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28 pages, 18995 KiB

Open AccessArticle

A Novel Classification Model of Date Fruit Dataset Using Deep Transfer Learning

by Amjad Alsirhani, Muhammad Hameed Siddiqi, Ayman Mohamed Mostafa, Mohamed Ezz and Alshimaa Abdelraof Mahmoud

Electronics 2023, 12(3), 665; https://doi.org/10.3390/electronics12030665 - 28 Jan 2023

Cited by 6 | Viewed by 3697

Abstract

Date fruits are the most common fruit in the Middle East and North Africa. There are a wide variety of dates with different types, colors, shapes, tastes, and nutritional values. Classifying, identifying, and recognizing dates would play a crucial role in the agriculture, [...] Read more.

Date fruits are the most common fruit in the Middle East and North Africa. There are a wide variety of dates with different types, colors, shapes, tastes, and nutritional values. Classifying, identifying, and recognizing dates would play a crucial role in the agriculture, commercial, food, and health sectors. Nevertheless, there is no or limited work to collect a reliable dataset for many classes. In this paper, we collected the dataset of date fruits by picturing dates from primary environments: farms and shops (e.g., online or local markets). The combined dataset is unique due to the multiplicity of items. To our knowledge, no dataset contains the same number of classes from natural environments. The collected dataset has 27 classes with 3228 images. The experimental results presented are based on five stages. The first stage applied traditional machine learning algorithms for measuring the accuracy of features based on pixel intensity and color distribution. The second stage applied a deep transfer learning (TL) model to select the best model accuracy of date classification. In the third stage, the feature extraction part of the model was fine-tuned by applying different retrained points to select the best retraining point. In the fourth stage, the fully connected layer of the model was fine-tuned to achieve the best classification configurations of the model. In the fifth stage, regularization was applied to the classification layer of the best-selected model from the fourth stage, where the validation accuracy reached 97.21% and the best test accuracy was 95.21%. Full article

(This article belongs to the Special Issue Deep Learning in Environmental, Electrical, and Biomedical Engineering: Recent Advances and Future Trends)

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16 pages, 3393 KiB

Open AccessArticle

Intelligent Diagnosis Method of Data Center Precision Air Conditioning Fault Based on Knowledge Graph

by Jinsong Wu, Xiangming Xu, Xiao Liao, Zhuohui Li, Shaofeng Zhang and Yong Huang

Electronics 2023, 12(3), 498; https://doi.org/10.3390/electronics12030498 - 18 Jan 2023

Cited by 7 | Viewed by 1919

Abstract

This study first digitizes, rules and structures complex unstructured data such as massive historical operation and maintenance data and fault judgment experience of operation and maintenance engineering based on semi-automatic entity extraction method; annotate the association or indirect relationship between 63,724 types of [...] Read more.

This study first digitizes, rules and structures complex unstructured data such as massive historical operation and maintenance data and fault judgment experience of operation and maintenance engineering based on semi-automatic entity extraction method; annotate the association or indirect relationship between 63,724 types of faults among triads by means of decision trees. Bayesian algorithm is used to further explore the relationship between triples, the realizes knowledge fusion, knowledge reasoning and knowledge update, and completes knowledge graph construction; combines with fault intelligent diagnosis method, realizes fault prediction, fast discovery, locates fault, type and business impact reasoning, and provides solutions to assist decision making. Full article

(This article belongs to the Special Issue Deep Learning in Environmental, Electrical, and Biomedical Engineering: Recent Advances and Future Trends)

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17 pages, 7105 KiB

Open AccessArticle

Fault Diagnosis of Diesel Engine Valve Clearance Based on Wavelet Packet Decomposition and Neural Networks

by Zhenyi Kuai and Guoyong Huang

Electronics 2023, 12(2), 353; https://doi.org/10.3390/electronics12020353 - 10 Jan 2023

Cited by 5 | Viewed by 1610

Abstract

In order to improve the accuracy of engine valve clearance fault diagnosis, in this study, a fault identification algorithm based on wavelet packet decomposition and an artificial neural network is proposed. Firstly, the vibration signals of the engine cylinder head were collected, and [...] Read more.

In order to improve the accuracy of engine valve clearance fault diagnosis, in this study, a fault identification algorithm based on wavelet packet decomposition and an artificial neural network is proposed. Firstly, the vibration signals of the engine cylinder head were collected, and different levels of noise were superimposed on the extended data sets. Then, the test data were decomposed into wavelet packets, and the power spectrum of the sub-band signal was analyzed using the autoregressive power spectrum density estimation method. A group of values were obtained from the power spectrum integration to form the fault eigenvalue. Finally, a neural network model was designed to classify the fault eigenvalues. In the training process, the test data set was divided into three parts, the training set, the verification set, and the test set, and the dropout layer was added to avoid the overfitting phenomenon of the neural network. The experimental results show that the wavelet packet neural network model in this paper has a good diagnostic accuracy for data with different levels of noise. Full article

(This article belongs to the Special Issue Deep Learning in Environmental, Electrical, and Biomedical Engineering: Recent Advances and Future Trends)

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14 pages, 1615 KiB

Open AccessArticle

Crop Disease Detection against Complex Background Based on Improved Atrous Spatial Pyramid Pooling

by Wei Ma, Helong Yu, Wenbo Fang, Fachun Guan, Dianrong Ma, Yonggang Guo, Zhengchao Zhang and Chao Wang

Electronics 2023, 12(1), 216; https://doi.org/10.3390/electronics12010216 - 1 Jan 2023

Cited by 7 | Viewed by 1550

Abstract

Timely crop disease detection, pathogen identification, and infestation severity assessments can aid disease prevention and control efforts to mitigate crop-yield decline. However, improved disease monitoring methods are needed that can extract high-resolution, accurate, and rich color and spatial features from leaf disease spots [...] Read more.

Timely crop disease detection, pathogen identification, and infestation severity assessments can aid disease prevention and control efforts to mitigate crop-yield decline. However, improved disease monitoring methods are needed that can extract high-resolution, accurate, and rich color and spatial features from leaf disease spots in the field to achieve precise fine-grained disease-severity classification and sensitive disease-recognition accuracy. Here, we propose a neural-network-based method incorporating an improved Rouse spatial pyramid pooling strategy to achieve crop disease detection against a complex background. For neural network construction, first, a dual-attention module was introduced into the cross-stage partial network backbone to enable extraction of multi-dimensional disease information from the channel and space perspectives. Next, a dilated convolution-based spatial pyramid pooling module was integrated within the network to broaden the scope of the collection of crop-disease-related information from images of crops in the field. The neural network was tested using a set of sample data constructed from images collected at a rate of 40 frames per second that occupied only 17.12 MB of storage space. Field data analysis conducted using the miniaturized model revealed an average precision rate approaching 90.15% that exceeded the corresponding rates obtained using comparable conventional methods. Collectively, these results indicate that the proposed neural network model simplified disease-recognition tasks and suppressed noise transmission to achieve a greater accuracy rate than is obtainable using similar conventional methods, thus demonstrating that the proposed method should be suitable for use in practical applications related to crop disease recognition. Full article

(This article belongs to the Special Issue Deep Learning in Environmental, Electrical, and Biomedical Engineering: Recent Advances and Future Trends)

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15 pages, 1517 KiB

Open AccessArticle

Load Restoration Flexible Optimization in Wind Power Integrated System Based on Conditional Value at Risk

by Rusi Chen, Haiguang Liu, Yan Liu, Sicong Han and Xiaodong Yu

Electronics 2023, 12(1), 178; https://doi.org/10.3390/electronics12010178 - 30 Dec 2022

Cited by 1 | Viewed by 1386

Abstract

In order to better accommodate the uncertainty induced from a high penetration of wind power during load recovery, a method of load restoration flexible optimization is developed in this paper. First, the idea of adopting flexible operational constraints when optimizing schemes is presented [...] Read more.

In order to better accommodate the uncertainty induced from a high penetration of wind power during load recovery, a method of load restoration flexible optimization is developed in this paper. First, the idea of adopting flexible operational constraints when optimizing schemes is presented so as not to be too conservative in the application of wind power output during restoration. Further, conditional value at risk (CVaR) is employed to define an operational constraint slacking factor (OCSF) through analyzing the extent of constraint exceeding the prescribed limits when wind speed is taking the value outside its confidence interval. Adopting OCSF, a mixed integer linear programming (MILP) model of flexible load restoration, is constructed based on the primary model by substituting adjustable constraints for rigid constraints, which can be solved with CPLEX. Finally, the New England 10-unit 39-bus power system is used to demonstrate the proposed method, and the results explicitly indicate that the amount of load picked up can be effectively increased and an operational security can be guaranteed as well. Full article

(This article belongs to the Special Issue Deep Learning in Environmental, Electrical, and Biomedical Engineering: Recent Advances and Future Trends)

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20 pages, 4234 KiB

Open AccessArticle

An Accurate Urine Red Blood Cell Detection Method Based on Multi-Focus Video Fusion and Deep Learning with Application to Diabetic Nephropathy Diagnosis

by Fang Hao, Xinyu Li, Ming Li, Yongfei Wu and Wen Zheng

Electronics 2022, 11(24), 4176; https://doi.org/10.3390/electronics11244176 - 14 Dec 2022

Cited by 5 | Viewed by 3092

Abstract

Background and Objective: Detecting urine red blood cells (U-RBCs) is an important operation in diagnosing nephropathy. Existing U-RBC detection methods usually employ single-focus images to implement such tasks, which inevitably results in false positives and missed detections due to the abundance of [...] Read more.

Background and Objective: Detecting urine red blood cells (U-RBCs) is an important operation in diagnosing nephropathy. Existing U-RBC detection methods usually employ single-focus images to implement such tasks, which inevitably results in false positives and missed detections due to the abundance of defocused U-RBCs in the single-focus images. Meanwhile, the current diabetic nephropathy diagnosis methods heavily rely on artificially setting a threshold to detect the U-RBC proportion, whose accuracy and robustness are still supposed to be improved. Methods: To overcome these limitations, a novel multi-focus video dataset in which the typical shape of all U-RBCs can be captured in one frame is constructed, and an accurate U-RBC detection method based on multi-focus video fusion (D-MVF) is presented. The proposed D-MVF method consists of multi-focus video fusion and detection stages. In the fusion stage, D-MVF first uses the frame-difference data of multi-focus video to separate the U-RBCs from the background. Then, a new key frame extraction method based on the three metrics of information entropy, edge gradient, and intensity contrast is proposed. This method is responsible for extracting the typical shapes of U-RBCs and fusing them into a single image. In the detection stage, D-MVF utilizes the high-performance deep learning model YOLOv4 to rapidly and accurately detect U-RBCs based on the fused image. In addition, based on U-RBC detection results from D-MVF, this paper applies the K-nearest neighbor (KNN) method to replace artificial threshold setting for achieving more accurate diabetic nephropathy diagnosis. Results: A series of controlled experiments are conducted on the self-constructed dataset containing 887 multi-focus videos, and the experimental results show that the proposed D-MVF obtains a satisfactory mean average precision (mAP) of 0.915, which is significantly higher than that of the existing method based on single-focus images (0.700). Meanwhile, the diabetic nephropathy diagnosis accuracy and specificity of KNN reach 0.781 and 0.793, respectively, which significantly exceed the traditional threshold method (0.719 and 0.759). Conclusions: The research in this paper intelligently assists microscopists to complete U-RBC detection and diabetic nephropathy diagnosis. Therefore, the work load of microscopists can be effectively relieved, and the urine test demands of nephrotic patients can be met. Full article

(This article belongs to the Special Issue Deep Learning in Environmental, Electrical, and Biomedical Engineering: Recent Advances and Future Trends)

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12 pages, 1827 KiB

Open AccessArticle

Stereoscopic Projection Policy Optimization Method Based on Deep Reinforcement Learning

by Jing An, Guang-Ya Si, Lei Zhang, Wei Liu and Xue-Chao Zhang

Electronics 2022, 11(23), 3951; https://doi.org/10.3390/electronics11233951 - 29 Nov 2022

Viewed by 1027

Abstract

Based on the good performance of deep reinforcement learning (DRL) in policy optimization, a stereoscopic projection policy optimization method is proposed, which combines the simulation experiment method with the DRL method. On the basis of policy optimization research, a deep learning framework is [...] Read more.

Based on the good performance of deep reinforcement learning (DRL) in policy optimization, a stereoscopic projection policy optimization method is proposed, which combines the simulation experiment method with the DRL method. On the basis of policy optimization research, a deep learning framework is selected according to the research problems, and a DRL stereoscopic project policy model based on the asynchronous advantage actor–critic (A3C) algorithm, which uses two groups of neural networks, is constructed. The optimized stereoscopic projection policy is obtained by the interactive learning between the DRL model and the simulation. The effectiveness of the cooperative optimization policy between the DRL and the simulation experiment is verified. Full article

(This article belongs to the Special Issue Deep Learning in Environmental, Electrical, and Biomedical Engineering: Recent Advances and Future Trends)

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23 pages, 4622 KiB

Open AccessArticle

Synthetic Deviation Correction Method for Tracking Satellite of the SOTM Antenna on High Maneuverability Carriers

by Lei Han, Guangxia Li, Jiao Ren and Xiaoxiang Ji

Electronics 2022, 11(22), 3732; https://doi.org/10.3390/electronics11223732 - 14 Nov 2022

Cited by 2 | Viewed by 1500

Abstract

Using Satcom-On-The-Move (SOTM) antenna on moving carriers to track communication satellites is a rapidly developing technology. The normal running of the SOTM system requires its antenna beam to track the target communication satellite accurately at all times. However, due to the errors of [...] Read more.

Using Satcom-On-The-Move (SOTM) antenna on moving carriers to track communication satellites is a rapidly developing technology. The normal running of the SOTM system requires its antenna beam to track the target communication satellite accurately at all times. However, due to the errors of the measurement system, tracking deviation will inevitably occur, especially when the moving carrier is in ahigh maneuvering state, which may cause communication failures. In this paper, we propose a synthetic deviation correction algorithm; when the carrier is in the high maneuvering state, the measurement error is converted into the deviation of the azimuth as well as the pitch of the antenna that needs to be corrected to correct the pointing of the SOTM antenna. Finally, the proposed algorithm is verified by experiments. The experimental results show that the proposed algorithm has a good isolation effect on the high maneuverability of the carrier, which means that the pointing to the communication satellite is more accurate and achieves better communication quality under the high maneuvering state. The effectiveness of the algorithm is illustrated. Full article

(This article belongs to the Special Issue Deep Learning in Environmental, Electrical, and Biomedical Engineering: Recent Advances and Future Trends)

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