Measurement Data Research Articles

Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks

Blood coagulation, which involves a group of complex biochemical reactions, is a crucial step in hemostasis to stop bleeding at the injury site of a blood vessel. Coagulation abnormalities, such as hypercoagulation and hypocoagulation, could either cause thrombosis or hemorrhage, resulting in severe clinical consequences. Mathematical models of blood coagulation have been widely used to improve the understanding of the pathophysiology of coagulation disorders, guide the design and testing of new anticoagulants or other therapeutic agents, and promote precision medicine. However, estimating the parameters in these coagulation models has been challenging as not all reaction rate constants and new parameters derived from model assumptions are measurable. Although various conventional methods have been employed for parameter estimation for coagulation models, the existing approaches have several shortcomings. Inspired by the physics-informed neural networks, we propose Coagulo-Net, which synergizes the strengths of deep neural networks with the mechanistic understanding of the blood coagulation processes to enhance the mathematical models of the blood coagulation cascade. We assess the performance of the Coagulo-Net using two existing coagulation models with different extents of complexity. Our simulation results illustrate that Coagulo-Net can efficiently infer the unknown model parameters and dynamics of species based on sparse measurement data and data contaminated with noise. In addition, we show that Coagulo-Net can process a mixture of synthetic and experimental data and refine the predictions of existing mathematical models of coagulation. These results demonstrate the promise of Coagulo-Nets in enhancing current coagulation models and aiding the creation of novel models for physiological and pathological research. These results showcase the potential of Coagulo-Net to advance computational modeling in the study of blood coagulation, improving both research methodologies and the development of new therapies for treating patients with coagulation disorders.

Hybrid Virtual Flow Metering on Arbitrary Well Patterns for Transient Multiphase Prediction Driven by Mechanistic and Data Model

Virtual Flow Meters (VFMs) have garnered considerable attention due to their substantial benefits in curtailing the expenses associated with multiphase flow measurement. To overcome the constraints of current technologies, we introduce the Distributed Hybrid Virtual Flow Meter (HVFM-Fleet) method. This innovative approach leverages a data-driven model that integrates multi-scale features, significantly enhancing the accuracy of flow characteristic identification. Moreover, a mechanical model is crafted to translate arbitrary well networks into a virtual layout, thereby amplifying the system's adaptability to a variety of scenarios. The HVFM-Fleet capitalizes on distributed computing to reduce computational costs in large-scale simulations, establishing a database that surpasses a thousand multi-well cases—the most extensive in the transient multiphase flow simulation sector. Case studies integrate simulation data with actual measurement data from oil fields in the North Sea of the UK, with a detailed analysis of key elements such as individual wells, multiple wells, and flow allocation. The HVFM-Fleet has notably reduced the mean absolute percentage error and achieved a remarkable 140% improvement in performance compared to existing models. Furthermore, this research pioneers new metrics for assessing the volatility of predictions, setting a new standard in the evaluation of flow measurement reliability.

Research on FPC coils with limited turns in planar electromagnetic tomography

Electromagnetic tomography (EMT) is a non-destructive testing method that reconstructs electrical parameter distribution based on boundary electromagnetic measurement data along electromagnetic rotation projection. The coil is a critical component of the EMT system, used to excite and detect changing magnetic fields. The performance of excitation-detection with specific coils directly affects the quality of the reconstructed image. The traditional EMT coils are wound coils. The excitation-detection performance of the coils is enhanced by increasing the number of turns to obtain better imaging quality. A larger coil volume has better sensitivity, but it cannot meet the space constraints of many applications. To solve this problem, this paper proposes a method using extremely thin FPC coil arrays as EMT sensors. To verify the feasibility of FPC coil imaging with a limited number of turns, firstly, this article analyses the excitation-detection capabilities of FPC coil with 1 to 12 turns through a combination of simulation and experiment. Secondly, the impact of excitation-detection capability on the EMT sensitivity matrix was studied. Finally, a 4×4 8-Turns FPC coil sensor was designed for image reconstruction experiments. The experimental results show that FPC coils with limited turns can be used for image reconstruction and to solve the problem of planar nondestructive testing in a small space.

An experimental study on beamforming architecture and full-duplex wireless across two operational outdoor massive MIMO networks

Full-duplex (FD) wireless communication refers to a communication system in which both ends of a wireless link transmit and receive data simultaneously in the same frequency band. One of the major challenges of FD communication is self-interference (SI), which refers to the interference caused by transmitting elements of a radio to its own receiving elements. Fully digital beamforming is a technique used to conduct beamforming and has been recently repurposed to also reduce SI. However, the cost of fully digital systems dramatically increases with the number of antennas, as each antenna requires an independent Tx-Rx RF chain. Hybrid beamforming systems use a much smaller number of RF chains to feed the same number of antennas, and hence can significantly reduce the deployment cost. In this paper, we aim to quantify the performance gap between these two radio architectures in terms of SI cancellation and system capacity in FD multi-user Multiple Input Multiple Output (MIMO) setups. We first obtained over-the-air channel measurement data on two outdoor massive MIMO deployments over the course of three months. We next study SoftNull and M-HBFD as two state-of-the-art transmit (Tx) beamforming based FD systems, and introduce two new joint transmit-receive (Tx-Rx) beamforming based FD systems named TR-FD2 and TR-HBFD for fully digital and hybrid radio architectures, respectively. We show that the hybrid beamforming systems can achieve 80%–99% of the fully digital systems capacity, depending on the number of users. Our results show that it is possible to get many benefits associated with fully digital massive MIMO systems with a hybrid beamforming architecture at a fraction of the cost.

Impact of upstream buildings on Wind-Driven Rain Loading: Refining Obstruction Factor in ISO semi-empirical model based on CFD

CFD is a valuable tool for assessing Wind-Driven Rain (WDR) loading, one of the most important environmental loads for façade design. The majority of previous studies on this topic have primarily concentrated on simple building configurations, i.e., stand-alone buildings. Hence, prior findings may not be applicable to consider the impact of upstream buildings in urban areas, which significantly alter wind flow field, consequently, change WDR loadings on downstream building facades compared to the stand-alone building. Part A: four different steady-state RANS models (i.e., standard k−ω, realizable k−ε, RNG k−ε, and standard k−ε) coupled with the Eulerian Multiphase (EM) technique (RANS-EM) are compared and implemented using OpenFOAM-7. These models are validated and verified based on wind-tunnel and field measurement data obtained from the literature for a six-story mid-rise residential building located in an urban area in Vancouver, Canada. The study considers 13 distinct rainfall events, for the test building with/without overhangs. All four RANS models are deemed suitable for modeling WDR in urban areas, while the steady-state standard k-ω RANS-EM approach without incorporating turbulent dispersion showing slightly better performance, thus utilized for the reminder of the study. Part B: a sensitivity analysis is presented on how the upstream buildings influence the WDR loading on a downstream building, denoted as Obstruction Factor. A comparison between the CFD and ISO semi-empirical model shows significant discrepancies, potentially reaching up to factors of 5. Thus, updated Obstruction Factors are suggested to enhance the ISO model for more accurate estimation of WDR loads.

Murmansk Marine Biological Institute of Kola Scientific Center of the Russian Academy of Sciences

The authors assess the wind potential indicators and the scale of its use for power supply of hard-to-reach off-grid settlements in the eastern Russian Arctic. The primary challenge facing autonomous power plants is fuel delivery due to long distances and underdeveloped transport infrastructure. The use of wind power plants will reduce fuel consumption and cut the cost of energy production. The researchers use ground-based measurement data and the characteristics of existing autonomous power plants as initial data when assessing the rational capacity of wind power plants. They employ authoring research methods. Via Wind-MCA program they estimate wind potential indicators: average wind speed, capacity factor, and coefficient of wind speed variation. The research results are presented in cartographic form. The rational capacities of wind power plants as part of hybrid energy systems are determined using the generalized dependencies method based on the maximum load of a populated area and calculated wind potential indicators, taking into account the characteristic intra-annual distribution of wind speeds. The best conditions for the development of wind energy are observed on the coast of the Arctic seas in the Krasnoyarsk Territory and the Chukot Autonomous Area. The rational capacity of wind power plants in zones of high wind potential in hard-to-reach areas of eastern Russian Arctic is estimated at 12,5 MW.

Scientific preparation for JRT: Wind pressure prediction model for large radio telescope based on real data from multi-sensors

Jingdong 120-meter radio telescope (JRT) is poised to become the world's largest single-aperture fully steerable medium-low frequency radio telescope. However, like other large-aperture radio telescopes, the JRT is vulnerable to wind loads, which can cause structural deformation and pointing errors. Addressing this challenge requires the ability to predict dynamic winds in real-time. This study developed a wind pressure preprocessing and prediction model using sensor data collected from the Kunming 40-meter radio telescope (KRT), enabling real-time prediction of wind pressure on the telescope. The model employs adaptive noise and Variational Mode Decomposition (VMD) techniques to eliminate random noise from the original wind pressure data. Subsequently, wind pressure predictions are made using a Bidirectional Long Short-term Memory (BiLSTM) model. By conducting predictions under various stabilization conditions and conducting a thorough analysis of measurement data from five sensors, the study has achieved impressive results in predicting wind pressure on the KRT reflector surface. The proposed model demonstrates the lowest MAE, RMSE, and MAPE, while achieving the highest R2 across various data sets. Where the average R2 of the proposed model is 0.9392 at 45° pitch angle attitude and the RMSE, MAE and MAPE values are 1.4923, 1.2377 and 1.82% respectively. This model helps wind load monitoring of real-time wind pressure monitoring of the telescope surface, to study the effects of wind load on pointing accuracy. By adjusting the control parameters to reduce wind load interference, to ensure the high-precision work of a large radio telescope, such as JRT.

Research on the identification and detection technology of electricity theft in power distribution areas under a smart grid environment

Abstract With the extensive application of smart meters, power companies can access a wealth of customer electricity usage data, which has not yet been fully utilized. This paper proposes a substation area user identification and electricity theft detection method based on smart meter data, aiming to enhance the level of intelligent management of power grid enterprises and reduce operational costs. By using the grey relational analysis method, analyzing the measurement data collected by smart meters at different time points, it is possible to determine the substation area and phase to which the user belongs and to detect electricity theft behavior. Finally, through simulation, the effectiveness of the proposed method is verified.

Medium-adaptive compressive diffuse optical tomography.

The low spatial resolution of diffuse optical tomography (DOT) has motivated the development of high-density DOT systems utilizing spatially-encoded illumination and detection strategies. Data compression methods, through the application of Fourier or Hadamard patterns, have been commonly explored for both illumination and detection but were largely limited to pre-determined patterns regardless of imaging targets. Here, we show that target-optimized detection patterns can yield significantly improved DOT reconstructions in both in silico and experimental tests. Applying reciprocity, we can further iteratively optimize both illumination and detection patterns and show that these simultaneously optimized source/detection patterns outperform predetermined patterns in simulation settings. In addition, we show media-adaptive measurement data compression methods enable wide-field DOT systems to recover highly complex inclusions inside optically-thick media with reduced background artifacts. Furthermore, using truncated optimized patterns shows an improvement of 2-4× in increased speed of data acquisition and reconstruction without significantly losing image quality. The proposed method can be readily extended for additional data dimensions such as spectrum and time.

Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow

Background and Objective:Computational models of hemodynamics can contribute to optimizing surgical plans, and improve our understanding of cardiovascular diseases. Recently, machine learning methods have become essential to reduce the computational cost of these models. In this study, we propose a method that integrates 1-D blood flow equations with Physics-Informed Graph Neural Networks (PIGNNs) to estimate the propagation of blood flow velocity and lumen area pulse waves along arteries. Methods:Our methodology involves the creation of a graph based on arterial topology, where each 1-D line represents edges and nodes in the blood flow analysis. The innovation lies in decoding the mathematical data connecting the nodes, where each node has velocity and lumen area pulse waveform outputs. The training protocol for PIGNNs involves measurement data, specifically velocity waves measured from inlet and outlet vessels and diastolic lumen area measurements from each vessel. To optimize the learning process, our approach incorporates fundamental physical principles directly into the loss function. This comprehensive training strategy not only harnesses the power of machine learning but also ensures that PIGNNs respect fundamental laws governing fluid dynamics. Results:The accuracy was validated in silico with different arterial networks, where PIGNNs achieved a coefficient of determination (R2) consistently above 0.99, comparable to numerical methods like the discontinuous Galerkin scheme. Moreover, with in vivo data, the prediction reached R2 values greater than 0.80, demonstrating the method’s effectiveness in predicting flow and lumen dynamics using minimal data. Conclusions:This study showcased the ability to calculate lumen area and blood flow rate in blood vessels within a given topology by seamlessly integrating 1-D blood flow with PIGNNs, using only blood flow velocity measurements. Moreover, this study is the first to compare the PIGNNs method with other classic Physics-Informed Neural Network (PINNs) approaches for blood flow simulation. Our findings highlight the potential to use this cost-effective and proficient tool to estimate real-time arterial pulse waves.

Effect of super-high water materials backfilling on stress decrease and energy release during strip coal pillar mining: A case study

Mining strip coal pillars left due to strip mining is important to resource-exhausted coal mines in eastern China. Backfilling mining is an effective means to mine strip coal pillars, which could decrease high stress and high energy release. However, materials with super-high water content were not widely applied in deeply isolated coal pillar mining due to lower strength characteristics and durability. In the paper, taking panel c8301 as engineering background, theoretical analysis, numerical simulation, and field monitoring were used to study the feasibility and application effect of the super-high-water backfilling technology in deep isolated coal pillar mining. The results showed that: (1) Panel c8301 is a strip-filling working face with insufficient mining on both sides, and the mining of the panel will trigger the rebalancing of the overlying rock structure on both sides, which increases the rock burst risk. (2) Simulation results indicate that the super-high-water filling method, in comparison to the traditional caving method, significantly reduces peak stress and elastic energy from 112.3 MPa to 4.6 × 106 J to 79.2 MPa and 2.23 × 106 J, respectively. This represents reductions of 29.6 % and 51.5 %, effectively mitigating the impact of mining activities on overburden movement. (3) On-site measurement data confirmed that the measured equivalent mining height was 1.25 m. The total number of microseisms and the amount of released energy decreased significantly, More specifically, three big energy release instances (>1.8 × 105 J) were recorded during the first roof weighting stage and panel in square meters. Super-high-water filling technology has achieved remarkable results in the mining of strip coal pillars and has significant application prospects.

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.

Psychological and socio-economic correlates of cardiovascular health among young adults in Puerto Rico

ObjectiveWe aimed to determine the relationship between socioeconomic and psychological factors and overall cardiovascular health (CVH), as defined by the American Heart Association's Life's Essential 8 (LE8), among young adults in Puerto Rico. MethodsParticipants were 2156 young adults, between the ages of 18–29 years, enrolled in the PR-OUTLOOK study. The analysis included survey, laboratory, and physical measurement data collected from September 2020 to November 2023. Assessed socioeconomic indicators included food insecurity, housing instability, economic insecurity, and subjective social standing. Evaluated psychological factors comprised symptoms of depression, anxiety, post-traumatic stress, and overall perceived stress. LE8 scores were calculated and classified as suboptimal (poor/intermediate range) vs. ideal CVH. Logistic regression models estimated associations between each socioeconomic and psychological measure and suboptimal CVH, and dominance analysis assessed the importance of each measure. ResultsParticipants' mean age was 22.6 (SD = 3.1), 60.9 % were female, about one-third (34.2 %) had high school education or less, and over one-third had public or no health insurance (38.4 %). Participants reporting socioeconomic adversity (i.e., high food insecurity, housing instability and economic insecurity, and low subjective social standing) and elevated psychological symptoms (i.e., symptoms of anxiety, depression, post-traumatic stress, and overall perceived stress) had lower CVH scores. However, in the adjusted analysis, only lower subjective social standing (OR = 1.38, 95 % CI = 1.13–1.69) and elevated symptoms of anxiety (OR = 1.63, 95 % CI = 1.25–2.13) and depression (OR = 1.30, 95 % CI = 1.03–1.65) emerged as the primary contributors to suboptimal CVH (vs. ideal). ConclusionEfforts to preserve and enhance CVH among young Puerto Ricans on the island should target these factors.

Maternal Exposure to Tap Water Disinfection By-Products and Risk of Selected Congenital Heart Defects.

The use of chlorine to treat drinking water produces disinfection by-products (DBPs), which have been associated with congenital heart defects (CHDs) in some studies. Using National Birth Defects Prevention Study data, we linked geocoded residential addresses to public water supply measurement data for DBPs. Self-reported water consumption and filtration methods were used to estimate maternal ingestion of DBPs. We estimated adjusted odds ratios and 95% confidence intervals using logistic regression controlling for maternal age, education, body mass index (BMI), race/ethnicity, and study site to examine associations between CHDs and both household DBP level and estimated ingestion of DBPs. Household DBP exposure was assessed for 2717 participants (1495 cases and 1222 controls). We observed a broad range of positive, null, and negative estimates across eight specific CHDs and two summary exposures (trihalomethanes and haloacetic acids) plus nine individual DBP species. Examining ingestion exposure among 2488 participants (1347 cases, 1141 controls) produced similarly inconsistent results. Assessing both household DBP level and estimated ingestion of DBPs, we did not find strong evidence of an association between CHDs and DBPs. Despite a large study population, DBP measurements were available for less than half of participant addresses, limiting study power.

The data recovery strategy on machine learning against false data injection attacks in power cyber physical systems

During the transmission of power measurement data through communication networks from remote terminal unit (RTU) to the state estimator in Supervisory Control and Data Acquisition (SCADA), power cyber-physical systems (PCPSs) are more susceptible to cyber-attacks. To mitigate that threat, this paper is concerned with a new data recovery strategy on machine learning against false data injection attacks (FDIAs) in PCPSs. Firstly, in view of the limited resources (such as limited energy) of adversaries and system protections, a sparse target false data injection attack (FDIA) is constructed. Then, the FDIA detection problem is transformed into a tripartite separation problem, and the alternating direction method of multipliers on proximal exchange (ADMM-PE) is adopted to complete the intrusion detection of FDIAs. In addition, with the help of reliable mask information and real incomplete measurement data provided by the FDIA detection, a similar supervised generative adversarial imputation networks (GAIN) is proposed to complete the measurement data recovery after FDIAs. Specifically, the pseudo labels generated by data analysis methods such as k-means clustering and support vector machine (SVM) to improve the accuracy of measurement data recovery. Finally, the experimental results of PCPSs show the effectiveness and superiority of the proposed data recovery strategy against FDIAs.

Analysis of Big Data Obtained from Continuous Measurement of 22 Malodor-producing Substances in Korean Cattle Farms

Objectives : This research collected continuous (20-minute interval) measurement data of 22 compounds from cattle farms during a year in order to understand malodor emission characteristics and trends and also big data analysis was performed to analyze the relationships between malodor concentration and environmental factors such as wind direction and speed, temperature, and humidity.Methods : The measured substances were 22 compounds defined as ‘designated malodor-producing substances’ by Korean Ministry of Environment. Detection and concentration measurement were performed by gas chromatography with two kinds of columns (VB-WAX, and VB-1), and detectors (FID and PID). Sampling was carried out with pumping every 2 or 5 minute and thermal desorption was followed by chromatograph injection. Data obtained from analysis were stored with environmental variables such as temperature, humidity, wind speed, and wind direction from 1 August 2022 to 31 July 2023. Total number of measurement data was 26,280.Results and Discussion : Correlation analysis showed that temperature was correlated with dimethyl disulfide, and iso-valeraldehyde, and humidity was correlated with toluene, iso-butylalcohol, and trimethylamine. Six principal components explained 60.7% of total variance and two principal components, PC 1 and PC 2, were selected and analyzed. PC 1 was strongly related with volatile organic compounds and PC 2 was related with acid compounds. Together with bipolar plot results, PC 1 was defined as volatile properties and PC 2 as acidic characteristics, respectively. Multiple regression with principal components as dependent variables and environmental variables as independent showed relatively small value of adjusted R2 except PC 1 of 0.539.Conclusion : Malodorous compounds tended to diffuse and move together with similar molecular characteristics which made the detected concentration similar in patterns. Though PC 1 had correlation with environmental variables through multiple regression analysis, it can be explained as chemical compound’s characteristics depends on temperature, humidity, and wind.

An Improved Laplace Satellite Tracking Method Based on the Kalman Filter

When photoelectric measuring equipment is used to track satellites, the extraction of the short-term or long-term target often fails because the target is weak, clouds block the target, and/or the sun’s angle is too small, resulting in the loss of the tracking target. In this study, an improved Laplacian satellite tracking method based on the Kalman filter is proposed. Firstly, the improved Laplacian algorithm was used for the initial fitting of the equation of motion of a small amount of measurement data. Judgment of the validity and Kalman filtering was carried out on the current frame’s measurement data to calculate the optimal estimate of the current frame’s orbit data, and the accurate equation of motion was iteratively fitted to obtain high-precision data for predicting the satellite’s orbit frame by frame. Numerical tracking of the equipment was carried out. This method was experimentally validated on an actual optical measurement device. The test results showed that this method can make up for the frequent loss of short-term targets. Under the condition that the maximum deviation is less than 3”, the length of extrapolated data can be up to 30 s and the length of the measurement data was less than 30 s. This method may improve the stability of tracking equipment as well as the accuracy and integrity of the measurement data.

Predicting resistance and pseudoprogression: are minimalistic immunoediting mathematical models capable of forecasting checkpoint inhibitor treatment outcomes in lung cancer?

Background:The increased application of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 in lung cancer treatment generates clinical need to reliably predict individual patients’ treatment outcomes. Methods:To bridge the prediction gap, we examine four different mathematical models in the form of ordinary differential equations, including a novel delayed response model. We rigorously evaluate their individual and combined predictive capabilities with regard to the patients’ progressive disease (PD) status through equal weighting of model-derived outcome probabilities. Results:Fitting the complete treatment course, the novel delayed response model (R2=0.938) outperformed the simplest model (R2=0.865). The model combination was able to reliably predict patient PD outcome with an overall accuracy of 77% (sensitivity = 70%, specificity = 81%), solely through calibration with primary tumor longest diameter measurements. It autonomously identified a subset of 51% of patients where predictions with an overall accuracy of 81% (sensitivity = 81%, specificity = 81%) can be achieved. All models significantly outperformed a fully data-driven machine learning-based approach. Implications: These modeling approaches provide a dynamic baseline framework to support clinicians in treatment decisions by identifying different treatment outcome trajectories with already clinically available measurement data. Limitations and future directions:Conjoint application of the presented approach with other predictive tools and biomarkers, as well as further disease information (e.g. metastatic stage), could further enhance treatment outcome prediction. We believe the simple model formulations allow widespread adoption of the developed models to other cancer types. Similar models can easily be formulated for other treatment modalities.

Stages and main tasks of the century-long control theory and system identification development. Part VI. Model predictive control

The article is devoted to one of the relatively new areas in the field of control theory and practice, called model predictive control. The ideas underlying it, the connection with the theory of optimal control and numerical methods for solving optimization problems are described. It should be noted that currently the number of problems solved and control systems developed within the framework of this approach has come out on top in comparison with all others. This is evidenced by numerous publications concerning the theory and practical application of control methods and algorithms using a predictive model. The main distinctive feature of this method is that the control is not synthesized in advance, but is calculated directly during the operation of the system, i.e. at the current time. This made it possible, with such control, to implement original feedback that ensures stabilization of the process when the model is inaccurate and there is uncertainty associated with existing disturbances and measurements. It describes how such feedback is organized based on current measurement data. In this case, there is no need to interpret somehow the nature of the disturbances and errors acting on the system. Much attention in the article is paid to the possibilities that are admitted with this method of control. The model predictive control method seems especially attractive for systems with discrete impulse processes that are well described with using cognitive maps. It is considered an example of cognitive modeling of evolutionary demographic processes in Ukraine and the possibility of control them using a predictive model, when the model contains unstable eigenvalues. When solving problems of predictive control, it is proposed to use trajectory models, which directly follow from the Cauchy formula, in particular for discrete systems, instead of a local description in the state space. This makes it possible to take into account, when solving predictive control problems, how well or poorly controlled, observable or identifiable the system is. Non-standard capabilities of this method with a trajectory description appear during terminal control on a sliding interval with a finite horizon. A number of original formulations of terminal control problems are presented in the article and some of their properties are described.

분청사기 가마의 연대에 대한 고고지자기학적 연구

Baked earth samples were collected from 15 kilns at 9 sites among the surveyed Buncheong kiln sites. Afterwards, archaeomagnetic analysis was conducted to estimate the age of the kilns. Except for one kiln, reliable data with an error range of about 10 to 20 years were obtained. Since the margin of error is relatively narrow, the time difference between the kilns is not expected to be large, but the relative order of the Buncheong kilns was organized through the date of archaeomagnetic measurement data in this paper. The range of dates established based on archaeomagnetic measurements is from 1340 to 1410 (the early 14th to mid-15th centuries, including the error range), which is older than estimates from the late 14th to mid-15th centuries (about 1390 to 1480) based on archaeological investigations. Although some of the kilns showed little difference in archaeomagnetic and archaeological dating, many others were dated to about 50 years earlier than the archaeological survey suggested. In considering the causes of this discrepancy, the possibility of raising archaeological dating estimates by about 50 years and the possibility of age errors that can occur when dating is estimated through the Japanese standard curve were discussed. If the accumulation of archaeomagnetic data on various contemporary baked earth remains and comparison with other natural scientific dating methods are made, the archaeomagnetic data presented in this paper can be used to complete a more detailed Korean archaeomagnetic standard curve of the construction period of the Buncheong kiln.