Wyniki wyszukiwania - Biblioteka Nauki

1

Useful energy prediction model of a Lithium-ion cell operating on various duty cycles

100%

Burzyński D.

Eksploatacja i Niezawodność

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2022

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tom Vol. 24, no. 2

317--329

EN

The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (RUEc) that can be transferred during a full cycle is variable, and also three different types of evolution of changes in RUEc were identified. The paper presents a new non-parametric RUEc prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the RUEc prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied to determine the significance of model input parameters – the variable importance method – and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on RUEc – the accumulated local effects model of the first and second order.

2

Wpływ wstępnego przetwarzania danych wejściowych na jakość modeli predykcyjnych budowanych technikami Data Mining

86%

Nęcka K.

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tom Nr 2 (113)

11--17

PL

Artykuł porusza problematykę wstępnego przetwarzania danych wejściowych wykorzystywanych do prognozowania godzinowego zapotrzebowania na energię elektryczną. Analizy zostały przeprowadzone na danych uzyskanych w wyniku badań własnych wykonanych w ubojni drobiu. Zaprezentowane w pracy wyniki dotyczą przykładowych prognoz godzinowego zapotrzebowania na energię elektryczną wykonanych technikami data mining z wykorzystaniem zmiennych wejściowych poddanych różnym przekształceniom..

EN

This article contains issue of data pre-processing used in prediction of hourly energy consumption. All analyses and studies were done based on own researching made in poultry abattoir and data achieved this way. Results presented in this article applies to hourly prediction for energy consumption achieved with Data Mining techniques with utilization of input variables subjected to various transformations..

3

New trends in the prevention of occupational noise-induced hearing loss

86%

Sliwinska-Kowalska M.

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nr 6

841-848

EN

Noise exposure during lifespan is one of the main causes of hearing loss. The highest risk of noise-induced hearing loss (NIHL) is related to exposures in the workplace, and affects about 7% of the population. Occupational NIHL is irreversible, thus its prevention must be considered a priority. Although current hearing conservation programs (HCPs) have proved to be very beneficial, the incidence of occupational NIHL is still high, reaching about 18% of overexposed workers. This paper reviews recent research on the effects of noise on hearing in pursuit of more effective methods for the prevention of occupational NIHL. The paper discusses the translational significance of noise-induced cochlear neuropathy, as recently shown in animals, and the concept of hidden hearing loss in relation to current NIHL damage risk criteria. The anticipated advantages of monitoring the incidents of the temporary threshold shift (TTS) in workers exposed to high levels of noise have been analyzed in regard to the preclinical diagnostics of NIHL, i.e., at the stage when hearing loss is still reversible. The challenges, such as introducing speech-in-noise audiometry and TTS computational predictive models into HCPs, have been discussed. Finally, the paper underscores the need to develop personalized medical guidelines for the prevention of NIHL and to account for several NIHL risk factors other than these included in the ISO 1999:2013 model. Implementing the steps mentioned above would presumably further reduce the incidence of occupational NIHL, as well as associated social costs.

4

Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction

58%

Acker A. , Wittkopp T. , Nedelkoski S. , Bogatinovski J. , Kao O.

Annals of Computer Science and Information Systems

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2020

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tom Vol. 21

7--10

EN

The rapid growth and distribution of IT systems increases their complexity and aggravates operation and maintenance. To sustain control over large sets of hosts and the connecting networks, monitoring solutions are employed and constantly enhanced. They collect diverse key performance indicators (KPIs) (e.g. CPU utilization, allocated memory, etc.) and provide detailed information about the system state. Storing such metrics over a period of time naturally raises the motivation of predicting future KPI progress based on past observations. This allows different ahead of time optimizations like anomaly detection or predictive maintenance. Predicting the future progress of KPIs can be defined as a time series forecasting problem. Although, a variety of time series forecasting methods exist, forecasting the progress of IT system KPIs is very hard. First, KPI types like CPU utilization or allocated memory are very different and hard to be modelled by the same model. Second, system components are interconnected and constantly changing due to soft- or firmware updates and hardware modernization. Thus a frequent model retraining or fine-tuning must be expected. Therefore, we propose a lightweight solution for KPI series prediction based on historic observations. It consists of a weighted heterogeneous ensemble method composed of two models - a neural network and a mean predictor. As ensemble method a weighted summation is used, whereby a heuristic is employed to set the weights. The lightweight nature allows to train models individually on each KPI series and makes model retraining feasible when system changes occur. The modelling approach is evaluated on the available FedCSIS 2020 challenge dataset and achieves an overall R^2 score of 0.10 on the preliminary 10\% test data and 0.15 on the complete test data. We publish our code on the following github repository: https://github.com/citlab/fed\_challenge.

5

Bezpomiarowa ocena narażenia zawodowego na substancje chemiczne - nowe wyzwanie dla pracodawców

58%

Gromiec J. P. , Kupczewska-Dobecka M. , Jankowska A. , Czerczak S.

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tom 64

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nr 5

699-716

EN

Employers are obliged to carry out and document the risk associated with the use of chemical substances. The best but the most expensive method is to measure workplace concentrations of chemicals. At present no "measureless" method for risk assessment is available in Poland, but predictive models for such assessments have been developed in some countries. The purpose of this work is to review and evaluate the applicability of selected predictive methods for assessing occupational inhalation exposure and related risk to check the compliance with Occupational Exposure Limits (OELs), as well as the compliance with REACH obligations. Based on the literature data HSE COSHH Essentials, EASE, ECETOC TRA, Stoffenmanager, and EMKG-Expo-Tool were evaluated. The data on validation of predictive models were also examined. It seems that predictive models may be used as a useful method for Tier 1 assessment of occupational exposure by inhalation. Since the levels of exposure are frequently overestimated, they should be considered as "rational worst cases" for selection of proper control measures. Bearing in mind that the number of available exposure scenarios and PROC categories is limited, further validation by field surveys is highly recommended. Predictive models may serve as a good tool for preliminary risk assessment and selection of the most appropriate risk control measures in Polish small and medium size enterprises (SMEs) providing that they are available in the Polish language. This also requires an extensive training of their future users. Med Pr 2013;64(5):699–716

PL

W Polsce nie ma obecnie wiarygodnej, uproszczonej, bezpomiarowej metody oceny narażenia na związki chemiczne, natomiast w niektórych państwach podjęto próby opracowania i wprowadzenia takich metod. Celem pracy jest przegląd wybranych modeli bezpomiarowego prognozowania narażenia zawodowego i związanego z nim ryzyka oraz ocena ich przydatności do szacowania inhalacyjnego narażenia zawodowego, zarówno dla potrzeb oceny zgodności warunków pracy z normatywami higienicznymi, jak i spełnienia wymagań rozporządzenia w sprawie rejestracji i oceny chemikaliów (tzw. REACH). Na podstawie danych literaturowych przeprowadzono przegląd i ocenę modeli: HSE COSHH Essentials, EASE, ECETOC TRA, Stoffenmanager oraz EMKG-Expo-Tool. Zapoznano się z zasadami funkcjonowania modelu i zakresem informacji dotyczących procesu technologicznego oraz innymi danymi, które są wymagane jako dane wejściowe do modelu, oraz z dostępnymi wynikami badań porównawczych, prowadzonych w celu weryfikacji modeli. Na podstawie przeprowadzonej oceny wybranych modeli można stwierdzić, że mogą być one stosowane do wstępnej oceny narażenia inhalacyjnego w zakładach pracy. Omówione modele na ogół dają jako wynik przeszacowane narażenie, a obliczone z ich wykorzystaniem poziomy narażenia należy rozpatrywać jako tzw. racjonalny najgorszy przypadek, niezbędny do prawidłowego doboru środków prewencji. Dostępna w modelach liczba kategorii procesowych i wzorcowych scenariuszy narażenia zawodowego jest obecnie stosunkowo niewielka w porównaniu z sytuacjami, które występują w przemyśle. Niezbędna jest więc dalsza walidacja programów oceny narażenia i/lub ryzyka za pomocą badań terenowych. Modele te mogą być przydatne do wstępnej oceny narażenia inhalacyjnego i doboru środków prewencji, jednak warunkiem ich stosowania w małych i średnich przedsiębiorstwach w Polsce jest ich dostępność w polskiej wersji oraz intensywne szkolenia przyszłych użytkowników w zakresie ich stosowania. Med. Pr. 2013;64(5):699–716

6

A HPC (High Performance Computing) based model for optimisation of the waste management systems in metropolitan regions

58%

Gaska K. , Generowicz A. , Ciuła J.

Technical Transactions

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2018

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tom Vol. 115, iss. 3

153--163

EN

The article presents the conception of an intelligent system for monitoring and managing the municipal waste disposal in metropolises. Applying advanced IT solutions using intelligent computational techniques enables the passage from the passive position of selfgovernment units (JST) in managing the waste disposal to the active position, especially in decision making during the problem solving of planning systems associated with the organisation management of the complex infrastructure of the waste disposal. The aim of using ICT systems is an increase in the reliability of the economy of systemic waste, monitoring in real time, the stabilization of the work of the system and the optimization of logistic and technological processes in the context of the raw material, energy application and simultaneously limiting the influence on all components of the environment.

PL

W artykule przedstawiono koncepcję inteligentnego systemu monitorowania i zarządzania gospodarką odpadami komunalnymi w metropoliach. Zastosowanie zaawansowanych rozwiązań informatycznych wykorzystujących inteligentne techniki obliczeniowe umożliwia na przejście z biernej pozycji jednostek samorządowych (JST) w zarządzaniu gospodarką odpadami do aktywnego działania, w tym szczególnie, podejmowanie decyzji podczas rozwiązywania problemów planistycznych związanych z organizacją systemu zbiórki i systemu transportu odpadów i kompleksowym zarządzaniem złożoną infrastrukturą systemów gospodarki odpadami. Celem wykorzystania systemów ICT jest zwiększenie niezawodności systemów gospodarki odpadami, monitoring w czasie rzeczywistym, stabilizacja pracy systemu oraz optymalizacja procesów logistycznych i technologicznych w kontekście wykorzystania surowcowego, energetycznego przy jednoczesnym ograniczeniu wpływu na wszystkie komponenty środowiska (woda, powietrze, gleba).

7

Data Mining-Based Phishing Detection

58%

Bohacik J. , Skula I. , Zabovsky M.

Annals of Computer Science and Information Systems

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2020

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tom Vol. 21

27--30

EN

Webpages can be faked easily nowadays and as there are many internet users, it is not hard to find some becoming victims of them. Simultaneously, it is not uncommon these days that more and more activities such as banking and shopping are being moved to the internet, which may lead to huge financial losses. In this paper, a developed Chrome plugin for data mining-based detection of phishing webpages is described. The plugin is written in JavaScript and it uses a C4.5 decision tree model created on the basis of collected data with eight describing attributes. The usability of the model is validated with 10-fold cross-validation and the computation of sensitivity, specificity and overall accuracy. The achieved results of experiments are promising.

8

Short-term air pollution forecasting based on environmental factors and deep learning models

58%

Arsov M. , Zdravevski E. , Lameski P. , Corizzo R. , Koteli N. , Mitreski K. , Trajkovik V.

Annals of Computer Science and Information Systems

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2020

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tom Vol. 21

15--22

EN

The effects of air pollution on people, the environment, and the global economy are profound - and often under-recognized. Air pollution is becoming a global problem. Urban areas have dense populations and a high concentration of emission sources: vehicles, buildings, industrial activity, waste, and wastewater. Tackling air pollution is an immediate problem in developing countries, such as North Macedonia, especially in larger urban areas. This paper exploits Recurrent Neural Network (RNN) models with Long Short-Term Memory units to predict the level of PM10 particles in the near future (+3 hours), measured with sensors deployed in different locations in the city of Skopje. Historical air quality measurements data were used to train the models. In order to capture the relation of air pollution and seasonal changes in meteorological conditions, we introduced temperature and humidity data to improve the performance. The accuracy of the models is compared to PM10 concentration forecast using an Autoregressive Integrated Moving Average (ARIMA) model. The obtained results show that specific deep learning models consistently outperform the ARIMA model, particularly when combining meteorological and air pollution historical data. The benefit of the proposed models for reliable predictions of only 0.01 MSE could facilitate preemptive actions to reduce air pollution, such as temporarily shutting main polluters, or issuing warnings so the citizens can go to a safer environment and minimize exposure.

9

When to Trust AI: Advances and Challenges for Certification of Neural Networks

58%

Kwiatkowska M. , Zhang X.

Annals of Computer Science and Information Systems

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2023

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tom Vol. 35

25--37

EN

Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing. Early adoption of AI technology for real-world applications has not been without problems, particularly for neural networks, which may be unstable and susceptible to adversarial examples. In the longer term, appropriate safety assurance techniques need to be developed to reduce potential harm due to avoidable system failures and ensure trustworthiness. Focusing on certification and explainability, this paper provides an overview of techniques that have been developed to ensure safety of AI decisions and discusses future challenges.

10

Defect Backlog Size Prediction for Open-Source Projects with the Autoregressive Moving Average and Exponential Smoothing Models

58%

Anioła P. , Pandey S. K. , Staron M. , Ochodek M.

Annals of Computer Science and Information Systems

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2023

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tom Vol. 35

83--92

EN

Context: predicting the number of defects in a defect backlog in a given time horizon can help allocate project resources and organize software development. Goal: to compare the accuracy of three defect backlog prediction methods in the context of large open-source (OSS) projects, i.e., ARIMA, Exponential Smoothing (ETS), and the state-of-the-art method developed at Ericsson AB (SM). Method: we perform a simulation study on a sample of 20 open-source projects to compare the prediction accuracy of the methods. Also, we use the Na\"{\i}ve prediction method as a baseline for sanity check. We use statistical inference tests and effect size coefficients to compare the prediction errors. Results: ARIMA, ETS, and SM were more accurate than the Na\"{\i}ve method. Also, the prediction errors were statistically lower for ETS than for SM (however, the effect size was negligible). Conclusions: ETS seems slightly more accurate than SM when predicting defect backlog size of OSS projects.

11

Predictive regression models of monthly seismic energy emissions induced by longwall mining

51%

Jakubowski J. , Tajduś A.

Archives of Mining Sciences

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2014

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tom Vol. 59, no. 3

705--720

EN

This article presents the development and validation of predictive regression models of longwall mining-induced seismicity, based on observations in 63 longwalls, in 12 seams, in the Bielszowice colliery in the Upper Silesian Coal Basin, which took place between 1992 and 2012. A predicted variable is the logarithm of the monthly sum of seismic energy induced in a longwall area. The set of predictors include seven quantitative and qualitative variables describing some mining and geological conditions and earlier seismicity in longwalls. Two machine learning methods have been used to develop the models: boosted regression trees and neural networks. Two types of model validation have been applied: on a random validation sample and on a time-based validation sample. The set of a few selected variables enabled nonlinear regression models to be built which gave relatively small prediction errors, taking the complex and strongly stochastic nature of the phenomenon into account. The article presents both the models of periodic forecasting for the following month as well as long-term forecasting.

PL

W artykule przedstawiono budowę i walidację predykcyjnych modeli regresyjnych sejsmiczności indukowanej eksploatacją w ścianie, opartych na obserwacjach w 63 ścianach kopalni Bielszowice prowadzonych w 12 pokładach w latach 1992-2012. Zmienna prognozowaną jest logarytm miesięcznej sumy energii sejsmicznej wstrząsów w ścianie. Zestaw predyktorów składa się z siedmiu zmiennych ilościowych i jakościowych opisujących wybrane czynniki górnicze i geologiczne w ścianach. Do budowy modeli zastosowano dwie metody uczenia się maszyn: drzewa wzmacniane oraz sieci neuronowe. Zastosowano dwa rodzaje walidacji modeli: na losowej próbie walidacyjnej oraz na czasowej próbie walidacyjnej. Zestaw kilku wybranych zmiennych pozwolił na zbudowanie nieliniowych modeli regresyjnych, które, biorąc pod uwagę złożoną i silnie stochastyczną naturę zjawiska, dają względnie małe błędy pro gnozy. W artykule przedstawiono zarówno modele do prognozy okresowej na kolejny miesiąc jak i do prognozy długoterminowej.