Journal articles on the topic 'First system of Estimation'
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Gao, Chao, Guorong Zhao, Jianhua Lu, and Shuang Pan. "Decentralized state estimation for networked spatial-navigation systems with mixed time-delays and quantized complementary measurements: The moving horizon case." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 11 (June 8, 2017): 2160–77. http://dx.doi.org/10.1177/0954410017712277.
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In this paper, the navigational state estimation problem is investigated for a class of networked spatial-navigation systems with quantization effects, mixed time-delays, and network-based observations (i.e. complementary measurements and regional estimations). A decentralized moving horizon estimation approach, featuring complementary reorganization and recursive procedure, is proposed to tackle this problem. First, through the proposed reorganized scheme, a random delayed system with complementary observations is reconstructed into an equivalent delay-free one without dimensional augment. Second, with this equivalent system, a robust moving horizon estimation scheme is presented as a uniform estimator for the navigational states. Third, for the demand of real-time estimate, the recursive form of decentralized moving horizon estimation approach is developed. Furthermore, a collective estimation is obtained through the weighted fusion of two parts, i.e. complementary measurements based estimation, and regional estimations directly from the neighbors. The convergence properties of the proposed estimator are also studied. The obtained stability condition implicitly establishes a relation between the upper bound of the estimation error and two parameters, i.e. quantization density and delay occur probability. Finally, an application example to networked unmanned aerial vehicles is presented and comparative simulations demonstrate the main features of the proposed method.
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Liu, Bing, Zhen Chen, Xiangdong Liu, and Fan Yang. "An Efficient Nonlinear Filter for Spacecraft Attitude Estimation." International Journal of Aerospace Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/540235.
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Increasing the computational efficiency of attitude estimation is a critical problem related to modern spacecraft, especially for those with limited computing resources. In this paper, a computationally efficient nonlinear attitude estimation strategy based on the vector observations is proposed. The Rodrigues parameter is chosen as the local error attitude parameter, to maintain the normalization constraint for the quaternion in the global estimator. The proposed attitude estimator is performed in four stages. First, the local attitude estimation error system is described by a polytopic linear model. Then the local error attitude estimator is designed with constant coefficients based on the robustH2filtering algorithm. Subsequently, the attitude predictions and the local error attitude estimations are calculated by a gyro based model and the local error attitude estimator. Finally, the attitude estimations are updated by the predicted attitude with the local error attitude estimations. Since the local error attitude estimator is with constant coefficients, it does not need to calculate the matrix inversion for the filter gain matrix or update the Jacobian matrixes online to obtain the local error attitude estimations. As a result, the computational complexity of the proposed attitude estimator reduces significantly. Simulation results demonstrate the efficiency of the proposed attitude estimation strategy.
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Dabush, Lital, Ariel Kroizer, and Tirza Routtenberg. "State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools." Sensors 23, no. 3 (January 26, 2023): 1387. http://dx.doi.org/10.3390/s23031387.
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This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to 30% in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.
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Rao, Zhushi, Qinzhong Shi, and Ichiro Hagiwara. "Optimal Estimation of Dynamic Loads for Multiple-Input System." Journal of Vibration and Acoustics 121, no. 3 (July 1, 1999): 397–401. http://dx.doi.org/10.1115/1.2893993.
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An optimal method is developed to estimate the dynamic loads for systems subjected to multiple inputs. The method focuses on minimizing the ensemble mean square error of the estimation. First, the inverse system analysis technique is employed to establish the error estimation equation. Then, by applying the noncausal Wiener filtering theory, the optimal estimator of dynamic loads is derived out. Numerical simulation work demonstrates that the method is of a good ability in suppressing the influence of measurement noises on estimation accuracy. Meanwhile, the simulating calculation of load estimation by a conventional method is also performed and the comparison of both results shows that the method proposed in this paper is rather effective and practicable for dynamic load estimation.
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PARK, J. W., B. K. CHOI, and K. B. SONG. "First Derivatives Estimation of Nonlinear Parameters in Hybrid System." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E89-A, no. 12 (December 1, 2006): 3736–38. http://dx.doi.org/10.1093/ietfec/e89-a.12.3736.
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Zhang, Xiao, Feng Ding, Ling Xu, Ahmed Alsaedi, and Tasawar Hayat. "A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise." Mathematics 7, no. 4 (April 17, 2019): 356. http://dx.doi.org/10.3390/math7040356.
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This paper is concerned with the joint state and parameter estimation methods for a bilinear system in the state space form, which is disturbed by additive noise. In order to overcome the difficulty that the model contains the product term of the system input and states, we make use of the hierarchical identification principle to present new methods for estimating the system parameters and states interactively. The unknown states are first estimated via a bilinear state estimator on the basis of the Kalman filtering algorithm. Then, a state estimator-based recursive generalized least squares (RGLS) algorithm is formulated according to the least squares principle. To improve the parameter estimation accuracy, we introduce the data filtering technique to derive a data filtering-based two-stage RGLS algorithm. The simulation example indicates the efficiency of the proposed algorithms.
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Timmons, Eric Michael, and Brian Charles Williams. "Best-First Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation." Journal of Artificial Intelligence Research 67 (January 12, 2020): 1–34. http://dx.doi.org/10.1613/jair.1.11892.
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There is an increasing desire for autonomous systems to have high levels of robustness and safety, attained through continuously planning and self-repairing online. Underlying this is the need to accurately estimate the system state and diagnose subtle failures. Estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing these estimates. However, existing methods have difficulty scaling to systems with more than a handful of components. Discrete, consistency based state estimation capabilities can scale to this level by combining best-first enumeration and conflict-directed search. While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts learn inconsistencies from constraint violation, but probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach to hybrid estimation that unifies best-first enumeration and conflict-directed search through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. This paper presents a general best-first enumeration algorithm based on bounding conflicts (A*BC) and a hybrid estimation method using this enumeration algorithm. Experiments show that an A*BC powered state estimator produces estimates up to an order of magnitude faster than the current state of the art, particularly on large systems.
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Li, Shaoshi, Xingjian Wang, Shaoping Wang, and Yuwei Zhang. "Distributed Bearing-Only Formation Control for UAV-UWSV Heterogeneous System." Drones 7, no. 2 (February 10, 2023): 124. http://dx.doi.org/10.3390/drones7020124.
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This paper investigates the bearing-only formation control problem of a heterogeneous multi-vehicle system, which includes unmanned aerial vehicles (UAVs) and unmanned surface vehicles (UWSVs). The interactions among vehicles are described by a particular class of directed and acyclic graphs, namely heterogeneous leader-first follower (HLFF) graphs. Under the HLFF structure, a UAV is selected as the leader, moving with the reference dynamics, while the followers, including both UAVs and UWSVs, are responsible for controlling the position with regard to the neighbors in the formation. To solve the problem, we propose a velocity-estimation-based control scheme, which consists of a distributed observer for estimating the reference velocity of each vehicle and a distributed formation control law for achieving the desired formation based on the estimations and bearing measurements. Moreover, it is shown that the translation and scale of the formation can be uniquely determined by the leader UAV. The theoretical analysis demonstrated the finite-time convergence of the velocity estimation and the asymptotic convergence of the formation tracking. Comparative simulation results are provided to substantiate the effectiveness of the proposed method.
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Shin, Yung C. "System Identification of Multivariate Systems With Feedback." Journal of Dynamic Systems, Measurement, and Control 112, no. 2 (June 1, 1990): 283–91. http://dx.doi.org/10.1115/1.2896137.
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System identification of linear stochastic systems has been the concern of many scientists and engineers. The same system can be described in many ways and consequently many different models have been proposed. However, the complexity of the systems and computational difficulty, especially under the presence of feedback and external disturbances, have rendered identification a difficult task. This paper deals with a general modeling procedure that can be used for identification of feedback systems. First, a brief discussion on the joint input-output process models is given, followed by the discussion on the canonical representation of the model. A simplified model is derived from the general vector model. This will lead to the validity of using the Modified Autoregressive Moving Average Vector (MARMAV) model in the identification of the general multivariate system with open-loop and closed-loop dynamics. Next, an estimation procedure is explained. The estimation is pursued to reach a maximum likelihood model by nonlinear iterative calculations. Since initial values are required to start the procedure which are critical to the estimation, two methods for the initial guess value estimation are provided in the appendices.
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Lim, Jae-Han, and Eun-Kyu Lee. "Modeling the Accuracy of Estimating a Neighbor’s Evolving Position in VANET." Applied Sciences 10, no. 19 (September 28, 2020): 6814. http://dx.doi.org/10.3390/app10196814.
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Accurate estimation of a neighbor’s evolving position is essential to enhancing safety in intelligent transport systems. A vehicle can estimate a neighbor’s evolving position via periodic beaconing wherein each vehicle periodically broadcasts a beacon including its own kinematic data (e.g., position, speed, and acceleration). Many researchers have proposed analytic models to describe periodic beaconing in vehicular ad-hoc networks (VANETs). However, those models have focused only on network performance, e.g., packet delivery ratio (PDR), or a delay, which fail to evaluate the accuracy of estimating a neighbor’s evolving position. In this paper, we present a new analytic model capable of providing an estimation error of a neighbor’s evolving position in VANET to assess the accuracy of the estimation. This model relies on a vehicle system using periodic beaconing and a constant speed and position estimator (CSPE) to estimate a neighbor’s evolving position. To derive an estimation error, we first calculate the estimation error using a simple equation, which is associated with a probability of successful reception. Then, we derive the probability of successful reception that is applied onto the error model. To our knowledge, this is the first paper to establish a mathematical model to assess the accuracy of estimating a neighbor’s evolving position. To validate the proposed model, we compared the numerical results of the model with those of the NS-2 simulation. We observed that numerical results of the proposed model were located within the 95% confidential intervals of simulations results.
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Munjulury, Raghu Chaitanya, Ingo Staack, Adrián Sabaté López, and Petter Krus. "Knowledge-based aircraft fuel system integration." Aircraft Engineering and Aerospace Technology 90, no. 7 (October 1, 2018): 1128–35. http://dx.doi.org/10.1108/aeat-01-2017-0046.
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Purpose This paper aims to present a knowledge-based fuel system, implementation and application, oriented towards its use in aircraft conceptual design. Design/methodology/approach Methodology and software tools oriented to knowledge-based engineering applications (MOKA) is used as a foundation for the implementation and integration of fuel systems. Findings Including fuel systems earlier in the design process creates an opportunity to optimize it and obtain better solutions by allocating suitable locations in an aircraft, thereby reflecting on the centre of gravity of the aircraft. Research limitations/implications All geometries are symbolic, representing a space allocation inside the aircraft for the fuel system. A realistic representation of the real components could be realized in detail design. Practical implications Fuel weight is a significant part of take-off weight and decisive in aircraft sizing and range estimations. The three-dimensional geometry provides a better estimation of the volume that is available to allocate the necessary entities. It also provides fast measures for weight and balance, fuel capacity, relative tank positions and a first estimation of piping length. Originality/value Fuel systems appear early in the design process, as they are involved in several first estimations. By using a knowledge-based engineering approach, several alternatives can be visualized and estimated in the conceptual design process. Furthermore, using the weights and centre of gravity at different angles of pitch and roll of each fuel tank, the aircraft could be optimized for handling qualities by using automatically generated system simulation models.
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Sun, Shaoxin, Huaguang Zhang, Jian Han, and Yuling Liang. "A novel double-level observer-based fault estimation for Takagi–Sugeno fuzzy systems with unknown nonlinear dynamics." Transactions of the Institute of Measurement and Control 41, no. 12 (February 5, 2019): 3372–84. http://dx.doi.org/10.1177/0142331219826655.
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In this paper we investigate the fault estimation problem against local unknown nonlinear dynamics, sensor and actuator faults for a class of Takagi–Sugeno (T-S) fuzzy systems. In addition, the exogenous disturbances and measurement noise are considered, which are presented in the operation of the systems and are various and independent of the systems. A novel double-level observer is designed to estimate the system states and faults. Compared with the current research results, the proposed observer has a wider range of application. By designing a fuzzy augmented system and a Kalman filter as the first-level observer, the estimations of system states, sensor faults and actuator faults can be obtained simultaneously. The second-level observer can estimate the unknown nonlinear dynamic function by establishing generalized fuzzy hyperbolic model. The robust stability of the estimation error systems is considered by H∞ performance. Finally, three simulation examples are provided to demonstrate the effectiveness of the proposed fault estimation method.
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Demir, Ridvan, and Murat Barut. "Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control." Transactions of the Institute of Measurement and Control 40, no. 13 (November 28, 2017): 3884–98. http://dx.doi.org/10.1177/0142331217734631.
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This paper presents a novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimation problems of the variations in stator resistance ([Formula: see text]) and rotor resistance ([Formula: see text]) for speed-sensorless induction motor control. The EKF simultaneously estimates the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator currents, the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator fluxes, rotor angular velocity ([Formula: see text]), load torque ([Formula: see text]) and [Formula: see text], while the AP-MRAS provides the online [Formula: see text] estimation to the EKF. Both the AP-MRAS, whose adaptation mechanism is developed with the help of the least mean squares method in this paper, and the EKF only utilize the measured stator voltages and currents. Performances of the proposed hybrid estimator in this paper are tested by challenging scenarios generated in simulations and real-time experiments. The obtained results demonstrate the effectiveness of the introduced hybrid estimator, together with a [Formula: see text] reduction in the processing time and size of the estimation algorithm in terms of previous studies performing the same estimations of the states and parameters. From this point of view, it is the first such study in the literature, to our knowledge.
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VOSS, HENNING U., JENS TIMMER, and JÜRGEN KURTHS. "NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS." International Journal of Bifurcation and Chaos 14, no. 06 (June 2004): 1905–33. http://dx.doi.org/10.1142/s0218127404010345.
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We review the problem of estimating parameters and unobserved trajectory components from noisy time series measurements of continuous nonlinear dynamical systems. It is first shown that in parameter estimation techniques that do not take the measurement errors explicitly into account, like regression approaches, noisy measurements can produce inaccurate parameter estimates. Another problem is that for chaotic systems the cost functions that have to be minimized to estimate states and parameters are so complex that common optimization routines may fail. We show that the inclusion of information about the time-continuous nature of the underlying trajectories can improve parameter estimation considerably. Two approaches, which take into account both the errors-in-variables problem and the problem of complex cost functions, are described in detail: shooting approaches and recursive estimation techniques. Both are demonstrated on numerical examples.
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Ross, Sheldon M., and Sridhar Seshadri. "HITTING TIME IN AN ERLANG LOSS SYSTEM." Probability in the Engineering and Informational Sciences 16, no. 2 (April 2002): 167–84. http://dx.doi.org/10.1017/s0269964802162036.
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In this article, we develop methods for estimating the expected time to the first loss in an Erlang loss system. We are primarily interested in estimating this quantity under light traffic conditions. We propose and compare three simulation techniques as well as two Markov chain approximations. We show that the Markov chain approximations proposed by us are asymptotically exact when the load offered to the system goes to zero. The article also serves to highlight the fact that efficient estimation of transient quantities of stochastic systems often requires the use of techniques that combine analytical results with simulation.
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Jiang, Bing, Zeqi Chen, and Feifan Chen. "Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method." Energies 12, no. 10 (May 16, 2019): 1878. http://dx.doi.org/10.3390/en12101878.
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The equivalent-circuit model (ECM) is widely used in online estimating the parameters and states of lithium-ion batteries. However, the sampling delay between the voltage and current of a battery is generally overlooked, which is unavoidable in a modular battery management system (BMS) and would lead to wrong results in the estimation of battery parameters and states. In this paper, with the first-order resistor–capacitor (RC) model as our battery model, we analyze the influence mechanism of sampling delay and then propose an optimized method for online estimating battery parameters. The mathematical model derived from the first-order RC model and the approximation method of first-order derivative are optimized. The recursive least squares (RLS) algorithm is used for identifying the parameters of the model. In order to verify the proposed method, a modular battery test system with high sampling frequency and high synchronization accuracy is developed. The experiment results indicate that the sampling delay would cause the estimation process to fluctuate, and the optimized method effectively improves the tolerance range of sampling delay.
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AOSHIMA, Nobuharu. "Estimation of Damped Oscillating System Parameters and Input Waveforms by Complex First Order System." Transactions of the Society of Instrument and Control Engineers 27, no. 10 (1991): 1138–43. http://dx.doi.org/10.9746/sicetr1965.27.1138.
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Liu, Lidong, Jifeng Hu, Zishu He, Chunlin Han, Huiyong Li, and Jun Li. "A Velocity Measurement Method Based on Scaling Parameter Estimation of a Chaotic System." Metrology and Measurement Systems 18, no. 2 (January 1, 2011): 275–82. http://dx.doi.org/10.2478/v10178-011-0009-1.
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A Velocity Measurement Method Based on Scaling Parameter Estimation of a Chaotic SystemIn this paper, we propose a new method of measuring the target velocity by estimating the scaling parameter of a chaos-generating system. First, we derive the relation between the target velocity and the scaling parameter of the chaos-generating system. Then a new method for scaling parameter estimation of the chaotic system is proposed by exploiting the chaotic synchronization property. Finally, numerical simulations show the effectiveness of the proposed method in target velocity measurement.
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Orłowski, Przemysław. "Estimation of the Output Deviation Norm for Uncertain, Discrete-Time Nonlinear Systems in a State Dependent Form." International Journal of Applied Mathematics and Computer Science 17, no. 4 (December 1, 2007): 505–13. http://dx.doi.org/10.2478/v10006-007-0042-z.
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Estimation of the Output Deviation Norm for Uncertain, Discrete-Time Nonlinear Systems in a State Dependent FormNumerical evaluation of the optimal nonlinear robust control requires estimating the impact of parameter uncertainties on the system output. The main goal of the paper is to propose a method for estimating the norm of an output trajectory deviation from the nominal trajectory for nonlinear uncertain, discrete-time systems. The measure of the deviation allows us to evaluate the robustness of any designed controller. The first part of the paper concerns uncertainty modelling for nonlinear systems given in the state space dependent form. The method for numerical estimation of the maximal norm of the output trajectory deviation with applications to robust control synthesis is proposed based on the introduced three-term additive uncertainty model. Theoretical deliberations are complemented with a numerical, water-tank system example.
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Yamada, Tetsuyasu, Hisao Ayame, Shigeyuki Nagasaka, and Hiroo Hirose. "Method of System Identification for Air Conditioning Systems in Operation." International Journal of Emerging Technology and Advanced Engineering 12, no. 5 (May 1, 2022): 38–48. http://dx.doi.org/10.46338/ijetae0522_05.
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This study aims to optimize the operation of an air conditioning (AC) system by tracking situational changes due to outside temperature, number of people and computers, and other factors. Therefore, we studied the accurate estimation of system parameters of an AC unit during operation. We modeled the AC system using the first-order plus dead time model and discretized it using the autoregressive with exogenous input model. We developed a technique to estimate the system parameters using Bayesian optimization. Here, the system parameters are values that determine the physical characteristics of the combined air conditioning system and room. Therefore, we determined that there are cases where the characteristics deteriorate after repeated estimation. By solving this problem, we were able to establish a practical system. Keywords—ARX time-series model, Bayesian optimization method, First-order plus dead time, Gaussian process, PID control
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AOKI, Shigeru. "Estimation Method for First Excursion Probability of Secondary System with Gap." Transactions of the Japan Society of Mechanical Engineers Series C 67, no. 660 (2001): 2450–55. http://dx.doi.org/10.1299/kikaic.67.2450.
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Aoki, Shigeru. "Estimation Method for First Excursion Probability of Secondary System with Friction." Transactions of the Japan Society of Mechanical Engineers Series C 59, no. 563 (1993): 2065–69. http://dx.doi.org/10.1299/kikaic.59.2065.
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Wang, Zhigang, Zihao Wang, Fusheng Zhu, Zezhou Luo, Fang Li, and Haopeng Liu. "Joint Doppler Shift and Channel Estimation for High-Speed Railway Wireless Communications in Tunnel Scenarios." Wireless Communications and Mobile Computing 2022 (January 31, 2022): 1–6. http://dx.doi.org/10.1155/2022/6804412.
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In this work, we study the problem of Doppler shift and channel estimation for wireless communication systems on high-speed railways (HSRs). We focus on tunnel scenario, one of the classical scenarios of HSRs. We first build up the mathematical system model, design a joint Doppler shift and channel estimator, and compare its performance with the typical Moose algorithm. We show that our estimator outperforms the Moose algorithm in Doppler estimation. Besides, since wireless channels in tunnel scenarios often contain several or multiple taps, we suggest an adaptive frame structure to improve transmission efficiency. Simulations are then provided to corroborate our proposed studies.
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Chinsatit, Warapon, and Takeshi Saitoh. "CNN-Based Pupil Center Detection for Wearable Gaze Estimation System." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8718956.
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This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. Potentially, the pupil center position of a user’s eye can be used in various applications, such as human-computer interaction, medical diagnosis, and psychological studies. However, users tend to blink frequently; thus, estimating gaze direction is difficult. The proposed method uses two CNN models. The first CNN model is used to classify the eye state and the second is used to estimate the pupil center position. The classification model filters images with closed eyes and terminates the gaze estimation process when the input image shows a closed eye. In addition, this paper presents a process to create an eye image dataset using a wearable camera. This dataset, which was used to evaluate the proposed method, has approximately 20,000 images and a wide variation of eye states. We evaluated the proposed method from various perspectives. The result shows that the proposed method obtained good accuracy and has the potential for application in wearable device-based gaze estimation.
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Lei, Ying, Chao Liu, and Yong Qiang Jiang. "System Identification of High-Rise Building under Unknown Seismic Excitation with Limited Output Measurements." Advanced Materials Research 163-167 (December 2010): 3947–51. http://dx.doi.org/10.4028/www.scientific.net/amr.163-167.3947.
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In this paper, a system identification approach is proposed for high-rise building under unknown seismic excitation with limited output measurements. A high-rise building is decomposed into small size substructures based on its finite element formulation. Interaction effect between adjacent substructures is considered as ‘equivalent known inputs’ to each substructure. Unknown seismic excitation is considered as ‘equivalent unknown inputs’ at the first floor. By sequentially utilizing the extended Kalman estimator for the extended state vectors and the least squares estimation for the ‘equivalent unknown inputs’, structural parameters above the first story of a shear building can be identified. Then, with the analysis of the measured absolute acceleration responses in frequency domain and the peak-picking method for the estimation of the first natural frequency of the building, structural parameters of the first story can be identified from the frequency equation. Finally, the unknown seismic excitation can be identified via the numerical solution of a first-order differential equation. It is shown by a numerical example that the proposed method can identify high-rise building parameters and the seismic excitation with good accuracy.
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Lakshminarayana, Subhash, Saurav Sthapit, and Carsten Maple. "Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation." Sustainability 14, no. 4 (February 11, 2022): 2051. http://dx.doi.org/10.3390/su14042051.
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Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parameter estimation problem. First, we propose a novel algorithm to solve the parameter estimation based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, which uses sparse regression to infer the parameters that best describe the observed data. We then compare its performance against another benchmark algorithm, namely, the physics-informed neural networks (PINN) approach applied to parameter estimation. We perform extensive simulations on IEEE bus systems to examine the performance of the aforementioned algorithms. Our results show that the SINDy algorithm outperforms the PINN algorithm in estimating the power grid parameters over a wide range of system parameters (including high and low inertia systems) and power grid architectures. Particularly, in case of the slow dynamics system, the proposed SINDy algorithms outperforms the PINN algorithm, which struggles to accurately determine the parameters. Moreover, it is extremely efficient computationally and so takes significantly less time than the PINN algorithm, thus making it suitable for real-time parameter estimation. Furthermore, we present an extension of the SINDy algorithm to a scenario where the operator does not have the exact knowledge of the underlying system model. We also present a decentralised implementation of the SINDy algorithm which only requires limited information exchange between the neighbouring nodes of a power grid.
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Allaw, Kamel, Jocelyne Adjizian Gerard, Makram Zouheir Chehayeb, and Nada Badaro Saliba. "Population estimation using geographic information system and remote sensing for unorganized areas." Geoplanning: Journal of Geomatics and Planning 7, no. 2 (January 1, 2021): 75–86. http://dx.doi.org/10.14710/geoplanning.7.2.75-86.
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Population estimation using remotely sensed data has been largely discussed in the literature relative to human geography. However, the previously established models can be applied on organized areas (mainly urban areas) but they are not suitable for unorganized areas which already suffer from a lack of population data. So, the aim of this study is the establish a statistical model for population estimation based on remote sensing data and suitable for unorganized areas. To do so, the morphological characteristics have been studied and a bivariate analysis was carried out to determine factors having a strong relationship with population data as a first step. Second, factors with strongest correlations have been chosen to establish the required model. As a result, an equation has been generated which relates the population data to building volume, density of roads, number of nodes, actual urban areas, and urban trend.
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Le-Tran, Manh, and Sunghwan Kim. "Deep Learning-Assisted Index Estimator for Generalized LED Index Modulation OFDM in Visible Light Communication." Photonics 8, no. 5 (May 19, 2021): 168. http://dx.doi.org/10.3390/photonics8050168.
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In this letter, we present the first attempt of active light-emitting diode (LED) indexes estimating for the generalized LED index modulation optical orthogonal frequency-division multiplexing (GLIM-OFDM) in visible light communication (VLC) system by using deep learning (DL). Instead of directly estimating the transmitted binary bit sequence with DL, the active LEDs at the transmitter are estimated to maintain acceptable complexity and improve the performance gain compared with those of previously proposed receivers. Particularly, a novel DL-based estimator termed index estimator-based deep neural network (IE-DNN) is proposed, which can employ three different DNN structures with fully connected layers (FCL) or convolution layers (CL) to recover the indexes of active LEDs in a GLIM-OFDM system. By using the received signal dataset generated in simulations, the IE-DNN is first trained offline to minimize the index error rate (IER); subsequently, the trained model is deployed for the active LED index estimation and signal demodulation of the GLIM-OFDM system. The simulation results show that the IE-DNN significantly improves the IER and bit error rate (BER) compared with those of conventional detectors with acceptable run time.
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Chen, Wei, Ruisheng Sun, and Weisheng Yan. "Optimal Position and Velocity Estimation for Multi-USV Positioning Systems with Range Measurements." Complexity 2018 (July 10, 2018): 1–12. http://dx.doi.org/10.1155/2018/5452723.
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This paper investigates the problem on simultaneously estimating the velocity and position of the target for range-based multi-USV positioning systems. According to the range measurement and kinematics model of the target, we formulate this problem in a mixed linear/nonlinear discrete-time system. In this system, the input and state represent the velocity and position of the target, respectively. We divide the system into two components and propose a three-step minimum variance unbiased simultaneous input and state estimation (SISE) algorithm. First, we estimate the velocity in the local level plane and predict the corresponding position. Then, we estimate the velocity in the heave direction. Finally, we estimate the 3-dimensional (3D) velocity and position. We establish the unbiased conditions of the input and state estimation for the MLBL system. Simulation results illustrate the effectiveness of the problem formulation and demonstrate the performance of the proposed algorithm.
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Aljabrine, Abdulwahab A., Abdallah A. Smadi, Yacine Chakhchoukh, Brian K. Johnson, and Hangtian Lei. "Resiliency Improvement of an AC/DC Power Grid with Embedded LCC-HVDC Using Robust Power System State Estimation." Energies 14, no. 23 (November 23, 2021): 7847. http://dx.doi.org/10.3390/en14237847.
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The growth of renewable energy generation in the power grid brings attention to high-voltage direct current (HVDC) transmission as a valuable solution for stabilizing the system. Robust hybrid power system state estimation could enhance the resilience of the control of these systems. This paper proposes a two-stage, highly robust least-trimmed squares (LTS)-based estimator. The first step combines the supervisory control and data acquisition (SCADA) measurements using the robust LTS-based estimator. The second step merges the obtained state results with the available phasor measurement units (PMUs) measurements using a robust Huber M-estimator. The proposed robust LTS-based estimator shows good performance in the presence of Gaussian measurement noise. The proposed estimator is shown to resist and correct the effect of false data injection (FDI) attacks and random errors on the measurement vector and the Jacobian matrix. The state estimation (SE) is executed on a modified version of the CIGRE bipole LCC-HVDC benchmark model integrated into the IEEE 12-bus AC dynamic test system. The obtained simulation results confirm the effectiveness and robustness of the proposed two-stage LTS-based SE.
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Chen, Jenn Yih. "Passivity-Based Parameter Estimation and Position Control of Induction Motors via Composite Adaptation." Applied Mechanics and Materials 284-287 (January 2013): 1894–98. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1894.
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This paper proposes the parameters estimation and position control of an induction motor drive by using the composite adaptation scheme. First, in the rotor reference frame, the input-output linearization theory was employed to decouple the mechanical rotor position and the rotor flux amplitude at the transient state. An open-loop current model rotor flux observer was utilized for estimating the flux, and then the adaptive laws for estimating the rotor resistance, moment of inertia, viscous friction coefficient, and load torque. The passive properties of the flux observer, rotor resistance estimator, and composite adaptive position controller were analyzed based on the passivity theorem. According to the properties, the overall position control system was proved to be globally stable without using Lyapunov-type arguments. Experimental results are finally provided to show that the proposed method is robust to variations of the motor mechanical parameters, rotor resistance, and load torque disturbances. Moreover, good position tracking response and characteristics on parameter estimation can be achieved.
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Wang, Xinwei, Jie Liu, Haijun Peng, and Xudong Zhao. "A fast-moving horizon estimation method based on the symplectic pseudospectral algorithm." Transactions of the Institute of Measurement and Control 43, no. 11 (February 22, 2021): 2500–2511. http://dx.doi.org/10.1177/0142331221992691.
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In this paper, a fast-moving horizon state estimation algorithm for nonlinear continuous systems with measurement noises and model disturbances is developed. The optimization problem required to be solved at each sampling instant is formulated into a backward nonlinear optimal control problem over the finite past. Once prior knowledge of the observed system is available, constraints can be further imposed. The highly efficient and accurate symplectic pseudospectral algorithm is taken as the core solver, which leads to the symplectic pseudospectral moving horizon estimation (SP-MHE) method. The developed SP-MHE is first evaluated by numerical simulations for a hovercraft. Then the developed method is extended to parameter estimation and applied to a chaotic system with an unknown parameter. Simulation results show that the SP-MHE can generate accurate estimations even under large sampling periods or large noise where regular filters fail. In addition, the SP-MHE exhibits excellent online efficiency, suggesting it can be used for scenarios where the sampling period is relatively small.
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Melo, A., V. Motta, N. Godoy, J. Mejia-Restrepo, R. J. Assef, E. Mediavilla, E. Falco, F. Ávila-Vera, and R. Jerez. "First black hole mass estimation for the quadruple lensed system WGD2038-4008." Astronomy & Astrophysics 656 (December 2021): A108. http://dx.doi.org/10.1051/0004-6361/202141869.
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Context. The quadruple lensed system WGD2038-4008 (zs = 0.777 ± 0.001) has recently been discovered with the help of new techniques and observations. Black hole masses have been estimated for lensed quasars, but they have mostly been calculated for one broad emission line of one image. However, the images could be affected by microlensing, which changes the results. Aims. We present black hole mass (MBH) estimations for images A and B of WGD2038-4008 using the three most prominent broad emission lines (Hα, Hβ, and Mg II) obtained in one single-epoch spectra. This is the first time the mass has been estimated in a lensed quasar in two images, allowing us to disentangle the effects of microlensing. The high S/N of our spectra allows us to get reliable results that can be compared with the existing data in the literature. Methods. We used the X-shooter instrument mounted on the Very Large Telescope at Paranal Observatory to observe this system, taking advantage of its wide spectral range (UVB, VIS, and NIR). The sky emission correction was performed using principal component analysis as the nodding was small compared to the image separation. We compared the lines profiles to identify the microlensing in the broad-line region and corrected each spectra by the image magification. Using the flux ratio of the continuum to the core of the emission lines, we analyzed whether microlensing was present in the continuum source. Results. We obtained MBH using the single-epoch method with the Hα and Hβ emission lines from the monochromatic luminosity and the velocity width. The luminosity at 3000 Å was obtained using the spectral energy distribution of image A, while the luminosity at 5100 Å was estimated directly from the spectra. The average MBH between the images obtained was log10(MBH/M⊙) = 8.27 ± 1.05, 8.25 ± 0.32, and 8.59 ± 0.35 for Mg II, Hβ, and Hα, respectively. We find Eddington ratios similar to those measured in the literature for unlensed low-luminosity quasars. Microlensing of −0.16 ± 0.06 mag in the continuum was found, but the induced error in the MBH is minor compared to that associated with the macromodel magnification. We also obtained the accretion disk size using the MBH for the three emission lines, obtaining an average value of log10(rs/cm)=15.3 ± 0.63, which is in agreement with theoretical estimates.
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AOKI, Shigeru. "Estimation Method for First Excursion Probability of Mechanical System with Plastic Deformation." Proceedings of the JSME annual meeting 2000.1 (2000): 873–74. http://dx.doi.org/10.1299/jsmemecjo.2000.1.0_873.
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Norouzi, Mahdi, and Efstratios Nikolaidis. "An efficient estimation of probability of first-passage in a linear system." Structural and Multidisciplinary Optimization 55, no. 5 (October 31, 2016): 1733–46. http://dx.doi.org/10.1007/s00158-016-1606-z.
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Aoki, Shigeru. "Simplified estimation method for first excursion probability of secondary system with gap." Nuclear Engineering and Design 212, no. 1-3 (March 2002): 193–99. http://dx.doi.org/10.1016/s0029-5493(01)00478-2.
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Yan, Hui, and Zhong Pei Zhang. "A Low-Complexity Algorithm of Joint Multiple Frequency Offsets and Channels Estimation in Cooperative Relay Systems." Advanced Materials Research 748 (August 2013): 1046–50. http://dx.doi.org/10.4028/www.scientific.net/amr.748.1046.
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A low complexity joint estimator of multiple CFOs and Channels is presented in cooperative relay systems. The new algorithm first utilizes correlation-based frequency estimator to get CFOs initial estimation, and then serial interference cancellation based on correlation properties of training sequence in different relays is done to obtain the initial channel estimation. Moreover, a parallel iteration scheme with interference cancellation is proposed to reduce time complexity of the traditional serial iteration. In the overall process, matrix inversion is avoided. Thus, the complexity of the proposed algorithm in both time and computation is much less than the existing algorithms. In last, simulation results verify the iterative algorithm achieves a good performance in Decode and Forward (DF) relay system.
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Wada, Massaki, Kang Sup Yoon, and Hideki Hashimoto. "Development of Advanced Parking Assistance System in the iCAN Framework." Journal of Robotics and Mechatronics 13, no. 4 (August 20, 2001): 402–8. http://dx.doi.org/10.20965/jrm.2001.p0402.
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This paper describes the advanced parking assistance system developed in the iCAN (intelligent CAr Navigation Systems) project framework. The main issues in implementing the assistance system are the vehicle state estimation and the human guidance. First, a guidance approach based on human interface and path generation are proposed. The parking assistance system sensor suite, estimation scheme, path generation scheme and human interface scheme are described. The developed system prototype and experimental results are also presented.
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Hamidi, Mohamed, Hassan Satori, Ouissam Zealouk, and Naouar Laaidi. "Estimation of ASR Parameterization for Interactive System." International Journal of Natural Computing Research 10, no. 1 (January 2021): 28–40. http://dx.doi.org/10.4018/ijncr.2021010103.
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In this study, the authors explore the integration of speaker-independent automatic Amazigh speech recognition technology into interactive applications to extract data remotely from a distance database. Based on the combined interactive voice response (IVR) and automatic speech recognition (ASR) technologies, the authors built an interactive speech system to allow users to interact with the interactive system through voice commands. The hidden Markov models (HMMs), Gaussian mixture models (GMMs), and Mel frequency spectral coefficients (MFCCs) are used to develop a speech system based on the ten first Amazigh digits and six Amazigh words. The best-obtained performance is 89.64% by using 3 HMMs and 16 GMMs.
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Şengüneş, Burcu, and Nursel Öztürk. "An Artificial Neural Network Model for Project Effort Estimation." Systems 11, no. 2 (February 9, 2023): 91. http://dx.doi.org/10.3390/systems11020091.
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Estimating the project effort remains a challenge for project managers and effort estimators. In the early phases of a project, having a high level of uncertainty and lack of experience cause poor estimation of the required work. Especially for projects that produce a highly customized unique product for each customer, it is challenging to make estimations. Project effort estimation has been studied mainly for software projects in the literature. Currently, there has been no study on estimating effort in customized machine development projects to the best of our knowledge. This study aims to fill this gap in the literature regarding project effort estimation for customized machine development projects. Additionally, this study focused on a single phase of a project, the automation phase, in which the machine is automated according to customer-specific requirements. Therefore, the effort estimation of this phase is crucial. In some cases, this is the first time that the company has experienced the requirements specific to the customer. For this purpose, this study proposed a model to estimate how much work is required to automate a machine. Insufficient effort estimation is one of the main reasons behind project failures, and nowadays, researchers prefer more objective approaches such as machine learning over expert-based ones. This study also proposed an artificial neural network (ANN) model for this purpose. Data from past projects were used to train the proposed ANN model. The proposed model was tested on 11 real-life projects and showed promising results with acceptable prediction accuracy. Additionally, a desktop application was developed to make this system easier to use for project managers.
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Jewel, Md Khalid Hossain, Rabiu Sale Zakariyya, and Fujiang Lin. "On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems." Electronics 10, no. 11 (May 24, 2021): 1246. http://dx.doi.org/10.3390/electronics10111246.
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Narrowband Internet of Things (NB-IoT) systems were specified by 3GPP in release 13 as a low power wide area network (LPWAN) technology to operate with a very narrow bandwidth of 180 kHz only. Due to fragile radio signal operating conditions (where a signal is weaker than noise), NB-IoT channel status becomes highly complex. Therefore, an effective and low complexity channel estimation will perform a significant role in the receiver operation. The linear minimum mean square error (LMMSE) scheme is very effective in estimating the channel but introduces massive complexity because of having complex matrix inversion. In this paper, we first derive the analytical model of the signal for long-term evolution (LTE)-based NB-IoT downlink systems and propose a reduced complexity LMMSE channel estimation for the downlink NB-IoT systems by applying singular value decomposition (SVD) technique along with partitioning the whole channel matrix into small submatrices. Furthermore, we apply the overlap banded technique to optimize the performance of the proposed channel estimator. As a result of exploiting several submatrices instead of a larger channel matrix, the operational complexity is significantly optimized. Lastly, we propose a polyphase filter structure for implementing the interpolation procedure instead of the conventional interpolation method to further optimize the performance and complexity of the proposed channel estimator further. The performance of the proposed technique has been justified by the mean square error (MSE), bit error rate (BER), and instantaneous throughput for the related signal-to-noise ratio (SNR). The system complexity is verified by the number of complex multiplications used. Simulation evaluations indicate that with the sacrifice of negligible performance, the proposed modified LMMSE technique along with the proposed interpolation possesses a good balance between the performance and the system complexity that could help the proposed techniques to be applied successfully in the low complexity NB-IoT systems.
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Li, Shusheng, Yongling Fu, and Ping Liu. "Position Estimation and Compensation Based on a Two-Step Extended Sliding-Mode Observer for a MSFESS." Sensors 18, no. 8 (July 30, 2018): 2467. http://dx.doi.org/10.3390/s18082467.
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This paper aims to deal with the problem of rotor position estimation and compensation for a magnetically suspended flywheel energy storage system under the consideration of measurement noise and unknown disturbances. First, the flywheel system working principle and description are analyzed and, based on this, the mathematical model as well as the coordinates transformation are introduced. For the purpose of the state estimation, a two-step extended sliding-mode observer is considered to obtain the estimates of the rotor angular position. In this control strategy, a traditional sliding-mode observer is adopted as a first-step original state estimator. After that, the relationship between the angular position and the estimation error is established and a second-step observer is designed to obtain the estimation of the error. The estimated error is then used to compensate the real values of the rotor angular position generated by the first-step observer. To reject the influences of the measurement noise and unknown disturbances, the H∞ optimization strategy is considered to determine the second-step observer structure. Finally, experimental results are presented to demonstrate the effectiveness of the proposed method. It is demonstrated that the proposed two-step observer method has a better estimation accuracy and control performance.
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Li, Shuo, Song Li, Haifeng Zhao, and Yuan An. "Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system." International Journal of Distributed Sensor Networks 15, no. 12 (December 2019): 155014771989452. http://dx.doi.org/10.1177/1550147719894526.
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In this article, a method for estimating the state of charge of lithium battery based on back-propagation neural network is proposed and implemented for uninterruptible power system. First, back-propagation neural network model is established with voltage, temperature, and charge–discharge current as input parameters, and state of charge of lithium battery as output parameter. Then, the back-propagation neural network is trained by Levenberg–Marquardt algorithm and gradient descent method; and the state of charge of batteries in uninterruptible power system is estimated by the trained back-propagation neural network. Finally, we build a state-of-charge estimation test platform and connect it to host computer by Ethernet. The performance of state-of-charge estimation based on back-propagation neural network is tested by connecting to uninterruptible power system and compared with the ampere-hour counting method and the actual test data. The results show that the state-of-charge estimation based on back-propagation neural network can achieve high accuracy in estimating state of charge of uninterruptible power system and can reduce the error accumulation caused in long-term operation.
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Liu, Peng, Shuyu Zhou, Peng Zhang, and Mengwei Li. "Distributed State Fusion Estimation of Multi-Source Localization Nonlinear Systems." Sensors 23, no. 2 (January 7, 2023): 698. http://dx.doi.org/10.3390/s23020698.
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For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise with unknown and bounded characteristics is modeled by using bounded ellipsoidal regions. At the same time, local estimators are designed at the sensor link nodes to filter out the noise interference in the localization system. The local estimator is designed using the dual set-membership filtering algorithm. It uses the dual principle to find the minimizing ellipsoid that can contain the nonlinear function by solving the optimization problem with semi-infinite constraints, and a first-order conditional gradient algorithm is used to solve the optimization problem with a low computational complexity. Meanwhile, the communication confusion among multiple sensors causes the problem of unknown correlation. The obtained estimates of local filters are fused at the fusion center by designing a distributed sequential ellipsoid intersection fusion estimation algorithm to obtain more accurate fusion localization results with lower computational cost. Finally, the stability and reliability of the proposed distributed fusion algorithm are verified by designing a simulation example of a multi-source nonlinear system.
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Yoo, Taesuk, Moonhwan Kim, Seonil Yoon, and Daejoong Kim. "Performance Enhancement for Conventional Tightly Coupled INS/DVL Navigation System Using Regeneration of Partial DVL Measurements." Journal of Sensors 2020 (January 11, 2020): 1–15. http://dx.doi.org/10.1155/2020/5324349.
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Inertial navigation systems/Doppler velocity log (INS/DVL) integrated navigation systems are widely used in underwater environments where GPS is unavailable. An INS/DVL integrated navigation system is generally loosely coupled; however, this does not work if any of the DVL transducers do not work. If a system is tightly coupled, velocity error can be estimated with fair accuracy even if some of the transducers fail. However, despite the robustness of a tightly coupled system compared to a loosely coupled one, velocity error estimation accuracy of the former decreases as the number of faulty transducers increases. Therefore, this paper proposes an INS/DVL/revolutions per minute (RPM) integrated navigation filter designed to improve the performance of conventional tightly coupled integrated systems by estimating data from faulty transducers using RPM data. Two salient features of the proposed filter are (1) estimating RPM data accounting for error from the effect of tidal currents and (2) continuous estimation of error in RPM data by selectively converting only the measurements of faulty transducers. The performance of the proposed filter was first verified using Monte Carlo numerical simulations with the analysis range set to 1 standard deviation (1σ, 68%) and then with real sea test measurement data.
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Tejeda-Ochoa, D. A., G. E. Peralta-Peñuñuri, V. A. González-Huitrón, H. Rodríguez-Rangel, R. Baray-Arana, and A. E. Rodríguez-Mata. "New System for Angular Velocity Estimation for a First-Order Manipulator Using Artificial Intelligence and Sliding Mode Differentiator." Memorias del Congreso Nacional de Control Automático 5, no. 1 (October 17, 2022): 362–67. http://dx.doi.org/10.58571/cnca.amca.2022.055.
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On a NVIDIA Jetson Nano device, this study illustrates a unique and original usage of automated control and artificial intelligence algorithms for angular velocity estimates of a first-order manipulator device. A platform can be used as a position estimation platform using computer vision and three state estimation algorithms: sliding mode differentiator, high gain observer, and static filter. A hybrid system for process performance improvement is proposed using computer vision and three state estimation algorithms: sliding mode differentiator, high gain observer, and static filter. The results of numerical simulations are provided, as well as real-time judgments. The ITAE and IAE indices reveal that the sliding mode differentiator is much superior in angular velocity estimation for position signals utilizing artificial intelligence sensors.
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Wang, Dan, Zhiqiang Mei, Jiamin Liang, and Jinzhi Liu. "An Improved Channel Estimation Algorithm Based on WD-DDA in OFDM System." Mobile Information Systems 2021 (July 31, 2021): 1–9. http://dx.doi.org/10.1155/2021/6540923.
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Channel estimation is the key technology to ensure reliable transmission in orthogonal frequency division multiplexing (OFDM) system. In order to improve the accuracy of the channel estimation algorithm in a low signal-to-noise ratio (SNR) channel environment, in this paper, we proposed an improved channel estimation algorithm based on the transform domain. The improved algorithm with wavelet denoising (WD) and distance decision analysis (DDA) to perform secondary denoising on the channel estimation algorithm based on the transform domain is proposed. First, after the least-squares (LS) algorithm, WD is used to denoise for the first time, then the DDA is used to further suppress the residual noise in the transform domain, and the important channel taps are screened out. Simulation results show that the proposed algorithm can improve the detection performance of existing channel estimation algorithms based on transform domain in low SNR.
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Oğuz, Emin Oğuzhan, Erdinç Şahin Çonkur, and Murat Sari. "SHTEREOM I SIMPLE WINDOWS® BASED SOFTWARE FOR STEREOLOGY. VOLUME AND NUMBER ESTIMATIONS." Image Analysis & Stereology 26, no. 1 (May 3, 2011): 45. http://dx.doi.org/10.5566/ias.v26.p45-50.
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Stereology has been earlier defined by Wiebel (1970) to be: "a body of mathematical methods relating to three dimensional parameters defining the structure from two dimensional measurements obtainable on sections of the structure." SHTEREOM I is a simple windows-based software for stereological estimation. In this first part, we describe the implementation of the number and volume estimation tools for unbiased design-based stereology. This software is produced in Visual Basic and can be used on personal computers operated by Microsoft Windows® operating systems that are connected to a conventional camera attached to a microscope and a microcator or a simple dial gauge. Microsoft NET Framework version 1.1 also needs to be downloaded for full use. The features of the SHTEREOM I software are illustrated through examples of stereological estimations in terms of volume and particle numbers for different magnifications (4X–100X). Point-counting grids are available for area estimations and for use with the most efficient volume estimation tool, the Cavalieri technique and are applied to Lizard testicle volume. An unbiased counting frame system is available for number estimations of the objects under investigation, and an on-screen manual stepping module for number estimations through the optical fractionator method is also available for the measurement of increments along the X and Y axes of the microscope stage for the estimation of rat brain hippocampal pyramidal neurons.
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Liu, Mengzhuo, Jifeng Zhu, Xiaohe Pan, Guolin Wang, Jun Liu, Zheng Peng, and Jun-Hong Cui. "A Distributed Intelligent Buoy System for Tracking Underwater Vehicles." Journal of Marine Science and Engineering 11, no. 9 (August 24, 2023): 1661. http://dx.doi.org/10.3390/jmse11091661.
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Underwater vehicles play a crucial role in various underwater applications, such as data collection in underwater sensor networks, target detection and tracking, and underwater pipeline monitoring. Real-time acquisition of their states, particularly their location and velocity, is vital for their operation and navigation. Consequently, the development of a remote tracking system to monitor these states is essential. In this paper, we propose a system that can track the underwater vehicle’s location and velocity. We take a systematic approach that encompasses the system architecture, system composition, signal processing, and mobility state estimation. We present the system architecture and define its components, along with their relationships and interfaces. The beacon signal employed in the system features dual-hyperbolic-frequency-modulated (HFM) waveform and an OFDM symbol with cyclic prefix (CP). Based on this beacon signal, we demonstrate how signal processing techniques are utilized to precisely determine the time of arrival and reduce false alarm rates in underwater acoustic channels affected by impulsive noise. Additionally, we explain how the CP-OFDM symbol is used to measure the Doppler scaling factor and transmit essential information for localization and velocity estimation purposes. Utilizing the measurements obtained through signal processing, least squares estimators are used for estimating both the location and velocity. To validate the effectiveness of our approach, we implement the system and conduct field trials. Two separate experiments were conducted in which the diagonal lengths of the square topology were designed to be 1000 m and 800 m. The minimum/maximum root mean square error of localization in the first and second experiment is 2.36/2.91 m and 1.47/2.49 m, respectively. And the minimum/maximum root mean square error of velocity estimation in the first and second experiment is 0.16/0.47 m/s and 0.21/0.76 m/s, respectively. Results confirm the effectiveness of the proposed method in estimating the location and velocity of the underwater vehicle. Overall, this paper provides a practical and effective design of a system to track the location and velocity of underwater vehicles. By leveraging the proposed system, signal processing, and mobility state estimation methods, our work offers a systematic solution. And, the successful field experiment serves as evidence of the feasibility and effectiveness of the proposed system, making it a valuable contribution to the field of tracking underwater vehicles.
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Lin, Bor-Shing, I.-Jung Lee, Chin-Shyurng Fahn, Yi-Fang Lee, Wei-Jen Chou, and Meng-Luen Wu. "Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm." Sensors 21, no. 12 (June 19, 2021): 4216. http://dx.doi.org/10.3390/s21124216.
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Insufficient physical activity is common in modern society. By estimating the energy expenditure (EE) of different physical activities, people can develop suitable exercise plans to improve their lifestyle quality. However, several limitations still exist in the related works. Therefore, the aim of this study is to propose an accurate EE estimation model based on depth camera data with physical activity classification to solve the limitations in the previous research. To decide the best location and amount of cameras of the EE estimation, three depth cameras were set at three locations, namely the side, rear side, and rear views, to obtain the kinematic data and EE estimation. Support vector machine was used for physical activity classification. Three EE estimation models, namely linear regression, multilayer perceptron (MLP), and convolutional neural network (CNN) models, were compared and determined the model with optimal performance in different experimental settings. The results have shown that if only one depth camera is available, optimal EE estimation can be obtained using the side view and MLP model. The mean absolute error (MAE), mean square error (MSE), and root MSE (RMSE) of the classification results under the aforementioned settings were 0.55, 0.66, and 0.81, respectively. If higher accuracy is required, two depth cameras can be set at the side and rear views, the CNN model can be used for light-to-moderate activities, and the MLP model can be used for vigorous activities. The RMSEs for estimating the EEs of standing, walking, and running were 0.19, 0.57, and 0.96, respectively. By applying the different models on different amounts of cameras, the optimal performance can be obtained, and this is also the first study to discuss the issue.