Information impactogram application for fast detection of temperature instability zone of a two-channel ceramic roller kiln

Za dvokanalnu peć firme Siti predložen je postupak brzog određivanja zone nestabilnosti u priproizvodnji keramičkih pločica. Osnova za određivanje zone nestabilnosti jest izračun kodne identičnosti signala kvara i signala senzora temperature dvokanalne peći u odgovarajućem dijelu kritične zone paljenja. Slučaj povećanja škarta iznad 6 % je upotrebljen kao indikator primjene postupka. Određuju se kodni znakovi i računa se jednodimenzionalna teselacijska entropija za te mjerne signale. Slučaj slaganja ekspandiranih kodova i teselacijskog entropijskog iznosa može se smatrati dovoljno indikativnim za određivanje zone proizvodne nestabilnosti s naslova povećanja škarta. U razdoblju praćenja procesa 2009. u mjesecima kolovoz/rujan to je bila zona signala s50 u kanalu dvokanalne peći u trajanju približno šest uzastopnih radnih smjena. Na temelju ovih rezultata, predložen je radni tok za korištenje entropijske analize u industrijskom postrojenju u stvarnom vremenu.A fast procedure for determination of instability zone of a two-channel ceramic roller kiln, Siti type, is proposed. The basis for determination of instability zone is the calculation of code identity of final fallout signal and temperature sensor signal from the roller kiln in relevant critical firing zone. The case of product fallout increase above 6 % is used as indicator for procedure application. Code letters for the signals are determined and their respective one-dimensional tessellation entropy is calculated. The case of expanded code and entropy congruence can be taken as indicative enough for determination of the firing instability zone from the fallout increase standpoint. The signal s50 temperature zone of two-channel roller kiln was indicated in the period August/September 2009 as responsible for product losses in approximately six consecutive working shifts. Based on the results, a workflow is proposed for the use of the entropy analysis in real-time industrial setting

Evaluating and comparing performance of feature combinations of heart rate variability measures for cardiac rhythm classification

Abstract Automatic classification of cardiac arrhythmias using heart rate variability (HRV) analysis has been an important research topic in recent years. Explorations reveal that various HRV feature combinations can provide highly accurate models for some rhythm disorders. However, the proposed feature combinations lack a direct and carefully designed comparison. The goal of this work is to assess the various HRV feature combinations in classification of cardiac arrhythmias. In this setting, a total of 56 known HRV features are grouped in eight feature combinations. We evaluate and compare the combinations on a difficult problem of automatic classification between nine types of cardiac rhythms using three classification algorithms: support vector machines, AdaBoosted C4.5, and random forest. The effect of analyzed segment length on classification accuracy is also examined. The results demonstrate that there are three combinations that stand out the most, with total classification accuracy of roughly 85% on time segments of 20 seconds duration. A simple combination of time domain features is shown to be comparable to the more informed combinations, with only 1-4% worse results on average than the three best ones. Random forest and AdaBoosted C4.5 are shown to be comparably accurate, while support vector machines was less accurate (4-5%) on this problem. We conclude that the nonlinear features exhibit only a minor influence on the overall accuracy in discerning different arrhythmias. The analysis also shows that reasonably accurate arrhythmia classification lies in the range of 10 to 40 seconds, with a peak at 20 seconds, and a significant drop after 40 seconds

HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm,

Abstract. Heart rate variability (HRV) analysis can be successfully applied to automatic classification of cardiac rhythm abnormalities. This paper presents a novel Java-based computer framework for feature extraction from cardiac rhythms. The framework called HRVFrame implements more than 30 HRV linear time domain, frequency domain, time-frequency domain, and nonlinear features. Output of the framework in the form of .arff files enables easier medical knowledge discovery via platforms such as RapidMiner or Weka. The scope of the framework facilitates comparison of models for different cardiac disorders. Some of the features implemented in the framework can also be applied to other biomedical time-series. The thorough approach to feature extraction pursued in this work is also encouraged for other types of biomedical time-series

Heart failure ontology

Abstract Ontology represents explicit specification of knowledge in a specific domain of interest in the form of concepts and relations among them. This paper presents a medical ontology describing the domain of heart failure (HF). Construction of ontology for a domain like HF is recognized as an important step in systematization of existing medical knowledge. The main virtue of ontology is that the represented knowledge is both computer and humanreadable. The current development of the HF ontology is one of the main results of the EU Heartfaid project. The ontology has been implemented using Ontology Web Language and Protégé editing tool. It consists of roughly 200 classes, 100 relations and 2000 instances. The ontology is a precise, voluminous, portable, and upgradable representation of the HF domain. It is also a useful framework for building knowledge based systems in the HF domain, as well as for unambiguous communication between professionals. In the process of developing the HF ontology there have been significant technical and medical dilemmas. The current result should not be treated as the ultimate solution but as a starting point that will stimulate further research and development activities that can be very relevant for both intelligent computer systems and precise communication of medical knowledge

A Systematic Evaluation of Profiling Through Focused Feature Selection

Profiled side-channel attacks consist of several steps one needs to take. An important, but sometimes ignored, step is a selection of the points of interest (features) within side-channel measurement traces. A large majority of the related works start the analyses with an assumption that the features are preselected. Contrary to this assumption, here, we concentrate on the feature selection step. We investigate how advanced feature selection techniques stemming from the machine learning domain can be used to improve the attack efficiency. To this end, we provide a systematic evaluation of the methods of interest. The experiments are performed on several real-world data sets containing software and hardware implementations of AES, including the random delay countermeasure. Our results show that wrapper and hybrid feature selection methods perform extremely well over a wide range of test scenarios and a number of features selected. We emphasize L1 regularization (wrapper approach) and linear support vector machine (SVM) with recursive feature elimination used after chi-square filter (Hybrid approach) that performs well in both accuracy and guessing entropy. Finally, we show that the use of appropriate feature selection techniques is more important for an attack on the high-noise data sets, including those with countermeasures than on the low-noise ones

Exploring Attitudes Toward “Sugar Relationships” Across 87 Countries: A Global Perspective on Exchanges of Resources for Sex and Companionship

The current study investigates attitudes toward one form of sex for resources: the so-called sugar relationships, which often involve exchanges of resources for sex and/or companionship. The present study examined associations among attitudes toward sugar relationships and relevant variables (e.g., sex, sociosexuality, gender inequality, parasitic exposure) in 69,924 participants across 87 countries. Two self-report measures of Acceptance of Sugar Relationships (ASR) developed for younger companion providers (ASR-YWMS) and older resource providers (ASR-OMWS) were translated into 37 languages. We tested cross-sex and cross-linguistic construct equivalence, cross-cultural invariance in sex differences, and the importance of the hypothetical predictors of ASR. Both measures showed adequate psychometric properties in all languages (except the Persian version of ASR-YWMS). Results partially supported our hypotheses and were consistent with previous theoretical considerations and empirical evidence on human mating. For example, at the individual level, sociosexual orientation, traditional gender roles, and pathogen prevalence were significant predictors of both ASR-YWMS and ASR-OMWS. At the country level, gender inequality and parasite stress positively predicted the ASR-YWMS. However, being a woman negatively predicted the ASR-OMWS, but positively predicted the ASR-YWMS. At country-level, ingroup favoritism and parasite stress positively predicted the ASR-OMWS. Furthermore, significant cross-subregional differences were found in the openness to sugar relationships (both ASR-YWMS and ASR-OMWS scores) across subregions. Finally, significant differences were found between ASR-YWMS and ASR-OMWS when compared in each subregion. The ASR-YWMS was significantly higher than the ASR-OMWS in all subregions, except for Northern Africa and Western Asia

Predictors of Enhancing Human Physical Attractiveness: Data from 93 Countries

People across the world and throughout history have gone to great lengths to enhance their physical appearance. Evolutionary psychologists and ethologists have largely attempted to explain this phenomenon via mating preferences and strategies. Here, we test one of the most popular evolutionary hypotheses for beauty-enhancing behaviors, drawn from mating market and parasite stress perspectives, in a large cross-cultural sample. We also test hypotheses drawn from other influential and non-mutually exclusive theoretical frameworks, from biosocial role theory to a cultural media perspective. Survey data from 93,158 human participants across 93 countries provide evidence that behaviors such as applying makeup or using other cosmetics, hair grooming, clothing style, caring for body hygiene, and exercising or following a specific diet for the specific purpose of improving ones physical attractiveness, are universal. Indeed, 99% of participants reported spending \u3e10 min a day performing beauty-enhancing behaviors. The results largely support evolutionary hypotheses: more time was spent enhancing beauty by women (almost 4 h a day, on average) than by men (3.6 h a day), by the youngest participants (and contrary to predictions, also the oldest), by those with a relatively more severe history of infectious diseases, and by participants currently dating compared to those in established relationships. The strongest predictor of attractiveness-enhancing behaviors was social media usage. Other predictors, in order of effect size, included adhering to traditional gender roles, residing in countries with less gender equality, considering oneself as highly attractive or, conversely, highly unattractive, TV watching time, higher socioeconomic status, right-wing political beliefs, a lower level of education, and personal individualistic attitudes. This study provides novel insight into universal beauty-enhancing behaviors by unifying evolutionary theory with several other complementary perspectives

Intelligent Biosignal Analysis Methods

This Editorial presents the accepted manuscripts for the special issue “Intelligent Biosignal Analysis Methods” of the Sensors MDPI journal [...

Semantic Web Ontology Utilization for Heart Failure Expert System Design. Organizing Committee

Abstract. In this work we present the usage of semantic web knowledge representation formalism in combination with general purpose reasoning for building a medical expert system. The properties of the approach have been studied on the example of the knowledge base construction for decision support tasks in the heart failure domain. The work consisted of descriptive knowledge presentation in the ontological form and its integration with the heart failure procedural knowledge. In this setting instance checking in description logic represents the main process of the expert system reasoning

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection