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Hazard-intense applications of cyber-physical systems (CPSs), such as the evacuation of a building on fire, require personalized informing on the basis of a real-time assessment of dynamic context. In this paper, a context-dependent message construction mechanism (CD-MCM) is proposed with the objectives (i) to inform people about the emergence and development of a situation unsafe for them, and (ii) to instruct them what they have to do according to an adaptively computed action plan. To achieve this, the concepts of 'situation' and 'impact indicator' have been introduced in order to facilitate the computation of personalized action plans and to send messages about the level of danger and the requested actions. In both activities, the inferred implications of situations are used as the basis of informing the involved people. The messages are adapted to actual situations. In addition, the concept of 'relevance indicator' was utilized to assess the significance of the standing situations for the concerned people in a quasi-real-time manner. The level of danger was evaluated for each person by totaling the values of the situation-related relevance indicators. This was also used to select the proper message templates from the predefined alternatives in the process of message construction. The personalized messages were generated based on the chosen message template and the various message components describing concerned situations or providing instructions. The CD-MCM was validated in a simulated indoor fire evacuation guiding application. In the practical evaluation of the quality of the generated messages, a sample of test people was involved. The results of the evaluation show that the messages generated by the proposed CD-MCM lead to more effective messaging about the personal context and the expected actions than the messages constructed by using static context information only. The reason is that the proposed template-based message construction mechanism facilitates the appropriateness as well as the articulation of the contents of context-sensitive personalized messages.

The aim of this section is to provide an overview on the state of the current research, the exposition of the challenges faced in various fields of knowledge formalisation and representation (KFR),...

Smart CPSs (S-CPSs) have been evolving beyond what was identified by the traditional definitions of CPSs. The objective of our research is to investigate the concepts and implementations of reasoning processes for S-CPSs, and more specifically, the frameworks proposed for the fuzzy front end of their reasoning mechanisms. The objectives of the paper are: (i) to analyze the framework concepts and implementations of CPS, (ii) to review the literature concerning system-level reasoning and its enablers from the points of view of the processed knowledge, building awareness, reasoning mechanisms, decision making, and adaptation. Our findings are: (i) awareness and adaptation behaviors are considered as system-level smartness of S-CPSs that are not achieved by traditional design approaches; (ii) model-based and composability approaches insufficiently support the development of reasoning mechanisms for S-CPSs; (iii) frameworks for development of reasoning in S-CPS should support compositional design. Based on the conclusions above, we argue that coping with the challenges of compositionality requires both software-level integration and holistic fusion of knowledge by means of semantic transformations. This entails the need for a multi aspect framework that is able to capture at least conceptual, functional, architectural, informational, interoperation, and behavioral aspects. It needs further investigation if a compositionality enabling framework should appear in the form of a meta-framework (abstract) or in the form of a semantically integrated (concrete) framework. Highlights Smartness in CPSs is a holistic and synergistic behavioral characteristic. Complex mental representations are compositional. Compositionality is necessary for smart CPSs. Without a rigorous unifying framework, designing synthesis reasoning remains ad hoc.

Mathematical modelling of the shape-forming process of multi-layer multi-bead parts dealing with (i) a single layer, (ii) inclination angles and (iii) various slopes, is carried out. In the layers-overlapping model, material shortage areas are generated at the edges of layers. To solve the problem, the deposition amount of beads at the edges of the second and above layers should be modified, in association with the parameters applied to fabricate the slope. Various basic inclined components were deposited to validate the proposed mathematical formulation. Findings from the basic experiments were: (i) depositing additional material to compensate for the material shortage areas is necessary for realizing the designed geometries of the layers, (ii) depositing the bead at the layer edges alongside an already-deposited neighboring bead enables a better shape-forming in the case of negative slopes and (iii) the deposition order of beads in a layer has little influence on the shape formation of positive slopes. A complex part was also fabricated as a case study to validate the model and findings in the real-life context. Practical issues with regard to using the model and the solutions to the issues were discussed.

The deposition process of wire and arc additive manufacturing (WAAM) is usually planned based on a bead geometry model (BGM), which represents the relationship between bead geometries (e.g. width, height) and required deposition parameters. However, the actual deposition situation may deviate from the one in which the BGM is built, such as varied heat dissipation conditions, resulting in morphological changes of deposited beads and geometrical errors in the formed parts. In this paper, a novel control mechanism for enhancing the fabrication accuracy of WAAM based on fuzzy-logic inference is proposed. It considers the geometrical errors measured on already deposited layers and deposition context to adjust deposition parameters of beads in the subsequent layer, forming an interlayer closed-loop control (ICLC) mechanism. This paper not only presents the theoretical fundamentals of the ICLC mechanism but also reports the technical details about utilizing this mechanism to control the forming height of multi-layer multi-bead (MLMB) components. A fuzzy-logic inference machine was applied as the core component for calculating speed change of bead deposition based on height error and previously applied change. In terms of validation, the effectiveness of the proposed control mechanism and the implemented controller was investigated through both simulative studies and real-life experiments. The fabricated cuboid blocks showed good accuracy in height with a maximum error of 0.20 mm. The experimental results implied that the proposed ICLC approach facilitates deposition continuity of WAAM, and thus enables process automation for robotic manufacturing.

Nautical traffic management in The Netherlands is shifting from local traffic control to corridor traffic management. Current traffic management systems do not sufficiently support operators in perceptual and cognitive process to interpret and understand the large amounts of information needed for corridor traffic management. Newly developed user interface concepts aim to overcome deficiencies of current interface designs that insufficiently support situation awareness assessment. The effects of these new user interfaces, however, are insufficiently known due to the intricate relations between situation awareness, task performance, and workload. The objective of this study is to evaluate the effects of the three previously developed user interface concepts on operators’ situation awareness, task performance, and workload to gain better insights into the benefits and limitations of the user interface design concepts. The effects were tested in a simulator environment. The results show that user interface features of an integrated user interface allowed operators to apply more effective information processing, which resulted in better task performance. Features of a context-dependent adaptable user interface triggered proactive behavior of operators, which resulted in better task performance for tasks in which operators require insight into future activities of the elements in the environment.

Conventionally, a technical system is defined in the design phase and considers all important requirements and aspects. The expected operations and the circumstances of operations are to be known in advance. If these are known, then the designed system can even be validated before it is produced or launched on the market. Validation is typically based on predictive analyses or simulations. However, these do not apply completely in the case of smart systems such as smart cyber-physical systems (S-CPSs) which self-manage their operation, or at least a part of it. Being able to adapt during run-time and evolve over time, S-CPSs cannot be validated using conventional deterministic approaches. Typical examples of these self-managing systems are S-CPSs already used as instrumentation in the medical field. The above circumscribed situation stimulated our background research, the results of which are concisely summarized and critically concluded in this paper. The literature has been found fairly narrow in terms of novel validation approaches for self-managing systems. The literature proposes to share the tasks of operational and behavioral validation among the system designers and the technical systems themselves. While designers need prognostic approaches to validate system operation, systems need to construct validation plans and execute them at run-time. This requires additional, validation-specific functionalities and context-dependent mechanisms such as run-time validation frameworks or meta-models, objective-sensitive self-monitoring mechanisms, self-constraining and self-supporting mechanisms, and other enablers. Extensive foundational research and system prototype testing are deemed to be indispensable. To make the first small step in this direction, this paper proposes a concept for the validation of smart medical CPSs. This relies on the following hypothesis: If a system has the freedom for self-adaptation, then it should also be equipped with a self-control mechanism, meta-knowledge, and a supervisory controller. These additional resources enable purpose- and context-dependent semantic reasoning about the operational objectives and behavioral states. This paper suggests a number of topics for future research towards a run-time validation engine.

Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments.

Continuous enhancements of connected products make them able to generate and communicate a huge amounts of middle-of-life data streams to their producers. This affordance also creates a challenge for current data analytics tools unable to keep up with the heterogeneous nature and characteristics of these type of data. Accordingly, a function able to combine data from multiple data streams and analyze them as one source of information is definitely needed in a next-generation data analytics toolbox to support product enhancements by designers. As a result of a recent Ph.D. project, this paper presents the conceptualization and the implementation of a novel function of merging middle-of-life data streams. The implemented computational mechanism (i) acquires middle-of-life data streams, (ii) pre-processes them individually, (iii) merges information from the concerned streams, (iv) derives recommendation based on the merged information, and (v) send a recommendation as a message to the designer. The performance of the computational implementation was tested in an application case of data steaming and management to white goods designers for enhancing a connected washing machine. From a computational point of view, the testing proved that the set of proprietary algorithms designed for the realization of computational merging, together with the existing ones taken from the literature, were able to efficiently perform the subtasks. The advantages of merges were: (i) it provides more information than the one obtained by processing sensors' data individually, (ii) it reflects the condition of the product with a higher fidelity, (iii) it communicates information about the product while it is in use by the customer, (iv) it reduces the sensors analyses time and effort, and (v) it provides recommendation as an action plan concerning the product at hand. The outcomes of this study will be used in a follow up research to develop a comprehensive smart data analytics toolbox to support product designers in product innovation.

Robotic wire and arc additive manufacturing (WAAM) systems are required to provide predictable and efficient operations to fabricate solid metallic parts with high morphological fidelity and geometric accuracy. Since the metallic parts are fabricated based on a layer-by-layer principle, the interactions between the neighboring beads and layers strongly influence the geometric accuracy of the fabricated part. The layers-overlapping process has been studied and a traditional layers-overlapping model (T-LOM) has been published in the literature. This paper proposes a layers-overlapping strategy (LOS), based on which a revised layers-overlapping model (R-LOM) was proposed for the fabrication of multi-layer multi-bead (MLMB) components with homogeneous layers. A mathematical model for layers-overlapping is presented, which considers the material shortage areas at the edges of the layers. This is important since the material shortage areas result in a situation that the component width is smaller than the expected value. In addition, they will be accumulated when multiple layers are overlapped through normal unidirectional parallel (NUP) paths. The proposed LOS addresses two aspects: (i) the deposition amount of the first bead and the last bead in the lap layers should be increased and (ii) the deposition position of the first bead and the last bead in the lap layers should be moved towards the edges with a given offset distance. Validation experiments were designed and conducted to test the proposed concepts and models. The experimental results indicated that (i) the R-LOM enables the MLMB components to achieve the expected width and (ii) for components deposited with NUP paths, the R-LOM eliminates the effect of accumulation of material shortage areas on the first bead and increases the surface flatness.

Wire and arc additive manufacturing (WAAM) is a competitive technology for fabricating metallic parts with complex structure and geometry. It enables the fabrication of multi-layer multi-bead (MLMB) parts. The basis of planning the deposition paths is the beads overlapping model (BOM). The existing overlapping models consider only the geometric area of adjacent beads, but ignore the spreading of the melted weld beads. The objective of the research was to develop an enhanced BOM (E.BOM) for WAAM, which takes the spreading of the weld beads into consideration. A deposited bead spreads to the already deposited neighboring bead and as a consequence, its center point deviates from the center point of the fed (to be melted) wire. Experiments were designed to explore the relationships between the geometries of the beads, and the offset distance between the center of a weld bead and the center of the fed wire. An artificial neural network was used to predict the offset distance of a certain weld bead based on the results of the experiments. In addition, a reasoning algorithm was implemented to calculate the optimal distance between the centers of adjacent deposition paths in order to achieve a planned center distance between adjacent beads. This enables the control of the actual center distance of the adjacent beads according to an expected value. The E.BOM has been tested by validation experiments. On the one hand, it improves the surface flatness of layers of MLMB parts produced by WAAM. On the other hand, it prevents formation of defects inside the parts.

Enhancing engagement of patients during stroke rehabilitation exercises are in the focus of current research. Various methods and computer supported tools have been developed for this purpose, which try to avoid mundane exercising that is prone to become a routine or even boring for the patients and leads to ineffective training. This paper introduces an engagement enhancing cyber-physical stroke rehabilitation system (CP-SRS) aiming at enhancing the patient's engagement during rehabilitation training exercises. This paper focuses on introducing the implementation and validation of the engagement monitoring subsystem (EMS) in the CP-SRS. The EMS is expected to evaluate the patient's actual engagement levels in motor, perceptive, cognitive and emotional aspects. Experiments in these four aspects were conducted separately, in order to characterize the range and accuracy of the engagement indicators by influencing the subjects into different engaged states. During the experiments, different setups were created to mimic the situations in which the subject was engaged or not engaged. The subjects involved in the experiments were healthy subjects. Results showed that the measurement in motor, perceptive, cognitive, and emotional aspects can represent the corresponding engagement level. More experiments will be conducted in the future to validate the efficiency of the CP-SRS in enhancing the engagement with stroke patients.