Architecting Self-adaptive Software Systems

Peer reviewe

A hybrid approach combining control theory and AI for engineering self-adaptive systems

Control theoretical techniques have been successfully adopted as methods for self-adaptive systems design to provide formal guarantees about the effectiveness and robustness of adaptation mechanisms. However, the computational effort to obtain guarantees poses severe constraints when it comes to dynamic adaptation. In order to solve these limitations, in this paper, we propose a hybrid approach combining software engineering, control theory, and AI to design for software self-adaptation. Our solution proposes a hierarchical and dynamic system manager with performance tuning. Due to the gap between high-level requirements specification and the internal knob behavior of the managed system, a hierarchically composed components architecture seek the separation of concerns towards a dynamic solution. Therefore, a two-layered adaptive manager was designed to satisfy the software requirements with parameters optimization through regression analysis and evolutionary meta-heuristic. The optimization relies on the collection and processing of performance, effectiveness, and robustness metrics w.r.t control theoretical metrics at the offline and online stages. We evaluate our work with a prototype of the Body Sensor Network (BSN) in the healthcare domain, which is largely used as a demonstrator by the community. The BSN was implemented under the Robot Operating System (ROS) architecture, and concerns about the system dependability are taken as adaptation goals. Our results reinforce the necessity of performing well on such a safety-critical domain and contribute with substantial evidence on how hybrid approaches that combine control and AI-based techniques for engineering self-adaptive systems can provide effective adaptation

Evaluating Architectural Safeguards for Uncertain AI Black-Box Components

Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability

On extending process monitoring and diagnosis to the electrical and mechanical utilities: an advanced signal analysis approach

This thesis is concerned with extending process monitoring and diagnosis to electrical and mechanical utilities. The motivation is that the reliability, safety and energy efficiency of industrial processes increasingly depend on the condition of the electrical supply and the electrical and mechanical equipment in the process. To enable the integration of electrical and mechanical measurements in the analysis of process disturbances, this thesis develops four new signal analysis methods for transient disturbances, and for measurements with different sampling rates. Transient disturbances are considered because the electrical utility is mostly affected by events of a transient nature. Different sampling rates are considered because process measurements are commonly sampled at intervals in the order of seconds, while electrical and mechanical measurements are commonly sampled with millisecond intervals. Three of the methods detect transient disturbances. Each method progressively improves or extends the applicability of the previous method. Specifically, the first detection method does univariate analysis, the second method extends the analysis to a multivariate data set, and the third method extends the multivariate analysis to measurements with different sampling rates. The fourth method developed removes the transient disturbances from the time series of oscillatory measurements. The motivation is that the analysis of oscillatory disturbances can be affected by transient disturbances. The methods were developed and tested on experimental and industrial data sets obtained during industrial placements with ABB Corporate Research Center, Kraków, Poland and ABB Oil, Gas and Petrochemicals, Oslo, Norway. The concluding chapters of the thesis discuss the merits and limitations of each method, and present three directions for future research. The ideas should contribute further to the extension of process monitoring and diagnosis to the electrical and mechanical utilities. The ideas are exemplified on the case studies and shown to be promising directions for future research.Open Acces

Standoff Sensing Technology Based on Laser-Induced Breakdown Spectroscopy: Advanced Targeting, Surveillance and Reconnaissance in Security and Architectural Heritage Applications

Due to the ability to perform simultaneous, multi-element and real-time analysis without pretreatment and doing from a distance, laser induced breakdown spectroscopy (LIBS) in standoff mode is now considered a cutting-edge analytical technology. All these features have allowed its application in various fields such as security, environment, cultural heritage protection and space exploration, among the more outstanding. Nonetheless, the fact of working to long distances involves greater difficulties than in a lab-scale. Thus, in a first part of this memory, the behavior of the analytical signal has been assessed. On the other hand, a second part demonstrates the applicability of the technique in standoff mode for solving real-life problems. • Fundamental studies 1. Main causes affecting the uncertainty of the analytical signal in standoff LIBS. One of the most sensitive issues in standoff LIBS is maybe the large variability observed in the analytical response of distant targets. Therefore, in this work, a standoff LIBS sensor has been used to assessment of the laser beam delivering up to a distant target as well as the properties of the light emitted from the plasma induced gathered by the sensor. • Applications standoff LIBS 1. Evaluation of the Cultural Heritage: Malaga Cathedral. Cultural heritage is a valuable source of history and a unique and irreplaceable legacy of our past. While sometimes an artwork can be transported to the laboratory for its analysis, in other cases this option is not feasible. The ease compaction in mobile platforms of LIBS instrumentation for in situ analysis, allows for moving the system sensor to the location of the sample. For first time a standoff LIBS system has been used to characterize and analyze the composition of building materials as well as potential sources of contamination in a historic building on difficult to access areas, since this technology only requires a clear line of sight to the target. I. Location and identification of explosive-contaminated fingerprint. Nowadays, it is clear that the detection of explosives due to numerous terrorist attacks requires a special attention. LIBS is an attractive technology to anticipating this type of threats. In the present work, the ability of a mobile LIBS sensor to locate and identify fingerprints of explosives residues (DNT, TNT, RDX, PETN and chloratite ) on different surfaces (aluminum and glass) from a minimum distance of 30m has been demonstrated. Chemical distribution maps of the different residues with 100% effectiveness were developed. However, despite the effectiveness of the technique in the localization and detection of explosives residues, one of the main problems is the identification of products that share a similar elemental composition, and thus a similarity in the analytical response. In this memory have been developed and implemented chemometric algorithms, which are capable of adapting to different working ranges, to distinguish residues of organic explosives of traces of dairy products, such as olive oil, motor oil, hand cream, gasoline, fuel oil, etc. on a metal surface (aluminum). This strategy allows categorize the residues assessed with a 100% accuracy and error rates below 5 %. II. Forensic studies for the determination of radiological material. Although radioactivity has numerous applications in everyday life, the danger of a radiological dispersal event, either by natural causes or malicious (dirty bombs) is more than evident. Therefore, the detection and identification of explosives as well as their monitoring and quantification from a safe location is demanded. The potential of standoff LIBS to scan, analyze and quickly characterize the radiological contamination in various objects of street furniture has been here evaluated. The results have demonstrated the selectivity and sensitivity of the technology to detect radioactive surrogates such as Co, Ba, Sr, Cs, Ir and U on substrates of aluminum, clay, concrete and glass. It have been also demonstrated the capabilities of the technique for simultaneous and in situ analysis of explosive and radiological evidence in a post-detonation scenario