A novel ensemble clustering for operational transients classification with application to a nuclear power plant turbine

International audienceThe objective of the present work is to develop a novel approach for combining in an ensemble multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final consensus clustering is unknown. A measure of pairwise similarity is used to quantify the co-association matrix that describes the similarity among the different base clusterings. Then, a Spectral Clustering technique of literature, embedding the unsupervised K-Means algorithm, is applied to the co-association matrix for finding the optimum number of clusters of the final consensus clustering, based on Silhouette validity index calculation. The proposed approach is developed with reference to an artificial case study, properly designed to mimic the signal trend behavior of a Nuclear Power Plant (NPP) turbine during shutdown. The results of the artificial case have been compared with those achieved by a state-of-art approach, known as Cluster-based Similarity Partitioning and Serial Graph Partitioning and Fill-reducing Matrix Ordering Algorithms (CSPA-METIS). The comparison shows that the proposed approach is able to identify a final consensus clustering that classifies the transients with better accuracy and robustness compared to the CSPA-METIS approach. The approach is, then, validated on an industrial case concerning 149 shutdown transients of a NPP turbine

Clustering aplicado à Bolsa de Valores de Lisboa

O objetivo desta dissertação foi estudar um conjunto de empresas cotadas na bolsa de valores de Lisboa, para identificar aquelas que têm um comportamento semelhante ao longo do tempo. Para isso utilizamos algoritmos de Clustering tais como K-Means, PAM, Modelos hierárquicos, Funny e C-Means tanto com a distância euclidiana como com a distância de Manhattan. Para selecionar o melhor número de clusters identificado por cada um dos algoritmos testados, recorremos a alguns índices de avaliação/validação de clusters como o Davies Bouldin e Calinski-Harabasz entre outros.The aim of this thesis was to study a set of companies from Lisbon stock exchange to identify those that have a similar behavior over time. For this we use clustering algorithms such as K-Means, PAM, hierarchical models, Funny and C-Means with Euclidean distance and Manhattan distance. To select the best number of clusters identified by each of the tested algorithms, we resort to some clusters validation such as the Davies Bouldin and Calinski-Harabasz among others

Quantitative analysis of hypoglycemia-induced EEG alterations in type 1 diabetes

The main risk for patients affected by type 1 diabetes (T1D) is to fall in hypoglycemia, an event which leads to both short and long-terms automatic failure and can be life-threatening especially when occurs at night without subject awareness. Moreover, T1D patients can develop asymptomatic hypoglycemia, reducing the prompt response of the counterregulatory system triggered by the fall in blood glucose. Avoiding hypoglycemia is important in children and adolescents because hypoglycemia episodes may have clinically relevant effects on cognition. Also in adults, cognitive tests assessed that hypoglycemia results in altered cerebral activity, most likely due to the complete dependence of the brain for glucose supply. The first organ influenced by this fall of glucose in the blood is the brain. Indeed, a lot of studies proved the mirroring of cognitive dysfunction due to hypoglycemia in the spectral power of the electroencephalogram (EEG) signal. In particular, the increase of the power in low frequency EEG bands is a well-known effect during hypoglycemia that seems more pronounced in the EEG recording in the posterior areas of the brain. Pilot studies about the real-time processing of the EEG signal to detect hypoglycemia have indicated that it might be possible to alert the patients by means of EEG analysis. The main advantages in exploiting EEG analysis is that the blood glucose threshold to enter in hypoglycemia has large inter-subjects variations, on the contrary the EEG onset in general occurs before the state of hypoglycemia is critical, i.e., the brain starts to experience neuroglycopoenia and its functions completely fail. The main aim of this work is to broaden out the quantitative analysis on the altered EEG activity due to hypoglycemia in T1D patients to identify potential margins of improvement in EEG processing and further features sensitive to hypoglycemia. In particular, the analyses are extended to different domains, i.e., time and frequency domains, to deepen the knowledge on the effects of hypoglycemia in the brain. So far, studies in the literature have mainly evaluated these changes only on a single EEG channel level on the frequency domain, but limited information is available on the hypoglycemia influence on brain network dynamics and on connection between different brain areas. To do so, this dissertation is structured in 7 chapters, briefly presented below. Chapter 1 will start with a brief overview about the impact of T1D and its main effects on daily life. Moreover, the main consequences of hypoglycemia in human brain will be described by reporting the main findings in the literature. Chapter 2 will present the database where EEG data and blood glucose samples were collected in parallel for about 8 h in 31 T1D hospitalized patients during an hyperinsulinemic - hypoglycemic clamp experiment. Chapter 3 will address on the main effects of hypoglycemia in the frequency domain. After testing the well-known changes in the spectral power of the EEG signal during hypoglycemia, a multivariate analysis based on the concept of Information Partial Directed Coherence will be presented. In particular, we will confirm the general slowing in the frequency domain and we will show how hypoglycemia affects the EEG functional connectivity. Chapter 4 will consider the effects of hypoglycemia on EEG complexity. Fractal dimension features, describing both amplitude and frequency properties, will be computed and compared with the results based on Sample Entropy. We will reveal a decrease of EEG signal complexity in the hypoglycemic condition. Chapter 5 will focus on the consequences of hypoglycemia in the so-called microstates or "athoms of thought". We will hypothesize that the changes in the frequency domain and the decrease of the EEG signal complexity in hypoglycemia have in common the same resting EEG electric potential amplitude map. Chapter 6 will describe how hypoglycemia influences the results of cognitive tests, and the relationship between the drop in the tests performance and the EEG quantitative measures presented in the previous chapters. We will find a direct correlation among the changes in the power spectra, the cognitive tests performance and the changes of one resting EEG electric potential amplitude map. Eventually, Chapter 7 will close the dissertation by interpreting the ensemble of the results from both the medical and engineering point of view, and presenting the possible future developments of this work

Multivarijaciono statističko modeliranje u funkciji merenja stepena ekonomske razvijenosti teritorijalnih jedinica

U doktorskoj disertaciji su razmatrana suštinska teorijska određenja odabranih multivarijacionih statističkih metoda međuzavisnosti i zavisnosti i sagledani njihovi aplikativni potencijali za modeliranje kompleksnih, multidimenzionih ekonomskih fenomena od interesa, čime je istovremeno opredeljen predmet istraživanja. Afirmaciju primene multivarijacionih statističkih metoda u domenu ekonomskih istraživanja odražava osnovni cilj disertacije, koji podrazumeva kreiranje inovativnog konceptualno-metodološkog okvira, zasnovanog na statistički validnoj implementaciji kako pojedinačnih metoda multivarijacione analize, tako i njihove kombinacije na određenom broju relevantnih pokazatelja, u funkciji merenja dostignutog stepena ekonomske razvijenosti i, shodno tome, klasifikacije teritorijalnih jedinica lokalne samouprave u Republici Srbiji. U tom smislu, u okviru svake metode detaljno su analizirani ciljevi, tipovi i postupak sprovođenja, uz jasno razgraničenje istraživačkih okolnosti pod kojima se njihova primena smatra prikladnom i statistički opravdanom. Na temeljima važnosti adekvatne pripreme podataka za sprovođenje bilo koje analize podataka u kontekstu obezbeđivanja naučne zasnovanosti dobijenih rezultata i izvedenih zaključaka, posebna pažnja je posvećena pretprocesiranju multivarijacionih opservacija i elaboriranju značaja ispunjenosti statističkih pretpostavki iz perspektive validne primene konkretne metode. Rasvetljavanje kompleksnog i značajnog pitanja validacije kvaliteta rezultata multivarijacionog modeliranja i, s tim u vezi, pronalaženja „statističkih“ argumenta za izbor optimalnog rešenja, izvršeno je analizom brojnih kriterijuma i metoda za evaluaciju rezultata. U empirijskom delu disertacije predstavljena su dva originalna konceptualnometodološka okvira analize multivarijacionih podataka, i to: prvi, zasnovan na integrisanoj primeni faktorske analize, analize grupisanja i multivarijacione analize varijanse u funkciji razvoja multivarijacionog modela (forma kompozitnog pokazatelja) za merenje stepena ekonomske razvijenosti i klasifikaciju jedinica lokalne samouprave u Republici Srbiji, i, drugi, zasnovan na primeni diskriminacione analize u funkciji razvoja klasifikacionog modela za razvrstavanje analiziranih teritorijalnih jedinica u jednu od, prema vrednostima prethodno utvrđenog kompozitnog pokazatelja stepena ekonomske razvijenosti, empirijski identifikovanih grupa. Rezultati istraživanja ukazuju na veliki potencijal kombinovane implementacije multivarijacionih statističkih metoda u koncipiranju inovativnih metodoloških rešenja za analizu i razumevanje ekonomskih fenomena.In this doctoral dissertation, the essential theoretical determinations of selected multivariate statistical methods of interdependence and dependence were examined, as well as their application potentials for modeling complex, multidimensional economic phenomena of interest were considered, which simultaneously defined the research subject. The affirmation of the application of multivariate statistical methods in the domain of economic research reflects the primary objective of the dissertation, which implies the development of an innovative conceptual-methodological framework, based on statistically valid implementation of individual methods of multivariate analysis, as well as their combinations on a number of relevant indicators in function of measuring the achieved degree of economic development and, accordingly, the classification of local self-government territorial units in the Republic of Serbia. In this sense, within each method, the objectives, types and implementation procedures have been thoroughly analyzed, with a clear distinction of research circumstances under which their application is considered appropriate and statistically justified. On the basis of the importance of adequate data preparation for implementation of any data analysis, in terms of ensuring the scientific basis of the obtained results and conclusions drawn, special attention has been devoted to preprocessing of multivariate observations and elaboration of importance of fulfilling statistical assumptions from the perspective of valid application of particular method. The clarification of complex and significant question of validating the quality of multivariate modeling results and, in this regard, finding “statistical” arguments for choosing the optimal solution, was done by analyzing a number of different criteria and methods for evaluation of results. Within the empirical part of the dissertation, the following two original conceptualmethodological frameworks of multivariate data analysis were presented: first, based on the integrated implementation of factor analysis, cluster analysis and multivariate analysis of variance in function of developing a specific multivariate model (in form of a composite indicator) for measuring the degree of economic development and classification of local selfgovernment units in the Republic of Serbia, and, second, based on the implementation of discriminant analysis in function of developing a classification model for allocation of analyzed territorial units into one of the empirically identified groups, according to the values of the previously proposed composite indicator of degree of economic development. The results of the conducted research indicate the great potential of the combined implementation of multivariate statistical methods in conceiving innovative methodological solutions for the analysis and understanding of economic phenomena

On the Number of Clusters in Block Clustering Algorithms

One of the major problems in clustering is the need of specifying the optimal number of clusters in some clustering algorithms. Some block clustering algorithms suffer from the same limitation that the number of clusters needs to be specified by a human user. This problem has been subject of wide research. Numerous indices were proposed in order to find reasonable number of clusters. In this paper, we aim to extend the use of these indices to block clustering algorithms. Therefore, an examination of some indices for determining the number of clusters in CROKI2 algorithm is conducted on synthetic data sets. The purpose of the paper is to test the performance and ability of some indices to detect the proper number of clusters on rows and columns partitions obtained by a block clustering algorithm