Visual Representation and Cognitive Models in Information Visualization

As a branch of computer science,information visualization aims to help users understand and analyze complex data through graphical interfaces and interactive technologies.Information visualization primarily includes various visual structures such as time-series structures,spatial relationship structures,statistical distribution structures,and geographic map structures,each with unique functions and application scenarios.To better explain the cognitive process of visualization,researchers have proposed various cognitive models based on interaction mechanisms,visual perception steps,and novice use of visualization.These models help understand user cognition in information visualization,enhancing the effectiveness of data analysis and decision-making.

Survey of lightweighting methods of huge 3D models for online Web3D visualization

Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of light-weighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache man-agement, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.

Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged people.Object detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so on.The test images include all these variants as well.These detection models alert them about their surroundings when they want to walk independently.This study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition applications.Based on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed.To further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the work.Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization approach.Additionally,Inception V3 can handle images of different sizes.This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

Visual Modflow在和田县农业经济新区地下水资源评价中的应用

为全面了解地下水系统受各种激励因素影响后的水动力场和地下水量变化规律,以及为当地地下水资源开发与利用提供科学的指导意见,本次在和田县农业经济新区地下水资源评价中采用可视化标准软件Visnal Modflow实现地下水资源的模拟与预报,通过构建三维非稳定流数值模型,使用迭代方法求解,并根据设计开采量对地下水水位变动做出预测,再基于建模预测结果,结合经济新区地下水资源量确定合理的地下水开采量。

Research status and application of artificial intelligence large models in the oil and gas industry

This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

基于Visual MODFLOW大水矿山深部开采矿坑涌水量预测

矿产资源的不断开发导致矿坑涌、突水现象愈加频繁,此类问题多发生于大水矿山,为更好指导大水矿山深部开采工作,准确预测矿山深部开采矿坑涌水量至关重要。结合某矿区勘察资料、水文地质条件和观测井实测水位数据,利用Visual MODFLOW软件建立矿区水文地质数值模型,进行稳态与非稳态校正以及相关参数敏感性分析;根据该矿区-150 m水平矿坑真实涌水量拟合分析Drain模块水力传导系数值,运用识别后模型对矿山深部开采矿坑涌水量预测,结果表明-200、-250、-300、-350 m等不同水平矿坑正常涌水量为17209.82、18896.20、21299.31、23494.10 m^(3)/d,预测结果可为矿山防治水工作提供依据,以及为工程安全开挖提供保障。

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.

Visual modeling reveals cryptic aspect in egg mimicry of Himalayan Cuckoo(Cuculus saturatus) on its host Blyth's Leaf Warbler(Phylloscopus reguloides)

Brood parasitism and egg mimicry of Himalayan Cuckoo（Cuculus saturatus） on its host Blyth＇s Leaf Warbler（Phylloscopus reguloides） were studied in south-western China from April to July 2009.The cuckoo laid a white egg with fine brown markings on the blunt end.The eggs were conspicuously bigger than the host＇s own,with 2.06 g in mass and 1.91 cm3 in volume.Visual modeling showed that the cuckoo eggs,which from the human eye appeared to mimic the host eggs to a great extent,were completely different from the host eggs in both hue and chroma.The characters of the Himalayan Cuckoo nestling,reported for the first time,included two triangular and black patches on its gape,which appeared from four days old and became darker with age and growth.While this character also exists in nestlings of Oriental Cuckoo（C.optatus）,it has not been found for other Cuculus species.Our results reveal cryptic aspects in the cuckoo-host egg color matching,which are not visible to the naked human eye,and indicate that high mimetic cuckoo eggs rejected by hosts,as determined by human observers in previous studies,might not be mimetic as birds see them.

Visualization of uncertainty associated with spatial prediction of continuous variables using HSI color model:a case study of prediction of pH for topsoil in peri-urban Beijing,China

Hue-Saturation-Intensity （HSI） color model, a psychologically appealing color model, was employed to visualize uncertainty represented by relative prediction error based on the case of spatial prediction of pH of topsoil in the peri-urban Beijing. A two-dimensional legend was designed to accompany the visualization-vertical axis （hues） for visualizing the predicted values and horizontal axis （whiteness） for visualizing the prediction error. Moreover, different ways of visualizing uncertainty were briefly reviewed in this paper. This case study indicated that visualization of both predictions and prediction uncertainty offered a possibility to enhance visual exploration of the data uncertainty and to compare different prediction methods or predictions of totally different variables. The whitish region of the visualization map can be simply interpreted as unsatisfactory prediction results, where may need additional samples or more suitable prediction models for a better prediction results.

Study upon Modeling and Visualization of 3D Geologic Body Based on Generalized Tri-Prism

According to the characteristics of bore data,a model of 3D geologic body with generalized tri-prism as the primitive modeling element is constructed while the modeling process and key algorithms of modeling are presented here in detail.Using this method,the original bore data go through Delaunay triangulation to generate irregular triangular network on the surface,and then links stratum segments on the adjoining bores in session to form tri-prisms which would be pinched out.Finally stratified 3D geologic body model is built by an iterated search which searches for consecutive layer of the same property.The result shows that this method can effectively simulate stratified stratum modeling.

DATA MODEL AND VISUALIZATION OF 3D CITY LANDSCAPE BASED ON INTEGRATED DATABASES

Data acquisition and modeling are the two important, difficult and costful aspects in a Cybercity project. 2D-GIS is mature and can manage a lot of spatial data. Thus 3D-GIS should make the best of data and technology of 2D-GIS. Construction of a useful synthetic environment requires integration of multiple types of information like DEM, texture images and 3D representation of objects such as buildings. In this paper, the method for 3D city landscape data model and visualization based on integrated databases is presented. Since the data volume of raster are very huge, special strategies(for example, pyramid gridded method) must be adopted in order to manage raster data efficiently. Three different methods of data acquisition, the proper data structure and a simple modeling method are presented as well. At last, a pilot project of Shanghai Cybercity is illustrated.

3D visualization model and key techniques for digital mine

Digital mine is the inevitable outcome of the information processing, and is also a complicated system engineering. Firstly, for the 3D visualization application of the digital mine, the ground and underground integrative visualization framework model was proposed based on the mine entity database. So, the visualization problem was availably resolved, as well as the professional analytical ability was improved. Secondly, aiming at the irregularities, non-uniformity, dynamics of mine entities, mix modeling method based on the entity character was put forward, in which 3D expression of mine entities was realized. Lastly, the 3D visualization project for a copper mine was experimentally studied. Satisfactory results were acquired, and the rationality of visualization model and feasibility of 3D modeling were validated.

Physical modeling and visualization of soil liquefaction under high confining stress

The mechanisms of seismically-induced liquefaction of granular soils underhigh confining stresses are still not fully understood.Evaluation of these mechanisms is generallybased on extrapolation of observed behavior at shallow depths.Three centrifuge model tests wereconducted at RPI's experimental facility to investigate the effects of confining stresses on thedynamic response of a deep horizontal deposit of saturated sand.Liquefaction was observed at highconfining stresses in each of the tests.A system identification procedure was used to estimate theassociated shear strain and stress time histories.These histories revealed a response marked byshear strength degradation and dilative patterns.The recorded accelerations and pore pressures wereemployed to generate visual animations of the models.These visualizations revealed a liquefactionfront traveling downward and leading to large shear strains and isolation of upper soil layers.

Analyzing Bandwidth on the Microwave Absorber by the Interface Reflection Model

Metallic flaky sendust particles are prepared for use as fillers in electromagnetic attenuation composites. We report the interface reflection model to divide the broad bandwidth into electromagnetic loss and quarter-wavelength （λ/4） cancelation. Combining with the face reflection calculation, we identify the electromagnetic loss originated from skin effect, which is used to explain over half of the absorbed energy in high frequency band. Most impor- tantly, the unique electromagnetic loss cannot generate the reflection loss （RL） peak. Using the phase relation of face reflection, we show evidence that the λ/4 cancelation is vital to generate the RL peak. The calculated energy loss agrees well with the experimental data and lays the foundation for further research.

Heavy metal pollution model of tailings and the pollution simulation by visualization

Based on the analysis of whole mining process in metal mines, it was pointed out that the investigation of the heavy metal pollution of tailings should be taken as an important project for a metal mine. Combined with the anlysis of the characteristics of tailings, it is found that the transformation of the heavy metal dissolution process, the heavy metal ions migration with groundwater and the heavy metal transport in porous media are three key aspects. Accordingly, the models of heavy metal pollution were established with providing boundary conditions. Depending upon a case of Ibnglushan Copper Mine railings and its relevant area from Google maps＇, a three-dimensional grid view of the tailings was set up. By application of Fluent software, the contaminated process of the heavy metal pollutants in the tailings was shown through digital visualization pattern.

Dynamic Visual Image Modeling Based on Mobile RobotSelf- Organizing Network in Internetof Things Perception Layer

The dynamic multichannel binocular visual image modeling is studied based on Internet of Things (IoT) Perception Layer, using mobile robot self-organizing network. By employing multigroup mobile robots with binocular visual system, the real visual images of the object will be obtained. Then through the mobile self-organizing network, a three-dimensional model is rebuilt by synthesizing the returned images. On this basis, we formalize a novel algorithm for multichannel binocular visual three-dimensional images based on fast three-dimensional modeling. Compared with the method based on single binocular visual system, the new algorithm can improve the Integrity and accuracy of the dynamic three-dimensional object modeling. The simulation results show that the new method can effectively accelerate the modeling speed, improve the similarity and not increase the data size.

Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Rate Control Algorithm of Wireless Video Based on Visual Saliency Map Model

In order to further improve the efficiency of video compression, we introduce a perceptual characteristics of Human Visual System (HVS) to video coding, and propose a novel video coding rate control algorithm based on human visual saliency model in H.264/AVC. Firstly, we modifie Itti's saliency model. Secondly, target bits of each frame are allocated through the correlation of saliency region between the current and previous frame, and the complexity of each MB is modified through the saliency value and its Mean Absolute Difference (MAD) value. Lastly, the algorithm was implemented in JVT JM12.2. Simulation results show that, comparing with traditional rate control algorithm, the proposed one can reduce the coding bit rate and improve the reconstructed video subjective quality, especially for visual saliency region. It is very suitable for wireless video transmission.

A virtual lock-in amplifier,spectrum analyzer,impedance meter and semiconductor analyzer implemented on an SR7265 hardware target

Lock-in amplifiers are used to detect and measure very small alternating current(AC)signals down to the range of nVs.Accurate measurements can be made even when the small signals are buried by noise thousands of times larger.With the digital signal processing(DSP)technology involved in modern instrumentation,a lock-in amplifier is more versatile in sensing and recovering small signals.Combining the virtual instrumentation technology,we reorganize the functional blocks of a programmable lock-in amplifier and build it as a virtual spectrum analyzer,virtual impedance meter,virtual network analyzer,virtual semiconductor parameter analyzer,signal generator,etc.A 4 layer model is used to implement these virtual instruments.The same virtual instrument can also be implemented on a general purpose FPGA developing board.