Real-time Estimation Method Research Articles

Development of a digital image analysis method for real-time estimation of chlorophyll content in micropropagated potato plants

The present work describes a digital image analysis method based on leaf color analysis to estimate chlorophyll content of leaves of micropropagated potato plantlets. For estimation of chlorophyll content, a simple leaf digital analysis procedure using a simple digital still camera was applied in parallel to a SPAD chlorophyll content meter. RGB features were extracted from the image and correlated with the SPAD values. None of the mean brightness parameters (RGB) were correlated with the actual chlorophyll content following simple correlation studies. However, a correlation between the chromaticity co-ordinates ‘r’, ‘b’ and chlorophyll content was observed, while co-ordinate ‘g’ was not significantly correlated with chlorophyll content. Linear regression and artificial neural networks (ANN) were applied for correlating the mean brightness (RGB) and mean brightness ratio (rgb) features to chlorophyll content of plantlet leaves determined through a SPAD meter. The chlorophyll content as determined by the SPAD meter was significantly correlated (RMSE = 3.97 and 3.59, respectively, for linear and ANN models) to the rgb values of leaf image analysis. Both the models indicate successful prediction of chlorophyll content of leaves of micropropagated plants with high correlation. The developed RGB-based digital image analysis has the advantage over conventional subjective methods for being objective, fast, non-invasive, and inexpensive. The system could be utilized for real-time estimation of chlorophyll content and subsequent analysis of photosynthetic and hyperhydric status of the micropropagated plants for better ex vitro survival.

Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system

This paper proposes a precise real-time wind speed estimation method and sensorless control for variable-speed variable-pitch wind turbine power generation system (WTPGS). The wind speed estimation is realized by a nonlinear input–output mapping extreme learning machine (ELM). A specific design characteristic of the wind turbine is used for improving the mapping accuracy with considering the variable pitch angle. Moreover, since the design is independent of the environmental air density, the proposed ELM wind speed estimation method is robust to the air density variations. The estimated wind speed is then used to determine the optimal rotational speed command for maximum power point tracking. A fast and effective ELM mapping based pitch controller is proposed too when the WTPGS operates in its high wind speed region. The ELM pitch controller can act much faster and more precise than conventional pitch controllers. Furthermore, the complicated design precess for the parameters of conventional pitch controllers will be avoided in the proposed method. The effectiveness of the proposed methods are verified both by simulations and experiments on a WTPGS installed with Permanent Magnet Synchronous Generator (PMSG).

Real-Time Identification of Hunt–Crossley Dynamic Models of Contact Environments

Real-time estimates of environment dynamics play an important role in the design of controllers for stable interaction between robotic manipulators and unknown environments. The Hunt-Crossley (HC) dynamic contact model has been shown to be more consistent with the physics of contact, compared with the classical linear models, such as Kelvin-Voigt (KV). This paper experimentally evaluates the author's previously proposed single-stage identification method for real-time parameter estimation of HC nonlinear dynamic models. Experiments are performed on various dynamically distinct objects, including an elastic rubber ball, a piece of sponge, a polyvinyl chloride (PVC) phantom, and a PVC phantom with a hard inclusion. A set of mild conditions for guaranteed unbiased estimation of the proposed method is discussed and experimentally evaluated. Furthermore, this paper rigorously evaluates the performance of the proposed single-stage method and compares it with those of a double-stage method for the HC model and a recursive least squares method for the KV model and its variations in terms of convergence rate, the sensitivity to parameter initialization, and the sensitivity to the changes in environment dynamic properties.

Designing similarity measurement with distance measure and application on laterally directional mode flight test

Similarity measure construction has been proposed as fault detection of flight test method in order to obtain the primary control surface stuck and the combination stuck of primary control. Similarity measures were obtained through analyzing the certainty and uncertainty of fuzzy membership functions, which were designed based on well-known Hamming distance. It was applied to the fault detection of primary control surface stuck of uninhabited aerial vehicle (UAV). At post-failure control surface, if the UAV is controllable and trimmable using other control surfaces, the UAV is able to fly or return to the safety region through reconfiguration of flight control system. To detect the fault, similarity measure computations were carried out. This result could be applicable with the real-time parameter estimation method. By monitoring the value of coefficients due to the control surface deviation, it becomes aware that the control surface fault occurs or not. The control surface stuck position and value were separated by comparing the trim value with the reference value. This is the advantage of increasing in reliability without adding sensors or with additional low cost.

A Method for Real-Time Estimating Pose of Large Aircraft Component in Process of Pose Adjustment Based on the Unscented Kalman Filter

To estimate the pose of large aircraft component in pose adjustment quickly and accurately, a real-time estimation method based on Unscented Kalman filter (UKF) is proposed. Firstly, in the process of the aircraft component adjustment, a rough value of aircraft component’s pose is acquired by using forward kinematic model and the displacement of positioners on real time. Then, position of a measuring point fixed on aircraft component is obtained by a laser tracker. At last, UKF is employed to integrate the previous rough value and the measuring point position for evaluating the accurate pose of aircraft component. Numerical simulation results show that the presented method is achieved easily, calculated fast and high accurate.

Engine Torque Estimation Based on BP Neural Network

Engine torque estimation function is the base of engine torque control. This paper establishes the model for engine torque estimation respectively under steady condition and unsteady condition based on BP Neural network, and develops a new engine torque real-time estimation method. The experiment results under steady condition and unsteady condition show that the engine torque estimation model can estimate the engine output torque and the precision is remarkable.

Biplane US-Guided Real-Time Volumetric Target Pose Estimation Method for Theragnostic HIFU System

This paper presents a real-time pose estimation method as a part of robotic HIFU treatment system for moving volumetric targets. For the acquired biplane US images, current pose of the preoperative model is calculated by iterative segmentation and registration. Seed contours for the segmentation in each iteration is provided by previously registered preoperative 3-D model. The segmented boundary points then update the pose of 3-D model. The boundary outlier-removal makes the algorithm robust against partially noisy boundaries as well as the spatial boundary points accelerates the algorithm to be calculated in real-time. By the phantom experiments, registration accuracy for a biplane US image data was evaluated, and the processing time was also investigated.

Real-time camera pose estimation for wide-area augmented reality applications.

Achieving accurate registration between real and synthetic worlds is one of augmented reality's biggest challenges. A real-time camera-pose estimation method, based on multiple maps and local bundle adjustment, enables the registration to work without prior knowledge of natural scenes. This method can significantly enhance AR systems' usability.

Improved numerical inverse kinematics for human pose estimation

We propose a real-time pose estimation method that addresses the weaknesses of the numerical inverse kinematics method. Using conventional inverse kinematics based on the numerical method requires many iterations; moreover, a singularity in the Jacobian matrix as well as a local minimum problem can occur. To solve these problems, we propose an inverse kinematics method combined with an unscented Kalman filter (UKF) to recover intermediate joint information. Because the numerical inverse kinematics method optimizes a state, the solution can often converge to the local minimum and require many iterations. We use several sigma points for analysis to find the optimum state by using an unscented transform. The improved method using a UKF converges faster than the numerical inverse kinematics method for the global minimum of the existing inverse kinematics. We use 2-D image processes to extract body areas from the input images, and a 3-D reconstruction algorithm is used to estimate the 3-D positions of the extracted human body area. Using the improved method, we generate intermediate joints for each body part and the results show that the proposed method reduces the computational complexity and increases the accuracy of estimation compared to conventional numerical inverse kinematics.

Adaptive Vehicle Speed Control With Input Injections for Longitudinal Motion Independent Road Frictional Condition Estimation

This paper presents a novel real-time tire-road friction coefficient estimation method that is independent of vehicle longitudinal motion for ground vehicles with separable control of the front and rear wheels. The tire-road friction coefficient information is of critical importance for vehicle dynamic control systems and intelligent autonomous vehicle applications. In this paper, the vehicle longitudinal-motion-independent tire-road friction coefficient estimation method consists of three main components: 1) an observer to estimate the internal state of a dynamic LuGre tire model; 2) an adaptive control law with a parameter projection mechanism to track the desired vehicle longitudinal motion in the presence of tire-road friction coefficient uncertainties and actively injected braking excitation signals; and 3) a recursive least square estimator that is independent of the control law, to estimate the tire-road friction coefficient in real time. Simulation results based on a high-fidelity CarSim full-vehicle model show that the system can reliably estimate the tire-road friction coefficient independent of vehicle longitudinal motion.

Human elbow joint angle estimation using electromyogram signal processing

This study proposes a real-time joint angle estimation method of human elbow by processing a biomedical signal of surface synergistic EMG (electromyogram) measured between biceps brachii and triceps brachii simultaneously. Actually, the EMG is known as a non-stationary signal, but the authors assume that it is quasi-stationary because a physical or physiological system has limitations in the rate at which it can change its characteristics. Based on the assumption, a pre-processing method to obtain pre-angle values from the raw synergistic EMG signal is firstly suggested, and then a method to estimate the joint angle through normalisation when there are external loads is discussed. In addition, an optimisation method to minimise the error between the normalised angle and real joint angle is proposed. Finally, the authors show the effectiveness of the suggested algorithm through experimental results.

Real Time Estimation Method for both the Location and the Size of Tumor Using NIR Fluorescence Images

A reconstruction technique, such as Fluorescence Molecular Tomography or Fluorescence Diffusion Optical Tomography, permits us to detect a tumor genesis, cancer detection and so on using the Photo Dynamic Diagnosis(PDD) images. However, these techniques require huge computational cost and the introduction into intra operation has some difficulty about the surgical possibility in real time and narrow field. In this study, we propose a method to identify the fluorescence source simulated as a tumor in real time process using Near Infrared(NIR) fluorescence images, the optimization approach with Weighted Residual Method (WRM). In order to verify the location accuracy of the proposed method, we demonstrated the tumor identification using tissue phantom and a fluorescence tube filled with fluorescence dye (AlPcS). The absolute errors between the true positions and the estimated positions in the experiments were less than 1mm. The estimated time was about seventy mill-seconds. To confirm the validity for living bodies, we carried out the experimental identification using small animals. The fluorescent micro-tubes (diameter 0.4mm) filled with AlPcS were inserted in the brains of the euthanized mice. The errors between the true positions and the estimated positions in the animal experiments were about 1.6mm. Consequently, we confirm that this proposed method is effective as an assisted system in a navigation surgery.

Method for On-line Estimation of Electrical Motor Parameter Variation and Current Sensor Offset for SPM Motor

The servo system of a permanent magnet (PM) motor should always maintain fine torque and fine speed responses. Accurate motor parameter identification is necessary for the PM motor servo system because the current control system is designed by considering the electric parameters of the PM motor. However, the motor parameters vary with the age of the motor and temperature. Moreover, current sensors have offset values. When the current sensor has offset values, the PM motor servo system produces torque ripple. In order to overcome these problems, this paper proposes a new real-time estimation method for both current sensor offsets and electrical parameters (resistance Ra, inductance La, and magnetic flux φfa) of the surface permanent magnet (SPM) motor. The proposed method involves the use of a real-time algorithm and a current simulator, which is operated using a DSP software system. In order to accurately estimate the motor parametera, the proposed method is using estimate currents, DC terms of sensor currents, and nominal motor parameter value. The experimental results of this study confirm that the proposed method satisfactorily estimates the current sensor offset of the U phase and V phase, as well as the electrical motor parametersRa, La, and φfa accurately.

Modeling data revisions: Measurement error and dynamics of “true” values

Policy makers must base their decisions on preliminary and partially revised data of varying reliability. Realistic modeling of data revisions is required to guide decision makers in their assessment of current and future conditions. This paper provides a new framework with which to model data revisions. Recent empirical work suggests that measurement errors typically have much more complex dynamics than existing models of data revisions allow. This paper describes a state-space model that allows for richer dynamics in these measurement errors, including the noise, news and spillover effects documented in this literature. We also show how to relax the common assumption that “true” values are observed after a few revisions. The result is a unified and flexible framework that allows for more realistic data revision properties, and allows the use of standard methods for optimal real-time estimation of trends and cycles. We illustrate the application of this framework with real-time data on US real output growth.

Real-Time Target Position Estimation Using Stereoscopic Kilovoltage/Megavoltage Imaging and External Respiratory Monitoring for Dynamic Multileaf Collimator Tracking

To develop a real-time target position estimation method using stereoscopic kilovoltage (kV)/megavoltage (MV) imaging and external respiratory monitoring, and to investigate the performance of a dynamic multileaf collimator tracking system using this method. The real-time three-dimensional internal target position estimation was established by creating a time-varying correlation model that connected the external respiratory signals with the internal target motion measured intermittently using kV/MV imaging. The method was integrated into a dynamic multileaf collimator tracking system. Tracking experiments were performed for 10 thoracic/abdominal traces. A three-dimensional motion platform carrying a gold marker and a separate one-dimensional motion platform were used to reproduce the target and external respiratory motion, respectively. The target positions were detected by kV (1 Hz) and MV (5.2 Hz) imaging, and external respiratory motion was captured by an optical system (30 Hz). The beam-target alignment error was quantified as the positional difference between the target and circular beam center on the MV images acquired during tracking. The correlation model error was quantified by comparing a model estimate and measured target positions. The root-mean-square errors in the beam-target alignment that had ranged from 3.1 to 7.6 mm without tracking were reduced to <1.5 mm with tracking, except during the model building period (6 s). The root-mean-square error in the correlation model was submillimeters in all directions. A novel real-time target position estimation method was developed and integrated into a dynamic multileaf collimator tracking system and demonstrated an average submillimeter geometric accuracy after initializing the internal/external correlation model. The method used hardware tools available on linear accelerators and therefore shows promise for clinical implementation.

스마트 홈에서 위치 기반 인간 적응형 냉난방기를 위한 신체 활동량 추정

If an appliance perceives the location or health condition of a resident in the smart home, it can provide more intelligent service actively. That is, while the conventional appliance is operated by manual input of a resident, the location-based human adaptive appliance detects the resident's information such as location, activity pattern, or health condition by itself and provides the most suitable living condition for the resident autonomously. This paper presents the real-time location-based metabolic rate estimation method that measures the amount of physical activity (metabolic rate) for location-based human adaptive air-conditioner. And, the feasibility of the algorithm is evaluated experimentally on a test bed using the pyroelectric infrared sensor-based indoor location aware system (PILAS) that is a non-terminal-based location-aware system.

Optical Sensor and Neural Networks for Real-Time Monitoring and Estimation of Hazardous Gas Release Rate

A real-time monitoring and estimation method is proposed for managing facilities that store hazardous materials. It relies on optical sensor networks and neural networks trained for Gaussian dispersion model for the detection and analysis of hazardous (gas) releases. This method generates estimated values of the release rate, which provide the basis for the corresponding planning and response actions. While previous monitoring methodologies take less accurate assumed values for release rates, resulting in overestimation of hazards, the proposed method provides calculated values that are within acceptable differences from those estimated after intensive, time-consuming trials with commercial software like PHAST or TRACE. The proposed method does the efficient calculation of the extent of damage, in a shorter period of time, because it directly estimates the release rate with the given inputs, while the commercially available software takes a trial-and-error approach because of the absence of information on the leak size, which is difficult to get in real cases of accidents. It also produces relatively accurate estimation results even for releases of untrained materials. The results indicate that the proposed method can improve the accuracy and availability of information that is crucial to the success of emergency information systems.

Video tracking and behaviour segmentation of laboratory rodents

In this paper a system for laboratory rodent video tracking and behavior segmentation is proposed. A new real-time mouse pose estimation method is proposed based on semi-automatically generated animal shape model. Behavior segmentation into separate behavior acts is considered as a signal segmentation problem using hidden Markov models (HMM). Conventional first order HMM supposes a geometric prior distribution on segment’s length, which is inadequate for behavior segmentation. We propose a modification of conventional first order HMM that allows any prior distribution on segment’s length. Experiments show that the developed approach can lead to more adequate results comparing to conventional HMM.

Implementation of a New Method for Dynamic Multileaf Collimator Tracking of Prostate Motion in Arc Radiotherapy Using a Single kV Imager

To implement a method for real-time prostate motion estimation with a single kV imager during arc radiotherapy and to integrate it with dynamic multileaf collimator (DMLC) target tracking. An arc field with a circular aperture and 358 degrees gantry rotation was delivered to a motion phantom with a fiducial marker under continuous kV X-ray imaging at 5 Hz, perpendicular to the treatment beam. A pretreatment gantry rotation of 120 degrees in 20 sec with continuous imaging preceded the treatment. During treatment, each kV image was first used together with all previous images to estimate the three-dimensional (3D) target probability density function and then used together with this probability density function to estimate the 3D target position. The MLC aperture was then adapted to the estimated 3D target position. Tracking was performed with five patient-measured prostate trajectories that represented characteristic prostate motion patterns. Two data sets were recorded during tracking: (1) the estimated 3D target positions, for off-line comparison with the actual phantom motion; and (2) continuous portal images, for independent off-line calculation of the 2D tracking error as the positional difference between the marker and the MLC aperture center in each portal image. All experiments were also made with 1- Hz kV imaging. The mean 3D root-mean-square error of the trajectory estimation was 0.6 mm. The mean root-mean-square tracking error was 0.7 mm, both parallel and perpendicular to the MLC. The accuracy degraded slightly for 1- Hz imaging. Single-imager DMLC prostate tracking that allows arbitrary beam modulation during arc radiotherapy was implemented. It has submillimeter accuracy for most prostate motion types.

Real-time estimation of combustion variability for model-based control and optimal calibration of spark ignition engines

A method for indirect real-time estimation of combustion variability by substitutive measurements is investigated in this paper. An impetus for improving combustion stability, particularly at near-idle conditions, is provided by increasing driver sensitivity to engine noise, vibration, and harshness. The combustion stability is generally quantified by the cycle-by-cycle variations, and represented by the coefficient of variation in the indicated mean effective pressure (COVIMEP). Since the COVIMEP is calculated from a set of IMEP data obtained by experiments, prediction of the COVIMEP in real time from measurements of IMEP is impossible. Instead, an indirect prediction methodology of combustion variability is proposed on the basis of statistical analysis of substitutive measurements. A set of relevant exploratory variables is determined first. Then, trends of the COVIMEP are investigated to determine the regression model, and an exponential form proved to be the best. A systematic regression analysis procedure based on statistical analysis enables the simplest equations to be found while maintaining accuracy. The results demonstrate that the COVIMEP behaviour can be captured as a function of 10–90 per cent burn duration and manifold absolute pressure. The implementation can utilize real-time pressure measurements if the engine is equipped with pressure transducers, or virtual sensing of 10–90 per cent burned using a neural network trained a priori on measured or predicted data. The resulting regression equations derived with and without the charge motion valve enable the estimation of the combustion variability for optimal calibration or model-based control.