Actual Environment Research Articles

Research on SLAM Localization Algorithm for Orchard Dynamic Vision Based on YOLOD-SLAM2

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments.

Intelligent luminescent microneedle and hydrogel patches for visual monitoring of lactic acid and dopamine

Dopamine (DA) and lactic acid (LA) are vital biochemical indicators of neurotransmitters and tissue oxidation monitoring, which can comprehensively assess the neurological and physical conditions. The combination of fluorescent sensor and artificial intelligence shows great potentials in monitoring and quantizing of human biochemicals. Herein, a Tb3+ functionalized hydrogen-bonded organic framework material (Tb@ME-TPA) is synthesized based on coordination post-synthesis modification, and its sensing mechanism for DA and LA is investigated by experiments and theoretical calculations. For visual detection of DA and LA in actual environment, Tb@ME-TPA is fabricated into PVA films and microneedle patches, which hold the advantages of high efficiency, sensitivity and anti-interference. In addition, microneedles-based probe for minimally invasive sensing of DA in the dermal interstitial fluid along with a portable smartphone platform are designed to monitor with high accuracy. Furthermore, an intelligent back-propagation neural network model based on fluorescence image recognition is constructed, which can combine optical sensing of LA with deep learning by computer, greatly improving the efficiency and accuracy. This work not only presents a novel idea for the integration of fluorescent materials, smartphones and deep learning, but also provides an effective method for the prediction of diseases as well as the real-time monitoring of overall well-being.

Novel utilization exploration for the dephosphorization waste of Ca–modified biochar: enhanced removal of heavy metal ions from water

Phosphorus-modified biochar has been proven to enhance the precipitation and complexation of heavy metal ions from wastewater. However, the current modification methods require large amounts of exogenous P and have high energy consumption. Hence, this study proposes and analyzes a strategy integrating biochar production, phosphorus wastewater treatment, dephosphorization waste recovery, and heavy metal removal. “BC-Ca-P” was derived from Ca-modified biochar after phosphorus wastewater treatment. The adsorption of Pb(II) by BC-Ca-P followed the Langmuir isotherm and pseudo–second–order kinetic models. The maximum adsorption capability of 361.20 mg·g−1 at pH 5.0 for 2 h was markedly greater than that of external phosphorous-modified biochar. The adsorption mechanisms were dominated by chemical precipitation and complexation. Furthermore, density functional theory calculations indicated that oxygen-containing functional groups (P-O and C-O) contributed the most to the efficient adsorption of Pb(II) onto BC-Ca-P. To explore its practical feasibility, the adsorption performance of BC-Ca-P recovered from an actual environment was evaluated. The continuous-flow adsorption behavior was investigated and well-fitted utilizing the Thomas and Yoon–Nelson models. There was a negligible P leakage risk of BC-Ca-P during heavy metal treatment. This study describes a novel and sustainable method to utilize dephosphorization waste for heavy metal removal.Graphical

Water temperature disturbance alters the conjugate transfer of antibiotic resistance genes via affecting ROS content and intercellular aggregation

Spread of antibiotic resistance genes (ARGs) in aquatic ecosystems poses a significant global challenge to public health. The potential effects of water temperature perturbation induced by specific water environment changes on ARGs transmission are still unclear. The conjugate transfer of plasmid-mediated ARGs under water temperature perturbation was investigated in this study. The conjugate transfer frequency (CTF) was only 7.16 × 10−7 at a constant water temperature of 5 °C, and it reached 2.18 × 10−5 at 30 °C. Interestingly, compared to the constant 5 °C, the water temperature perturbations (cooling and warming models between 5–30 °C) significantly promoted the CTF. Intracellular reactive oxygen species was a dominant factor, which not only directly affected the CTF of ARGs, but also functioned indirectly via influencing the cell membrane permeability and cell adhesion. Compared to the constant 5 °C, water temperature perturbations significantly elevated the gene expression associated with intercellular contact, cell membrane permeability, oxidative stress responses, and energy driven force for CTF. Furthermore, based on the mathematical model predictions, the stabilization times of acquiring plasmid maintenance were shortened to 184 h and 190 h under cooling and warming model, respectively, thus the water temperature perturbations promoted the ARGs transmission in natural conditions compared with the constant low temperature conditions.

Research on the Detection of Steel Plate Defects Based on SimAM and Twin-NMF Transfer

Pulsed eddy current thermography can detect surface or subsurface defects in steel, but in the process of combining deep learning, it is expensive and inefficient to build a complete sample of defects due to the complexity of the actual industrial environment. Consequently, this study proposes a transfer learning method based on Twin-NMF and combines it with the SimAM attention mechanism to enhance the detection accuracy of the target domain task. First, to address the domain differences between the target domain task and the source domain samples, this study introduces a Twin-NMF transfer method. This approach reconstructs the feature space of both the source and target domains using twin non-negative matrix factorization and employs cosine similarity to measure the correlation between the features of these two domains. Secondly, this study integrates a parameter-free SimAM into the neck of the YOLOv8 model to enhance its capabilities in extracting and classifying steel surface defects, as well as to alleviate the precision collapse phenomenon associated with multi-scale defect recognition. The experimental results show that the proposed Twin-NMF model with SimAM improves the detection accuracy of steel surface defects. Taking NEU-DET and GC10-DET as source domains, respectively, in the ECTI dataset, mAP@0.5 reaches 99.3% and 99.2%, and the detection accuracy reaches 98% and 98.5%.

A concept for non-uniform thermal irradiation emulation in an immersive virtual environment

This study addresses the challenge of emulating human thermal environments without the need to build physical mock-ups. The aim is to complement the established technology of emulating visual environments with head-mounted displays. For the user, the virtual environment (VE) emulates an arbitrary indoor environment including visual and thermal aspects. In contrast to conventional climate chambers, we describe a concept to emulate arbitrary non-uniform environments and room geometries without the need to construct or modify separate physical mock-ups for each scenario being tested. By fully emulating air temperatures as well as the thermal irradiation, a user can experience the same thermal sensation as in the actual environment. An air conditioning system and nine supply air diffusers are used for the air temperature and humidity control. For thermal radiation, 45 temperature-controlled surfaces are arranged in a hemicube around the user. An arbitrary environment’s thermal irradiation is emulated by a projection of temperatures on the hemicube surface elements. This study compares two methods for setting the surface temperatures in the VE to emulate thermal irradiation. Finally, it presents the results of computational fluid dynamic simulations that benchmark the capability of the VE to emulate a non-uniform indoor thermal environment. For the non-uniform thermal environment benchmark scenario, the assumption of a uniform enclosure (using the surface average radiant temperature) resulted in a surface root mean square error of 2.2 K (local deviation of up to 4.6 K) of radiant temperature on the person dummy surface. The projection method was implemented for the VE, utilising temperature-controlled surfaces. Depending on the projection method used to determine the surface temperatures, it was possible to decrease the root mean square error of the radiant temperature in the emulated environment to a range of 0.4 K to 0.7 K.

Signal simulation based on improved doppler filter

Abstract In satellite communication, the signal is transmitted through a wireless channel, and distorted signals will appear under the influence of the actual environment. In this paper, an improved Doppler filter is proposed by mathematically fitting the actual signals, and the similarity of the time-frequency map of the signals output by this improved filter reaches 94.357% of the standard value, realizing the simulation of a kind of aberration appearing in the actual environment on the time-frequency image and finally realizing the self-adaptation.

Intra-pulse modulation discrimination using a self-supervised attention-driven CNN-BiLSTM-VAE combination

Identification of intra-pulse modulation (IPM) of radar signals is a crucial part of contemporary electronic support systems and electronic intelligence reconnaissance. Artificial intelligence (AI)-based methods can be very effective in recognising the IPM of radar signals. In this direction, an automatic method is proposed for recognising a few IPMs of radar signals based on continuous wavelet transform (CWT) and a hybrid model of self-attention (SA)-aided convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Firstly, time–frequency attributes of different radar signals are obtained using CWT, and thereafter CNN-SA-BiLSTM is utilised for feature extraction from the 2D scalograms formed by the time–frequency components. The CNN extracts features from the scalograms, SA enhances the discriminative power of the feature map, and BiLSTM detects radar signals based on these features. Additionally, the study addresses real-world data imbalance issues by incorporating a generative AI model, namely the Variational Autoencoder (VAE). The VAE-based approach effectively mitigates challenges arising from data imbalance situations. This method is tested at varying noise levels to give a proper representation of the actual electronic warfare environment. The simulation results demonstrate that the best overall recognition accuracy of the proposed method is 98.4%, even at low signal-to-noise ratios (SNR).

A three-band narrow-band terahertz perfect absorber for patch antennas and other sensors

We have studied a multi-mode resonance and actively tunable high-sensitivity terahertz perfect absorber using a microstructured Dirac semimetal. The absorber is composed of gold-dielectric-Dirac semimetal three-layer structure, which has good optical sensing characteristics. The absorber produces three perfect absorption modes (>95%) in the range of 2–6 THz, with a minimum bandwidth (FWHM) of 0.18 THz. We studied the adaptability of the device response mode to the actual complex electromagnetic environment by simulating the deflection incident angle. In addition, we also analyzed the influence of various parameter variables on the absorption performance, which provides a clearer idea and more possibilities for the wider application of the device. In terms of sensing detection, the absorber has a maximum refractive index sensitivity S of 610 GHz/RIU and a maximum figure of merit FOM of 3.4 in the ambient refractive index range of 1 to 1.8. Finally, we also realized the tunable absorption frequency of the absorber in 60–80 meV. We believe that based on the good optical properties of the absorber, the device has potential application value in communication technology, space exploration and so on.

A detection method for dead caged hens based on improved YOLOv7

In large-scale laying hen farms, daily inspection of dead hens is a relevant task to monitor the health of the flock and prevent disease spreading. The current manual inspection method used in caged hen farms is inefficient, costly, and particularly difficult for high-rise cages. To address this issue, a dead caged hen detection method based on improved You Only Look Once version 7 (YOLOv7) was proposed in this study, which was optimized to improve detection performance and speed in complex farming environments, such as cage wire mesh occlusion and crowded hen occlusion. First, the Convolutional Block Attention Module was used to enable the model to learn target features accurately. Second, the Distance Intersection over Union Non-maximum Suppression and repulsion loss were introduced to improve crowded hen occlusion and reduce missed detections. Additionally, to facilitate the deployment of the proposed method on mobile devices, the MobileNetv3 lightweight network was used to replace the backbone of YOLOv7. Furthermore, the lightweight model was trained using the knowledge distillation method to enhance its performance. Finally, a comparison experiment of different object detection networks and an ablation experiment were conducted to evaluate the proposed method. The experimental results reveal that the improved YOLOv7 model proposed in this study performs optimally. Its precision, recall, F1 score, and mAP@0.5 for the dead hens in the test set are 95.7 %, 86.8 %, 0.910, and 86.2 %, respectively. Compared with the original YOLOv7 model, precision, recall, and mAP@0.5 were increased by 6 %, 10 %, and 13.4 %, respectively. The model parameters and Giga Floating-point Operations were decreased by 31.95 % and 60.56 %, respectively, resulting in a detection speed increase of 43 Frames Per Second. Furthermore, with the assistance of an inspection robot, the proposed dead hen detection model was deployed in the actual farming environments. Compared with methods proposed by other researchers, the proposed model is more suitable for complex actual farming environments and achieves higher detection accuracy, which can offer a reference for automated caged hen detection.

Liver Disease Detection using Machine Learning

Abstract: Liver disease is a serious worldwide health issue, and prompt diagnosis and treatment are essential for successful outcomes. Traditional diagnostic techniques, however, may be expensive and time-consuming and can require intrusive procedures. In this work, we suggest a machine learning-based method for liver disease identification that makes use of the Support Vector Machine (SVM) and Random Forest Decision Tree algorithms. Our approach uses a large dataset with pertinent clinical characteristics including biochemical signs and patient demographics to categorize people into liver disease-positive or - negative groups. Furthermore, we incorporate an intuitive UI with Streamlit, improving accessibility and usability for end users and healthcare providers alike. By means of meticulous testing and assessment, we exhibit the efficiency and functionality of our suggested framework. In terms of precision, specificity, and sensitivity, our model performs admirably, demonstrating its promise as a trustworthy instrument for the identification of liver illness. Moreover, the incorporation of Streamlit improves the system's practicality and usability, making it easier to install and use in actual healthcare environments.

Optimized identification process of equivalent wind load calculations for offshore wind turbines under standstill conditions

In engineering, the wind excitations acting on the offshore wind turbine (OWT) structure cannot be obtained directly by the measured method. The traditional load simulation way may lead to poor accuracy because of the deviation between actual operational conditions and simulation environment parameters or load coefficients, which are always selected based on load spectrums, small scale model tests and engineering experience. To solve this project problem, an equivalent wind load optimization model and parameter identification process of OWT was established under the standstill conditions. Afterwards, two key load parameters in load model respectively called constant drag coefficient and unsteady lift coefficient can be obtained through recursive call on numerical model and feedback analysis using particle swarm optimization (PSO) algorithm based on measured vibration data. The applicability and accuracy of proposed calculation process was verified through an equivalent numerical model of OWT considering different particle position and wind speed. Further, the optimization identification on equivalent wind load and the drag and lift coefficients of one OWT was achieved based on measured data. It is illustrated that the identified drag coefficients show a drop trend from 0.629 to 0.154, consistent with the parameter range of 0.20–0.60 obtained from model experiments, before the wind speed reaches 6.0 m/s and then rise obviously with the increase of wind speed. On the contrary, the range of optimized lift coefficients form 0.133–0.480, which is as familiar as the experimental parameter range of 0.10–0.30, reflects a certain increase trend. Finally, the comparison between identified wind excitations and calculated loads based on the measured strain data explain that the proposed load optimization identification process have a good credibility on actual OWT.

Machine learning-based prediction method for drying shrinkage of recycled aggregate concrete

The objective of this study is to develop a broadly applicable, high-precision, and robust prediction model for the drying shrinkage of recycled aggregate concrete, a material that exhibits significantly greater shrinkage compared to natural aggregate concrete due to its complex characteristics. To achieve this, the study began by selecting relevant characteristic parameters based on international concrete codes, followed by the application of various machine learning algorithms including Backpropagation Neural Network, Support Vector Machine, Random Forest, eXtreme Gradient Boosting, Gaussian Process Regression, k-Nearest Neighbor, Linear Regression, and Long Short-Term Memory to model and forecast the drying shrinkage of recycled aggregate concrete. Subsequently, the SHapley Additive exPlanations (SHAP) method was employed to identify the crucial factors influencing RAC drying shrinkage, such as drying age, elastic modulus, and water-binder ratio, thereby optimizing the input parameters of the ML model. To further enhance the XGBoost algorithm, the sparrow search algorithm (SSA) and the whale optimization algorithm (WOA) were utilized. The optimized WOA-XGBoost model exhibits superior predictive performance, with a determination coefficient of 0.980 and a mean ratio of predicted to experimental values of 1.025, significantly outperforming traditional specification models. The model's applicability may be limited since the dataset is mainly derived from laboratory conditions, which may differ from actual engineering environments. Future research could consider different types of recycled aggregates and curing conditions and test the model on larger data sets to improve its robustness and applicability.

Mobile phone conversation during nighttime driving: Effects on driving behavior

Objectives This study aims to investigate the impact of mobile phone use (specifically, conversation), considering various use modes, on driving behavior at night. Mobile phone use is a source of driver distraction and has been associated with increased accident risk. Driving at night also entails higher accident risk and severity compared to daytime driving. Several studies have investigated the impact of mobile phone use on driving behavior; however, only a few have explored the differences between the different use modes. Most present studies involved daytime driving, although mobile phone use at night is equally if not more prevalent. Method A driving simulator experiment was designed in which 55 participants drove under nighttime simulator conditions, in different road environments (urban and rural) and under different types of distraction: no distraction, handheld, wired earphone, and speaker mode. The drives were performed during late afternoon and evening hours to resemble nighttime conditions both in the simulator and in the actual environment. Participants also completed a questionnaire for collection of different types of data. Results Results highlight the effect of mobile phone use on driving behavior, through specific indicators. Mobile phone use resulted in reduced 85th percentile driving speed and 85th percentile acceleration and increased reaction time and lateral deviation. However, safer stopping distance was observed in rural roads. Parameters relative to mobile phone use familiarity and exposure were found to mitigate mobile phone use effects. Conclusions Mobile phones affect driving behavior at night in a similar manner to that noted in several different studies considering daytime driving. The hands-free regulation should be revisited, because driver distraction also occurred under this particular use mode. Further research is required considering mobile phone use familiarity and exposure and effects of mobile phone use, because the latter is reduced with an increase in the former. Stopping distance, an understudied but more immediate surrogate measure of road safety, was increased with mobile phone use, mainly as a result of the risk compensation behavior that drivers adopt, indicating that more research is required in this field.

Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review.

Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational efficiency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders. As adoption is a key factor in the successful proliferation of an innovation, this scoping review aimed at presenting an overview of the barriers to and facilitators of AI adoption in health care. A scoping review was conducted using the guidance provided by the Joanna Briggs Institute and the framework proposed by Arksey and O'Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This review focused on articles published between January 2011 and December 2023. The review did not have any limitations regarding the health care setting (hospital or community) or the population (patients, clinicians, physicians, or health care administrators). A thematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care. A total of 2514 articles were identified in the initial search. After title and abstract reviews, 50 (1.99%) articles were included in the final analysis. These articles were reviewed for the barriers to and facilitators of AI adoption in health care. Most articles were empirical studies, literature reviews, reports, and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations for AI development, implementation, and the overall structure needed to facilitate adoption. The literature review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients' perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.

Chinese expert consensus on the application of electrolytes in parenteral nutrition (2024 edition)

As essential nutrients for human beings, electrolytes play a central role in nutritional and metabolic support treatment. To further standardize the use of electrolyte preparations in parenteral nutrition, the China International Exchange and Promotive Association for Medical and Health Care Clinical Nutrition Health Branch, Chinese Medical Doctor Association Nutritionist Professional Committee, Chinese Society of Parenteral and Enteral Nutrition, and Chinese Nutrition Society Branch of Clinical Nutrition, invited authoritative experts in the fields of critical medicine, emergency medicine, internal medicine, surgery, and pharmacology in China to create this expert consensus. This consensus was based on the latest guidelines and evidence-based medicine at home and abroad, and combined with the actual clinical medical environment in China. This consensus covered the following topics: electrolytes monitoring during parenteral nutrition, risk assessment and nutritional treatment of refeeding syndrome, electrolytes supplementation during parenteral nutrition for various diseases (including severe diseases, heart failure, post-surgical short bowel syndrome, perioperative period, trauma, diabetic foot, chronic kidney disease, liver disease, neonates), and the compatibility principles of electrolyte preparations in parenteral nutrition. Finally, 13 clinical questions and 26 recommendations were proposed aiming at providing scientific, standardized, and practical guidance for the rational application of electrolytes in parenteral nutrition.

A visible light positioning system for coal mine personnel based on convolutional recurrent neural network

Visible Light Positioning (VLP) technology exhibits promising potential for underground coal mine positioning. While numerous studies have explored VLP in indoor environments, attention to VLP systems for underground mines still needs to be improved. In this paper, based on the traditional indoor visible light communication channel model, the effects of unique features on VLP, such as irregular walls, inclined and rotating receiving ends, and coal dust particles in underground mines, are considered and modeled in detail. In order to improve the accuracy and reliability of personnel VLP in complex mine environments, this paper proposes a deep-learning-based VLP system for underground personnel in coal mines, which consists of two LEDs and four photodetectors (PDs), where the four PDs are mounted on miners' helmets, in conjunction with the actual mine environment. Due to environmental factors and noise, there are apparent spatio-temporal characteristics in the received signal strength of the PDs. Therefore, the system adopts a convolutional recurrent neural network (CRNN) for feature extraction. In the experimental phase, the method's average localization error was 11.24 cm, with 90% of the localization errors within 20.4 cm. The experimental results show that the system has high positioning accuracy and meets the requirements of personnel positioning in underground mines.

Automated Measurement of Cattle Dimensions Using Improved Keypoint Detection Combined with Unilateral Depth Imaging.

Traditional measurement methods often rely on manual operations, which are not only inefficient but also cause stress to cattle, affecting animal welfare. Currently, non-contact cattle dimension measurement usually involves the use of multi-view images combined with point cloud or 3D reconstruction technologies, which are costly and less flexible in actual farming environments. To address this, this study proposes an automated cattle dimension measurement method based on an improved keypoint detection model combined with unilateral depth imaging. Firstly, YOLOv8-Pose is selected as the keypoint detection model and SimSPPF replaces the original SPPF to optimize spatial pyramid pooling, reducing computational complexity. The CARAFE architecture, which enhances upsampling content-aware capabilities, is introduced at the neck. The improved YOLOv8-pose achieves a mAP of 94.4%, a 2% increase over the baseline model. Then, cattle keypoints are captured on RGB images and mapped to depth images, where keypoints are optimized using conditional filtering on the depth image. Finally, cattle dimension parameters are calculated using the cattle keypoints combined with Euclidean distance, the Moving Least Squares (MLS) method, Radial Basis Functions (RBFs), and Cubic B-Spline Interpolation (CB-SI). The average relative errors for the body height, lumbar height, body length, and chest girth of the 23 measured beef cattle were 1.28%, 3.02%, 6.47%, and 4.43%, respectively. The results show that the method proposed in this study has high accuracy and can provide a new approach to non-contact beef cattle dimension measurement.

Collective noise-resistant multi-party semi-quantum secret sharing protocols

Semi-quantum secret sharing facilitates the sharing of private data between quantum users and ‘classical’ users with limited quantum capabilities, thereby lowering the barrier to utilizing quantum technology. However, most current semi-quantum secret sharing protocols are confined to ideal environments and two-party scenarios. In this paper, we design two collective noise-resistant multi-party semi-quantum secret sharing protocols based on decoherence-free states to address potential noise interference during transmission. These protocols use decoherence-free states as information carriers for data interaction and exhibit strong resilience to both internal and external threats. We also conduct simulation experiments using IBM Qiskit to verify the stability and feasibility of the protocols in the noisy environments. The results of these experiments underscore the robustness of the protocols, particularly in the presence of collective noise. Compared with previous related protocols, our protocols have advantages in noise resistance and applicability to multi-party scenarios. Therefore, the proposed protocols may be more in line with the secret sharing needs of actual environments.

Experimental study on identifying anomaly azimuth based on acoustic vector array of borehole acoustic reflection imaging

Accurate anomaly azimuth identification is a major challenge in borehole acoustic reflection imaging. We propose an azimuth estimation method using an acoustic vector array to address this issue. This method was rigorously tested through a physical simulation experiment in a water-filled tank using a monopole acoustic source and an acoustic receiver station comprised of eight azimuthal elements. The proposed method involves the synthesis of multiazimuth acoustic pressure waveforms recorded by the receiver into multicomponent particle velocity waveforms. These waveforms are paired and subjected to coordinate conversion, yielding multiple groups of orthogonal component particle velocity waveforms. Each group underwent polarization analysis to obtain the direction of particle polarization on the horizontal plane and isolate the principal component particle velocity waveform. Using the particle polarization direction, the amplitude of the multiazimuth acoustic pressure waveform, and the type of echo mode, we accurately determined the azimuth of the anomaly, facilitating the creation of a spatial distribution image of the anomaly. Further validation of this method was conducted through a large-scale physical simulation experiment in a deep-water lake using a borehole acoustic reflection imaging instrument. The obtained experimental data were used to validate the effectiveness of this method. Finally, considering an actual logging environment, the feasibility of this method in a borehole environment was verified using numerical simulations and field data of acoustic detection of nearby wells. The present findings demonstrate that this novel method considerably enhances the accuracy and stability of the measurement of anomaly azimuth based on the particle polarization direction. In addition, this method effectively harnesses both acoustic pressure and particle velocity in borehole acoustic reflection imaging, exhibiting superior noise robustness, mitigating noise, and improving the signal-to-noise ratio of the echoes.