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Processes
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Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems
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Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 40064

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

Special Issue Editors

Prof. Dr. Xiong Luo

E-Mail Website1 Website2
Guest Editor
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: machine learning; optimization; complex system; intelligent processing
Special Issues, Collections and Topics in MDPI journals
Dr. Manman Yuan

E-Mail Website
Guest Editor
School of Computer Science, Inner Mongolia University, Hohhot 010021, China
Interests: machine learning; complex system; intelligent processing

Special Issue Information

Dear Colleagues,

The modern world depends more on technology than ever before. A huge amount of data is generated and gathered with the large implementation of booming technologies such as the Internet of Things and cloud computing. Although data can be used to better serve the corresponding business needs, intelligent processing often poses major challenges. The key problem with the traditional processing method is that it cannot fully extract the information contained in big data, and therefore, it cannot run intelligently according to needs. Intelligent processing through the use of machine learning is typically a collection of target-related data from different relevant sources, such as network behavior, database activity, application activity, user activity, etc., and algorithms are chosen to operate on these data to deduce the performance; as a result, the resultant machine-learning-based models can make intelligent decisions by analyzing data from the huge amount of cyber events. Therefore, machine-learning-assisted intelligent processing for big data has been integrated into several research types to develop intelligent data modeling systems that can meet the need for sustainability and maintenance in smart city infrastructure, brain computation, smart industrial applications, etc.

Based on the above, then, we can conclude that machine-learning-assisted intelligent processing and optimization for complex systems would be able to alter the future of many applications and industries because of their data learning capabilities and could play a major role in the domain of AI-driven systems.

This Special Issue seeks high-quality works focusing on the latest novel advances in modeling technology for both machine-learning-assisted intelligent optimization and its applications. Topics include but are not limited to:

  • Machine-learning-based intelligent processing used in complex manufacturing system modeling;
  • Metaheuristic algorithms for system identification and optimization;
  • Multisource data fusion for complex industrial systems;
  • Mobile computing and sensing for real-time system simulation;
  • Distributed multiagent modeling algorithms and their industrial applications;
  • Industrial applications of complex system theory;
  • Data-driven intelligent modeling for brain computing;
  • Stability and qualitative analysis of complex networks;
  • Malware detection and classification for industrial control systems;
  • Diagnosis and treatment of human brain diseases based on intelligent dynamic modeling;
  • Other related topics.

Prof. Dr. Xiong Luo
Dr. Manman Yuan
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. Processes is an international peer-reviewed open access monthly 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

  • intelligent processing
  • machine learning
  • optimization
  • complex system

Published Papers (15 papers)

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Editorial

Jump to: Research, Review

4 pages, 185 KiB  
Editorial
Special Issue on “Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems”
by Xiong Luo and Manman Yuan
Processes 2023, 11(9), 2595; https://doi.org/10.3390/pr11092595 - 30 Aug 2023
Cited by 1 | Viewed by 579
Abstract
Complex systems and their various characteristics have been widely considered in economic and industrial systems [...] Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)

Research

Jump to: Editorial, Review

17 pages, 5470 KiB  
Article
An Artificial Intelligence Method for Flowback Control of Hydraulic Fracturing Fluid in Oil and Gas Wells
by Ruixuan Li, Hangxin Wei, Jingyuan Wang, Bo Li, Xue Zheng and Wei Bai
Processes 2023, 11(6), 1773; https://doi.org/10.3390/pr11061773 - 10 Jun 2023
Cited by 2 | Viewed by 989
Abstract
Hydraulic fracturing is one of the main ways to increase oil and gas production. However, with existing methods, the diameter of the nozzle cannot be easily adjusted. This therefore results in ‘sand production’ in flowback fluid, affecting the application of hydraulic fracturing. This [...] Read more.
Hydraulic fracturing is one of the main ways to increase oil and gas production. However, with existing methods, the diameter of the nozzle cannot be easily adjusted. This therefore results in ‘sand production’ in flowback fluid, affecting the application of hydraulic fracturing. This is because it is difficult to identify the one-dimensional series signal of fracturing fluid collected on site. In order to avoid ‘sand production’ in the flowback fluid, the nozzle should be properly controlled. Aiming to address this problem, a novel augmented residual deep learning neural network (AU-RES) is proposed that can identify the characteristics of multiple one-dimensional time series signals and effectively predict the diameter of the nozzle. The AU-RES network includes three parts: signal conversion layer, residual and convolutional layer, fully connected layer (including regression layer). Firstly, a spatial conversion algorithm for multiple one-dimensional time series signals is proposed, which can transform the one-dimensional time series signals into images in high dimensional space. Secondly, the features of the images are extracted and identified by the residual network. Thirdly, the network hyperparameters are optimized to improve the prediction accuracy of the network. Simulations and experiments performed on the field data samples show that the RMSE and LOSS when training the AU-RES network are 0.131 and 0.00021, respectively, and the prediction error of the test samples is 0.1689. In the gas field experiments, fracturing fluid sand production could be controlled, thus demonstrating the validity and reliability of the AU-RES network. By using the AU-RES neural network, sand particles will not be present in the flowback of fracturing fluid, thus improving the efficiency of hydraulic fracturing and reducing the cost of hydraulic fracturing. In addition, the AU-RES network can also be used in other similar situations. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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30 pages, 7905 KiB  
Article
Fault Diagnosis Algorithm of Gearboxes Based on GWO-SCE Adaptive Multi-Threshold Segmentation and Subdomain Adaptation
by Yangshuo Liu, Jianshe Kang, Liang Wen, Yunjie Bai, Chiming Guo and Weibo Yu
Processes 2023, 11(2), 556; https://doi.org/10.3390/pr11020556 - 11 Feb 2023
Cited by 3 | Viewed by 1323
Abstract
The data distribution of the vibration signal under different speed conditions of the gearbox is different, which leads to reduced accuracy of fault diagnosis. In this regard, this paper proposes a deep transfer fault diagnosis algorithm combining adaptive multi-threshold segmentation and subdomain adaptation. [...] Read more.
The data distribution of the vibration signal under different speed conditions of the gearbox is different, which leads to reduced accuracy of fault diagnosis. In this regard, this paper proposes a deep transfer fault diagnosis algorithm combining adaptive multi-threshold segmentation and subdomain adaptation. First of all, in the data acquisition stage, a non-contact, easy-to-arrange, and low-cost sound pressure sensor is used to collect equipment signals, which effectively solves the problems of contact installation limitations and increasingly strict layout requirements faced by traditional vibration signal-based methods. The continuous wavelet transform (CWT) is then used to convert the original vibration signal of the device into time–frequency image samples. Further, to highlight the target fault characteristics of the samples, the gray wolf optimization algorithm (GWO) is combined with symmetric cross entropy (SCE) to perform adaptive multi-threshold segmentation on the image samples. A convolutional neural network (CNN) is then used to extract the common features of the source domain samples and the target domain samples. Additionally, the local maximum mean discrepancy (LMMD) is introduced into the parameter space of the deep fully connected layer of the network to align the sub-field edge distribution of deep features so as to reduce the distribution difference of sub-class fault features under different working conditions and improve the diagnostic accuracy of the model. Finally, to verify the effectiveness of the proposed diagnosis method, a fault preset experiment of the gearbox under variable speed conditions is carried out. The results show that compared to other diagnostic methods, the method in this paper has higher diagnostic accuracy and superiority. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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22 pages, 10547 KiB  
Article
A Study on a Knowledge Graph Construction Method of Safety Reports for Process Industries
by Zhiqiang Yin, Lin Shi, Yang Yuan, Xinxin Tan and Shoukun Xu
Processes 2023, 11(1), 146; https://doi.org/10.3390/pr11010146 - 03 Jan 2023
Cited by 5 | Viewed by 2839
Abstract
There are some representative reports in industrial safety engineering, such as the Hazard and Operability Analysis and Pre-Hazard Analysis; however, a large amount of industrial safety knowledge in the report has not been fully explored. In order to reuse and release the value [...] Read more.
There are some representative reports in industrial safety engineering, such as the Hazard and Operability Analysis and Pre-Hazard Analysis; however, a large amount of industrial safety knowledge in the report has not been fully explored. In order to reuse and release the value of industrial safety knowledge, this paper constructs a new industrial safety knowledge extraction framework. The framework combines the asset management shell to summarize the knowledge concept entities of machine description language and model description language. According to the safety report template, the framework also constructs a new industrial safety knowledge-mapping standard structure. Specifically, firstly, considering that the knowledge structure of safety reports is different in different processes of the process industry, this paper innovatively proposes a general industrial safety knowledge-mapping standard structure, which provides a practical solution for the integration of industrial knowledge representation problems in different processes. Secondly, based on the research progress of named entities, this paper presents an industrial named entity extraction method (INERM) for the process industry. This method designs an entity weight model to calculate the entity weight of each sentence, and adds part-of-speech weight to improve the entity extraction algorithm, which alleviates the problem that the existing entity extraction methods cannot reasonably use the semantic information and context of word. Finally, we construct a triple of industrial safety knowledge based on the rules and store it in Neo4j. In this paper, four semantic-type templates and five semantic relation templates are constructed based on the new industrial safety knowledge map standardization construction process of the process industry. The comparative experiments show that the accuracy of the INERM on the test set is improved by 17 percentage points on average compared with other key entity extraction algorithms. A total of 1329 entities are constructed in the directional application example of the fluid transportation process, which provides a large number of references for the safety of the fluid transportation process and is more conducive to improving the safety guarantee of the fluid transport process. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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17 pages, 2062 KiB  
Article
Task-Offloading and Resource Allocation Strategy in Multidomain Cooperation for IIoT
by Zuojun Dai, Ying Zhou, Hui Tian and Nan Ma
Processes 2023, 11(1), 132; https://doi.org/10.3390/pr11010132 - 02 Jan 2023
Cited by 2 | Viewed by 1120
Abstract
This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of [...] Read more.
This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of traditional industrial wireless network architecture, which produces low efficiency of static resource allocation and high delay in closed-loop data processing. Based on the closed-loop process of task interaction of intelligent terminals in wireless networks, the proposed strategy constructs a network model of multidomain collaborative task-offloading and resource allocation in IIoT for flexible and dynamic resource allocation among intelligent terminals, edge servers, and cluster networks. Considering the partial offloading mechanism, various tasks were segmented into multiple subtasks marked at bit-level per demand, which enabled local and edge servers to process all subtasks in parallel. Moreover, this study established a utility function for the closed-loop delay and terminal energy consumption of task processing, which transformed the process of multidomain collaborative task-offloading and resource allocation into the problem of task computing revenue. Furthermore, an improved Cuckoo Search algorithm was developed to derive the optimal offloading position and resource allocation decision through an alternating iterative method. The simulation results revealed that TARMC performed better than strategies. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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24 pages, 5704 KiB  
Article
Assessing Waste Marble Powder Impact on Concrete Flexural Strength Using Gaussian Process, SVM, and ANFIS
by Nitisha Sharma, Mohindra Singh Thakur, Raj Kumar, Mohammad Abdul Malik, Ahmad Aziz Alahmadi, Mamdooh Alwetaishi and Ali Nasser Alzaed
Processes 2022, 10(12), 2745; https://doi.org/10.3390/pr10122745 - 19 Dec 2022
Cited by 9 | Viewed by 1492
Abstract
The study’s goal is to assess the flexural strength of concrete that includes waste marble powder using machine learning methods, i.e., ANFIS, Support vector machines, and Gaussian processes approaches. Flexural strength has also been studied by using the most reliable approach of sensitivity [...] Read more.
The study’s goal is to assess the flexural strength of concrete that includes waste marble powder using machine learning methods, i.e., ANFIS, Support vector machines, and Gaussian processes approaches. Flexural strength has also been studied by using the most reliable approach of sensitivity analysis in order to determine the influential independent variable to predict the dependent variable. The entire dataset consists of 202 observations, of which 120 were experimental and 82 were readings from previous research projects. The dataset was then arbitrarily split into two subsets, referred to as the training dataset and the testing dataset, each of which contained a weighted percentage of the total observations (70–30). Output was concrete mix flexural strength, whereas inputs comprised cement, fine and coarse aggregates, water, waste marble powder, and curing days. Using statistical criteria, an evaluation of the efficacy of the approaches was carried out. In comparison to other algorithms, the results demonstrate that the Gaussian process technique has a lower error bandwidth, which contributes to its superior performance. The Gaussian process is capable of producing more accurate predictions of the results of an experiment due to the fact that it has a higher coefficient of correlation (0.7476), a lower mean absolute error value (1.0884), and a smaller root mean square error value (1.5621). The number of curing days was identified as a significant predictor, in addition to a number of other factors, by sensitivity analysis. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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13 pages, 6129 KiB  
Article
Applicability of Convolutional Neural Network for Estimation of Turbulent Diffusion Distance from Source Point
by Takahiro Ishigami, Motoki Irikura and Takahiro Tsukahara
Processes 2022, 10(12), 2545; https://doi.org/10.3390/pr10122545 - 30 Nov 2022
Cited by 4 | Viewed by 1523
Abstract
For locating the source of leaking gas in various engineering fields, several issues remain in the immediate estimation of the location of diffusion sources from limited observation data, because of the nonlinearity of turbulence. This study investigated the practical applicability of diffusion source-location [...] Read more.
For locating the source of leaking gas in various engineering fields, several issues remain in the immediate estimation of the location of diffusion sources from limited observation data, because of the nonlinearity of turbulence. This study investigated the practical applicability of diffusion source-location prediction using a convolutional neural network (CNN) from leaking gas instantaneous distribution images captured by infrared cameras. We performed direct numerical simulation of a turbulent flow past a cylinder to provide training and test images, which are scalar concentration distribution fields integrated along the view direction, mimicking actual camera images. We discussed the effects of the direction in which the leaking gas flows into the camera’s view and the distance between the camera and the leaking gas on the accuracy of inference. A single learner created by all images provided an inference accuracy exceeding 85%, regardless of the inflow direction or the distance between the camera and the leaking gas within the trained range. This indicated that, with sufficient training images, a high-inference accuracy can be achieved, regardless of the direction of gas leakage or the distance between the camera and the leaking gas. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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16 pages, 1846 KiB  
Article
Research on Discrete Artificial Bee Colony Cache Strategy of UAV Edge Network
by Yang Hong, Yuexia Zhang and Shaoshuai Fan
Processes 2022, 10(9), 1838; https://doi.org/10.3390/pr10091838 - 13 Sep 2022
Cited by 1 | Viewed by 1141
Abstract
Unmanned aerial vehicle edge networks (UENs) can reduce the cache load of the core network and improve system performance to provide users with efficient content services. However, the time-varying characteristics of content popularity in UENs lead to a low accuracy of popularity prediction, [...] Read more.
Unmanned aerial vehicle edge networks (UENs) can reduce the cache load of the core network and improve system performance to provide users with efficient content services. However, the time-varying characteristics of content popularity in UENs lead to a low accuracy of popularity prediction, and the capacity limitations of wireless channel conditions lead to a lower cache hit rate than the rates of traditional fiber-optic-based cache strategies. Therefore, this paper proposes the discrete artificial bee colony cache strategy of UENs (DABCCSU). First, the information–dynamics–dissemination model of UENs (IDDMU) is established to deduce the coupling relationship between the channel capacity and the service probability in IDDMU. The influence of the service probability change on the content dissemination process is discussed, and the content popularity in UENs is predicted by the state iteration matrix. Then, the discrete artificial bee colony cache (DABCC) optimization algorithm is proposed. The action function of the artificial bee colony is designed as a random action based on the historical cache strategy. The discrete cache strategy is used as an optimization variable, and the popularity prediction result obtained by IDDMU is used to maximize the cache hit rate. DABCC provides the optimal cache strategy for the UENs, and effectively improves the cache hit rate. The simulation result shows that the accuracy of DABCCSU in content popularity prediction is more than 90%, which achieves a good prediction effect. In terms of cache performance, the average cache hit rate of DABCCSU is 91.62%, which is better than the 51.09% of the Least Recently Used (LRU) strategy, 89.27% of the Greedy Algorithm (GA) and 54.26% of Binary Particle Swarm Optimization (BPSO). In addition, the cache hit rate of DABCCSU under different cache capacities is better than that of LRU, GA, and BPSO, showing a relatively stable performance. It shows that DABCCSU can achieve excellent content popularity prediction, and it can also maximize the cache hit rate under limited communication resources and cache resources to provide UENs with the optimal content cache strategy, and provides users with high-quality content services. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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12 pages, 1610 KiB  
Article
Intelligent Facemask Coverage Detector in a World of Chaos
by Sadaf Waziry, Ahmad Bilal Wardak, Jawad Rasheed, Raed M. Shubair and Amani Yahyaoui
Processes 2022, 10(9), 1710; https://doi.org/10.3390/pr10091710 - 27 Aug 2022
Cited by 8 | Viewed by 1406
Abstract
The recent outbreak of COVID-19 around the world has caused a global health catastrophe along with economic consequences. As per the World Health Organization (WHO), this devastating crisis can be minimized and controlled if humans wear facemasks in public; however, the prevention of [...] Read more.
The recent outbreak of COVID-19 around the world has caused a global health catastrophe along with economic consequences. As per the World Health Organization (WHO), this devastating crisis can be minimized and controlled if humans wear facemasks in public; however, the prevention of spreading COVID-19 can only be possible only if they are worn properly, covering both the nose and mouth. Nonetheless, in public places or in chaos, a manual check of persons wearing the masks properly or not is a hectic job and can cause panic. For such conditions, an automatic mask-wearing system is desired. Therefore, this study analyzed several deep learning pre-trained networks and classical machine learning algorithms that can automatically detect whether the person wears the facemask or not. For this, 40,000 images are utilized to train and test 9 different models, namely, InceptionV3, EfficientNetB0, EfficientNetB2, DenseNet201, ResNet152, VGG19, convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), to recognize facemasks in images. Besides just detecting the mask, the trained models also detect whether the person is wearing the mask properly (covering nose and mouth), partially (mouth only), or wearing it inappropriately (not covering nose and mouth). Experimental work reveals that InceptionV3 and EfficientNetB2 outperformed all other methods by attaining an overall accuracy of around 98.40% and a precision, recall, and F1-score of 98.30%. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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22 pages, 3705 KiB  
Article
Lean Optimization Techniques for Improvement of Production Flows and Logistics Management: The Case Study of a Fruits Distribution Center
by Ana P. Proença, Pedro Dinis Gaspar and Tânia M. Lima
Processes 2022, 10(7), 1384; https://doi.org/10.3390/pr10071384 - 15 Jul 2022
Cited by 11 | Viewed by 5786
Abstract
The organizations of horticultural producers and, particularly, those that deal with extremely perishable endogenous fruits, such as peaches and cherries, have a greater need to optimize production flows and processes. A particularity of the horticultural industry is the short shelf life of raw [...] Read more.
The organizations of horticultural producers and, particularly, those that deal with extremely perishable endogenous fruits, such as peaches and cherries, have a greater need to optimize production flows and processes. A particularity of the horticultural industry is the short shelf life of raw materials and the seasonality of products. In this paper, optimization techniques are used to improve the production flows and the management of cold storage and distribution in a fruit central. The application of Lean tools allowed reducing the cycle time by 4.37 min and the lead time by 7.10 min of the whole process, i.e., a reduction of 35.5% and 10.6% of the cycle time and lead time, respectively, excluding the cold conservation operation. The study shows that it is possible to reduce, or even eliminate, waste throughout the process, reduce unnecessary movement, adapt the layout, maximize workspace, and level stocks, as well as greater supplier involvement in a continuous improvement approach. This approach can be outlined as a good practice for the optimization of productive flows and management logistics that may benefit productivity, energy efficiency, human resources distribution, food quality, and reduce food waste. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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14 pages, 421 KiB  
Article
A Machine Learning Approach for Predicting the Maximum Spreading Factor of Droplets upon Impact on Surfaces with Various Wettabilities
by Moussa Tembely, Damien C. Vadillo, Ali Dolatabadi and Arthur Soucemarianadin
Processes 2022, 10(6), 1141; https://doi.org/10.3390/pr10061141 - 07 Jun 2022
Cited by 8 | Viewed by 2549
Abstract
Drop impact on a dry substrate is ubiquitous in nature and industrial processes, including aircraft de-icing, ink-jet printing, microfluidics, and additive manufacturing. While the maximum spreading factor is crucial for controlling the efficiency of the majority of these processes, there is currently no [...] Read more.
Drop impact on a dry substrate is ubiquitous in nature and industrial processes, including aircraft de-icing, ink-jet printing, microfluidics, and additive manufacturing. While the maximum spreading factor is crucial for controlling the efficiency of the majority of these processes, there is currently no comprehensive approach for predicting its value. In contrast to the traditional approach based on scaling laws and/or analytical models, this paper proposes a data-driven approach for estimating the maximum spreading factor using supervised machine learning (ML) algorithms such as linear regression, decision tree, random forest, and gradient boosting. For this purpose, a dataset of hundreds of experimental results from the literature and our own—spanning the last thirty years—is collected and analyzed. The dataset was divided into training and testing sets, each representing 70% and 30% of the input data, respectively. Subsequently, machine learning techniques were applied to relate the maximum spreading factor to relevant features such as flow controlling dimensionless numbers and substrate wettability. In the current study, the gradient boosting regression model, capable of handling structured high-dimensional data, is found to be the best-performing model, with an R2-score of more than 95%. Finally, the ML predictions agree well with the experimental data and are valid across a wide range of impact conditions. This work could pave the way for the development of a universal model for controlling droplet impact, enabling the optimization of a wide variety of industrial applications. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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16 pages, 1532 KiB  
Article
A Study of Text Vectorization Method Combining Topic Model and Transfer Learning
by Xi Yang, Kaiwen Yang, Tianxu Cui, Min Chen and Liyan He
Processes 2022, 10(2), 350; https://doi.org/10.3390/pr10020350 - 11 Feb 2022
Cited by 13 | Viewed by 3240
Abstract
With the development of Internet cloud technology, the scale of data is expanding. Traditional processing methods find it difficult to deal with the problem of information extraction of big data. Therefore, it is necessary to use machine-learning-assisted intelligent processing to extract information from [...] Read more.
With the development of Internet cloud technology, the scale of data is expanding. Traditional processing methods find it difficult to deal with the problem of information extraction of big data. Therefore, it is necessary to use machine-learning-assisted intelligent processing to extract information from data in order to solve the optimization problem in complex systems. There are many forms of data storage. Among them, text data is an important data type that directly reflects semantic information. Text vectorization is an important concept in natural language processing tasks. Because text data can not be directly used for model parameter training, it is necessary to vectorize the original text data and make it numerical, and then the feature extraction operation can be carried out. The traditional text digitization method is often realized by constructing a bag of words, but the vector generated by this method can not reflect the semantic relationship between words, and it also easily causes the problems of data sparsity and dimension explosion. Therefore, this paper proposes a text vectorization method combining a topic model and transfer learning. Firstly, the topic model is selected to model the text data and extract its keywords, to grasp the main information of the text data. Then, with the help of the bidirectional encoder representations from transformers (BERT) model, which belongs to the pretrained model, model transfer learning is carried out to generate vectors, which are applied to the calculation of similarity between texts. By setting up a comparative experiment, this method is compared with the traditional vectorization method. The experimental results show that the vector generated by the topic-modeling- and transfer-learning-based text vectorization (TTTV) proposed in this paper can obtain better results when calculating the similarity between texts with the same topic, which means that it can more accurately judge whether the contents of the given two texts belong to the same topic. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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18 pages, 3107 KiB  
Article
Dynamic Prediction of Chilo suppressalis Occurrence in Rice Based on Deep Learning
by Siqiao Tan, Yu Liang, Ruowen Zheng, Hongjie Yuan, Zhengbing Zhang and Chenfeng Long
Processes 2021, 9(12), 2166; https://doi.org/10.3390/pr9122166 - 01 Dec 2021
Cited by 4 | Viewed by 2371
Abstract
(1) Background: The striped rice stem borer (SRSB), Chilo suppressalis, has severely diminished the yield and quality of rice in China. A timely and accurate prediction of the rice pest population can facilitate the designation of a pest control strategy. (2) Methods: [...] Read more.
(1) Background: The striped rice stem borer (SRSB), Chilo suppressalis, has severely diminished the yield and quality of rice in China. A timely and accurate prediction of the rice pest population can facilitate the designation of a pest control strategy. (2) Methods: In this study, we applied multiple linear regression (MLR), gradient boosting decision tree (GBDT), and deep auto-regressive (DeepAR) models in the dynamic prediction of the SRSB population occurrence during the crop season from 2000 to 2020 in Hunan province, China, by using weather factors and time series of related pests. (3) Results: This research demonstrated the potential of the deep learning method used in integrated pest management through the qualitative and quantitative evaluation of a reasonable validating dataset (the average coefficient of determination Rmean2 for the DeepAR, GBDT, and MLR models were 0.952, 0.500, and 0.166, respectively). (4) Conclusions: The DeepAR model with integrated ground-based meteorological variables, time series of related pests, and time features achieved the most accurate dynamic forecasting of the population occurrence quantity of SRSB as compared with MLR and GBDT. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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Review

Jump to: Editorial, Research

40 pages, 6791 KiB  
Review
Methods for Detecting and Classifying Weeds, Diseases and Fruits Using AI to Improve the Sustainability of Agricultural Crops: A Review
by Ana Corceiro, Khadijeh Alibabaei, Eduardo Assunção, Pedro D. Gaspar and Nuno Pereira
Processes 2023, 11(4), 1263; https://doi.org/10.3390/pr11041263 - 19 Apr 2023
Cited by 7 | Viewed by 2229
Abstract
The rapid growth of the world’s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, [...] Read more.
The rapid growth of the world’s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, their excessive and random use can lead to environmental pollution and herbicide resistance. To address these challenges, in the agricultural industry, deep learning models have become a possible tool for decision-making by using massive amounts of information collected from smart farm sensors. However, agriculture’s varied environments pose a challenge to testing and adopting new technology effectively. This study reviews recent advances in deep learning models and methods for detecting and classifying weeds to improve the sustainability of agricultural crops. The study compares performance metrics such as recall, accuracy, F1-Score, and precision, and highlights the adoption of novel techniques, such as attention mechanisms, single-stage detection models, and new lightweight models, which can enhance the model’s performance. The use of deep learning methods in weed detection and classification has shown great potential in improving crop yields and reducing adverse environmental impacts of agriculture. The reduction in herbicide use can prevent pollution of water, food, land, and the ecosystem and avoid the resistance of weeds to chemicals. This can help mitigate and adapt to climate change by minimizing agriculture’s environmental impact and improving the sustainability of the agricultural sector. In addition to discussing recent advances, this study also highlights the challenges faced in adopting new technology in agriculture and proposes novel techniques to enhance the performance of deep learning models. The study provides valuable insights into the latest advances and challenges in process systems engineering and technology for agricultural activities. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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15 pages, 2160 KiB  
Review
Is Industry 5.0 a Human-Centred Approach? A Systematic Review
by Joel Alves, Tânia M. Lima and Pedro D. Gaspar
Processes 2023, 11(1), 193; https://doi.org/10.3390/pr11010193 - 07 Jan 2023
Cited by 35 | Viewed by 9814
Abstract
Industry 5.0 presents itself as a strategy that puts the human factor at the centre of production, where the well-being of the worker is prioritized, as well as more sustainable and resilient production systems. For human centricity, it is necessary to empower human [...] Read more.
Industry 5.0 presents itself as a strategy that puts the human factor at the centre of production, where the well-being of the worker is prioritized, as well as more sustainable and resilient production systems. For human centricity, it is necessary to empower human beings and, respectively, industrial operators, to improve their individual skills and competences in collaboration or cooperation with digital technologies. This research’s main purpose and distinguishing point are to determine whether Industry 5.0 is truly human-oriented and how human centricity can be created with Industry 5.0 technologies. For that, this systematic literature review article analyses and clarifies the concepts and ideologies of Industry 5.0 and its respective technologies (Artificial Intelligence, Robotics, Human-robot collaboration, Digitalization), as well as the strategies of human centricity, with the aim of achieving sustainable and resilient systems, especially for the worker. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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