Interference Environment Research Articles

MCNet: Magnitude consistency network for domain adaptive object detection under inclement environments

Deep learning-based object detection methods have achieved promising results in normal scenarios, while such methods often fail to locate objects from the disturbed images captured in inclement environments. Most existing methods utilized denoise modules to assist the detection network or exploit prior knowledge to reduce the environment interference remaining in the features, which ignores the essential role of the frequency element. From a novel perspective, we observe that the inclement environment alters the frequency content in the features, which in turn crushes the detection. To tickle this problem, we present the Magnitude Consistency Network (MCNet) to distill the irrelevant contents in the frequency domain. The MCNet is composed of the detection network and the magnitude corrector. The detection network is able to locate and classify objects. However, in inclement environments, the crucial information about the objects of interest is disrupted by the nuisance noise introduced from the inclement environments. The magnitude corrector can recover relevant information about the objects in the frequency domain by distilling the irrelevant factors and refining the affected features. Alternately optimizing the magnitude corrector and the detection network gradually makes the frequency content between the disturbed image and the clear image to be consistent. By distilling the irrelevant noise in the feature, the detection network can learn domain-invariant representations. Extensive experiments prove that the proposed method is effective and outperforms existing methods by a clear margin on four datasets.

ISA: Ingenious Siamese Attention for object detection algorithms towards complex scenes

The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-based tasks in complex environments. In order to ameliorate the detection accuracy of object detection algorithms under various complex environment transformations, this work proposes the Siamese Attention YOLO (SAYOLO) object detection algorithm based on ingenious siamese attention structure. The ingenious siamese attention structure includes three aspects: Attention Neck YOLOv4 (ANYOLOv4), siamese neural network structure and special designed network scoring module. In the Complex Mini VOC dataset, the detection accuracy of SAYOLO algorithm is 12.31%, 48.93%, 17.80%, 10.12%, 18.79% and 1.12% higher than Faster-RCNN (Resnet50), SSD (Mobilenetv2), YOLOv3, YOLOv4, YOLOv5-l and YOLOX-x, respectively. Compared with traditional object detection algorithms based on image preprocessing, the detection accuracy of SAYOLO is 4.88%, 11.51%, 1.73%, 23.27%, 18.12%, and 5.76% higher than Image-Adaptive YOLO, MSBDN-DFF + YOLOv4, Dark Channel Prior + YOLOv4, Zero-DCE + YOLOv4, MSBDN-DFF + Zero-DCE + YOLOv4, and Dark Channel Prior + Zero-DCE + YOLOv4, respectively.

Application of Artificial Intelligence Technology in the Optimal Deployment of Wireless Sensor Network Nodes

WSN (Wireless Sensor Network) is a network composed of a large number of sensor nodes self-organizing through wireless communication technology. Nodes are prone to failures due to environmental impact and energy depletion, and environmental interference and node failures can easily cause changes in the network topology. This article conducts research on the application of AI (Artificial Intelligence) technology in the optimal deployment of WSN nodes. Using AI technology to optimize the deployment of hybrid WSN mobile nodes, ensuring that the node density of each sub target area reaches its expected node density to ensure effective coverage of the entire target monitoring area. Given the target area range, the number and position of sensor nodes are determined through AI technology, which is the network layout for a given target area. Effectively reducing the data flow of the entire network. Due to the limited energy of sensor nodes, minimizing energy consumption and maximizing network lifespan are the main goals of designing WSN. Deployment strategies must optimize the deployment strategies of sensor nodes, intermediate nodes, and base stations to ensure coverage, connectivity, and robustness.

Distributed Joint Resource Optimization for Federated Learning Task Distribution

The effective utilization of computation and communication resources is one of the primary challenges for Artificial Intelligence (AI)-based applications. The complexity of the edge computation environment and the diversity of communication interference in the wireless environment make it particularly difficult to quickly and accurately complete multiple federated learning tasks. This paper examines energy cost, communication capacity, computation capacity, and other factors in a network-slicing environment. The virtual queue vector and the associated Lyapunov drift are presented to analyze queue backlog performance and optimize each slice’s Key Performance Indicator (KPI) performance, including energy and other Quality of Experience (QoE) factors. In response, we design a Federated Learning (FL) Service-Oriented Allocation Policy Calculation Algorithm to address the allocation policy calculation problem in a time-varying environment. Finally, our simulation experiments demonstrate that our proposed algorithm outperforms other benchmarks.

A Survey of Deep Learning-based Facial Expression Recognition Research

Facial expression is one of the ways to convey emotional expression. Deep learning is used to analyze facial expression to understand people's true feelings, and human-computer interaction is integrated into it. However, in the natural real environment and various interference (such as lighting, age and ethnicity), facial expression recognition will face many challenges. In recent years, with the development of artificial intelligence, scholars have studied more and more facial expression recognition in the case of interference, which not only promotes the theoretical research, but also makes it popularized in the application. Facial expression recognition is to identify facial expressions to carry out emotion analysis, and emotion analysis can be analyzed with the help of facial expressions, speech, text, video and other signals. Therefore, facial expression recognition can be regarded as a research direction of emotion analysis. This paper focuses on the perspective of facial expression recognition to summarize. In the process of facial expression recognition, researchers usually try to combine multiple modal information such as voice, text, picture and video for analysis. Due to the differences between single-modal data set and multi-modal data set, this paper will analyze static facial expression recognition, dynamic facial expression recognition and multi-modal fusion. This research has a wide range of applications, such as: smart elderly care, medical research, detection of fatigue driving and other fields.

Efficient sulfamethoxazole degradation via boosting nonradical-based peroxymonosulfate activation by biochar supported Co-Ni bimetal oxide

Nonradical-based peroxymonosulfate (PMS) activation has recently caught increasing attention due to its less susceptible to environmental interference in the complex water matrix, but the performance of activators still requires further development for efficient pollutants degradation in wastewater treatment. Herein, a novel Co-Ni bimetal oxide loaded biochar was fabricated to boost nonradical PMS activation for enhancing sulfamethoxazole (SMX) degradation. According to the results, the as-obtained catalyst exhibited excellent SMX degradation efficiency of 96.2% within 30 min through complete nonradical oxidation pathway (including both 1O2 and mediated electron transfer). Co was confirmed to be the active sites to generate 1O2 dominated nonradical pathway. The biochar acted as the porous material to achieve the electron transfer oxidation process, as well as Ni ensured sufficient electron transfer rate and bound Co active sites to prevent the leaching of metal ions. Meanwhile, the prepared Co-Ni bimetal oxide loaded biochar possessed a remarkable catalytic degradation durability in the complex water environment conditions and showed universality for various organic contaminants. This study offers a valuable clue on the rational design of novel catalyst to induce the nonradical pathway-based persulfate activation.

7T Magnetic Compatible Multimodality Electrophysiological Signal Recording System

This paper developed a comprehensive magnetic resonance imaging (MRI)-compatible electrophysiological (EP) acquisition system, which can acquire various physiological electrical signals, including electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG) and electrocorticogram (ECoG), and EP recording combined with multimodal stimulation. The system is designed to be compatible with the 7-Tesla (7T) ultra-high field MRI environment, providing convenience for neuroscience and physiological research. To achieve MRI compatibility, the device uses magnetically compatible materials and shielding measures on the hardware and algorithm processing on the software side. Different filtering algorithms are adopted for different signals to suppress all kinds of interference in the MRI environment. The system can allow input signals up to ±0.225 V and channels up to 256. The equipment has been tested and proven to be able to collect a variety of physiological electrical signals effectively. When scanned under the condition of a 7T high-intensity magnetic field, the system does not generate obvious heating and can meet the safety requirements of MRI and EEG acquisition requirements. Moreover, an algorithm is designed and improved to efficiently and automatically remove the gradient artifact (GA) noise generated by MRI, which is a thousand-fold gradient artifact. Overall, this work proposes a complete, portable, MRI-compatible system that can collect a variety of physiological electrical signals and integrate more efficient GA removal algorithms.

High-sensitivity fiber optic Fabry-Perot ultrasonic sensor based on a grooved silicon diaphragm for partial discharge detection.

An extrinsic fiber optic Fabry-Perot interferometric (EFPI) ultrasonic sensor based on a grooved silicon diaphragm for partial discharge (PD) detection has been proposed. The size of the groove is determined by finite element simulation, which allows the resonant frequency of the sensor to meet the requirements of PD ultrasonic detection and improves the sensitivity of the sensor by 5.07 times compared with that based on a traditional circular diaphragm. The microelectro-mechanical system process is used to fabricate the diaphragm on a silicon-on-insulator wafer, and the prepared diaphragm has a grooved section with a diameter of 829.34µm and a thickness of only 2.09µm. At its resonant frequency of 61.5kHz, the acoustic pressure sensitivity of the sensor is 172.42mV/Pa. The ultrasonic signal detection capability of the sensor is verified in the PD experiment. Furthermore, the characteristics of the corona discharge are successfully manifested based on the ultrasonic waves detected by the EFPI sensor. It is demonstrated that the proposed sensor is suitable for PD detection due to its high sensitivity, simple production process, and good resistance to environmental interference.

Robust Space-Time Adaptive Processing Method for GNSS Receivers in Coherent Signal Environments

In the coherent signal environments caused by multipath propagation, the interference suppression performance of the global navigation satellite systems (GNSS) receivers decreases sharply. In this paper, a robust space-time adaptive processing (STAP) method for GNSS receivers is proposed to suppress interferences in coherent signal environments, by using the modified space-time two-dimensional iterative adaptive approach (ST2D-IAA) spectrum estimation. This method applies the IAA algorithm to the ST2D signal model of GNSS receivers, and further modifies the ST2D-IAA algorithm to accurately estimate the power spectrum and noise power simultaneously. The space-time interference-plus-noise covariance matrix (STINCM) is reconstructed by using the estimated power spectrum and noise power in the interference angle region. Based on the reconstructed STINCM, we construct the STAP beamforming optimization problem for the space-time steering vector (STSV) error vector, and further correct the STSV of GNSS signal. Finally, the weight vector of STAP beamforming is calculated by using the reconstructed STINCM and the corrected STSV of GNSS signal. Simulation results show that the proposed method can suppress interferences in coherent signal environments and outperforms the current methods.

Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5

The images captured using UAVs during inspection often contain a great deal of small targets related to transmission lines. These vulnerable elements are critical for ensuring the safe operation of these lines. However, due to various factors such as the small size of the targets, low resolution, complex background, and potential target aggregation, achieving accurate and real-time detection becomes challenging. To address these issues, this paper proposes a detection algorithm called P2-ECA-EIOU-YOLOv5 (P2E-YOLOv5). Firstly, to tackle the challenges posed by the issues of complex background and environmental interference impacting small targets, an ECA attention module is integrated into the network. The module effectively enhances the network’s focus on small targets, while concurrently mitigating the influence of environmental interference. Secondly, considering the characteristics of small target size and low resolution, a new high-resolution detection head is introduced, making the network more sensitive to small targets. Lastly, the network utilizes the EIOU_Loss as the regression loss function to improve the positioning accuracy of small targets, especially when they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P (precision) of 96.0% and an average accuracy (mAP) of 97.0% for small-target detection in transmission lines.

Temperature‐Independent Conductive Ceramic for High‐Temperature Strain‐Sensing Applications

Temperature‐independent properties are critical for high‐temperature thin‐film strain gauges (TFSGs). In this study, by controlling the electron scattering and tunneling effects in the TiB2/SiCN composites, the environmental interference of temperature fluctuations is successfully eliminated, and a temperature‐independent TFSG is fabricated. The effects of pyrolysis temperature and TiB2 content on the microstructural evolution and electrical properties of the ceramic films are studied. The temperature insensitivity is mainly attributed to the balance between the intrasheet resistance with a positive temperature coefficient of resistance (TCR) and the intersheet resistance with a negative TCR. This composite shows nearly constant resistance values over an ultrawide temperature range of 300–700 °C, with less than 0.05% deviation of the normalized resistance and TCR values as low as 1.6 ppm °C−1. In addition, the TiB2/SiCN films exhibited stable piezoresistive responses, with a gauge factor of 4.28, and the temperature‐independent strain response in the high‐temperature range is verified.

Same Wavelength Noise Filtering via Quantum Orbital Angular Momentum Emission.

In laser active detection, detection performance is affected by optical noise, laser interference, and environmental background interference. Conventional methods to filter optical noise take advantage of the differences between signal and noise in wavelength and polarization. Due to the limitations of traditional methods in the physical dimension, noise cannot be completely filtered out. In this manuscript, a new method of noise filtering based on the spatial distribution difference between the quantum orbital angular momentum beam and the background noise is proposed. The use of beams containing quantum orbital angular momentum can make the signal light have a new physical dimension and enrich the information of emitted light. We conduct a complete theoretical analysis and provide a proof-of-principle experiment. The experimental results are in good agreement with the theoretical analysis results, and there is a signal-to-noise ratio improvement of more than five times in laser active detection. Our method meets the urgent needs of laser active detection and can be applied in the field of high-quality target detection.

Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction

Due to the possible interference of non-Gaussian noise in Li-ion battery management systems, there is no guarantee of reliable accuracy when using the extended Kalman filter (EKF) algorithm for battery state of charge (SOC) estimation. A novel EKF algorithm based on the adaptive maximum correntropy criterion (AMCCEKF) is proposed to enhance the robustness of SOC estimation in the paper. The Gaussian kernel function is chosen as the cost function to reconstruct the state error variance and the measurement noise variance. And a kernel width adaptive update strategy is designed to address the constraints of fixed kernel width on SOC estimation performance. In addition, an open circuit voltage (OCV) correction strategy based on terminal voltage innovation and OCV-SOC curve gradient is designed to reduce the impact of OCV error caused by non-Gaussian noise on SOC estimation. Incorporating the AMCCEKF method and the OCV correction strategy, a novel estimation method based on the dOCV-AMCCEKF is offered to perform the SOC estimation. Simulation results under different operating conditions and temperatures show that the maximum absolute error of the dOCV-AMCCEKF method is close to 0.5%, which verifies it can reduce the SOC estimation error in a non-Gaussian noisy interference environment compared with the EKF based method.

Numerical analysis and test of vibration and interference characteristics of dual U-tube Coriolis flowmeter

The Coriolis flowmeter is a resonant instrument that can be easily affected by environmental vibration interference factors. Therefore, it is necessary to study the vibration characteristics of the Coriolis flowmeter, as well as the on-site environmental interference vibrations. In this paper, a full-scale numerical model of the flowmeter, including the support pipe and flange, was established, and the vibration characteristics of the Dual U-Tube Coriolis flowmeter were analyzed using structural dynamics and fluid-structure coupling calculation methods. The first to sixth natural frequencies and vibration modes were obtained. A mass flowmeter of the same model was selected from the oil transportation station, and vibration analysis and testing were carried out on the Dual U-Tube Coriolis flowmeter. The driving frequency of the Coriolis mass flowmeter and the frequency of external interference vibration signals during the distribution process were obtained through Fast Fourier transform, which verified the correctness of the numerical calculation model. It was found that the natural frequency of the Coriolis flowmeter varies with the installation conditions, and it was pointed out that external vibration signals can interfere with the operation of the Coriolis flowmeter during field operations. This work in this paper has significant reference value for the actual field installation and noise reduction of the Coriolis mass flowmeter.

Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators

Robot manipulators as an indispensable part of automatic operation are becoming increasingly important in intelligent manufacturing systems, such as grinding and assembly. Although control methods of robot manipulators have been extensively studied, high-precision robots still face new challenges from uncertainties caused by changes in the environment and enhancement of interference. As a consequence, the neural network-based observer is utilized to reduce the influence of uncertainties and external disturbances. In this work, a new kind of nonlinear disturbance observer is designed which could well function with observed states. To improve the control efficiency and response characteristic, a kind of new integral sliding manifold is devised and the control input is generated via backstepping techniques. The stability is proved with Lyapunov theory, and the experimental results are given to demonstrate the effectiveness of the proposed controller.

Temperature-dependent dielectric and wide-temperature-range microwave absorption properties of La0.8Sr0.2MnO3/Al2O3–13%TiO2 coatings

The advancement of absorption materials and coating preparation technology is crucial to address the challenges posed by electromagnetic interference and pollution in high-temperature extreme environments. Here, by using a granulation and plasma spraying strategy, we successfully fabricated a series of La0.8Sr0.2MnO3/Al2O3–13%TiO2 (LSMO/AT13) high-temperature microwave absorption coatings that have excellent dielectric properties and extremely thermal stability. The results showed that the LSMO/AT13 coatings exhibited remarkable oxidation resistance, withstanding high-temperature aerobic environments of up to 1000 °C. Based on the temperature-dependent permittivity of the coatings, the establishment process of polarization and the transformation of loss mechanism were observed and explained. With the increase of temperature, the main dielectric loss mechanism of the coatings transitions from polarization loss to conductive loss. Thanks to the coordinated effects of impedance and loss, the coatings are capable of effectively absorbing microwave over a wide temperature range. When the LSMO content was 30 wt% and the thickness was 1.9 mm, the effective bandwidth ranged from 1.7 GHz to 2.4 GHz at temperatures below 773 K. This study demonstrates the promising application of LSMO/AT13 microwave absorption coatings for ultra-high temperature environment, and provides a reference for the design and optimization of high-temperature microwave absorption coatings.

DCMF-AFNet: An anchor-free photovoltaic hot-spot fault detection network based on deformable context transformer and bi-branch multi-level feature fusion

In the process of photovoltaic hot-spot detection by thermal infrared sensors, the fault features cannot be effectively represented due to low pixel ratios and complex environmental interference, which makes it difficult for the detection network to accurately detect hot-spot faults. Therefore, an anchor-free photovoltaic hot-spot fault detection algorithm based on deformable context Transformer and bi-branch multi-level feature fusion is proposed. First, to improve the feature extraction ability of the backbone network for small-scale hot-spot faults, a deformable context Transformer module is constructed. By building an offset network in a multi-headed self-attention mechanism, dynamic and static context information with small-scale fault features can be explored from shallow feature maps. Second, to solve the problem of low target saliency due to the complex background interference, a bi-branch multi-level feature fusion module is designed to aggregate global and local multi-level features in a parallel fusion manner, enabling the detection network to rapidly focus on the fault target region in complex environments. Then, an anchor-free mechanism is introduced, and a dynamic task alignment prediction module is proposed to avoid feature misalignment in the classification and localization tasks and further improve the algorithm detection accuracy. Finally, to verify the superiority of the proposed network, seven detection algorithms are selected for comparison experiments. The experimental results show that the DCMF-AFNet network can accurately detect multi-scale hot-spot targets under various harsh conditions, and the detection accuracy can reach 87.3%.

Soft Smart Biopatch for Continuous Authentication‐Enabled Cardiac Biometric Systems

AbstractBiometric locking systems offer a seamless integration of an individual's physiological characteristics with secure authentication. However, they suffer from limitations such as false positive and negative authentication, environmental interference, and varying disadvantages across multiple authentication methods. To address these limitations, this study develops a soft smart biopatch for a continuous cardiac biometric wearable device that can continuously gather novel biometric data from an individual's heart sound for authentication with minimal error (less than 0.5%). The device is designed to be discreet and user‐friendly, and it employs soft biocompatible materials to ensure comfort and ease of use. The patch system incorporates a miniaturized microphone to monitor sounds over long periods and multiple dimensions, enhancing the reliability of the biometric data. Furthermore, the use of machine‐learning algorithms has enabled the creation of unique identification keys for individuals based on the continuous monitoring properties of the low‐cost device. These advantages make it more effective and efficient than traditional biometric systems, with the potential to enhance the security of mobile devices and door locks.

Early warning method of unsafe behavior accidents for offshore drilling operators based on eye-tracking trajectory

Despite the implementation of highly automated and intelligent equipment and facilities at the offshore drilling platform in recent years, drilling operators remain indispensable. During offshore drilling operations, the behavior of operators is susceptible to numerous factors including personal attributes, environmental interferences, and equipment feedback. When the factors that affect the operation of drilling change adversely, unsafe behavior of drilling operators can easily occur, resulting in offshore drilling accidents. As such, the early warning of unsafe behavior accidents of drilling operators is essential to reduce the occurrence of offshore drilling accidents. Firstly, attempts were made to use eye-tracking technology to record the eye movement data of unsafe behavior of drilling operators in real-time. In total, 42 drilling operation trainers had their eye-tracking trajectories recorded in real-time during normal tripping operations throughout the drilling process. A total of 252 eye-tracking trajectory data points were collected for six drilling behaviors, including normal operation, unskilled operation, fatigue operation, action errors, omitted actions and wrong order. Secondly, based on the IETTSM (Improved Eye-tracking Trajectory Sequence Method) algorithm, the eye-tracking trajectory of drilling operators was serialized. Thirdly, an evaluation model of drilling operator operation behavior was constructed based on the DLD (Damerau-Levenshtein Distance) algorithm. Fourthly, the IETTSM-DLD model was used to prompt abnormal behavior among drilling operators, which was accomplished by referencing the appropriate trajectory nodes and issuing early warnings of unsafe behavior based on the coupling of said trajectory nodes. The results reveal that the proposed IETTSM-DLD model can realize real-time prompting of abnormal operation of offshore drilling operators and early warning of accidents caused by unsafe behavior. The average early warning accuracy rate was found to be 91.9%, with an average early warning time of 120.97 s.

High-Efficiency Ce3+ Activated Orthorhombic Lanthanide Silicate Blue Phosphors for Plant Growth Lighting.

Plant growth can be controlled and freed from natural environmental interference through indoor plant cultivation. Artificial light sources with better quality are required to promote indoor plant growth. In this study, we used a simple high-temperature solid-state reaction to synthesize high-efficiency Ce3+-activated NaGdSiO4 (NGSO) phosphors. X-ray diffraction and Rietveld refinement were performed to determine the detailed crystal structure of the NGSO:Ce3+ phosphors. The morphology of NGSO:Ce3+ and the elemental state of Ce3+ were measured and analyzed. Under near-ultraviolet (n-UV) light excitation, the Ce3+-activated NGSO phosphors exhibit a broad emission band from 375 to 500 nm, and their emission peaks are at approximately 401 nm. This asymmetrical blue emission band is caused by the spin-allowed 5d → 4f transition of Ce3+ and overlaps well with the blue absorption region of carotenoids and chlorophyll. The temperature-dependent luminescence spectra were utilized to assess the thermal stability of NGSO:Ce3+. The external quantum efficiency (EQE) was measured to be 60.91%, and the internal quantum efficiency (IQE) was measured to be 73.39%. A blue LED device assembled from the NGSO:Ce3+ phosphor has demonstrated the application potential in accelerating plant growth.