Journal Description
Sensors
Sensors
is an international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB) and International Society for the Measurement of Physical Behaviour (ISMPB) are affiliated with Sensors and their members receive a discount on the article processing charges.
- Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
- Journal Rank: JCR - Q2 (Instruments & Instrumentation) / CiteScore - Q1 (Instrumentation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Sensors.
- Companion journals for Sensors include: Chips, Automation, JCP and Targets.
Impact Factor:
3.9 (2022);
5-Year Impact Factor:
4.1 (2022)
Latest Articles
Robust Point Cloud Registration for Aircraft Engine Pipeline Systems
Sensors 2024, 24(11), 3358; https://doi.org/10.3390/s24113358 (registering DOI) - 24 May 2024
Abstract
Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using
[...] Read more.
Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line–Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine’s piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.
Full article
(This article belongs to the Section Remote Sensors)
►
Show Figures
Open AccessArticle
Feasibility Analysis of ECG-Based pH Estimation for Asphyxia Detection in Neonates
by
Nadia Muhammad Hussain, Bilal Amin, Barry James McDermott, Eoghan Dunne, Martin O’Halloran and Adnan Elahi
Sensors 2024, 24(11), 3357; https://doi.org/10.3390/s24113357 (registering DOI) - 24 May 2024
Abstract
Birth asphyxia is a potential cause of death that is also associated with acute and chronic morbidities. The traditional and immediate approach for monitoring birth asphyxia (i.e., arterial blood gas analysis) is highly invasive and intermittent. Additionally, alternative noninvasive approaches such as pulse
[...] Read more.
Birth asphyxia is a potential cause of death that is also associated with acute and chronic morbidities. The traditional and immediate approach for monitoring birth asphyxia (i.e., arterial blood gas analysis) is highly invasive and intermittent. Additionally, alternative noninvasive approaches such as pulse oximeters can be problematic, due to the possibility of false and erroneous measurements. Therefore, further research is needed to explore alternative noninvasive and accurate monitoring methods for asphyxiated neonates. This study aims to investigate the prominent ECG features based on pH estimation that could potentially be used to explore the noninvasive, accurate, and continuous monitoring of asphyxiated neonates. The dataset used contained 274 segments of ECG and pH values recorded simultaneously. After preprocessing the data, principal component analysis and the Pan–Tompkins algorithm were used for each segment to determine the most significant ECG cycle and to compute the ECG features. Descriptive statistics were performed to describe the main properties of the processed dataset. A Kruskal–Wallis nonparametric test was then used to analyze differences between the asphyxiated and non-asphyxiated groups. Finally, a Dunn–Šidák post hoc test was used for individual comparison among the mean ranks of all groups. The findings of this study showed that ECG features (T/QRS, T Amplitude, Tslope, Tslope/T, Tslope/|T|, HR, QT, and QTc) based on pH estimation differed significantly (p < 0.05) in asphyxiated neonates. All these key ECG features were also found to be significantly different between the two groups.
Full article
(This article belongs to the Special Issue ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition)
►▼
Show Figures
Figure 1
Open AccessCommunication
Propagation Analysis of an RFID System in the UHF Band in the Honeycomb Frame of a Beehive
by
José Lorenzo-López and Leandro Juan-Llácer
Sensors 2024, 24(11), 3356; https://doi.org/10.3390/s24113356 - 23 May 2024
Abstract
In recent years, communication systems, including RFID, have been used in intelligent beehives for beekeeping. RFID systems in the UHF frequency band offer reading distances of tens of centimetres, allowing the localisation and identification of the queen bee inside the hive. With this
[...] Read more.
In recent years, communication systems, including RFID, have been used in intelligent beehives for beekeeping. RFID systems in the UHF frequency band offer reading distances of tens of centimetres, allowing the localisation and identification of the queen bee inside the hive. With this purpose, this work proposes an analysis of an environment of propagation that consists of a honeycomb frame, where the reader is placed within the frame, and the tag is placed in different positions over it. A honeycomb frame consists of a wooden box containing a honey wax panel, supported by metallic wires. The environment is modelled theoretically using its S-parameters and simulated in CST Studio. An analysis of these results and empirical measurements is performed. The results show that a periodicity in the received power of the tag is found with respect to the distance to the reader when the tag is located in a direction parallel to the wire, where local maximum and minimum values are found. Additionally, when the tag is placed over a wire of the frame, a higher received power is obtained compared to the case where the tag is placed between two wires. Furthermore, it has been observed that the reading range has increased with respect to free space, covering the full frame.
Full article
(This article belongs to the Section Communications)
Open AccessArticle
Weakly Supervised Pose Estimation of Surgical Instrument from a Single Endoscopic Image
by
Lihua Hu, Shida Feng and Bo Wang
Sensors 2024, 24(11), 3355; https://doi.org/10.3390/s24113355 - 23 May 2024
Abstract
Instrument pose estimation is a key demand in computer-aided surgery, and its main challenges lie in two aspects: Firstly, the difficulty of obtaining stable corresponding image feature points due to the instruments’ high refraction and complicated background, and secondly, the lack of labeled
[...] Read more.
Instrument pose estimation is a key demand in computer-aided surgery, and its main challenges lie in two aspects: Firstly, the difficulty of obtaining stable corresponding image feature points due to the instruments’ high refraction and complicated background, and secondly, the lack of labeled pose data. This study aims to tackle the pose estimation problem of surgical instruments in the current endoscope system using a single endoscopic image. More specifically, a weakly supervised method based on the instrument’s image segmentation contour is proposed, with the effective assistance of synthesized endoscopic images. Our method consists of the following three modules: a segmentation module to automatically detect the instrument in the input image, followed by a point inference module to predict the image locations of the implicit feature points of the instrument, and a point back-propagatable Perspective-n-Point module to estimate the pose from the tentative 2D–3D corresponding points. To alleviate the over-reliance on point correspondence accuracy, the local errors of feature point matching and the global inconsistency of the corresponding contours are simultaneously minimized. Our proposed method is validated with both real and synthetic images in comparison with the current state-of-the-art methods.
Full article
(This article belongs to the Section Sensors and Robotics)
Open AccessArticle
Strain Virtual Sensing Applied to Industrial Presses for Fatigue Monitoring
by
Bartomeu Mora, Jon Basurko, Urko Leturiondo and Joseba Albizuri
Sensors 2024, 24(11), 3354; https://doi.org/10.3390/s24113354 - 23 May 2024
Abstract
The techniques that allow one to estimate measurements at the unsensed points of a system are known as virtual sensing. These techniques are useful for the implementation of condition monitoring systems in industrial equipment subjected to high cyclic loads that can cause fatigue
[...] Read more.
The techniques that allow one to estimate measurements at the unsensed points of a system are known as virtual sensing. These techniques are useful for the implementation of condition monitoring systems in industrial equipment subjected to high cyclic loads that can cause fatigue damage, such as industrial presses. In this article, three different virtual sensing algorithms for strain estimation are tested using real measurement data obtained from a scaled bed press prototype: two deterministic algorithms (Direct Strain Observer and Least-Squares Strain Estimation) and one stochastic algorithm (Static Strain Kalman Filter). The prototype is subjected to cyclic loads using a hydraulic fatigue testing machine and is sensorized with strain gauges. Results show that sufficiently accurate strain estimations can be obtained using virtual sensing algorithms and a reduced number of strain gauges as input sensors when the monitored structure is subjected to static and quasi-static loads. Results also show that is possible to estimate the initiation of fatigue cracks at critical points of a structural component using virtual strain sensors.
Full article
(This article belongs to the Section Industrial Sensors)
Open AccessArticle
NMR in Battery Anode Slurries with a V-Shaped Sensor
by
Eric Schmid, Louis Kontschak, Hermann Nirschl and Gisela Guthausen
Sensors 2024, 24(11), 3353; https://doi.org/10.3390/s24113353 - 23 May 2024
Abstract
Inline analytics in industrial processes reduce operating costs and production rejection. Dedicated sensors enable inline process monitoring and control tailored to the application of interest. Nuclear Magnetic Resonance is a well-known analytical technique but needs adapting for low-cost, reliable and robust process monitoring.
[...] Read more.
Inline analytics in industrial processes reduce operating costs and production rejection. Dedicated sensors enable inline process monitoring and control tailored to the application of interest. Nuclear Magnetic Resonance is a well-known analytical technique but needs adapting for low-cost, reliable and robust process monitoring. A V-shaped low-field NMR sensor was developed for inline process monitoring and allows non-destructive and non-invasive measurements of materials, for example in a pipe. In this paper, the industrial application is specifically devoted to the quality control of anode slurries in battery production. The characterization of anode slurries was performed with the sensor to determine chemical composition and detect gas inclusions. Additionally, flow properties play an important role in continuous production processes. Therefore, the in- and outflow effects were investigated with the V-shaped NMR sensor as a basis for the future determination of slurry flow fields.
Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
►▼
Show Figures
Figure 1
Open AccessArticle
FP-GCN: Frequency Pyramid Graph Convolutional Network for Enhancing Pathological Gait Classification
by
Xiaoheng Zhao, Jia Li and Chunsheng Hua
Sensors 2024, 24(11), 3352; https://doi.org/10.3390/s24113352 - 23 May 2024
Abstract
Gait, a manifestation of one’s walking pattern, intricately reflects the harmonious interplay of various bodily systems, offering valuable insights into an individual’s health status. However, the current study has shortcomings in the extraction of temporal and spatial dependencies in joint motion, resulting in
[...] Read more.
Gait, a manifestation of one’s walking pattern, intricately reflects the harmonious interplay of various bodily systems, offering valuable insights into an individual’s health status. However, the current study has shortcomings in the extraction of temporal and spatial dependencies in joint motion, resulting in inefficiencies in pathological gait classification. In this paper, we propose a Frequency Pyramid Graph Convolutional Network (FP-GCN), advocating to complement temporal analysis and further enhance spatial feature extraction. specifically, a spectral decomposition component is adopted to extract gait data with different time frames, which can enhance the detection of rhythmic patterns and velocity variations in human gait and allow a detailed analysis of the temporal features. Furthermore, a novel pyramidal feature extraction approach is developed to analyze the inter-sensor dependencies, which can integrate features from different pathways, enhancing both temporal and spatial feature extraction. Our experimentation on diverse datasets demonstrates the effectiveness of our approach. Notably, FP-GCN achieves an impressive accuracy of 98.78% on public datasets and 96.54% on proprietary data, surpassing existing methodologies and underscoring its potential for advancing pathological gait classification. In summary, our innovative FP-GCN contributes to advancing feature extraction and pathological gait recognition, which may offer potential advancements in healthcare provisions, especially in regions with limited access to medical resources and in home-care environments. This work lays the foundation for further exploration and underscores the importance of remote health monitoring, diagnosis, and personalized interventions.
Full article
(This article belongs to the Special Issue Human-Centered Solutions for Ambient Assisted Living)
Open AccessArticle
Developing a Portable Autofluorescence Detection System and Its Application in Biological Samples
by
Jiaxing Zhou, Yunfei Li, Jinfeng Zhang and Fuhong Cai
Sensors 2024, 24(11), 3351; https://doi.org/10.3390/s24113351 - 23 May 2024
Abstract
Advanced glycation end-products (AGEs) are complex compounds closely associated with several chronic diseases, especially diabetes mellitus (DM). Current methods for detecting AGEs are not suitable for screening large populations, or for long-term monitoring. This paper introduces a portable autofluorescence detection system that measures
[...] Read more.
Advanced glycation end-products (AGEs) are complex compounds closely associated with several chronic diseases, especially diabetes mellitus (DM). Current methods for detecting AGEs are not suitable for screening large populations, or for long-term monitoring. This paper introduces a portable autofluorescence detection system that measures the concentration of AGEs in the skin based on the fluorescence characteristics of AGEs in biological tissues. The system employs a 395 nm laser LED to excite the fluorescence of AGEs, and uses a photodetector to capture the fluorescence intensity. A model correlating fluorescence intensity with AGEs concentration facilitates the detection of AGEs levels. To account for the variation in optical properties of different individuals’ skin, the system includes a 520 nm light source for calibration. The system features a compact design, measuring only 60 mm × 50 mm × 20 mm, and is equipped with a miniature STM32 module for control and a battery for extended operation, making it easy for subjects to wear. To validate the system’s effectiveness, it was tested on 14 volunteers to examine the correlation between AGEs and glycated hemoglobin, revealing a correlation coefficient of 0.49. Additionally, long-term monitoring of AGEs’ fluorescence and blood sugar levels showed a correlation trend exceeding 0.95, indicating that AGEs reflect changes in blood sugar levels to some extent. Further, by constructing a multivariate predictive model, the study also found that AGEs levels are correlated with age, BMI, gender, and a physical activity index, providing new insights for predicting AGEs content and blood sugar levels. This research supports the early diagnosis and treatment of chronic diseases such as diabetes, and offers a potentially useful tool for future clinical applications.
Full article
(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments)
Open AccessArticle
Portable Facial Expression System Based on EMG Sensors and Machine Learning Models
by
Paola A. Sanipatín-Díaz, Paul D. Rosero-Montalvo and Wilmar Hernandez
Sensors 2024, 24(11), 3350; https://doi.org/10.3390/s24113350 - 23 May 2024
Abstract
One of the biggest challenges of computers is collecting data from human behavior, such as interpreting human emotions. Traditionally, this process is carried out by computer vision or multichannel electroencephalograms. However, they comprise heavy computational resources, far from final users or where the
[...] Read more.
One of the biggest challenges of computers is collecting data from human behavior, such as interpreting human emotions. Traditionally, this process is carried out by computer vision or multichannel electroencephalograms. However, they comprise heavy computational resources, far from final users or where the dataset was made. On the other side, sensors can capture muscle reactions and respond on the spot, preserving information locally without using robust computers. Therefore, the research subject is the recognition of the six primary human emotions using electromyography sensors in a portable device. They are placed on specific facial muscles to detect happiness, anger, surprise, fear, sadness, and disgust. The experimental results showed that when working with the CortexM0 microcontroller, enough computational capabilities were achieved to store a deep learning model with a classification store of 92%. Furthermore, we demonstrate the necessity of collecting data from natural environments and how they need to be processed by a machine learning pipeline.
Full article
(This article belongs to the Special Issue Sensors Applications on Emotion Recognition)
Open AccessReview
A Review on the State of the Art in Copter Drones and Flight Control Systems
by
Janis Peksa and Dmytro Mamchur
Sensors 2024, 24(11), 3349; https://doi.org/10.3390/s24113349 - 23 May 2024
Abstract
This paper presents an overview on the state of the art in copter drones and their components. It starts by providing an introduction to unmanned aerial vehicles in general, describing their main types, and then shifts its focus mostly to multirotor drones as
[...] Read more.
This paper presents an overview on the state of the art in copter drones and their components. It starts by providing an introduction to unmanned aerial vehicles in general, describing their main types, and then shifts its focus mostly to multirotor drones as the most attractive for individual and research use. This paper analyzes various multirotor drone types, their construction, typical areas of implementation, and technology used underneath their construction. Finally, it looks at current challenges and future directions in drone system development, emerging technologies, and future research topics in the area. This paper concludes by highlighting some key challenges that need to be addressed before widespread adoption of drone technologies in everyday life can occur. By summarizing an up-to-date survey on the state of the art in copter drone technology, this paper will provide valuable insights into where this field is heading in terms of progress and innovation.
Full article
(This article belongs to the Section Vehicular Sensing)
Open AccessArticle
A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN)
by
Anna Strzoda and Krzysztof Grochla
Sensors 2024, 24(11), 3348; https://doi.org/10.3390/s24113348 - 23 May 2024
Abstract
Despite the ability of Low-Power Wide-Area Networks to offer extended range, they encounter challenges with coverage blind spots in the network. This article proposes an innovative energy-efficient and nature-inspired relay selection algorithm for LoRa-based LPWAN networks, serving as a solution for challenges related
[...] Read more.
Despite the ability of Low-Power Wide-Area Networks to offer extended range, they encounter challenges with coverage blind spots in the network. This article proposes an innovative energy-efficient and nature-inspired relay selection algorithm for LoRa-based LPWAN networks, serving as a solution for challenges related to poor signal range in areas with limited coverage. A swarm behavior-inspired approach is utilized to select the relays’ localization in the network, providing network energy efficiency and radio signal extension. These relays help to bridge communication gaps, significantly reducing the impact of coverage blind spots by forwarding signals from devices with poor direct connectivity with the gateway. The proposed algorithm considers critical factors for the LoRa standard, such as the Spreading Factor and device energy budget analysis. Simulation experiments validate the proposed scheme’s effectiveness in terms of energy efficiency under diverse multi-gateway (up to six gateways) network topology scenarios involving thousands of devices (1000–1500). Specifically, it is verified that the proposed approach outperforms a reference method in preventing battery depletion of the relays, which is vital for battery-powered IoT devices. Furthermore, the proposed heuristic method achieves over twice the speed of the exact method for some large-scale problems, with a negligible accuracy loss of less than 2%.
Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Open AccessArticle
Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization
by
Misganaw Abebe, Yonggeun Cho, Seung Chul Han and Bonyong Koo
Sensors 2024, 24(11), 3347; https://doi.org/10.3390/s24113347 - 23 May 2024
Abstract
The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design’s performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent
[...] Read more.
The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design’s performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.
Full article
(This article belongs to the Section Vehicular Sensing)
►▼
Show Figures
Figure 1
Open AccessArticle
Light and Displacement Compensation-Based iPPG for Heart-Rate Measurement in Complex Detection Conditions
by
Shubo Bi, Haipeng Wang and Shuaishuai Zhang
Sensors 2024, 24(11), 3346; https://doi.org/10.3390/s24113346 - 23 May 2024
Abstract
A light and displacement-compensation-based iPPG algorithm is proposed in this paper for heart-rate measurement in complex detection conditions. Two compensation sub-algorithms, including light compensation and displacement compensation, are designed and integrated into the iPPG algorithm for more accurate heart-rate measurement. In the light-compensation
[...] Read more.
A light and displacement-compensation-based iPPG algorithm is proposed in this paper for heart-rate measurement in complex detection conditions. Two compensation sub-algorithms, including light compensation and displacement compensation, are designed and integrated into the iPPG algorithm for more accurate heart-rate measurement. In the light-compensation sub-algorithm, the measurement deviation caused by the ambient light change is compensated by the mean filter-based light adjustment strategy. In the displacement-compensation sub-algorithm, the measurement deviation caused by the subject motion is compensated by the optical flow-based displacement calculation strategy. A series of heart-rate measurement experiments are conducted to verify the effectiveness of the proposed method. Compared with conventional iPPG, the average measurement accuracy increases by 3.8% under different detection distances and 5.0% under different light intensities.
Full article
(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis)
Open AccessReview
A Rapid Review on the Effectiveness and Use of Wearable Biofeedback Motion Capture Systems in Ergonomics to Mitigate Adverse Postures and Movements of the Upper Body
by
Carl M. Lind
Sensors 2024, 24(11), 3345; https://doi.org/10.3390/s24113345 - 23 May 2024
Abstract
Work-related diseases and disorders remain a significant global health concern, necessitating multifaceted measures for mitigation. One potential measure is work technique training utilizing augmented feedback through wearable motion capture systems. However, there exists a research gap regarding its current effectiveness in both real
[...] Read more.
Work-related diseases and disorders remain a significant global health concern, necessitating multifaceted measures for mitigation. One potential measure is work technique training utilizing augmented feedback through wearable motion capture systems. However, there exists a research gap regarding its current effectiveness in both real work environments and controlled settings, as well as its ability to reduce postural exposure and retention effects over short, medium, and long durations. A rapid review was conducted, utilizing two databases and three previous literature reviews to identify relevant studies published within the last twenty years, including recent literature up to the end of 2023. Sixteen studies met the inclusion criteria, of which 14 were of high or moderate quality. These studies were summarized descriptively, and the strength of evidence was assessed. Among the included studies, six were rated as high quality, while eight were considered moderate quality. Notably, the reporting of participation rates, blinding of assessors, and a-priori power calculations were infrequently performed. Four studies were conducted in real work environments, while ten were conducted in controlled settings. Vibration feedback was the most common feedback type utilized (n = 9), followed by auditory (n = 7) and visual feedback (n = 1). All studies employed corrective feedback initiated by the system. In controlled environments, evidence regarding the effectiveness of augmented feedback from wearable motion capture systems to reduce postural exposure ranged from strong evidence to no evidence, depending on the time elapsed after feedback administration. Conversely, for studies conducted in real work environments, the evidence ranged from very limited evidence to no evidence. Future reach needs are identified and discussed.
Full article
(This article belongs to the Special Issue Sensor-Based Human Motor Learning)
Open AccessArticle
Vibration Signal Noise-Reduction Method of Slewing Bearings Based on the Hybrid Reinforcement Chameleon Swarm Algorithm, Variate Mode Decomposition, and Wavelet Threshold (HRCSA-VMD-WT) Integrated Model
by
Zhuang Li, Xingtian Yao, Cheng Zhang, Yongming Qian and Yue Zhang
Sensors 2024, 24(11), 3344; https://doi.org/10.3390/s24113344 - 23 May 2024
Abstract
To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic
[...] Read more.
To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm’s (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA’s superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance.
Full article
(This article belongs to the Special Issue Sensing Technology and Applications for Industrial Maintenance and Automation)
►▼
Show Figures
Figure 1
Open AccessArticle
Validation of Step Detection and Distance Calculation Algorithms for Soccer Performance Monitoring
by
Gabriele Santicchi, Susanna Stillavato, Marco Deriu, Aldo Comi, Pietro Cerveri, Fabio Esposito and Matteo Zago
Sensors 2024, 24(11), 3343; https://doi.org/10.3390/s24113343 - 23 May 2024
Abstract
This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running
[...] Read more.
This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created. Experiments were conducted on a specially designed soccer-specific testing circuit performed by 15 athletes, simulating a range of locomotion activities such as walking, jogging, and high-intensity actions. The algorithm outcome was compared with manually tagged data from a high-quality video camera-based system for validation, by assessing the agreement between the paired values using limits of agreement, concordance correlation coefficient, and further metrics. Results returned a step detection accuracy of 95.8% and a distance estimation Root Mean Square Error (RMSE) of 17.6 m over about 202 m of track. A sub-sample (N = 6) also wore two pairs of devices concurrently to evaluate inter-unit reliability. The performance analysis suggested that the algorithm was effective and reliable in tracking diverse soccer-specific movements. The proposed algorithm offered a robust and efficient solution for tracking step count and distance covered in soccer, particularly beneficial in indoor environments where global navigation satellite systems are not feasible. This advancement in sports technology widens the spectrum of tools for coaches and athletes in monitoring soccer performance.
Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport—2nd Edition)
►▼
Show Figures
Figure 1
Open AccessArticle
PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism
by
Yi Deng, Jiazheng Liu, Kuihu Zhu, Quan Xie and Hai Liu
Sensors 2024, 24(11), 3342; https://doi.org/10.3390/s24113342 - 23 May 2024
Abstract
Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and
[...] Read more.
Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction–attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.
Full article
(This article belongs to the Section Electronic Sensors)
►▼
Show Figures
Figure 1
Open AccessArticle
An Enhanced Indoor Three-Dimensional Localization System with Sensor Fusion Based on Ultra-Wideband Ranging and Dual Barometer Altimetry
by
Le Bao, Kai Li, Joosun Lee, Wenbin Dong, Wenqi Li, Kyoosik Shin and Wansoo Kim
Sensors 2024, 24(11), 3341; https://doi.org/10.3390/s24113341 - 23 May 2024
Abstract
Accurate three-dimensional (3D) localization within indoor environments is crucial for enhancing item-based application services, yet current systems often struggle with localization accuracy and height estimation. This study introduces an advanced 3D localization system that integrates updated ultra-wideband (UWB) sensors and dual barometric pressure
[...] Read more.
Accurate three-dimensional (3D) localization within indoor environments is crucial for enhancing item-based application services, yet current systems often struggle with localization accuracy and height estimation. This study introduces an advanced 3D localization system that integrates updated ultra-wideband (UWB) sensors and dual barometric pressure (BMP) sensors. Utilizing three fixed UWB anchors, the system employs geometric modeling and Kalman filtering for precise tag 3D spatial localization. Building on our previous research on indoor height measurement with dual BMP sensors, the proposed system demonstrates significant improvements in data processing speed and stability. Our enhancements include a new geometric localization model and an optimized Kalman filtering algorithm, which are validated by a high-precision motion capture system. The results show that the localization error is significantly reduced, with height accuracy of approximately ±0.05 m, and the Root Mean Square Error (RMSE) of the 3D localization system reaches 0.0740 m. The system offers expanded locatable space and faster data output rates, delivering reliable performance that supports advanced applications requiring detailed 3D indoor localization.
Full article
(This article belongs to the Special Issue Simultaneous Localization and Mapping (SLAM) and Artificial Intelligence (AI) Based Localization for Positioning Applications and Mobile Robot Navigation—Second Edition)
►▼
Show Figures
Figure 1
Open AccessCommunication
Proposal for a New Differential High-Sensitivity Refractometer for the Simultaneous Measurement of Two Refractive Indices and Their Differences
by
Šimons Svirskis, Dmitrijs Merkulovs and Vladimirs Kozlovs
Sensors 2024, 24(11), 3340; https://doi.org/10.3390/s24113340 - 23 May 2024
Abstract
The refractive index of a liquid serves as a fundamental parameter reflecting its composition, thereby enabling the determination of component concentrations in various fields such as chemical research, the food industry, and environmental monitoring. Traditional methods for refractive index (RI) measurement rely on
[...] Read more.
The refractive index of a liquid serves as a fundamental parameter reflecting its composition, thereby enabling the determination of component concentrations in various fields such as chemical research, the food industry, and environmental monitoring. Traditional methods for refractive index (RI) measurement rely on light deflection angles at interfaces between the liquid and a material with a known refractive index. In this paper, the authors present a new differential refractometer for the highly sensitive measurement of RI differences between two liquid samples. Using a configuration with two cells equipped with flat parallel plates as measuring elements, the instrument facilitates accurate analysis. Namely, the sensor signals from both the solution and the solvent cuvette are generated simultaneously with one laser pulse, reducing the possible fluctuations in the laser radiation intensity. Our evaluation shows the high sensitivity of RI measurements < 7 × 10−6, so this differential refractometer can be proposed not only as a high-sensitivity sensing tool that can be used for mobile detection of nanoparticles in solution samples but also to determine the level of environmental nano-pollution using water (including rain, snow) samples from various natural as well as industrial sources, thus helping to solve some important environmental problems.
Full article
(This article belongs to the Section Optical Sensors)
Open AccessArticle
Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach
by
Thuan Minh Nguyen, Hanh Hong-Phuc Vo and Myungsik Yoo
Sensors 2024, 24(11), 3339; https://doi.org/10.3390/s24113339 - 23 May 2024
Abstract
Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm
[...] Read more.
Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying a new three-population division strategy with a proposed conditional inherited choice (CIC) to overcome premature convergence in WOA. The proposed approach achieves a balance between exploration and exploitation and enhances global search abilities. Additionally, the CatBoost model is employed for classification, effectively handling categorical data with complex patterns. A new technique for fine-tuning CatBoost’s hyperparameters is introduced, using effective quantization and the GSWO strategy. Extensive experimentation on various datasets demonstrates the superiority of GSWO-CatBoost, achieving higher accuracy rates on the WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017 datasets than the existing approaches. The comprehensive evaluations highlight the real-time applicability and accuracy of the proposed method across diverse data sources, including specialized WSN datasets and established benchmarks. Specifically, our GSWO-CatBoost method has an inference time nearly 100 times faster than deep learning methods while achieving high accuracy rates of 99.65%, 99.99%, 99.76%, and 99.74% for WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017, respectively.
Full article
(This article belongs to the Section Sensor Networks)
Journal Menu
► ▼ Journal Menu-
- Sensors Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal Browser-
arrow_forward_ios
Forthcoming issue
arrow_forward_ios Current issue - Vol. 24 (2024)
- Vol. 23 (2023)
- Vol. 22 (2022)
- Vol. 21 (2021)
- Vol. 20 (2020)
- Vol. 19 (2019)
- Vol. 18 (2018)
- Vol. 17 (2017)
- Vol. 16 (2016)
- Vol. 15 (2015)
- Vol. 14 (2014)
- Vol. 13 (2013)
- Vol. 12 (2012)
- Vol. 11 (2011)
- Vol. 10 (2010)
- Vol. 9 (2009)
- Vol. 8 (2008)
- Vol. 7 (2007)
- Vol. 6 (2006)
- Vol. 5 (2005)
- Vol. 4 (2004)
- Vol. 3 (2003)
- Vol. 2 (2002)
- Vol. 1 (2001)
Highly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Energies, JMSE, Safety, Sensors, Processes
Safety, Reliability and Effectiveness of Internal Combustion Engines
Topic Editors: Leszek Chybowski, Jarosław Myśków, Przemysław Kowalak, Andrzej JakubowskiDeadline: 31 May 2024
Topic in
Applied Sciences, Electricity, Electronics, Energies, Sensors
Power System Protection
Topic Editors: Seyed Morteza Alizadeh, Akhtar KalamDeadline: 20 June 2024
Topic in
Applied Sciences, Energies, Machines, Sensors, Vehicles
Vehicle Dynamics and Control
Topic Editors: Peter Gaspar, Junnian WangDeadline: 30 June 2024
Topic in
Acoustics, Environments, Remote Sensing, Sensors, Vehicles
Environmental Noise Prediction, Measurement and Control
Topic Editors: Bowen Hou, Jinhan MoDeadline: 20 July 2024
Conferences
Special Issues
Special Issue in
Sensors
Early Detection Techniques for Sensor Aging/Biasing/Degrading/Faulty Issues
Guest Editors: Yangquan Chen, Junyi Cao, Naipeng LiDeadline: 25 May 2024
Special Issue in
Sensors
Selected Papers from 20th World Conference on Non-Destructive Testing (WCNDT 2024)
Guest Editor: Seunghee ParkDeadline: 31 May 2024
Special Issue in
Sensors
Novel Sensors and Algorithms for Outdoor Mobile Robot
Guest Editors: Levente Tamás, Andras MajdikDeadline: 20 June 2024
Special Issue in
Sensors
Deep Learning Methods for Human Activity Recognition and Emotion Detection
Guest Editor: Mario Munoz-OrganeroDeadline: 30 June 2024
Topical Collections
Topical Collection in
Sensors
Robotic and Sensor Technologies in Environmental Exploration and Monitoring
Collection Editors: Jacopo Aguzzi, Corrado Costa, Sergio Stefanni, Valerio Funari
Topical Collection in
Sensors
Microfluidic Sensors
Collection Editors: Sabina Merlo, Klaus Stefan Drese