Way Of Data Processing Research Articles

TECHNIQUE OF PROCESSING OF VIBRATION MONITORING DATA, RECEIVED BY THE USE OF MICROELECTROMECHANICAL SYSTEMS

During the operation of such machines as aviation engines and land based gas turbines, the obligatory vibration monitoring is carried out which is focused on the prevention of their possible damages and destructions during the work on resonant modes or because of material fatigue. Nowadays, as a rule, the standard or additional equipment is used for such control which includes as a component various types of one-axial vibration gauges. In most cases, the control is carried out continuously, and the frequency of registration can differ from several values for a flight to several values in a second. The data received during routine vibration monitoring is peak values of vibrations. They are diagnosed, using the pre-start control and some ways of fit and the tendency of changes of vibration during the operation. Microelectromechanical systems gaining now the increasing distribution (MEMS), as a rule, allow to obtain the data about vibro-acceleration without giving data about the frequency vibration characteristics. But also the regular equipment of vibrational control used during the operation of considered machines does not give data about the frequency vibration characteristics. However, microelectromechanical devices allow to obtain the data with the essential higher frequency of sample rate (in tens and hundreds times) in comparison with mass equipment used now for control, and to carry out the simultaneous control of vibration on three axes using one gauge. Apart from the vibration peak value the position relative to a reference point is fixed. Does the information received according to mentioned above features have better diagnostic potential? Will the array dimension received during data verification be an obstacle to the operational processing? Materials of the present article are an attempt to give answers to these questions and to make representation about possibility and features of an estimation of a technical state of machines by the results of processing of time series vibro-accelerations received with the use of such processing microelectromechanical systems. It is represented that the way of data processing of vibrating monitoring considered in the article at sufficient simplicity of realization allows to solve the problem of an estimation of a technical state of monitoring item.

The study of weather conditions favourable to the accretion of icing that pose a threat to transmission power lines

In order to prevent power failures caused by atmospheric phenomena you need to identify where such phenomena occur and to explore the scale of impact thereof on individual elements of the power grid infrastructure. It is important to create IT systems which automate the process of evaluating the current state of the power line based on historical data. The paper presents the results of an extended analysis of historical meteorological data in order to investigate conditions favourable to the adhesion and accretion of icing on power lines, which is one of the most common causes of failures triggered by wet snow and freezing rain precipitation. An additional aim of the analysis was to detail and verify the prior research. To that end, the information retrieved and analysed measuring locations where the favourable conditions were investigated has been significantly increased. This article compares two ways of data processing for determining the conditions that lead to icing of power lines, i.e. an algorithm that takes into account the specific ambient temperature, wind velocity and type of precipitation (TWP) and the combined methods for determining wet snow and freezing rain precipitation based on an analysis of wet-bulb temperature and type of precipitation (WS+FR). Additionally, detailed analyses were presented of atmospheric conditions recorded in selected locations (Katowice, Kraków) during the occurrence of significant power failures.

Testing a model predictive control algorithm for a PV-CHP hybrid system on a laboratory test-bench

In order to reduce the global warming to less than two degrees, a large increase in renewable energy resources like photovoltaic systems is necessary. A business case based on feed-in tariffs does not exist in every countries and self-consumption is limited due to the unsteady nature of solar radiation on earth. A combination with other systems such as batteries, heat-pumps or even combined heat and power plants can enhance the use of generated power by photovoltaic systems, particularly in private households and small businesses. Rule-based controllers and optimization algorithms (model predictive control) can both realise the efficient operation of a photovoltaic system in combination with storage systems and a combined heat and power plant. However, different controllers and energy management systems have hitherto only been compared theoretically. A comparison of such controllers in a real, controllable hardware environment has not yet been carried out. In this study, a test-bench is introduced to test different control algorithms for photovoltaic systems in combination with storage systems and a micro- combined heat and power plant. The operation has been tested for a one day period. Key performance parameters have been derived and compared for a rule-based control, an optimized control and simulation results including a forecast. The results show that the operational costs can be reduced by 7.3% for the chosen test-period using the optimized algorithm in the laboratory compared to the same system with a rule-based control. The results also indicate that even under perfect forecast conditions the hardware, metering and energy management cause latencies and inaccuracies leading to deviations, which are not accounted for in simulations. Hence, the accuracy of the forecast methods need not be higher than the deviations introduced by the hardware. These deviations often lead to unwanted charging and discharging events of the battery. A faster way of processing data and a second order or low level control is needed for short term reaction and higher efficiency.

Distributed least-squares estimation applied to GNSS networks

In view of the recent proliferation of low-cost mass-market receivers, the number of network receivers and GNSS users will be growing rapidly, demanding an efficient way of data processing in terms of computational power and capacity. One way of improving the computational capacity is to decentralize the underlying data processing and distribute the task of the computer center across individual network receivers. In this invited contribution we review the problem of distributed estimation and present an algorithm for distributed least-squares estimation using the alternating direction method of multipliers. Applying the algorithm to a network of GNSS receivers, we show how the distributed data processing of individual receivers can deliver parameter solutions comparable to their centralized network-derived counterparts. With distributed estimation techniques, GNSS single-receiver users can therefore obtain high-precision solutions without the need of having a centralized computing center.

Monitoring patient health based on medical records using fuzzy logic method

The Fuzzy Logic concept was first introduced by Prof. Lotfi Zadeh from the University of California at Berkeley in 1965, and presented not as a control methodology, but as a way of processing data by allowing the use of partial set membership compared to the crisp set membership or non-membership. Along with the development of computer technology, the concept of fuzzy logic is increasingly needed by people, because this concept is able to provide information needed in the decision making process. This study aims to analyze and design intelligent systems to monitor the health development of inpatients. The method used is the Fuzzy Logic method. This method will predict the level (degree) of health of each patient based on the amount of drug use and the durationof diagnosis process. The tools used to analyze and design the system are Unified Modeling Language. The results of this research are monitoring the health development of patients using the Fuzzy Logic method.

A multi-agent security framework for cloud data storage

In the last years, cloud computing has emerged as a new information technology (IT) model. Which provides an easy way for data processing and storage remotely through a network. Nowadays, cloud storage has become an attractive storage scheme for users to store their data. When users store their fil es remotely in a cloud storage system, they still worried about the security of their files and the guarantee of the confidentiality and the integrity of them. In this work, we propose a multi-agent framework to provide cloud data storage security. The proposed multi-agent framework enables the security of data in cloud storage by providing both integrity and confidentiality of data. For this purpose, we proposed a client-side encryption method, which is based on the use of both AES and RSA algorithms, it provides the confidentiality and privacy of data besides the performance. Proof of retrievability based on the use of hash function is adopted in our multi-agent framework, to enable data integrity check.

Experimental comparison of altitudes with modern geodetic methods

Levelling is one of the most important geodesic works in construction and other interventions in space. Mostly, it is used for the needs of the altitude presentations of the terrain, to determine shifts, to determine the height of the object, and for various precise laboratory and scientific researches. When determining the shifts, the values are confirmed with the results of levelling, and deformations are calculated. There are a lot of methods to determine height differences; which one will be used, depends on the complexity of the works. Recently, the GNSS method is mostly used. It offers 2D positionally reliable results, whereas the vertical component does not provide reliable results. For this purpose, a series of tests and GNSS-measurements analysis were performed at our institution, which is also presented in the article as the GNSS-measurements analysis in comparison to the results obtained with the robotic total station of accuracy 0,5ˇ. Prior to the experiment, a temporal GNSS data analysis based on an individual axis and with a different way of data processing was carried out. The planning of GNSS-measurements for the needs of more demanding measurements is emphasised. To improve the determination of the vertical component, the data capture with GNSS method was increased from 10 Hz to 100 Hz, which partly improved the final values and is presented in the study.

Společnost 4.0 – vybrané aspekty

Components of Society 4.0 are taken as those currently most significantly influencing human being, common activities related to the existence of individual in society. It is closely related to changes in approach to information and its use. Modern ways of data sharing and processing are used massively, what is reflected also in changes of interpersonal communication generally. Positive impacts of this state are discussed at conferences and other events closely related to the issue of Society 4.0. On the other hand, it is also necessary to deal with possible negatives and risks related to use of new technologies that may arise in the future. Objective of this contribution is to explain and discuss selected aspects of society transformation to Society 4.0 which is based on specific principles. To reach the defined objective, literary research of relevant area and initial analyses of current state in the area of understanding principles of Society 4.0 by the representatives of small and medium sized companies specified in the research were used. Main focus was given to the new ways of data processing, changes of current conditions in relation to job positions, education and overall quality of life. Selected findings are presented in individual sections of the contribution. The emphasis was given on approaches to information and communication technologies and approach of individuals to changes visible in the whole society. This contribution should be taken as the summary of selected information coming from the initial research phase.

Implementation of polarization-encoded quantum Toffoli gate

Optics plays an important role in data processing. In this context, optical nonlinear material is very useful for superfast switching operations. Again polarization-based encoding and decoding technique plays a convenient way for data processing. In this paper, a new method to implement a quantum Toffoli gate with the active use of optical nonlinear material and polarization-based encoding technique is reported.

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis

Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF). Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.

Potencijali za primenu cloud tehnologije u računovodstvu

Cloud technology, as an innovative way of data processing and storage, is one of the latest trends in the world of information technology. Adopting these technological solutions is one of the primary ways to ensure the efficiency of the accounting information system, so today the focus is more and more on cloud accounting, or, how it can often be heard, online accounting, web accounting, or virtual accounting system. The aim of this paper is to point to specific features of applying cloud technology in accounting - opportunities it provides, as well as risks arising from it. In addition, by presenting the benefits that cloud-based accounting brings to companies implementing this technology as well as users of financial information, the authors aim to encourage and stimulate companies in the Republic of Serbia to use the growing cloud services market and introduce this technology into their accounting information systems.

RETRACTED ARTICLE: Data mining and visualization of data-driven news in the era of big data

With the continuous deepening of study of data mining, the application area of data mining gradually expanded, its influence also spread to the media industry. Data visualization technology has changed the traditional narrative mode, make the news becomes a product that be produced. This paper analyzes the history of computer aided reporting to data news, the main models of data news visualization, and the process of data news production through data mining. The study found that data news focuses on the way of data processing in the entire news workflow, it involves not only classical computer graphics technology, image-processing technology and computer audio technology, but also more data analysis and visual processing technologies based on new media and cloud computing involved in. Research data mining and visualization of data-driven journalism can help journalists use big data to do news work better, deepen people’s cognition of news events, find the logic which cannot be reflected in traditional news, and maximize the connotation of news report.

Digital iconoclasm and the new challenges of cognitive ethics: the case of web project Fiber

ABSTRACTThis article takes the form of an analytical commentary by the developers of ‘Fiber’, a web project, which actualizes the potential to influence the intentionality of users’ attention. The application raises the question of shifts in cognitive reality in the wake of the ‘Data Revolution’, as well as the means by which digital tools may provide access to them. Attention has become one of the most important phenomena in the formation of social reality in the era of late capitalism. Fiber examines attention as data in the age of cognitive capitalism. This article posits a relationship between the visualization of invisible cognitive processes (‘iconoclastic imagery’), emergent means of access to them, and cognitive ethics – a way of processing data that in the predigital age belonged to the individual world of the subject, and now, having being converted into data, has been included in and co-opted by economic and communication exchange.

Paradigm shift: cognitive surgery.

In the last hundred years surgery has experienced a dramatic increase of scientific knowledge and innovation. The need to consider best available evidence and to apply technical innovations, such as minimally invasive approaches, challenges the surgeon both intellectually and manually. In order to overcome this challenge, computer scientists and surgeons within the interdisciplinary field of “cognitive surgery” explore and innovate new ways of data processing and management. This article gives a general overview of the topic and outlines selected pre-, intra- and postoperative applications. It explores the possibilities of new intelligent devices and software across the entire treatment process of patients ending in the consideration of an “Intelligent Hospital” or “Hospital 4.0”, in which the borders between IT infrastructures, medical devices, medical personnel and patients are bridged by technology. Thereby, the “Hospital 4.0” is an intelligent system, which gives the right information, at the right time, at the right place to the individual stakeholder and thereby helps to decrease complications and improve clinical processes as well as patient outcome.

Inquiry Based Science Education and Getting Immediate Students’ Feedback about Their Motivation

The paper is based on collecting evidence of the Establish project impact on students. For the purpose two questionnaires based on the existing tools have been used. Questionnaire 1 is a part of Intrinsic Motivation Inventory (IMI) based on the Self-determination theory. It is aimed at assessing students’ interests, their perceived choice and usefulness of implemented learning units and should be answered after each learning unit/several IBSE activities. Several items of CLES questionnaire are included there as well. Questionnaire 2 assesses the impact on students’ attitudes towards science and technology and on their knowledge about nature of building up science knowledge. Both questionnaires exist in the lower and upper secondary school versions. The paper presents selected data and results which were obtained by addressing the Questionnaire 1, so that the focus is on getting students’ feedback about their intrinsic motivation. Our assumption is that active learning is associated with positive intrinsic motivation of students. That is why we find as very important that educators have a possibility to understand the phenomenon more deeply. We aim to present the reliable tool for getting the feedback and to present a way of data processing which does not need advanced statistical methods, so that teachers (as well as science education researchers) can use and analyze data obtained by the tool. Means and standard deviations for items of the subscales Interest/Enjoyment, Perceived choice and Value/Usefulness were computed. To determine the consistency of results, the Standard Pearson correlation cofficient was computed for all items within the subscales. Based on the findings, we can conclude that participants’ answers (questionnaire results) were consistent (not responded mechanically).

Artificial immune systems applied to fault detection and isolation: A brief review of immune response-based approaches and a case study

This paper aims to document the application of a new generation of artificial immune systems (AIS) in fault detection and isolation problems. These kind of algorithms are able to explore normal and anomalous behavior evidences, however, they may often require a more explicit prior knowledge provided by experts, usually difficult to obtain in some practical cases. Thus, many immune inspired approaches applied to fault detection and isolation (FDI) in the literature are based on negative selection algorithms. Considering these points, this work presents a review on three AIS approaches. Once reviewed and contextualized, the evaluated techniques are properly adjusted considering their main parameters and ways of processing data, and then, applied to a case study of fault detection and isolation in order to provide a performance analysis of these techniques, according to their applicability to these problems.

Method of immersion of a problem of comparison financial conditions of the enterprises in an expert cover in a class algorithms of artificial intelligence

The financial condition of the enterprise can be estimated by a set of characteristics (solvency and liquidity, structure of the capital, profitability, etc.). The part of financial coefficients is low-informative, and other part contains the interconnected sizes. Therefore for elimination of ambiguity we will pass to the generalized indicators – rating numbers, and as the main means of research it is offered to use the theory of expert systems. As characteristic of the modern theory of expert systems it is necessary to consider application of intellectual ways of data processing of data mining, or simply data mining. The method of immersion of a problem of comparison of a financial condition of economic objects in an expert cover in a class of systems of artificial intelligence is offered (algorithms of a method of the analysis of hierarchies, contiguity leaning of a neural network, algorithm of training with function of activation softmax). The generalized indicator of structure of the capital in the form of rating number is entered and the sign (factorial) space for seven concrete enterprises is created. Quantitative signs (financial coefficients of structure of the capital) are allocated and their normalization by rules of the theory of expert systems is carried out. To the received set of the generalized indicators the method of the analysis of hierarchies is applied: on the basis of a linguistic scale of T. Saaty the ranks of signs reflecting the relative importance of various financial coefficients are defined and the matrix of pair comparisons is constructed. The vector of priority signs on the basis of the solution of the equation for own numbers and own vectors of the mentioned matrix is calculated. As a result the visualization of the received results which has allowed to eliminate difficulties of interpretation of small and negative values of the generalized indicator is carried out. The neural network with contiguity leaning and function of activation softmax is applied to further smoothing of dispersion of indicators. Application of this method allows to facilitate considerably problems of interpretation of results of comparison of a financial condition of various enterprises.

Mass Spectrometry Imaging of Drug Related Crystal-Like Structures in Formalin-Fixed Frozen and Paraffin-Embedded Rabbit Kidney Tissue Sections.

A multimodal mass spectrometry imaging (MSI) based approach was used to characterize the molecular content of crystal-like structures in a frozen and paraffin embedded piece of a formalin-fixed rabbit kidney. Matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging and desorption electrospray ionization (DESI) mass spectrometry imaging were combined to analyze the frozen and paraffin embedded sample without further preparation steps to remove the paraffin. The investigated rabbit kidney was part of a study on a drug compound in development, in which severe renal toxicity was observed in dosed rabbits. Histological examination of the kidney showed tubular degeneration with precipitation of crystal-like structures in the cortex, which were assumed to cause the renal toxicity. The MS imaging approach was used to find out whether the crystal-like structures were composed of the drug compound, metabolites, or an endogenous compound as a reaction to the drug administration. The generated MALDI-MSI data were analyzed using principal component analysis. In combination with the MS/MS results, this way of data processing demonstrates that the crystal structures were mainly composed of metabolites and relatively little parent drug.Graphical ᅟ

Эффективная обработка и классификация энергетических спектров морского волнения на основе распределенного вычислительного конвейера

Обработка больших массивов данных обычно происходит в несколько последовательно выполняемых этапов, таких как пред- и постобработка, после каждого из которых промежуточные данные записываются на диск; однако, для каждой задачи этап предварительной обработки может отличаться, и в таком случае непосредственная передача данных по вычислительному конвейеру от одного этапа (звена) к другому бу- дет более эффективным с точки зрения производительности решением. В более общем случае некоторые этапы можно разделить на параллельные части, сформировав таким образом распределенный вычислительный конвейер, каждое звено которого может иметь несколько входов и выходов. Такой принцип обработки данных применяется в задаче о классификации энергетических спектров морского волнения, которая основана на аппроксимациях, позволяющих извлекать параметры отдельных систем волн (тип волн, генеральное направление волн и т. п.). Система, построенная на этом принципе показывает более высокую производительность по сравнению с часто применяемой поэтапной обработкой данных.

A Method for Missing Data Recovery of Waste Gas Monitoring in Animal Building Based on GA-SVM

In order to solve the data missing problem caused by sensor faults during the waste gas monitoring in animal building, a method for missing data recovery was presented based on support vector machine (SVM) combined with genetic algorithm (GA). Multiple factors that influence monitoring values of the waste gas in animal building such as temporal, spatial and environmental, were considered to established a SVM regression prediction model to estimate the missing data of the waste gas monitoring. Meanwhile, to obtain better prediction accuracy, model parameters were optimized by the GA. The data processing of the ammonia (NH3) concentration was taken as an example; monitoring data of 3 days were randomly selected in a farm to test the presented model in this paper. It is shown that there was a very little error between the estimated data and the monitoring data, the maximal relative error was 6.99 % (percent), and the average relative error was 2.15 % (percent). It is an effective method for missing data recovery and a practical way of data processing for waste gas monitoring in animal building.