Intelligent Data Processing System Research Articles

ДОСЛІДЖЕННЯ ЕФЕКТИВНОСТІ ВИКОРИСТАННЯ РОЗПОДІЛЕНОЇ МАТРИЧНОЇ ФАКТОРИЗАЦІЇ В ПРОМИСЛОВИХ СИСТЕМАХ

The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis. A prime example of an intelligent industrial system is the smart grid, which efficiently distributes electricity to users by considering demand, network parameters, load, etc. Processing large amounts of information necessitates the use of machine learning methods and mathematical data analysis. Matrix factorization serves as an exemplary technique for transforming information into a more convenient form for further processing, establishing relationships between elements, and optimizing outcomes. In particular, the SVD (Singular Value Decomposition) and Funk-SVD algorithms are employed to address big data processing challenges, and they were discussed in this work. The key features of processing large data volumes in industrial smart grid systems were analyzed in the paper. The advantages of distributed computing for more efficient information analysis were identified. The recommendation algorithms that enable faster and more accurate processing of extensive data were explored in the study. Specifically, the SVD and Funk-SVD algorithms, used in recommendation systems for large data processing, were examined. A method of distributed matrix factorization to provide recommendations to smart grid system users was proposed in the paper. This approach involves the exchange of public data between devices and the local processing of private data. The advantages of this distributed model include flexibility in adjusting parameters, improved calculation accuracy through result exchange between nodes, high data processing speed, and scalability were identified. The conclusion that the proposed method can be effectively used in recommendation systems within the smart grid context, enhancing automated management processes and resource distribution was exclaimed.

DESIGN AND SYNTHESIS OF TERNARY LOGIC ELEMENTS

The aim of this paper is creating some ternary elements. The threshold element of ternary logic on bipolar transistors and elements of ternary systems based on it are considered. The main disadvantages of this approach are identified. The multi-threshold element of multivalued logic and its specific four-threshold implementation are considered. The scientific nowelty is the using of a multithreshold element of multivalued logic as a basis for constructing elements of ternary systems. It is shown that the advantages of a multi-threshold element of multi-valued logic are a larger number of thresholds of input signals, a larger number of levels that it can distinguish and a larger number of output signals. The implementation of some ternary elements, such as half-adder, disjunction and strong conjunction, is given. The practical significance of obtained results is that the multithreshold element of multivalued logic allows us to use it for synthesis basic elements of ternary logic and use one approach for all of them. Additional practical advantage is that in practice it allows you to build more diverse logical and arithmetic devices with a simplified implementation. The implementation of ternary devices based on threshold logic is a way to create ternary devices that can compete with binary devices in terms of equipment, capacity, operational capabilities, and variety. This, in turn, leads to greater speed and simplification of the structure of devices, as well as increasing the speed of data processing. These advantages are significant in such areas of computer use as intelligent data processing systems, expert systems, decision theory, i.e., where data analysis is performed. Prospects for further research are to use the method of construction and synthesis of nodes of ternary computer systems, based on MTEML. Their optimization, and development of principles of mathematical modeling and software of such systems and their elements is expedient.

Preparation of Innovative Development Rating of Russian Regions by the Level of University Involvement

The paper presents method of clustering and grouping the university performance indicators to prepare an integral rating of Russian regions by the level of university involvement in innovative development of Russian regions. The following problems that require the management influence of state structures are considered: the role of the research base of regional universities in strengthening the innovative potential of regions, the degree of involvement of universities in the innovative regional space.The aim of the study is to develop a rating system for Russian regions according to the degree of university involvement in innovative development using mathematical tools and an intelligent data processing system. The main hypothesis of the article is the existence of a link between regional innovative development and the effectiveness of university contributions. The mathematical approach involves the consideration and multidimensional ranking of the main groups of university performance indicators in order to obtain cluster classifications within each of the problems posed. The solution to the problems is to build a barometer of innovation activity in the form of a multi-purpose problem-oriented rating.Materials and methods. An approach is applied that includes the assessment and multidimensional ranking of the contribution of universities using groups of integral indices created on the basis of aggregation of several important indicators of the university’s activity. Computational experiments were conducted according to the data of the Ministry of Education and Science for the regions of Russia and regional universities. 16 indicators of Russian regions for 2016 are considered.Results and discussion. The ranking and comparison of universities according to the degree of involvement in the innovative development of the region was performed using an indicator model and aggregation depending on the target problem. Quantitative indicators of the quality of university’s activities by regions are obtained, which allow us to obtain an integral rating of Russian regions by the level of university involvement. Unlike other approaches, the author’s method includes three components, which are independent integral indexes and indicate the level of involvement of universities in regional innovative development. Statistical data on the leading indicators of Russian universities for 2016 were processed. The methodology Science-model with three factors - the model with regrouping, named by the author A-B-C, showed the high potential of Russian universities to balance their regional demand as management research centers.Conclusion. The results of the study are compared with the ratings of the well-known agencies. The author hopes that soon Russia will have a reliable scientometric system at the level of rating universities on their involvement in the innovative development of Russia, such a rating will be an indisputable argument in favor of financing regional universities. The author laid down a high requirement: compliance with the three models, only in this case, regional universities can receive funding from the municipality, after redistribution from the center. At the same time, it is necessary to carefully choose universities in which projects will receive development and perspective. Regional authorities must meet the requirements to receive the necessary investments in promising projects. The scientific potential and demand for theoretical research for their full application at all enterprises, the combination of theoretical science and practical implementation will reduce the cost of stabilizing outdated technologies in all areas of knowledge and use the experience of older generations and the strength of young people for high-tech production growth in Russia. Therefore, the results of the study will be useful to federal authorities and financial and credit organizations that provide financing.

Real-time intelligent big data processing: technology, platform, and applications

Human beings keep exploring the physical space using information means. Only recently, with the rapid development of information technologies and the increasing accumulation of data, human beings can learn more about the unknown world with data-driven methods. Given data timeliness, there is a growing awareness of the importance of real-time data. There are two categories of technologies accounting for data processing: batching big data and streaming processing, which have not been integrated well. Thus, we propose an innovative incremental processing technology named after Stream Cube to process both big data and stream data. Also, we implement a real-time intelligent data processing system, which is based on real-time acquisition, real-time processing, real-time analysis, and real-time decision-making. The real-time intelligent data processing technology system is equipped with a batching big data platform, data analysis tools, and machine learning models. Based on our applications and analysis, the real-time intelligent data processing system is a crucial solution to the problems of the national society and economy.

Intelligent data processing received from radio frequency identification system

The present article is devoted to the issue of improvement of utilization efficiency of modern information technologies in order to enhance the quality and safety of healthcare delivery. The introduction of Real-time Locating Systems (RTLS), in particular Radio Frequency Identification System (RFID), applied in conjunction with intelligent data processing system can reduce the severity of the consequences of event risks implementation, as well as avoidance of a number of such consequences. The authors of the present article suggest using mathematical tools of fuzzy logic theory for event analysis in online mode and recommendation formation on their basis for timely management decision-taking.

МОДУЛЬНА СТРУКТУРНО-АЛГОРИТМІЧНА ОРГАНІЗАЦІЯ КОМП'ЮТЕРНИХ СИСТЕМ ІНТЕЛЕКТУАЛЬНОЇ ОБРОБКИ ДАНИХ З ЕЛЕМЕНТАМИ ВБУДОВАНИХ СИСТЕМ

МОДУЛЬНА СТРУКТУРНО-АЛГОРИТМІЧНА ОРГАНІЗАЦІЯ КОМП'ЮТЕРНИХ СИСТЕМ ІНТЕЛЕКТУАЛЬНОЇ ОБРОБКИ ДАНИХ З ЕЛЕМЕНТАМИ ВБУДОВАНИХ СИСТЕМ

Making of the Intelligent Data Processing System for the Experiment in the Determination of the Thermal Coefficient

For the problem that the intelligent physics experimental data processing system based on Multimedia ToolBook is only suit for 32 bit computer, the new developing intelligent experimental data processing system based on Flash is put forward. The intelligent experimental data processing system is used to quickly checking the results of the student Physics Experiment report. The results of the Physics Experiment report are descripted on the average and the uncertainty which the reliability is 99.7%. The uncertainty is kept one digit and it is aligned with the last digit of the significant figure. The uncertainty carried and the significant figure rounded. The intelligent experimental data processing system of the Thermal Coefficient Determination is an example. There are two aspects based on Flash. The first one is the interface design. Another is program design. In the program design the intelligent look-up table from the temperature difference electromotive force to temperature is designed with the principle that if it is the same the temperature is given directly and it is not the same the temperature is obtained with linear interpolation. The coordination transformation is used to show the experimental data points. The Least Square Method is used to fitting curve of the temperature and the cooling time with logarithm operation. The successful development of the intelligent data processing system of the Thermal Coefficient Determination is good for development of other experimental data processing system.

Technologies for Complex Intelligent Clinical Data Analysis

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient's features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented. Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality. the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center. Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as "negation" (indicates that the disease is absent), "no patient" (indicates that the disease refers to the patient's family member, but not to the patient), "severity of illness", disease course", "body region to which the disease refers". Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the methodfor determining the most informative patients'features are also proposed. Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records ofpatients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases. The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare.

The Choice of Metrics for Clustering Algorithms

Methods of data analysis and automatic processing are treated as knowledge discovery. In many cases it is necessary to classify data in some way or find regularities in the data. That is why the notion of similarity is becoming more and more important in the context of intelligent data processing systems. It is frequently required to ascertain how the data are interrelated, how various data differ or agree with each other, and what the measure of their comparison is. An important part in detection of similarity in clustering algorithms plays the accuracy in the choice of metrics and the correctness of the clustering algorithms operation.

A novel application of artificial neural network for wind speed estimation

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.

Development of the WDS Russian-Ukrainian Segment

Establishment of the Russian-Ukrainian WDS Segment and its state of the art, main priorities and research activities are described. One of the high priority tasks for Segment members is development of a common information space - transition from Legacy Systems and individual services to a common, globally interoperable, distributed data system that incorporates emerging technologies and new scientific data activities. The new system will build on the potential and added value offered by advanced interconnections between data management and data processing components for disciplinary and multidisciplinary applications. Thus, the principles of the architectural organization of intelligent data processing systems are discussed in this paper.