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Traditionally, the preferred means of documentation used by safety engineers have been sheets- and text-based solutions. However, in the last decades, the introduction of model-driven engineering in conjunction with Component-Based Design has been influencing the way safety engineers perform their tasks; especially in the area of fault analysis, model-driven approaches have been developed aimed at coupling fault trees with architecture models. Doing this fosters communication between engineers, may reduce design effort, and makes artifacts easier to maintain and reuse. In this paper, we want to move forward in this direction and take another step in the modeling of Component Fault Trees in combination with the modeling of the architecture design. We propose a hazard-centric approach for the definition of multiple realization views for fault analysis using Component Fault Trees. The approach is composed of a modeling method and a tool solution. We illustrate our approach with a real-life example from the automotive industry. [ABSTRACT FROM AUTHOR]

This paper presents a component-based approach tailored for the modelling of avionic systems. The system is defined as a set of applications developed following time partitioning principle. The toolset provides means to help the designer configure the system and compute a partition schedule for each available processing unit. The model and the generated schedule can then be used as inputs for a response-time analysis engine that calculates the worst-case response time of each task and therefore, assesses the overall system schedulability. [ABSTRACT FROM AUTHOR]

In order to deal with extremely large quantities of information, embedded systems need high capabilities in order to process the whole amount of data in real time. Two trends are present in the field: the usage of boards with Graphics Processing Units (GPUs) and the usage of component-based development (CBD). Components with GPU capabilities have the great advantage to be platform-independent. However, developing embedded systems with GPUs by using CBD was considered until very recently a problem with restricted availability and exibility. By introducing specific GPU support for CBD in the form of exible components and by improving their communication, a solution was identified and checked. Present paper aims to present a state-of-the-art and highlights the newest knowledge to date, articulating encountered confronted issues and describing existing solution approaches. [ABSTRACT FROM AUTHOR]

Event-based communication is used in different domains including telecommunications, transportation, and business information systems to build scalable distributed systems. Such systems typically have stringent requirements for performance and scalability as they provide business and mission critical services. While the use of event-based communication enables loosely-coupled interactions between components and leads to improved system scalability, it makes it much harder for developers to estimate the system's behavior and performance under load due to the decoupling of components and control flow. In this paper, we present our approach enabling the modeling and performance prediction of event-based systems at the architecture level. Applying a model-to-model transformation, our approach integrates platform-specific performance influences of the underlying middleware while enabling the use of different existing analytical and simulation-based prediction techniques. In summary, the contributions of this paper are: (1) the development of a meta-model for event-based communication at the architecture level, (2) a platform aware model-to-model transformation, and (3) a detailed evaluation of the applicability of our approach based on two representative real-world case studies. The results demonstrate the effectiveness, practicability and accuracy of the proposed modeling and prediction approach. [ABSTRACT FROM AUTHOR]

To generate efficient and low-energy software test case and by combination of UML model and component information, the paper has put forward a component-based energy consumption test case to distribute component test quantity in software systems. This allocation method takes into account system structural features and relationship between the test quantity required by each system component and reliability which can be achieved. Therefore, when achieving the certain target value of system reliability, the total test quantity required will be the minimum. Meanwhile, by combining with the importance degree of software architecture angle to system, visiting rate of component and difficulty degree of improving component reliability, the test quantity can be effectively allocated. In a simulation, we adopt two models to simulate the relationship between component test quantity and reliability. In two models, when structural optimization method achieves certain reliability value, it will require less test quantity than the quantity required in equal allocation strategy and experts' experience. Therefore, this is a more effective test quantity allocation strategy. [ABSTRACT FROM AUTHOR]

Inter-module connections (IMC) are a research focus closely related to the robustness of steel volumetric buildings (VB). Many IMC have been proposed by numerous researchers and engineers, experimentally tested and numerically studied using finite element models. However, there are insufficient IMC macro models available, which imposes challenges for engineers to construct a global numerical VB model. Hence, this study aims to close the gap with a component-based macro-mechanical model for the macro-modelling of IMC in steel VB. In this paper, a comprehensive IMC database was collected to identify and characterise the active components. Two types of macro-mechanical models (H-shape and Q-shape) consisting of P-V-M links have been proposed and a novel uplifting mechanism has been derived for a typical IMC (bolted tie plate with shear key). The proposed macro-mechanical model and other existing macro-models were then compared with existing pushover experiments from an IMC subassembly. The proposed macro-mechanical model shows a good match to the existing experimental results, and it is adaptable to existing IMC. • A component-based macro-mechanical model for inter-module connections (IMC) is proposed. • A comprehensive IMC database was collected to identify and characterise the active components. • A novel uplifting mechanism is derived for a typical IMC (bolted tie plate with shear key). • The proposed macro-mechanical model is compared with existing pushover experiments of IMC subassembly. [ABSTRACT FROM AUTHOR]

High-strength structural bolts are used in nearly every steel beam-to-column connection in typical steel building construction practice. Thus, accurately modeling the behavior of high-strength bolts at elevated temperatures is crucial for properly evaluating the connection capacity, and is also important in evaluating the strength and stability of steel buildings subjected to fires. This paper uses a component-based modeling approach to empirically derive the ultimate tensile strength and modulus of elasticity for grade A325 and A490 bolt materials based on data from double-shear testing of high-strength 25 mm (1 in.) diameter bolts at elevated temperatures. Using these derived mechanical properties, the component-based model is then shown to accurately account for the temperature-dependent degradation of shear strength and stiffness for bolts of other diameters, while also providing the capability to model load reversal. [ABSTRACT FROM AUTHOR]

In this paper, we propose a face perception model to mimic the biological mechanism of face perception and memory in human brain. We are mainly inspired by the fact that there are two functionally and neurologically distinct pathways after the early face perception and they both interact to process the changeable features of faces. Accordingly, our model consists of three perception parts, facial structure perception, facial expression perception and facial identity perception, which are all component-based. The structure perception has a feed-forward projection to the expression and identity perception, while the expression affects the identity perception with a modulation process. We embody the three parts referring to three bio-inspired computational models. For the facial structure perception, we utilize a cascaded-CNN (convolutional neural networks) approach to estimate the center locations of key facial components. For the facial expression perception, we propose a novel approach which exploits convolutional deep belief networks (CDBN) to spontaneously locate the places containing the most discriminative information and synchronously complete the feature learning and feature selection. For the facial identity perception, we propose an approach which adopts the hierarchical max-pooling (HMAX) model to encode notable characteristics of facial components and utilizes a new memory formation integrating the preliminary decision, expression modulation and final decision process. We evaluate our model through a series of experiments and the experimental results demonstrate its rationality and effectiveness. [ABSTRACT FROM AUTHOR]

Software reliability is prioritised as the most critical quality attribute. Reliability prediction models participate in the prevention of software failures which can cause vital events and disastrous consequences in safety-critical applications or even in businesses. Predicting reliability during design allows software developers to avoid potential design problems, which can otherwise result in reconstructing an entire system when discovered at later stages of the software development life-cycle. Several reliability models have been built to predict reliability during software development. However, several issues still exist in these models. Current models suffer from a scalability issue referred to as the modeling of large systems. The scalability solutions usually come at a high computational cost, requiring solutions. Secondly, consideration of the nature of concurrent applications in reliability prediction is another issue. We propose a reliability prediction model that enhances scalability by introducing a system-level scenario synthesis mechanism that mitigates complexity. Additionally, the proposed model supports modeling of the nature of concurrent applications through adaption of formal statistical distribution toward scenario combination. The proposed model was evaluated using sensors-based case studies. The experimental results show the effectiveness of the proposed model from the view of computational cost reduction compared to similar models. This reduction is the main parameter for scalability enhancement. In addition, the presented work can enable system developers to know up to which load their system will be reliable via observation of the reliability value in several running scenarios. [ABSTRACT FROM AUTHOR]

• A detailed EMT-type component-based load model is capable of almost accurately reproducing the measured dynamic behavior of load during and following a voltage sag. • Using measured data to derive relevant characteristics of the modelled load (e.g., connection or reconnection pattern of motor loads during and following voltage sags or load torque characteristics of single-phase motors) is beneficial to provide a more realistic representation of the load. • Approximate, yet reasonable load composition can reproduce the actual measured load behavior. All circumstantial data (i.e., geographical location, time, temperature, etc.) should be accounted for to achieve the best estimate of load composition. Nevertheless, load component data will always have some uncertainty no matter how hard one tries to obtain this information. Load modeling is one of the most important parts in power system simulation. This paper describes the efforts to develop a detailed EMT-type component-based load model. The aim of this new model is to capture the accurate behavior of loads in commercial and residential sectors. The developed model is validated and refined against field data which were recorded by Hydro-Quebec during voltage sag events. The simulations are performed using the time-domain simulation tool EMTP. [ABSTRACT FROM AUTHOR]

High accuracy contour error estimation and direct contour error control are two major approaches to reduce the contour error. However, two key factors make them complex for five-axis machine tools: the nonlinear kinematics and the coupling between the tool position and orientation. In this study, by finding the reference point nearest to the current actual position, and interpolating the point with two neighboring reference points and using the distance ratio, a new contour error estimation method for five-axis machine tools is proposed, which guarantees high accuracy while depending on only the reference points. By adding a weighted contour error on the tracking error in the workpiece coordinate system, and specifying a desired second-order error dynamics based on the error variable, an effective contouring control method is proposed, which can alleviate the problem: when the contour error components are introduced into the controller, the contour errors increase instead in some regions of the tracking trajectory. A series of experiments are performed on a tilting-rotary-table (TRT) type five-axis machine tool. The results reveal that the proposed estimation method has high accuracy, and compared with the case without contour error control, the proposed control approach can reduce the contour error along the whole trajectory. [ABSTRACT FROM AUTHOR]

Enabling early validation of extra functional properties can play an important role in the development process of reliable and safe cyber physical systems (CPS); this is particularly relevant in the automotive industry where the ever-increasing need to reduce electronic control unit (ECU) production development cost, enhance quality, and shorten the development cycle time is a crucial aspect. Building on the results of the recently ended CONCERTO project, CHESS pursues this objective by proposing a methodology and technology which fits into the well-defined AUTOSAR workflow. [ABSTRACT FROM AUTHOR]

Versatility, flexibility and robustness are essential requirements for software forensic tools. Researchers and practitioners need to put more effort into assessing this type of tool. A Markov model is a robust means for analyzing and anticipating the functioning of an advanced component based system. It is used, for instance, to analyze the reliability of the state machines of real time reactive systems. This research extends the architecture-based software reliability prediction model for computer forensic tools, which is based on Markov chains and COSMIC-FFP. Basically, every part of the computer forensic tool is linked to a discrete time Markov chain. If this can be done, then a probabilistic analysis by Markov chains can be performed to analyze the reliability of the components and of the whole tool. The purposes of the proposed reliability assessment method are to evaluate the tool's reliability in the early phases of its development, to improve the reliability assessment process for large computer forensic tools over time, and to compare alternative tool designs. The reliability analysis can assist designers in choosing the most reliable topology for the components, which can maximize the reliability of the tool and meet the expected reliability level specified by the end-user. The approach of assessing component-based tool reliability in the COSMIC-FFP context is illustrated with the Forensic Toolkit Imager case study. [ABSTRACT FROM AUTHOR]

Building demolition is one of the main sources of waste generation in urban areas and is a growing problem for cities due to the generated environmental impacts. To promote high levels of circular economy, it is necessary to better understand the waste-flow composition; nevertheless, material flow studies typically focus on low levels of detail. This article presents a model based on a bottom-up macro-component approach, which allows the multiscale characterization of construction materials and the estimation of demolition waste flows, a model that we call the BTP-flux model. Data mining, analytical techniques, and geographic information system (GIS) tools were used to assess different datasets available at the national level and develop a common database for French buildings: BDNB. Generic information for buildings in the BDNB is then enriched by coupling every building with a catalog of macro-components (TyPy), thus allowing the building's physical description. Subsequently, stock and demolition flows are calculated by aggregation and classified into 32 waste categories. The BTP-flux model was applied in Île-de-France in a sample of 101,320 buildings for residential and non-residential uses, representative of the assessed population (1,968,242 buildings). In the case of Île-de-France, the building stock and the total demolition flows were estimated at 1382 Mt and 4065 kt, respectively. For its inter-regional areas—departments—, stock and demolition waste can vary between 85 and 138 tons/cap and 0.263 and 0.486 tons/cap/year, respectively. The mean of the total demolition wastes was estimated at 0.33 tons/cap/year for the region. Results could encourage scientists, planners, and stakeholders to develop pathways towards a circular economy in the construction sector by implementing strategies for better management of waste recovery and reintegrating in economic circuits, while preserving a maximum of their added value. [ABSTRACT FROM AUTHOR]