Modelling and Verification of a Correction Factor to Evaluate the Efficiency of Solar Thermal Collectors with the Rapid Test Method

A new method for the evaluation of the efficiency of parabolic trough collectors, called Rapid Test Method, is investigated at the Solar Institut Jülich. The basic concept is to carry out measurements under stagnation conditions. This allows a fast and inexpensive process due to the fact that no working fluid is required. With this approach, the temperature reached by the inner wall of the receiver is assumed to be the stagnation temperature and hence the average temperature inside the collector. This leads to a systematic error which can be rectified through the introduction of a correction factor. A model of the collector is simulated with COMSOL Multipyisics to study the size of the correction factor depending on collector geometry and working conditions. The resulting values are compared with experimental data obtained at a test rig at the Solar Institut Jülich. These results do not match with the simulated ones. Consequentially, it was not pos-sible to verify the model. The reliability of both the model with COMSOL Multiphysics and of the measurements are analysed. The influence of the correction factor on the rapid test method is also studied, as well as the possibility of neglecting it by measuring the receiver’s inner wall temperature where it receives the least amount of solar rays. The last two chapters analyse the specific heat capacity as a function of pressure and tem-perature and present some considerations about the uncertainties on the efficiency curve obtained with the Rapid Test Method.

Verification and Implementation of Call Sequence Analysing Algorithm

Työssä arvioidaan ja verifioidaan puheluiden luokitteluun suunniteltu Call Sequence Analysing Algorithm (CSA-algoritmi). Algoritmin tavoitteena on luokitella riittävän samankaltaiset puhelut ryhmiksi tarkempaa vika-analyysia varten. Työssä esitellään eri koneoppimisalgoritmien pääluokitukset ja niiden tyypilliset eroavaisuudet, eri luokitteluprosesseille ominaiset datatyypit, sekä toimintaympäristöt, joissa kyseinen toteutus on suunniteltu toimivaksi. CSA-algoritmille syötetään verkon ylläpitoviesteistä koostuvia viestisarjoja, joiden sisällön perusteella samankaltaiset sarjat ryhmitellään kokonaisuuksiksi. Algoritmin suorituskykyä arvioidaan 94 käsin luokitellun verrokkisarjan avulla. Sarjat on kerätty toimivasta 3G-verkon kontrollerista. Kahta sarjaa vertailemalla sarjaparille muodostetaan keskinäinen tunnusluku: sarjojen samanlaisuutta kuvaava etäisyys. Tässä työssä keskitytään erityisesti Hamming-etäisyyteen. Etäisyyden avulla sarjat koostetaan ryhmiksi. Muuttamalla hyväksyttävää maksimietäisyyttä, jonka perusteella kaksi sarjaa lasketaan kuuluvaksi samaan ryhmään, saadaan aikaiseksi alaryhmiä, joihin kuuluu ainoastaan samankaltaisia sarjoja. Hyväksyttävän etäisyyden kasvaessa, myös virheluokitusten määrä kasvaa. Oikeiden lajittelutulosten vertailukohteena toimii käsin luokiteltu ryhmittely. CSA-algoritmin luokittelutuloksen tarkkuus esitetään prosentuaalisena osuutena tavoiteryhmittelystä maksimietäisyyden funktiona. Työssä osoitetaan, miten etäisyysattribuutiksi valittu Hamming-etäisyys ei sovellu tämän datan luokitteluun. Työn lopussa ehdotetaan menetelmää ja työkalua, joiden avulla useampaa eri lajittelija-algoritmia voidaan testata nopealla kehityssyklillä.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Neuromorphic model for sound source segregation

While humans can easily segregate and track a speaker's voice in a loud noisy environment, most modern speech recognition systems still perform poorly in loud background noise. The computational principles behind auditory source segregation in humans is not yet fully understood. In this dissertation, we develop a computational model for source segregation inspired by auditory processing in the brain. To support the key principles behind the computational model, we conduct a series of electro-encephalography experiments using both simple tone-based stimuli and more natural speech stimulus. Most source segregation algorithms utilize some form of prior information about the target speaker or use more than one simultaneous recording of the noisy speech mixtures. Other methods develop models on the noise characteristics. Source segregation of simultaneous speech mixtures with a single microphone recording and no knowledge of the target speaker is still a challenge. Using the principle of temporal coherence, we develop a novel computational model that exploits the difference in the temporal evolution of features that belong to different sources to perform unsupervised monaural source segregation. While using no prior information about the target speaker, this method can gracefully incorporate knowledge about the target speaker to further enhance the segregation.Through a series of EEG experiments we collect neurological evidence to support the principle behind the model. Aside from its unusual structure and computational innovations, the proposed model provides testable hypotheses of the physiological mechanisms of the remarkable perceptual ability of humans to segregate acoustic sources, and of its psychophysical manifestations in navigating complex sensory environments. Results from EEG experiments provide further insights into the assumptions behind the model and provide motivation for future single unit studies that can provide more direct evidence for the principle of temporal coherence.

JCML: A specification language for the runtime verification of Java Card programs

COSTA, Umberto Souza; MOREIRA, Anamaria Martins; MUSICANTE, Matin A.; SOUZA NETO, Plácido A. JCML: A specification language for the runtime verification of Java Card programs. Science of Computer Programming. [S.l]: [s.n], 2010.

Specification and Runtime Verification of Java Card Programs

COSTA, Umberto Souza da; MOREIRA, Anamaria Martins; MUSICANTE, Martin A. Specification and Runtime Verification of Java Card Programs. Electronic Notes in Theoretical Computer Science. [S.l:s.n], 2009.

Probabilistic verification of satellite systems for mission critical applications

In this thesis, we present a quantitative approach using probabilistic verification techniques for the analysis of reliability, availability, maintainability, and safety (RAMS) properties of satellite systems. The subject of our research is satellites used in mission critical industrial applications. A strong case for using probabilistic model checking to support RAMS analysis of satellite systems is made by our verification results. This study is intended to build a foundation to help reliability engineers with a basic background in model checking to apply probabilistic model checking to small satellite systems. We make two major contributions. One of these is the approach of RAMS analysis to satellite systems. In the past, RAMS analysis has been extensively applied to the field of electrical and electronics engineering. It allows system designers and reliability engineers to predict the likelihood of failures from the indication of historical or current operational data. There is a high potential for the application of RAMS analysis in the field of space science and engineering. However, there is a lack of standardisation and suitable procedures for the correct study of RAMS characteristics for satellite systems. This thesis considers the promising application of RAMS analysis to the case of satellite design, use, and maintenance, focusing on its system segments. Data collection and verification procedures are discussed, and a number of considerations are also presented on how to predict the probability of failure. Our second contribution is leveraging the power of probabilistic model checking to analyse satellite systems. We present techniques for analysing satellite systems that differ from the more common quantitative approaches based on traditional simulation and testing. These techniques have not been applied in this context before. We present the use of probabilistic techniques via a suite of detailed examples, together with their analysis. Our presentation is done in an incremental manner: in terms of complexity of application domains and system models, and a detailed PRISM model of each scenario. We also provide results from practical work together with a discussion about future improvements.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Necessary and sufficient checkpoint selection for temporal verification of high-confidence cloud workflow systems

On-time completion is an important temporal QoS (Quality of Service) dimension and one of the fundamental requirements for high-confidence workflow systems. In recent years, a workflow temporal verification framework, which generally consists of temporal constraint setting, temporal checkpoint selection, temporal verification, and temporal violation handling, has been the major approach for the high temporal QoS assurance of workflow systems. Among them, effective temporal checkpoint selection, which aims to timely detect intermediate temporal violations along workflow execution plays a critical role. Therefore, temporal checkpoint selection has been a major topic and has attracted significant efforts. In this paper, we will present an overview of work-flow temporal checkpoint selection for temporal verification. Specifically, we will first introduce the throughput based and response-time based temporal consistency models for business and scientific cloud workflow systems, respectively. Then the corresponding benchmarking checkpoint selection strategies that satisfy the property of “necessity and sufficiency” are presented. We also provide experimental results to demonstrate the effectiveness of our checkpoint selection strategies, and finally points out some possible future issues in this research area.

Temporal verification for scientific cloud workflows: State-of-the-art and research challenges

Cloud computing is establishing itself as the latest computing paradigm in recent years. As doing science in the cloud is becoming a reality, scientists are now able to access public cloud centers and employ high-performance computing resources to run scientific applications. However, due to the dynamic nature of the cloud environment, the usability of scientific cloud workflow systems can be significantly deteriorated if without effective service quality assurance strategies. Specifically, workflow temporal verification as the major approach for workflow temporal QoS (Quality of Service) assurance plays a critical role in the on-time completion of large-scale scientific workflows. Great efforts have been dedicated to the area of workflow temporal verification in recent years and it is high time that we should define the key research issues for scientific cloud workflows in order to keep our research on the right track. In this paper, we systematically investigate this problem and present four key research issues based on the introduction of a generic temporal verification framework. Meanwhile, state-of-the-art solutions for each research issue and open challenges are also presented. Finally, SwinDeW-V, an ongoing research project on temporal verification as part of our SwinDeW-C cloud workflow system, is also demonstrated.

Workflow temporal verification for monitoring parallel business processes

Workflow temporal verification is conducted to guarantee on-time completion, which is one of the most important QoS (Quality of Service) dimensions for business processes running in the cloud. However, as today's business systems often need to handle a large number of concurrent customer requests, conventional response-time based process monitoring strategies conducted in a one-by-one fashion cannot be applied efficiently to a large batch of parallel processes because of significant time overhead. Similar situations may also exist in software companies where multiple software projects are carried out at the same time by software developers. To address such a problem, based on a novel runtime throughput consistency model, this paper proposes a QoS-aware throughput based checkpoint selection strategy, which can dynamically select a small number of checkpoints along the system timeline to facilitate the temporal verification of throughput constraints and achieve the target on-time completion rate. Experimental results demonstrate that our strategy can achieve the best efficiency and effectiveness compared with the state-of-the-art as and other representative response-time based checkpoint selection strategies.

A modified approach to modeling of diffusive transformation kinetics from nonisothermal data and experimental verification

An inverse model is proposed to construct the mathematical relationship between continuous cooling transformation (CCT) kinetics with constant rates and the isothermal one. The kinetic parameters in JMAK equations of isothermal kinetics can be deduced from the experimental CCT kinetics. Furthermore, a generalized model with a new additive rule is developed for predicting the kinetics of nucleation and growth during diffusional phase transformation with arbitrary cooling paths based only on CCT curve. A generalized contribution coefficient is introduced into the new additivity rule to describe the influences of current temperature and cooling rate on the incubation time of nuclei. Finally, then the reliability of the proposed model is validated using dilatometry experiments of a microalloy steel with fully bainitic microstructure based on various cooling routes.