Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

Systems Modeling and Modularity Assessment for Embedded Computer Control Applications

AbstractThe development of embedded computer control systems(ECS) requires a synergetic integration of heterogeneoustechnologies and multiple engineering disciplines. Withincreasing amount of functionalities and expectations for highproduct qualities, short time-to-market, and low cost, thesuccess of complexity control and built-in flexibility turn outto be one of the major competitive edges for many ECS products.For this reason, modeling and modularity assessment constitutetwo critical subjects of ECS engineering.In the development ofECS, model-based design is currently being exploited in most ofthe sub-systems engineering activities. However, the lack ofsupport for formalization and systematization associated withthe overall systems modeling leads to problems incomprehension, cross-domain communication, and integration oftechnologies and engineering activities. In particular, designchanges and exploitation of "components" are often risky due tothe inability to characterize components' properties and theirsystem-wide contexts. Furthermore, the lack of engineeringtheories for modularity assessment in the context of ECS makesit difficult to identify parameters of concern and to performearly system optimization. This thesis aims to provide a more complete basis for theengineering of ECS in the areas of systems modeling andmodularization. It provides solution domain models for embeddedcomputer control systems and the software subsystems. Thesemeta-models describe the key system aspects, design levels,components, component properties and relationships with ECSspecific semantics. By constituting the common basis forabstracting and relating different concerns, these models willalso help to provide better support for obtaining holisticsystem views and for incorporating useful technologies fromother engineering and research communities such as to improvethe process and to perform system optimization. Further, amodeling framework is derived, aiming to provide a perspectiveon the modeling aspect of ECS development and to codifyimportant modeling concepts and patterns. In order to extendthe scope of engineering analysis to cover flexibility relatedattributes and multi-attribute tradeoffs, this thesis alsoprovides a metrics system for quantifying componentdependencies that are inherent in the functional solutions.Such dependencies are considered as the key factors affectingcomplexity control, concurrent engineering, and flexibility.The metrics system targets early system-level design and takesinto account several domain specific features such asreplication and timing accuracy. Keywords:Domain-Specific Architectures, Model-basedSystem Design, Software Modularization and Components, QualityMetrics.QC 2010052

Adopting Graph Neural Networks to Analyze Human–Object Interactions for Inferring Activities of Daily Living

Human Activity Recognition (HAR) refers to a field that aims to identify human activitiesby adopting multiple techniques. In this field, different applications, such as smart homes andassistive robots, are introduced to support individuals in their Activities of Daily Living (ADL)by analyzing data collected from various sensors. Apart from wearable sensors, the adoption ofcamera frames to analyze and classify ADL has emerged as a promising trend for achieving theidentification and classification of ADL. To accomplish this, the existing approaches typically rely onobject classification with pose estimation using the image frames collected from cameras. Given theexistence of inherent correlations between human–object interactions and ADL, further efforts areoften needed to leverage these correlations for more effective and well justified decisions. To this end,this work proposes a framework where Graph Neural Networks (GNN) are adopted to explicitlyanalyze human–object interactions for more effectively recognizing daily activities. By automaticallyencoding the correlations among various interactions detected through some collected relational data,the framework infers the existence of different activities alongside their corresponding environmentalobjects. As a case study, we use the Toyota Smart Home dataset to evaluate the proposed framework.Compared with conventional feed-forward neural networks, the results demonstrate significantlysuperior performance in identifying ADL, allowing for the classification of different daily activitieswith an accuracy of 0.88. Furthermore, the incorporation of encoded information from relational dataenhances object-inference performance compared to the GNN without joint prediction, increasingaccuracy from 0.71 to 0.77. QC 20240527</p

A Metrics System for Quantifying Operational Coupling in Embedded Computer Control Systems

One central issue in system structuring and quality prediction is the interdependencies of system modules. This paper proposes a novel technique for determining the operational coupling in embedded computer control systems. It allows us to quantify dependencies between modules, formed by different kinds of relationships in a solution, and therefore promotes a more systematic approach to the reasoning about modularity. Compared to other existing coupling metrics, which are often implementation-technology specific such as confining to the inheritance and method invocation relationships in OO software, this metrics system considers both communication and synchronization and can be applied throughout system design. The metrics system has two parts. The first part supports a measurement of coupling by considering individual relationship types separately. The quantification is performed by considering the topology of connections, as well as the multiplicity, replication, frequency, and accuracy of component properties that appear in a relationship. The second part provides a methodology for combining coupling by individual relationship types into an overall coupling, where domain specific heuristics and technology constraints are used to determine the weighting

Combining Self-Organizing Map with Reinforcement Learning for Multivariate Time Series Anomaly Detection

Anomaly detection plays a critical role in condition monitors to support the trustworthiness of Cyber-Physical Systems (CPS). Detecting multivariate anomalous data in such systems is challenging due to the lack of a complete comprehension of anomalous behaviors and features. This paper proposes a framework to address time series multivariate anomaly detection problems by combining the Self-Organizing Map (SOM) with Deep Reinforcement Learning (DRL). By clustering the multivariate data, SOM creates an environment to enable the DRL agents interacting with the collected system  operational data in terms of a tabular dataset. In this environment, Markov chains reveal the likely anomalous features to support the DRL agent exploring and exploiting the state-action space to maximize anomaly detection performance. We use a time series dataset, Skoltech Anomaly Benchmark (SKAB), to evaluate our framework. Compared with the best results by some currently applied methods, our framework improves the F1 score by 9%, from 0.67 to 0.73. QC 20231220</p