Modeling Tracer Flow Characteristics in Different Types of Pores: Visualization and Mathematical Modeling

Structure of porous media and fluid distribution in rocks can significantly affect the transport characteristics during the process of microscale tracer flow.To clarify the effect of micro heterogeneity on aqueous tracer transport,this paper demonstrates microscopic experiments at pore level and proposes an improved mathematical model for tracer transport.The visualization results show a faster tracer movement into movable water than it into bound water,and quicker occupancy in flowing pores than in storage pores caused by the difference of tracer velocity.Moreover,the proposed mathematical model includes the effects of bound water and flowing porosity by applying interstitial flow velocity expression.The new model also distinguishes flowing and storage pores,accounting for different tracer transport mechanisms(dispersion,diffusion and adsorption)in different types of pores.The resulting analytical solution better matches with tracer production data than the standard model.The residual sum of squares(RSS)from the new model is 0.0005,which is 100 times smaller than the RSS from the standard model.The sensitivity analysis indicates that the dispersion coefficient and flowing porosity shows a negative correlation with the tracer breakthrough time and the increasing slope,whereas the superficial velocity and bound water saturation show a positive correlation.

Experimental investigation on migration and retention mechanisms of elastic gel particles(EGPs)in pore-throats using multidimensional visualized models

Knowledge of migration and retention mechanisms of elastic gel particles(EGPs)in pore-throats is essential for the effective application of EGPs as a smart sweep improvement and profile control agent for enhanced oil recovery(EOR).The matching coefficient(defined as the ratio of particle size to pore-throat size)is used to investigate its influence on migration,retention and profile control performance of EGPs.A 1-D continuous pore-throat visualization model(PTVM),a 2-D heterogeneous PTVM and a 3-D heterogeneous core model were constructed and used to investigate pore-scale migration,retention and controlling mechanism of migration and retention characteristics on EGPs profile control.The results of the 1-D continuous PTVM indicated that while the matching coefficient was in the optimal range(i.e.,0.20-0.32),the EGPs could not only smoothly migrate to the deeper pore-throats,but also form stable retention in the pores to resist the erosion of injected water,which was conducive to the effective indepth profile control.The results of the 2-D heterogeneous PTVM verified that the sweep efficiency in low-permeability regions could be significantly improved by in-depth migration and stable retention of EGPs in the pore-throats with an optimal matching coefficient(0.29),which was much better than that in cases with a smaller matching coefficient(0.17)or an excessive matching coefficient(0.39).Moreover,the NMR displacement experiments of 3-D heterogeneous cores were carried out to simulate the EGPs profile control in actual reservoir porous media.Saturation images and T2 spectrum curves of crude oil showed that EOR in the low-permeability layer was highest(56.1%)using EGPs profile control with an optimal matching coefficient,attributing to the in-depth migration and stable retention of EGPs.

Optimized Modeling Method for Unbalanced Data in High-Level Visual Semantic Concept Classification

To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine （PPSVM） is presented. A neighborbased posterior probability estimator for visual concepts is provided. The proposed method has been applied in a high-level visual semantic concept classification system and the experiment results show that it results in enhanced performance over the baseline SVM models, as well as in improved robustness with respect to high-level visual semantic concept classification.

Educational Assistance Through Modeling in Tsukuba University of Technology-Professional Education for the Hearing or Visually Impaired

The National University Corporation Tsukuba University of Technology（NTUT） is the only institute of higher education for the hearing and the visually impaired in Japan. In our university, hearing or visually impaired students are studying to be technicians after they graduate, toward social independence. From previous experience of higher education for students with disabilities, effects are increased when modeling is used by the teachers involved in professional education. In the Mechanical Engineering Course, we are using modeling, to match the drawing and shape for beginning students. It includes support for enhancing one＇s view, and how to draw out the ability of mechanical engineering students for the basics. For students to study Mechanical Design and Drawing, Modeling of Gear Pump, Jack and Globe Valve are easily shown through drawings and the operation of each mechanism through sample drawings in the textbook. It is possible to make an opportunity to think about the machine mechanism. It will be shown by students＇ works. The assembling of the model triggers the need for form accuracy by making a function, and improves the quality of learning. It is possible that a three-dimensional molding machine can be produced through experiential learning by the model, and modeling with the dimension numerical data. Moreover, it is also embodied in a three-dimensional modeling which results in the image processing programming created. Confirming the improvement of the program through the shape with the quality. In the Department of Synthetic Design, students have chances to realize and self-evaluate from the design of the lamp shade with a complicated shape. In the Faculty of Health Science from Department of Health, high quality teaching of visually-impaired students through the use of bone model teaching materials has become possible in the medical-related courses.

Chromatic and achromatic differences of melanin-and carotenoid-based plumage coloration in five minivet species(Pericrocotus spp.)under conspecific and predator visual systems

Adaptive mate choice has been accepted as the leading theory to explain the colorful plumage of birds.This theory hypothesizes that conspicuous colors act as signals to advertise the qualities of the owners.However,a dilemma arises in that conspicuous colors may not only attract mates,but also alert predators.The"private channels of communication"hypothesis proposes that some intraspecific signals may not be visible to heterospecific animals because of different visual systems.To better understand the evolution of plumage colors and sexual selection in birds,here we studied the chromatic difference and achromatic differences of melanin-and carotenoid-based plumage coloration in five minivet species(Pericrocotus spp.)under conspecific and predator visual systems.We found that either the chromatic or achromatic difference among male or female minivets’plumage was consistently higher under conspecific vision than under predator vision for all five studied species of minivets.This result indicated that individual differences in plumage colors of minivets were visible to the conspecific receivers and hidden from potential predators as a result of evolution under predation risk and conspecific communication.However,males were under a higher risk of predation because they were more conspicuous than females to the vision of a nocturnal predator.

Dixit Player with Open CLIP

A computer vision approach through Open AI’s CLIP, a model capable of predicting text-image pairs, is used to create an AI agent for Dixit, a game which requires creative linking between images and text. This paper calculates baseline accuracies for both the ability to match the correct image to a hint and the ability to match up with human preferences. A dataset created by previous work on Dixit is used for testing. CLIP is utilized through the comparison of a hint to multiple images, and previous hints, achieving a final accuracy of 0.5011 which surpasses previous results.

A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis:Applied COVID-19 aggregated datasets

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis.However,most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation,resulting in a lack of quantitative and qualitative evidence.To address this issue,we developed a portrait-based visual modeling method called+msRNAer.This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities,enabling portrait-based exploration and comparison in epidemiological analysis.We applied+msRNAer to aggregate COVID-19-related datasets in New South Wales,Australia,combining COVID-19 case number trends,geo-information,intervention events,and expert-supervised risk factors extracted from local government area-based censuses.We perfected the+msRNAer workflow with collaborative views and evaluated its feasibility,effectiveness,and usefulness through one user study and three subject-driven case studies.Positive feedback from experts indicates that+msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical,timeline,and other factor comparisons.By adopting interactions,experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic.Experts confirmed that+msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

Unsuspected implications arising from assumptions in simulations: insights from recasting a forest growth model in system dynamics

Background:Familiarity with a simulation platform can seduce modellers into accepting untested assumptions for convenience of implementation.These assumptions may have consequences greater than commonly suspected,and it is important that modellers remain mindful of assumptions and remain diligent with sensitivity testing.Methods:Familiarity with a technique can lead to complacency,and alternative approaches and software can reveal untested assumptions.Visual modelling environments based on system dynamics may help to make critical assumptions more evident by offering an accessible visual overview and empowering a focus on representational rather than computational efficiency.This capacity is illustrated using a cohort-based forest growth model developed for mixed species forest.Results:The alternative model implementation revealed that untested assumptions in the original model could have substantial influence on simulated outcomes.Conclusions:An important implication is that modellers should remain conscious of all assumptions,consider alternative implementations that reveal assumptions more clearly,and conduct sensitivity tests to inform decisions.

Discussion on the sweep efficiency of hybrid steam-chemical process in heavy oil reservoirs: An experimental study

Non-condensable gas(NCG),foam and surfactant are the three commonly-used additives in hybrid steam-chemical processes for heavy oil reservoirs.Their application can effectively control the steam injection profile and increase the sweep efficiency.In this paper,the methods of microscale visualized experiment and macroscale 3D experiment are applied to systematically evaluate the areal and vertical sweep efficiencies of different hybrid steam-chemical processes.First,a series of static tests are performed to evaluate the effect of different additives on heavy oil properties.Then,by a series of tests on the microscale visualized model,the areal sweep efficiencies of a baseline steam flooding process and different follow-up hybrid EOR processes are obtained from the collected 2D images.Specifically,they include the hybrid steam-N_(2)process,hybrid steam-N2/foam process,hybrid steam-surfactant process and hybrid steam-N2/foam/surfactant process(N2/foam slug first and steam-surfactant co-injection then).From the results of static tests and visualized micromodels,the pore scale EOR mechanisms and the difference between them can be discussed.For the vertical sweep efficiencies,a macroscale 3D experiment of steam flooding process and a follow-up hybrid EOR process is conducted.Thereafter,combing the macroscale 3D experiment and laboratory-scaled numerical simulation,the vertical and overall sweep efficiencies of different hybrid steam-chemical processes are evaluated.Results indicate that compared with a steam flooding process,the areal sweep efficiency of a hybrid steam-N2process is lower.It is caused by the high mobility ratio in a steam-N2-heavy oil system.By contrast,the enhancement of sweep efficiency by a hybrid steam-N2/foam/surfactant process is the highest.It is because of the high resistance capacity of NCG foam system and the performance of surfactant.Specifically,a surfactant can interact with the oil film in chief zone and reduce the interfacial energy,and thus the oil droplets/films formed during steam injection stage are unlocked.For NCG foam,it can plug the chief steam flow zone and thus the subsequent injected steam is re-directed.Simultaneously,from the collected 2D images,it is also observed that the reservoir microscopic heterogeneity can have an important effect on their sweep efficiencies.From the 3D experiment and laboratory-scaled numerical simulation,it is found that a N2/foam slug can increase the thermal front angle by about 150 and increase the vertical sweep efficiency by about 26%.Among the four processes,a multiple hybrid EOR process(steam-N2/foam/surfactant process) is recommended than the other ones.This paper provides a novel method to systematically evaluate the sweep efficiency of hybrid steam-chemical process and some new insights on the mechanisms of sweep efficiency enhancement are also addressed.It can benefit the expansion of hybrid steam-chemical processes in the post steamed heavy oil reservoirs.

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.

基于物联网感知层的移动机器人自组织网动态视觉图像建模(英文)

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.

INDUSTRIAL ENGINEERING AND VISUALISATION—A PRODUCT DEVELOPMENT PERSPECTIVE

Taking the actual project of teaching and researching process for example, the relationship between the industrial engineering and product development is discussed. And use the novel visualization technology to support the industrial engineering and product development. How to use the new computer modeling and simulating technologies to support the product development and industrial engineering, is introduced especially. The support includes both domestic products and industrial systems. The visualization and computer technologies take a very impo[tant role in some system or multi-direction modeling, those technologies mentioned above can help the industrial engineers study the effect of design on the whole life circle, including the producing steps. So the engineers can avoid making the wrong decision which may cause bad effects on the whole industrial engineering.

氯碱企业数字化工厂培训平台构建与实施

氯碱生产工艺连续性强,开停车过程繁琐,自动化水平高,模拟现场实际生产情况比较困难,通过引进烧碱PVC仿真系统,加强上岗前培训工作,提高企业运转安全性。

Sensor planning method for visual tracking in 3D camera networks

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.

Effective Video Summarization Approach Based on Visual Attention

Video summarization is applied to reduce redundancy and developa concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes,the frames that stand out visually are extracted as key frames based on humanattention modeling theories. The schemes for modeling visual attention haveproven to be effective for video summaries. Nevertheless, the high cost ofcomputing in such techniques restricts their usability in everyday situations.In this context, we propose a method based on KFE (key frame extraction)technique, which is recommended based on an efficient and accurate visualattention model. The calculation effort is minimized by utilizing dynamicvisual highlighting based on the temporal gradient instead of the traditionaloptical flow techniques. In addition, an efficient technique using a discretecosine transformation is utilized for the static visual salience. The dynamic andstatic visual attention metrics are merged by means of a non-linear weightedfusion technique. Results of the system are compared with some existing stateof-the-art techniques for the betterment of accuracy. The experimental resultsof our proposed model indicate the efficiency and high standard in terms ofthe key frames extraction as output.

Natural Scene Classification Inspired by Visual Perception and Cognition Mechanisms

The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.

From neurogenesis to neuronal regeneration: the amphibian olfactory system as a model to visualize neuronal development in vivo

How do individual neurons develop and how are they in- tegrated into neuronal circuitry？ To answer this question is essential to understand how the nervous system develops and how it is maintained during the adult life. A neural stem cell must go through several stages of maturation, including proliferation, migration, differentiation, and integration, to become fully embedded to an existing neuronal circuit. The knowledge on this topic so far has come mainly from cell culture studies. Studying the development of individual neurons within intact neuronal networks in vivo is inherently difficult. Most neurons are generated form neural stem cells during embryonic and early postnatal development.

A bibliometric analysis of research on nursing models in countries other than China

Objectives＂ To deepen our understanding of the status quo and to identify the hot topics and develop- mental trends of research on nursing models in countries other than China in the most recent decade. Methods： The study subjects were the publications retrieved from the PubMed database using the MeSH terms of ＂Models, Nursing＂. Bibliographic item co-occurrence mining system （BICOMS） software was used for conventional bibliometric analysis of publications during two time periods, 2005-2009 and 2010-2014. The number of published journal articles, journal distribution, authors of publications, country of origin of journals, and language of publications were analyzed to establish a high-frequency keyword profile and co-occurrence matrix. Graphical clustering toolkit （gCLUTO） software was applied for two-way clustering analysis and visualized analysis. Results： A total of 1472 journal articles with a key theme of nursing models were retrieved for final analysis, including 771 published during 2005-2009 and 701 during 2010-2014. The bibliometric analysis revealed that publications other than China concerning nursing models were mostly concentrated in the United States and the United Kingdom and that the number of relevant publications has been continuously decreasing. The two-way clustering analysis showed that there were mainly four types of research themes in the relevant publications in countries other than China during 2005-2009, i.e., nursing education and theoretical research, clinical nursing and psychological care, nursing administration, and models of nursing education, whereas there were five types during 2010-2014, i.e., nursing theories and clinical nursing practice, nursing administration models and assessments of nurses＇ knowledge and skills, community nursing administration models, nursing human resource management, and nursing education models and approaches. Conclusions： Research on nursing models in countries other than China is relatively mature and stable with a broader view, but it has shown a declining trend in recent years. It emphasizes both theory and practice, with research content tending to be structured into four modules, i.e., nursing education, administration, clinical practice, and theoretical research. Community nursing models may become a key research direction in the international research on nursing models in the future.

Visual Modeling of Rice Root Growth Based on B-Spline Curve

As a major food production crop in China,the growth and development of rice is an extremely complex systemic process,and the root system is the main organ for rice to obtain nutrients.Therefore,3D modeling and visualization of the rice root system can help to further understand its morphology,structure and function,and provide an aid for scientific cultivation of rice and improving rice yield for decision making.In this paper,a mathematical model of the rice root system is established based on the B spline curve combined with the L-system approach,using mathematical knowledge based on the 3D morphological characteristics of the real rice root system.The B-Spline Curve is chosen to simulate this,and the recursive definition of B-Spline Curve and its formula are used to realize the modeling of the rice root system curve.Based on the mathematical method of rice root system integration,the bending effect of rice root system at different periods and different growth positions is realized.Finally,the L-system combined with B-Spline Curve is used to construct a rice root system model and realize the rice root system visualization simulation.The simulated image is closer to the real rice root system image in terms of morphological structure and has a strong sense of realism.

Modeling and Analyzing of Breast Tumor Deterioration Process with Petri Nets and Logistic Regression

It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease risk.Because the occurrence of the disease is caused by many factors,and the pathogenesis process is also complicated.It is necessary to use interpretable and visual modeling methods to characterize this complex process.Machine learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic decisions.However,in most cases,it is unexplainable.Using formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent,and can well visualize the analysis process.Coloured Petri Nets(CPN)is a powerful formal model.This paper presents a modeling approach with CPN and machine learning in breast cancer,which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of disease.By evaluating the performance of several common machine learning algorithms,we finally choose the logistic regression algorithm to analyze the data,and integrate the obtained prediction model into the CPN model.Our method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.