Museum Informs History

BRIGHT blue sky, a fleet of diplomats＇ cars outside, flocks of journalists inside, a memorial site in the size of a small town： right next to the Lugou Bridge - better known in the West as the Marco Polo Bridge - is the location of the Museum of the War of Chinese People＇s Resistance Against Japanese Aggression.

LiDAR-Visual SLAM with Integrated Semantic and Texture Information for Enhanced Ecological Monitoring Vehicle Localization

Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environmental science research,ecological and environmental monitoring projects,disaster response,and emergency management.A key method employed in these vehicles for achieving high-precision positioning is LiDAR(lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping(SLAM).However,maintaining highprecision localization in complex scenarios,such as degraded environments or when dynamic objects are present,remains a significant challenge.To address this issue,we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data registration.Specifically,semantic information simplifies the modeling of scene elements,reducing the reliance on dense point clouds,which can be less efficient.Meanwhile,visual texture information complements LiDAR-Visual localization by providing additional contextual details.By incorporating semantic and texture details frompaired images and point clouds,we significantly improve the quality of data association,thereby increasing the success rate of localization.This approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.

Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

Joint Feature Encoding and Task Alignment Mechanism for Emotion-Cause Pair Extraction

With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Navigating the ethical terrain:Off-label and experimental treatments in medical case reports

This article explores the ethical considerations surrounding the reporting of offlabel and experimental treatments in medical case reports,with a focus on fields such as oncology,psychiatry,and pediatrics.It emphasizes the balance between innovation and evidence-based medicine,highlighting the critical role of case reports in disseminating clinical experiences and advancing medical knowledge.The discussion delves into the ethical framework guiding case reporting,including principles of patient autonomy,informed consent,non-maleficence,beneficence,justice,and transparency.Challenges such as negative outcome reporting,commercial interests,and the balance between innovation and caution are examined.Recommendations for ethical vigilance,the development of comprehensive guidelines,and the role of regulatory bodies are proposed to ensure patient safety and uphold scientific integrity.The article concludes by underscoring the importance of a collaborative effort among clinicians,researchers,ethicists,and regulatory bodies to foster the responsible advancement of medical science while adhering to the highest ethical standards.

ARCHITECTURE INFORMS HISTORY

Clusters of ancient architecture in central China have recently been entered on the world heritage list A group of ancient architecture in Dengfeng,central China’s Henan Province,was added to the world heritage list at the 34th session of the World Heritage Committee in Brazil on August 1 this year.The architectural collection is China’s 39th property inscribed on the list,and the third world heritage site in the province after the Longmen Grottoes and Yinxu in Anyang,site of the capital of the late Shang Dynasty(1600-1046 B.C.).

高速铁路日常客运量的EMD-Informer组合预测方法

铁路客流需求的科学预测是进行运输组织方案决策的重要依据。以高速铁路历史客票数据为基础,结合经验模态分解(empirical mode decomposition,EMD)与机器深度学习中的注意力机制,提出高速铁路日客流量的EMD-Informer组合预测方法。首先采用EMD方法分解高速铁路客流量序列,获得具有周期特征和线路客流内在特征的模态分量,再利用Informer模型分别训练和预测各模态分解分量,并通过多头注意力机制高效挖掘客流数据内在规律和捕捉数据序列中的关键特征,在此基础上重组各分量预测值,从而得到高速铁路日常客流的整体高精度预测值。同时,根据结合问题特征的大量实验,明确可供实际运用参考的超参数设置规则。基于京沪高速铁路全线的实例计算分析表明,相对对比预测方法,EMD-Informer组合预测方法在高速铁路客流的单步预测及超前预测上均具有明显更小的预测误差。

机场能见度临近预测方法

能见度是保障机场航班安全、正常运行的重要标准之一。为精准预测能见度,使用2020年天津机场气象和常规空气质量监测数据,构建基于方差膨胀因子(Variance Inflation Factor,VIF)、主成分分析(Principal Components Analysis,PCA)和Informer的能见度预测模型,并将均方根误差、平均绝对误差、平均绝对百分比误差作为评价指标进行误差分析。结果显示,VIF PCA Informer模型比单一的Informer和简单组合模型效果更优,能更好地捕捉长时间序列特征的关系。相比于单一的Informer、长短期记忆神经网络和门控循环单元模型,VIF PCA Informer模型均方根误差下降了0.2141~0.3486,平均绝对误差下降了0.1842~0.2753,平均绝对百分比误差下降了0.3224~0.5270;VIF PCA Informer模型对能见度的临近预测(1 h)更为精准。使用高效的机场能见度预测模型可在保障航班安全高效运行方面发挥较大支撑作用。

基于时间序列的发电机设备异常分析

为提高发电机组设备运行维护管理水平,提出一种基于PCA-Informer方法的发电设备故障预测技术。首先使用主成分分析(PCA)算法降低时间序列数据的特征维度;其次将数据分批次输入Encoder中,由Encoder执行蒸馏操作,提取长时间序列输入间的Long-Range依赖,通过蒸馏操作为重要特征赋予更高的权重,并在下一层生成聚焦的Self-Attention Feature Map;最后由Decoder通过一个正向过程一步生成长序列输出。通过实验验证,该方法能够有效地对发电设备的故障进行预测。

寒冷条件下高拱坝施工过程仿真方法研究

众多高拱坝施工面临着恶劣寒冷气候的考验。然而,现有的高拱坝施工过程仿真方法较少直接考虑气温对施工的影响,且未考虑基于气温预测成果进行动态分块,以分析寒冷条件下利用正温时段进行浇筑的特点。针对以上问题,本文提出了基于Informer的寒冷条件下高拱坝施工过程仿真方法。首先,提出基于Informer气温预测模型,实现未来气温序列的预测,在此基础上实现可施工时段分析;其次,建立考虑动态分块的寒冷条件下高拱坝施工进度仿真模型,提出更加贴近实际施工情况的分块施工仿真策略,以更好地模拟和分析不同气温条件下的施工过程;最后,以西南地区的叶巴滩高拱坝工程为例进行研究,采用Informer模型对气温进行预测,平均误差为±1.49℃,每日的可施工时长平均误差为±1.16 h。通过对比三种不同的仿真策略,发现寒冷条件下采用动态分块策略可以充分利用可浇筑时段,施工效率更高,也表明在仿真中“考虑气温”比简单降效处理更加贴近实际情况。

考虑多时间尺度信息的风力发电机滚动轴承故障预测

风电机组滚动轴承故障会造成风电机组长时间停机,为准确预测风电机组滚动轴承故障,提出一种考虑多时间尺度信息的风力发电机滚动轴承故障预测方法。首先,采用连续变分模式分解(successive variational mode decomposition,SVMD)自适应提取轴承健康数据温度多维特征;其次,将分解的本征模态函数(intrinsic mode functions,IMFs)输入Informer模型提取多尺度时间信息训练,基于树状结构Parzen密度估计的非标准贝叶斯优化算法(tree structure Parzen density estimation,TPE)优化Informer模型超参数;然后,构建基于残差的故障指标,采用核密度估计(kernel density estimation,KDE)确定故障预警阈值;最后,将运行数据输入训练后的Informer模型进行故障预测。选取某风电场的风力发电机轴承温度数据进行故障预测,仿真结果表明,考虑多时间尺度信息的SVMD-TPE-Informer模型在发电机轴承温度预测上具有更高的预测精度和计算效率,所提方法在两个故障案例上分别能够提前15.5 h和10 h预测到故障,且不会出现误报现象,验证所提模型的有效性和稳定性。

基于STL-Informer-BiLSTM-XGB模型的供热负荷预测

供热负荷预测是指导供热系统调控的重要手段。提高供热负荷预测精度十分重要,针对机器学习中输出目标的分解预测,提出了一种基于季节和趋势分解(seasonal and trend decomposition using loess,STL)的供热负荷预测方法,构建了适用于供热负荷预测的输出目标。首先利用STL算法将供热负荷时间序列数据分解为趋势分量、周期分量和残差分量,分别训练Informer、BiLSTM和XGB模型,将构建好的3个分量预测模型的输出叠加作为初步预测结果,分析误差序列,以BiLSTM预测误差提高模型精度,构建出STL-Informer-BiLSTM-XGB预测模型。将上述模型与常用预测模型进行对比,结果表明所构建的STL-Informer-BiLSTM-XGB模型的MAPE、MAE和MSE分别为0.871%、96.18和13202.2,预测效果最优,验证了所提出的方法具有较高的供热负荷预测精度。

基于集成学习与深度学习的洪水径流预报研究

深度学习模型凭借其对水文因素间复杂作用的优秀处理能力,在水文预报领域得到了一定的应用,然而,针对集成学习与深度学习耦合模型的研究仍有所缺失。通过融合集成学习AdaBoost算法与深度学习Informer模型,提出了一种组合模型,称为AdaBoost-Informer模型,以提高洪水径流预报的精度。该模型以历史雨量和径流数据作为数据输入,将具备长时序依赖捕获能力的Informer作为集成学习的弱预测器,使用网格搜索法进行超参数调优,使用AdaBoost集成学习算法对弱预测器进行加权组合得到强预测器。在浙江省椒江流域的应用分析表明:对比Random Forest、AdaBoost、Transformer、Informer等模型,AdaBoost-Informer模型表现最佳,RMSE为62.08 m^(3)/s,MAE为23.83 m^(3)/s,NSE为0.980,预报合格率为100%。所提模型可有效提高洪水预报精度,为防汛抢险和防洪系统调度提供决策依据。

TimeGAN-Informer长时机场能见度预测

能见度的预测对机场的业务决策、保障飞机的安全起降具有重要的意义。针对现有能见度预测模型预测时间较短的问题,提出一种基于TimeGAN Informer(Time Generative Adversarial Network-Informer)的机场能见度预测方法。利用2018—2022年气象和污染物数据,通过相关系数法和递归特征消除法提取出能见度的主要影响因素,使用TimeGAN时间序列生成对抗网络对数据进行扩充,并将Informer长时间序列预测模型应用于能见度预测。结果显示:当预测步长为1 d、2 d、3 d时,TimeGAN Informer的绝对误差(Mean Absolute Error,MAE)分别为2.42、3.13、3.57,比Informer分别降低了0.29、0.27、0.28,比长短时记忆网络(Long Short-Term Memory,LSTM)分别降低了0.28、0.49、0.63;均方根误差(Root Mean Square Error,RMSE)分别为3.03、3.7、4.09,比Informer分别降低了0.38、0.22、0.24,比长短时记忆网络(LSTM)分别降低了0.3、0.5、1.04;百分误差小于30%的分别占测试样本集的78.07%、70.68%、63.84%。尽管随着步长的增加预测效果变差,但在预测步长为3 d时,多数样本的预测误差仍小于30%,实现了对机场区域较为准确的长时能见度预测。

基于GAT-Informer的采空区煤自燃温度预测模型

现有的煤自燃温度预测模型仅考虑监测数据前后的时间关联性,未考虑监测点之间的空间关系,并存在多步长煤自燃温度预测精度低的问题。针对上述问题,提出了一种基于图注意力网络(GAT)和Informer模型(GAT-Informer)的采空区煤自燃温度预测模型。首先,采用随机森林回归法和Savitzky-Golay滤波器对采空区沿空侧煤自燃监测数据中的异常值、缺失值和噪声进行处理,并使用Z-score方法对数据进行标准化。其次,采用GAT提取多个监测点煤自燃监测数据间的空间特征。然后,使用Informer模型的编码器对包含空间特征的数据进行编码,利用多头概率稀疏自注意力机制捕捉数据之间的长期依赖关系和时间特征;解码器通过交叉注意力机制与编码器交互,结合编码器提取的全局特征与目标序列的上下文依赖关系,生成特征矩阵并输入全连接层,得到煤自燃温度预测值。最后,对Informer模型输出的煤自燃温度预测值进行反标准化处理,恢复到原始数据尺度,得到最终的预测结果。实验结果表明,相较于循环神经网络(RNN)、长短期记忆网络(LSTM)、门控循环单元(GRU)和Informer模型,GAT-Informer模型在6个监测点上预测24步长煤自燃温度时,均方误差(MSE)分别平均降低了15.70%,22.15%,25.45%,36.49%,平均绝对误差(MAE)分别平均降低了16.01%,14.60%,20.30%,26.27%,表明GAT-Informer模型能有效提高煤自燃温度多步长预测精度。

基于Informer融合模型的油田开发指标预测方法

为解决油田开发指标的预测问题,提出了一种基于物质平衡方程和Informer的融合模型。首先,通过物质平衡方程领域知识建立油田开发产量递减前后的机理模型;其次,将所建机理模型作为约束与Informer模型损失函数进行融合建立符合油田开发物理规律的指标预测模型;最后,采用油田实际生产数据进行实验分析,结果表明相比于纯数据驱动的几种循环结构预测模型,本融合模型在相同数据条件下的预测效果更优。该模型的机理约束部分能引导模型的训练过程,使其收敛速度更快,且波峰波谷处的预测更准确。该融合模型具有更好的预测能力和泛化能力和比较合理的物理可解释性。

基于KCR-Informer的长期风电功率预测研究

准确的长期风电功率预测对电网系统稳定运行至关重要,传统预测方法在处理长序列预测时效果并不理想,近期研究表明Informer模型在长序列预测领域取得良好效果。然而,该模型在捕捉数据的局部特征以及处理网络层数堆叠问题上还有待改进。文章提出一种基于卡尔曼滤波器-卷积神经网络-残差网络-Informer(Kalman filter-convolutional neural network-residual network-informer,KCR-Informer)模型的长期风电功率预测方法,首先分析气象数据对风电功率的影响,使用卡尔曼滤波器对风电气象数据进行数据平滑处理,以减轻噪声对数据的影响,然后基于Informer模型建立风电功率预测模型,根据气象数据以及历史功率数据进行长期功率预测;在此基础上,引入卷积神经网络和残差连接模块,使模型能够更好的捕捉到局部特征,同时加快模型收敛,解决模型网络退化问题。算例的结果表明,与长短期记忆网络(long short-term memory,LSTM)算法、Transformer算法、Informer算法相比,文章方法在不同预测步长下的平均绝对误差(mean absolute error,MAE)降低5.7%~30%,均方误差(mean square error,MSE)降低8.3%~35%,长期风功率预测的精度得到提升,验证了模型的有效性。

基于改进Informer的云计算资源负载预测

负载预测是云计算资源管理中的重要组成部分,准确预测云资源的使用情况可提高云平台性能及防止资源浪费,然而云计算资源使用的动态性和不确定性使得负载预测较为困难,尽管Informer在时序预测领域取得了较好的效果,但未对时间的因果依赖关系加以限制造成未来信息泄露,也未考虑网络深度的增加导致模型性能下降的问题。为解决上述问题,提出一种基于改进Informer的多步负载预测模型(Informer-DCR)。将编码器中各注意力块之间的正则卷积替换为扩张因果卷积,使深层网络中的高层能够接收更大范围的输入信息来提高模型预测精度,并保证时序预测过程的因果性。在编码器中添加残差连接,使网络中低层的输入信息直接传到后续的高层,解决了深层网络退化问题。实验结果表明,Informer-DCR模型在不同预测步长下的平均绝对误差比Informer、时间卷积网络等主流预测模型降低了8.4%~40.0%,并且在训练过程中表现出比Informer更好的收敛性。

基于Informer神经网络的锂离子电池容量退化轨迹预测

通过对锂离子电池容量退化轨迹的精确预测可以大幅提升电池材料的研究效率。针对Transformer网络在锂电池容量退化轨迹预测这种长时间序列预测任务中存在的问题,本工作采用滑动窗口策略,构建了一种基于Informer网络的锂离子电池容量退化轨迹预测方法。首先,利用滑动窗口对数据集进行划分和再拼接,便于神经网络挖掘数据序列内部的相关性;然后,根据Informer网络的周期性时间特征捕捉能力设计适用于锂电池数据的全局时间戳;最后,使用前10%容量数据通过多步滚动预测方法实现模型输出,缓解预测中的误差累积问题,进而得到完整的预测轨迹。通过选取不同的误差评价指标和训练过程中的时间开销,在美国马里兰大学提供的锂电池数据集上验证了所建立模型的准确性和训练效率,并在美国航空航天局提供的电池数据集上验证了模型的泛用性。本工作模型的预测结果与多层感知机神经网络、循环神经网络及Transformer网络模型对比,退化轨迹与真实轨迹最为拟合,且训练时间开销小,预测结果的平均绝对误差和均方根误差控制在2.57%和3.5%,验证了所提预测方法的有效性。