Attack-Resilient Distributed Cooperative Control of Virtually Coupled High-Speed Trains via Topology Reconfiguration

Dear Editor,This letter addresses the resilient distributed cooperative control problem of a virtually coupled train convoy under stochastic disturbances and cyber attacks.The main purpose is to achieve distributed coordination of virtually coupled high-speed trains with the prescribed inter-train distance and same cruise velocity.

Speed limit effect during lane change in a two-lane lattice model under V2X environment

Speed limit measures are ubiquitous due to the complexity of the road environment,which can be supplied with the help of vehicle to everything(V2X)communication technology.Therefore,the influence of speed limit on traffic system will be investigated to construct a two-lane lattice model accounting for the speed limit effect during the lane change process under V2X environment.Accordingly,the stability condition and the mKdV equation are closely associated with the speed limit effect through theory analysis.Moreover,the evolution of density and hysteresis loop is simulated to demonstrate the positive role of the speed limit effect on traffic stability in the cases of strong reaction intensity and high limited speed.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

Warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography

High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.

Preheating-assisted solid-state friction stir repair of Al-Mg-Si alloy plate at different rotational speeds

Additive friction stir deposition(AFSD)is a novel structural repair and manufacturing technology has become a research hotspot at home and abroad in the past five years.In this work,the microstructural evolution and mechanical performance of the Al-Mg-Si alloy plate repaired by the preheating-assisted AFSD process were investigated.To evaluate the tool rotation speed and substrate preheating for repair quality,the AFSD technique was used to additively repair 5 mm depth blind holes on 6061 aluminum alloy substrates.The results showed that preheat-assisted AFSD repair significantly improved joint bonding and joint strength compared to the control non-preheat substrate condition.Moreover,increasing rotation speed was also beneficial to improve the metallurgical bonding of the interface and avoid volume defects.Under preheating conditions,the UTS and elongation were positively correlated with rotation speed.Under the process parameters of preheated substrate and tool rotation speed of 1000 r/min,defect-free specimens could be obtained accompanied by tensile fracture occurring in the substrate rather than the repaired zone.The UTS and elongation reached the maximum values of 164.2MPa and 13.4%,which are equivalent to 99.4%and 140%of the heated substrate,respectively.

Piecewise Acoustic Source Imaging with Unknown Speed of Sound Using a Level-Set Method

We investigate the following inverse problem:starting from the acoustic wave equation,reconstruct a piecewise constant passive acoustic source from a single boundary temporal measurement without knowing the speed of sound.When the amplitudes of the source are known a priori,we prove a unique determination result of the shape and propose a level set algorithm to reconstruct the singularities.When the singularities of the source are known a priori,we show unique determination of the source amplitudes and propose a least-squares fitting algorithm to recover the source amplitudes.The analysis bridges the low-frequency source inversion problem and the inverse problem of gravimetry.The proposed algorithms are validated and quantitatively evaluated with numerical experiments in 2D and 3D.

Corrosion Test of the Steel Plate in a WJ-8 Fastener for High Speed Rail

It was found that the steel plate in the composite plate in the WJ-8 fastener used in high speed rail is rusty. The objective of this study is to test the zinc coating of the steel plate. A literature review was conducted to identify the zinc coating techniques, and the companies that can provide different coating service was identified. A salt fog chamber was built that was in compliance with the ANSI B117 code, and the steel plates that were coated by the identified companies were tested using the salt fog chamber. The results indicated that the coating technique that had the best performance in preventing corrosion was the Greenkote plates with passivation. The galvanized option had the roughest coating layer, and it was the most reactive in the salt water solution. This makes it non-ideal for the dynamic rail environment because the increased friction of the plate could damage the supports, especially during extreme temperatures that would cause the rail to expand or contract. Greenkote with Phosphate and ArmorGalv also provided increased corrosion prevention with a smooth, strong finish, but it had more rust on the surface area than the Greenkote with ELU passivation. The ArmorGalv sample had more rust on the surface area than the Greenkote samples. This may not be a weakness in the ArmorGalv process;rather, it likely was the result of this particular sample not having the added protection of a colored coating.

Study on the pattern of train arrival headway time in high-speed railway

Purpose-The design goal for the tracking interval of high-speed railway trains in China is 3 min,but it is difficult to achieve,and it is widely believed that it is mainly limited by the tracking interval of train arrivals.If the train arrival tracking interval can be compressed,it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval.The goal of this article is to study how to compress the train arrival tracking interval.Design/methodologylapproach-By simulating the process of dense train groups arriving at the station and stopping,the headway between train arrivals at the station was calculated,and the pattern of train arrival headway was obtained,changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains.Findings-When the running speed of trains is high,the headway between trains is short,the length of the station approach throat area is considerable and frequent train arrivals at the station,the arrival headway for the first group or several groups of trains will exceed the headway,but the subsequent sets of trains will havea headway equal to the arrival headway.This convergence characteristic is obtained by appropriately increasing the running time.Originality/value-According to this pattern,there is no need to overly emphasize the impact of train arrival headway on the headway.This plays an important role in compressing train headway and improving high-speedrailwaycapacity.

Key technologies of earthquake early warning system for China’s high-speed railway

Purpose–The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China,which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.Design/methodology/approach–The paper describes the key technologies that are involved in the development of the system,such as P-wave identification and earthquake early-warning,multi-source seismic information fusion and earthquake emergency treatment technologies.The paper also presents the test results of the system,which show that it has complete functions and its major performance indicators meet the design requirements.Findings–The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety.The key technical indicators of the system have excellent performance:The first report time of the P-wave is less than three seconds.From the first arrival of P-wave to the beginning of train braking,the total delay of onboard emergency treatment is 3.63 seconds under 95%probability.The average total delay for power failures triggered by substations is 3.3 seconds.Originality/value–The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions.It also contributes to the earthquake prevention and disaster reduction efforts.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.

Temperature-Based Fan Speed Optimization Strategy

As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study aims to develop a temperature-feedback-based fan speed optimization strategy to achieve higher energy efficiency and user comfort.Firstly,by analyzing existing fan speed control technologies,their main limitations are identified,such as the inability to achieve smooth speed transitions.To address this issue,a BP-PID speed control algorithm is designed,which dynamically adjusts fan speed based on indoor temperature changes.Experimental validation demonstrates that the designed system can achieve smooth speed transitions compared to traditional fan systems while maintaining stable indoor temperatures.Furthermore,the real-time responsiveness of the system is crucial for enhancing user comfort.Our research not only demonstrates the feasibility of temperature-based fan speed optimization strategies in both theory and practice but also provides valuable insights for energy management in future smart home environments.Ultimately,this research outcome will facilitate the development of smart home systems and have a positive impact on environmental sustainability.

关节镜下speed-bridge技术修复中大型肩袖撕裂的临床应用

目的 探讨关节镜下speed-bridge技术修复中大型肩袖撕裂的临床应用效果。方法 回顾性选择18例中大型肩袖撕裂的患者,均采用关节镜下speed-bridge技术修复肩袖撕裂,术后指导康复锻炼。记录患者术前、术后1个月、术后3个月和术后6个月的肩关节前屈及外展活动度、疼痛视觉模拟评分(VAS)、Constant-Murley肩关节功能评分。结果 术前、术后1个月、术后3个月、术后6个月患者肩关节前屈活动度分别为(83.33±3.75)°、(98.06±3.12)°、(120.00±2.49)°、(139.72±2.74)°;肩关节外展活动度分别为(69.17±4.40)°、(82.50±4.24)°、(111.39±3.19)°、(138.89±2.74)°;VAS评分分别为(5.78±0.30)分、(3.28±0.22)分、(2.06±0.25)分、(0.89±0.17)分;Constant-Murley评分分别为(53.17±1.15)分、(60.33±1.07)分、(71.61±1.11)分、(80.83±0.91)分。术后随着康复锻炼的进行,肩关节前屈及外展活动度、VAS评分、Constant-Murley肩关节功能评分均明显改善,差异均有统计学意义(P<0.05)。结论 采用关节镜下speed-bridge技术修复中大型肩袖撕裂,操作简便,并发症少,可取得满意疗效。

基于UPLC-QTOF-MS解析苦荞黄酮组成及药理学网络分析

为优化苦荞黄酮提取工艺并明确其具体成分结构和含量及药理作用,本研究以四川省凉山彝族自治州甘洛县优质苦荞为研究对象,对超声提取苦荞黄酮工艺进行了优化,采用超高效液相色谱-四级杆-飞行时间质谱(UPLC-QTOF-MS)对主要黄酮成分进行结构鉴定,并采用超高效液相色谱-三重四级杆质谱联用仪对鉴定到的黄酮进行准确定量,同时利用中药系统药理学(TCMSP)系统分析其网络药理学特性。结果表明,超声辅助乙醇提取苦荞黄酮的最优条件为乙醇浓度90%(V/V)、超声功率140 W、超声温度70℃、超声时间60 min、提取溶剂与苦荞原料比50∶1(mL·g^(-1)),该条件下,提取液中共鉴定到黄酮16种,其中芦丁、表儿茶素和儿茶素含量分别达到115.81、72.99和23.08μg·mL^(-1)。这16种黄酮在防治心血管疾病、慢性炎症性疾病上具有重要作用。本研究可为苦荞精深加工综合利用尤其是功能性苦荞产品开发利用奠定基础。

水资源-能源-粮食-生态系统耦合协调及驱动力分析

为加深对我国水资源、能源、粮食、生态系统协同演变趋势的认识,构建水资源-能源-粮食-生态多维系统指标体系,运用耦合协调度模型对我国2005—2020年水资源-能源-粮食-生态系统耦合协调度进行评价,并采用多因素归因分析法进行驱动力分析。结果表明:我国水资源-能源-粮食-生态系统耦合协调度从2005年的0.55增长到2020年的0.84,各地区耦合协调度从勉强协调发展水平过渡到中级协调发展水平,各子系统对耦合协调度上升的驱动分别经历了由粮食子系统到生态子系统再到水资源子系统主导的过程;能源子系统的贡献率虽然比较小,但是未来可能是各地区提升水资源-能源-粮食-生态系统多维系统协调发展水平的突破口。

基于SPI-RRV指数中国气象干旱及其风险时空演变特征研究

为全面揭示变化环境下我国多维气象干旱特征,耦合气象干旱指数(SPI)和可靠性-回弹性-脆弱性(RRV)指数,提出了一种基于SPI-RRV指数的干旱风险评价方法,定量评价了中国气象干旱及其风险的时空演变特征。结果表明:SPI-RRV指数具有特征稳定和时空可比性强的特点,能够较为准确地评估气象干旱风险时空演变特征;南方平均干旱栅格比、干旱月占比和频次大于北方,湿润区和半湿润区干旱历时短、烈度大,半干旱区和干旱区干旱历时长、烈度相对较小;干旱高风险区转移具有显著年代际变化规律,空间上从西北向西南地区转移。

老年急性脑梗死患者血清Del-1和IL-17水平变化及与梗死面积和预后的关系

目的 探究老年急性脑梗死(ACI)患者血清内皮发育调节基因-1(Del-1)、白细胞介素-17(IL-17)水平与脑梗死面积、病情严重程度及预后的关系。方法 回顾性收集2019-01-2021-12厦门大学附属东南医院收治的126例老年ACI患者(病例组)及105名健康体检者(对照组)的临床资料,根据脑梗死面积将病例组患者分为大面积梗死组(梗死最大直径>5 cm,n=14)、中面积梗死组(梗死最大直径3~5 cm,n=53)和小面积梗死组(梗死最大直径<3 cm,n=59);根据NIHSS评分分为重症组(NIHSS评分≥16分,n=13)、中症组(5分<NIHSS评分<15分,n=58)和轻症组(NIHSS评分≤5分,n=55);根据m RS评分分为预后良好组(mRS评分≤2分,n=81)和预后不良组(mRS评分>2分,n=45)。采用荧光免疫法检测血清Del-1、IL-17水平,受试者工作特征(ROC)曲线评估血清Del-1、IL-17水平预测老年ACI患者预后的价值。结果 病例组吸烟史比例、收缩压、舒张压、糖化血红蛋白、血清IL-17水平均高于对照组,血清Del-1水平低于对照组(P<0.05)。不同脑梗死面积患者血清Del-1水平比较,小面积梗死组>中面积梗死组>大面积梗死组;血清IL-17水平比较,小面积梗死组<中面积梗死组<大面积梗死组(P<0.05)。轻症组血清Del-1水平高于中症组、重症组(P<0.05),不同病情严重程度患者血清IL-17水平比较,重症组>中症组>轻症组(P<0.05)。预后良好组患者血清Del-1水平高于预后不良组,血清IL-17水平低于预后不良组(P<0.05)。ROC曲线分析显示,血清Del-1、IL-17预测ACI患者预后的AUC值分别为0.763、0.747(P<0.05)。结论 老年ACI患者血清Del-1水平较低,IL-17水平较高,二者与患者脑梗死面积、病情严重程度和预后关系密切,可作为预测老年ACI患者预后的可靠指标。

基于混合分解和PCG-BiLSTM的风速短期预测

为降低风速的随机性对风力发电的影响,提高风速短期预测的精准度,提出一种基于混合分解、双通道输入、多分支PCG-BiLSTM深度学习模型的短期风速预测方法。首先,将全年风速数据分为春、夏、秋、冬4个季度,选取春季作为主要实验对象;其次,利用奇异谱分解(SSD)和变分模态分解(VMD)以降低原始春季风速数据复杂度,生成具有不同模态且复杂度低的子分量,两种不同模式子分量组合为混合分量,实现不同模式分解算法的优势互补;最后,将混合分量以双通道的形式输入到多分支PCG-BiLSTM深度学习模型中,其模型的每个分支由卷积神经网络(CNN)与门控循环单元(GRU)并联组成时空特征提取模块,用于提取两种分解分量组合的混合分量的时空特征,各分支提取对应混合分量的时空特征经聚合后再由双向长短期记忆网络(BiLSTM)进一步提取风速信号的正向和反向双向波动规律,进而得到最终的风速预测结果。多组实验结果表明:提出的组合预测方法在短期风速预测中具有较高的精度和泛化能力,优于其他传统预测方法。

基于混合式教学的“教-学-评”一体化实验教学体系探索

实验能力是医学专业研究生的重要科研能力,实验能力的培养与实验教学的方法与质量密切相关。文章针对传统实验教学受到时间和空间限制且评价指标较为单一的现状,提出构建基于线上自主学习和现场实践相结合的“教-学-评”一体化实验教学体系,建立覆盖课前线上自主学习、课堂现场实践、课后作业提交与考核教学全过程的评价体系,以评促学,以评促教,通过评价反馈激发学生的学习热情,改进教学内容和方法,不断探索与完善实验教学体系,提高实验教学质量,以期为培养具有实践能力与创新精神的医学科研人才发挥积极作用。

高速铁路主跨320 m钢-混部分斜拉桥无砟轨道适应性研究

南玉高铁六景郁江特大桥设计将钢-混部分斜拉桥结构引入时速350 km高速铁路领域,而300 m级以上大跨度桥上无砟轨道的竖向变形极易超限,影响列车通过的安全性和舒适性,因此,系统研究在此大跨桥梁结构上铺设无砟轨道的适应性十分必要。通过建立有限元及动力学模型,分析不同组合工况下无砟轨道结构的变形特点及动力特性,运用60 m弦测法探究各工况下无砟轨道的线形变化规律,从而确定大跨度钢-混部分斜拉桥铺设无砟轨道的适应性,并对设计和施工提出合理化建议。主要结论如下:在各种不利组合荷载作用下,桥上无砟轨道结构强度满足规范要求,列车通过大桥的各项安全性与舒适性指标均满足规范要求;混凝土收缩徐变和斜拉索升降温是影响无砟轨道线形标准的两大主因,应在无砟轨道施工前确保足够的沉降观测期和收缩徐变释放期,并充分考虑拉索的保温设计;在温度组合荷载作用下,桥上无砟轨道的60 m弦测不平顺幅值为6.79 mm,满足高速铁路静态验收标准;但在叠加列车荷载和收缩徐变后,变形弦测值均出现Ⅱ级及以上超限,通过合理设置预拱度后可有效改善轨道平顺性标准。