Main Modeling Tools Research Articles

Functional incremental least square regression algorithm

Functional linear regression is one of the main modeling tools for working with functional data. Since functional data are usually stream data essentially and there are some noises in functional data. Many numerical research studies of machine learning indicate that the noise samples not only increase the amount of storage space, but also affect the performance of algorithm. Therefore, in this paper we consider a new learning strategy by introducing incremental learning, Markov sampling for functional linear regression and propose a novel functional incremental linear square regression algorithm based on Markov sampling (FILSR-MS). To have a better understanding of the proposed FILSR-MS, we not only estimate the generalization bound of the proposed algorithm and establish the fast learning rate, but also present some useful discussions. The performance of the proposed algorithm is validated by the numerical experiments for benchmark repository.

Integration of Natural Scenery in Chinese Landscape Painting and Application of 3D Modeling Technology

Abstract We determine the content of natural scenery material in landscape painting based on the artistic characteristics and techniques of traditional Chinese landscape paintings. The 3Dmax software in 3D modeling technology is selected as the main modeling tool, and the construction of the 3D scene of landscape painting is completed based on the IFS theory (Iterative Function System Theory), and then the scene is rendered and texture synthesized, and the virtual landscape mood space is displayed, and the fusion of natural scenery and 3D modeling technology is finally realized. The analysis of the research data reveals the integration of natural scenery and 3D modeling technology. The results show that the iterative function system quickly generates the natural scenery scene in the landscape painting by continuously regulating, changing, and controlling the parameters in the affine transformation (the influence factor of each branch is 0.9, 0.7, 0.4, and 0.15). While the static rendering speed of 47 is slower than the dynamic rendering speed of 34, the overall rendering speed still meets the real-time detection criteria, confirming that 3Dmax is capable of achieving dynamic real-time rendering of 3D mountain models. The regression coefficient for high efficiency is 0.208, with a significance level of 0.005, indicating a significant positive impact on user experience. The higher the efficiency, the better the user experience, revealing the mechanism behind the interactive 3D scene. This study promotes the integration of natural scenery and 3D modeling technology to further develop the digital research of Chinese landscape painting.

BIM to BEM for Building Energy Analysis: A Review of Interoperability Strategies

The main objective of this review is to summarize and thoroughly investigate the most popular and promising BIM (building information modeling) and BEM (building energy modeling) interoperability strategies employed in the last years (2004–2023), highlighting pros and cons of each strategy and trying to understand the reason for the still limited BIM–BEM interaction. This review summarizes the main countries, areas, modeling tools, and interoperability strategies, with the advantages and disadvantages of each one. The methodology is based on the PRISMA protocol, and two databases were used for the research: Scopus and Google Scholar. A total of 532 publications were selected and 100 papers were deemed useful for the purposes of this review. The main findings led to the identification of four different interoperability strategies between BIM and BEM tools: (1) real-time connection; (2) standardized exchange formats and middleware corrective tools; (3) adherence to model view definitions; (4) proprietary tool-chain. These strategies were found to be characterized by different degrees of complexity, time required for information exchange, proprietary or opensource software, ability to reduce information loss, and detailed energy results. The results of this study showed that, to date, there is no better interoperability strategy, and that further efforts are needed so that interoperability between the two tools can become commonplace.

Adiabatic Flame Temperatures for Oxy-Methane, Oxy-Hydrogen, Air-Methane, and Air-Hydrogen Stoichiometric Combustion using the NASA CEARUN Tool, GRI-Mech 3.0 Reaction Mechanism, and Cantera Python Package

The Adiabatic Flame Temperature (AFT) in combustion represents the maximum attainable temperature at which the chemical energy in the reactant fuel is converted into sensible heat in combustion products without heat loss. AFT depends on the fuel, oxidizer, and chemical composition of the products. Computing AFT requires solving either a nonlinear equation or a larger minimization problem. This study obtained the AFTs for oxy-methane (methane and oxygen), oxy-hydrogen (hydrogen and oxygen), air-methane (methane and air), and air-hydrogen (hydrogen and air) for stoichiometric conditions. The reactant temperature was 298.15 K (25°C), and the pressure was kept constant at 1 atm. Two reaction mechanisms were attempted: a global single-step irreversible reaction for complete combustion and the GRI-Mech 3.0 elementary mechanism (53 species, 325 steps) for chemical equilibrium with its associated thermodynamic data. NASA CEARUN was the main modeling tool used. Two other tools were used for benchmarking: an Excel and a Cantera-Python implementation of GRI-Mech 3.0. The results showed that the AFTs for oxy-methane were 5,166.47 K (complete combustion) and 3,050.12 K (chemical equilibrium), and dropped to 2,326.35 K and 2,224.25 K for air-methane, respectively. The AFTs for oxy-hydrogen were 4,930.56 K (complete combustion) and 3,074.51 K (chemical equilibrium), and dropped to 2,520.33 K and 2,378.62 K for air-hydrogen, respectively. For eight combustion modeling cases, the relative deviation between the AFTs predicted by CEARUN and GRI-Mech 3.0 ranged from 0.064% to 3.503%.

A Novel Mirror Neuron Inspired Decision-Making Architecture for Human–Robot Interaction

Inspired by the role of mirror neurons and the importance of predictions in joint action, a novel decision-making structure is proposed, designed and tested for both individual and dyadic action. The structure comprises models representing individual decision policies, policy integration layer(s), and a negotiation layer. The latter is introduced to prevent and resolve conflicts among individuals through internal simulation rather than via explicit agent-agent communication. As the main modelling tool, Dynamic Neural Fields (DNFs) were chosen. Data was captured from human-human experiments with a decision-making task performed by either one or two participants. The task involves choosing and picking blocks one by one from seven wooden blocks to create an alpha/numeric character on a 7-segment. The task is designed to be as generic as possible. Recorded hand and blocks movements were used for developing DNF-based models by optimising parameters using a genetic algorithm. Results show that decision policies can be modelled and integrated with acceptable accuracy for individual performances. In the dyadic experiment, using only individual models without the negotiation layer, the model failed to resolve conflicts. However, with the implementation of a negotiation layer, this problem could be overcome. The proposed decision-making structure based on DNFs is developed and tested for a simple pick-and-place task. However, the main primitive underlying action of this task, pick-and-place, is indeed part of many more complex tasks people perform in their day-to-day life. Paired with the possibility to gradually evolve the architecture by adding new policies on demand, the architecture provides a general framework for modelling decision-making in joint action tasks.

Analysis of Bank Credit Risk Evaluation Model Based on BP Neural Network.

Commercial banks are of great value to social and economic development. Therefore, how to accurately evaluate their credit risk and establish a credit risk prevention system has important theoretical and practical significance. This paper combines BP neural network with a mutation genetic algorithm, focuses on the credit risk assessment of commercial banks, applies neural network as the main modeling tool of the credit risk assessment of commercial banks, and uses the mutation genetic algorithm to optimize the main parameter combination of neural network, so as to give better play to the efficiency of neural network. After verification of various evaluation models, the accuracy of the evaluation model designed in this paper is more than 65%, while the acceptability of the evaluation results optimized by the mutation genetic algorithm is more than 85%. Compared with the accuracy of about 50% of the traditional credit scoring method, the accuracy of the credit risk evaluation using neural network technology is improved by more than 10%. It is proved that the performance of the optimized algorithm is better than that of the traditional neural network algorithm. It has important theoretical and practical significance for the establishment of the credit risk prevention system of commercial banks.

Formal grammar methodology for digital visualization of Islamic geometric patterns

In the oriental practice of art and architecture, and among the regions under their influence, Islamic geometric patterns (IGPs) have been widely used, not only due to aesthetics and decoration but also to make it possible to cover wide flat surfaces, curved surface of domes, and perforated surfaces of window and partitions, with perfectly tessellated shapes. However, with advances in time and technology, these techniques could not connect to the new technologies and benefit from the capacities of digitalization. Recent progress in science and technology tends to open new doors to study geometrical patterns by digitalizing the old ones and developing new variations. This study looks at formal grammar and computer science to introduce a new approach to digital visualization of available IGPs, particularly, star patterns. We investigate the potentials of developing a re-writing system for simulation of IGPs to provide a flexible platform, which allows introducing IGP to CAD/CAM software without previous knowledge on their design or drawing techniques. This methodology allows designers to directly develop various scenarios of IGP applications and implement them on related CAD/CAM tools. Formal language and grammar theories, based on applied mathematics are contributing to the advancements of computer science and digital modeling. They can provide an opportunity to express relational definition and written equivalents of the geometries by using strings and symbols. It is supposed that by using the formal grammar frameworks, certain languages could be developed to visualize IGPs in a machine-friendly way, and consequently, this computational interpretation of IGPs facilitates their application and further developments, for example, regards to digital fabrication. The presented method of IGP visualization is developed as a C#-based add-on for Grasshopper in Rhino3D, one of the main modeling tools used by architects and product designers.

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

Purpose. The aim of the research is to develop fuzzy impact models of the natural and anthropogenic influence, which allows to integrate different physical factors, which makes it possible to bring them to a single environmental assessment system and comparison of different assessed areas. Methodology. The basis of the proposed modeling is a traditional approach on the development of such models, which includes conceptual, logical and physical modeling levels. The Unified Modeling Language (UML) is used for conceptual modeling level, which is recommended as the main modeling tool in the set of international standards in geographic information / geomatics and software that supports the interactive mode of UML diagrams creation Visio. The geospatial database and SQL-functions are implemented and the extension of the standard SQL-99 language with a new data type geometry and built-in functions which provides storage, processing and analysis of geospatial data in database management systems is used. The proposed models are realized in the environment of object-relational DBMS PostgreSQl / Postgis and geographic information system QGIS. Results. A review of the experience of using fuzzy logic to assess the state of the environment is done. Technological models for computation of indicators of administrative unit provision by social infrastructure objects, influence of greenery, industrial territories and transport on the environment are offered and realized. An example of approbation of the proposed approach based on OpenStreetMaps open data for the Popasnianskyi distinct of Luhansk region territory is given. Scientific novelty. Theoretical generalizations are made and practical results are received of resolving applied problem of the development of the fuzzy impact assessment model of various factors influence on the environment with use of GIS. Such assessment can be used at the stage of community spatial development strategies preparation to determine the most acceptable development version, as well as to unify the means of strategies implementation monitoring, organically linking local, national and global tasks. Practical significance. The application of the proposed approach of GRID modeling and fuzzy impact assessment use in assessing the quality of the environment allows to integrate different indicators, compare them, by bringing them into a single evaluation system.

Making resilience explicit in FRAM: Shedding light on desired outcomes

AbstractResilience plays a key role in desired outcomes of socio‐technical systems. However, the Functional Resonance Analysis Method (FRAM), which has been the main modeling tool in light of resilience engineering, does not make explicit the role of resilience. This paper addresses this gap by proposing new procedures for the development of FRAM models of desired outcomes. They are: (i) the active search for functions that display resilient performance; (ii) the assessment of the frequency at which the function output is expected to occur at the same way as it occurred in the desired outcome – frequent unwanted variabilities that occur despite desired outcomes tend to be hidden; (iii) understanding of the reasons for desired outcomes based on the analysis of the logical associations between each function, the abilities of resilient systems and guidelines for coping with complexity; and (iv) the proposal of recommendations for sustaining the observed successful performance. Two case studies of events with desired outcomes in an intensive care unit illustrate the applicability of the proposal. The proposal is expected to be useful for making systems more resilient to everyday work, in which vulnerabilities might be hidden by desired outcomes.

Approval of the technology of carbon dioxide injection into the V-16 water driven reservoir of the Hadiach field (Ukraine) under the conditions of the water pressure mode pages 1–2

The object of research is water-driven gas-condensate reservoirs. Using the main hydrodynamic modeling tools Eclipse and Petrel from Schlumberger (USA), the study was carried out to improve the existing technologies for the displacement of residual gas reserves by carbon dioxide from the water-driven gas-condensate reservoirs. The carbon dioxide injection technology was tested in the V-16 reservoir of the Hadiach oil and gas condensate field (Ukraine). According to the study results, it was found that due to the injection of non-hydrocarbon gas, the cumulative water production are reduced compared to the depletion. Based on the obtained modeling results, the calculation of the predicted hydrocarbon recovery factors at the moment of carbon dioxide breakthrough into the production well was carried out according to the cumulative formation water production. According to the calculations, it was found that the implementation of the enhanced gas recovery technology provides significantly higher ultimate hydrocarbon recovery compared to the depletion. The predicted gas recovery factor when injecting carbon dioxide into the V-16 reservoir increases by 2.95 % of the residual gas reserves, and the condensate recovery factor for these conditions by 1.24 %. Based on the study results, the technological efficiency of using carbon dioxide as an injection agent to increase the hydrocarbon recovery from water-driven reservoirs was established. According to the simulation results, the implementation of the technology of carbon dioxide injection into the V-16 reservoir of the Hadiach oil and gas condensate field can significantly increase the hydrocarbon recovery from the deposit, thereby increasing the production capacity of the field.

Predicting Breast Cancer Survivability

this study concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods: two from machine learning (artificial neural network and decision trees) and one from statistics (logistic regression), and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 87 variables and the total of the records are 457,389; which became 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we find the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2 which is in our view of point is the suitable system to use according to the facilities and the results given to us. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. However, we have found out that the neural network has a much better performance than the other two techniques. Finally, we can say that the model we chose has the highest accuracy which specialists in the breast cancer field can use and depend on.

Application of 4D BIM modelling in planning and construction of zero cycle works

4D BIM modeling is a unique and effective management technology in construction projects. However, it is not widely used in geotechnical construction. This paper summarizes main aspects that characterize the essence of this new technology. An extensive literature review and analysis of ‘pilot’ projects were conducted. The main 4D modeling tools are presented. As a result of the study of ongoing projects, a calculation of the economic efficiency by implementation of this technology was made. The use of this method in planning and construction can increase the main criteria for assessing the success and effectiveness of zero-cycle work.

Nonlinear acoustics of complex solids and granular media

This talk is an introduction to nonlinear acoustic processes in complex solids such as cracked metals, damaged composites or concrete, and granular media. These materials, belong to the same class of complex solids often referred to as mesoscopic solids, and share a number of similar nonlinear acoustic behaviors originating mainly from the presence of internal solid contacts. The specific nonlinear acoustic signatures and processes will be described (slow dynamics, memory, contact acoustic nonlinearity, i.e. nonclassical nonlinearities) through several examples, and an overview of the main modeling tools will be provided. Finally, the current trends and several applications of such nonclassical nonlinear processes (nondestructive testing and characterization, wave control, etc.) will be given.

Edification in creation of Lattice Boltzmann models for materials science students

Education of new engineers is accompanied with the presentation of novel technologies. Development and optimisation of technologies is impossible without student learning of new computational methods. Theoretical complexity of computational techniques makes self-learning an extra-long and not effective. This paper is an approach that helps student to understand the whole process of development and testing of a model. The technology of selective laser melting was chosen as a sample, and the Lattice Boltzmann method (LBM) was used as the main modelling tool. This paper provides practical instructions for creating the first model for a free-surface fluid flow, providing examples of desired results. Then, the paper presents some of students' results and achievements, and also compares the results received without and with the proposed learning approach. The instructions presented in this paper may be useful for beginners who are trying to self-learning LBM, because it describes operations in easily understandable language.

A simulation-based framework for concurrent safety and productivity improvement in construction projects

PurposeSafety and productivity are key concerns in the construction projects. While safety looks to the construction workers need to work in a safe environment, productivity affects the project’s profitability and is of a paramount importance from the project owner’s view. The different perspective to the safety and productivity from these two major players in construction projects poses a potential for the conflict between the two. This problem can be fundamentally addressed by methods concurrently improving project safety and productivity. The paper aims to discuss this issue.Design/methodology/approachTo this aim, a discrete event simulation (DES) based framework applicable was proposed for complex and hazardous operations. The utility of the framework was tested using a case study of an eight-story residential building in the north-east part of Tehran, Iran. The excavation and stabilization operation was identified as the most hazardous and critical operation in this case. The framework could improve safety and productivity of this operation by 38 and 4 percent, respectively.FindingsThis framework is a complement to the conventional construction project safety and productivity planning methods. Its main application is in complex and hazardous construction operations.Originality/valueFor the first time, a comprehensive framework for concurrently improving safety and productivity of an entire project was proposed in this research. DES was used as the main modeling tool in the framework to provide anex-anteevaluation foundation applicable to a wide range of construction projects.

Cohesion versus friction in controlling the long‐term strength of a self‐healing experimental fault

AbstractThe Coulomb's failure criterion, which postulates that failure occurs along a fault plane when the applied shear stress overcomes a resistance made of two parts of different nature, cohesion, and friction, remains the standard conceptual framework of faulting mechanics. More recently, rate‐and‐state friction laws became the main modeling tool of the seismic cycle. These laws implicitly assume that only frictional resistance sets the fault strength and its evolution. We therefore raise the question of the role of cohesion and related healing/sealing mechanisms on fault mechanics. We designed an original laboratory experiment based on a model material, an ice thin layer sitting on top of a water tank and mechanically deformed at various rates with a circular Couette‐like geometry. This allowed sliding along the fault plane over arbitrarily large slip distances, and analyzing the competition between faulting and cohesion‐healing rates, whereas frictional resistance, resulting from the fault roughness, is limited in magnitude. We show that cohesion‐healing plays an essential role in the time evolution of the interface strength under constant loading rate. In particular, the magnitudes of shear stress fluctuations are observed to be temperature and velocity weakening. The present experiment shares several properties similar to that of active faults during seismic cycles, suggesting that cohesive healing could control some of these features: scale invariance properties, Gutenberg‐Richter law of rupture event size distribution, Omori's law of energy dissipation, at least after major ruptures, time asymmetry in energy dissipation, and periods of creep under high shear stress alternating with major earthquake‐like ruptures.

Pre-Processing and Modeling Tools for Bigdata

AbstractModeling tools and operators help the user / developer to identify the processing field on the top of the sequence and to send into the computing module only the data related to the requested result. The remaining data is not relevant and it will slow down the processing. The biggest challenge nowadays is to get high quality processing results with a reduced computing time and costs. To do so, we must review the processing sequence, by adding several modeling tools. The existing processing models do not take in consideration this aspect and focus on getting high calculation performances which will increase the computing time and costs. In this paper we provide a study of the main modeling tools for BigData and a new model based on pre-processing.

DYNAMIC MODELLING OF HIGH SPEED BALLASTED RAILWAY TRACKS: ANALYSIS OF THE BEHAVIOUR

The aim of the paper is to present a numerical model for a ballasted railway track that includes the dynamic effect of a moving train load and predicts the values of the vertical stiffness of the infrastructure. This model is therefore deemed to be a tool for the evaluation of the state of the track during service situations as well as a predictive model of the behaviour of the system. Consequently, it will be very useful when sizing the cross section of a new railway line is required.The main modelling tool is the finite element method. In regard to this, the application of damping elements to avoid the elastic wave reflection on the boundaries of the numerical domain will be studied. The proposed dynamic analysis consider the change in time of the value of the train load, but not the change in position along the tracks.In the end, a set of suggestions for the numerical model with moving loads will be summarize aiming for the mitigation of the unusual behaviour of the contact surface between the ballast and the sleepers. DOI: http://dx.doi.org/10.4995/CIT2016.2016.4218

Dynamic Modelling of High Speed Ballasted Railway Tracks: Analysis of the Behaviour

The aim of the paper is to present a numerical model for a ballasted railway track that includes the dynamic effect of a moving train load and predicts the values of the vertical stiffness of the infrastructure. This model is therefore deemed to be a tool for the evaluation of the state of the track during service situations as well as a predictive model of the behaviour of the system. Consequently, it will be very useful when sizing the cross section of a new railway line is required.The main modelling tool is the finite element method. In regard to this, the application of damping elements to avoid the elastic wave reflection on the boundaries of the numerical domain will be studied. The proposed dynamic analysis consider the change in time of the value of the train load, but not the change in position along the tracks.In the end, a set of suggestions for the numerical model with moving loads will be summarize aiming for the mitigation of the unusual behaviour of the contact surface between the ballast and the sleepers.

Predicción y Análisis de la Contribución de PM10 desde Pilas de Carbón en una Mina a Cielo Abierto

The contributions of PM10 to the environment were determined in a mining industrial zone in northern Colombia for activities involved in storing 3.2 Mton of coal. As the main modeling tool the software CALPUFF was used to determine the contribution of PM10 for 10 days modeling during a month period. Ambient concentrations of PM10 in five receivers were measured. Although higher emissions occur during daytime, characterized by high values of wind speed and higher solar radiation, the greater receptivity of PM10 measurements stations occurred during night hours due to the decreasing the mixing height. The modeling results show that the activities involved in carbon storage can provide 14.5% of daily ambient concentrations at receptors located 16.2 km downwind from the source.