Discrete-time dynamic modeling for software and services composition as an extension of the Markov chain approach

Discrete Time Markov Chains (DTMCs) and Continuous Time Markov Chains (CTMCs) are often used to model various types of phenomena, such as, for example, the behavior of software products. In that case, Markov chains are widely used to describe possible time-varying behavior of “self-adaptive” software systems, where the transition from one state to another represents alternative choices at the software code level, taken according to a certain probability distribution. From a control-theoretical standpoint, some of these probabilities can be interpreted as control signals and others can just be observed. However, the translation between a DTMC or CTMC model and a corresponding first principle model, that can be used to design a control system is not immediate. This paper investigates a possible solution for translating a CTMC model into a dynamic system, with focus on the control of computing systems components. Notice that DTMC models can be translated as well, providing additional information

Human Performance Modelling for Adaptive Automation

The relentless march of technology is increasingly opening new possibilities for the application of automation and new horizons for human machine interaction. However there is insufficient scientific evidence on human factors for modern socio-technical systems supporting the guidelines currently used to design Human Machine Interfaces (HMI) (ISA 2014). This dearth of knowledge presents a particular risk in safety critical industries. The continuing 60–90% of accidents currently that are rooted in Human Factors (HF) and the rapid developments in the Internet of Things (IoT) and its novel automation archetypes means that the requirements for new interfaces are becoming more demanding, and creating new failure modes. To address this gap it is necessary to face the issue of modelling the human factor element and be ready to incorporate that knowledge into the design of adaptive automation

Human performance in manufacturing tasks: Optimization and assessment of required workload and capabilities

This paper discusses some examples where human performance and or human error prediction was achieved by using a modified version of the Rasch model(1980), where the probability of a specified outcome is modelled as a logistic function of the difference between the person capacity and item difficulty. The model needs to be modified to take into account an outcome that may not be dichotomous and o take into account the interaction between two macro factors: (a) Task complexity: that summarises all factors contributing to physical and mental workload requirements for execution of a given operative task & (b) Human capability: that considered the skills, training and experience of the people facing the tasks, representing a synthesis of their physical and cognitive abilities to verify whether or not they are matching the task requirements. Task complexity can be evaluated as a mathematical construct considering the compound effects of Mental Workload Demands and Physical Workload Demands associated to an operator task. Similarly, operator capability can be estimated on the basis of the operators' set of cognitive capabilities and physical conditions. The examples chosen for the application of the model were quite different: one is a set of assembly workstation in large computer manufacturing company and the other a set of workstation in the automotive sector. This paper presents and discusses the modelling hypothesis, the interim field data collection, results and possible future direction of the studies.

Hybrid Predictive Models for Accurate Forecasting in PV Systems

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error

Light Unmanned Aerial Vehicles (UAVs) for cooperative inspection of PV plants

After a fast photovoltaic (PV) expansion in the past decade supported by many governments in Europe, in this postincentive era, one of the most significant open issues in the PV sector is to find appropriate inspection methods to evaluate real PV plant performance and failures. In this context, PV modules are surely the key components affecting the overall system performance; therefore, there is a main concern about the occurrence of any kind of failure in PV modules. This paper aims to propose a novel concept for monitoring PV plants by using light unmanned aerial vehicles (UAVs) or systems (UASs) during their operation and maintenance. The main objectives of this study are to explore and evaluate the use of different UAV technologies and to propose a reliable, cost-effective, and time-saving method for the inspection of PV plants. In this research, different UAVs were employed to inspect a PV array field. For this purpose, some thermal imaging cameras and a visual camera were chosen as monitoring tools to suitably scan PV modules. The first results show that the procedure of utilizing UAV was effective in the detection of different failures of PV modules. Moreover, such a process was much faster and cost effective than traditional methods

Risk based approach for procedures' optimization

Despite an increase in the process automation, different activities remain mainly operator driven, as the loading and unloading of tankers, maintenance operations, and so on. In these cases, the activities performed by the operator can be critical, both for the safety and for the product quality. Optimizing the operational procedures is thus a key factor for quality and safety. A risk assessment of the procedure can be adopted as a base for optimisation, highlighting which of the tasks within the procedure mainly contributes to the risk of the working activity. Usually the analysis of the procedures is carried on through a task analysis as in Builes et al. (2014). In this paper the task analysis is used as a starting point for a quantitative risk assessment carried on through an integrated dynamic decision analysis. The logical-probabilistic model of the procedure is elaborated jointly with a consequences analysis, obtaining a risk assessment for all the sequences of tasks of the work procedure under analysis. The risk assessment considered both possible equipment failures and the potential operational errors in executing the tasks. The proposed approach is in this paper demonstrated through the application of the integrated decision analysis for the operation of unloading of ammonia in a plant for the production and storage of fertilizers

Sloshing dynamics estimation for liquid-filled containers performing 3-dimensional motions: modeling and experimental validation

Many industrial applications require the displacement of liquid-filled containers on planar paths (namely, paths on a horizontal plane), by means of linear transport systems or serial robots. The movement of the liquid inside the container, known as sloshing, is usually undesired, thus there is the necessity to keep under control the peaks that the liquid free-surface exhibits during motion. This paper aims at validating a model for estimating the liquid sloshing height, taking into account 2-dimensional motions of a cylindrical container occurring on a horizontal plane, with accelerations up to 9.5 m/s2. This model can be exploited for assessment or optimization purposes. Experiments performed with a robot following three paths, each one of them with different motion profiles, are described. Comparisons between experimental results and model predictions are provided and discussed. Finally, the previous formulation is extended in order to take into account the addition of a vertical acceleration, up to 5 m/s2. The resulting 3-dimensional motions are experimentally validated to prove the effectiveness of the extended technique

Correlation between magnetic interactions and domain structure in A1 FePt ferromagnetic thin films

We have investigated the relationship between the domain structure and the magnetic interactions in a series of FePt ferromagnetic thin films of varying thickness. As-made films grow in the magnetically soft and chemically disordered A1 phase that may have two distinct domain structures. Above a critical thickness $d_{cr}\sim 30$ nm the presence of an out of plane anisotropy induces the formation of stripes, while for $d<d_{cr}$ planar domains occur. Magnetic interactions have been characterized using the well known DCD-IRM remanence protocols, $\delta M$ plots, and magnetic viscosity measurements. We have observed a strong correlation between the domain configuration and the sign of the magnetic interactions. Planar domains are associated with positive exchange-like interactions, while stripe domains have a strong negative dipolar-like contribution. In this last case we have found a close correlation between the interaction parameter and the surface dipolar energy of the stripe domain structure. Using time dependent magnetic viscosity measurements, we have also estimated an average activation volume for magnetic reversal, $\langle V_{ac}\rangle \sim 1.37\times 10^{4}$ nm$^{3},$ which is approximately independent of the film thickness or the stripe period.Comment: 25 pages, 11 figure