基于Guided BERTopic模型的产业链关键核心技术识别与发展趋势研判--以未来工业互联网为例

产业链关键核心技术不仅是国之重器,而且对推动我国经济的高质量发展和保障国家安全起着至关重要的作用。当前,工业互联网产业链的关键技术发展成为我国的研究重点,该领域的重要性在2023年被提升至国家级重大研究计划。综合收集了1989-2024年中美两国在工业互联网领域的专利数据,基于《关键和新兴技术清单》和《未来工业互联网基础理论与关键技术重大研究计划》,构建了一套工业互联网产业链核心关键技术的种子词库。利用Guided BERTopic模型提取了产业链关键技术主题,并采用Logistic模型评估了这些技术的生命周期,进而对比中美两国的发展情况,提出了针对未来发展的建议。研究结果表明,相比美国,我国在大多数产业链核心技术方面仍处于成长阶段,而美国则已步入技术饱和阶段。展望未来,设备监测系统、设备管理系统、无线通信技术及能耗监测分析系统等被认为是具有巨大发展潜力的技术。研究提出了一种基于政策文件的产业链关键技术识别方法,并通过对工业互联网专利技术的实证分析,为制定相关政策提供了科学指导和建议。

Research on the navigation method of large-scale differential tail-control improvised guided munitions based on rotational speed constraints

In the process of launching guided projectile under the conventional system, it is difficult to effectively obtain the precise navigation parameters of the projectile in the high dynamic environment. Aiming at this problem, this paper describes a new system of guided ammunition based on tail spin reduction. After analyzing the mechanism of the ammunition's tail spin reduction, a navigation method of large scale difference tail control simple guided ammunition based on speed constraint is proposed. In this method,the corresponding navigation constraints can be carried out by combining the rotation speed state of the ammunition itself, and the optimal solution of navigation parameters during the flight of the missile can be obtained by Extended Kalman Filter(EKF). Finally, the performance of the proposed method was verified by the simulation environment, and the hardware-in-the-loop simulation test and flight test were carried out to verify the performance of the method in the real environment. The experimental results show that the proposed method can achieve the optimal estimation of navigation parameters for simple guided ammunition with large-scale difference tail control. Under the conditions of simulation test and hardware-in-loop simulation test, the position and velocity errors calculated by the method in this paper converged. Under the condition of flight test, the spatial average error calculated by the method described in this paper is 6.17 m, and the spatial error of the final landing point is 3.50 m.Through this method, the accurate acquisition of navigation parameters in the process of projectile launching is effectively realized.

Pipeline thickness estimation using the dispersion of higher-order SH guided waves

Thickness measurement plays an important role in the monitoring of pipeline corrosion damage. However, the requirement for prior knowledge of the shear wave velocity in the pipeline material for popular ultrasonic thickness measurement limits its widespread application. This paper proposes a method that utilizes cylindrical shear horizontal(SH) guided waves to estimate pipeline thickness without prior knowledge of shear wave velocity. The inversion formulas are derived from the dispersion of higher-order modes with the high-frequency approximation. The waveform of the example problems is simulated using the real-axis integral method. The data points on the dispersion curves are processed in the frequency domain using the wave-number method. These extracted data are then substituted into the derived formulas. The results verify that employing higher-order SH guided waves for the evaluation of thickness and shear wave velocity yields less than1% error. This method can be applied to both metallic and non-metallic pipelines, thus opening new possibilities for health monitoring of pipeline structures.

Guided Care护理模式对老年骨质疏松症患者自我管理能力和生活质量的影响

目的探讨分析Guided Care护理模式对老年骨质疏松症患者自我管理能力和生活质量的影响。方法简单随机选取2022年1月—2023年9月山东省泰安荣军医院收治的60例老年骨质疏松症患者为研究对象,按随机数表法将其分为常规组(实施常规护理干预),模式组(在常规组的基础上给予Guided Care护理模式干预),每组30例,两组均持续干预2个月,比较两组患者干预前后的健康认知水平、跌倒风险、自我管理能力、生活质量、护理满意度。结果干预2个月后,模式组的健康认知水平评分高于常规组,差异有统计学意义(P<0.05)。干预2个月后,模式组的跌倒风险评分低于常规组,差异有统计学意义(P<0.05)。干预2个月后,模式组的自我管理能力评分高于常规组,生活质量评分低于常规组,差异有统计学意义(P均<0.05)。干预2个月后,模式组的护理满意度为93.33%,高于常规组的护理满意度73.33%,差异有统计学意义(χ^(2)=4.320,P<0.05)。结论在老年骨质疏松症患者中,应用Guided Care护理模式,可以有效地提升患者的健康认知水平及自我管理能力,提高患者的生活质量,降低患者跌倒的风险。

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

SMSTracker:A Self-Calibration Multi-Head Self-Attention Transformer for Visual Object Tracking

Visual object tracking plays a crucial role in computer vision.In recent years,researchers have proposed various methods to achieve high-performance object tracking.Among these,methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information.However,current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information.In this paper,we introduce self-calibration multi-head self-attention Transformer(SMSTracker)as a solution to these challenges.It employs a hybrid tensor decomposition self-organizing multihead self-attention transformermechanism,which not only compresses and accelerates Transformer operations but also significantly reduces redundant data,thereby enhancing the accuracy and efficiency of tracking.Additionally,we introduce a self-calibration attention fusion block to resolve common issues of attention ambiguities and inconsistencies found in traditional trackingmethods,ensuring the stability and reliability of tracking performance across various scenarios.By integrating a hybrid tensor decomposition approach with a self-organizingmulti-head self-attentive transformer mechanism,SMSTracker enhances the efficiency and accuracy of the tracking process.Experimental results show that SMSTracker achieves competitive performance in visual object tracking,promising more robust and efficient tracking systems,demonstrating its potential to providemore robust and efficient tracking solutions in real-world applications.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

An Aerial Target Recognition Algorithm Based on Self-Attention and LSTM

In the application of aerial target recognition,on the one hand,the recognition error produced by the single measurement of the sensor is relatively large due to the impact of noise.On the other hand,it is difficult to apply machine learning methods to improve the intelligence and recognition effect due to few or no actual measurement samples.Aiming at these problems,an aerial target recognition algorithm based on self-attention and Long Short-Term Memory Network(LSTM)is proposed.LSTM can effectively extract temporal dependencies.The attention mechanism calculates the weight of each input element and applies the weight to the hidden state of the LSTM,thereby adjusting the LSTM’s attention to the input.This combination retains the learning ability of LSTM and introduces the advantages of the attention mechanism,making the model have stronger feature extraction ability and adaptability when processing sequence data.In addition,based on the prior information of the multidimensional characteristics of the target,the three-point estimation method is adopted to simulate an aerial target recognition dataset to train the recognition model.The experimental results show that the proposed algorithm achieves more than 91%recognition accuracy,lower false alarm rate and higher robustness compared with the multi-attribute decision-making(MADM)based on fuzzy numbers.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.

Stroke Electroencephalogram Data Synthesizing through Progressive Efficient Self-Attention Generative Adversarial Network

Early and timely diagnosis of stroke is critical for effective treatment,and the electroencephalogram(EEG)offers a low-cost,non-invasive solution.However,the shortage of high-quality patient EEG data often hampers the accuracy of diagnostic classification methods based on deep learning.To address this issue,our study designed a deep data amplification model named Progressive Conditional Generative Adversarial Network with Efficient Approximating Self Attention(PCGAN-EASA),which incrementally improves the quality of generated EEG features.This network can yield full-scale,fine-grained EEG features from the low-scale,coarse ones.Specially,to overcome the limitations of traditional generative models that fail to generate features tailored to individual patient characteristics,we developed an encoder with an effective approximating self-attention mechanism.This encoder not only automatically extracts relevant features across different patients but also reduces the computational resource consumption.Furthermore,the adversarial loss and reconstruction loss functions were redesigned to better align with the training characteristics of the network and the spatial correlations among electrodes.Extensive experimental results demonstrate that PCGAN-EASA provides the highest generation quality and the lowest computational resource usage compared to several existing approaches.Additionally,it significantly improves the accuracy of subsequent stroke classification tasks.

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

Detection of internal crack growth in polyethylene pipe using guided wave ultrasonic testing

Despite the success of guided wave ultrasonic inspection for internal defect detection in steel pipes,its application on polyethylene(PE)pipe remains relatively unexplored.The growth of internal cracks in PE pipe severely affects its pressure-holding capacity,hence the early detection of internal cracks is crucial for effective pipeline maintenance strategies.This study extends the scope of guided wave-based ultrasonic testing to detect the growth of internal cracks in a natural gas distribution PE pipe.Laboratory experiments and a finite element model were planned to study the wave-crack interaction at different stages of axially oriented internal crack growth with a piezoceramic transducer-based setup arranged in a pitch-catch configuration.Mode dispersion analysis supplemented with preliminary experiments was performed to isolate the optimal inspection frequency,leading to the selection of the T(0,1)mode at 50-kHz for the investigation.A transmission index based on the energy of the T(0,1)mode was developed to trace the extent of simulated crack growth.The findings revealed an inverse linear correlation between the transmission index and the crack depth for crack growth beyond 20%crack depth.

Role of endoscopic ultrasound-guided biliary drainage for palliation of malignant biliary obstruction

Endoscopic ultrasound-guided biliary drainage(EUS-BD)directs bile flow into the digestive tract and has been mostly used in patients with malignant biliary obstruction(MBO)where endoscopic retrograde cholangiopancreatography-guided biliary drainage was unsuccessful or was not feasible.Lumen apposing metal stents(LAMS)are deployed during EUS-BD,with the newer electrocautery-enhanced LAMS reducing procedure time and complication rates due to the inbuilt cautery at the catheter tip.EUS-BD with electrocautery-enhanced LAMS has high technical and clinical success rates for palliation of MBO,with bleeding,cholangitis,and stent occlusion being the most common adverse events.Recent studies have even suggested comparable efficacy between EUS-BD and endosc-opic retrograde cholangiopancreatography as the primary approach for distal MBO.In this editorial,we commented on the article by Peng et al published in the recent issue of the World Journal of Gastrointestinal Surgery in 2024.

Research on High-Velocity Impact Damage Monitoring Method of CFRP Based on Guided Wave

Carbon fiber-reinforced polymer(CFRP)is widely used in aerospace applications.This kind of material may face the threat of high-velocity impact in the process of dedicated service,and the relevant research mainly considers the impact resistance of the material,and lacks the high-velocity impact damage monitoring research of CFRP.To solve this problem,a real high-velocity impact damage experiment and structural health monitoring(SHM)method of CFRP plate based on piezoelectric guided wave is proposed.The results show that CFRP has obvious perforation damage and fiber breakage when high-velocity impact occurs.It is also proved that guided wave SHM technology can be effectively used in the monitoring of such damage,and the damage can be reflected by quantifying the signal changes and damage index(DI).It provides a reference for further research on guided wave structure monitoring of high/hyper-velocity impact damage of CFRP.

Clinical Efficacy of GBR Technique Combined with Temporary Bridgework-Guided Gingival Contouring in Treating Upper Anterior Tooth Loss with Labial Bone Defects

Objective:To investigate the clinical effect of the guided bone regeneration(GBR)technique combined with temporary bridgework-guided gingival contouring in treating upper anterior tooth loss with labial bone defects.Methods:From July 2023 to April 2024,80 patients with upper anterior tooth loss and labial bone defects were admitted to the hospital and selected as evaluation samples.They were divided into an observation group(n=40)and a control group(n=40)using a numerical table lottery scheme.The control group received treatment with the GBR technique,while the observation group received treatment with the GBR technique combined with temporary bridges to guide gingival contouring.The two groups were compared in terms of clinical red aesthetic scores(PES),labial alveolar bone density,labial bone wall thickness,gingival papillae,gingival margin levels,and patient satisfaction.Results:The PES scores of patients in the observation group were higher than those in the control group after surgery(P<0.05).The bone density of the labial alveolar bone and the thickness of the labial bone wall in the observation group were higher than those in the control group.The levels of gingival papillae and gingival margins were lower in the observation group after surgery(P<0.05).Additionally,patient satisfaction in the observation group was higher than in the control group(P<0.05).Conclusion:The GBR technique combined with temporary bridge-guided gingival contouring for treating upper anterior tooth loss with labial bone defects can improve the aesthetic effect of gingival soft tissue,increase alveolar bone density and the thickness of the labial bone wall,and enhance patient satisfaction.This approach is suitable for widespread application in healthcare institutions.

Comparison of Cognitive Registration Transrectal Ultrasound-Guided Targeted Biopsy of Prostate to Systematic 12-Core Biopsy: A Retrospective, Multicentre Study

Introduction: Prostate cancer (PCa) is the third most prevalent cancer among Malaysian males, often diagnosed at advanced stages, leading to suboptimal outcomes. While transrectal ultrasound-guided systematic biopsy (TRUS-SB) is the primary diagnostic method, prebiopsy multiparametric magnetic resonance imaging (mpMRI) is gaining popularity in identifying suspicious lesions. This study addresses the lack of comprehensive investigations into the efficacy of cognitive registration TRUS targeted biopsy (COG-TB) compared to conventional TRUS-SB, considering the resource limitations of the Malaysian healthcare system. Materials and Methods: A retrospective cohort study was conducted in two Malaysian healthcare facilities. 116 adult patients with a prostate-specific antigen (PSA) level of more than 4 ng/mL who underwent both COG-TB and TRUS-SB between October 2020 and March 2022 were included. Primary outcomes were cancer detection rate and histopathological outcomes, including Gleason score. Results: COG-TB showed a higher overall cancer detection rate (50%) compared to TRUS-SB (44%). Clinically significant cancer detection rates were similar between COG-TB and TRUS-SB (37.1%). Further analysis revealed that both COG-TB and TRUS-SB detected clinically significant cancer in 30.2% of patients, did not detect it in 56.0%, and had conflicting findings in 16 patients (p Conclusion: COG-TB and TRUS-SB have comparable detection rates for clinically significant prostate cancer, with COG-TB showing a higher tendency to detect insignificant prostate cancer. Further studies comparing these methods are warranted.

Guided Care在老年慢性病管理中的效果研究

目的研究老年慢性病患者实施Guided Care的管理效果。方法应用生活质量量表对120例老年慢性病患者进行实施前后效果观察评价,其中研究组60例,实施Guided Care,对照组60例,定期门诊随访,以分析两组老年慢性病患者生活质量、血糖达标情况。结果两组老年慢性病患者在实施前后生活质量、血糖达标情况效果有显著性差异(P<0.01)。结论实施Guided Care为老年慢性病患者提供科学的综合服务,为临床提供更具体有效适合中国实际新的管理模式,值得临床推广应用。

肠造口病人实施Guided Care护理模式对生活质量的影响

[目的]研究肠造口病人实施Guided Care护理模式对生活质量的影响。[方法]选择80例肠造口病人,随机分为试验组和对照组,试验组实施Guided Care护理模式,对照组实施常规护理模式。干预后6个月比较两组病人并发症的发生情况,并应用简明生存质量量表(SF-36)评定实施前后两组病人的生活质量。[结果]两组病人在实施护理干预后并发症的发生情况、生活质量差异均有统计学意义(P<0.05或P<0.01)。[结论]对肠造口病人实施Guided Care护理模式,可以减少肠造口病人的并发症,提高肠造口病人生活质量。