Construction Of System Model Research Articles

Safety Verification Utilizing Model-based Development for Safety Critical Cyber-Physical Systems

The application of cyber-physical systems (CPSs) in safety-critical application domain requires rigorous verification of their functional correctness and safety-relevant properties. We propose a practical verification process which enables to conduct safety verification of safety critical CPSs. The verification process consists of (a) a system model construction method, which generates a system model by combining software described in C and plant model code reused from model-based development, (b) a model transformation method, which transforms the plant models including differential algebraic equations (DAE) to approximate models without DAE to reduce verification complexity induced by DAE solver execution, (c) a model simplification framework, which enables the simplification of bond-graph plant models using domain-knowledge-based replacement of complex model components for further verification overhead reductions, and (d) a formal verification based on symbolic execution. We implemented the proposed methods and framework, and successfully applied the proposed verification process for safety verification of automotive brake control systems. The results of the study demonstrate that the verification detects a complex failure condition in a real-world brake control system from the generated system model and that the automated model transformations of the CPS models yield significant verification complexity reductions without impairing the ability to detect unsafe behavior. © 2017 Information Processing Society of Japan.

Quantifying uncertainty in future ocean carbon uptake

AbstractAttributing uncertainty in ocean carbon uptake between societal trajectory (scenarios), Earth System Model construction (structure), and inherent natural variation in climate (internal) is critical to make progress in identifying, understanding, and reducing those uncertainties. In the present issue of Global Biogeochemical Cycles, Lovenduski et al. (2016) disentangle these drivers of uncertainty in ocean carbon uptake over time and space and assess the resulting implications for the emergence timescales of structural and scenario uncertainty over internal variability. Such efforts are critical for establishing realizable and efficient monitoring goals and prioritizing areas of continued model development. Under recently proposed climate stabilization targets, such efforts to partition uncertainty also become increasingly critical to societal decision‐making in the context of carbon stabilization.

Study on Extraction of Sub-element and Construction of System Model for the Technological Communication Skills

Study on Extraction of Sub-element and Construction of System Model for the Technological Communication Skills

Centrifuge Modelling of Diaphragm Wall Construction Adjacent to Piled Foundations

Abstract Diaphragm walls are often implemented to support the sides of deep excavations and in urban environments situations increasingly occur where it is necessary to install them close to existing piled structures. There is a lack of good quality field monitoring case studies to help understand this complex three-dimensional soil–structure interaction problem. It can be investigated using numerical analysis or an alternative approach is to perform small-scale model tests in a geotechnical centrifuge. This paper describes a sophisticated model construction system that was developed to simulate the construction sequence (excavation and casting of concrete) of a single or series of three diaphragm wall panels in sand as part of a study to investigate the influence of constructing diaphragm walls adjacent to piled foundations. Prior to wall construction an adjacent instrumented model pile was driven in-flight and a constant force maintained at the pile head to simulate axially loaded piles. The system response was captured through the use of miniature soil stress cells, LVDTs, and laser displacement sensors. A detailed description of the centrifuge model and test procedures developed is presented and the necessary simplifications and associated errors discussed. The effectiveness of the small-scale physical model is demonstrated by presenting some selected test results (soil stresses and deformations).

An Approach to Recognize Machining Features for In-Process Models by Using a Hybrid Graph and Rule-Based Method

A hybrid graph and rule-based method for recognizing machining features is proposed in this paper, which aims to get the machining feature in 3D in-process model construction system. Initially, the attributed adjacency graph (AAG) of the input MBD models is constructed and represented in matrix form. After that, by deleting the convex edges of the AAG, the concave sub-graphs are generated. For obtaining the volumetric features, their concave sub-graphs are matched with the feature template graphs in a pre-defined library; for those which can be generated by sweeping the edge loops, such as open pocket, closed pocket, through pocket and slot, their features are recognized by using rules. In experimental section, an example is given to illustrate the validity and effectiveness of the proposed method.

Multi-Functional Loader Moving Hydraulic System Model Construction and Simulation Based on Power Bond Graphs

Hydraulic moving system has been extensively applied in small type of loader and other engineering vehicles do to its special superority, how to forecast its dynamic behavior in design stage is most impotant problem. This paper firstly analyse a typical moving hydraulic system of multi-functional loader, and construct its powder bond graphs model and encapsulate these key parts model in soft Mworks. at last, a simpled moving hydraulic dynamaic simulation model was constructed by these components, and some characteristic curves of system can be easily abtained.

Cardiovascular Model Construction System of Congenital Heart Disease Based on Neonatal Echocardiographic Images

Cardiovascular Model Construction System of Congenital Heart Disease Based on Neonatal Echocardiographic Images

Multi-Functional Loader Steering Hydraulic System model construction and simulation Based on Power Bond Graphs

Hydraulic steering system has been extensively applied in small type of loader and other engineering vehicles do to its special superority, how to forecast its dynamic behavior in design stage is most impotant problem. This paper firstly analyse a typical steering hydraulic system of multi-functional loader, and construct its powder bond graphs, then decuce its state equations.with these equations, some hydraulic component have been encapsulated in soft Mworks, at last, a simpled steering hydraulic dynamaic simulation model was constructed by these components, and some characteristic curves of system can be easily abtained.

Evaluation of malware phylogeny modelling systems using automated variant generation

A malware phylogeny model is an estimation of the derivation relationships between a set of malware samples. Systems that construct phylogeny models are expected to be useful for malware analysts. While several such systems have been proposed, little is known about the consistency of their results on different data sets, about their generalizability across different types of malware evolution. This paper explores these issues using two artificial malware history generators: systems that simulate malware evolution according to different evolution models. A quantitative study was conducted using two phylogeny model construction systems and multiple samples of artificial evolution. High variability was found in the quality of their results on different data sets, and the systems were shown to be sensitive to the characteristics of evolution in the data sets. The results call into question the adequacy of evaluations typical in the field, raise pragmatic concerns about tool choice for malware analysts, and underscore the important role that model-based simulation is expected to play in evaluating and selecting suitable malware phylogeny construction systems.

Injecting Creativity into Teaching Simulation Modeling to Computer Science Students

The computer simulation classes in the Computer and Information Sciences and Engineering Department at the University of Florida emphasize a skill set focusing on dynamic system model construction, syntax, and execution. Simulation models are treated as types of formal languages, resulting in instruction that concentrates on key facets of language such as syntax, semantics, and pragmatics. For the past 12 years, the class was predicated on learning a collection of model types and their applications. In the Fall of 2004 and 2005, we modified the class to emphasize student creativity in model construction, allowing students to design their own model representations for a fixed model structure. This experiment resulted in significant positive student feedback, and has the potential to suggest visual language exploration in other computer science classes as a means of comprehending the relevant topical material.

Extension of Companion Modeling Using Classification Learning

Companion Modeling is a methodology of refining initial models for understanding reality through a role-playing game (RPG) and a multi-agent simulation. In this research, we propose a novel agent model construction methodology in which classification learning is applied to the RPG log data in Companion Modeling. This methodology enables a systematic model construction that handles multi-parameters, independent of the modelers ability. There are three problems in applying classification learning to the RPG log data: 1) It is difficult to gather enough data for the number of features because the cost of gathering data is high. 2) Noise data can affect the learning results because the amount of data may be insufficient. 3) The learning results should be explained as a human decision making model and should be recognized by the expert as being the result that reflects reality. We realized an agent model construction system using the following two approaches: 1) Using a feature selection method, the feature subset that has the best prediction accuracy is identified. In this process, the important features chosen by the expert are always included. 2) The expert eliminates irrelevant features from the learning results after evaluating the learning model through a visualization of the results. Finally, using the RPG log data from the Companion Modeling of agricultural economics in northeastern Thailand, we confirm the capability of this methodology. (Resume d'auteur)

A modelling shell for participatory assessment and management of natural resources

Recent structural and jurisdictional developments in natural resources management have resulted in requirements for more open and inclusive management processes. These processes generally use participatory approaches to involve a wide stakeholder audience in management investigations and policy development. Along with these changes have come requirements for more open approaches to the combined spatial and temporal modelling often used in management, leading to the development of tools that are designed to meet the needs of managers and other stakeholders. This paper presents a modelling shell that has been developed to support the participatory assessment and management of natural resources. The modelling shell concept is based upon experience gained during development of a number of natural resources modelling applications over the past 10 years. The shell supports the basic functions of data manipulation, simulation management and output presentation, and is underlain by a simple, accessible and easily understood system of data storage and manipulation. By providing these common functions, the modelling shell allows the people involved in system model construction to concentrate on the important issues of algorithm selection and development, rather than the often messy and time consuming issues of input–output.

Performance measurement indices for simulated construction operations

This paper describes the development of indices useful in automating the experimentation process of a computer simulation. Simulation methodologies have been developed to model construction systems, but most of these systems require the experimentation process to be carried out manually. In achieving optimum performance, one has to repeat an exhaustive number of experiments. The indices can be used to automate this process, as they are indicators of bottlenecks in the system that can be tracked through the simulation output. They are based upon user-defined performance guidelines for the resources. Where a performance index falls outside the acceptable range, remedial action may be taken. Belief networks, a probabilistic form of artificial intelligence, were used to automate the analysis of the indices to determine the most likely causal factor of poor performance.Key words: construction engineering, computer simulation, performance indices, belief networks, performance improvement.

Performance measurement indices for simulated construction operations

This paper describes the development of indices useful in automating the experimentation process of a computer simulation. Simulation methodologies have been developed to model construction systems, but most of these systems require the experimentation process to be carried out manually. In achieving optimum performance, one has to repeat an exhaustive number of experiments. The indices can be used to automate this process, as they are indicators of bottlenecks in the system that can be tracked through the simulation output. They are based upon user-defined performance guidelines for the resources. Where a performance index falls outside the acceptable range, remedial action may be taken. Belief networks, a probabilistic form of artificial intelligence, were used to automate the analysis of the indices to determine the most likely causal factor of poor performance.Key words: construction engineering, computer simulation, performance indices, belief networks, performance improvement.

R&D Decision Advisor: An interactive approach to normative decision system model construction

This paper describes the architecture of R&D Decision Advisor ™, a commercial intelligent decision system for evaluating corporate research and development projects and portfolios. In analyzing projects, R & D Decision Advisor interactively guides a user in constructing an influence diagram model for an individual research project. The system's interactive approach can be clearly explained from a blackboard perspective. The system guides the user to choose among general project features but offers flexibility to capture unique project details. Related research generalizes the underlying architecture and addresses resource-constrained decision situations.

Computer-aided model construction

We examine ways in which the construction of mathematical models may be supported, and review several approaches for computer-aided model construction. The construction of complex models can be a challenging task even for expert modelers. The aim of computer-aided model construction systems is to simplify this task. We view model construction as a state transformation process, and suggest that the process can be facilitated by supporting the creation of certain representations or by transforming an existing representation to another. For example, declarative modeling languages facilitate the specification of an executable statement of the mathematical model, while knowledge-based model construction systems assist the creation of the mathematical formulation. Knowledge-based model construction systems are the focus of our review of existing systems. To facilitate a comparison of several such systems, we characterize them in terms of their cognitive bases, the representations developed in them, and the methods for transformation of these representations.

A production model construction system: PM statement to math programming

The first three generations of software for mathematical programming were (1) coding of the simplex algorithm, (2) matrix generation programs, and (3) modeling languages. The fourth generation will provide improved knowledge-based and graphical interfaces to permit modeling by users who have little familiarity with mathematical programming, viz. Krishnan (1988) which is the focus of this paper. Since many economists and management scientists have experience with production and distribution modeling but limited familiarity as yet with knowledge-based systems and logic programming, a series of papers have been prepared to elucidate the elements of Krishnan's system to this community. This paper, the second in the series, covers a part of the model construction system.

Knowledge-based simulation model specification

A Knowledge-Based Model Construction (KBMC) system has been developed to automate the model construction phase of the simulation life cycle. The system's underlying rule base in corporates several types of knowledge. This includes domain knowledge that facilitates a structured interactive dialog for the acquisition of a complete model specification from a user. An executable discrete simulation model in SIMAN is automatically constructed by the system from this specification, utilizing model ing knowledge and SIMAN knowledge. We present an overview of the KBMC system and focus on the knowledge-based specifica tion method used in the KBMC system.

Real Time Simulation of Conductivity in River-Lake Systems

Conductivity is modeled in real time in a natural water system using the finite section method. Dispersion coefficients are estimated using methods available in the literature. Calibration is performed together with sensitivity analyses. Small scale effects are defined by a dye study. The resultant model is eventually used in the construction of system models describing the transport and fate of various pollutants in the Ford Lake - Melleville Lake (Mich.) System.