Topics in MDPI journals
Dr. Shuo Zhang

E-Mail Website
Guest Editor
School of mathematics and statistics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: applied mathematics; nonlinear dynamics; control; information science; neural network; complex network system
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Zhang

E-Mail Website1 Website2
Guest Editor
School of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China
Interests: rotor dynamics; squeeze film damper; fault diagnosis; deep learning; mechanical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of the journal Mathematics entitled “Applied Computing and Artificial Intelligence”. This initiative focuses on advances in algorithmic research and practical applications of applied computing and artificial intelligence methods, which have been attracting growing interest in recent years due to their effectiveness in solving technical problems. The recent developments in applied mathematics have largely led to benefits in many industrial tasks in different fields, including the aerospace industry, manufacturing, transportation, energy, robotics, materials, informatics, etc. A large number of practical industrial problems have been well addressed, such as system condition monitoring, parameter identification, time-series data prediction, fault diagnosis, signal processing, dynamics analysis, system optimization, data-driven modeling, etc.

This Special Issue invites high-quality original research or review papers on recent advanced methods in applied computing and artificial intelligence to address the practical challenges in the related areas. The topics of interest are listed in the following, but not limited to:

  • Applied computing
  • Applied mathematics
  • Neural networks
  • Deep learning
  • Parameter identification
  • Dynamics
  • System optimization
  • Machine learning
  • Control engineering
  • Intelligent maintenance
  • Computational methods
  • Fault diagnosis
  • Signal processing
  • Rotor dynamics
  • Prognosis
  • Remaining useful life prediction
  • Data mining
  • Fractional differential equation
  • Complex network system
  • Stochastic dynamics
  • Structural health monitoring
  • Nonlinear dynamics and control
  • Spacecraft dynamics

Dr. Xiang Li
Dr. Shuo Zhang
Dr. Wei Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Mathematics 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 2600 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

  • applied mathematics
  • neural networks
  • dynamics
  • control engineering
  • intelligent maintenance
  • computational methods
  • fault diagnosis
  • fractional differential equation
  • complex network system
  • stochastic dynamics
  • nonlinear dynamics and control
  • spacecraft dynamics

Published Papers (23 papers)

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Editorial

Jump to: Research, Review

4 pages, 185 KiB  
Editorial
Applied Computing and Artificial Intelligence
by Xiang Li, Shuo Zhang and Wei Zhang
Mathematics 2023, 11(10), 2309; https://doi.org/10.3390/math11102309 - 15 May 2023
Viewed by 835
Abstract
Applied computing and artificial intelligence methods have been attracting growing interest in recent years due to their effectiveness in solving technical problems [...] Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)

Research

Jump to: Editorial, Review

20 pages, 4886 KiB  
Article
Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis
by Fahman Saeed, Muhammad Hussain, Hatim A. Aboalsamh, Fadwa Al Adel and Adi Mohammed Al Owaifeer
Mathematics 2023, 11(2), 307; https://doi.org/10.3390/math11020307 - 06 Jan 2023
Cited by 3 | Viewed by 3083
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged diabetic patients. Regular screening for DR using fundus imaging aids in detecting complications and delays the progression of the disease. Because manual screening takes time and is subjective, deep learning has been [...] Read more.
Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged diabetic patients. Regular screening for DR using fundus imaging aids in detecting complications and delays the progression of the disease. Because manual screening takes time and is subjective, deep learning has been used to help graders. Pre-trained or brute force CNN models are used in existing DR grading CNN-based approaches that are not suited to fundus image complexity. To solve this problem, we present a method for automatically customizing CNN models based on fundus image lesions. It uses k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to determine the CNN model’s depth and width. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging Kaggle datasets: EyePACS and APTOS2019. The auto-designed models outperform well-known pre-trained CNN models such as ResNet152, DenseNet121, and ResNeSt50, as well as Google’s AutoML and Auto-Keras models based on neural architecture search (NAS). The proposed method outperforms current CNN-based DR screening methods. The proposed method can be used in various clinical settings to screen for DR and refer patients to ophthalmologists for further evaluation and treatment. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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22 pages, 3389 KiB  
Article
Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text
by Ahmed Hamza Osman and Hani Moaiteq Aljahdali
Mathematics 2022, 10(23), 4613; https://doi.org/10.3390/math10234613 - 05 Dec 2022
Cited by 3 | Viewed by 1421
Abstract
Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when [...] Read more.
Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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15 pages, 1108 KiB  
Article
Improved Large Covariance Matrix Estimation Based on Efficient Convex Combination and Its Application in Portfolio Optimization
by Yan Zhang, Jiyuan Tao, Zhixiang Yin and Guoqiang Wang
Mathematics 2022, 10(22), 4282; https://doi.org/10.3390/math10224282 - 16 Nov 2022
Cited by 3 | Viewed by 1519
Abstract
The estimation of the covariance matrix is an important topic in the field of multivariate statistical analysis. In this paper, we propose a new estimator, which is a convex combination of the linear shrinkage estimation and the rotation-invariant estimator under the Frobenius norm. [...] Read more.
The estimation of the covariance matrix is an important topic in the field of multivariate statistical analysis. In this paper, we propose a new estimator, which is a convex combination of the linear shrinkage estimation and the rotation-invariant estimator under the Frobenius norm. We first obtain the optimal parameters by using grid search and cross-validation, and then, we use these optimal parameters to demonstrate the effectiveness and robustness of the proposed estimation in the numerical simulations. Finally, in empirical research, we apply the covariance matrix estimation to the portfolio optimization. Compared to the existing estimators, we show that the proposed estimator has better performance and lower out-of-sample risk in portfolio optimization. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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15 pages, 26807 KiB  
Article
Non-Contact Detection of Delamination in Composite Laminates Coated with a Mechanoluminescent Sensor Using Convolutional AutoEncoder
by Seogu Park, Jinwoo Song, Heung Soo Kim and Donghyeon Ryu
Mathematics 2022, 10(22), 4254; https://doi.org/10.3390/math10224254 - 15 Nov 2022
Cited by 4 | Viewed by 1695
Abstract
Delamination is a typical defect of carbon fiber-reinforced composite laminates. Detecting delamination is very important in the performance of laminated composite structures. Structural Health Monitoring (SHM) methods using the latest sensors have been proposed to detect delamination that occurs during the operation of [...] Read more.
Delamination is a typical defect of carbon fiber-reinforced composite laminates. Detecting delamination is very important in the performance of laminated composite structures. Structural Health Monitoring (SHM) methods using the latest sensors have been proposed to detect delamination that occurs during the operation of laminated composite structures. However, most sensors used in SHM methods measure data in the contact form and do not provide visual information about delamination. Research into mechanoluminescent sensors (ML) that can address the limitations of existing sensors has been actively conducted for decades. The ML sensor responds to mechanical deformation and emits light proportional to mechanical stimuli, thanks it can provide visual information about changes in the physical quantity of the entire structure. Many researchers focus on detecting cracks in structures and impact damage with the ML sensor. This paper presents a method of detecting the delamination of composites using ML sensors. A Convolutional AutoEncoder (CAE) was used to automatically extract the delamination positions from light emission images, which offers better performance compared to edge detection methods. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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21 pages, 751 KiB  
Article
SemG-TS: Abstractive Arabic Text Summarization Using Semantic Graph Embedding
by Wael Etaiwi and Arafat Awajan
Mathematics 2022, 10(18), 3225; https://doi.org/10.3390/math10183225 - 06 Sep 2022
Cited by 8 | Viewed by 1916
Abstract
This study proposes a novel semantic graph embedding-based abstractive text summarization technique for the Arabic language, namely SemG-TS. SemG-TS employs a deep neural network to produce the abstractive summary. A set of experiments were conducted to evaluate the performance of SemG-TS and to [...] Read more.
This study proposes a novel semantic graph embedding-based abstractive text summarization technique for the Arabic language, namely SemG-TS. SemG-TS employs a deep neural network to produce the abstractive summary. A set of experiments were conducted to evaluate the performance of SemG-TS and to compare the results to those of a popular baseline word embedding technique called word2vec. A new dataset was collected for the experiments. Two evaluation methodologies were followed in the experiments: automatic and human evaluations. The Rouge evaluation measure was used for the automatic evaluation, while for the human evaluation, Arabic native speakers were tasked to evaluate the relevancy, similarity, readability, and overall satisfaction of the generated summaries. The obtained results prove the superiority of SemG-TS. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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18 pages, 1598 KiB  
Article
A Deep Learning Approach for Predicting Two-Dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)
by Yue Lu and Gang Mei
Mathematics 2022, 10(16), 2949; https://doi.org/10.3390/math10162949 - 16 Aug 2022
Cited by 10 | Viewed by 2514
Abstract
The unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multidirectional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications but is much more complicated in terms of index determination and solution. [...] Read more.
The unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multidirectional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications but is much more complicated in terms of index determination and solution. To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation. In the proposed method, (1) a fully connected neural network is constructed; (2) the computational domain, partial differential equation (PDE), and constraints are defined to generate data for model training; and (3) the PDE of two-dimensional soil consolidation and the model of the neural network are connected to reduce the loss of the model. The effectiveness of the proposed method is verified by comparison with the numerical solution of PDE for two-dimensional consolidation. Moreover, the FEM and the proposed PINN-based method are applied to predict the consolidation of foundation soils in a real case of Sichuan Railway in China, and the results are quite consistent. The proposed deep learning approach can be used to investigate large and complex multidirectional soil consolidation. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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15 pages, 3551 KiB  
Article
Deep Learning-Based Remaining Useful Life Prediction Method with Transformer Module and Random Forest
by Lefa Zhao, Yafei Zhu and Tianyu Zhao
Mathematics 2022, 10(16), 2921; https://doi.org/10.3390/math10162921 - 13 Aug 2022
Cited by 9 | Viewed by 2205
Abstract
This paper focuses on the prognosis problem in manufacturing of the electronic chips for devices. Electronic devices are of great importance at present, which are popularly applied in daily life. The basis of supporting the electronic device is the powerful electronic chip and [...] Read more.
This paper focuses on the prognosis problem in manufacturing of the electronic chips for devices. Electronic devices are of great importance at present, which are popularly applied in daily life. The basis of supporting the electronic device is the powerful electronic chip and its manufacturing technology. Chip manufacturing has been one of the most important technologies in recent years. The etching machine is the key equipment in the etching process of the wafers in chip manufacturing. Due to the high demands for precise manufacturing, monitoring the health state and predicting the remaining useful life (RUL) of the etching system is quite important. However, the task is very hard because of the lack of knowledge of exact onset of failure or degradation and the multiple operating conditions, etc. This paper proposes a novel deep learning-based RUL prediction method for the etching system. The transformer module and random forest are integrated in the methodology to identify the health state of the machine and predict its RUL, through training with the complex data of the etching machine’s sensors and exploring its underlying features. The experiments are based on the subject of the 2018 PHM Data Challenge—for estimating time-to-failure or RUL of Ion Mill Etching Systems in an online fashion using data from multiple sensors. The results indicate the proposed method is promising for the real applications of the prognosis of the etching system for electronic devices. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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15 pages, 7196 KiB  
Article
Study on Dynamic Characteristics of a Rotating Sandwich Porous Pre-Twist Blade with a Setting Angle Reinforced by Graphene Nanoplatelets
by Jiapei Peng, Lefa Zhao and Tianyu Zhao
Mathematics 2022, 10(15), 2814; https://doi.org/10.3390/math10152814 - 08 Aug 2022
Cited by 3 | Viewed by 1221
Abstract
Lightweight blades with high strength are urgently needed in practical rotor engineering. Sandwich structures with porous core and reinforced surfaces are commonly applied to achieve these mechanical performances. Moreover, blades with large aspect ratios are established by the elastic plate models in theory. [...] Read more.
Lightweight blades with high strength are urgently needed in practical rotor engineering. Sandwich structures with porous core and reinforced surfaces are commonly applied to achieve these mechanical performances. Moreover, blades with large aspect ratios are established by the elastic plate models in theory. This paper studies the vibration of a rotating sandwich pre-twist plate with a setting angle reinforced by graphene nanoplatelets (GPLs). Its core is made of foam metal, and GPLs are added into the surface layers. Supposing that nanofillers are perfectly connected with matrix material, the effective mechanical parameters of the surface layers are calculated by the mixing law and the Halpin–Tsai model, while those of the core layers are determined by the open-cell scheme. The governing equation of the rotating plate is derived by employing the Hamilton principle. By comparing with the finite element method obtained by ANSYS, the present model and vibration analysis are verified. The material and structural parameters of the blade, including graphene nanoplatelet (GPL) weight faction, GPL distribution pattern, porosity coefficient, porosity distribution pattern, length-to-thickness ratio, length-to-width ratio, setting angle and pre-twist angle of the plate are discussed in detail. The finds provide important inspiration in the designing of a rotating sandwich blade. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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16 pages, 3121 KiB  
Article
An Intelligent Athlete Signal Processing Methodology for Balance Control Ability Assessment with Multi-Headed Self-Attention Mechanism
by Nannan Xu, Xinze Cui, Xin Wang, Wei Zhang and Tianyu Zhao
Mathematics 2022, 10(15), 2794; https://doi.org/10.3390/math10152794 - 06 Aug 2022
Cited by 4 | Viewed by 1259
Abstract
In different kinds of sports, the balance control ability plays an important role for every athlete. Therefore, coaches and athletes need accurate and efficient assessments of the balance control ability to improve the athletes’ training performance scientifically. With the fast growth of sport [...] Read more.
In different kinds of sports, the balance control ability plays an important role for every athlete. Therefore, coaches and athletes need accurate and efficient assessments of the balance control ability to improve the athletes’ training performance scientifically. With the fast growth of sport technology and training devices, intelligent and automatic assessment methods have been in high demand in the past years. This paper proposes a deep-learning-based method for a balance control ability assessment involving an analysis of the time-series signals from the athletes. The proposed method directly processes the raw data and provides the assessment results, with an end-to-end structure. This straight-forward structure facilitates its practical application. A deep learning model is employed to explore the target features with a multi-headed self-attention mechanism, which is a new approach to sports assessments. In the experiments, the real athletes’ balance control ability assessment data are utilized for the validation of the proposed method. Through comparisons with different existing methods, the accuracy rate of the proposed method is shown to be more than 95% for all four tasks, which is higher than the other compared methods for tasks containing more than one athlete of each level. The results show that the proposed method works effectively and efficiently in real scenarios for athlete balance control ability evaluations. However, reducing the proposed method’s calculation costs is an important task for future studies. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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9 pages, 229 KiB  
Article
Single-Machine Group Scheduling Model with Position-Dependent and Job-Dependent DeJong’s Learning Effect
by Jin Qian and Yu Zhan
Mathematics 2022, 10(14), 2454; https://doi.org/10.3390/math10142454 - 14 Jul 2022
Cited by 1 | Viewed by 833
Abstract
This paper considers the single-group scheduling models with Pegels’ and DeJong’s learning effect and the single-group scheduling models with Pegels’ and DeJong’s aging effect. In a classical scheduling model, Pegels’ and DeJong’s learning effect is a constant or position-dependent, while the learning effect [...] Read more.
This paper considers the single-group scheduling models with Pegels’ and DeJong’s learning effect and the single-group scheduling models with Pegels’ and DeJong’s aging effect. In a classical scheduling model, Pegels’ and DeJong’s learning effect is a constant or position-dependent, while the learning effect and aging effect are job-dependent in this paper. Compared with the classical learning model and aging model for scheduling, the proposed models are more general and realistic. The objective functions are to minimize the total completion time and makespan. We propose polynomial time methods to solve all the studied problems. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
19 pages, 4624 KiB  
Article
Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model
by Zian Lin, Yuanfa Ji, Weibin Liang and Xiyan Sun
Mathematics 2022, 10(13), 2203; https://doi.org/10.3390/math10132203 - 24 Jun 2022
Cited by 10 | Viewed by 1442
Abstract
In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model, the local mean decomposition-bidirectional long short-term [...] Read more.
In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model, the local mean decomposition-bidirectional long short-term memory (LMD-BiLSTM), is proposed based on the time-frequency analysis method. The model uses the local mean decomposition (LMD) algorithm to decompose landslide displacement and obtains several subsequences of landslide displacement with different frequencies. This paper analyzes the internal relationship between the landslide displacement and rainfall, reservoir water level, and landslide state. The maximum information coefficient (MIC) algorithm is used to calculate the intrinsic correlation between each subsequence of landslide displacement and rainfall, reservoir water level, and landslide state. Subsequences of influential factors with high correlation are selected as input variables of the bidirectional long short-term memory (BiLSTM) model to predict each subsequence. Finally, the predicted results of each of the subsequences are added to obtain the final predicted displacement. The proposed LMD-BiLSTM model effectiveness is verified based on the Baishuihe landslide. The prediction results and evaluation indexes show that the model can accurately predict landslide displacement. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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7 pages, 251 KiB  
Article
Analyzing the Collatz Conjecture Using the Mathematical Complete Induction Method
by Mercedes Orús-Lacort and Christophe Jouis
Mathematics 2022, 10(12), 1972; https://doi.org/10.3390/math10121972 - 08 Jun 2022
Cited by 3 | Viewed by 4726
Abstract
In this paper, we demonstrate the Collatz conjecture using the mathematical complete induction method. We show that this conjecture is satisfied for the first values of natural numbers, and in analyzing the sequence generated by odd numbers, we can deduce a formula for [...] Read more.
In this paper, we demonstrate the Collatz conjecture using the mathematical complete induction method. We show that this conjecture is satisfied for the first values of natural numbers, and in analyzing the sequence generated by odd numbers, we can deduce a formula for the general term of the Collatz sequence for any odd natural number n after several iterations. This formula is used in one case that we analyze using the mathematical complete induction method in the process of demonstrating the conjecture. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
16 pages, 246 KiB  
Article
The Due Window Assignment Problems with Deteriorating Job and Delivery Time
by Jin Qian and Yu Zhan
Mathematics 2022, 10(10), 1672; https://doi.org/10.3390/math10101672 - 13 May 2022
Cited by 4 | Viewed by 1177
Abstract
This paper considers the single machine scheduling problem with due window, delivery time and deteriorating job, whose goal is to minimize the window location, window size, earliness and tardiness. Common due window and slack due window are considered. The delivery time depends on [...] Read more.
This paper considers the single machine scheduling problem with due window, delivery time and deteriorating job, whose goal is to minimize the window location, window size, earliness and tardiness. Common due window and slack due window are considered. The delivery time depends on the actual processing time of past sequences. The actual processing time of the job is an increasing function of the start time. Based on the small perturbations technique and adjacent exchange technique, we obtain the propositions of the problems. For common and slack due window assignment, we prove that the two objective functions are polynomial time solvable in O(nlogn) time. We propose the corresponding algorithms to obtain the optimal sequence, window location and window size. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
22 pages, 24753 KiB  
Article
Unsupervised Fault Diagnosis of Sucker Rod Pump Using Domain Adaptation with Generated Motor Power Curves
by Dezhi Hao and Xianwen Gao
Mathematics 2022, 10(8), 1224; https://doi.org/10.3390/math10081224 - 08 Apr 2022
Cited by 4 | Viewed by 1654
Abstract
The poor real-time performance and high maintenance costs of the dynamometer card (DC) sensors have been significant obstacles to the timely fault diagnosis in the sucker rod pumping system (SRPS). In contrast to the DCs, the motor power curves (MPCs), which are accessible [...] Read more.
The poor real-time performance and high maintenance costs of the dynamometer card (DC) sensors have been significant obstacles to the timely fault diagnosis in the sucker rod pumping system (SRPS). In contrast to the DCs, the motor power curves (MPCs), which are accessible easily and highly associated with the entire system, have been attempted to predict the working conditions of the SRPS in recent years. However, the lack of labeled MPCs limits the successful applications in the industrial scenario. Thereby, this paper presents an unsupervised fault diagnosis methodology to leverage the generated MPCs of different working conditions to diagnose the actual unlabeled MPCs. Firstly, the MPCs of six working conditions are generated with an integrated dynamics mathematical model. Secondly, a framework named mechanism-assisted domain adaptation network (MADAN) is proposed to minimize the distribution discrepancy between the generated and actual MPCs. Specifically, benefiting from introducing the mechanism analysis to label the collected MPCs preliminarily, a conditional distribution discrepancy metric is defined to guarantee a more accurate distribution matching with respect to different working conditions. Eventually, validation experiments are performed to evaluate the mathematical model and the diagnosis method with a set of actual MPCs collected by a self-developed device. The experimental result demonstrates that the proposed method offers a promising approach for the unsupervised diagnosis of the SRPS. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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16 pages, 3883 KiB  
Article
An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System
by Xin Wang, Seyed Mehdi Abtahi, Mahmood Chahari and Tianyu Zhao
Mathematics 2022, 10(6), 976; https://doi.org/10.3390/math10060976 - 18 Mar 2022
Cited by 10 | Viewed by 1624
Abstract
In recent decades, one of the scientists’ main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, [...] Read more.
In recent decades, one of the scientists’ main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, an adaptive neuro-fuzzy integrated system (ANFIS) for satellite attitude estimation and control was developed. The controller was trained with the data provided by an optimal controller. Furthermore, a pulse modulator was used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the proposed controller in closed-loop simulation, an ANFIS observer was also used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor, and data gyro data. However, a new ANFIS system was proposed that can jointly control and estimate the system attitude. The performance of the proposed controller was compared to the optimal PID controller in a Monte Carlo simulation with different initial conditions, disturbance, and noise. The results show that the proposed controller can surpass the optimal PID controller in several aspects including time and smoothness. In addition, the ANFIS estimator was examined and the results demonstrate the high ability of this designated observer. Consequently, evaluating the performance of PID and the proposed controller revealed that the proposed controller consumed less control effort for satellite attitude estimation under noise and uncertainty. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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28 pages, 6247 KiB  
Article
Adaptive Guided Spatial Compressive Cuckoo Search for Optimization Problems
by Wangying Xu and Xiaobing Yu
Mathematics 2022, 10(3), 495; https://doi.org/10.3390/math10030495 - 03 Feb 2022
Cited by 2 | Viewed by 1674
Abstract
Cuckoo Search (CS) is one of the heuristic algorithms that has gradually drawn public attention because of its simple parameters and easily understood principle. However, it still has some disadvantages, such as its insufficient accuracy and slow convergence speed. In this paper, an [...] Read more.
Cuckoo Search (CS) is one of the heuristic algorithms that has gradually drawn public attention because of its simple parameters and easily understood principle. However, it still has some disadvantages, such as its insufficient accuracy and slow convergence speed. In this paper, an Adaptive Guided Spatial Compressive CS (AGSCCS) has been proposed to handle the weaknesses of CS. Firstly, we adopt a chaotic mapping method to generate the initial population in order to make it more uniform. Secondly, a scheme for updating the personalized adaptive guided local location areas has been proposed to enhance the local search exploitation and convergence speed. It uses the parent’s optimal and worst group solutions to guide the next iteration. Finally, a novel spatial compression (SC) method is applied to the algorithm to accelerate the speed of iteration. It compresses the convergence space at an appropriate time, which is aimed at improving the shrinkage speed of the algorithm. AGSCCS has been examined on a suite from CEC2014 and compared with the traditional CS, as well as its four latest variants. Then the parameter identification and optimization of the photovoltaic (PV) model are applied to examine the capacity of AGSCCS. This is conducted to verify the effectiveness of AGSCCS for industrial problem application. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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17 pages, 2744 KiB  
Article
Intelligent Robust Cross-Domain Fault Diagnostic Method for Rotating Machines Using Noisy Condition Labels
by Abhijeet Ainapure, Shahin Siahpour, Xiang Li, Faray Majid and Jay Lee
Mathematics 2022, 10(3), 455; https://doi.org/10.3390/math10030455 - 30 Jan 2022
Cited by 10 | Viewed by 2727
Abstract
Cross-domain fault diagnosis methods have been successfully and widely developed in the past years, which focus on practical industrial scenarios with training and testing data from numerous machinery working regimes. Due to the remarkable effectiveness in such problems, deep learning-based domain adaptation approaches [...] Read more.
Cross-domain fault diagnosis methods have been successfully and widely developed in the past years, which focus on practical industrial scenarios with training and testing data from numerous machinery working regimes. Due to the remarkable effectiveness in such problems, deep learning-based domain adaptation approaches have been attracting increasing attention. However, the existing methods in the literature are generally lower compared to environmental noise and data availability, and it is difficult to achieve promising performance under harsh practical conditions. This paper proposes a new cross-domain fault diagnosis method with enhanced robustness. Noisy labels are introduced to significantly increase the generalization ability of the data-driven model. Promising diagnosis performance can be obtained with strong noise interference in testing, as well as in practical cases with low-quality data. Experiments on two rotating machinery datasets are carried out for validation. The results indicate that the proposed algorithm is well suited to be applied in real industrial environments to achieve promising performance with variations of working conditions. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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15 pages, 3310 KiB  
Article
Design on Intelligent Feature Graphics Based on Convolution Operation
by Ying Li and Ye Tang
Mathematics 2022, 10(3), 384; https://doi.org/10.3390/math10030384 - 26 Jan 2022
Cited by 10 | Viewed by 2198
Abstract
With the development and application of artificial intelligence, the technical methods of intelligent image processing and graphic design need to be explored to realize the intelligent graphic design based on traditional graphics such as pottery engraving graphics. An optimized method is aimed to [...] Read more.
With the development and application of artificial intelligence, the technical methods of intelligent image processing and graphic design need to be explored to realize the intelligent graphic design based on traditional graphics such as pottery engraving graphics. An optimized method is aimed to be explored to extract the image features from traditional engraving graphics on historical relics and apply them into intelligent graphic design. For this purpose, an image feature extracted model based on convolution operation is proposed. Parametric test and effectiveness research are conducted to evaluate the performance of the proposed model. Theoretical and practical research shows that the image-extracted model has a significant effect on the extraction of image features from traditional engraving graphics because the image brightness processing greatly simplifies the process of image feature extraction, and the convolution operation improves the accuracy. Based on the brightness feature map output from the proposed model, the design algorithm of intelligent feature graphic is presented to create the feature graphics, which can be directly applied to design the intelligent graphical interface. Taking some pottery engraving graphics from the Neolithic Age as an example, we conduct the practice on image feature extraction and feature graphic design, the results of which further verify the effectiveness of the proposed method. This paper provides a theoretical basis for the application of traditional engraving graphics in intelligent graphical interface design for AI products such as smart tourism products, smart museums, and so on. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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28 pages, 3306 KiB  
Article
Parameter Identification of Photovoltaic Models by Hybrid Adaptive JAYA Algorithm
by Xiaobing Yu, Xuejing Wu and Wenguan Luo
Mathematics 2022, 10(2), 183; https://doi.org/10.3390/math10020183 - 07 Jan 2022
Cited by 13 | Viewed by 1488
Abstract
As one of the most promising forms of renewable energy, solar energy is increasingly deployed. The simulation and control of photovoltaic (PV) systems requires identification of their parameters. A Hybrid Adaptive algorithm based on JAYA and Differential Evolution (HAJAYADE) is developed to identify [...] Read more.
As one of the most promising forms of renewable energy, solar energy is increasingly deployed. The simulation and control of photovoltaic (PV) systems requires identification of their parameters. A Hybrid Adaptive algorithm based on JAYA and Differential Evolution (HAJAYADE) is developed to identify these parameters accurately and reliably. The HAJAYADE algorithm consists of adaptive JAYA, adaptive DE, and the chaotic perturbation method. Two adaptive coefficients are introduced in adaptive JAYA to balance the local and global search. In adaptive DE, the Rank/Best/1 mutation operator is put forward to boost the exploration and maintain the exploitation. The chaotic perturbation method is applied to reinforce the local search further. The HAJAYADE algorithm is employed to address the parameter identification of PV systems through five test cases, and the eight latest meta-heuristic algorithms are its opponents. The mean RMSE values of the HAJAYADE algorithm from five test cases are 9.8602 × 10−4, 9.8294 × 10−4, 2.4251 × 10−3, 1.7298 × 10−3, and 1.6601 × 10−2. Consequently, HAJAYADE is proven to be an efficient and reliable algorithm and could be an alternative algorithm to identify the parameters of PV systems. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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21 pages, 11982 KiB  
Article
Similitude for the Dynamic Characteristics of Dual-Rotor System with Bolted Joints
by Lei Li, Zhong Luo, Fengxia He, Zhaoye Qin, Yuqi Li and Xiaolu Yan
Mathematics 2022, 10(1), 3; https://doi.org/10.3390/math10010003 - 21 Dec 2021
Cited by 3 | Viewed by 1509
Abstract
The dual-rotor system has been widely used in aero-engines and has the characteristics of large axial size, the interaction between the high-pressure rotor and low-pressure rotor, and stiffness nonlinearity of bolted joints. However, the testing of a full-scale dual-rotor system is expensive and [...] Read more.
The dual-rotor system has been widely used in aero-engines and has the characteristics of large axial size, the interaction between the high-pressure rotor and low-pressure rotor, and stiffness nonlinearity of bolted joints. However, the testing of a full-scale dual-rotor system is expensive and time-consuming. In this paper, the scaling relationships for the dual-rotor system with bolted joints are proposed for predicting the responses of full-scale structure, which are developed by generalized and fundamental equations of substructures (shaft, disk, and bolted joints). Different materials between prototype and model are considered in the derived scaling relationships. Moreover, the effects of bolted joints on the dual-rotor system are analyzed to demonstrate the necessity for considering bolted joints in the similitude procedure. Furthermore, the dynamic characteristics for different working conditions (low-pressure rotor excitation, high-pressure rotor excitation, two frequency excitations, and counter-rotation) are predicted by the scaled model made of a relatively cheap material. The results show that the critical speeds, vibration responses, and frequency components can be predicted with good accuracy, even though the scaled model is made of different materials. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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14 pages, 374 KiB  
Article
The Due Date Assignment Scheduling Problem with Delivery Times and Truncated Sum-of-Processing-Times-Based Learning Effect
by Jin Qian and Yu Zhan
Mathematics 2021, 9(23), 3085; https://doi.org/10.3390/math9233085 - 30 Nov 2021
Cited by 12 | Viewed by 1307
Abstract
This paper considers a single-machine scheduling problem with past-sequence-dependent delivery times and the truncated sum-of-processing-times-based learning effect. The goal is to minimize the total costs that comprise the number of early jobs, the number of tardy jobs and due date. The due date [...] Read more.
This paper considers a single-machine scheduling problem with past-sequence-dependent delivery times and the truncated sum-of-processing-times-based learning effect. The goal is to minimize the total costs that comprise the number of early jobs, the number of tardy jobs and due date. The due date is a decision variable. There will be corresponding penalties for jobs that are not completed on time. Under the common due date, slack due date and different due date, we prove that these problems are polynomial time solvable. Three polynomial time algorithms are proposed to obtain the optimal sequence. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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Review

Jump to: Editorial, Research

26 pages, 6514 KiB  
Review
Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey
by Izaz Raouf, Asif Khan, Salman Khalid, Muhammad Sohail, Muhammad Muzammil Azad and Heung Soo Kim
Mathematics 2022, 10(18), 3233; https://doi.org/10.3390/math10183233 - 06 Sep 2022
Cited by 12 | Viewed by 3405
Abstract
Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers. Improvement of passenger safety is one of the key factors for evolving AVs. An automated system provided by the ADAS in autonomous vehicles is a salient [...] Read more.
Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers. Improvement of passenger safety is one of the key factors for evolving AVs. An automated system provided by the ADAS in autonomous vehicles is a salient feature for passenger safety in modern vehicles. With an increasing number of electronic control units and a combination of multiple sensors, there are now sufficient computing aptitudes in the car to support ADAS deployment. An ADAS is composed of various sensors: radio detection and ranging (RADAR), cameras, ultrasonic sensors, and LiDAR. However, continual use of multiple sensors and actuators of the ADAS can lead to failure of AV sensors. Thus, prognostic health management (PHM) of ADAS is important for smooth and continuous operation of AVs. The PHM of AVs has recently been introduced and is still progressing. There is a lack of surveys available related to sensor-based PHM of AVs in the literature. Therefore, the objective of the current study was to identify sensor-based PHM, emphasizing different fault identification and isolation (FDI) techniques with challenges and gaps existing in this field. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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