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Wu, Di, Si Jing Cai, and Wen Xiao Wang. "Numerical Modeling and Solution of Ground Water Inrush at the Baixiangshan Iron Mine." Advanced Materials Research 383-390 (November 2011): 2464–70. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2464.
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The Baixiangshan iron mine is a large scale underground mine under construction, and will be put into production in 2011. As hydro-geological conditions of the mine are extremely complicated, ground water inrush happened twice during excavation of the ventilation shaft in 2006 and 2009 respectively. The purpose of this paper is to control the ground water inrush and guide the safety production of the mine. Hence, the ground water state was firstly analyzed through the data of borehole and hydrologic geology. After that, hydro-geological models of the ground water system were built up. According to the hydro-geological models, mathematical models of the ground water were established. With the help of the software of Processing Modflow for Windows (PMWIN), the numerical model of the ground water was set up. Then, by using the SOR (Successive Over-Relaxation) iterative method, the numerical equations were solved. Finally, the district ground water inflow rate on the main mining levels and the dangerous districts of the ground water inrush was figured out. The results of the numerical solution are significant to the control of ground water inrush in the Baixiangshan iron mine.
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Sawada, Yohei, and Risa Hanazaki. "Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration." Hydrology and Earth System Sciences 24, no. 10 (October 5, 2020): 4777–91. http://dx.doi.org/10.5194/hess-24-4777-2020.
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Abstract. In socio-hydrology, human–water interactions are simulated by mathematical models. Although the integration of these socio-hydrological models and observation data is necessary for improving the understanding of human–water interactions, the methodological development of the model–data integration in socio-hydrology is in its infancy. Here we propose applying sequential data assimilation, which has been widely used in geoscience, to a socio-hydrological model. We developed particle filtering for a widely adopted flood risk model and performed an idealized observation system simulation experiment and a real data experiment to demonstrate the potential of the sequential data assimilation in socio-hydrology. In these experiments, the flood risk model's parameters, the input forcing data, and empirical social data were assumed to be somewhat imperfect. We tested if data assimilation can contribute to accurately reconstructing the historical human–flood interactions by integrating these imperfect models and imperfect and sparsely distributed data. Our results highlight that it is important to sequentially constrain both state variables and parameters when the input forcing is uncertain. Our proposed method can accurately estimate the model's unknown parameters – even if the true model parameter temporally varies. The small amount of empirical data can significantly improve the simulation skill of the flood risk model. Therefore, sequential data assimilation is useful for reconstructing historical socio-hydrological processes by the synergistic effect of models and data.
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Thorndahl, Søren, Thomas Einfalt, Patrick Willems, Jesper Ellerbæk Nielsen, Marie-Claire ten Veldhuis, Karsten Arnbjerg-Nielsen, Michael R. Rasmussen, and Peter Molnar. "Weather radar rainfall data in urban hydrology." Hydrology and Earth System Sciences 21, no. 3 (March 7, 2017): 1359–80. http://dx.doi.org/10.5194/hess-21-1359-2017.
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Abstract. Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology necessitate an updated review of the state of the art in such radar rainfall data and applications. Three key areas with significant advances over the past decade have been identified: (1) temporal and spatial resolution of rainfall data required for different types of hydrological applications, (2) rainfall estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological applications. The paper also reviews how the focus in urban hydrology research has shifted over the last decade to fields such as climate change impacts, resilience of urban areas to hydrological extremes, and online prediction/warning systems. It is discussed how radar rainfall data can add value to the aforementioned emerging fields in current and future applications, but also to the analysis of integrated water systems.
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Unnikrishnan, Poornima, and V. Jothiprakash. "Data-driven multi-time-step ahead daily rainfall forecasting using singular spectrum analysis-based data pre-processing." Journal of Hydroinformatics 20, no. 3 (August 9, 2017): 645–67. http://dx.doi.org/10.2166/hydro.2017.029.
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Abstract Accurate forecasting of rainfall, especially daily time-step rainfall, remains a challenging task for hydrologists' invariance with the existence of several deterministic, stochastic and data-driven models. Several researchers have fine-tuned the hydrological models by using pre-processed input data but improvement rate in prediction of daily time-step rainfall data is not up to the expected level. There are still chances to improve the accuracy of rainfall predictions with an efficient data pre-processing algorithm. Singular spectrum analysis (SSA) is one such technique found to be a very successful data pre-processing algorithm. In the past, the artificial neural network (ANN) model emerged as one of the most successful data-driven techniques in hydrology because of its ability to capture non-linearity and a wide variety of algorithms. This study aims at assessing the advantage of using SSA as a pre-processing algorithm in ANN models. It also compares the performance of a simple ANN model with SSA-ANN model in forecasting single time-step as well as multi-time-step (3-day and 7-day) ahead daily rainfall time series pertaining to Koyna watershed, India. The model performance measures show that data pre-processing using SSA has enhanced the performance of ANN models both in single as well as multi-time-step ahead daily rainfall prediction.
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Taysaev, K. K., and L. G. Petrova. "Mathematical Models Analysis of Combined Processing Methods of Parts." Materials Science Forum 992 (May 2020): 901–6. http://dx.doi.org/10.4028/www.scientific.net/msf.992.901.
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In the article researches mathematical models that ensure effectiveness of use combined methods for parts processing. The use of combined processing methods is always associated with the search for technological compromise and boils down to technical and economic indicators comparative assessment. In this case, it is necessary to rely on mathematical models that objectively reflect manufacturing parts technological processes. Mathematical methods and models for optimizing production processes for manufacturing parts that are applicable in combined methods for processing parts are a complex formalized scientific abstraction that describes production functioning process at all stages of its implementation. In the synthesis of various processing methods, it is necessary to ensure that a number of conditions are met that determine necessary and sufficient conditions for implementing feasibility a particular technology in the combined method of processing parts. Multiple regression analysis methods allow minimizing experiments number in mathematical model determining which adequate to processes under study and form the baseline data for the transition from multi-factor to multi-criteria models. Using this approach, it is necessary to determine objective function optimal values parameters and influence factors in each specific technological process, which will allow us to bring the uncertainty removal in the processing materials technology to a new qualitative level.
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Liu, Yan Shu. "Long Tube Hole Straightness Data Processing Method." Advanced Materials Research 756-759 (September 2013): 1494–97. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1494.
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In this paper, applying planar straightness error assessment methods, in other words, the principle of minimum conditions and roundness error approach, further more, the least square circle will be used to the idea of space. By establishing the two space mathematical models to deal with the long-tube straightness error. It dramatically solves the problem to the space straightness error. In practice, the use of the ideas of the software can gain high accurate and high precise data, it can also consistent with the actual situation.
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Al Salaimeh, Safwan. "MATHEMATICAL MODELS FOR COMPUTERIZED CONTROL SYSTEM." Gulustan-Black Sea Scientific Journal of Academic Research 48, no. 05 (July 5, 2019): 119–23. http://dx.doi.org/10.36962/gbssjar119.
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The software is a set of mathematical methods, and algorithms of information processing, which used in creating the control system. When designing control systems, Initial data for the design of control system. The tasks of the computerized control system are understood as a part of the computerized functions of the computerized control system characterized by the outcomes and outputs in specific form. control function is: commutative action for computerized control system, aimed to achieve a criterion goal. Depending on the properties of the process and their mathematical description can be combined into different classes; This paper shows the designing the mathematical models which need to computerized control systems (models (3) – (8)). In the same time this paper shows the main methods which were used to formulate the mathematical models as: • Stochastic and deterministic; • One dimensional and multidimensional; • Linear and nonlinear; • Static and dynamic; • Stationary and non – stationary; • With distributed and lumped parameters.
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Rezaie-Balf, Mohammad, and Ozgur Kisi. "New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine." Hydrology Research 49, no. 3 (March 27, 2017): 939–53. http://dx.doi.org/10.2166/nh.2017.283.
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Abstract Streamflow forecasting is crucial in hydrology and hydraulic engineering since it is capable of optimizing water resource systems or planning future expansion. This study investigated the performances of three different soft computing methods, multilayer perceptron neural network (MLPNN), optimally pruned extreme learning machine (OP-ELM), and evolutionary polynomial regression (EPR) in forecasting daily streamflow. Data from three different stations, Soleyman Tange, Perorich Abad, and Ali Abad located on the Tajan River of Iran were used to estimate the daily streamflow. MLPNN model was employed to determine the optimal input combinations of each station implementing evaluation criteria. In both training and testing stages in the three stations, the results of comparison indicated that the EPR technique would generally perform more efficiently than MLPNN and OP-ELM models. EPR model represented the best performance to simulate the peak flow compared to MLPNN and OP-ELM models while the MLPNN provided significantly under/overestimations. EPR models which include explicit mathematical formulations are recommended for daily streamflow forecasting which is necessary in watershed hydrology management.
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Liang, Xin-Zhong, Hyun I. Choi, Kenneth E. Kunkel, Yongjiu Dai, Everette Joseph, Julian X. L. Wang, and Praveen Kumar. "Surface Boundary Conditions for Mesoscale Regional Climate Models." Earth Interactions 9, no. 18 (October 1, 2005): 1–28. http://dx.doi.org/10.1175/ei151.1.
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Abstract This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (land-cover category, and static fractional vegetation cover and varying leaf-plus-stem-area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe.
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Dozier, Jeff, and James Frew. "Computational provenance in hydrologic science: a snow mapping example." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1890 (December 16, 2008): 1021–33. http://dx.doi.org/10.1098/rsta.2008.0187.
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Computational provenance—a record of the antecedents and processing history of digital information—is key to properly documenting computer-based scientific research. To support investigations in hydrologic science, we produce the daily fractional snow-covered area from NASA's moderate-resolution imaging spectroradiometer (MODIS). From the MODIS reflectance data in seven wavelengths, we estimate the fraction of each 500 m pixel that snow covers. The daily products have data gaps and errors because of cloud cover and sensor viewing geometry, so we interpolate and smooth to produce our best estimate of the daily snow cover. To manage the data, we have developed the Earth System Science Server (ES 3 ), a software environment for data-intensive Earth science, with unique capabilities for automatically and transparently capturing and managing the provenance of arbitrary computations. Transparent acquisition avoids the scientists having to express their computations in specific languages or schemas in order for provenance to be acquired and maintained. ES 3 models provenance as relationships between processes and their input and output files. It is particularly suited to capturing the provenance of an evolving algorithm whose components span multiple languages and execution environments.
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Ponchateau, Cyrille, Ladjel Bellatreche, Carlos Ordonez, and Mickael Baron. "A Mathematical Database to Process Time Series." International Journal of Data Warehousing and Mining 14, no. 3 (July 2018): 1–21. http://dx.doi.org/10.4018/ijdwm.2018070101.
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In scientific research, the results of an experiment commonly take the form of a time series, in which such time series consists of measurements collected from a sensor over time. After time series are stored, mathematical models are derived using numerical methods. Even though there exist plenty of tools to store and analyze time series data, there is scarce research aimed at storing and querying derived models, which are the most important mechanism for a scientist to understand data. In this article, the authors propose to help scientists with a flexible database structure to persist and manage mathematical models with a mathematical models store, with extended features, to handle time series. In this article, the authors introduce the concept of a mathematical models store enriched with numerical processing methods to allow queries based on raw time series data. Then they introduce a prototype, that is an implementation of such a data store with PostgreSQL.
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Li, Kang, Mikiko Kadohisa, Makoto Kusunoki, John Duncan, Claus Bundesen, and Susanne Ditlevsen. "Distinguishing between parallel and serial processing in visual attention from neurobiological data." Royal Society Open Science 7, no. 1 (January 2020): 191553. http://dx.doi.org/10.1098/rsos.191553.
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Serial and parallel processing in visual search have been long debated in psychology, but the processing mechanism remains an open issue. Serial processing allows only one object at a time to be processed, whereas parallel processing assumes that various objects are processed simultaneously. Here, we present novel neural models for the two types of processing mechanisms based on analysis of simultaneously recorded spike trains using electrophysiological data from prefrontal cortex of rhesus monkeys while processing task-relevant visual displays. We combine mathematical models describing neuronal attention and point process models for spike trains. The same model can explain both serial and parallel processing by adopting different parameter regimes. We present statistical methods to distinguish between serial and parallel processing based on both maximum likelihood estimates and decoding the momentary focus of attention when two stimuli are presented simultaneously. Results show that both processing mechanisms are in play for the simultaneously recorded neurons, but neurons tend to follow parallel processing in the beginning after the onset of the stimulus pair, whereas they tend to serial processing later on.
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Kuczynska-Siehien, Joanna, Dimitrios Piretzidis, Michael G. Sideris, Tomasz Olszak, and Viktor Szabó. "Monitoring of extreme land hydrology events in central Poland using GRACE, land surface models and absolute gravity data." Journal of Applied Geodesy 13, no. 3 (July 26, 2019): 229–43. http://dx.doi.org/10.1515/jag-2019-0003.
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Abstract This study deals with the analysis of temporal gravity variations in central Poland, deduced from multiple sources and covering the period from 2002–2016. The gravity data sets used comprise GRACE monthly solutions, GLDAS land surface models and absolute gravimeter measurements from the FG-5 gravimeter located in Józefosław, Poland. All data are corrected using standard processing methods in order to include the same gravity effects. After removing the annual and semi-annual components from all data using least-squares spectral analysis and seasonal-trend decomposition, the deseasoned time series are derived and examined for signatures of extreme hydrological events. The signatures of several severe drought and flood conditions affecting Poland and central Europe are identified. A complementary correlation analysis is performed to assess the level of agreement between different data sources. A higher correlation is shown when the analysis is restricted in the 2009–2012 period that includes the 2010 extreme flood and 2011 increased precipitation events, both affecting Poland.
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Simonov, V. M. "Combinatorial linearization of data processing algoritmic complexes. Theory and Heuristics." Informatization and communication 1 (January 2021): 97–104. http://dx.doi.org/10.34219/2078-8320-2021-12-1-97-104.
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theoretical redundancy of the proof theorem about data processing algorithmic complexes’s combinatorial linearization [ 1 ] opens additional heuristic possibilities for decision of this problem. The correlation between theory and heuristics is illustrated in article on concrete examples. It had been shown, that heuristic transformations of DPAC’s mathematical models are able not only correspond to theoretical recommendations, but also break them, realizing, nevertheless, the purpose of combinatorial linearization itself. As a hole, the heuristic approach widenes possibilities of DPAC’s combinatorial linearization, that is essential for applications.
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Yang, Ming Qiang, and Liang Wei. "Construction of Processing Model for Massive Terrain Data Used in Three-Dimensional Terrain Visualization." Applied Mechanics and Materials 99-100 (September 2011): 259–63. http://dx.doi.org/10.4028/www.scientific.net/amm.99-100.259.
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Based on the analysis of various kinds of terrain data used in applications of three-dimensional terrain visualization, this paper puts forward a preprocessing method named hierarchical blocked processing for massive terrain data, and obtains relevant mathematical models and algorithm requirements.
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REINERTH, G. "P1567 Simulation of cardiac electrophysiology using mathematical models and computer based processing of digital image data." European Heart Journal 24, no. 5 (March 2003): 285. http://dx.doi.org/10.1016/s0195-668x(03)94705-1.
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Gaidar, S. M., A. V. Pydrin, M. Yu Karelina, M. S. Gaidar, and A. V. Sukhodolya. "Laboratory Study of Tribological Characteristics of Innovative Lubricant Coolants." Materials Science Forum 992 (May 2020): 590–97. http://dx.doi.org/10.4028/www.scientific.net/msf.992.590.
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In the article researches mathematical models that ensure effectiveness of use combined methods for parts processing. The use of combined processing methods is always associated with the search for technological compromise and boils down to technical and economic indicators comparative assessment. In this case, it is necessary to rely on mathematical models that objectively reflect manufacturing parts technological processes. Mathematical methods and models for optimizing production processes for manufacturing parts that are applicable in combined methods for processing parts are a complex formalized scientific abstraction that describes production functioning process at all stages of its implementation. In the synthesis of various processing methods, it is necessary to ensure that a number of conditions are met that determine necessary and sufficient conditions for implementing feasibility a particular technology in the combined method of processing parts. Multiple regression analysis methods allow minimizing experiments number in mathematical model determining which adequate to processes under study and form the baseline data for the transition from multi-factor to multi-criteria models. Using this approach, it is necessary to determine objective function optimal values parameters and influence factors in each specific technological process, which will allow us to bring the uncertainty removal in the processing materials technology to a new qualitative level.
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Gómez Salazar, Julián Andrés, Gabriela Clemente Polo, and Neus Sanjuán Pelliccer. "Review of mathematical models to describe the food salting process." DYNA 82, no. 190 (May 11, 2015): 23–30. http://dx.doi.org/10.15446/dyna.v82n190.42016.
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Salting and subsequent curing are part of the traditional processing method used in the meat and fish industry. Different preserving agents (sodium chloride, nitrite, nitrate, among others) are added in this process. Nowadays, more attention is paid to the amount of salts added and the salting time employed. For this reason, it is important to know the factors governing salt penetration and the most convenient process conditions. The transfer mechanism of the salts through the structure is an interesting aspect in meat and fish processing technology. Mathematical models are the best way to discover the factors, which govern this process, which may represent and explain the observed data and predict the behaviour under different conditions. The objective of this review is to describe the use of the mathematical models to simulate meat and fish salting and the benefits derived from their use. Most of the models used to describe the salting process are diffusional, based on Fick’s second law, although empirical ones are also used. For modelling purposes a good description of the experimental results should be guaranteed. The complexity of the model will depend on the objective to be reached and will be analysed in each case.
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Burinskienė, Marija, Denis Kapski, Valery Kasyanik, Anton Pashkevich, Aleksandra Volynets, and Oleg Kaptsevich. "Estimating Parameters for Traffic Flow Using Navigation Data on Vehicles." Baltic Journal of Road and Bridge Engineering 15, no. 4 (September 28, 2020): 1–21. http://dx.doi.org/10.7250/bjrbe.2020-15.492.
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The article describes the method for estimating transport flow parameters using the two-fluid Herman-Prigogine mathematical model developed considering the proposed method of estimating parameters for the system based on the passive processing of navigation data on the movement of vehicles. The efficiency of the suggested algorithms and mathematical models for estimating road traffic flow parameters and the system as a whole was confirmed performing tests using a set of tracks on the main highways of Belarus.
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Wang, Yingxu, and Jun Peng. "Big Data Analytics." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 2 (April 2017): 41–56. http://dx.doi.org/10.4018/ijcini.2017040103.
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Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is the recursively typed hyperstructure (RTHS). The RTHS model reveals the inherited complexities and unprecedented difficulty in big data engineering. This finding leads to a set of mathematical and computational models for efficiently processing big data systems. The cognitive relationship between data, information, knowledge, and intelligence is formally described.
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Florescu, Dorian, and Daniel Coca. "Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data." Neural Computation 30, no. 3 (March 2018): 670–707. http://dx.doi.org/10.1162/neco_a_01051.
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Inferring mathematical models of sensory processing systems directly from input-output observations, while making the fewest assumptions about the model equations and the types of measurements available, is still a major issue in computational neuroscience. This letter introduces two new approaches for identifying sensory circuit models consisting of linear and nonlinear filters in series with spiking neuron models, based only on the sampled analog input to the filter and the recorded spike train output of the spiking neuron. For an ideal integrate-and-fire neuron model, the first algorithm can identify the spiking neuron parameters as well as the structure and parameters of an arbitrary nonlinear filter connected to it. The second algorithm can identify the parameters of the more general leaky integrate-and-fire spiking neuron model, as well as the parameters of an arbitrary linear filter connected to it. Numerical studies involving simulated and real experimental recordings are used to demonstrate the applicability and evaluate the performance of the proposed algorithms.
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Kinar, Nicholas J. "Introducing electronic circuits and hydrological models to postsecondary physical geography and environmental science students: systems science, circuit theory, construction, and calibration." Geoscience Communication 4, no. 2 (April 13, 2021): 209–31. http://dx.doi.org/10.5194/gc-4-209-2021.
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Abstract. A classroom activity involving the construction, calibration, and testing of electronic circuits was introduced to an advanced hydrology class at the postsecondary level. Two circuits were constructed by students: (1) a water detection circuit and (2) a hybrid relative humidity (RH)/air temperature sensor and pyranometer. The circuits motivated concepts of systems science, modelling in hydrology, and model calibration. Students used the circuits to collect data useful for providing inputs to mathematical models of hydrological processes. Each student was given the opportunity to create a custom hydrological model within the context of the class. This is an example of constructivist teaching where students engage in the creation of meaningful knowledge, and the instructor serves as a facilitator to assist students in the achievement of a goal. Analysis of student-provided feedback showed that the circuit activity motivated, engaged, and facilitated learning. Students also found the activity to be a novel and enjoyable experience. The theory of circuit operation and calibration is provided along with a complete bill of materials (BOM) and design files for replication of this activity in other postsecondary classrooms. Student suggestions for improvement of the circuit activity are presented along with additional applications.
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Jin, Rong Rong, and Jia Hang Wang. "Data Processing in Moving Boundary Problems of One-Dimensional Non-Darcy Flow in Semi-Infinite Porous Media." Advanced Materials Research 977 (June 2014): 515–19. http://dx.doi.org/10.4028/www.scientific.net/amr.977.515.
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The paper establishes dimensionless mathematical models of the fluid flow in semi-infinite porous media with constant flow rate. Exact analytical solutions of these dimensionless mathematical models are derived by new definitions of dimensionless variables and Laplace transformation. Comparison curves of dimensionless moving boundary under different values of dimensionless Threshold Pressure Gradient (TPG) are plotted from newly proposed exact analytical solutions. An example is used to demonstrate pressure distribution in different positions with different TPG. It is shown that for the constant flow rate condition, the moving boundary extends to infinite in porous media with increasing production time. Steeper pressure curve is observed in larger TPG, which also exhibits greater pressure drop gradient and shorter pressure propagation distance at the same production time.
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Li, C. Y., G. Q. Zhou, X. Zhou, and D. Q. Liu. "STUDY AND ANALYSIS OF REMOTE SENSING DATA PARALLEL PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 443–50. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-443-2020.
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Abstract. This paper analyzes a varieties of procedure of remote sensing data processing, and explores the common mathematical models, common algorithm models, and public function processing units of data processing shared by different tasks or even different parts within an individual task. Public modules are established to improve the parallelism of remote sensing data processing based on FPGA, which has excellent parallel processing performance. In addition, in order to reduce the resource consumption and increase the calculation efficiency of the designed FPGA program, the method of avoiding floating-point arithmetic and division operation in FPGA programming are discussed in this paper. There are a large number of common calculation modules between different tasks, such as the rotation matrix calculation module in attitude solution, geometric correction, and orthorectification task. Image preprocessing, feature information extraction, image threshold separation, and connected region markers are all common processing modules for a target detection task. In the same task, there is also a common calculation module. When using the FPGA design program, the power series of 2 can be used to convert the floating-point operation to fixed-point operation with an acceptable precision. A similar approach can transform the division operation into multiplication and shift operations, thereby improve the computational performance of FPGA programming.
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Zhuravlev, G. M., A. E. Gvozdev, S. V. Sapozhnikov, S. N. Kutepov, and E. V. Ageev. "DECISIONS ON STATISTICAL MODELS IN QUALITY CONTROL OF PRODUCTS." Proceedings of the Southwest State University 21, no. 5 (October 28, 2017): 78–92. http://dx.doi.org/10.21869/2223-1560-2017-21-5-78-92.
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Development of methods for registration, description and analysis of statistical experimental data, obtained by monitoring mass random phenomena is the subject of a special science - mathematical statistics. All tasks of mathematical statistics concerns the treatment of observations of mass random phenomena, but depending on the nature of the solved practical question and amount of available experimental material these tasks can take a particular form. One of the main objectives of mathematical statistics is to develop methods of studying mass phenomena or processes on the basis of the relatively small number of observations or experiments. These methods have their scientific justification, his theory, called the theory of samples. The aim of this work is to build mathematical models of influence of various factors on a single number using the method of multifactor experiment planning, and their use results in the appointment of modes of technological operations. To study processes incomplete hot deformation uses a complex viscoplastic model of the environment, the mechanical properties which are characterized by a yield stress and viscosity. The yield strength depends on temperature and strain rate. On this basis, was carried out processing of experimental data by the method of multifactor experiment planning and statistical treatment of experimental data by definition of the yield strength depending on temperature and speed of deformation of steel U12A. From the analysis of the obtained regression equations, we can conclude that the most highly specific force depends on temperature. Regression equations mathematically describe the mutual influence of technological factors on yield strength and specific strength, in addition they allow you to correctly set processing modes that yield products of the required quality.
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Kikin, Pavel, Alexey Kolesnikov, Alexey Portnov, and Denis Grischenko. "Natural language processing systems for data extraction and mapping on the basis of unstructured text blocks." InterCarto. InterGIS 26, no. 3 (2020): 53–61. http://dx.doi.org/10.35595/2414-9179-2020-3-26-53-61.
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The state of ecological systems, along with their general characteristics, is almost always described by indicators that vary in space and time, which leads to a significant complication of constructing mathematical models for predicting the state of such systems. One of the ways to simplify and automate the construction of mathematical models for predicting the state of such systems is the use of machine learning methods. The article provides a comparison of traditional and based on neural networks, algorithms and machine learning methods for predicting spatio-temporal series representing ecosystem data. Analysis and comparison were carried out among the following algorithms and methods: logistic regression, random forest, gradient boosting on decision trees, SARIMAX, neural networks of long-term short-term memory (LSTM) and controlled recurrent blocks (GRU). To conduct the study, data sets were selected that have both spatial and temporal components: the values of the number of mosquitoes, the number of dengue infections, the physical condition of tropical grove trees, and the water level in the river. The article discusses the necessary steps for preliminary data processing, depending on the algorithm used. Also, Kolmogorov complexity was calculated as one of the parameters that can help formalize the choice of the most optimal algorithm when constructing mathematical models of spatio-temporal data for the sets used. Based on the results of the analysis, recommendations are given on the application of certain methods and specific technical solutions, depending on the characteristics of the data set that describes a particular ecosystem
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Barnard, Richard C., Kai Huang, and Cory Hauck. "A mathematical model of asynchronous data flow in parallel computers*." IMA Journal of Applied Mathematics 85, no. 6 (September 25, 2020): 865–91. http://dx.doi.org/10.1093/imamat/hxaa031.
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Abstract We present a simplified model of data flow on processors in a high-performance computing framework involving computations necessitating inter-processor communications. From this ordinary differential model, we take its asymptotic limit, resulting in a model which treats the computer as a continuum of processors and data flow as an Eulerian fluid governed by a conservation law. We derive a Hamilton–Jacobi equation associated with this conservation law for which the existence and uniqueness of solutions can be proven. We then present the results of numerical experiments for both discrete and continuum models; these show a qualitative agreement between the two and the effect of variations in the computing environment’s processing capabilities on the progress of the modelled computation.
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Kovtun, I. I., and G. S. Romanenko. "Mathematical models, numerical methods and complexes of programs for efficiency improvement of multiproduct production." Informatization and communication, no. 1 (March 31, 2020): 48–54. http://dx.doi.org/10.34219/2078-8320-2020-11-1-48-54.
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The problems of analysis and planning of diversified production, including the lack of effective methods for search, systematization and primary processing of initial data for mathematical programming methods used in the process of its optimization are considered. Methodology IDEF0 is proposed as a data collection system. For the purpose of practical implementation of the proposed solution, the conceptual apparatus of this methodology is put in accordance with the formal apparatus of these methods and, for its effective realization by computer, relational data model as metamodel is used.
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Kuleshov, S. V., A. A. Zaytseva, and S. P. Levashkin. "Technologies and principles of unstructured distributed data processing in the context of modern media content providing." Informatization and communication 5 (December 2020): 22–28. http://dx.doi.org/10.34219/2078-8320-2020-11-5-22-28.
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Purpose The COVID-19 pandemic is creating serious challenges for modern society that leads to develop new information models and methods of digital monitoring not only of the spread of the virus, but also of the socio-economic environment. Materials and methods: As sources for clarifying the parameters of such models, it is advisable to choose not a limited set of predefi ned Internet sources, but unstructured media data on an unlimited set of resources, which leads to the need to build a system for complex monitoring of social phenomena. Such system can supplement and correct mathematical and information models for the spread of viruses, aimed at minimizing the damage caused by any pandemic. Results: It is proposed to create a software system that includes a Data Retrieving subsystem (for collecting and preprocessing media data) combined with a headless browser. This allows to build a system for monitoring of social phenomena, complementing mathematical and information models of the spread of viruses, aimed at minimizing the damage they cause. The feature of developed system is the using of a natural language processing framework based on the associative-ontological approach, and software implementation of the adaptive-behavioral SEIR model, as well as a subsystem for interpreting the collected data, generating metadata for identifying and correcting the model. Conclusions: The proposed system allows to make more balanced management decisions based on the analysis of the current situation in the infosphere. An additional advantage of the system is the ability to identify poorly predictable reactions of society to certain events expressed in media content.
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Wang, Yan Ping, Xin Bing Yang, Yao Hui Jin, Bao Quan Liu, and Jun Sheng Wang. "The Mathematical Models and Simulation on Work Roll Bending Control System of Cold Rolling Mill." Applied Mechanics and Materials 556-562 (May 2014): 2337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2337.
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The calculated result of the mathematical model based on the conventional transfer function has bigger deviation than the measured. In this paper the mathematical model of the work roll bending is derived. The modeling is different from conventional modeling used many presumptions and linear processing. The modeling calculated accurately the equivalent load spring using the method of influence function. The simulation results agree well with actual data of the bending system.
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Janickas, Robertas. "CONSTRUCTION AND RESEARCH OF SYSTEM IDENTIFIABLE MATHEMATICAL MODELS / IDENTIFIKUOTINOS SISTEMOS MATEMATINIO MODELIO SUDARYMAS IR TYRIMAS." Mokslas - Lietuvos ateitis 3, no. 1 (August 22, 2011): 87–90. http://dx.doi.org/10.3846/mla.2011.018.
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Paper discusses about control and data acquisition, processing, visualization, which must be adapted to the investigation and examination of identification process. A description of the device, the functionality and customization possibilities are presented. The relevant experimental model and its characteristics are obtained for measurement, control results using this model.
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Bondar, O. G., E. O. Brezhneva, O. G. Dobroserdov, K. G. Andreev, and N. V. Polyakov. "Synthesis and Parameterization of Gas Sensor Models." Proceedings of the Southwest State University 25, no. 1 (May 30, 2021): 138–61. http://dx.doi.org/10.21869/2223-1560-2021-25-1-138-161.
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Purpose of research: search and analysis of existing models of gas-sensitive sensors. Development of mathematical models of gas-sensitive sensors of various types (semiconductor, thermocatalytic, optical, electrochemical) for their subsequent use in the training of artificial neural networks (INS). Investigation of main physicochemical patterns underlying the principles of sensor operation, consideration of the influence of environmental factors and cross-sensitivity on the sensor output signal. Comparison of simulation results with actual characteristics produced by the sensor industry. The concept of creating mathematical models is described. Their parameterization, research and assessment of adequacy are carried out.Methods. Numerical methods, computer modeling methods, electrical circuit theory, the theory of chemosorption and heterogeneous catalysis, the Freundlich and Langmuir equations, the Buger-Lambert-Behr law, the foundations of electrochemistry were used in creating mathematical models. Standard deviation (MSE) and relative error were calculated to assess the adequacy of the models.Results. The concept of creating mathematical models of sensors based on physicochemical patterns is described. This concept allows the process of data generation for training artificial neural networks used in multi-component gas analyzers for the purpose of joint information processing to be automated. Models of semiconductor, thermocatalytic, optical and electrochemical sensors were obtained and upgraded, considering the influence of additional factors on the sensor signal. Parameterization and assessment of adequacy and extrapolation properties of models by graphical dependencies presented in technical documentation of sensors were carried out. Errors (relative and RMS) of discrepancy of real data and results of simulation of gas-sensitive sensors by basic parameters are determined. The standard error of reproduction of the main characteristics of the sensors did not exceed 0.5%.Conclusion. Multivariable mathematical models of gas-sensitive sensors are synthesized, considering the influence of main gas and external factors (pressure, temperature, humidity, cross-sensitivity) on the output signal and allowing to generate training data for sensors of various types.
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Slavutskaya, Elena, and Leonid Slavutskii. "PRETEEN AGE: THE ANALYSIS OF THE MULTILEVEL PSYCHO-DIAGNOSTIC DATA BASED ON NEURAL NETWORK MODELS." SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference 5 (May 25, 2018): 455. http://dx.doi.org/10.17770/sie2018vol1.3348.
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The use of the artificial neural network (ANN) models for vertical system analysis of psycho-diagnostic data is suggested. It is shown that the ANN training, as the problem of nonlinear multi-parameter optimization, allows to create effective algorithms for the psycho-diagnostic data processing when the results of psychological testing for the different level’s characteristics have different numerical scales. On the example of processing the author's data of psycho-diagnostics (preadolescent schoolchildren), it is shown that neural network models can be used to estimate latent (hidden) connections between psychological characteristics. The proposed algorithms are based on a statistical assessment of the quality of such models, do not require a large sample of respondents. The quantitative statistical criteria for evaluating the quality of the models are estimated. The approach is sufficiently clear for practical use by psychologists who do not have a special mathematical preparation.
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Lombardo, F., E. Volpi, D. Koutsoyiannis, and S. M. Papalexiou. "Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology." Hydrology and Earth System Sciences Discussions 10, no. 4 (April 11, 2013): 4627–54. http://dx.doi.org/10.5194/hessd-10-4627-2013.
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Abstract. The need of understanding and modelling the space-time variability of natural processes in hydrological sciences produced a large body of literature over the last thirty years. In this context, multifractal framework provides parsimonious models which can be applied to a wide scale range of hydrological processes, and are based on the empirical detection of some patterns in observational data, i.e. a scale invariant mechanism repeating scale after scale. Hence, multifractal analyses heavily rely on available data series and their statistical processing. In such analyses, high order moments are often estimated and used in model identification and fitting as if they were reliable. This paper warns practitioners for blind use in geophysical time series analyses of classical statistics, which is based upon independent samples typically following distributions of exponential type. Indeed, the study of natural processes reveals scaling behaviours in state (departure from exponential distribution tails) and in time (departure from independence), thus implying dramatic increase of bias and uncertainty in statistical estimation. Surprisingly, all these differences are commonly unaccounted for in most multifractal analyses of hydrological processes, which may result in inappropriate modelling, wrong inferences and false claims about the properties of the processes studied. Using theoretical reasoning and Monte Carlo simulations we find that the reliability of multifractal methods that use high order moments (> 3) is questionable. In particular, we suggest to use the first two moments in all problems as they suffice to define the most important characteristics of the distribution.
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Dinu, Cristian, Radu Drobot, Claudiu Pricop, and Tudor Viorel Blidaru. "Genetic Programming Technique Applied for Flash-Flood Modelling Using Radar Rainfall Estimates." Mathematical Modelling in Civil Engineering 13, no. 4 (December 20, 2017): 27–38. http://dx.doi.org/10.1515/mmce-2017-0012.
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AbstractThe rainfall-runoff transformation is a highly complex dynamic process and the development of fast and robust modelling instruments has always been one of the most important topics for hydrology. Over time, a significant number of hydrological models have been developed with a clear trend towards a process-based approach. The downside of these types of models is the significant amount of data required for building the model and for the calibration process: in practice, the collection of all necessary data for such models proves to be a difficult task. In order to cope with this issue, various data-driven modelling techniques have been introduced for hydrological modelling as an alternative to more traditional approaches, on the basis of their capacity of mapping out complex relationships from observation data. Having the capacity to generate meaningful mathematical structures as results, genetic programming (GP) presents a high potential for rainfall-runoff modelling as a data-driven method. Using ground and radar rainfall observation, the aim of this study is to investigate the GP technique capability for modelling the rainfall-runoff process, taking into consideration a flash-flood event.
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Kuznetsov, V. A., L. I. Gapon, L. M. Malishevskii, D. S. Lobuntsov, E. A. Dziabenko, A. M. Soldatova, G. S. Pushkarev, V. V. Todosiychuk, and E. I. Yaroslavskaya. "Mathematical models in cardiology: From formulas to real clinical practice." Siberian Journal of Clinical and Experimental Medicine 35, no. 4 (December 25, 2020): 39–48. http://dx.doi.org/10.29001/2073-8552-2020-35-4-39-48.
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Aim. To develop and implement into clinical practice six calculators of risk for various cardiovascular diseases in the form of mobile application for iOS/Android and module for the 1C: Medicine software.Material and Methods. At the premises of Tyumen Cardiology Research Center (TCRC) of Tomsk NRMC, we developed the mobile application for iOS/Android and module for the 1C: Medicine software based on six mathematical models that were invented and patented in our center earlier.Results and Discussion. The use of mobile application improved the convenience of working with the mathematical formulas and reduces the time for obtaining results of calculations. Implementation of 1C as a programming environment allowed to perform automatic filling out the calculator fields with medical data from individual patients, which significantly simplified and accelerated the rate of work with mathematical models.Conclusion. The developed mobile application and external processing for 1C allowed to implement research products of TCRC in the form of mathematical formulas into real-life clinical practice. These developments contributed to speeding up the process for acquisition of results and partial automatization of filling out the form fields.
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Liu, Xiyu, and Qianqian Ren. "Spiking Neural Membrane Computing Models." Processes 9, no. 5 (April 21, 2021): 733. http://dx.doi.org/10.3390/pr9050733.
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As third-generation neural network models, spiking neural P systems (SNP) have distributed parallel computing capabilities with good performance. In recent years, artificial neural networks have received widespread attention due to their powerful information processing capabilities, which is an effective combination of a class of biological neural networks and mathematical models. However, SNP systems have some shortcomings in numerical calculations. In order to improve the incompletion of current SNP systems in dealing with certain real data technology in this paper, we use neural network structure and data processing methods for reference. Combining them with membrane computing, spiking neural membrane computing models (SNMC models) are proposed. In SNMC models, the state of each neuron is a real number, and the neuron contains the input unit and the threshold unit. Additionally, there is a new style of rules for neurons with time delay. The way of consuming spikes is controlled by a nonlinear production function, and the produced spike is determined based on a comparison between the value calculated by the production function and the critical value. In addition, the Turing universality of the SNMC model as a number generator and acceptor is proved.
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Fedorenko, Mykola Ivanovych. "MODELS OF GENERATION OF INPUT DATA OF TRAINING OF NEURAL NETWORK MODULES FOR DIAGNOSTIC OF DISEASES IN UROLOGY." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (December 25, 2019): 116–22. http://dx.doi.org/10.32620/reks.2019.4.13.
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The subject of the research presented in the article is neural network modules (NNMs), which are used to solve problems in the practice of diagnosing diseases in urology. This work aims to develop a mathematical model for generating a multitude of uroflowmetric parameters, in particular, graphs of uroflowrograms of the required volume, used as input data for NNM training. Objective: to develop a mathematical model for the formation of uroflowmetric parameters using a probabilistic approach based on a uniform "white noise". To develop an effective algorithm for the procedure for generating new parameter values and tools for its implementation. Methods used: NNM training methods, mathematical modeling methods, digital signal processing methods, tools for generating and processing random numerical sequences, digital data filtering methods. The following results were obtained: when creating and implementing a mathematical model for generating a large amount of training data, the requirements of randomness are taken into account when obtaining new values of uroflowmetric parameters. And at the same time, the obtained noise values are filtered to values of a given range, which are percentage-wise comparable to the amplitude value of the uroflowmetric parameter. Conclusions. The scientific novelty of the results is as follows: the NNM training method for recognizing diseases in urology has been improved by developing a mathematical model to generate uroflowmetric parameters for NNM training. The presented model allows you to create the necessary amount of data for training neural network modules in the course of experimental research on the recognition of diseases. The generation of uroflowmetric parameters is based on adding noise to the parameter values. This allows you to change the input data of the NNM training in a given range. This ensures the creation of the required input volume of the NNM training procedure. In the future, this contributes to the testing process of trained neural network modules with reliable information on the diagnosis of diseases in urology.
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Kapski, Denis, Valery Kasyanik, Oleksii Lobashov, Aleksandra Volynets, Oleg Kaptsevich, and Andrii Galkin. "Estimating the Parameters of Traffic Flows on the Basis of Processing of Localization Data on the Movement of Vehicles." Communications - Scientific letters of the University of Zilina 21, no. 2 (May 24, 2019): 89–99. http://dx.doi.org/10.26552/com.c.2019.2.89-99.
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The article describes the method of estimating the parameters of transport flows using the two-fluid mathematical model of Herman-Prigogine and developed and based on the proposed method of estimating the parameters of the system on the basis of passive processing of navigation data on the movement of vehicles. The efficiency of the proposed algorithms, mathematical models for estimating the parameters of road traffic flow and system as a whole was confirmed during its testing using a set of tracks on the main highways of Commonwealth of Independent States.
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Palguev, D. A., and A. N. Shentyabin. "More on assessing the probability of radar information association during tertiary information processing in network structures." Radio industry (Russia) 30, no. 2 (June 6, 2020): 32–41. http://dx.doi.org/10.21778/2413-9599-2020-30-2-32-41.
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The use of network structures in information systems allows for single-level tertiary processing of radar information. This feature makes it possible to significantly reduce the time of delivery of information from sources to consumers, as well as rigorously determine mathematically the probability of combining information during tertiary processing in network structures. The article contains mathematical expressions for assessing the probability of combining information during tertiary processing from incoherent spatially separated sources. Determining the size of the strobe for accurate selection by the given probability of combining information using the tertiary network processing algorithm allows reducing the number of calculations in the subsequent pairwise comparison of messages due to the reduction of the size of the strobe. Such an approach creates the conditions for building a cognitive model of radar data processing in modern information systems. The reduction of data processing intervals in an information system increases the accuracy and reliability of information transmitted to consumers. The resulting mathematical expressions can be used to assess the reliability of the information in mathematical models of network information systems.
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Harmel, R. Daren, Claire Baffaut, and Kyle Douglas-Mankin. "Review and Development of ASABE Engineering Practice 621: “Guidelines for Calibrating, Validating, and Evaluating Hydrologic and Water Quality Models”." Transactions of the ASABE 61, no. 4 (2018): 1393–401. http://dx.doi.org/10.13031/trans.12806.
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Abstract. In 2010, the Natural Resources and Environmental Systems Hydrology Committee (NRES-21) of ASABE initiated a long-term process to develop guidelines to improve modeling practice through better understanding of the calibration, validation, and evaluation process across applications and more effective interpretation and communication of model performance. This effort generated a compilation of 23 articles with model-specific descriptions and guidance (2012), a position paper outlining guidance for evaluating, interpreting, and communicating performance of hydrologic and water quality models considering intended use (2014), and a compilation of ten articles addressing key topics related to model calibration and validation (2015). In 2016, the first draft of ASABE Engineering Practice 621 (EP621), “Guidelines for Calibrating, Validating, and Evaluating Hydrologic and Water Quality (H/WQ) Models,” was developed, subsequently revised, and ultimately approved by the ASABE Standards Committee in 2017. EP621 provides guidelines, not prescriptive requirements, and as such recommends “good” modeling practices to enhance calibration, validation, evaluation, and communication of H/WQ models through establishment of consistent terminology; model selection; compilation and processing of input data and calibration, validation, and evaluation data; determination of model performance measures; model parameterization and calibration; re-examination of input and calibration data and/or consideration of model refinement; re-evaluation of model performance; and documentation of modeling process and results. EP621 can be obtained from the ASABE Technical Library at https://elibrary.asabe.org/abstract.asp?aid=47804. The objectives of this technical note are to review the process and rationale used to develop EP621 and to briefly summarize its major components. Keywords: Hydrologic processes, Hydrology, Model calibration, Modeling, Model validation, Water quality.
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Wang, Bao Feng. "Analysis for Packet Dropout in State Estimation Problem over Sensor Network." Applied Mechanics and Materials 568-570 (June 2014): 523–27. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.523.
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In this paper, we discuss the data losses problem in state estimation process. In sensor network, due to the energy constrained and communication constrained, the system suffering varying data dropped problem, which include independent package dropped, related dropped in single data package transmission processing and partial package dropout in multiple data package transmission processing. Based on the proposed state estimation over sensor network model, the corresponding mathematical models are built and analyzed.
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Faddeev, Alexander O., Svetlana A. Pavlova, and Tatiana M. Nevdakh. "Mathematical Models and Evaluation Software for Stress-Strain State of the Earth’s Lithosphere." Engineering Technologies and Systems, no. 1 (March 29, 2019): 51–66. http://dx.doi.org/10.15507/2658-4123.029.201901.051-066.
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Introduction. For the purposes of this article, geodeformation processes mean processes associated with deformations arising from the movement of species and blocks of the lithosphere at various depths, including surfaces. The objective is to reconstruct geodynamic stress fields, which cause modern shifts and deformations in the Lithosphere. A mathematical model and software for estimating the stress-strain state of the Earth Lithosphere are considered. Materials and Methods.For mathematical modeling of stresses, isostatically reduced data on abnormal gravitation field were used. The methods of continuum mechanics and methods of the theory of differential equations were used to design a model for estimating the stressstrain state of the Earth Lithosphere. For processing input, intermediate and outcoming data, the Fourier transform method of spectral analysis for constructing grid functions and spectral-temporal method were used. To model for the stress-strain state of the Lithosphere globally, stress calculation was corrected on the basis of sputnik-derived velocity data at the surface of the earth crust. The data on the rates of horizontal and vertical movements at the surface of the Earth crust were processed to obtain a distribution of velocities in the uniform grid embracing longitudes and latitudes. The processing procedure was carried out on the basis of the Kraiging method. The software was developed in Borland Delphi 7.0 programming environment. Results. Based on the data on the abnormal gravitation field in isostatic reduction and information on the distribution of velocities of horizontal motions on the surface of the Earth crust, a mathematical model of the stress-strain state of the Lithosphere was constructed. With the help of the obtained mathematical model and software complex, the stress-strain state of the Lithosphere was calculated at various depth using elastic and elastic-viscous models, and maps of equipotential distribution of shear elastic-viscous deformations in the lithosphere at the depth of 10 km were constructed. Discussion and Conclusion. The presented mathematical model and software allow restoring fields of both elastic and elastic-viscous deformations that is fundamental for quantification of elastic-viscous shear stresses deep in the Earth Lithosphere.
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Damian Wierzbicki and Kamil Krasuski. "Determining the Elements of Exterior Orientation in Aerial Triangulation Processing Using UAV Technology." Communications - Scientific letters of the University of Zilina 22, no. 1 (January 2, 2020): 15–24. http://dx.doi.org/10.26552/com.c.2020.1.15-24.
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Unmanned Aerial Vehicles (UAVs) are still an interesting and current research topic in photogrammetry. An important issue in this area is determining the elements of exterior orientation of image data acquired at low altitudes. The article presents selected mathematical methods (TGC, TIC, TAD) of estimating elements of exterior orientation for image data obtained at low altitudes. The measurement data for the experimental test were recorded by the Unmanned Aerial Vehicle platform Trimble UX-5. In the framework of the test photogrammetric flight, the authors obtained 506 images and navigation data specifying the position and orientation of the Unmanned Aerial Vehicle. As a result of the research, it is proven possible to show the usefulness of the mathematical models (TGC, TIC, TAD) in estimation of elements of exterior orientation.
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Kapskiy, D. V., V. V. Kasyanik, A. V. Evtuh, and O. F. Kaptsevich. "EVALUATION OF TRANSPORT FLOW EFFICIENCY ON THE BASIS OF TRAFFIC MOVEMENT DATA." Science & Technique 16, no. 5 (October 5, 2017): 444–54. http://dx.doi.org/10.21122/2227-1031-2017-16-5-444-454.
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The paper considers a method for evaluation of transport flow parameters and determine an efficiency of variants concerning organization of traffic movement and provision of qualitative transportation. A method for evaluation of main traffic flow parameters while using a two-fluid mathematical model of Herman – Prigogine has been developed and it is based on passive processing of navigation data for road transport facilities on highways and urban road and street networks. The main advantages and novelty of the method (in contrast with the traditional usage of a mathematical model of Her- man – Prigogine on the basis of active processing of navigation data) are the following: a passive treatment of GPS-tracks and support of their formats that make it possible significantly improve quality and adequacy of the model results; cloud com-puting (usage of cloud storage for storing large bulk of data); presence of client mobile application for Android operating system which is used for data collection in addition to operation with already collected navigation data from navigation and information center and other sources; application for calculation and processing of modern web technologies; scalability and configurability of the system. Efficiency of the proposed algorithms, mathematical models for evaluation of transport flow parameters and system as a whole have been proved in the process of their approbation while using a set of tracks on main highways of Belarus
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Mitrofanov, Georgy Mikhailovich, and Viatcheslav Ivanovich Priimenko. "PRONY FILTERING OF SEISMIC DATA: MATHEMATICAL AND PHYSICAL MODELLING." Revista Brasileira de Geofísica 31, no. 1 (March 1, 2013): 151. http://dx.doi.org/10.22564/rbgf.v31i1.253.
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This article presents results of the Prony filtering method testing using mathematical and physical models. They illustrate features and capabilities of the proposed method of processing and analysing the recorded signals, which also include reflected seismic signals. Guidelines for choosing the optimal method parameters are demonstrated using examples. The use of such parameters provides the best extraction and parameters estimation of target signals. Based on them, it allows us to analyse the absorbing and scattering properties of the media for the local areas, for the analysed reflectors in particular. RESUMO: Este artigo teve por objetivo apresentar e analisar os resultados da aplicação do método de filtragem de Prony em modelos físicos e matemáticos. Tais resultados ilustram as características e o potencial do método proposto no processamento e na análise de sinais, incluindo também os eventos sísmicos de reflexão. As diretrizes para escolher os parâmetros otimizados do método são demonstradas através de exemplos. O uso de tais parâmetros proporciona a melhor estimativa, e viabiliza a separação dos sinais alvo. Com base em tais sinais, é possível analisar as propriedades de absorção e dispersão dos meios em áreas locais, em particular, nos refletores analisados.Palavras-chave: transformada de prony; decomposição de sinal e filtragem; dispersão e absorção de energia sísmica; efeitos de frequência
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Lombardo, F., E. Volpi, D. Koutsoyiannis, and S. M. Papalexiou. "Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology." Hydrology and Earth System Sciences 18, no. 1 (January 17, 2014): 243–55. http://dx.doi.org/10.5194/hess-18-243-2014.
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Abstract. The need of understanding and modelling the space–time variability of natural processes in hydrological sciences produced a large body of literature over the last thirty years. In this context, a multifractal framework provides parsimonious models which can be applied to a wide-scale range of hydrological processes, and are based on the empirical detection of some patterns in observational data, i.e. a scale invariant mechanism repeating scale after scale. Hence, multifractal analyses heavily rely on available data series and their statistical processing. In such analyses, high order moments are often estimated and used in model identification and fitting as if they were reliable. This paper warns practitioners against the blind use in geophysical time series analyses of classical statistics, which is based upon independent samples typically following distributions of exponential type. Indeed, the study of natural processes reveals scaling behaviours in state (departure from exponential distribution tails) and in time (departure from independence), thus implying dramatic increase of bias and uncertainty in statistical estimation. Surprisingly, all these differences are commonly unaccounted for in most multifractal analyses of hydrological processes, which may result in inappropriate modelling, wrong inferences and false claims about the properties of the processes studied. Using theoretical reasoning and Monte Carlo simulations, we find that the reliability of multifractal methods that use high order moments (>3) is questionable. In particular, we suggest that, because of estimation problems, the use of moments of order higher than two should be avoided, either in justifying or fitting models. Nonetheless, in most problems the first two moments provide enough information for the most important characteristics of the distribution.
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Chekalin, V. F., and S. L. Korneev. "The analysis of principles constructions of space images geometry and mathematical models of its processing." Geodesy and Cartography 928, no. 10 (November 20, 2017): 30–39. http://dx.doi.org/10.22389/0016-7126-2017-928-10-30-39.
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In this article essential principles of constructing geometry of contemporary space images with the purpose of definitions their common regularity and distinctions of generations’ inner photogrammetric relations and then – mathematical models, expedients for securing of strict processing are considered. It is significant, that scanning survey is the general case/way, but strip and panoramic surveys are particular cases. For the first time it was established, that under solving the problem of photogrammetric resection for scanner images on the basis of using dependences of well-known collinearity models the ambiguity of external orientation of images arises. The basic reason of its arising is discovered and established, by way of which it is determined poor domain the systems of initial equations’ corrections. A rigorous model of a push broom space image is introduced, which incorporates both a static a photogrammetric model and a dynamic one. The static model gives the only mean position and orientation of image, meanwhile the dynamic one makes them more exact. The table of condition numbers (cond A) of initial equations’ correction linear system is given and it confirms the fundamental idea of this article. A practical way of solving this problem is indicated for cases of using source data, both outboard measurements and control points.
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Markelov, N. "ANALYTICAL MODEL OF PACKET TRANSMISSION OF SPEECH AND DATA IN HF RADIO CHANNELS IN A COMPLEX SIGNAL-INTERFERENCE ENVIRONMENT." National Association of Scientists 1, no. 67 (June 15, 2021): 45–49. http://dx.doi.org/10.31618/nas.2413-5291.2021.1.67.422.
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The author has carried out an analytical simulation of the process of signal formation and processing in the HF channel during packet transmission of speech and data in a complex signal-interference environment. The totality of the result obtained can be the basis for the development of complex mathematical models of HF channels.
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Kowalczyk, Adam, Anna Szlachta, Robert Hanus, and Rafał Chorzępa. "Estimation of Conditional Expected Value for Exponentially Autocorrelated Data." Metrology and Measurement Systems 24, no. 1 (March 1, 2017): 69–78. http://dx.doi.org/10.1515/mms-2017-0005.
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AbstractAutocorrelation of signals and measurement data makes it difficult to estimate their statistical characteristics. However, the scope of usefulness of autocorrelation functions for statistical description of signal relation is narrowed down to linear processing models. The use of the conditional expected value opens new possibilities in the description of interdependence of stochastic signals for linear and non-linear models. It is described with relatively simple mathematical models with corresponding simple algorithms of their practical implementation.The paper presents a practical model of exponential autocorrelation of measurement data and a theoretical analysis of its impact on the process of conditional averaging of data. Optimization conditions of the process were determined to decrease the variance of a characteristic of the conditional expected value. The obtained theoretical relations were compared with some examples of the experimental results.