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In recent works in manpower planning interest has been awakened in modeling manpower systems in departmentalized framework. This, as a form of disaggregation, may solve the problem of observable heterogeneity but not latent heterogeneity; it rather opens up other aspects of latent heterogeneity hitherto unaccounted for in classical (non departmentalized) manpower models. In this article, a multinomial Markov-switching model is formulated for investigating latent heterogeneity in intra-departmental and interdepartmental transitions in departmentalized manpower systems. The formulation incorporates extensions of the mover-stayer principle resulting in several competing models. The best manpower model is chosen based on the optimum number of hidden states established by the use of Expectation-Maximization iterative algorithm for estimation of the model parameters and a search procedure for assessing model performance against one another. The illustration establishes the usefulness of the model formulation in highlighting hidden disparities in personnel transitions in a departmentalized manpower system and in avoiding wrong model specification. [ABSTRACT FROM AUTHOR]

Background: There are currently five different kinds of transcranial magnetic stimulation (TMS) motor mapping algorithms available, from ordinary point-based algorithms to advanced field-based algorithms. However, there have been only a limited number of comparison studies conducted, and they have not yet examined all of the currently available algorithms. This deficiency impedes the judicious selection of algorithms for application in both clinical and basic neuroscience, and hinders the potential promotion of a potential superior algorithm. Considering the influence of algorithm complexity, further investigation is needed to examine the differences between fMRI peaks and TMS cortical hotspots that were identified previously. Methods: Twelve healthy participants underwent TMS motor mapping and a finger-tapping task during fMRI. The motor cortex TMS mapping results were estimated by five algorithms, and fMRI activation results were obtained. For each algorithm, the prediction error was defined as the distance between the measured scalp hotspot and optimized coil position, which was determined by the maximum electric field strength in the estimated motor cortex. Additionally, the study identified the minimum number of stimuli required for stable mapping. Finally, the location difference between the TMS mapping cortical hotspot and the fMRI activation peak was analyzed. Results: The projection yielded the lowest prediction error (5.27 ± 4.24 mm) among the point-based algorithms and the association algorithm yielded the lowest (6.66 ± 3.48 mm) among field-based estimation algorithms. The projection algorithm required fewer stimuli, possibly resulting from its suitability for the grid-based mapping data collection method. The TMS cortical hotspots from all algorithms consistently deviated from the fMRI activation peak (20.52 ± 8.46 mm for five algorithms). Conclusion: The association algorithm might be a superior choice for clinical applications and basic neuroscience research, due to its lower prediction error and higher estimation sensitivity in the deep cortical structure, especially for the sulcus. It also has potential applicability in various other TMS domains, including language area mapping and more. Otherwise, our results provide further evidence that TMS motor mapping intrinsically differs from fMRI motor mapping. [ABSTRACT FROM AUTHOR]

Magnetic pole position detection is the core of the closed-loop control system of the permanent magnet linear synchronous motor (PMLSM), and its position estimation accuracy directly affects control performance and dynamic response speed. In order to solve the problem of the increased estimation error of magnetic pole position caused by magnetic field distortion at the end of PMLSM while also considering the cost of control hardware, the paper uses four linear Hall sensors as magnetic pole position detection components and adopts an optimized estimation algorithm to improve the dynamic performance of the motor. Firstly, a numerical simulation of the magnetic field of poles was conducted using Ansoft Maxwell software, and combined with theoretical analysis, the optimal installation position range of four linear Hall orthogonal placements relative to the motor was obtained. Meanwhile, based on the existing vector tracking position observer, an improved observer detection model is proposed. The Matlab/Simulink software was used to compare the Hall-based detection model with the Hall-based improved observer detection model, verifying the feasibility of the improved detection algorithm. Finally, the rationality of the spatial layout design of linear Hall and the feasibility of improving the estimation algorithm were verified through experiments. [ABSTRACT FROM AUTHOR]

Creating SoC algorithms for Li-ion batteries based on neural networks requires a large amount of training data, since it is necessary to test the batteries under different conditions so that the algorithm learns the relationship between the different inputs and the output. Obtaining such data through laboratory tests is costly and time consuming; therefore, in this article, a neural network has been trained with data generated synthetically using electrochemical models. These models allow us to obtain relevant data related to different conditions at a minimum cost over a short period of time. By means of the different training rounds carried out using these data, it has been studied how the different hyperparameters affect the behaviour of the algorithm, creating a robust and accurate algorithm. To adapt this approach to new battery references or chemistries, transfer learning techniques can be employed. [ABSTRACT FROM AUTHOR]

In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multi-dimensional stochastic Graphical Modeling model based on multivariate normal hypothesis is constructed, and the relationship between the Graphical Gaussian model and the linear regression model is determined. The parameters of the multi-dimensional stochastic model are estimated by using the parameter estimation algorithm of the multi-dimensional stochastic model containing intermediate variables. According to the parameter estimation results of the multi-dimensional stochastic model, the structural change point estimation results of the multi-dimensional stochastic model are obtained by using the accurate estimation algorithm of the structural change point based on the MLE identification local drift time. The experimental results show that the proposed algorithm has higher estimation accuracy of structural change points than the control algorithms, which shows that it can effectively estimate the structural change points of multi-dimensional random models and has higher practicability. [ABSTRACT FROM AUTHOR]

The use of lithium-ion battery energy storage (BES) has grown rapidly during the past year for both mobile and stationary applications. For mobile applications, BES units are used in the range of 10–120 kWh. Power grid applications of BES are characterized by much higher capacities (range of MWh) and this area particularly has great potential regarding the expected energy system transition in the next years. The optimal operation of BES by an energy storage management system is usually predictive and based strongly on the knowledge about the state of charge (SOC) of the battery. The SOC depends on many factors (e.g. material, electrical and thermal state of the battery), so that an accurate assessment of the battery SOC is complex. The SOC intermediate prediction methods are based on the battery models. The modeling of BES is divided into three types: fundamental (based on material issues), electrical equivalent circuit (based on electrical modeling) and balancing (based on a reservoir model). Each of these models requires parameterization based on measurements of input/output parameters. These models are used for SOC modelbased calculation and in battery system simulation for optimal battery sizing and planning. Empirical SOC assessment methods currently remain the most popular because they allow practical application, but the accuracy of the assessment, which is the key factor for optimal operation, must also be strongly considered. This scientific contribution is divided into two papers. Paper part I will present a holistic overview of the main methods of SOC assessment. Physical measurement methods, battery modeling and the methodology of using the model as a digital twin of a battery are addressed and discussed. Furthermore, adaptive methods and methods of artificial intelligence, which are important for the SOC calculation, are presented. In paper part II, examples of the application areas are presented and their accuracy is discussed.

The paper studies the estimation of state of charge (SOC) of batteries. Firstly, the research status of battery management system, battery equivalent model and SOC estimation algorithm is introduced, and the performance of common equivalent circuit model and SOC estimation algorithm in complexity and accuracy is compared and analyzed. On this basis, this paper proposes an extended Kalman filter (EKF) algorithm based on the first-order RC model, and optimizes it by piecewise fitting. The accuracy of the optimized EKF algorithm is greatly improved. Finally, the modeling and simulation are completed through MATLAB/SIMULINK, and the experimental platform is designed and built to test the SOC estimation algorithm based on EKF. The simulation and experimental results verify the accuracy of the estimation algorithm.

Creating SoC algorithms for Li-ion batteries based on neural networks requires a large amount of training data, since it is necessary to test the batteries under different conditions so that the algorithm learns the relationship between the different inputs and the output. Obtaining such data through laboratory tests is costly and time consuming; therefore, in this article, a neural network has been trained with data generated synthetically using electrochemical models. These models allow us to obtain relevant data related to different conditions at a minimum cost over a short period of time. By means of the different training rounds carried out using these data, it has been studied how the different hyperparameters affect the behaviour of the algorithm, creating a robust and accurate algorithm. To adapt this approach to new battery references or chemistries, transfer learning techniques can be employed.

Magnetic pole position detection is the core of the closed-loop control system of the permanent magnet linear synchronous motor (PMLSM), and its position estimation accuracy directly affects control performance and dynamic response speed. In order to solve the problem of the increased estimation error of magnetic pole position caused by magnetic field distortion at the end of PMLSM while also considering the cost of control hardware, the paper uses four linear Hall sensors as magnetic pole position detection components and adopts an optimized estimation algorithm to improve the dynamic performance of the motor. Firstly, a numerical simulation of the magnetic field of poles was conducted using Ansoft Maxwell software, and combined with theoretical analysis, the optimal installation position range of four linear Hall orthogonal placements relative to the motor was obtained. Meanwhile, based on the existing vector tracking position observer, an improved observer detection model is proposed. The Matlab/Simulink software was used to compare the Hall-based detection model with the Hall-based improved observer detection model, verifying the feasibility of the improved detection algorithm. Finally, the rationality of the spatial layout design of linear Hall and the feasibility of improving the estimation algorithm were verified through experiments.

The insulin secretion rate (ISR) contains information that can provide a personal, quantitative understanding of endocrine function. If the ISR can be reliably inferred from measurements, it could be used for understanding and clinically diagnosing problems with the glucose regulation system. Objective: This study aims to develop a model-based method for inferring a parametrization of the ISR and related physiological information among people with different glycemic conditions in a robust manner. The developed algorithm is applicable for both dense or sparsely sampled plasma glucose/insulin measurements, where sparseness is defined in terms of sampling time with respect to the fastest time scale of the dynamics. Methods: An algorithm for parametrizing and validating a functional form of the ISR for different compartmental models with unknown but estimable ISR function and absorption/decay rates describing the dynamics of insulin accumulation was developed. The method and modeling applies equally to c-peptide secretion rate (CSR) when c-peptide is measured. Accuracy of fit is reliant on reconstruction error of the measured trajectories, and when c-peptide is measured the relationship between CSR and ISR. The algorithm was applied to data from 17 subjects with normal glucose regulatory systems and 9 subjects with cystic fibrosis related diabetes (CFRD) in which glucose, insulin and c-peptide were measured in course of oral glucose tolerance tests (OGTT). Results: This model-based algorithm inferred parametrization of the ISR and CSR functional with relatively low reconstruction error for 12 of 17 control and 7 of 9 CFRD subjects. We demonstrate that when there are suspect measurements points, the validity of excluding them may be interrogated with this method. Significance: A new estimation method is available to infer the ISR and CSR functional profile along with plasma insulin and c-peptide absorption rates from sparse measurements of insulin, c-peptide, and plasma glucose concentrations. We propose a method to interrogate and exclude potentially erroneous OGTT measurement points based on reconstruction errors. [ABSTRACT FROM AUTHOR]

Virtual environment (VE), as the proxy of slave contact environment, is the most promising technology to solve the time-delay problems in teleoperation. The accuracy of the predicted force depends not only on the reliability of the contact model but also on the estimation algorithm's adaptability. A new contact model is proposed to be applicable in various materials, which includes both the Kelvin–Voigt model (KVM) and Hunt–Crossley model (HCM). An extra parameter is set in the model to express the capacity of continuous switching between KVM and HCM, whose rationality is proved based on the energy loss. The energy loss is proportional to a power of impact velocity, and the exponent is bounded at [2,3], which exactly lies between KVM and HCM. Furthermore, to estimate the parameters with a single-stage method, the nonlinear model is linearized approximatively with logarithm function and polynomials. Then, the recursive least squares (RLS) algorithm combining forgetting factor and self-perturbing action is designed to identify the four parameters online. Finally, the model's continuous switching is verified with ideal simulation, and the model parameters are continuously changed without jumpy switch error. In the experiment, sponge, foam, and human hand represent the complex contact materials of the slave environment where the predicted force is shown to follow the real contact force with enough accuracy. Therefore, the virtual model can be considered the substitution of slave contact environment so that the feedback force in master can be calculated in real-time. [ABSTRACT FROM AUTHOR]

The insulin secretion rate (ISR) contains information that can provide a personal, quantitative understanding of endocrine function. If the ISR can be reliably inferred from measurements, it could be used for understanding and clinically diagnosing problems with the glucose regulation system.Objective: This study aims to develop a model-based method for inferring a parametrization of the ISR and related physiological information among people with different glycemic conditions in a robust manner. The developed algorithm is applicable for both dense or sparsely sampled plasma glucose/insulin measurements, where sparseness is defined in terms of sampling time with respect to the fastest time scale of the dynamics.Methods: An algorithm for parametrizing and validating a functional form of the ISR for different compartmental models with unknown but estimable ISR function and absorption/decay rates describing the dynamics of insulin accumulation was developed. The method and modeling applies equally to c-peptide secretion rate (CSR) when c-peptide is measured. Accuracy of fit is reliant on reconstruction error of the measured trajectories, and when c-peptide is measured the relationship between CSR and ISR. The algorithm was applied to data from 17 subjects with normal glucose regulatory systems and 9 subjects with cystic fibrosis related diabetes (CFRD) in which glucose, insulin and c-peptide were measured in course of oral glucose tolerance tests (OGTT).Results: This model-based algorithm inferred parametrization of the ISR and CSR functional with relatively low reconstruction error for 12 of 17 control and 7 of 9 CFRD subjects. We demonstrate that when there are suspect measurements points, the validity of excluding them may be interrogated with this method.Significance: A new estimation method is available to infer the ISR and CSR functional profile along with plasma insulin and c-peptide absorption rates from sparse measurements of insulin, c-peptide, and plasma glucose concentrations. We propose a method to interrogate and exclude potentially erroneous OGTT measurement points based on reconstruction errors.

Climate change is driving the transformation of energy systems from fossil to renewable energies. In industry, power supply systems and electro-mobility, the need for electrical energy storage is rising sharply. Lithium-based batteries are one of the most widely used technologies. Operating parameters must be determined to control the storage system within the approved operating limits. Operating outside the limits, i.e., exceeding or falling below the permitted cell voltage, can lead to faster aging or destruction of the cell. Accurate cell information is required for optimal and efficient system operation. The key is high-precision measurements, sufficiently accurate battery cell and system models, and efficient control algorithms. Increasing demands on the efficiency and dynamics of better systems require a high degree of accuracy in determining the state of health and state of charge (SOC). These scientific contributions to the above topics are divided into two parts. In the first part of the paper, a holistic overview of the main SOC assessment methods is given. Physical measurement methods, battery modeling, and the methodology of using the model as a digital twin of a battery are addressed and discussed. In addition, adaptive methods and artificial intelligence methods that are important for SOC calculation are presented. Part two of the paper presents examples of the application areas and discusses their accuracy. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to identify the components of the enterprise intellectual potential, which are promising areas of intellectual potential management that is the basis of a strategic partnership between business, science and education in the brewing industry of Ukraine. The novelty of the research is represented by the created theoretical model disclosing the specific advantages of such partnership for business, science and education in the context of intellectual potential management. The object of the scientific research is the process of the intellectual potential management and its role in forming strategic partnership of science-business-education. The research method includes the method of structural-logical analysis, the method of analysis and synthesis, the method of comparative analysis, statistical data and information analysis etc. The proposed algorithm for estimating the enterprise intellectual potential at the appropriate level of management includes ten stages, performing of which allows to get the calculated use level of the intellectual potential and its components: 1) providing the definition of generalized components of the enterprise’s potential and its important subspecies; 2) selecting and calculating the list of the indicators for defining the relative efficiency; 3) calculating the relative coefficients’ values for each enterprise considering average values in dependence of the type of activity; 4) aligning the contribution of the each subspecies of the potential; 5) calculating the efficiency coefficient of the consolidated relative use for each subspecies; 6) rationing the consolidated relative efficiency coefficient depending on their product; 7) calculating the potential share of each subspecies in the total value; 8) redistributing the obtained results by the components of the potential; 9) calculating the realized value of intellectual potential; 10) calculating the use level of intellectual potential and its components. The research is based on the Ukrainian brewing industry’s data for the period of 2016-2019. The obtained results highlight the most problematic components of the intellectual resources of the brewing companies in Ukraine such as "human resources", "technological resources" and "management resources". These components are recommended to be considered as the most promising areas of the intellectual potential development of the brewing enterprises in strategic partnership with educational and scientific institutions. The practical value of the proposed instruments for the brewing enterprises is in the use of the adapted graphanalytical model, which gives the opportunity to assess the structural components of intellectual potential of the enterprise and define the strategic directions of perspective partnership.

Climate change is driving the transformation of energy systems from fossil to renewable energies. In industry, power supply systems and electro-mobility, the need for electrical energy storage is rising sharply. Lithium-based batteries are one of the most widely used technologies. Operating parameters must be determined to control the storage system within the approved operating limits. Operating outside the limits, i.e., exceeding or falling below the permitted cell voltage, can lead to faster aging or destruction of the cell. Accurate cell information is required for optimal and efficient system operation. The key is high-precision measurements, sufficiently accurate battery cell and system models, and efficient control algorithms. Increasing demands on the efficiency and dynamics of better systems require a high degree of accuracy in determining the state of health and state of charge (SOC). These scientific contributions to the above topics are divided into two parts. In the first part of the paper, a holistic overview of the main SOC assessment methods is given. Physical measurement methods, battery modeling, and the methodology of using the model as a digital twin of a battery are addressed and discussed. In addition, adaptive methods and artificial intelligence methods that are important for SOC calculation are presented. Part two of the paper presents examples of the application areas and discusses their accuracy.

The use of lithium-ion battery energy storage (BES) has grown rapidly during the past year for both mobile and stationary applications. For mobile applications, BES units are used in the range of 10-120 kWh. Power grid applications of BES are characterized by much higher capacities (range of MWh) and this area particularly has great potential regarding the expected energy system transition in the next years. The optimal operation of BES by an energy storage management system is usually predictive and based strongly on the knowledge about the state of charge (SOC) of the battery. The SOC depends on many factors (e.g. material, electrical and thermal state of the battery), so that an accurate assessment of the battery SOC is complex. The SOC intermediate prediction methods are based on the battery models. The modeling of BES is divided into three types: fundamental (based on material issues), electrical equivalent circuit (based on electrical modeling) and balancing (based on a reservoir model). Each of these models requires parameterization based on measurements of input/output parameters. These models are used for SOC modelbased calculation and in battery system simulation for optimal battery sizing and planning. Empirical SOC assessment methods currently remain the most popular because they allow practical application, but the accuracy of the assessment, which is the key factor for optimal operation, must also be strongly considered. This scientific contribution is divided into two papers. Paper part I will present a holistic overview of the main methods of SOC assessment. Physical measurement methods, battery modeling and the methodology of using the model as a digital twin of a battery are addressed and discussed. Furthermore, adaptive methods and methods of artificial intelligence, which are important for the SOC calculation, are presented. In paper part II, examples of the application areas are presented and their accuracy is discussed. [ABSTRACT FROM AUTHOR]

The authors consider an algorithm for estimating the unknown parameters of the infection spread model based on the Markov field tools using the maximum likelihood method. It is assumed that each state of the Markov chain represents some configuration of a finite random Markov field, and the probability distribution of the chain states is the same as general probability distribution of the states of elements of the Gibbs random field. [ABSTRACT FROM AUTHOR]

Copyright of Cybernetics & Systems Analysis / Kibernetiki i Sistemnyj Analiz is the property of V.M. Glushkov Institute of Cybernetics of NAS of Ukraine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this note, the author discusses the problem of maximum likelihood (ML) estimation for the Kronecker product of two positive definite variance–covariance matrices, with focus on necessary and sufficient conditions of existence and uniqueness for the ML estimator. Recent and earlier references, from the author and others, are scrutinized. The discussion includes computational aspects, as an estimation algorithm is used in the absence of analytical solutions. A timely update is provided as a result. [ABSTRACT FROM AUTHOR]

The performance of the process network in the smart substation is particularly important for relaying functions implementation, and the transmission quality of sampled value packets is becoming the key constraint on protections because they carry practical parameters of the power system without repeated transmission. By means of phasor form and simulation, the model is built to analyze the operating characteristics of differential protection during the normal condition, external faults, and internal faults of the transformer, while packet loss/delay occurring at a different time and using different corrective measures. Considering the change of power equipment capacity, external fault types, starting points on wave and sampling frequency, the security boundaries are constructed for consecutive loss of sampled value packets. In addition, two indices are put forward to weigh the tolerance thresholds. Finally, the degree of dependability of differential protection due to the packet loss is further analyzed under the transformer internal faults.

Electrically excited synchronous machines have become one of the potential alternatives to permanent magnet synchronous machines in electric vehicles to avoid rare-earth materials. Utilizing high-frequency brushless exciters for rotor excitation is a promising choice to reduce friction losses and maintenance effort and cost. However, with the usage of brushless exciters, the field current and temperature cannot be measured directly. In this article, an algorithm is proposed to dynamically estimate the field current as well as the field winding temperature. The dc-link current is utilized as a feedback to correct the estimation. The performance of the estimation algorithm is initially evaluated in simulations and then verified by experimental measurements in a prototype. Based on the estimation algorithm, closed-loop control of the field current is developed and verified experimentally. [ABSTRACT FROM AUTHOR]

A complementary filter is presented for the attitude estimation using inertial and magnetic sensors. The proposed filter is based on a new attitude representation, where 4-D parameters (a 3-D unit vector and one angle quantity) are used. The proposed representation has an intuitive interpretation and the proposed filter has a separation property (magnetic disturbance does not affect pitch and roll angle estimation). Kinematics of the 4-D parameters and other properties are investigated. The proposed filter is verified through simulation and experiments. [ABSTRACT FROM AUTHOR]

Mean arterial blood pressure (MAP) dynamics estimation and its automated regulation could benefit the clinical resuscitation of patients in critical conditions. To address the variability and complexity of the MAP response of patients to vasoactive drug infusion, a parameter‐varying model with a varying delay is considered to describe such dynamics. The estimation of the varying parameters and delay is performed via a Bayesian‐based multiple‐model square‐root cubature Kalman filtering (MMSRCKF) approach. The estimation results substantiate the effectiveness of the utilized identification method using experimental data. Next, an automated drug delivery scheme to regulate the real‐time MAP response of patients is developed via time‐delay linear parameter‐varying (LPV) control techniques. To this end, a gain‐scheduled outputfeedback LPV controller is designed to track a desired reference MAP target and guarantee robustness against norm‐bounded uncertainties and disturbances in terms of the closed‐loop system induced L2 ‐norm. Parameter‐dependent Lyapunov–Krasovskii functionals (LKFs) are used to derive sufficient conditions in the convex linear matrix inequality (LMI) constraint framework for the robust stabilization of LPV systems with arbitrarily varying delay. Finally, to evaluate the performance of the proposed MAP regulation approach, closed‐loop simulations are conducted, and the results confirm the effectiveness of the proposed method against various simulated clinical scenarios. [ABSTRACT FROM AUTHOR]

The parameters were selected and the analysis of their compliance to assess the processing complexity of castings was made. A variant of the organization of process design for the castings of varying difficulty, defined on a set of proposed geometrical parameters was proposed.

Here we consider a corner target positioning problem under the condition of unknown walls' positions using time-division multiple-input-multiple-output (MIMO) radar. Firstly, we propose an estimation algorithm for the walls' positions using the Back-Projection (BP) imaging algorithm. Then, after obtaining the BP image of target, we extract the focus regions produced by different multipaths. Finally, we obtain the position of the target based on the extracted walls' positions and the focus regions. Simulations and experiments results validate the proposed algorithm.

Background: There are currently five different kinds of transcranial magnetic stimulation (TMS) motor mapping algorithms available, from ordinary point-based algorithms to advanced field-based algorithms. However, there have been only a limited number of comparison studies conducted, and they have not yet examined all of the currently available algorithms. This deficiency impedes the judicious selection of algorithms for application in both clinical and basic neuroscience, and hinders the potential promotion of a potential superior algorithm. Considering the influence of algorithm complexity, further investigation is needed to examine the differences between fMRI peaks and TMS cortical hotspots that were identified previously., Methods: Twelve healthy participants underwent TMS motor mapping and a finger-tapping task during fMRI. The motor cortex TMS mapping results were estimated by five algorithms, and fMRI activation results were obtained. For each algorithm, the prediction error was defined as the distance between the measured scalp hotspot and optimized coil position, which was determined by the maximum electric field strength in the estimated motor cortex. Additionally, the study identified the minimum number of stimuli required for stable mapping. Finally, the location difference between the TMS mapping cortical hotspot and the fMRI activation peak was analyzed., Results: The projection yielded the lowest prediction error (5.27 ± 4.24 mm) among the point-based algorithms and the association algorithm yielded the lowest (6.66 ± 3.48 mm) among field-based estimation algorithms. The projection algorithm required fewer stimuli, possibly resulting from its suitability for the grid-based mapping data collection method. The TMS cortical hotspots from all algorithms consistently deviated from the fMRI activation peak (20.52 ± 8.46 mm for five algorithms)., Conclusion: The association algorithm might be a superior choice for clinical applications and basic neuroscience research, due to its lower prediction error and higher estimation sensitivity in the deep cortical structure, especially for the sulcus. It also has potential applicability in various other TMS domains, including language area mapping and more. Otherwise, our results provide further evidence that TMS motor mapping intrinsically differs from fMRI motor mapping., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor YT declared a past co-authorship with the author CZ., (Copyright © 2023 Chen, Jiang, Zhang, Li and Zhu.)

Here we consider a corner target positioning problem under the condition of unknown walls' positions using time-division multiple-input-multiple-output (MIMO) radar. Firstly, we propose an estimation algorithm for the walls' positions using the Back-Projection (BP) imaging algorithm. Then, after obtaining the BP image of target, we extract the focus regions produced by different multipaths. Finally, we obtain the position of the target based on the extracted walls' positions and the focus regions. Simulations and experiments results validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

This study suggests a non‐linear control algorithm for the exciter controlling a single‐machine infinite‐bus system, considering the non‐linear dynamics with parameter and sudden input power variations. This study provides three contributions: (i) an estimation algorithm is designed to estimate the power angle operating point for the terminal voltage to be regulated to its desired value, incorporating the disturbance observer (DOB); (ii) a DOB‐based proportional‐type non‐linear stabilising algorithm is systematically derived using the estimated power angle operating point; and (iii) it is rigorously shown that the proposed proportional‐type non‐linear controller always removes the steady‐state errors without integral actions for error variables. The simulation results confirm the effectiveness of the proposed technique under several transmission line fault scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract We propose a frequency-sweeping interferometry (FSI) with single external cavity diode laser that can be used to measure the absolute distances of a dynamic target. Generally, the FSI-Doppler effect induced by target drift restricts the measurement accuracy severely, but the Doppler effect can also be used to measure the actual movement during optical frequency sweeps. Thus, a mathematical model of the Doppler effect in FSI is presented and a novel FSI method based on Doppler beat frequency tracking model is proposed to realize the absolute distance measurement of a dynamic target. The method was verified successfully by measuring vibrating targets of 50 ∼ 5000 Hz at 38 mm in the laboratory. [ABSTRACT FROM AUTHOR]

In this paper, a low-cost small-sized strap-down inertial navigation system (SINS)—Gyrolab GL-VG 109—is studied. When the system is installed on an unmanned vehicle and works in autonomous mode, it is difficult to determine the navigation parameters of the unmanned vehicle. Correcting the SINS information from the Global Navigation Satellite System (GNSS) can significantly increase the determination accuracy of the navigation parameters. However, this is only available when the GNSS signals are stable. A new adaptive estimation algorithm that can automatically detect, evaluate, and process the abnormal measurements is proposed in the present work. The determination of the navigation parameters can reach the third accuracy class using the proposed method. The effectiveness of the algorithm is verified by the mathematical simulation and the experimental tests (with a real SINS GL-VG 109), which are conducted in urban environments with a GNSS signal containing 15% and 40% abnormal measurements. The results show that the proposed method can significantly reduce the impact of abnormal measurements and improve the estimation accuracy.

Abstract: A method of identification of induction motor parameters based on a phase-locked loop frequency control algorithm was considered. The existing identification methods of the equivalent circuit parameters were analyzed and classified. The methods operating in real time and in the adaptation mode of electric drive during the first operative startup of electric drive were distinguished. In addition, using the methods of active identification, in which an additional test information signal is injected into the power circuit of an electric drive, is unacceptable during electric drive operation and they are used at the time of prior adjustment of electric drive until its operative startup or for a short time to adaptation of changing parameters. An evaluation algorithm of motor parameters was proposed, and functional and structural diagrams of parametric identification system were developed. The developed identification method based on a phase-locked loop frequency control algorithm is constructed upon leveling of the phase difference between the real (measured) stator current vector and the stator current vector calculated by the model. The simulation results of the implemented parametric identification system are presented. According to the results of model experiments, the error of estimation of motor parameters did not exceed 1%. The proposed parametric identification system can be used as part of a vector control system to determine the coefficients of internal regulators of electric drives. [ABSTRACT FROM AUTHOR]

We consider a special case of the scheduling problem on unrelated machines, namely the restricted assignment problem with two different processing times. We show that the configuration LP has an integrality gap of at most 53≈1.667 for this problem. This allows us to estimate the optimal makespan within a factor of 53, improving upon the previously best known estimation algorithm with ratio 116≈1.833 due to Chakrabarty et al. (in: Proceedings of the twenty-sixth annual ACM-SIAM symposium on discrete algorithms (SODA 2015), pp 1087-1101, 2015). [ABSTRACT FROM AUTHOR]

This paper aims to introduce an estimation algorithm for the joint densityof a circular-circular random variable, which is expressible in the form asdiscussed by Fernández-Durán (2007). The performance of the algorithm hasbeen checked with the help of a simulation study and it is found to performeciently even for small sample sizes. Furthermore, the performance of theproposed algorithm is compared with that of an existing method of densityestimation and is found to perform better than the existing one, which isindicated by the higher mean square error values for the estimates obtainedby the latter method. Finally, the application of the algorithm is displayedby estimating the joint density of a circular-circular random variable arisingin a real-life data set. [ABSTRACT FROM AUTHOR]

Sea eutrophication is a natural process of water enrichment caused by increased nutrient loading that severely affects coastal ecosystems by decreasing water quality. The degree of eutrophication can be assessed by chlorophyll-a concentration. This study aims to develop a remote sensing method suitable for estimating chlorophyll-a concentrations in tropical coastal waters with abundant phytoplankton using Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra imagery and to improve the spatial resolution of MODIS/Terra-based estimation from 1 km to 100 m by geostatistics. A model based on the ratio of green and blue band reflectance (rGBr) is proposed considering the bio-optical property of chlorophyll-a. Tien Yen Bay in northern Vietnam, a typical phytoplankton-rich coastal area, was selected as a case study site. The superiority of rGBr over two existing representative models, based on the blue-green band ratio and the red-near infrared band ratio, was demonstrated by a high correlation of the estimated chlorophyll-a concentrations at 40 sites with values measured in situ. Ordinary kriging was then shown to be highly capable of predicting the concentration for regions of the image covered by clouds and, thus, without sea surface data. Resultant space-time maps of concentrations over a year clarified that Tien Yen Bay is characterized by natural eutrophic waters, because the average of chlorophyll-a concentrations exceeded 10 mg/m3 in the summer. The temporal changes of chlorophyll-a concentrations were consistent with average monthly air temperatures and precipitation. Consequently, a combination of rGBr and ordinary kriging can effectively monitor water quality in tropical shallow waters.

This study is concerned with asynchronous data fusion problem in the non‐linear multisensor multirate system, where the observation noises are coupled with the system noise of the previous step. An estimation algorithm is designed based on covariance intersection (CI) fusion algorithm. First, the system is reformulated by using multiscale system theory. The local filtering algorithm processes the cross‐correlated noises by exploiting the conditional Gaussian distributions. Furthermore, the first‐order Stirling's interpolation algorithm and third‐degree spherical‐radial rule are used for non‐linear approximate estimation. The fusion of the local estimate results is based on CI fusion algorithm. This study extends the currently available asynchronous data fusion algorithm for a multirate multisensor non‐linear system, and considers the asynchronous correlation, then a novel algorithm is proposed, which has the ability to deal with the correlation, and is robust to the correlation between any two local filters. The effectiveness and superiority of the proposed algorithm are indicated by the results of numerical simulation. [ABSTRACT FROM AUTHOR]