The perception of corporate social responsibility by employees of international IT corporations

PURPOSE: The purpose of the study is to research the employees' understanding of the CSR concept in IT corporations, the actions taken by the employees, and to find out their opinions on whether these actions are effective.DESIGN/METHODOLOGY/APPROACH: The research method was a diagnostic survey with the use of a research tool – a questionnaire. The basic source of data presented in this article is data collected from the survey carried out in January 6th-30th 2020. In the process of preparing this publication, literature analysis of the discussed issue and questionnaire research were used. The article presents in detail the diversity of perception of CSR due to gender with regards to inequalities concerning wage differences and professional development between men and women.FINDINGS: The conclusions from the study indicate that, it can be considered that CSR activities undertaken by companies are perceived by employees as bringing positive results. It is recommended for employees to know and be involved in creating a company strategy, including CSR records. Companies should eliminate all kinds of inequality and discrimination. The knowledge obtained can be used both for practical purposes and for further theoretical considerations.PRACTICAL IMPLICATIONS: One of the manifestations of CSR is to ensure equal opportunities and opportunities for development, expansion of qualifications, but also salaries. The lack of consistency between the stipulations of the company's strategy and the actual principles causes employees to perceive CSR as serving only to improve the image and good reputation. Thanks to this knowledge multinational corporations from the IT sector can change their procedures in terms of real equal opportunities for employees.ORIGINALITY/VALUE: This article presents the results of a study on the implementation of the CSR concept in IT companies operating in Poland with different capital. To date, there has been no research among employees of multinational corporations in the IT industry.peer-reviewe

Energy reduction with super-resolution convolutional neural network for ultrasound tomography

This study addresses the issue of energy optimization by investigating solutions for the reduction of energy consumption in the diagnostics and monitoring of technological processes. The implementation of advanced process control is identified as a key approach for achieving energy savings and improving product quality, process efficiency, and production flexibility. The goal of this research is to develop a cost-effective system with a minimal number of ultrasound sensors, thus reducing the energy consumption of the overall system. To accomplish this, a novel method for obtaining high-resolution reconstruction in transmission ultrasound tomography (t-UST) is proposed. The method involves utilizing a convolutional neural network to take low-resolution measurements as input and output high-resolution sinograms that are used for tomography image reconstruction. This approach allows for the construction of a super-resolution sinogram by utilizing information hidden in the low-resolution measurement. The model is trained on simulation data and validated on real measurement data. The results of this technique demonstrate significant improvement compared to state-of-the-art methods. The study also highlights that UST measurements contain more information than previously thought, and this hidden information can be extracted and utilized with the use of machine learning techniques to further improve image quality and object recognition

Management of early failure detection of production process : the case of the clutch shaft alignment using LSTM deep learning algorithm

PURPOSE: In this paper the neural networks model based on long short-term memory (LSTM) for early failure detection of the clutch shaft alignment system is developed. This issue is of particular importance when assessing the condition of the tool and predicting its durability, which are keys to the reliability and quality of the production process.DESIGN/METHODOLOGY/APPROACH: Based on real fault data of the measuring system, 500 clutch fault runs were simulated. Then, the time of failure was modelled with two neural networks, the conventional neural network of the ANN and the LSTM deep learning network. The study examined and compared the effectiveness and quality of both networks in the context of fault prediction.PRACTICAL IMPLICATIONS: In vibroacoustic diagnostics, we often deal with machines operating in various conditions, which makes it difficult to diagnose them using standard methods. In such cases, spectral methods require analysis of frequency bands, which may contain other components in addition to information about the diagnosed parameter. The algorithm for predicting impending failure gives the possibility to monitor the current degradation status of the device. This makes it possible to streamline planning processes in the areas of inspection, preventive replacement of parts, warranty, service, or storage of spare parts.FINDINGS: The objective of the paper is to introduce an improved computational method for failure detection based on a deep learning algorithm. It was proven that LSTM networks are suitable for successfully solving this scope of tasks.ORIGINALITY/VALUE: The research showed that the proposed LSTM algorithm is more effective and accurate than conventional artificial neural networks (ANN) based on the multilayer perceptron model.peer-reviewe

Statystyczne modele w procesach zarządzania ochroną środowiska przed hałasem /

Promotor: Wojciech Batko.Niepublikowana praca doktorska.Praca doktorska. Akademia Górniczo-Hutnicza im. Stanisława Staszica. Wydział Inżynierii Mechanicznej i Robotyki. Katedra Mechaniki i Wibroakustyki, 2015.Bibliogr. k. 130-136.Rodzaje niepewności i metody jej wyznaczania oraz modele, przedziały ufności, niepewność według przewodnika Guide to the expression of uncertainty in measurement, metody wyznaczania niepewności stosowane w literaturze, formalizm modelowy, transformacje rozkładów prawdopodobieństw, modele wskaźników hałasu, metody estymacji funkcji gęstości, porównanie metod estymacji na przykładzie przesuniętego rozkładu gamma, problem asymetrii w wynikach pomiarów poziomów dźwięku, asymetria wyników pomiarów poziomów dźwięku i poziomów energii w monitoringu akustycznym, brak normalności wyników pomiarów poziomów energii i decybelowych poziomów dźwięku w monitoringu akustycznym, brak normalności w wynikach pomiarów długookresowych wskaźników hałasu, efekt wygładzania, dopasowanie rozkładów prawdopodobieństw do próbek otrzymanych z monitoringu akustycznego, dopasowanie rozkładów dobowych wskaźników hałasu, dopasowanie rozkładów długookresowych wskaźników hałasu, analiza porównawcza modelu wyznaczania niepewności metodą propagacji rozkładów z klasycznie stosowanymi metodami, analiza porównawcza metody propagacji rozkładów dla dobowych wskaźników hałasu, analiza porównawcza metody propagacji rozkładów dla długookresowych wskaźników hałas

Fault diagnosis of machines operating in variable conditions using artificial neural network not requiring training data from a faulty machine

The fault diagnosis for maintenance of machines operating in variable conditions requires special dedicated methods. Variable load or temperature conditions affect the vibration signal values. The article presents a new approach to diagnosing rotating machines using an artificial neural network, the training of which does not require data from the damaged machine. This is a new approach not previously found in the literature. Until now, neural networks have been used for machine diagnosis in the form of classifiers, where data from individual faults were required. A new diagnostic parameter rDPNS (Relative Differences Product of Network Statistics) as a function of the machine's shaft order was proposed as a kind of new order spectrum independent of the machine's operating conditions. The presented work analyses the use of the proposed method to diagnose misalignment and unbalance. The results of an experiment carried out in the laboratory demonstrated the effectiveness of the proposed method

The Use of Deep Learning Methods in Diagnosing Rotating Machines Operating in Variable Conditions

This paper presents the use of artificial neural networks in diagnosing the technical condition of drive systems operating under variable conditions. The effects of temperature and load variations on the values of diagnostic parameters were considered. An experiment was conducted on a testing rig where a variable load was introduced corresponding to the load of the main gearbox of the bucket wheel excavator. The signals of vibration acceleration on the gearbox body, rotational speed, and current consumption of the drive motor for different values of oil temperature were measured. Synchronous analysis was performed, and the values of order amplitudes and the corresponding values of current, speed, and temperature were determined. Such datasets were the learning vectors for a set of artificial deep learning neural networks. A new approach proposed in this paper is to train the network using a learning set consisting only of data from the efficient system. The responses of the trained neural networks to new data from the undamaged system were performed against the response to data recorded for three damage states: misalignment, unbalance, and simultaneous misalignment and unbalance. As a result, a diagnostic parameter as a normalized measure of the deviation of the network results was developed for the faulted system from the result for the undamaged condition

Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale gas extraction site. The water quality index was determined from the obtained parameters. A secondary objective of the study was to test the capacity of the artificial neural network (ANN) methods to model the water quality index in groundwater. The number of ANN input parameters was optimized and limited to seven, which was derived using a multiple regression model. Subsequently, using the stepwise regression method, models with ever fewer variables were tested. The best parameters were obtained for a network with five input neurons (electrical conductivity, pH as well as calcium, magnesium and sodium ions), in addition to five neurons in the hidden layer. The results showed that the use of the parameters is a convenient approach to modeling water quality index with satisfactory and appropriate accuracy. Artificial neural network methods exhibited the capacity to predict water quality index at the desirable level of accuracy (RMSE = 0.651258, R = 0.9992 and R2 = 0.9984). Neural network models can thus be used to directly predict the quality of groundwater, particularly in industrial areas. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. The novelty of these studies is the use of the ANN network to forecast WQI groundwater in an area in eastern Poland that was not previously studied—in Lublin

Statistical analysis with Kolmogorov-Smirnov distance for reflections’ directions of arrival and amplitudes for sound field diffuseness estimation

Many parameters are used for rating the quality of the sound field inside qualified acoustic halls describing the strength, clarity, and definition of the sound. Sound field diffuseness level and spatial impression parameters are used rarely because of the problem in their measurements and interpretation. Previous research on that topic provided some sound field diffuseness coefficients. Some of them are complicated in estimation and measurement. This paper presents a method for the sound field diffuseness level estimation basing on example measurements of the Arthur Rubinstein Philharmonic in Łódź, Poland. New directional parameters are proposed based on the statistical analysis of the sound reflections’ incidence angles and their amplitudes with Kolmogorov-Smirnov distance. The paper contains a discussion on the quality evaluation with the proposed method, including analysing the sound field diffuseness and non-uniform spatial distributions of sound reflections. The usability of the selected parameters and their importance for the spatial impression is discussed. The performed experiments allow setting the direction of future work in the field taken of the study, especially applying the proposed method for extended sound field diffuseness ratings with methods based on different physical principles, including directional, energetic, and time coefficients