Model Generation Technique Research Articles

Automatic Detection and 3D Reconstruction of Buildings from Historical Maps

ABSTRACT This paper presents an automatic 3D building reconstruction methodology for historical urban maps. It uses facade and openings detection on the maps, followed by rectification, regularization, and 3D model generation techniques. Evaluation metrics confirm the effectiveness of the approach, with high accuracy in detecting facades and their openings while maintaining geometric integrity. The flexibility and interoperability of the chosen 3D building representation method allow for adjustments in dimensions without compromising layout, making it suitable for completing the urban environments where facades may not be directly visible. This methodology represents a significant step toward automating the reconstruction of 3D historical urban landscapes, contributing to heritage preservation and architectural understanding.

LOD2-Level+ Low-Rise Building Model Extraction Method for Oblique Photography Data Using U-NET and a Multi-Decision RANSAC Segmentation Algorithm

Oblique photography is a regional digital surface model generation technique that can be widely used for building 3D model construction. However, due to the lack of geometric and semantic information about the building, these models make it difficult to differentiate more detailed components in the building, such as roofs and balconies. This paper proposes a deep learning-based method (U-NET) for constructing 3D models of low-rise buildings that address the issues. The method ensures complete geometric and semantic information and conforms to the LOD2 level. First, digital orthophotos are used to perform building extraction based on U-NET, and then a contour optimization method based on the main direction of the building and the center of gravity of the contour is used to obtain the regular building contour. Second, the pure building point cloud model representing a single building is extracted from the whole point cloud scene based on the acquired building contour. Finally, the multi-decision RANSAC algorithm is used to segment the building detail point cloud and construct a triangular mesh of building components, followed by a triangular mesh fusion and splicing method to achieve monolithic building components. The paper presents experimental evidence that the building contour extraction algorithm can achieve a 90.3% success rate and that the resulting single building 3D model contains LOD2 building components, which contain detailed geometric and semantic information.

Automated digital twin generation of manufacturing systems with complex material flows: graph model completion

Industry 4.0 determined the emergence of technologies that enable data-driven production planning and control approaches. A digital model can be used to make decisions based on the current state of a manufacturing system, and its efficacy strictly depends on the capability to correctly represent the physical counterpart at any time. Automated model generation techniques such as process mining can significantly accelerate the development of digital twins for manufacturing systems. However, complex production environments are characterized by the convergence of different material and information flows. The corresponding data logs present multiple part identifiers, resulting in the wrong finding of the system structure with traditional process mining techniques. This paper describes the problem of discovering manufacturing systems with complex material flows, such as assembly lines. An algorithm is proposed for the proper digital model generation, aided by the new concept of object-centric process mining. The proposed approach has been applied successfully to two test cases and a real manufacturing system. The results show the applicability of the proposed technique to realistic settings.

Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements

Modern manufacturing systems can benefit from the use of digital tools to support both short- and long-term decisions. Meanwhile, such systems reached a high level of complexity and are frequently subject to modifications that can quickly make the digital tools obsolete. In this context, the ability to dynamically generate models of production systems is essential to guarantee their exploitation on the shop-floors as decision-support systems. The literature offers approaches for generating digital models based on real-time data streams. These models can represent a system more precisely at any point in time, as they are continuously updated based on the data. However, most approaches consider only isolated aspects of systems (e.g., reliability models) and focus on a specific modeling purpose (e.g., material flow identification). The research challenge is therefore to develop a novel framework that systematically enables the combination of models extracted through different process mining algorithms. To tackle this challenge, it is critical to define the requirements that enable the emergence of automated modeling and simulation tasks. In this paper, we therefore derive and define data requirements for the models that need to be extracted. We include aspects such as the structure of the manufacturing system and the behavior of its machines. The paper aims at guiding practitioners in designing coherent data structures to enable the coupling of model generation techniques within the digital support system of manufacturing companies.

Recent advances in knowledge‐based model structure optimization and extrapolation techniques for microwave applications

AbstractArtificial neural network modeling techniques have been recognized as important vehicles in the microwave computer‐aided design (CAD) area in addressing the growing challenges of designing next generation microwave device, circuits, and systems. This article provides an overview of recent advances in knowledge‐based neural network model generation and extrapolation techniques for microwave applications. We first introduce the unified knowledge‐based neural network structure optimization technique. Using the distinctive property for feature selection of l1 optimization, this unified modeling technique efficiently determines the type and topology of the mapping structure in a knowledge‐based model. This knowledge‐based model structure optimization technique is more flexible and systematic, and can further speed up the knowledge‐based neural model development. As a further advancement, we also discuss the advanced multi‐dimensional extrapolation technique for neural‐based microwave modeling. The purpose is to make the neural network model can be reliably used not only inside the training range but also outside the training range. Multi‐dimensional cubic polynomial extrapolation formulation and optimization over grids outside the training range are utilized to make neural models more robust and reliable when they are used outside the training range. The validity of these techniques is demonstrated by microwave modeling examples.

Semantic model of an exploit’s source code for data protection in automation systems

Data protection is especially relevant in automation systems nowadays. The paper proposes a semantic model of exploit’s source code as a basis for unknown attacks detection, forecasting and assessment and therefore for automated data protection from cyber attacks. The proposed model combines a control flow graph with a function call dependencies graph. The paper describes model generation technique that is based on the analysis of exploit’s source code and incorporates code compilation, its decompilation, building the functional semantic models of the exploits, and, finally, comparing these models and generating the standard semantic model of exploits’ source code. Application of the technique for generation of exploit’s model is demonstrated on a case study.

Automated Digital Twins Generation for Manufacturing Systems: a Case Study

Abstract The recent industrial scenario was defined by the emergence of digital twins and cyber physical systems as key elements for manufacturers leadership. Digital models can perform good in terms of production planning and control decisions if they are correctly representing their physical counterparts at anytime. Discrete event simulation can be considered as established digital models of manufacturing system, thanks to the proven capabilities of correctly estimating the system performances. Automated simulation model generation techniques can significantly reduce model development phases and allow for using simulation models for short term decisions in production. Application studies and test cases are scarce in the literature. In this paper, we present the application of a digital model generation method. The test case is done exploiting a lab-scale model of a manufacturing system composed by six stations. We investigate how the model generation works online, during the transient phase of a manufacturing system. Results confirm the real-time applicability of the approach provided that sufficient data points are available from the production event logs.

Geostatistical Seismic Inversion with Self-Updating of Local Probability Distributions

Three-dimensional subsurface elastic models inverted from seismic reflection data are the basis of the geo-modeling workflow. These models are often used to predict the spatial distribution of reservoir rock properties such as porosity, volume of minerals and fluid saturations. Stochastic seismic inversion methods are important modeling tools, as they allow one to infer high-resolution subsurface models and assess uncertainties related to the spatial distribution of the inverted petro-elastic properties. Within this framework, iterative geostatistical seismic inversion methods use stochastic sequential simulation and co-simulation as a model generation and perturbation technique based on the mismatch between synthetic and real seismic data. This work proposes an alternative approach of iterative geostatistical seismic inversion based on the concept of self-updating of local probability distributions of the elastic property of interest to be inverted. The model perturbation is conditioned by local probability distribution functions, which are iteratively updated based on the data misfit at previous iterations. This approach allows for better exploration of the model parameter space, avoiding local fast convergence at early steps of the inversion, and wider exploration of the model parameter space. The method is applied to a two-dimensional nonstationary synthetic dataset and to a three-dimensional real case example with a blind well test.

Automated Generation of Consistent Graph Models With Multiplicity Reasoning

Advanced tools used in model-based systems engineering (MBSE) frequently represent their models as graphs. In order to test those tools, the automated generation of well-formed (or intentionally malformed) graph models is necessitated which is often carried out by solver-based model generation techniques. In many model generation scenarios, one needs more refined control over the generated unit tests to focus on the more relevant models. Type scopes allow to precisely define the required number of newly generated elements, thus one can avoid the generation of unrealistic and highly symmetric models having only a single type of elements. In this paper, we propose a 3-valued scoped partial modeling formalism, which innovatively extends partial graph models with predicate abstraction and counter abstraction. As a result, well-formedness constraints and multiplicity requirements can be evaluated in an approximated way on incomplete (unfinished) models by using advanced graph query engines with numerical solvers (e.g., IP or LP solvers). Based on the refinement of 3-valued scoped partial models, we propose an efficient model generation algorithm that generates models that are both well-formed and satisfy the scope requirements. We show that the proposed approach scales significantly better than existing SAT-solver techniques or the original graph solver without multiplicity reasoning. We illustrate our approach in a complex design-space exploration case study of collaborating satellites introduced by researchers at NASA JPL.

Iterative geostatistical seismic inversion incorporating local anisotropies

Geostatistical seismic inversion methods use stochastic sequential simulation as the model generation and perturbation technique. These stochastic simulation methods use a global variogram model to express the expected spatial continuity pattern of the subsurface elastic properties of interest. The conditioning to a single variogram model is not suitable for complex and non-stationary geological environments, resulting in poor inverted models unable to reproduce non-stationary features such as channels, folds, and faults. The proposed method uses a stochastic sequential simulation and co-simulation method able to cope with spatially varying information using local and independent variogram models. The information about the dip, azimuth, and ranges of the local variogram model is inferred directly from the observed data. First, local dip and azimuth structural volumes are computed from seismic attribute analysis. Then, local variogram models are fitted along the directions estimated from the previous step. This information is used as steering data during the inversion, acting as proxy of the true subsurface geological complexities. Application examples in synthetic and real datasets with complex geometries show the impact of using local anisotropy models in both the reproduction of the original seismic data and the reliability of the inverted models. The resulting inverted models show enhanced consistency, where small-to-large scale discontinuities and complex geometries are better reproduced, allowing reducing the spatial uncertainty associated with the subsurface properties. This work represents a step forward in integrating geological consistency into geostatistical seismic inversion, surpassing the limitation of using a single variogram model to reproduce complex geological patterns.

Ultra close-range digital photogrammetry in skeletal anthropology: A systematic review.

Ultra close-range digital photogrammetry (UCR-DP) is emerging as a robust technique for 3D model generation and represents a convenient and low-cost solution for rapid data acquisition in virtual anthropology. This systematic review aims to analyse applications, technical implementation, and performance of UCR-DP in skeletal anthropology. The PRISMA guidelines were applied to the study. The bibliographic search was performed on March 1st, 2019 using Scopus and MEDLINE databases to retrieve peer-reviewed studies accessible in English full-text. The authors worked independently to select the articles meeting inclusion criteria, upon discussion. Studies underwent to quantitative and qualitative syntheses. Twenty-six studies were selected. The majority appeared in 2016 or after and were focused on methodological aspects; the applications mainly dealt with the documentation of skeletal findings and the identification or comparison of anatomical features and trauma. Most authors used commercial software packages, and an offline approach. Research is still quite heterogeneous concerning methods, terminology and quality of results, and proper validation is still lacking. UCR-DP has great potential in skeletal anthropology, with many significant advantages: versatility in terms of application range and technical implementation, scalability, and photorealistic restitution. Validation of the technique, and the application of the cloud-based approach, with its reduced requirements relating to hardware, labour, time, and cost, could further facilitate the sharing of large collections for research and communication purposes.

Towards trusted and efficient SDN topology discovery: A lightweight topology verification scheme

Discovering network topology is critical for Software-defined Networking (SDN) controllers to establish the centralized network visibility. However, during the process of SDN topology discovery, SDN controllers tend to suffer from the topology poisoning attacks due to a lack of authentication. Existing methods can hardly solve the problems of SDN topology poisoning attacks in a comprehensive and lightweight manner. To fill this gap, we propose TrustTopo, a lightweight and efficient SDN topology verification scheme. Specifically, we first establish a security threat model to analyze key service vulnerabilities in the SDN controller. Then, we design two verification strategies (i.e., host location verification and link verification) to cope with the host hijacking attack and link fabrication attack, respectively. In more detail, our host location verification strategy is based on the path tracking and asynchronous rollback technique, while our link verification strategy is designed using the chaotic model and dynamic password generation technique. We implement TrustTopo in the mainstream SDN controller (i.e., Floodlight). Experimental results in the Mininet virtual network environment show that TrustTopo can effectively secure the network topology against the representative topology poisoning attacks, while bringing negligible runtime overhead.

Statistical Data Analyses for Investigating Recent Major Earthquakes and Mitigating their Damages in Japan

Japan has experienced many large-scale natural disasters such as earthquakes, typhoons with heavy rain and landslides, tornadoes and so on. Every time we have such serious damages due to major natural disasters, public utilities such as electricity, gas, and water have been cut off and stopped. In this paper we take four major earthquakes, which occurred very recently with serious damages. Using data such as deaths and missing people, refugees, and refugee camps, water supply suspension and its recovery and applying various statistical data analyses techniques and mathematical models, we show our findings and policy suggestions obtained from those analyses. In addition, we propose a mathematical modeling technique for quantitatively measuring the robust-ness of the water supply network system. Our approach is based upon the network flow optimization model and Monte Carlo random generation technique. We illustrate numerical results obtained from our numerical experiments.

Diversity of graph models and graph generators in mutation testing

When custom modeling tools are used for designing complex safety-critical systems (e.g., critical cyber-physical systems), the tools themselves need to be validated by systematic testing to prevent tool-specific bugs reaching the system. Testing of such modeling tools relies upon an automatically generated set of models as a test suite. While many software testing practices recommend that this test suite should be diverse, model diversity has not been studied systematically for graph models. In the paper, we propose different diversity metrics for models by generalizing and exploiting neighborhood and predicate shapes as abstraction. We evaluate such shape-based diversity metrics using various distance functions in the context of mutation testing of graph constraints and access policies for two separate industrial DSLs. Furthermore, we evaluate the quality (i.e., bug detection capability) of different (random and consistent) model generation techniques for mutation testing purposes.

First demonstration of airborne multi‐channel InSAR processing based on measured data

Azimuth multi-channel interferometric SAR (AMC-InSAR) mode integrates the azimuth multichannel high-resolution wide swath (HRWS) imaging technique and high-precision InSAR digital elevation model (DEM) generation technique. More equivalent phase centres are obtained through the azimuth single-input and multiple-output technique, which would provide more azimuth samples. Hence, the reliable and accurate DEM measurement of more wide-swath terrain profile would be obtained. Here, the performance of this AMC-InSAR mode is evaluated theoretically. A new processing method based on measured data for this mode is provided and the results of measured data-processing validate the analysed results and the new method.

Modified stacking ensemble approach to detect network intrusion

Detecting intrusions in a network traffic has remained an issue for researchers over the years. Advances in the area of machine learning provide opportunities to researchers to detect network intrusion without using a signature database. We studied and analyzed the performance of a stacking technique, which is an ensemble method that is used to combine different classification models to create a better classifier, on the KDD'99 dataset. In this study, the stacking method is improved by modifying the model generation and selection techniques and by using different classifications algorithms as a combiner method. Model generation is performed using subsets of the dataset with randomly selected features and not all of these models are used as input for the combiner. Various metrics are used in model selection and only selected models are used as input for the combiner method. In our experiments, the stacking technique provided higher accuracy results all the time compared to pure machine learning techniques. The second important result in our experiments was obtaining the highest detection rate for user-to-root attacks compared to other studies.

Review of Patient-Specific Vascular Modeling: Template-Based Isogeometric Framework and the Case for CAD

We review the literature on patient-specific vascular modeling, with particular attention paid to three-dimensional arterial networks. Patient-specific vascular modeling typically involves three main steps: image processing, analysis suitable model generation, and computational analysis. Analysis suitable model generation techniques that are currently utilized suffer from several difficulties and complications, which often necessitate manual intervention and crude approximations. Because the modeling pipeline spans multiple disciplines, the benefits of integrating a computer-aided design (CAD) component for the geometric modeling tasks has been largely overlooked. Upon completion of our review, we adopt this philosophy and present a CAD-integrated template-based modeling framework that streamlines the construction of solid non-uniform rational B-spline vascular models for performing isogeometric finite element analysis. Examples of arterial models for mouse and human circles of Willis and a porcine coronary tree are presented.

State Transition Based Test Model and Test Case Generation Technique for Embedded System: An Empirical Approach

Studies on the testing techniques required during process of black box testing in embedded system have been increasing tremendously. This paper suggests test model and test case generation method for model based testing of embedded system. Considering the complicated internal architecture and real-time characteristics of embedded system, test cases are generated by using the state based behavior modeling method. The proposed state transition model based test case generation method suggests systematic procedures ranged from the use case specification to the test case generation. Entire procedure of test case generation in MP3 player case which uses the suggested method is presented in this paper. With the created test cases, the interaction among the components of embedded system can be easily figured out, thereby helping easy error detection. Moreover, the test cases applied with characteristics and constraints of embedded system could be achieved by modeling system behavior using SysML.

효과적인 모델 기반 안드로이드 GUI 테스팅을 위한 GUI 상태 비교 기법

Graphical user interface testing (GUI testing) techniques have been widely used to test the functionality of Android applications (apps) and to detect faults for verification of the reliability and usability of apps. To adequately test the behaviors of apps, a number of studies on model-based GUI testing techniques have been performed on Android apps. However, the effectiveness of model-based techniques greatly depends on the quality of the GUI model, because model-based GUI testing techniques generate test inputs based on this model. Therefore, in order to improve testing effectiveness in model-based techniques, accurate and efficient GUI model generation has to be achieved using an improved model generation technique with concrete definition of GUI states. For accurate and efficient generation of a GUI model and test inputs, this study suggests a hierarchical GUI state comparison technique and evaluates this technique through comparison with the existing model-based techniques, considering activities as GUI states. Our results show that the proposed technique outperforms existing approaches and has the potential to improve the performance of model-based GUI testing techniques for Android apps.

A novel method to assess short-term forest cover changes based on digital surface models from image-based point clouds

Assessing forestcoverchange is a key issue forany national forestinventory.This wastested in two studyareas in Switzerland on thebasis of stereo airborne digital sensor (ADS) imagesand advanced digital surface model (DSM) generation techniques based on image point clouds. In the present study, an adaptive multi-scale approach to detectforestcoverchangewithhighspatialandtemporalresolutionwasappliedtotwostudyareasinSwitzerland. The challenge of this approach is to minimize DSM height uncertainties that mayaffect the accuracyof the forest coverchange results. The approach consisted of two steps. In the first step, a ‘change index’ parameter indicated the overallchange status atacoarser scale.The tendencytowards changewas indicated byderivative analysis of the normalized histograms of the difference between the two canopy height models (DCHMs) in different years. In the second step, detection of forest cover change at a refined scale was based on an automatic threshold and a moving window technique. Promising results were obtained and reveal that real forest cover changes can be distinguished from non-changes with a high degree of accuracy in managed mixed forests. Results had a loweraccuracyforforestslocatedonsteepalpineterrain.Amajorbenefitoftheproposedmethodisthatthemagnitude of forestcoverchange of anyspecific region canbe made availablewithin a short timeas often requiredby forest managers or policy-makers, especially after unexpected natural disturbances.