基于Photo Sphere Viewer的轻量化可量测街景系统研究

随着新型基础测绘体系发展,测绘技术和装备不断升级,大量街景数据随着车载激光扫描工作一并被采集。针对该数据路线随机性大、密集程度高等特点,以及传统街景地图系统插件受限、扩展性不强、无法量测等局限,本文设计了基于Photo Sphere Viewer的轻量化可量测街景系统,在开源插件的基础上实现了对其组件的扩充、实现了行进方向、地图交互等系统功能的设计优化,进一步实现了基于深度图的街景地图量测功能。生产作业队伍对系统的使用结果表明,该系统可以辅助生产作业队伍进行属性采集、纹理更新、数据检查等多项工作,操作灵活便捷,且具有较强的可扩展性,便于基于生产需求进行改进调整,提高了生产效率,为新型基础测绘成果进一步深化应用提供了良好的借鉴作用。

Application of meta-learning in cyberspace security:a survey

In recent years,machine learning has made great progress in intrusion detection,network protection,anomaly detection,and other issues in cyberspace.However,these traditional machine learning algorithms usually require a lot of data to learn and have a low recognition rate for unknown attacks.Among them,“one-shot learning”,“few-shot learning”,and“zero-shot learning”are challenges that cannot be ignored for traditional machine learning.The more intractable problem in cyberspace security is the changeable attack mode.When a new attack mode appears,there are few or even zero samples that can be learned.Meta-learning comes from imitating human problem-solving methods as humans can quickly learn unknown things based on their existing knowledge when learning.Its purpose is to quickly obtain a model with high accuracy and strong generalization through less data training.This article first divides the meta-learning model into five research directions based on different principles of use.They are model-based,metric-based,optimization-based,online-learning-based,or stacked ensemble-based.Then,the current problems in the field of cyberspace security are categorized into three branches:cyber security,information security,and artificial intelligence security according to different perspectives.Then,the application research results of various meta-learning models on these three branches are reviewed.At the same time,based on the characteristics of strong generalization,evolution,and scalability of meta-learning,we contrast and summarize its advantages in solving problems.Finally,the prospect of future deep application of meta-learning in the field of cyberspace security is summarized.

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

Crop Disease Recognition Based on Improved Model-Agnostic Meta-Learning

Currently,one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development.Therefore,further research in the field of crop disease and pest detection is necessary to address the mentioned problem.Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses,this article proposes a Model-Agnostic Meta-Learning(MAML)attention model based on the meta-learning paradigm.The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention(ECA)mod-ule.The module follows the local cross-channel interactive strategy of non-dimensional reduction to strengthen the weight parameters corresponding to certain disease characteristics.The proposed meta-learning-based algorithm has the advantage of strong generalization capability and,by integrating the ECA module in the original model,can achieve more efficient detection in new tasks.The proposed model is verified by experiments,and the experimental results show that compared with the original MAML model,the proposed improved MAML-Attention model has a better performance by 1.8–9.31 percentage points in different classification tasks;the maximum accuracy is increased by 1.15–8.2 percentage points.The experimental results verify the strong generalization ability and good robustness of the proposed MAML-Attention model.Compared to the other few-shot methods,the proposed MAML-Attention performs better.

A Meta-Learning Approach for Aircraft Trajectory Prediction

The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short- to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.

MetaPINNs:Predicting soliton and rogue wave of nonlinear PDEs via the improved physics-informed neural networks based on meta-learned optimization

Efficiently solving partial differential equations(PDEs)is a long-standing challenge in mathematics and physics research.In recent years,the rapid development of artificial intelligence technology has brought deep learning-based methods to the forefront of research on numerical methods for partial differential equations.Among them,physics-informed neural networks(PINNs)are a new class of deep learning methods that show great potential in solving PDEs and predicting complex physical phenomena.In the field of nonlinear science,solitary waves and rogue waves have been important research topics.In this paper,we propose an improved PINN that enhances the physical constraints of the neural network model by adding gradient information constraints.In addition,we employ meta-learning optimization to speed up the training process.We apply the improved PINNs to the numerical simulation and prediction of solitary and rogue waves.We evaluate the accuracy of the prediction results by error analysis.The experimental results show that the improved PINNs can make more accurate predictions in less time than that of the original PINNs.

A Novel Deep Model with Meta-Learning for Rolling Bearing Few-Shot Fault Diagnosis

Machine learning,especially deep learning,has been highly successful in data-intensive applications;however,the performance of these models will drop significantly when the amount of the training data amount does not meet the requirement.This leads to the so-called few-shot learning(FSL)problem,which requires the model rapidly generalize to new tasks that containing only a few labeled samples.In this paper,we proposed a new deep model,called deep convolutional meta-learning networks,to address the low performance of generalization under limited data for bearing fault diagnosis.The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data.The proposed method was compared to several FSL methods,including methods with and without pre-training the embedding mapping,and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain.The comparisons are carried out on 1-shot and 10-shot tasks using the Case Western Reserve University bearing dataset and a cylindrical roller bearing dataset.The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions.In addition,we found that the pretraining process does not always improve the prediction accuracy.

Non-contact wide-field viewing system-assisted scleral buckling surgery for retinal detachment in silicone oilfilled eyes

AIM:To evaluate scleral buckling(SB)surgery using a noncontact wide-field viewing system and 23-gauge intraocular illumination for the treatment of rhegmatogenous retinal detachment in silicone oil(SO)-filled eyes.METHODS:Totally 9 patients(9 eyes)with retinal detachment in SO-filled eyes were retrospectively analyzed.All patients underwent non-contact wide-field viewing system-assisted buckling surgery with 23-gauge intraocular illumination.SO was removed at an appropriate time based on recovery.The patients were followed up for at least 3mo after SO removal.Retinal reattachment,complications,visual acuity and intraocular pressure(IOP)before and after surgery were observed.RESULTS:Patients were followed up for a mean of 8.22mo(3-22mo)after SO removal.All patients had retinal reattachment.At the final follow-up,visual acuity showed improvement for 8 patients,and no change for 1 patient.The IOP was high in 3 patients before surgery,but it stabilized after treatment;it was not affected in the other patients.None of the patients had infections,hemorrhage,anterior ischemia,or any other complication.CONCLUSION:This new non-contact wide-field viewing system-assisted SB surgery with 23-gauge intraocular illumination is effective and safe for retinal detachment in SO-filled eyes.

The Youth’s View of Marriage in Chinese Mainstream Media Discourse-A Corpus-assisted Three-dimensional Discourse Analysis

The rising of aging and the declining of birth rates have forced the public to focus on the youth’s view on marriage.Based on critical discourse analysis and combined with Fairclough’s three-dimensional discourse analysis model,this paper builds a“Chinese media News Report Corpus on the topic of‘marriage’”whose news are collected from China Daily.It is found that the discourses are neutral and objective with regard to the advantages and disadvantages of marriage,but in general,it is still a traditional view of marriage that is inevitable and closely related to fertility.Although this is controlled by the policies and the social reasons including declining fertility rate,it deviates from the current view of the youth towards marriage,resulting in many serious consequences such as young people’s rejection.In addition,this research found that male and female have great differences in their views on marriage,and men’s resistance to marriage is far greater than that of women,which is departure from the public’s cognition.The reasons behind this need to be explored in order to solve the marriage and love problems of young people in today’s era and realize the healthy development of young marriage.

探究新课标下高中英语读思课中Viewing技能与学科素养的融合策略

本文率先介绍了Viewing渗透下高中英语阅读中的核心素养,再精准找出Viewing在高中英语读思课中的运用模块,然后全面探究出阅读思维和Viewing技能融合的有效策略,内容包含搭建多模态互动场景、丰富语言教学活动、科学运用思维导图及拓展英语阅读资源等,更好地培养学生的学习能力、语言能力、思维品质、文化意识与知识扩展能力,增强学生英语阅读综合素质。

Low-Rank Multi-View Subspace Clustering Based on Sparse Regularization

Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.

跨学科视野下对Point of View的重新界定

在西方叙事作品研究界,“point of view”自20世纪初以来一直是一个核心概念和热门话题。叙事学和文体学都十分关注这一话题。表面上看,两个学派对“point of view”进行了大致相同的探讨,而实际上两者在理念和路径上各行其道且相互排斥。叙事学界在探讨“point of view”时,将其囿于观察者的感知(区分“谁说”和“谁感知”),这种自我限制不合情理。与此相对照,文体学在探讨“point of view”时,聚焦于叙述者(说话者)的立场态度,这种自我限制也造成了很大问题。这两个学派在探讨“point of view”时各自的片面性以及相互之间的排他性还导致了不少理论上的混乱。这也对国内的研究造成了影响。本文在指出以往问题的基础上,对“point of view”提出切合实际的新的界定,旨在通过这一新的界定帮助推进理论探讨的发展并促进跨学科研究,以便对叙事作品进行更好、更为全面的分析。

Relationships among achievement motivation,meta-learning capacity and creativity tendencies among Chinese baccalaureate nursing students

capacity and creativity tendencies among Chinese baccalaureate nursing students.Design:Cross-sectional study design.Methods:A convenient sample of 445 baccalaureate nursing students were surveyed in two universities in Tianjin,China.Students completed a questionnaire that included their demographic information,Achievement Motivation Scale,Meta-Learning Capacity Questionnaire,and Creativity Tendencies Scale.Pearson correlation was performed to test the correlation among achievement motivation,meta-learning capacity and creativity tendencies.Hierarchical linear regression analyses were performed to explore the mediating role of meta-learning capacity.Results:The participants had moderate levels of achievement motivation(mean score 2.383±0.240)and meta-learning capacity(mean score 1.505±0.241)and a medium-high level of creativity tendency(mean score 1.841±0.288).Creativity tendencies was significantly associated with both achievement motivation and meta-learning capacity(both P<0.01).Furthermore,meta-learning capacity mediated the relationship between achievement motivation and high creativity tendencies.Conclusion:The study hypotheses were supported.Higher achievement motivation,and meta-learning capacity can increase creativity tendencies of baccalaureate nursing students,and meta-learning capacity was found to mediate the relationship between achievement motivation and creativity tendencies.Nursing educators should pay attention to the positive role of meta-learning capacity in nursing students’learning,and make them more confident when they finish their studies.

3D view序列联合压缩感知技术精准诊断踝周韧带损伤

目的应用3 Dview序列联合压缩感知(Compressed Sensing,CS)技术观察踝关节扭伤患者韧带损伤情况,探讨3D view序列与常规2D序列相比能否提高诊断效能。方法前瞻性分析2021年2月至2021年11月来我院急诊就诊的踝关节急性扭伤患者共26例(26个踝关节)。上述受试均行MRI检查,扫描序列包括常规2D及3D view T2联合压缩感知序列。应用Mann-Whitney U检验评价常规序列及3D view序列的扫描时间、跟腱的信噪比及对比噪声比及评估患者在两序列下腓距前韧带、腓跟韧带、三角韧带、分歧韧带的损伤程度比较有无区别。应用Fisher确切概率法比较两序列对腓距前韧带、腓跟韧带、三角韧带、分歧韧带损伤的检出率有无差别。结果共纳入踝关节扭伤患者26例,3D view+CS序列的扫描时间为376.2±0s,略短于常规序列(381.479±7.3s),两者无明显差别(Z=-1.396,P=0.163)。3D view序列跟腱信噪比明显高于2D常规序列(Z=-3.441,P=0.001);对比噪声比明显高于2D常规序列(Z=-6.094,P<0.01);跟腓韧带和分歧韧带的损伤程度明显高于2D常规序列(Z=-2.67,P=0.008;Z=-2.67,P<0.001)。两序列对于距腓前韧带和内侧三角韧带的损伤程度差异无统计学差别(P<0.05)。3D View+CS序列对分歧韧带损伤的检出率明显高于2D常规序列(P<0.001)。3D View+CS序列较2D常规序列对距腓前韧带、跟腓韧带、内侧三角韧带损伤的检出率无明显差别(P>0.05)。结论在3D View+CS序列与常规2D序列扫描时间相当的情况下,3D View+CS序列图像质量较常规2D序列更高,踝周韧带的检出率更高,并更准确地评估韧带损伤程度。

Smoother manifold for graph meta-learning

Meta-learning provides a framework for the possibility of mimicking artificial intelligence.How-ever,data distribution of the training set fails to be consistent with the one of the testing set as the limited domain differences among them.These factors often result in poor generalization in existing meta-learning models.In this work,a novel smoother manifold for graph meta-learning(SGML)is proposed,which derives the similarity parameters of node features from the relationship between nodes and edges in the graph structure,and then utilizes the similarity parameters to yield smoother manifold through embedded propagation module.Smoother manifold can naturally filter out noise from the most important components when generalizing the local mapping relationship to the global.Besides suiting for generalizing on unseen low data issues,the framework is capable to easily perform transductive inference.Experimental results on MiniImageNet and TieredImageNet consistently show that applying SGML to supervised and semi-supervised classification can improve the performance in reducing the noise of domain shift representation.

Meta-Learning of Evolutionary Strategy for Stock Trading

Meta-learning algorithms learn about the learning process itself so it can speed up subsequent similar learning tasks with fewer data and iterations. If achieved, these benefits expand the flexibility of traditional machine learning to areas where there are small windows of time or data available. One such area is stock trading, where the relevance of data decreases as time passes, requiring fast results on fewer data points to respond to fast-changing market trends. We, to the best of our knowledge, are the first to apply meta-learning algorithms to an evolutionary strategy for stock trading to decrease learning time by using fewer iterations and to achieve higher trading profits with fewer data points. We found that our meta-learning approach to stock trading earns profits similar to a purely evolutionary algorithm. However, it only requires 50 iterations during test, versus thousands that are typically required without meta-learning, or 50% of the training data during test.

Integral imaging-based tabletop light field 3D display with large viewing angle

Light field 3D display technology is considered a revolutionary technology to address the critical visual fatigue issues in the existing 3D displays.Tabletop light field 3D display provides a brand-new display form that satisfies multi-user shared viewing and collaborative works,and it is poised to become a potential alternative to the traditional wall and portable display forms.However,a large radial viewing angle and correct radial perspective and parallax are still out of reach for most current tabletop light field 3D displays due to the limited amount of spatial information.To address the viewing angle and perspective issues,a novel integral imaging-based tabletop light field 3D display with a simple flat-panel structure is proposed and developed by applying a compound lens array,two spliced 8K liquid crystal display panels,and a light shaping diffuser screen.The compound lens array is designed to be composed of multiple three-piece compound lens units by employing a reverse design scheme,which greatly extends the radial viewing angle in the case of a limited amount of spatial information and balances other important 3D display parameters.The proposed display has a radial viewing angle of 68.7°in a large display size of 43.5 inches,which is larger than the conventional tabletop light field 3D displays.The radial perspective and parallax are correct,and high-resolution 3D images can be reproduced in large radial viewing positions.We envision that this proposed display opens up possibility for redefining the display forms of consumer electronics.

Speckle structured illumination endoscopy with enhanced resolution at wide field of view and depth of field

Structured illumination microscopy(SIM)is one of the most widely applied wide field super resolution imaging techniques with high temporal resolution and low phototoxicity.The spatial resolution of SIM is typically limited to two times of the diffraction limit and the depth of field is small.In this work,we propose and experimentally demonstrate a low cost,easy to implement,novel technique called speckle structured illumination endoscopy(SSIE)to enhance the resolution of a wide field endoscope with large depth of field.Here,speckle patterns are used to excite objects on the sample which is then followed by a blind-SIM algorithm for super resolution image reconstruction.Our approach is insensitive to the 3D morphology of the specimen,or the deformation of illuminations used.It greatly simplifies the experimental setup as there are no calibration protocols and no stringent control of illumination patterns nor focusing optics.We demonstrate that the SSIE can enhance the resolution 2–4.5 times that of a standard white light endoscopic(WLE)system.The SSIE presents a unique route to super resolution in endoscopic imaging at wide field of view and depth of field,which might be beneficial to the practice of clinical endoscopy.

Rough Set Assisted Meta-Learning Method to Select Learning Algorithms

In this paper,we propose a Rough Set assisted Meta-Learning method on how to select the most-suited machine-learning algorithms with minimal effort for a new given dataset. A k-Nearest Neighbor (k-NN) algorithm is used to recognize the most similar datasets that have been performed by all of the candidate algorithms.By matching the most similar datasets we found,the corresponding performance of the candidate algorithms is used to generate recommendation to the user.The performance derives from a multi-criteria evaluation measure-ARR,which contains both accuracy and time.Furthermore,after applying Rough Set theory,we can find the redundant properties of the dataset.Thus,we can speed up the ranking process and increase the accuracy by using the reduct of the meta attributes.

基于Lab VIEW的地下管道挖掘机远程状态监测探讨

针对地下管道挖掘机远程状态监测精准度低的弊端,研究基于Lab VIEW的地下管道挖掘机远程状态监测方法。设计远程监测服务机制,在挖掘机库函数中设置数据采集卡功能;添加有关挖掘机故障的编程信息,使口令与动作程序间相关联,建立调用关联函数,用于后续监测数据完成目标特征对比。设计周期性函数,运用Lab VIEW程序自动监测地下管道挖掘机中存在符合畸变成分的信号,提取信号特性,完成精准监测。实验数据证明,所提方法远程监测精准度高,具有一定实用价值。