Revisiting the Ability of Interest Rate Spreads to Predict Recessions: Evidence for a

In this paper we examine the power of the interest rate spread and of other financial variables as predictors of economic recessions in Spain. The domestic term spread is found to have little information about future real activity. However, term spreads in big economies to which Spain is related, specifically Germany and the US, are found to have significant predicting power but at different time horizons. Both these findings are in line with the facts that the monetary policy of Spain has not been independent and that it has been conditioned by that of other big economies, most notably Germany.

Risk Assessment of a Wind Turbine: A New FMECA-Based Tool With RPN Threshold Estimation

A wind turbine is a complex system used to convert the kinetic energy of the wind into electrical energy. During the turbine design phase, a risk assessment is mandatory to reduce the machine downtime and the Operation & Maintenance cost and to ensure service continuity. This paper proposes a procedure based on Failure Modes, Effects, and Criticality Analysis to take into account every possible criticality that could lead to a turbine shutdown. Currently, a standard procedure to be applied for evaluation of the risk priority number threshold is still not available. Trying to fill this need, this paper proposes a new approach for the Risk Priority Number (RPN) prioritization based on a statistical analysis and compares the proposed method with the only three quantitative prioritization techniques found in literature. The proposed procedure was applied to the electrical and electronic components included in a Spanish 2 MW on-shore wind turbine

On the usage of the probability integral transform to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems

We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems. The proposed algorithm builds a fixed number of fuzzy sets for all variables and adjusts their shape and position to the real distribution of training data. A two-step process is applied : 1) transformation of the original distribution into a standard uniform distribution by means of the probability integral transform. Since the original distribution is generally unknown, the cumulative distribution function is approximated by computing the q-quantiles of the training set; 2) construction of a Ruspini strong fuzzy partition in the transformed attribute space using a fixed number of equally distributed triangular membership functions. Despite the aforementioned transformation, the definition of every fuzzy set in the original space can be recovered by applying the inverse cumulative distribution function (also known as quantile function). The experimental results reveal that the proposed methodology allows the state-of-the-art multi-way fuzzy decision tree (FMDT) induction algorithm to maintain classification accuracy with up to 6 million fewer leaves.Comment: Appeared in 2018 IEEE International Congress on Big Data (BigData Congress). arXiv admin note: text overlap with arXiv:1902.0935

Maintenance Knowledge Management with Fusion of CMMS and CM

Abstract- Maintenance can be considered as an information, knowledge processing and management system. The management of knowledge resources in maintenance is a relatively new issue compared to Computerized Maintenance Management Systems (CMMS) and Condition Monitoring (CM) approaches and systems. Information Communication technologies (ICT) systems including CMMS, CM and enterprise administrative systems amongst others are effective in supplying data and in some cases information. In order to be effective the availability of high-quality knowledge, skills and expertise are needed for effective analysis and decision-making based on the supplied information and data. Information and data are not by themselves enough, knowledge, experience and skills are the key factors when maximizing the usability of the collected data and information. Thus, effective knowledge management (KM) is growing in importance, especially in advanced processes and management of advanced and expensive assets. Therefore efforts to successfully integrate maintenance knowledge management processes with accurate information from CMMSs and CM systems will be vital due to the increasing complexities of the overall systems. Low maintenance effectiveness costs money and resources since normal and stable production cannot be upheld and maintained over time, lowered maintenance effectiveness can have a substantial impact on the organizations ability to obtain stable flows of income and control costs in the overall process. Ineffective maintenance is often dependent on faulty decisions, mistakes due to lack of experience and lack of functional systems for effective information exchange [10]. Thus, access to knowledge, experience and skills resources in combination with functional collaboration structures can be regarded as vital components for a high maintenance effectiveness solution. Maintenance effectiveness depends in part on the quality, timeliness, accuracy and completeness of information related to machine degradation state, based on which decisions are made. Maintenance effectiveness, to a large extent, also depends on the quality of the knowledge of the managers and maintenance operators and the effectiveness of the internal & external collaborative environments. With emergence of intelligent sensors to measure and monitor the health state of the component and gradual implementation of ICT) in organizations, the conceptualization and implementation of E-Maintenance is turning into a reality. Unfortunately, even though knowledge management aspects are important in maintenance, the integration of KM aspects has still to find its place in E-Maintenance and in the overall information flows of larger-scale maintenance solutions. Nowadays, two main systems are implemented in most maintenance departments: Firstly, Computer Maintenance Management Systems (CMMS), the core of traditional maintenance record-keeping practices that often facilitate the usage of textual descriptions of faults and actions performed on an asset. Secondly, condition monitoring systems (CMS). Recently developed (CMS) are capable of directly monitoring asset components parameters; however, attempts to link observed CMMS events to CM sensor measurements have been limited in their approach and scalability. In this article we present one approach for addressing this challenge. We argue that understanding the requirements and constraints in conjunction - from maintenance, knowledge management and ICT perspectives - is necessary. We identify the issues that need be addressed for achieving successful integration of such disparate data types and processes (also integrating knowledge management into the “data types” and processes)

"La tragedia de Barracas" : Elaboración pública de la muerte y el heroísmo en la figura de bomberos

Fil: Calandrón, Sabrina. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación. Instituto de Investigaciones en Humanidades y Ciencias Sociales (UNLP-CONICET); Argentina.Fil: Galar, Santiago. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación. Instituto de Investigaciones en Humanidades y Ciencias Sociales (UNLP-CONICET); Argentina

Generative Adversarial Networks for Bitcoin Data Augmentation

In Bitcoin entity classification, results are strongly conditioned by the ground-truth dataset, especially when applying supervised machine learning approaches. However, these ground-truth datasets are frequently affected by significant class imbalance as generally they contain much more information regarding legal services (Exchange, Gambling), than regarding services that may be related to illicit activities (Mixer, Service). Class imbalance increases the complexity of applying machine learning techniques and reduces the quality of classification results, especially for underrepresented, but critical classes. In this paper, we propose to address this problem by using Generative Adversarial Networks (GANs) for Bitcoin data augmentation as GANs recently have shown promising results in the domain of image classification. However, there is no "one-fits-all" GAN solution that works for every scenario. In fact, setting GAN training parameters is non-trivial and heavily affects the quality of the generated synthetic data. We therefore evaluate how GAN parameters such as the optimization function, the size of the dataset and the chosen batch size affect GAN implementation for one underrepresented entity class (Mining Pool) and demonstrate how a "good" GAN configuration can be obtained that achieves high similarity between synthetically generated and real Bitcoin address data. To the best of our knowledge, this is the first study presenting GANs as a valid tool for generating synthetic address data for data augmentation in Bitcoin entity classification.Comment: 8 pages, 5 figures, 4 table

Komentarze do artykułu Antoniego Kuklińskiego

Krzysztof Porwit’s article polemicizes with the thinking expressed in Antoni Kukliński’s article, saying that the new paradigm of associations between a KBE and a KBS is meant to apply to all aspects of the economy and society. The obstacles and conflicts in these associations are pointed out, and doubts are expressed about the rate of progress along the road to a KBE and KBS described in the article, creating a circle of progress and success. The article relates to Antoni Kukliński’s thesis about the reasons behind the stoppage of the development of a KBE in countries that are poorer than the countries of the European Union. In his article, Kukliński presents the chances of a remedy; but Porwit claims that first it is necessary to remove the causes of the malady by means of work from the very bottom - by means of an improvement to the institutional order and moral renewal. Only on such foundations is it possible to attain a high quality of formal-legal characteristics and institutional order.Roman Galar’s paper relates to the basic thesis of Antoni Kukliński’s article, i.e. the trap of low-level efficiency equilibrium. While underlining the appropriateness of this diagnosis, he nevertheless undermines it by noting that cause-effect mechanisms operate on a different level. He offers his own interpretation for the causes of this “ low level” trap, pointing out, among other things, the dangers of copying institutional standards from “high level” countries, and suggests a solution to the problem by creating a network of social enclaves which the processes of creating a KBE may function safely. He supports the idea of forming a Society of Friends of a Knowledge-Based Economy, providing exhaustive arguments in its favour, and indicates the need for such a Society to explain what a KBE really means, because misunderstandings of the purpose of a KBE may discredit the entire idea of such an economy. Krzysztof PORWIT:Autor polemizuje z tezą artykułu Antoniego Kuklińskiego, iż nowy paradygmat sprzężeń między gospodarką opartą na wiedzy a społeczeństwem opartym na wiedzy ma dotyczyć zarówno gospodarki, jak i społeczeństwa. Zwraca uwagę na przeszkody i konflikty w tym współdziałaniu, a także podaje w wątpliwość stan zaawansowania na drodze do gospodarki i społeczeństwa opartych na wiedzy nakreślony w komentowanym artykule, stwarzający magiczny krąg postępu i sukcesu. Autor ustosunkowuje się do tezy Antoniego Kuklińskiego na temat przyczyn blokady rozwoju gospodarki opartej na wiedzy krajach biedniejszych niż państwa Unii Europejskiej. Kukliński w swym artykule zarysował szanse terapii; według Porwita konieczne jest najpierw usunięcie przyczyn schorzeń poprzez pracę od podstaw, ulepszanie ładu instytucjonalnego, moralną odnowę i dopiero na takich fundamentach możliwe będzie osiąganie wysokiej jakości cech formalnoprawnych i ładu instytucjonalnego.Roman GALAR:Autor ustosunkowuje się do podstawowej tezy artykułu Antoniego Kuklińskiego, tzn. pułapki równowagi niskiego poziomu efektywności. Podkreśla trafność diagnozy, ale jednocześnie podważa ją, zauważając, iż mechanizmy przyczynowo-skutkowe funkcjonują na innym poziomie. Podaje własną interpretację przyczyn pułapki „niskiego poziomu” , zwracając uwagę m.in. na niebezpieczeństwa związane z kopiowaniem standardów instytucjonalnych z krajów „wysokiego poziomu” i proponuje jako rozwiązanie problemu tworzenie sieci społecznych enklaw, w ramach których procesy kreowania gospodarki opartej na wiedzy będą mogły bezpiecznie funkcjonować. Wspiera pomysł powołania Towarzystwa Przyjaciół Gospodarki Opartej na Wiedzy, podając rozwiniętą argumentację, zwraca także uwagę na znaczenie wyjaśnienia przez to Towarzystwo, o co naprawdę chodzi w idei tworzenia tego rodzaju gospodarki, gdyż nieporozumienia wokół kwestii związanych z gospodarką opartą na wiedzy grożą dyskredytacją całej idei

Predictive Maintenance on the Machining Process and Machine Tool

This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces

Railway Assets: A Potential Domain for Big Data Analytics

AbstractTwo concepts currently at the leading edge of todays information technology revolution are Analytics and Big Data. The public transportation industry has been at the forefront in utilizing and implementing Analytics and Big Data, from ridership forecasting to transit operations Rail transit systems have been especially involved with these IT concepts, and tend to be especially amenable to the advantages of Analytics and Big Data because they are generally closed systems that involve sophisticated processing of large volumes of data. The more that public transportation professionals and decision makers understand the role of Analytics and Big Data in their industry in perspective, the more effectively they will be able to utilize its promise. This paper gives an overview of Big Data technologies in context of transportation with specific to Railways. This paper also gives an insight on how the existing data modules from the transport authority combines Big Data and how can be incorporated in providing maintenance decision making