Science Mapping:A Systematic Review of the Literature

Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach： We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain＇s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings： The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages： conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations： The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.Practical implications： The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.Originality/value： Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.

Information visualization analysis on Advances in Psychological Science:1983-2014

Objective： To describe the revolution and research status of Advances in Psychological Science. Methods： A total of 3060 articles published in Advances in Psychological Science from 1983 to 2014 were analyzed with the information visualization method using Citespace software from the aspects of pub- lications, cited frequency and downloads, funding, organizations, authors and keywords. Results： The results showed that the amount of literature published annually had an upward tendency, and 49.4% of the papers were supported by national or provincial projects. Institutions such as the Chinese Academy of Sciences （CAS） and the normal universities were rated in the forefront of the sci- entific research output. Xiting Huang, Hong Li and Yuejia Luo were at the top of the list of prolific authors. Conclusions： A new pattern of cooperative development of the theory and application in the field of psychological research is forming.

Self-similarity Based Editing of 3D Surface Textures Using Height and Albedo Maps

This paper presents an inexpensive method for self-similarity based editing of real-world 3D surface textures by using height and albedo maps. Unlike self-similarity based 2D texture editing approaches which only make changes to pixel color or inten- sity values, this technique also allows surface geometry and reflectance of the captured 3D surface textures to be edited and relit us- ing illumination conditions and viewing angles that differ from those of the original. A single editing operation at a given location affects all similar areas and produces changes on all images of the sample rendered under different conditions. Since surface height and albedo maps can be used to describe seabed topography and geologic features, which play important roles in many oceanic proc- esses, the proposed method can be effectively employed in applications regarding visualization and simulation of oceanic phenom- ena.

Bibliometrics study on the Journal of American College Health： 1994-2014

Objective： To help readers around the world comprehensively understand the development of the journal and evolution of cooperation study, we employed a bibliometrics analysis for the Journal of American College Health. Methods： One-thousand-one-hundred-forty-three articles published in this journal from 1994 were analyzed using the bibliometrics and visualization software CiteSpace. Results： The annual number of published articles and cited studies increased. The published studies by RP Keeling and H Wechsler were at the forefront. ＂College student＂ and ＂alcohol＂ were prevalent key- words. University of Wisconsin and Harvard University were the institutional leaders of contributions. Conclusions： This journal provides an important platform for sharing research achievements and promoting cooperation in this field. The level of articles published is continually improving. A research cooperative network promoted by famous scholars and institutions is developing. However, crossregional and international cooperation is relatively limited.

High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

Purified terephthalic acid(PTA) is an important chemical raw material. P-xylene(PX) is transformed to terephthalic acid(TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to improve the product quality, as well as to visualize the fault type clearly, a fault diagnosis method based on selforganizing map(SOM) and high dimensional feature extraction method, local tangent space alignment(LTSA),is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously,and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process indicate that the LTSA–SOM can well detect and visualize the fault type.

Deep Stacked Ensemble Learning Model for COVID-19 Classification

COVID-19 is a growing problem worldwide with a high mortality rate.As a result,the World Health Organization(WHO)declared it a pandemic.In order to limit the spread of the disease,a fast and accurate diagnosis is required.A reverse transcript polymerase chain reaction(RT-PCR)test is often used to detect the disease.However,since this test is time-consuming,a chest computed tomography(CT)or plain chest X-ray(CXR)is sometimes indicated.The value of automated diagnosis is that it saves time and money by minimizing human effort.Three significant contributions are made by our research.Its initial purpose is to use the essential finetuning methodology to test the action and efficiency of a variety of vision models,ranging from Inception to Neural Architecture Search(NAS)networks.Second,by plotting class activationmaps(CAMs)for individual networks and assessing classification efficiency with AUC-ROC curves,the behavior of these models is visually analyzed.Finally,stacked ensembles techniques were used to provide greater generalization by combining finetuned models with six ensemble neural networks.Using stacked ensembles,the generalization of the models improved.Furthermore,the ensemble model created by combining all of the finetuned networks obtained a state-of-the-art COVID-19 accuracy detection score of 99.17%.The precision and recall rates were 99.99%and 89.79%,respectively,highlighting the robustness of stacked ensembles.The proposed ensemble approach performed well in the classification of the COVID-19 lesions on CXR according to the experimental results.

A bibliometric study of the Journal of School Health:1965-2014

Background：As an important international journal in the field of school health,the Journal of School Health has drawn wide attention from researchers and readers around the world.Therefore,it is important to conduct a systematic retrospective study of the journal.With the aim of understanding the development of the journal and the evolutionary process of cooperative study of this field comprehensively,we employed bibliometric analysis using the articles published in the Journal of School Health from 1965.Methods：Using bibliometrics,5242 articles published in the journal were extracted and then analyzed using the visualization software CiteSpace Ⅲ.Results：The annual published amount of literature showed a declining tendency;however,the frequency of citation displayed an increase year by year.Among prolific authors,the number of reports published by JH Price,L Kann and RJ McDermott are at the top.Among the high frequency keywords used in the research journal, ＂adolescents＂, ＂children＂ and ＂programs＂ have become popular in the journal＇s vocabulary.CDCP,Univ Texas and Univ Calif are positioned in the forefront of the involved institutions when ranked by degree of contribution.Conclusions：The Journal of School Health provides an important platform for sharing research achievements and promoting cooperation in this field.The amount of articles published in the journal is continually improving;its cooperative research network promoted by famous scholars and institutions is forming.As more researchers and institutions join,the network will grow and relationships will become increasingly close.However,limitations to cooperation at the regional or interagency scope remain.

Benchmarking visual SLAM methods in mirror environments

Visual simultaneous localisation and mapping(vSLAM)finds applications for indoor and outdoor navigation that routinely subjects it to visual complexities,particularly mirror reflections.The effect of mirror presence(time visible and its average size in the frame)was hypothesised to impact localisation and mapping performance,with systems using direct techniques expected to perform worse.Thus,a dataset,MirrEnv,of image sequences recorded in mirror environments,was collected,and used to evaluate the performance of existing representative methods.RGBD ORB-SLAM3 and BundleFusion appear to show moderate degradation of absolute trajectory error with increasing mirror duration,whilst the remaining results did not show significantly degraded localisation performance.The mesh maps generated proved to be very inaccurate,with real and virtual reflections colliding in the reconstructions.A discussion is given of the likely sources of error and robustness in mirror environments,outlining future directions for validating and improving vSLAM performance in the presence of planar mirrors.The MirrEnv dataset is available at https://doi.org/10.17035/d.2023.0292477898.

基于深度学习的室内动态场景下视觉SLAM技术研究

视觉同步定位与建图(visual simultaneous localization and mapping,VSLAM)技术是近年来机器人和计算机视觉领域的重点研究方向之一,但当前的主流算法主要面向静态环境,当场景中存在运动的物体时,算法的定位精度和稳定性会受到很大影响。为了解决上述问题,提出了一种惯性测量单元(inertial measurement unit,IMU)积分与YOLOv4语义分割结合的VSLAM前端动态特征点剔除算法,通过YOLOv4网络对图像进行语义分割,识别图像中有运动可能的物体;再将IMU积分与语义分割结合,对目标检测框内有运动可能的特征点进行重投影误差的解算,识别并剔除环境中运动的特征点。在TUM Visual-Inertial Dataset上验证该算法,结果表明,在包含运动物体的室内场景下,该算法可以有效剔除环境中的运动物体,显著提升SLAM系统的定位精度和稳定性。

Approach for improved development of advanced driver assistance systems for future smart mobility concepts

To use the benefits of Advanced Driver Assistance Systems(ADAS)-Tests in simulation and reality a new approach for using Augmented Reality(AR)in an automotive vehicle for testing ADAS is presented in this paper.Our procedure provides a link between simulation and reality and should enable a faster development process for future increasingly complex ADAS tests and future mobility solutions.Test fields for ADAS offer a small number of orientation points.Furthermore,these must be detected and processed at high vehicle speeds.That requires high computational power both for developing our method and its subsequent use in testing.Using image segmentation(IS),artificial intelligence(AI)for object recognition,and visual simultaneous localization and mapping(vSLAM),we aim to create a three-dimensional model with accurate information about the test site.It is expected that using AI and IS will significantly improve performance as computational speed and accuracy for AR applications in automobiles.

RB-SLAM:visual SLAM based on rotated BEBLID feature point description

The extraction and description of image features are very important for visual simultaneous localization and mapping(V-SLAM).A rotated boosted efficient binary local image descriptor(BEBLID)SLAM(RB-SLAM)algorithm based on improved oriented fast and rotated brief(ORB)feature description is proposed in this paper,which can solve the problems of low localization accuracy and time efficiency of the current ORB-SLAM3 algorithm.Firstly,it uses the BEBLID to replace the feature point description algorithm of the original ORB to enhance the expressiveness and description efficiency of the image.Secondly,it adds rotational invariance to the BEBLID using the orientation information of the feature points.It also selects the rotationally stable bits in the BEBLID to further enhance the rotational invariance of the BEBLID.Finally,it retrains the binary visual dictionary based on the BEBLID to reduce the cumulative error of V-SLAM and improve the loading speed of the visual dictionary.Experiments show that the dictionary loading efficiency is improved by more than 10 times.The RB-SLAM algorithm improves the trajectory accuracy by 24.75%on the TUM dataset and 26.25%on the EuRoC dataset compared to the ORB-SLAM3 algorithm.

A geocomputational analysis of Twitter activity around different world cities

The penetration and use of social media services differs from city to city.This paper is aimed to provide a comparison of the use of Twitter between different cities of the world.We present a temporal analysis of activity on Twitter in 15 cities.Our study consists of two parts:First,we created temporal graphs of the activity in the 15 cities,through which hours of high and low activity could be identified.Second,we created heat map visualizations of the Twitter activities during the period of 19 September 2012–25 September 2013.The heat map visualizations make the periods of intense and sparse activity apparent and provide a snapshot of the activity during the whole year.

Bearing-only Visual SLAM for Small Unmanned Aerial Vehicles in GPS-denied Environments

This paper presents a hierarchical simultaneous localization and mapping（SLAM） system for a small unmanned aerial vehicle（UAV） using the output of an inertial measurement unit（IMU） and the bearing-only observations from an onboard monocular camera.A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match.This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter（EKF） for attitude and velocity estimation.Then,another EKF is employed to estimate the position of the vehicle and the locations of the features in the map.Both simulations and experiments are carried out to test the performance of the proposed system.The result of the comparison with the referential global positioning system/inertial navigation system（GPS/INS） navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.

Real-time Visual Odometry Estimation Based on Principal Direction Detection on Ceiling Vision

In this paper,we present a novel algorithm for odometry estimation based on ceiling vision.The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error accumulation problem in most visual odometry estimation approaches.The principal direction is defned based on the fact that our ceiling is flled with artifcial vertical and horizontal lines which can be used as reference for the current robot s heading direction.The proposed approach can be operated in real-time and it performs well even with camera s disturbance.A moving low-cost RGB-D camera（Kinect）,mounted on a robot,is used to continuously acquire point clouds.Iterative closest point（ICP） is the common way to estimate the current camera position by registering the currently captured point cloud to the previous one.However,its performance sufers from data association problem or it requires pre-alignment information.The performance of the proposed principal direction detection approach does not rely on data association knowledge.Using this method,two point clouds are properly pre-aligned.Hence,we can use ICP to fne-tune the transformation parameters and minimize registration error.Experimental results demonstrate the performance and stability of the proposed system under disturbance in real-time.Several indoor tests are carried out to show that the proposed visual odometry estimation method can help to signifcantly improve the accuracy of simultaneous localization and mapping（SLAM）.