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The traditional target tracking algorithm can only utilize the information of a single sensor and cannot fully utilize the advantages of multiple sensors. To address this issue, this study designs a multi-sensor target tracking algorithm based on node capacity planning and distributed collaboration. First, aiming at the problem of distributed tracking application in multi-sensor networks, a wireless sensor target tracking algorithm based on node energy constraints is constructed. By constructing a node tracking model, the energy consumption is reduced according to the distributed connection network and node planning is introduced. Then, for sensor networks with limited energy, a node planning algorithm using convex relaxation method is developed to solve complex integer programming problems. Finally, in view of the various complex problems existing in the sensor network, a distributed social learning algorithm based on its own data and neighboring decision-making is established. This algorithm ultimately improves the performance of the multi-sensor target tracking algorithm by transmitting decisions about environmental states. The results demonstrated that in the convergence comparison between the TrackingNet dataset and the MOT 15 dataset, the convergence of research algorithm began to stabilize when the system was iterated 106 and 48 times, respectively. In addition, when the system iterated to 200 and 157 times, the error of the algorithm slowly began to level off and was at a smaller value. The above results show that the research algorithm has achieved better performance in multi-sensor target tracking tasks. It can better handle the relationship between targets and improve the effect of target tracking. [ABSTRACT FROM AUTHOR]

In order to reduce the probability of leakage and improve detection accuracy, multiple radar equipment is required to perform multiple detections on the same airspace. Due to the overlap of airspace, there are multiple local tracks reported by various radar equipment that belong to the same target. The local tracks reported by each radar require track fusion to form a system track, and track association is a prerequisite for track fusion. This paper proposes a multi-target and multi-sensor track association analysis method, which can distinguish whether local tracks come from the same target and associate local tracks with the same target, providing conditions for track fusion to form system tracks. This method divides track association into two steps: Coarse association and fine association, which greatly improves the accuracy of track association. [ABSTRACT FROM AUTHOR]

Yoga, as a kind of body building exercise, has always been loved by people. However, many people suffer from yoga training injuries due to long‐term incorrect posture and wrong exercise methods. There is an urgent need for a technology to help people detect and improve yoga training methods. Based on the past computer assistance method, this paper started from a new idea, and adopted the method of multi‐sensor information fusion to detect yoga training, aiming to help the masses better participate in yoga training. In this study, 50 volunteers were invited to participate in the comparative experiment. Based on multi‐sensor information fusion, and by building a human model, the tension and compression data before and after yoga training were compared to analyze the differences before and after calculation. It was concluded that the more sensors, the higher the degree of information fusion, and the lower the yoga training injury index. The injury index of yoga training without multi‐sensor information fusion technology in the early stage was 0.39. With the increase of the number of sensors, the injury index of yoga training has gradually decreased to 0.02, which was more than 5 percentage points lower than that of the previous methods. The experiment showed that the method of yoga training damage detection based on multi‐sensor information fusion was feasible, which also provided a new idea for the research of yoga training injury detection methods. [ABSTRACT FROM AUTHOR]

The issues of state estimations based on distributed observers for linear time-invariant (LTI) systems with multiple sensors are discussed in this paper. We deal with the scenario when the information exchange has known time delays, and aim at designing a distributed observer for each subsystem such that each distributed observer can estimate the system state asymptotically by rejecting the time delay. To begin with, by rewriting the target system in a connecting form, a subsystem which is affected by the time-delay states of other nodes is established. And then, for this subsystem, a distributed observer with time delay is constructed. Moreover, an equivalent state transformation is made for the observer error dynamic system based on the observable canonic decomposition theorem. Further, in order to ensure that the distributed observer error dynamic system is asymptotically stable even if there exists a time delay, a linear matrix inequality (LMI) which is relative to the Laplace matrix is elaborately set up, and a special Lyapunov function candidate based on the LMI is considered. Next, based on the Lyapunov function and Lyapunov stability theory, we prove that the error dynamic system of the distributed observer is asymptotically stable, and the observer gain is determined by a feasible solution of the LMI. Finally, a simulation example is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Bogie hunting instability is one of the common faults in railway vehicles. It not only affects ride comfort but also threatens operational safety. Due to the lower operating speed of metro vehicles, their bogie hunting stability is often overlooked. However, as wheel tread wear increases, metro vehicles with high conicity wheel–rail contact can also experience bogie hunting instability. In order to enhance the operational safety of metro vehicles, this paper conducts field tests and simulation calculations to study the bogie hunting instability behavior of metro vehicles and proposes corresponding solutions from the perspective of wheel–rail contact relationships. Acceleration and displacement sensors are installed on metro vehicles to collect data, which are processed in real time in 2 s intervals. The lateral acceleration of the frame is analyzed to determine if bogie hunting instability has occurred. Based on calculated safety indicators, it is determined whether deceleration is necessary to ensure the safety of vehicle operation. For metro vehicles in the later stages of wheel wear (after 300,000 km), the stability of their bogies should be monitored in real time. To improve the stability of metro vehicle bogies while ensuring the longevity of wheelsets, metro vehicle wheel treads should be reprofiled regularly, with a recommended reprofiling interval of 350,000 km. [ABSTRACT FROM AUTHOR]

Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. In this paper, we propose a stack of Transformer encoders for fire prediction using multiple sensors. Our model takes the time-series values collected from the sensors as input, and predicts the potential fire based on the sequential patterns underlying the time-series data. We compared our model with traditional machine learning models and recurrent neural networks on two datasets. For a simple dataset, we found that the machine learning models are better than ours, whereas our model gave better performance for a complex dataset. This implies that our model has a greater potential for real-world applications that probably have complex patterns and scenarios. [ABSTRACT FROM AUTHOR]

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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine learning (ML) algorithms are increasingly applied to structure health monitoring (SHM) problems. However, their application to pile damage detection (PDD) is hindered by the complexity of the problem. A novel multi-sensor pile damage detection (MSPDD) method is proposed in this paper to extend the application of ML algorithms in the automatic identification of PDD. The time-series signals collected by multiple sensors during the pile integrity test are first processed by the traveling wave decomposition (TWD) theory and are then input into a hybrid one-dimensional (1D) convolutional and recurrent neural network. The hybrid neural network can achieve the automatic multi-task identification of pile damage detection based on the time series of MSPDD results. Finally, the analytical solution-based sample set is utilized to evaluate the performance of the proposed hybrid model. The outputs of the multi-task learning framework can provide a detailed description of the actual pile quality and provide strong support for the classification of pile quality as well. [ABSTRACT FROM AUTHOR]

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient's progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings. [ABSTRACT FROM AUTHOR]

The issues of state estimations based on distributed observers for linear time-invariant (LTI) systems with multiple sensors are discussed in this paper. We deal with the scenario when the information exchange has known time delays, and aim at designing a distributed observer for each subsystem such that each distributed observer can estimate the system state asymptotically by rejecting the time delay. To begin with, by rewriting the target system in a connecting form, a subsystem which is affected by the time-delay states of other nodes is established. And then, for this subsystem, a distributed observer with time delay is constructed. Moreover, an equivalent state transformation is made for the observer error dynamic system based on the observable canonic decomposition theorem. Further, in order to ensure that the distributed observer error dynamic system is asymptotically stable even if there exists a time delay, a linear matrix inequality (LMI) which is relative to the Laplace matrix is elaborately set up, and a special Lyapunov function candidate based on the LMI is considered. Next, based on the Lyapunov function and Lyapunov stability theory, we prove that the error dynamic system of the distributed observer is asymptotically stable, and the observer gain is determined by a feasible solution of the LMI. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

Bogie hunting instability is one of the common faults in railway vehicles. It not only affects ride comfort but also threatens operational safety. Due to the lower operating speed of metro vehicles, their bogie hunting stability is often overlooked. However, as wheel tread wear increases, metro vehicles with high conicity wheel–rail contact can also experience bogie hunting instability. In order to enhance the operational safety of metro vehicles, this paper conducts field tests and simulation calculations to study the bogie hunting instability behavior of metro vehicles and proposes corresponding solutions from the perspective of wheel–rail contact relationships. Acceleration and displacement sensors are installed on metro vehicles to collect data, which are processed in real time in 2 s intervals. The lateral acceleration of the frame is analyzed to determine if bogie hunting instability has occurred. Based on calculated safety indicators, it is determined whether deceleration is necessary to ensure the safety of vehicle operation. For metro vehicles in the later stages of wheel wear (after 300,000 km), the stability of their bogies should be monitored in real time. To improve the stability of metro vehicle bogies while ensuring the longevity of wheelsets, metro vehicle wheel treads should be reprofiled regularly, with a recommended reprofiling interval of 350,000 km.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. In this paper, we propose a stack of Transformer encoders for fire prediction using multiple sensors. Our model takes the time-series values collected from the sensors as input, and predicts the potential fire based on the sequential patterns underlying the time-series data. We compared our model with traditional machine learning models and recurrent neural networks on two datasets. For a simple dataset, we found that the machine learning models are better than ours, whereas our model gave better performance for a complex dataset. This implies that our model has a greater potential for real-world applications that probably have complex patterns and scenarios.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the sensing period of a signal generally depends on the used sensor, it may be different from another, even in a single control system if it involves multiple sensors. This paper investigates a design problem of state observers for linear time-invariant discrete-time plants under a multi-rate sensing environment. The sensing periods of the sensors in the plant are assumed as mutually rational ratios. First, we characterize a state observer for a plant with multi-rate sensing as a periodically time-varying state observer. Then, we discuss the $l_{2}$ performance analysis of a state estimation error with the given periodically time-varying state observer. A linear matrix inequality (LMI) condition is provided for the analysis. By extending the LMI condition for analysis, we also provide that for multi-rate observer synthesis. Finally, we numerically illustrate the effectiveness of the proposed multi-rate state observer. Even if all the sensors have the same period, the sensing timing is not unique. Therefore, we numerically analyze whether the performance changes when the observation timing between multiple sensors is different.

As one of the promising human—machine interfaces, wearable sensors play an important role in modern society, which advances the development of wearable fields, especially in the promising applications of electronic skin (e-skin), robotics, prosthetics, and healthcare. In the last decades, wearable sensors tend to be capable of attractive capabilities such as miniaturization, multifunction, and smart integration, and wearable properties such as lightweight, flexibility, stretchability, and conformability for wider applications. In this work, we developed a stretchable multifunctional sensor based on porous silver nanowire/silicone rubber conductive film (P-AgNW/SR). Its unique structural configuration, i.e., an assembly of the P-AgNW/SR with good conductivity, stability, and resistance response, and the insulated silicone rubber layer, provided the feasibility for realizing multiple sensing capabilities. Specifically, porous microstructures of the P-AgNW/SR made the device to be used for pressure sensing, exhibiting outstanding dynamic and static resistive responsive behaviors and having a maximum sensitivity of 9.062 %·N−1 in a continuous compressive force range of ∼ 16 N. With the merit of the good piezoresistive property of AgNW/SR networks embedded into the surface of micropores of the P-AgNW/SR, the device was verified to be a temperature sensor for detecting temperature changes in the human body and environment. The temperature sensor had good sensitivity of 0.844 %·°C−1, high linearity of 0.999 in the range of 25–125 °C, and remarkable dynamic stability. Besides, the developed sensor was demonstrated to be a single electrode-triboelectric sensor for active sensing, owing to the unique assembly of the conductive P-AgNW/SR electrode and the silicone rubber friction layer. Based on the coupling effect of the triboelectrification and electrostatic induction, the generated electrical signals could be used to sense the human motions, according to the quantitative correlation between the human motions and the features in amplitude and waveform of the output signals. Thus, the developed stretchable sensor successfully achieved the integration of two types of passive sensing capabilities, i.e., pressure and temperature sensing, and one type of active sensing capability, i.e., triboelectric sensing, demonstrating the feasibility of monitoring multiple variables of the human body and environment. [ABSTRACT FROM AUTHOR]

Tool condition monitoring is one of the emerging areas in the manufacturing industry. This paper proposes HDL simulator-based simulation model using Modelsim to detect and classify the tool condition using the hybrid network. The system uses multiple sensors for data collection from the CNC machine. Multiple sensor data such as vibration and temperature as well as actual machine parameters are taken into consideration for the system design. The data collected is pre-processed and fed to the self-organizing map (SOM) and a Hebbian network which is a hybrid model. The data is classified according to its range, and which are mapped to get the SOM neurons. The Hebbian network designed is the single-layer feedforward neural network. The recognition process is robust to the number of changes in the input samples. The system operates in training and testing modes. The tool condition is indicated in Matlab with a message window. The system correctly detects the tool condition, with a simulation accuracy of 97.16% which is promising and insists on hardware model development. [ABSTRACT FROM AUTHOR]

Cloud and fog droplets are essential in atmospheric chemistry since they affect the distribution and chemical transformation of pollutants. Collecting sufficient volumes using cloud/fog samplers is the premise of cloud fog chemical studies. Accurate identification of fog events and high collection efficiency are the basic principles of sampler design. Therefore, an automatic cloud/fog collector (ACFC) has been designed, fabricated, and extensively tested for collecting samples of cloud/fog water. The control box and standard sensors for air temperature, relative humidity, and instantaneous rainfall were used to ensure sampling automation. Airflow measurement was used to guarantee the stability of the airspeed on the inlet section, and the airspeed is 7.5 m s−1. Moreover, the median collection rate of ACFC was 160–220 mL h−1, which was tested via a simulation experiment. To evaluate the actual performance of the device in the field, we obtained eight samples of cloud fog water from Shanghuang Observatory in eastern China from the summer through the fall of 2022. Collection rates varied from 62 to 169 mL h−1. For a cloud/fog sampler equipped with multiple sensors, the ACFC has excellent sampling efficiency in thick fog, sufficient cloud fog water samples can be collected in weak fog, and it can precisely identify fog mingled with rain. [ABSTRACT FROM AUTHOR]

With the rapid development of unmanned aerial vehicle (UAV) technology, there are more and more fields of UAV application. This research mainly discusses the UAV patrol path planning based on machine vision and multi-sensor fusion. This article studies how to apply ultrasonic, a classic ranging sensor, to obstacle avoidance of UAVs. The designed ultrasonic obstacle avoidance system is a complete set of hardware and software systems. The hardware part consists of a forward ultrasonic module and a central signal processing module. Among them, a single-axis stabilization gimbal is designed for the forward ultrasonic module, which decouples the attitude angle of the UAV and the pitch detection angle of the ultrasonic sensor. In the central signal processing module, Kalman filtering is performed on the ultrasonic data in the four directions of front, rear, left, right, and left, and the obstacle avoidance control signal is sent to the flight controller according to the filtered sensor data. At the same time, a human–computer interaction interface is also designed to set various parameters of the obstacle avoidance system. For the route planning method of the tower, the routine steps are used to inspect the tower with a single-circuit line, and the specific targets are the insulator string, the ground wire, and the conductor. In this study, the average statistical result of the straight-line distance of the UAV patrolling 100 m is 99.80 m, and the error is only 0.2%. The fusion obstacle avoidance control method based on machine vision is suitable for the engineering application of UAV perception obstacle avoidance. The obstacle avoidance method adopted in this article can be extended to most flight control platforms, and it is a control method with broad application prospects.

Machine learning (ML) algorithms are increasingly applied to structure health monitoring (SHM) problems. However, their application to pile damage detection (PDD) is hindered by the complexity of the problem. A novel multi-sensor pile damage detection (MSPDD) method is proposed in this paper to extend the application of ML algorithms in the automatic identification of PDD. The time-series signals collected by multiple sensors during the pile integrity test are first processed by the traveling wave decomposition (TWD) theory and are then input into a hybrid one-dimensional (1D) convolutional and recurrent neural network. The hybrid neural network can achieve the automatic multi-task identification of pile damage detection based on the time series of MSPDD results. Finally, the analytical solution-based sample set is utilized to evaluate the performance of the proposed hybrid model. The outputs of the multi-task learning framework can provide a detailed description of the actual pile quality and provide strong support for the classification of pile quality as well.

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient’s progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients’ quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings.

Bad conditions during machining cause tool chatter, wear, or breakage, which affect the tool life and consequently the surface quality and dimensional accuracy of the machined workpiece. Therefore, for the purposes of production efficiency and economics, monitoring and diagnostics of the tool's condition are important. This study presents a one-dimensional convolutional neural network (1D-CNN) and deep generalized canonical correlation analysis (DGCCA) for multiple sensors-based tool wear diagnosis. In particular, 1D-CNN is used to extract features from 1D raw data, such as force, vibration, and sound, whereas DGCCA with attention mechanism is used to fuse the feature output from each 1D-CNN by removing irrelevant or redundant information. Experiments are performed using PHM2010 and NASA data sets. The experimental results show that our proposed approach can achieve satisfactory accuracy of 95.6% and near real-time performance. Results of our study can be implemented in real tool wear diagnosis, and thus identify novel opportunities toward realizing Industry 4.0. [ABSTRACT FROM AUTHOR]

Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear diagnosis was proposed. The ResNet18 structure based on a one-dimensional convolutional neural network is adopted to make the basic model architecture. The one-dimensional convolutional neural network is more suitable for feature extraction of time series data. Add the channel attention mechanism of CaAt1 to the residual network block and the channel attention mechanism of CaAt5 automatically learns the features of different channels. The proposed method is validated on the PHM2010 dataset. Validation results show that CaAt-ResNet-1d can reach 89.27% accuracy, improving by about 7% compared to Gated-Transformer and 3% compared to Resnet18. The experimental results demonstrate the capacity and effectiveness of the proposed method for tool wear monitor. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we consider the performance of exclusive-OR (XOR) rule in detecting the presence or absence of a deterministic signal in bivariate Gaussian noise. Signals, when present at the two sensors, are assumed unequal, whereas the noise components have identical marginal distribution but are correlated. The sensors send their one-bit quantized data to a fusion center, which then employs the XOR rule to arrive at the final decision. Here we prove that, in the limit as the correlation coefficient $r$ approaches 1, the optimum fusion rule for both parallel and tandem topologies is XOR with identical, alternating partitions (XORAP) of the observations at the sensors. We further quantify the asymptotic decrease of the Bayes error of XORAP towards zero as a constant multiplied by $\sqrt {1-r}$ , as $r$ approaches 1. When compared to the asymptotic Bayes error of CLRT, which decreases to zero exponentially fast, as a function of $1/(1-r)$ , the Bayes error of XORAP decreases to zero much slower.

Without integrating various sensor data sets, sensing information in the presence of leakage for large-scale pipeline systems is very challenging. A data fusion methodology, wherein more sensor data is merged to give relevant information, is necessary to transform the challenging process into a straightforward step-by-step operation. Ultrasonic sensors are used in stage 1 to identify any ambiguities in pipeline systems, and various sites are used to gauge the rate of leak detection. As a result, a novel model for estimating various types of gas leakage in pipeline systems is examined, put to the test, and contrasted. Five distinct scenarios are seen during the leakage testing procedure using data fusion, where the optimization is done using the fuzzy interface technique. This integration procedure detects leakage rates with high accuracy, and in every test instance, the best outcomes are obtained. Additionally, the predicted model can be used in real-time with a low failure rate of numerous sensors, with MATLAB being used to simulate the results.

A large bionic flapping wing robot has unique advantages in flight efficiency. However, the fluctuation of fuselage centroid during flight makes it difficult for traditional state sensing and estimation methods to provide stable and accurate data. In order to provide stable and accurate positioning and attitude information for a flapping wing robot, this paper proposes a flight state sensing and estimation method integrating multiple sensors. Combined with the motion characteristics of a large flapping wing robot, the autonomous flight, including the whole process of takeoff, cruise and landing, is realized. An explicit complementary filtering algorithm is designed to fuse the data of inertial sensor and magnetometer, which solves the problem of attitude divergence. The Kalman filter algorithm is designed to estimate the spatial position and speed of a flapping wing robot by integrating inertial navigation with GPS (global positioning system) and barometer measurement data. The state sensing and estimation accuracy of the flapping wing robot are improved. Finally, the flying state sensing and estimation method is integrated with the flapping wing robot, and the flight experiments are carried out. The results verify the effectiveness of the proposed method, which can provide a guarantee for the flapping wing robot to achieve autonomous flight beyond the visual range. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The dependable guarantee of very high seam quality requirements in laser welding of demanding material combinations and highly stressed structures, such as powertrain components, is becoming increasingly important. The combination of sensor-based inline process monitoring and real-time data analysis using machine learning shows enormous potential for ensuring this. The subject of this paper is the assessment of process monitoring based on acoustic and optical sensor data by means of machine learning during laser welding on rotationally symmetric test specimens. The results show that typical welding defects caused by process variations can be detected with an accuracy of approx. 96 %, almost in real-time. Furthermore, approaches for predictive maintenance of system components and predictive modeling of component properties, supported by numerical simulations, are presented. [ABSTRACT FROM AUTHOR]

Cloud and fog droplets are essential in atmospheric chemistry since they affect the distribution and chemical transformation of pollutants. Collecting sufficient volumes using cloud/fog samplers is the premise of cloud fog chemical studies. Accurate identification of fog events and high collection efficiency are the basic principles of sampler design. Therefore, an automatic cloud/fog collector (ACFC) has been designed, fabricated, and extensively tested for collecting samples of cloud/fog water. The control box and standard sensors for air temperature, relative humidity, and instantaneous rainfall were used to ensure sampling automation. Airflow measurement was used to guarantee the stability of the airspeed on the inlet section, and the airspeed is 7.5 m s−1. Moreover, the median collection rate of ACFC was 160–220 mL h−1, which was tested via a simulation experiment. To evaluate the actual performance of the device in the field, we obtained eight samples of cloud fog water from Shanghuang Observatory in eastern China from the summer through the fall of 2022. Collection rates varied from 62 to 169 mL h−1. For a cloud/fog sampler equipped with multiple sensors, the ACFC has excellent sampling efficiency in thick fog, sufficient cloud fog water samples can be collected in weak fog, and it can precisely identify fog mingled with rain.

Accidental fall is one of the most prevalent causes of loss of autonomy, deaths and injuries among the elderly people. Fall detection and rescue systems with the advancement of technology help reduce the loss of lives and injuries, as well as the cost of healthcare systems by providing immediate emergency services to the victims of accidental falls. The aim of this paper is to perform a systematic review of the existing sensor-based fall detection and rescue systems and to facilitate further research in this field. The systems are reviewed based on their architecture, used sensors, performance metrics, limitations, etc. This review also provides a taxonomy for classifying the fall detection systems. The systems have been divided into two main categories: single sensor-based fall detection systems, and multiple sensor-based fall detection systems. Although single sensor-based systems are very accurate in detecting falls, multiple sensor-based systems are more efficient. The low power consumption of most single sensor-based systems especially those which are based on the accelerometer is perfect for wearable solutions, while most multiple sensor-based systems are perfect for indoor monitoring. [ABSTRACT FROM AUTHOR]

Weight-bearing is a common behavior, whereas a load impact on human gait symmetry is seldom considered. This study aims to propose and verify a gait symmetry detection model, by using the gait symmetry under different weight-bearing states to establish a weight-bearing symmetry classification rule, and to measure the consistency of movement through multiple gait symmetry experiments. In details, a classifier combined with statistical learning is presented to quantify the signal difference between left and right legs. The quantization result is incorporated as an index to judge the magnitude of symmetry. Meanwhile, a dynamic time warping (DTW) algorithm is introduced to calculate the signal similarity of two legs in time domain as well as another index. The symmetry trend on leg signals is also considered and described as a third index. By casting these indexes in the proposed detection model, we employ several existing nonlinear classification methods to effectively disclose the difference between legs. Moreover, the information between legs can also be extracted through time warping, yielding the detection model competitive. The validity of the model is verified by hypothetical test. Compared with some state-of-the-art methods, our model achieves an improvement over 5% in the consistency measure of the calf. The symmetry measure of calf and thigh is also test. The proposed method outperforms other methods with the improvement of 12.5%. [ABSTRACT FROM AUTHOR]

The Structure-from-Motion (SfM) algorithm is widely used for point cloud reconstruction. However, one drawback of conventional SfM based methods is that the obtained final point sets may contain holes and noise, which could degrade the estimation of reconstructed objects especially for smooth surfaces with few features. The other drawback is the accuracy and speed of SfM based methods depend on the uncertain number of images. To overcome these limitations, this paper proposes a novel 3D reconstruction method for unstructured objects based on the structure from motion in combination with the structured light, in which the point sets of structured light and the point sets of structure from motion can come from different target objects. Since the two point sets coming from multiple sensors do not scale well for register, making it difficult to find corresponding points, a scaled principal component analysis algorithm is proposed for the registration to overcome the impact due to large scale variance. With a large scale factor, a recalculated registration center is proposed via feature region segmentation to achieve point cloud registration again. The two point sets are matched using the proposed optimization method to complete 3D reconstruction. Surface reconstruction is performed using the Poisson algorithm to obtain a smooth surface. The proposed method is tested on some simple structured objects and real-life data of complex unstructured objects collected using range sensors. Compared with several state-of-the-art algorithms, experimental results confirm its potential for surface reconstruction from depth data calculated from the two sets. [ABSTRACT FROM AUTHOR]

Journal bearing is an essential part of the propulsion shafting system, whose performance directly affects the safety and performance of the shaft and even the entire ship. It is an important, but also a difficult task to obtain the accurate dynamic stiffness values. In order to determine the dynamic characteristics of the journal bearing with specific structural characteristics under specific working conditions, an inverted test platform with multiple sensors for the dynamic stiffness of the heavy-load, low-speed journal bearings (HLLSJBs) was developed, and a novel dynamic test system calibration method was proposed to obtain accurate baseline data, and then the effective characteristic frequency response was gotten based on a refined fast Fourier transform method (RFFT). The present study proposed a simplified identification method to analyze the constitutive relationships between the oil film dynamic parameters and the dynamic stiffness under different rotation and loading conditions in the journal bearing, and then the accurate values of the oil film dynamic stiffness of HLLSJBs were obtained. Furthermore, the values of the structural dynamic stiffness of HLLSJBs were also obtained using a hammer impact method. A comparison with the classic theoretical analysis results reveals that the method proposed is more accurate and effective.

An inertial navigation system is commonly used in various marine platforms above and below the sea surface to calculate the position, velocity and orientation of its carrier platform. Such systems contain an inertial measurement unit (IMU) to measure the specific force and angular velocity which in turn are integrated to obtain the navigation state. Due to sensor noises and other error terms, the navigation solution drifts in time. In situations of pure inertial navigation (no external aiding), multiple IMUs (MIMU) can be used to improve the performance of a single unit. In this paper, we explore the benefits of using a MIMU system for common navigation operations. To that end, a 32 MIMU architecture (192 inertial sensors) was designed and constructed for the experimental evaluations. Utilizing this system we examined the effect of the number of sensors in the architecture versus position accuracy, stationary calibration, coarse alignment and gyro free design. We derive closed form empirical expressions enabling insight to the connection between number of IMUs to the expected performance.

Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear diagnosis was proposed. The ResNet18 structure based on a one-dimensional convolutional neural network is adopted to make the basic model architecture. The one-dimensional convolutional neural network is more suitable for feature extraction of time series data. Add the channel attention mechanism of CaAt1 to the residual network block and the channel attention mechanism of CaAt5 automatically learns the features of different channels. The proposed method is validated on the PHM2010 dataset. Validation results show that CaAt-ResNet-1d can reach 89.27% accuracy, improving by about 7% compared to Gated-Transformer and 3% compared to Resnet18. The experimental results demonstrate the capacity and effectiveness of the proposed method for tool wear monitor.

Plant potassium accumulation (PKA) plays an important role in evaluating the production capacity of crops. Remote sensing from unmanned aerial vehicles (UAVs) is increasingly used in precision agriculture, but relatively little is known about its use for determining the potassium (K) nutritional status of rice (Oryza sativa L.). This study compares the performance of different sets of spectral and textural indices derived from UAV data for estimating rice K nutritional status. A UAV equipped with three cameras (RGB, color-infrared (CIR) and multispectral cameras) was used to acquire imagery of rice canopies at different key growth stages. Immediately following the overflights, rice canopies were sampled for rice PKA. Regression models were then built to predict rice PKA with spectral and textural indices as predictor variables. Finally, stepwise multiple linear regression (SMLR) was used to determine if fusing spectral and textural indices significantly improved UAV-based PKA estimates. The renormalized difference vegetation index (RDVI) calculated from multispectral imagery proved to be the best predictor of rice PKA (R2 = 0.72, RMSE = 5.42 g m−2), though spectral vegetation indices calculated from RGB and CIR imagery also showed moderately strong prediction capability (R2 ≤ 0.56, RMSE ≥ 7.33 g m−2). Among the textural indices, the renormalized difference texture index [RDTI (MEA800, MEA680)] calculated by mean texture (MEA) of the multispectral imagery was the best predictor of rice PKA (R2 = 0.74, RMSE = 5.57 g m−2) while textural indices calculated from the RGB and CIR imagery showed only weak relationships with rice PKA (R2 ≤ 0.40, RMSE ≥ 8.11 g m−2). Fusing textural and spectral vegetation indices improved our ability to remotely sense PKA, with the SMLR model combination of RDTI (MEA800, MEA680) and spectral vegetation index DATT performing best (R2 increased by 11.11% to 0.80 and RMSE decreased by 7.5% to 5.15 g m−2). Our findings suggest that spectral and textural indices derived from UAV data allow for accurate mapping of PKA and that fusion of the two further improves the accuracy.

Hydrogels that are both highly conductive and mechanically robust have demonstrated great potential in various applications ranging from healthcare to soft robotics; however, the creation of such materials remains an enormous challenge. This study presents an in situ synthesis strategy for developing bioinspired chemically integrated silica‐nanofiber‐reinforced hydrogels (SFRHs) with robust mechanical and electronic performance. The strategy is to synthesize soft hydrogel matrices from acrylamide monomers in the presence of well‐dispersed silica nanofibers and vinyl silane, which generates homogenous SFRHs with innovative interfacial chemical bonds. The resultant SFRHs exhibit excellent mechanical properties including high mechanical strength of 0.3 MPa at a fracture strain of 1400%, high Young's modulus of 0.11 MPa (comparable to human skin), and superelasticity over 1000 tensile cycles without plastic deformation, while maintaining high transmittance (≥83%). In parallel, the SFRHs show enhanced ionic conductivity (3.93 S m−1) and can monitor multiple stimuli (stretching, compressing, and bending) with high sensitivity (gauge factor of 2.67) and ultra‐durability (10 000 cycles). This work may shed light on the design and development of tough and stretchable hydrogels for various applications. [ABSTRACT FROM AUTHOR]

The biochemical reactions of aerobic microbial respiration (AMR) suggest that silage temperature (T si) rise, oxygen (O 2) consumption and carbon dioxide (CO 2) emission may be equally useful as indicators of silage deterioration during feed-out, but only temperature has been used extensively to assess aerobic stability. Here we extend the study of aerobic stability to incorporate AMR of silage by developing a novel experimental cell integrated with multiple sensors. Silage samples, ensiled from a triticale crop, were made in twelve air-tight barrels (60 L), packed to bulk densities of 190 or 250 kg m−3 dry matter (DM). T si and O 2 measurements were co-located at 15- and 30-cm behind the working face. CO 2 was measured as flux across the working face. The experimental period of aerobic exposure was 7 days. We provide the first reports of: (i) distinct aerobic responses of these parameters, showing that T si varied with CO 2 in phase but with O 2 out-of phase; (ii) CO 2 flux was dominated initially by anaerobic discharge and subsequently by aerobic products; (iii) linear relationships between aerobic reheating and both O 2 consumption (0.994 ≥ R2 ≥ 0.815, P < 0.01) and CO 2 flux (0.981 ≥ R2 ≥ 0.464, P < 0.01); and (iv) variable magnitude of daily aerobic production of CO 2 per kg DM from 2.3 to 133.4 mmol kg d−1. These results demonstrate that the novel multi-sensor technique has powerful capacity to provide insight into AMR of silage and thus provide more detailed information to guide silage management than previous measurements of aerobic stability. • We extend study of silage aerobic stability to aerobic microbial respiration (AMR). • Develop a multi-sensor instrument to monitor O 2 , CO 2 and reheating (T si) for silage. • Better linear relationships between T si ↑ and O 2 ↓ than T si ↑and CO 2 ↑. • CO 2 flux was dominated first by anaerobic metabolite release then by AMR. • Assessment of CO 2 and O2 as aerobic indicators compared with aerobic stability (T si). [ABSTRACT FROM AUTHOR]