systems identification
Recently Published Documents

Biobanking is one of the most important elements of the modern infrastructure for biomedical research. Organization of a biobank on the basis of the N. P. Bochkov Medical Genetics Research Center provides a centralized infrastructure for preparing biomaterial for research. Biobank has the format of a research equipment sharing center and works with two types of unique biomaterials from patients with genetic diseases: blood/blood components and vital cells of various tissue origin. The storage facility of the Biobank is equipped with low-temperature (-80° C) and cryostorage (-196° C) systems. Identification and search of samples is carried out using a bar-coding system and is implemented through the information interface of the biobank, which is integrated into the general database of patients at the Medical Genetics Research Center. Information on biomaterial samples is presented in periodically updated catalogs on the page of equipment sharing center “Biobank”. Biobank collection is available to internal and external users.

Use of the system approach to management of social and economic processes is connected with existence of the immanent contradiction caused by a high level of abstraction of the definition "system" and a considerable level of concreteness of real economic tasks. The mentioned thesis needs formalization of descriptors of socio-economic systems and identification of basic management contours. The purpose of the article is to substantiate the necessity of using a systematic approach to the construction of the socio-economic system construct based on the identification of relevant descriptors and management contours. The methodological basis of the study was a combination of the Marxist school of political economy, the modern economic mainstream and unorthodox currents within the systemic paradigm. The main hypothesis of the study was the assumption regarding the possibility and feasibility of using descriptive methodology of systems identification to the needs of management of socio-economic systems. Statement of the basic material. On the basis of the analysis, a certain basis is provided for the formation of correlations of correspondence of the descriptors of the national socio-economic system to the corresponding axiomatic conditions and basic components. It is proved that the dualistic understanding of the individual as an economic and institutional basic element of the socio-economic system is fully appropriate to the understanding of the essence of the national economy. Institutions should be seen as a structure of connections that combine the elements into a unified integrity. It is proved that the state and its institutions play a dialectical role in ensuring the integral existence of the national socio-economic system of market type. Originality and practical significance of the research. The resulting theoretical and methodological concept has theoretical significance and can be transformed into relevant tools for the development of scientific and practical recommendations on the management of socio-economic systems. Conclusions and prospects for further research. The results of generalization of theoretical statements on the essence and content of the definitions of "socio-economic system" and "national economy" led to the conclusion about the impossibility of considering the economic subsystem (national economy) separately from other subsystems. The expediency and sufficiency of the use of the definition "national socio-economic system" as more acceptable for solving the dialectical unity of social and economic subsystems of society was proved. Further research will focus on the development of the methodical support for the identification of management contours on the basis of socio-economic descriptors

The Danube and its tributaries have been crossing mountains and plains in their almost unchanged riverbeds for thousands of years, regardless of national and administrative boundaries. Nevertheless, even decades ago, several countries provided access to only limited data and information concerning the water level and flood protection status of their rivers. In recent years, information was exchanged mainly on the basis of bilateral agreements and on successful activities of basin-wide organizations, but for others, information could only be obtained by browsing the Internet, which is sometimes rather complicated and definitely time-consuming. The EU Strategy for the Danube Region Environmental Risks Priority Area initiated a project aimed at developing the Danube Hydrological Information System, which was supported by the International Commission for the Protection of the Danube River. A comprehensive overview of the complex national flood and ice forecasting systems, identification of the shortcomings of the existing forecasting practices as well as an improvement of the exchange and availability of hydrological and meteorological data between the involved countries constituted crucial fields of interests for the project. Hence the main aim of the article is to present and discuss key data and functionalities of the system. The key findings show that all authorized meteorological and hydrological data of the Danube River are stored in a central database and made available online to all licensed hydrological and flood protection institutions for further processing in virtually real time. At this moment 12 countries of the Danube have joined forces to work out the proposals that are essential for the future, for safer Danube.

System identification is an important task in the control theory. Classical control theory is usually known for integer-order processes. Nowadays real processes are fractional order usually. According to a large number of fractional-order systems, identification of these systems is so important. This paper aims to evaluate an improved Biogeography-based Optimization (BBO) approach to estimate the parameters and orders of fractional-order systems. After that, a method based on this algorithm has been introduced to synchronization of chaotic systems. Results show that the proposed scheme has high accuracy.

Abstract The work carried out in this paper focused on "control-oriented modeling of a thermal engine using a deep learning approach". The goal of this work is to develop a neural network model of a thermal engine and to make a prediction of parameters related to engine management and directly impacting pollutant emissions and fuel consumption. For it, data was taken from an experimentation engine and made it possible to make maps of its operation. These maps enabled the calibration of a Simulink model of a thermal engine. Through a systems identification approach, the temporal response of the motor was estimated and made it possible to develop a database which was used for the training the LSTM artificial neural network. The work carried out showed that the learning phase of the neural network proceeded in a consistent way (overall decrease in cost functions) and converged towards a value of RMSE = 1.09 better than those observed in the literature. The resulting neural engine model made it possible to predict several variables (fuel mass flow rate and pollutant mass flow rates) with acceptable residual errors. These results reveal that the neural model obtained correctly predicts the said variables and can therefore be used in closed-loop simulations of the operation of a vehicle or for a context of simulation of the operation of the engine.

Molecular interactions are significant resources for analyzing sophisticated biological systems. Identification of multifarious molecular interactions attracts increasing attention in biomedicine, bioinformatics, and human healthcare communities. Recently, a plethora of methods have been proposed to reveal molecular interactions in one specific domain. However, existing methods heavily rely on features or structures involving molecules, which limits the capacity of transferring the models to other tasks. Therefore, generalized models for the multifarious molecular interaction prediction (MIP) are in demand. In this paper, we propose a contrastive self-supervised graph neural network (CSGNN) to predict molecular interactions. CSGNN injects a mix-hop neighborhood aggregator into a graph neural network (GNN) to capture high-order dependency in the molecular interaction networks and leverages a contrastive self-supervised learning task as a regularizer within a multi-task learning paradigm to enhance the generalization ability. Experiments on seven molecular interaction networks show that CSGNN outperforms classic and state-of-the-art models. Comprehensive experiments indicate that the mix-hop aggregator and the self-supervised regularizer can effectively facilitate the link inference in multifarious molecular networks.

The content of this article is the presentation of methods used to identify systems before actual control, namely decentralized control of systems with Two Inputs, Two Outputs (TITO) and with two interactions. First, theoretical assumptions and reasons for using these methods are given. Subsequently, two methods for systems identification are described. At the end of this article, these specific methods are presented as the pre-identification of the chosen example. The Introduction part of the paper deals with the description of decentralized control, adaptive control, decentralized control in robotics and problem formulation (fixing the identification time at the existing decentralized self-tuning controller at the beginning of control and at the beginning of any set-point change) with the goal of a new method of identification. The Materials and methods section describes the used decentralized control method, recursive identification using approximation polynomials and least-squares with directional forgetting, recursive instrumental variable, self-tuning controller and suboptimal quadratic tracking controller, so all methods described in the section are those ones that already exist. Another section, named Assumptions, newly formulates the necessary background information, such as decentralized controllability and the system model, for the new identification method formulated in Pre-identification section. This section is followed by a section showing the results obtained by simulations and in real-time on a Coupled Drives model in the laboratory.

Airborne magnetic and radiometric datasets have, over the past few years, become powerful tools in the identification of porphyry systems which may host economic porphyry copper–gold–molybdenum ore bodies. Magnetisation contrasts with the unaltered host rocks, coupled with the elevated radiometric signature, compared to the host rock, makes identification of large-scale porphyry copper systems possible. Integrating these two different datasets with stream sediment data and other geochemical exploration methods results in a higher degree of confidence. Stream sediment data were analysed to see the distribution of copper and gold elements throughout the study area, located within the Eastern Papuan Peninsula of Papua New Guinea. Airborne geophysics data over the same area were also processed for magnetic and radiometric responses. The processing of the magnetic data revealed several magnetic anomalies related to concealed intrusive rock units, with associated radiometric signatures. The distribution of gold and copper anomalism was correlated with the geology and geophysical signatures. Results indicate varying degrees of correlation, with some areas showing a strong correlation between gold/copper occurrence and geophysical signatures, compared to other areas. Some factors that we believe impact the level of correlation may include tectonic history, volcanic cover, and weathering patterns. We recommend caution when applying multi-data exploration for porphyry copper systems.