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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 22917

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Special Issue Editors

Prof. Dr. Shengzong Zhou

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1. Federation of Chinese Professional Associations in Europe, Franz-Schubert-Weg 70, 61118 Bad Vilbel, Germany
2. Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China
Interests: computer software and theory; software engineering and computer application technology research
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jingsha He

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Guest Editor

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: information security; wireless networks; blockchain technology; digital forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to comprise regular papers and the extended versions of conference papers from the FCPAE Europe Forum and the 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2022), which will be held in October 7-9 2022 in Hamburg, Germany. AIAM2022 is mainly jointly organized by the Federation of Chinese Professional Associations in Europe (FCPAE) and the International Association of Applied Science and Technology (IAAST). It aims to bring together researchers and scientists from artificial intelligence and advanced manufacture, as well as from various application areas, to discuss problems and solutions in this area, to identify new issues, and to shape future directions for research.

This Special Issue will focus on introducing the research and applications in the field of sensors and AI, such as AI systems, IoT, machine learning, networking systems of AI, computer science, and other related topics. We invite you and your colleagues to submit a contribution in the form of an original scientific research article for this Special Issue. We thank presenters and speakers in advance for your attendance at this conference and look forward to a stimulating exchange.

Potential topics include, but are not limited to:

Smart sensors;
Smart industrial IoT;
Machine learning and AI;
Intelligent decision support systems;
Human–computer interaction;
Industry 4.0 and advanced manufacturing;
Automatic control;
Intelligent information system;
The application of AI and sensors in management, transportation, energy, bioengineering, etc.

Prof. Dr. Shengzong Zhou
Prof. Dr. Jingsha He
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. Sensors 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

sensor and AI
Industry 4.0
advanced manufacturing
automatic
IoT
machine learning

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Published Papers (11 papers)

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Research

20 pages, 5519 KiB

Open AccessArticle

Thermal Load Model of a Proportional Solenoid Valve Based on Random Load Conditions

by Chenyu Liu, Anlin Wang, Xiaotian Li and Xiaoxiang Li

Sensors 2023, 23(23), 9474; https://doi.org/10.3390/s23239474 - 28 Nov 2023

Cited by 1 | Viewed by 1000

Abstract

Drastic changes in the random load of an electromechanical system bring about a reliability problem for the proportional solenoid valve based on a thermal effect. In order to accurately and effectively express the thermal load of a proportional solenoid valve under random load conditions and to meet the requirements of online acquisition, adaptive anomaly detection, and the missing substitution of thermal load data, a thermal load prediction model based on the Kalman filter algorithm is proposed. Taking the compound operation process of an excavator as the object and based on the field testing of an excavator and the independent testing experiment of a proportional solenoid valve in a non-installed state, a method of obtaining historical samples of the proportional solenoid valve’s power and thermal load is given. The k-means clustering algorithm is used to cluster the historical samples of the power and thermal load corresponding to the working posture of a multi-tool excavator. The Grubbs criterion is used to eliminate the outliers in the clustering samples, and unbiased estimation is performed on the clustering samples to obtain the prediction model. The results show that the cross-validation of the sample data under the specific sample characteristics of the thermal load model was carried out. Compared with other methods, the prediction accuracy of the thermal load model based on the Kalman filter is higher, the adaptability is strong, and the maximum prediction deviation percentage is stable within 5%. This study has value as a reference for random cycle thermal load analyses of low-frequency electromechanical products. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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14 pages, 3763 KiB

Open AccessCommunication

Soft Sensor Modeling Method for the Marine Lysozyme Fermentation Process Based on ISOA-GPR Weighted Ensemble Learning

by Na Lu, Bo Wang and Xianglin Zhu

Sensors 2023, 23(22), 9119; https://doi.org/10.3390/s23229119 - 11 Nov 2023

Cited by 4 | Viewed by 1013

Abstract

Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot describe the dynamic characteristics of the entire fermentation process. Therefore, this study proposes a weighted ensemble learning soft sensor modeling method based on an improved seagull optimization algorithm (ISOA) and Gaussian process regression (GPR). First, an improved density peak clustering algorithm (ADPC) was used to divide the sample dataset into multiple local sample subsets. Second, an improved seagull optimization algorithm was used to optimize and transform the Gaussian process regression model, and a sub-prediction model was established. Finally, the fusion strategy was determined according to the connectivity between the test samples and local sample subsets. The proposed soft sensor model was applied to the prediction of key biochemical parameters of the marine lysozyme fermentation process. The simulation results show that the proposed soft sensor model can effectively predict the key biochemical parameters with relatively small prediction errors in the case of limited training data. According to the results, this model can be expanded to the soft sensor prediction applications in general nonlinear systems. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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18 pages, 2460 KiB

Open AccessArticle

6D Object Pose Estimation Based on Cross-Modality Feature Fusion

by Meng Jiang, Liming Zhang, Xiaohua Wang, Shuang Li and Yijie Jiao

Sensors 2023, 23(19), 8088; https://doi.org/10.3390/s23198088 - 26 Sep 2023

Viewed by 2202

Abstract

The 6D pose estimation using RGBD images plays a pivotal role in robotics applications. At present, after obtaining the RGB and depth modality information, most methods directly concatenate them without considering information interactions. This leads to the low accuracy of 6D pose estimation in occlusion and illumination changes. To solve this problem, we propose a new method to fuse RGB and depth modality features. Our method effectively uses individual information contained within each RGBD image modality and fully integrates cross-modality interactive information. Specifically, we transform depth images into point clouds, applying the PointNet++ network to extract point cloud features; RGB image features are extracted by CNNs and attention mechanisms are added to obtain context information within the single modality; then, we propose a cross-modality feature fusion module (CFFM) to obtain the cross-modality information, and introduce a feature contribution weight training module (CWTM) to allocate the different contributions of the two modalities to the target task. Finally, the result of 6D object pose estimation is obtained by the final cross-modality fusion feature. By enabling information interactions within and between modalities, the integration of the two modalities is maximized. Furthermore, considering the contribution of each modality enhances the overall robustness of the model. Our experiments indicate that the accuracy rate of our method on the LineMOD dataset can reach 96.9%, on average, using the ADD (-S) metric, while on the YCB-Video dataset, it can reach 94.7% using the ADD-S AUC metric and 96.5% using the ADD-S score (<2 cm) metric. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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10 pages, 4529 KiB

Open AccessArticle

Dynamic Lane Reversal Strategy in Intelligent Transportation Systems in Smart Cities

by Wenting Li, Jianqing Li and Di Han

Sensors 2023, 23(17), 7402; https://doi.org/10.3390/s23177402 - 25 Aug 2023

Cited by 2 | Viewed by 1354

Abstract

Route guidance strategies are an important part of advanced traveler information systems, which are a subsystem of intelligent transportation systems (ITSs). In previous research, many scholars have proposed a variety of route guidance strategies to guide vehicles in order to relieve traffic congestion, but few scholars have considered a strategy to control transportation infrastructure. In this paper, to cope with tidal traffic, we propose a dynamic lane reversal strategy (DLRS) based on the density of congestion clusters over the total road region. When the density reaches 0.37, the reversible lane converts to the opposite direction. When the density falls off to below 0.22, the reversible lane returns back to the conventional direction. The simulation results show that the DLRS has better adaptability for coping with the fluctuation in tidal traffic. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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18 pages, 2909 KiB

Open AccessArticle

Innovative DMHS Algorithm Application in Wireless Sensor Networks for Efficient Routing in High-Risk Environments

by Yuanjia Ma and Xiangwu Deng

Sensors 2023, 23(16), 7223; https://doi.org/10.3390/s23167223 - 17 Aug 2023

Viewed by 1007

Abstract

Efficient routing is essential for the proper functioning of wireless sensor networks (WSNs). Recent research has focused on optimizing energy and delay for these networks. Nevertheless, there is a dearth of studies that have examined the effects of volatile settings, such as chemical plants, coal mines, nuclear power plants, and battlefields, where connectivity is inconsistent. In such contexts, sensor networks may face security incidents, and environmental factors such as node movement and death can result in dynamic changes to the network topology. A novel design algorithm grounded on Dynamic Minimum Hop Selection (DMHS) was introduced in this paper. The key principle behind DMHS is to use a probabilistic forwarding decision-making process through a distributed route discovery strategy that utilizes dynamically adjusted minimum hop counts of nodes. Simulation results indicate that the life cycle of the DMHS algorithm increases by more than 12% over 700 nodes when compared to the traditional energy-saving algorithm. Furthermore, our algorithm performs better in the average delivery rate of node, and has a 10% to 21% improvement compared to the other algorithms. Overall, the DMHS algorithm represents an important contribution to the development of WSNs that can function robustly in high-risk and unstable environments. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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16 pages, 7932 KiB

Open AccessArticle

A Picosecond Delay Generator Optimized by Layout and Routing Based on FPGA

by Min Zhu, Tang Cui, Xihan Qi and Qiang Gao

Sensors 2023, 23(13), 6144; https://doi.org/10.3390/s23136144 - 4 Jul 2023

Cited by 2 | Viewed by 2120

Abstract

A delay generator is a timing control device that can generate a delay for the input signal according to the actual requirements. A delay generator with a combination of rough delay and precise delay is implemented on a Xilinx Kintex-7 series FPGA with a design scheme based on carry delay chain. The delay generator uses the delay time parameters sent by the upper monitor to work and to reflect the current working state to the upper monitor. In this article, a theoretical model of the delay generator is designed, and a delay compensation scheme is proposed to make the working state of the theoretical model closer to the actual circuit. Through simulation experiments, the time resolution of the delay generator is 54 ps, and the time accuracy is less than 50 ps. The delay scheme adopted in this article is highly scalable, and the time resolution and time accuracy can be further improved. Finally, a theoretical model of the delay generator with relatively high time resolution is implemented through low resource occupancy rate and little workload. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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30 pages, 3375 KiB

Open AccessArticle

Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones

by Albandari Alsumayt, Nahla El-Haggar, Lobna Amouri, Zeyad M. Alfawaer and Sumayh S. Aljameel

Sensors 2023, 23(11), 5148; https://doi.org/10.3390/s23115148 - 28 May 2023

Cited by 15 | Viewed by 4691

Abstract

Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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11 pages, 1743 KiB

Open AccessCommunication

SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation

by Kunal Chaturvedi, Ali Braytee, Jun Li and Mukesh Prasad

Sensors 2023, 23(7), 3649; https://doi.org/10.3390/s23073649 - 31 Mar 2023

Cited by 1 | Viewed by 2071

Abstract

This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel and global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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15 pages, 9808 KiB

Open AccessArticle

Uneven Terrain Walking with Linear and Angular Momentum Allocation

by Zhicheng He, Songhao Piao, Xiaokun Leng and Yucong Wu

Sensors 2023, 23(4), 2027; https://doi.org/10.3390/s23042027 - 10 Feb 2023

Viewed by 1961

Abstract

Uneven terrain walking is hard to achieve for most child-size humanoid robots, as they are unable to accurately detect ground conditions. In order to reduce the demand for ground detection accuracy, a walking control framework based on centroidal momentum allocation is studied in this paper, enabling a child-size humanoid robot to walk on uneven terrain without using ground flatness information. The control framework consists of three controllers: momentum decreasing controller, posture controller, admittance controller. First, the momentum decreasing controller is used to quickly stabilize the robot after disturbance. Then, the posture controller restores the robot posture to adapt to the unknown terrain. Finally, the admittance controller aims to decrease contact impact and adapt the robot to the terrain. Note that the robot uses a mems-based inertial measurement unit (IMU) and joint position encoders to calculate centroidal momentum and use force-sensitive resistors (FSR) on the robot foot to perform admittance control. None of these is a high-cost component. Experiments are conducted to test the proposed framework, including standing posture balancing, structured non-flat ground walking, and soft uneven terrain walking, with a speed of 2.8 s per step, showing the effectiveness of the momentum allocation method. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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13 pages, 2881 KiB

Open AccessArticle

An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2

by Ge Jin, Yanghe Liu, Peiliang Qin, Rongjing Hong, Tingting Xu and Guoyu Lu

Sensors 2023, 23(4), 1953; https://doi.org/10.3390/s23041953 - 9 Feb 2023

Cited by 8 | Viewed by 1816

Abstract

In the production process of steel products, it is very important to find defects, which can not only reduce the failure rate of industrial production but also can reduce economic losses. All deep learning-based methods need many labeled samples for training. However, in the industrial field, there is a lack of sufficient training samples, especially in steel surface defects. It is almost impossible to collect enough samples that can be used for training. To solve this kind of problem, different from traditional data enhancement methods, this paper constructed a data enhancement model dependent on GAN, using our designed EDCGAN to generate abundant samples that can be used for training. Finally, we mixed different proportions of the generated samples with the original samples and tested them through the MobileNet V2 classification model. The test results showed that if we added the samples generated by EDCGAN to the original samples, the classification results would gradually improve. When the ratio reaches 80%, the overall classification result reaches the highest, achieving an accuracy rate of more than 99%. The experimental process proves the effectiveness of this method and can improve the quality of steel processing. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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15 pages, 3317 KiB

Open AccessArticle

Learning 3D Bipedal Walking with Planned Footsteps and Fourier Series Periodic Gait Planning

by Song Wang, Songhao Piao, Xiaokun Leng and Zhicheng He

Sensors 2023, 23(4), 1873; https://doi.org/10.3390/s23041873 - 7 Feb 2023

Cited by 1 | Viewed by 1893

Abstract

Reinforcement learning provides a general framework for achieving autonomy and diversity in traditional robot motion control. Robots must walk dynamically to adapt to different ground environments in complex environments. To achieve walking ability similar to that of humans, robots must be able to perceive, understand and interact with the surrounding environment. In 3D environments, walking like humans on rugged terrain is a challenging task because it requires complex world model generation, motion planning and control algorithms and their integration. So, the learning of high-dimensional complex motions is still a hot topic in research. This paper proposes a deep reinforcement learning-based footstep tracking method, which tracks the robot’s footstep position by adding periodic and symmetrical information of bipedal walking to the reward function. The robot can achieve robot obstacle avoidance and omnidirectional walking, turning, standing and climbing stairs in complex environments. Experimental results show that reinforcement learning can be combined with real-time robot footstep planning, avoiding the learning of path-planning information in the model training process, so as to avoid the model learning unnecessary knowledge and thereby accelerate the training process. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence)

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