The Use of Stochastic Processes in Bridge Maintenance Optimization

We introduce an approach for modelling the structural deterioration of components of bridges for maintenance optimization purposes. The Markov chain model is found in the maintenance and repair problems since the early 60's, is introduced to the maintenance of road infrastructure in the 1980's, and is made to drive the current bridge maintenance optimization systems. While this model results into solvable programming problems and provides a solution, there are a number of criticisms associated with it. We highlight the shortfalls of the Markov model for bridge lifetime assessment and promote the use of stochastic processes

Sectoral dynamics of financial contagion in Europe - The cases of the recent crises episodes

In this paper, we investigate the existence of financial contagion in the European Union during the recent Global Financial Crisis (GFC) of 2007-2009 and the European Sovereign Debt Crisis (ESDC) that started in 2009. Our sample includes sectorial equity indices for 15 countries from 2004 to 2014. We adopt an ADCC-GJR-GARCH model for the time-varying correlations and a Markov-Switching model to identify the lead/lag relationship in crisis transition dates across the countries and the sectors. We assess the patterns of financial contagion by sector and by country. Our results support the existence of financial contagion in all business sectors under the GFC and the ESDC. Financials and Telecommunications are the most affected, while the Industrials and the Consumer Goods the least in each crisis respectively. Stock markets in the Core EU are the most affected in both crises. We find evidence of a non-synchronized transition of all countries to the crisis regime, in both crises. We believe that our results may provide useful insights for investors and policy makers

Pregled algoritmov za analizo slike za prepoznavanje registrske tablice

Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate. Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates. Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%. Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.Ozadje in namen: V članku raziskujemo problem prepoznavanja registrskih tablic (LPR), in podamo pregled števil­nih algoritmov, ki jih lahko uporabimo pri problemih analize slik. V sistemih za podporo vodenju, ki uporabljajo za prepoznavanje slikovnih objektov, je inteligenca vgrajena v statistične algoritme, ki jih je mogoče uporabiti v različnih korakih razpoznavanja. Opisujemo več rešitev, od začetnega koraka do lokalizacije in prepoznavanja slikovnih el­ementov. Cilj tega prispevka je predstaviti več verjetnostnih pristopov v korakih razpoznavanja, nato pa združiti te pristope v en sistem. Večina pristopov uporablja deterministične modele, ki so občutljivi na številne nenadzorovane vplive, kot so osvetlitev, razdalja vozila do kamere, šum pri procesiranju itd. Bistvo naših pristopov je v statističnih algoritmih, ki lahko natančno lokalizirajo in prepoznajo registrsko tablico. Oblikovanje / metodologija / pristop: Predstavimo enostavne in poceni metode za reševanje relativno pomemb­nih problemov z uporabo verjetnostnih pristopov. Pri teh pristopih opisujemo številne statistične rešitve od stopnje začetnega praga do lokalizacije in prepoznavanja slikovnih elementov. V koraku lokalizacije uporabljamo frekvenčne signale iz slik registrskih tablic, ki jih analiziramo z uporabo diskretne Fourier-jeve transformacije. Pri prepoznavanju znakov na tablicah smo uporabili tudi verjetnostni model. Na koncu prikazujemo, kako združiti rezultate iz dvojezičnih tablic, kot so na primer tablice Saudove Arabije. Rezultati: Algoritmi so učinkoviti pri razpoznavanju znakov na vozilih, v stavbah in drugod. Rezultat kaže prednost uporabe verjetnostnega pristopa v vseh korakih razpoznavanja registrskih tablic. Povprečne stopnje uspešnega raz­poznavanja pri uporabi lokalnega nabora podatkov so dosegle 79,13%. Zaključek: Izboljšanje stopnje razpoznavanja je mogoče doseči, če obstajata dva vira informacij, še posebej na registrskih tablicah, na katerih sta dve neodvisni besedili

A Statistical Model for Shutdowns due to Air Quality Control for a Copper Production Decision Support System

Background: In the mid-1990s, a decision support system for copper production was developed for one of the largest mining companies in Australia. The research was conducted by scientists from the largest Australian research center and involved the use of simulation to analyze options to increase production of a copper production facility

Statistični model zaustavitev zaradi nadzora kakovosti zraka pri proizvodnji bakra: sistem za podporo odločanju

Background: In the mid-1990s, a decision support system for copper production was developed for one of the largest mining companies in Australia. The research was conducted by scientists from the largest Australian research center and involved the use of simulation to analyze options to increase production of a copper production facility. Objectives: We describe a statistical model for shutdowns due to air quality control and some of the data analysis conducted during the simulation project. We point to the fact that the simulation was a sophisticated exercise that consisted of many modules and the statistical model for shutdowns was essential for valid simulation runs. Method: The statistical model made use of a full year of data on daily downtimes and used a combination of techniques to generate replications of the data. Results: The study was conducted with a high level of cooperation between the scientists and the mining company. This contributed to the development of accurate estimates for input into a support system with an EXCEL based interface. Conclusion: The environmental conditions affected greatly the operations of the production facility. A good statistical model was essential for the successful simulation and the high budget expansion decision that ensued.Ozadje: V sredini 1990-ih let je bil razvit sistem za podporo odločanju za proizvodnjo bakra za eno od največjih rudarskih podjetij v Avstraliji. Raziskava je bila izvedena s strani znanstvenikov iz največjega avstralskega raziskovalnega centra in vključuje uporabo simulacije za analizo možnosti za povečanje proizvodnje bakra. Cilji: Opisan je razvoj statističnega modela zaustavitev zaradi nadzora kakovosti zraka in prikazane so nekatere analize podatkov, izvedene v okviru projekta simulacije. Simulacija, ki je vključevala več modulov in statistični model zaustavitev je bistvenega pomena za veljavnost simulacijskih tekov. Metoda: uporabili smo statistični model, ki izhaja iz podatkov o dnevnih zastojev za eno leto, in kombinacijo tehnik za ustvarjanje replik podatkov. Rezultati: Raziskava je zahtevala visoko stopnjo sodelovanja med znanstveniki in rudarsko družbo. Na ta način je bil z uporabo preglednice izdelan vmesnik za vnos ustrezno natančnih ocen v simulacijski sistem. Zaključek: Okoljske razmere močno vplivajo na poslovanje v proizvodnem obratu bakra. Zato je dober statistični model bistvenega pomena za uspešno simulacijo in podporo odločanju

Statistični model zaustavitev zaradi nadzora kakovosti zraka pri proizvodnji bakra: sistem za podporo odločanju

Background: In the mid-1990s, a decision support system for copper production was developed for one of the largest mining companies in Australia. The research was conducted by scientists from the largest Australian research center and involved the use of simulation to analyze options to increase production of a copper production facility. Objectives: We describe a statistical model for shutdowns due to air quality control and some of the data analysis conducted during the simulation project. We point to the fact that the simulation was a sophisticated exercise that consisted of many modules and the statistical model for shutdowns was essential for valid simulation runs. Method: The statistical model made use of a full year of data on daily downtimes and used a combination of techniques to generate replications of the data. Results: The study was conducted with a high level of cooperation between the scientists and the mining company. This contributed to the development of accurate estimates for input into a support system with an EXCEL based interface. Conclusion: The environmental conditions affected greatly the operations of the production facility. A good statistical model was essential for the successful simulation and the high budget expansion decision that ensued.Ozadje: V sredini 1990-ih let je bil razvit sistem za podporo odločanju za proizvodnjo bakra za eno od največjih rudarskih podjetij v Avstraliji. Raziskava je bila izvedena s strani znanstvenikov iz največjega avstralskega raziskovalnega centra in vključuje uporabo simulacije za analizo možnosti za povečanje proizvodnje bakra. Cilji: Opisan je razvoj statističnega modela zaustavitev zaradi nadzora kakovosti zraka in prikazane so nekatere analize podatkov, izvedene v okviru projekta simulacije. Simulacija, ki je vključevala več modulov in statistični model zaustavitev je bistvenega pomena za veljavnost simulacijskih tekov. Metoda: uporabili smo statistični model, ki izhaja iz podatkov o dnevnih zastojev za eno leto, in kombinacijo tehnik za ustvarjanje replik podatkov. Rezultati: Raziskava je zahtevala visoko stopnjo sodelovanja med znanstveniki in rudarsko družbo. Na ta način je bil z uporabo preglednice izdelan vmesnik za vnos ustrezno natančnih ocen v simulacijski sistem. Zaključek: Okoljske razmere močno vplivajo na poslovanje v proizvodnem obratu bakra. Zato je dober statistični model bistvenega pomena za uspešno simulacijo in podporo odločanju

Pregled algoritmov za analizo slike za prepoznavanje registrske tablice

Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate. Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates. Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%. Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.Ozadje in namen: V članku raziskujemo problem prepoznavanja registrskih tablic (LPR), in podamo pregled števil­nih algoritmov, ki jih lahko uporabimo pri problemih analize slik. V sistemih za podporo vodenju, ki uporabljajo za prepoznavanje slikovnih objektov, je inteligenca vgrajena v statistične algoritme, ki jih je mogoče uporabiti v različnih korakih razpoznavanja. Opisujemo več rešitev, od začetnega koraka do lokalizacije in prepoznavanja slikovnih el­ementov. Cilj tega prispevka je predstaviti več verjetnostnih pristopov v korakih razpoznavanja, nato pa združiti te pristope v en sistem. Večina pristopov uporablja deterministične modele, ki so občutljivi na številne nenadzorovane vplive, kot so osvetlitev, razdalja vozila do kamere, šum pri procesiranju itd. Bistvo naših pristopov je v statističnih algoritmih, ki lahko natančno lokalizirajo in prepoznajo registrsko tablico. Oblikovanje / metodologija / pristop: Predstavimo enostavne in poceni metode za reševanje relativno pomemb­nih problemov z uporabo verjetnostnih pristopov. Pri teh pristopih opisujemo številne statistične rešitve od stopnje začetnega praga do lokalizacije in prepoznavanja slikovnih elementov. V koraku lokalizacije uporabljamo frekvenčne signale iz slik registrskih tablic, ki jih analiziramo z uporabo diskretne Fourier-jeve transformacije. Pri prepoznavanju znakov na tablicah smo uporabili tudi verjetnostni model. Na koncu prikazujemo, kako združiti rezultate iz dvojezičnih tablic, kot so na primer tablice Saudove Arabije. Rezultati: Algoritmi so učinkoviti pri razpoznavanju znakov na vozilih, v stavbah in drugod. Rezultat kaže prednost uporabe verjetnostnega pristopa v vseh korakih razpoznavanja registrskih tablic. Povprečne stopnje uspešnega raz­poznavanja pri uporabi lokalnega nabora podatkov so dosegle 79,13%. Zaključek: Izboljšanje stopnje razpoznavanja je mogoče doseči, če obstajata dva vira informacij, še posebej na registrskih tablicah, na katerih sta dve neodvisni besedili

The information system for bridge networks condition monitoring and prediction

This paper introduces an information system for estimating lifetime characteristics of elements of bridges and predicting the future conditions of networks of bridges. The Information System for Bridge Networks Condition Monitoring and Prediction was developed for the Roads and Traffic Authority of the state of New South Wales, Australia. The conceptual departure from the standard bridge management systems is the use of a novel stochastic process built out of the gamma process. The statistical model was designed for the estimation of infrastructure lifetime, based on the analysis of more than 15 years of bridge inspection data. The predictive curve provides a coherent mathematical model for conducting target level constrained and funding based maintenance optimization

Sistem za podporo odločanju vzdrževanja na podlagi Weibullove porazdelitve

Background: The Weibull distribution is one of the most important lifetime distributions in applied statistics. Weibull analysis is the leading method in the world for fitting and analyzing lifetime data. We discuss one of the earliest decision support system for the assessment of a distribution for the parameters of the Weibull reliability model using expert information. We then present a different approach to assess the parameters distribution. Objectives: The studies mentioned in this paper aimed to construct a distribution of the parameters of the Weibull reliability model and apply it in the domain of Maintenance Optimization. Method: The parameters of the Weibull reliability model are considered random variables and a distribution for the parameters is assessed using informed judgment in the form of reliability estimates from vendor information, engineering knowledge or experience in the field. Results: The results are the development of modern maintenance optimization models that can be embodied in decision support systems. Conclusion: While the information management part is important in the building of maintenance optimization decision systems, the accuracy of the mathematical and statistical algorithms determines the level of success of the maintenance solution.Ozadje: Weibullova porazdelitev je ena od najbolj pomembnih na področju porazdelitev življenjske dobe v uporabni statistiki. Ona je vodilna metoda za oceno in analizo podatkov na področju življenjske dobe. V prispevku razpravljamo o enem od prvih sistemov za podporo odločanju za oceno porazdelitve parametrov zanesljivosti Weibull-ovega modela na podlagi razpoložljive informacije. Nato smo predstavili drugačen pristop za oceno porazdelitve parametrov . Cilji: Cilj študije je zgraditi model porazdelitve parametrov zanesljivosti Weibullove porazdelitve in ga uporabiti na področju optimizacije vzdrževanja. Metoda: Parametri modela Weibullove zanesljivosti obravnavamo kot naključne spremenljivke katerih distribucija je ocenjena z strani ekspertov v obliki zanesljivosti s pomočjo informacij prodajalca z inženirskim znanjem in izkušnjami na tem področju. Rezultat: Rezultati so razvoj sodobnih optimizacijskih modelov za vzdrževanje kot sistem za podporo odločanju vzdrževanja. Zaključek: Medtem ko je del za upravljanje informacij pomemben pri gradnji sistemov za podporo odločanja optimizacije vzdrževanja, zanesljivost matematičnih in statističnih algoritmov določa stopnjo uspešnosti rešitev za vzdrževanje

Potrebe po simulaciji kompleksnih industrijskih sistemov

We discuss the concept of simulation and its application in the resolution of problems in complex industrial systems. Most problems of serious scale, be it an inventory problem, a production and distribution problem, a management of resources or process improvement, all real world problems require a mix of generic, data algorithmic and Ad-hoc solutions making the best of available information. We describe two projects in which analytical solutions were applied or contemplated. The first case study uses linear programming in the optimal allocation of advertising resources by a major internet service provider. The second study, in a series of projects, analyses options for the expansion of the production and distribution network of mining products, as part of a sensitive strategic business review. Using the examples, we make the case for the need of simulation in complex industrial problems where analytical solutions may be attempted but where the size and complexity of the problem forces a Monte Carlo approach.V članku razpravljamo o konceptu simulacije in njeni uporabi pri obravnavi kompleksnih industrijskih procesov. Večina zapletenih industrijskih problemov, kot so vodenje zalog, proizvodnja in distribucija, upravljanje virov ali izboljšave procesa, zahtevajo kombinacijo različnih metod kot na primer linearno programiranje, ad-hoc algoritmi ali simulacija za reševanje problemov na podlagi razpoložljivih informacij. V članku smo opisali dva projekta kjer smo ubrali analitično pot in kritično razmišljali o njej. V prvem primeru smo uporabili linearno programiranje za optimalno alokacijo marketinških virov za glavnega internetnega ponudnika storitev. Drugi primer sestoji od vrste projektov, kjer analiziramo možnost razširitve proizvodnje in mreže dostave rudarskih izdelkov kot del občutljive strategije poslovne politike podjetja. Na podlagi primerov smo utemeljili potrebo po Monte Carlo simulaciji v kompleksnih industrijskih problemih kot bolj učinkovitega pristopa od analitičnega