Artificial Intelligence in Sensors, 2nd Volume

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Open AccessArticle

Fault Classification for Cooling System of Hydraulic Machinery Using AI

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Sensors 2023, 23(16), 7152; https://doi.org/10.3390/s23167152 - 13 Aug 2023

Viewed by 841

Abstract

Hydraulic systems are used in all kinds of industries. Mills, manufacturing, robotics, and Ports require the use of Hydraulic Equipment. Many industries prefer to use hydraulic systems due to their numerous advantages over electrical and mechanical systems. Hence, the growth in demand for hydraulic systems has been increasing over time. Due to its vast variety of applications, the faults in hydraulic systems can cause a breakdown. Using Artificial-Intelligence (AI)-based approaches, faults can be classified and predicted to avoid downtime and ensure sustainable operations. This research work proposes a novel approach for the classification of the cooling behavior of a hydraulic test rig. Three fault conditions for the cooling system of the hydraulic test rig were used. The spectrograms were generated using the time series data for three fault conditions. The CNN variant, the Residual Network, was used for the classification of the fault conditions. Various features were extracted from the data including the F-score, precision, accuracy, and recall using a Confusion Matrix. The data contained 43,680 attributes and 2205 instances. After testing, validating, and training, the model accuracy of the ResNet-18 architecture was found to be close to 95%. Full article

(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)

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Open AccessArticle

Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN

Ramy Mohammed Aiesh Qaisi

Farhad Fouladinia

Abdulilah Mohammad Mayet

John William Grimaldo Guerrero

Hassen Loukil

M. Ramkumar Raja

Mohammed Abdul Muqeet

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Ehsan Eftekhari-Zadeh

Sensors 2023, 23(15), 6959; https://doi.org/10.3390/s23156959 - 05 Aug 2023

Viewed by 391

Abstract

Two-phase fluids are widely utilized in some industries, such as petrochemical, oil, water, and so on. Each phase, liquid and gas, needs to be measured. The measuring of the void fraction is vital in many industries because there are many two-phase fluids with a wide variety of liquids. A number of methods exist for measuring the void fraction, and the most popular is capacitance-based sensors. Aside from being easy to use, the capacitance-based sensor does not need any separation or interruption to measure the void fraction. In addition, in the contemporary era, thanks to Artificial Neural Networks (ANN), measurement methods have become much more accurate. The same can be said for capacitance-based sensors. In this paper, a new metering system utilizing an 8-electrode sensor and a Multilayer Perceptron network (MLP) is presented to predict an air and water volume fractions in a homogeneous fluid. Some characteristics, such as temperature, pressure, etc., can have an impact on the results obtained from the aforementioned sensor. Thus, considering temperature changes, the proposed network predicts the void fraction independent of pressure variations. All simulations were performed using the COMSOL Multiphysics software for temperature changes from 275 to 370 degrees Kelvin. In addition, a range of 1 to 500 Bars, was considered for the pressure. The proposed network has inputs obtained from the mentioned software, along with the temperature. The only output belongs to the predicted void fraction, which has a low MAE equal to 0.38. Thus, based on the obtained result, it can be said that the proposed network precisely measures the amount of the void fraction. Full article

(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)

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Open AccessArticle

MFF-YOLO: An Accurate Model for Detecting Tunnel Defects Based on Multi-Scale Feature Fusion

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Sensors 2023, 23(14), 6490; https://doi.org/10.3390/s23146490 - 18 Jul 2023

Viewed by 546

Abstract

Tunnel linings require routine inspection as they have a big impact on a tunnel’s safety and longevity. In this study, the convolutional neural network was utilized to develop the MFF-YOLO model. To improve feature learning efficiency, a multi-scale feature fusion network was constructed within the neck network. Additionally, a reweighted screening method was devised at the prediction stage to address the problem of duplicate detection frames. Moreover, the loss function was adjusted to maximize the effectiveness of model training and improve its overall performance. The results show that the model has a recall and accuracy that are 7.1% and 6.0% greater than those of the YOLOv5 model, reaching 89.5% and 89.4%, respectively, as well as the ability to reliably identify targets that the previous model error detection and miss detection. The MFF-YOLO model improves tunnel lining detection performance generally. Full article

(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)

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Open AccessReview

Deep Learning for Optical Sensor Applications: A Review

Nagi H. Al-Ashwal

Khaled A. M. Al Soufy

Mohga E. Hamza

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Mohamed A. Swillam

Sensors 2023, 23(14), 6486; https://doi.org/10.3390/s23146486 - 18 Jul 2023

Viewed by 768

Abstract

Over the past decade, deep learning (DL) has been applied in a large number of optical sensors applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as a promising technology for modern intelligent sensing platforms. These sensors are widely used in process monitoring, quality prediction, pollution, defence, security, and many other applications. However, they suffer major challenges such as the large generated datasets and low processing speeds for these data, including the high cost of these sensors. These challenges can be mitigated by integrating DL systems with optical sensor technologies. This paper presents recent studies integrating DL algorithms with optical sensor applications. This paper also highlights several directions for DL algorithms that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future development of related research. Full article

(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)

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Open AccessArticle

Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose

Joseph Peña-Echevarría

Angela Pinedo

Freddy Rojas

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María R. Sun-Kou

Sensors 2023, 23(13), 5864; https://doi.org/10.3390/s23135864 - 24 Jun 2023

Viewed by 814

Abstract

Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin”. For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation–extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks. Full article

(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)

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Open AccessArticle

A LiDAR–Inertial SLAM Method Based on Virtual Inertial Navigation System

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Electronics 2023, 12(12), 2639; https://doi.org/10.3390/electronics12122639 - 12 Jun 2023

Cited by 1 | Viewed by 664

Abstract

In scenarios with insufficient structural features, LiDAR-based SLAM may suffer from degeneracy, resulting in impaired robot localization and mapping and potentially leading to subsequent deviant navigation tasks. Therefore, it is crucial to develop advanced algorithms and techniques to mitigate the degeneracy issue and ensure the robustness and accuracy of LiDAR-based SLAM. This paper presents a LiDAR–inertial simultaneous localization and mapping (SLAM) method based on a virtual inertial navigation system (VINS) to address the issue of degeneracy. We classified different gaits and match each gait to its corresponding torso inertial measurement unit (IMU) sensor to construct virtual foot inertial navigation components. By combining an inertial navigation system (INS) with zero-velocity updates (ZUPTs), we formed the VINS to achieve real-time estimation and correction. Finally, the corrected pose estimation was input to the IMU odometry calculation procedure to further refine the localization and mapping results. To evaluate the effectiveness of our proposed VINS method in degenerate environments, we conducted experiments in three typical scenarios. The results demonstrate the high suitability and accuracy of the proposed method in degenerate scenes and show an improvement in the point clouds mapping effect. The algorithm’s versatility is emphasized by its wide applicability on GPU platforms, including quadruped robots and human wearable devices. This broader potential range of applications extends to other related fields such as autonomous driving. Full article

(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)

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Open AccessArticle

A Robust Pedestrian Re-Identification and Out-Of-Distribution Detection Framework

Abdelhamid Bouzid

Daniel Sierra-Sosa

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Adel Elmaghraby

Drones 2023, 7(6), 352; https://doi.org/10.3390/drones7060352 - 27 May 2023

Viewed by 714

Abstract

Pedestrian re-identification is an important field due to its applications in security and safety. Most current solutions for this problem use CNN-based feature extraction and assume that only the identities that are in the training data can be recognized. On the one hand, the pedestrians in the training data are called In-Distribution (ID). On the other hand, in real-world scenarios, new pedestrians and objects can appear in the scene, and the model should detect them as Out-Of-Distribution (OOD). In our previous study, we proposed a pedestrian re-identification based on von Mises–Fisher (vMF) distribution. Each identity is embedded in the unit sphere as a compact vMF distribution far from other identity distributions. Recently, a framework called Virtual Outlier Synthetic (VOS) was proposed, which detects OOD based on synthesizing virtual outliers in the embedding space in an online manner. Their approach assumes that the samples from the same object map to a compact space, which aligns with the vMF-based approach. Therefore, in this paper, we revisited the vMF approach and merged it with VOS to detect OOD data points. Experiment results showed that our framework was able to detect new pedestrians that do not exist in the training data in the inference phase. Furthermore, this framework improved the re-identification performance and holds a significant potential in real-world scenarios. Full article

(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)

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Open AccessArticle

MFPCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation

Yicheng Di

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Yuan Liu

Appl. Sci. 2023, 13(7), 4407; https://doi.org/10.3390/app13074407 - 30 Mar 2023

Viewed by 1094

Abstract

Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches frequently require the centralized storage of user data, which poses a substantial risk of privacy breaches. Compared to traditional recommendation systems with centralized data, federated recommendation systems with multiple clients trained collaboratively have significant privacy benefits in terms of user data. While users’ interests are often personalized, meta-learning can be used to learn users’ personalized preferences, and personalized preferences can help models make recommendations in cold-start scenarios. We use meta-learning to learn the personalized preferences of cold-start users. Therefore, we offer a unique meta-learning-based federated personalized cross-domain recommendation model that discovers the personalized preferences for cold-start users via a server-side meta-recommendation module. To avoid compromising user privacy, an attention mechanism is used on each client to find transferable features that contribute to knowledge transfer while obtaining embeddings of users and items; each client then uploads the weights to the server. The server accumulates weights and delivers them to clients for update. Compared to traditional recommendation system models, our model can effectively protect user privacy while solving the user cold-start problem, as we use an attention mechanism in the local embedding module to mine the source domain for transferable features that contribute to knowledge transfer. Extensive trials on real-world datasets have demonstrated that our technique effectively guarantees speed while protecting user privacy. Full article

(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)

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Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	15.8 Days	CHF 2300	Submit
Drones drones	4.8	6.1	2017	14.8 Days	CHF 2600	Submit
Electronics electronics	2.9	4.7	2012	15.8 Days	CHF 2200	Submit
Remote Sensing remotesensing	5.0	7.9	2009	21.1 Days	CHF 2700	Submit
Sensors sensors	3.9	6.8	2001	16.4 Days	CHF 2600	Submit
Technologies technologies	3.6	5.5	2013	13.6 Days	CHF 1400	Submit

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