Graph-based Modeling Approach Research Articles

Experimental Validation of Switched Moving Boundary Modeling of Phase Change Thermal Energy Storage Systems

Abstract Thermal Energy Storage (TES) devices use Phase Change Materials (PCMs) to store and release thermal energy. Control-oriented models are needed to predict the behavior of TES devices and experimental validation is necessary to demonstrate the predictive capabilities of these models. This paper presents an experimental validation of a switched Moving Boundary (MB) approach for modeling TES devices, where the dynamics of the device are captured with fewer states than traditional models. A graph-based modeling approach is used to model heat flow, while the moving boundary captures the time-varying liquid and solid regions of the TES. The model uses a Finite State Machine (FSM) to switch between four modes of operation based on the State-of-Charge (SOC) of the TES. Results show that the switched MB approach has similar accuracy and lower computational cost compared to traditional modeling approaches when predicting the SOC of an experimental TES device.

Mutual Information-Based Modeling for Services Dependency

Web services composition has drawn a great attention in computing industries to build complex and large systems. However, web services composition modeling has major challenges, including dependency determination, complexity, user requests dependency, handling cycles within a composition, service redundancy and scalability concerns. The service dependency graph (SDG) between services in a repository should be accurate to ensure the quality of composition and the associated user requests' responses. Despite of the crucial importance of accurate dependencies for adequate web services compositions, current modeling approaches do not provide any metric to evaluate the dependency between services for quality estimation. In this paper, the Mutual Information-based Services Dependency (MISD) model is proposed as a graph-based modeling approach, independent to any given user request. It constructs services dependency graphs based on Web Services Mutual Information (WSMI), a proposed modified version of mutual information as the dependency metric, along with other criteria for an accurate, efficient dependency evaluation. It finds the optimum composition representing the structure of the web services composition in a repository rather than the path of given user requests. The experimental dependency analysis emphasizes the efficiency of the generated OC and accuracy of the constructed SDG to be 76% and 86% higher than the state-of-the-art models respectively. The time cost to build the SDG and to find the OC is reduced dramatically up to 99% for different public repositories compared to the state-of-the-art studies as the number of user requests increases.

Improved NMR-data-compliant protein structure modeling captures context-dependent variations and expands the scope of functional inference.

Nuclear magnetic resonance (NMR) spectroscopy can reveal conformational states of a protein in physiological conditions. However, sparsely available NMR data for a protein with large degrees of freedom can introduce structural artifacts in the built models. Currently used state-of-the-art methods deriving protein structure and conformation from NMR deploy molecular dynamics (MD) coupled with simulated annealing for building models. We provide an alternate graph-based modeling approach, where we first build substructures from NMR-derived distance-geometry constraints combined in one shot to form the core structure. The remaining molecule with inadequate data is modeled using a hybrid approach respecting the observed distance-geometry constraints. One-shot structure building is rarely undertaken for large and sparse data systems, but our data-driven bottom-up approach makes this uniquely feasible by suitable partitioning of the problem. A detailed comparison of select models with state-of-art methods reveals differences in the secondary structure regions wherein the correctness of our models is confirmed by NMR data. Benchmarking of 106 protein-folds covering 38-282 length structures shows minimal experimental-constraint violations while conforming to other structure quality parameters such as the proper folding, steric clash, and torsion angle violation based on Ramachandran plot criteria. Comparative MD studies using select protein models from a state-of-art method and ours under identical experimental parameters reveal distinct conformational dynamics that could be attributed to protein structure-function. Our work is thus useful in building enhanced NMR-evidence-based models that encapsulate the contextual secondary and tertiary structure variations present during the experimentation and expand the scope of functional inference.

Data continuity and traceability in complex manufacturing systems: a graph-based modeling approach

ABSTRACT Modern industrial production systems are facing the challenge of interconnecting and exploiting the increasing amount of data from smart products, software systems, and IIoT devices. One of the main tasks in this context is the implementation of a data-continuous traceability system, which interconnects the heterogeneous data objects over the entire production flow. While traceability maturity is already high in regulated and process-driven industries, the established traceability solutions cannot be successfully applied to customized and volatile industries. In this research, a graph-based traceability modeling methodology is presented that allows the systematic development of data continuity and integration in manufacturing. Based on the traceability methodology, the graph data model and architecture are specified, which are aligned to the requirements of customized and complex manufacturing systems. The model is implemented and validated based on a Neo4j graph database for the use case of the manufacturing process of automotive electrical systems. This research overcomes the shortcomings of state-of-the-art traceability models by shifting the focus to the relationships between traceability-relevant data objects. The proposed graph-based traceability model is able to capture multi-hierarchical product structures and is less dependent on physical object identification, making it more applicable to customized and complex manufacturing.

Multilevel Hierarchical Estimation for Thermal Management Systems of Electrified Vehicles With Experimental Validation

Abstract This paper presents a multilevel model-based hierarchical estimation framework for complex thermal management systems of electrified vehicles. System dynamics are represented by physics-based lumped parameter models derived from a graph-based modeling approach. The complexity of the hierarchical models is reduced by applying an aggregation-based model-order reduction technique that preserves the physical correspondence between a reduced-order model and the physical system. This paper also presents a case study in which a hierarchical observer is designed to estimate the dynamics of a candidate system. The hierarchical observer is connected to a previously developed hierarchical controller for closed-loop control, and the closed-loop performance is demonstrated through simulation and real-time experimental results. A comparison between the proposed hierarchical observer and a centralized observer shows the tradeoff between the estimation accuracy and the computational complexity of the two approaches.

A novel graph-based modelling approach for reducing complexity in model-based systems engineering environment

Field of systems engineering is developing rapidly and becoming more complex, where multiple issues arise like overcomplexity, lack of communication or understanding of the design process. Model-based systems engineering (MBSE) has been introduced to overcome these issues and reduce systems complexity. Nonetheless, the system model remains static and the interactions among submodels are modelled in the form of hard-coded rules. Therefore, systems interaction in MBSE is not dynamic enough to satisfy the evolving nature of system models with growing complexity. In this paper, a novel approach for modelling logic is proposed to address the above challenge. The aim of the research is to improve existing methodologies and interaction modelling as well as deal with inconsistencies. The approach is based on graph theory, where pre-defined rules and relationships are substituted and reorganised dynamically with graphical constructs while metagraphs being the most applicable for systems modelling. Framework for reducing complexity is presented.

A novel graph-based modelling approach for reducing complexity in model-based systems engineering environment

Field of systems engineering is developing rapidly and becoming more complex, where multiple issues arise like overcomplexity, lack of communication or understanding of the design process. Model-based systems engineering (MBSE) has been introduced to overcome these issues and reduce systems complexity. Nonetheless, the system model remains static and the interactions among submodels are modelled in the form of hard-coded rules. Therefore, systems interaction in MBSE is not dynamic enough to satisfy the evolving nature of system models with growing complexity. In this paper, a novel approach for modelling logic is proposed to address the above challenge. The aim of the research is to improve existing methodologies and interaction modelling as well as deal with inconsistencies. The approach is based on graph theory, where pre-defined rules and relationships are substituted and reorganised dynamically with graphical constructs while metagraphs being the most applicable for systems modelling. Framework for reducing complexity is presented.

Experimental Validation of Graph-Based Hierarchical Control for Thermal Management

This paper proposes and experimentally validates a hierarchical control framework for fluid flow systems performing thermal management in mobile energy platforms. A graph-based modeling approach derived from the conservation of mass and energy inherently captures coupling within and between physical domains. Hydrodynamic and thermodynamic graph-based models are experimentally validated on a thermal-fluid testbed. A scalable hierarchical control framework using the graph-based models with model predictive control (MPC) is proposed to manage the multidomain and multi-timescale dynamics of thermal management systems. The proposed hierarchical control framework is compared to decentralized and centralized benchmark controllers and found to maintain temperature bounds better while using less electrical energy for actuation.

Multimedia story creation on social networks

The paper aims at proposing an original summarization technique from Online Social Networks (OSNs) for multimedia stories’ creation. In particular, for each Multimedia Social Network (MuSN) – i.e. an OSN focusing on the management and sharing of multimedia information – we leverage a graph-based modeling approach and exploit influence analysis methodologies to detect the most important multimedia objects related to one or more topics of interest. Consecutively, from the list of candidate objects we obtain a multimedia summary on the basis of a novel summarization model and some heuristics, whose purpose is to generate multimedia stories with priority (w.r.t. some user keywords), continuity, variety and not receptiveness features. The effectiveness of the proposed approach is shown by the performed experiments on Flickr.

Stability of decentralized model predictive control of graph-based power flow systems via passivity

This work presents a passivity-based stability guarantee for the decentralized control of nonlinear power flow systems. This class of systems is characterized using a graph-based modeling approach, where vertices represent capacitive elements that store energy and edges represent power flow between these capacitive elements. Due to their complexity and size, these power flow systems are often decomposed into dynamically coupled subsystems, where this coupling stems from the exchange of power between subsystems. Each subsystem has a corresponding model predictive controller that can be part of a decentralized, distributed, or larger hierarchical control structure. By exploiting the structure of the coupling between subsystems, stability of the closed-loop system is guaranteed by augmenting each model predictive controller with a local passivity constraint.

Spatial modelling of agro-ecosystem dynamics across scales: A case in the cotton region of West-Burkina Faso

Models are increasingly being used to investigate agro-ecosystems dynamics, although processes interacting at different scales remain difficult to consider. When upscaled or downscaled based on aggregation or disaggregation methods, information is generally distorted. This study explores agro-ecosystem modelling using an interaction graph-based modelling approach that explicitly link elements at different scales without up or downscaling. The study area/time frame is the cotton region of West Burkina Faso over the last fifteen years. Field, plot, farm and climate entities are linked in graphs that evolve according to functions computed along different time steps. Three main processes and their interrelations are simulated, occurring at different spatial and temporal scales: crop area expansion, crop rotation and crop production. Three simulation examples are presented to illustrate the analytical possibilities allowed by the approach. These examples test i) the geographical distribution of plots as a means to face climatic risks, ii) the effect of fallowing practice in a spatially constrained cotton dominated landscape and iii) the consequences of reduced access to credit for farmers to buy fertilizers. Model outputs enable quantifying and mapping the respective effects of processes at different scales. Results show that modelling across scales is achievable without resorting to methods of aggregation or disaggregation, which opens new perspectives in multi-scalar analyses of agro-ecosystems that link land production and land use and land cover.

A Novel Method of Modeling Wireless Sensor Network Using Fuzzy Graph and Energy Efficient Fuzzy Based k-Hop Clustering Algorithm

Clustering is one of the widely used methods to save energy, increase spatial re usability, and scalability. In this paper, we have proposed a new fuzzy graph based modeling approach for wireless sensor network which takes into account the dynamic nature of network, volatile aspects of radio links and physical layer uncertainty. The fuzzy graph constructs fuzzy neighborhoods which are used to identify all the prospective member nodes of a cluster. For computation of optimum centrality of a cluster, we have defined a new centrality metric namely fuzzy k-hop centrality. The proposed centrality metric considers residual energy of individual nodes, link quality, hop distance between the prospective cluster head and respective member nodes to ensure better cluster head selection and cluster quality. Finally, a new computationally inexpensive clustering algorithm has been developed. The simulation results demonstrate that the proposed algorithm resulted in prolonged network lifetime in terms of clustering rounds, scalability, higher energy efficiency and uniform cluster head and cluster members distribution, as compare to LEACH-ERE and CHEF.

Causality assignment and model approximation for quantitative hybrid bond graph-based fault diagnosis

Bond graph (BG) is an effective tool for modeling complex systems and it has been proven to be useful for fault detection and isolation (FDI) purposes for large continuous systems. BG provides causality between system's variables which allows FDI algorithms to be developed systematically from the graph. Similarly, Hybrid bond graph (HBG) is a bond graph-based modeling approach which provides an avenue to model complex hybrid systems; however, due to the lack of understanding, HBG has not been well-utilized for fault diagnosis. This is the first of a two-part paper that investigates the feasibility of utilizing HBG for quantitative FDI applications for hybrid systems. In this first paper, we present an analysis on the causality properties of the HBG where useful properties and insights associated with FDI applications are gained. Based on these properties, a causality assignment procedure and modeling approximation techniques are developed to achieve a HBG with a causality that facilitates efficient and effective FDI design for hybrid systems.