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González, Francisco, David Greiner, Vicente Mena, Ricardo M. Souto, Juan J. Santana, and Juan J. Aznárez. "Fitting procedure based on Differential Evolution to evaluate impedance parameters of metal–coating systems." Engineering Computations 36, no. 9 (November 11, 2019): 2960–82. http://dx.doi.org/10.1108/ec-11-2018-0513.
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Purpose Impedance data obtained by electrochemical impedance spectroscopy (EIS) are fitted to a relevant electrical equivalent circuit to evaluate parameters directly related to the resistance and the durability of metal–coating systems. The purpose of this study is to present a novel and more efficient computational strategy for the modelling of EIS measurements using the Differential Evolution paradigm. Design/methodology/approach An alternative method to non-linear regression algorithms for the analysis of measured data in terms of equivalent circuit parameters is provided by evolutionary algorithms, particularly the Differential Evolution (DE) algorithms (standard DE and a representative of the self-adaptive DE paradigm were used). Findings The results obtained with DE algorithms were compared with those yielding from commercial fitting software, achieving a more accurate solution, and a better parameter identification, in all the cases treated. Further, an enhanced fitting power for the modelling of metal–coating systems was obtained. Originality/value The great potential of the developed tool has been demonstrated in the analysis of the evolution of EIS spectra due to progressive degradation of metal–coating systems. Open codes of the different differential algorithms used are included, and also, examples tackled in the document are open. It allows the complete use, or improvement, of the developed tool by researchers.
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Alhasa, Kemal, Mohd Mohd Nadzir, Popoola Olalekan, Mohd Latif, Yusri Yusup, Mohammad Iqbal Faruque, Fatimah Ahamad, et al. "Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System." Sensors 18, no. 12 (December 11, 2018): 4380. http://dx.doi.org/10.3390/s18124380.
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Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.
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Miñambres, J. J., and M. de la Sen. "Fast Adaptive Control Algorithms in pH Measurement." IFAC Proceedings Volumes 19, no. 15 (December 1986): 313–16. http://dx.doi.org/10.1016/s1474-6670(17)59440-9.
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Kübler, Jonas M., Andrew Arrasmith, Lukasz Cincio, and Patrick J. Coles. "An Adaptive Optimizer for Measurement-Frugal Variational Algorithms." Quantum 4 (May 11, 2020): 263. http://dx.doi.org/10.22331/q-2020-05-11-263.
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Variational hybrid quantum-classical algorithms (VHQCAs) have the potential to be useful in the era of near-term quantum computing. However, recently there has been concern regarding the number of measurements needed for convergence of VHQCAs. Here, we address this concern by investigating the classical optimizer in VHQCAs. We introduce a novel optimizer called individual Coupled Adaptive Number of Shots (iCANS). This adaptive optimizer frugally selects the number of measurements (i.e., number of shots) both for a given iteration and for a given partial derivative in a stochastic gradient descent. We numerically simulate the performance of iCANS for the variational quantum eigensolver and for variational quantum compiling, with and without noise. In all cases, and especially in the noisy case, iCANS tends to out-perform state-of-the-art optimizers for VHQCAs. We therefore believe this adaptive optimizer will be useful for realistic VHQCA implementations, where the number of measurements is limited.
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Crompvoets, Elise A. V., Anton A. Béguin, and Klaas Sijtsma. "Adaptive Pairwise Comparison for Educational Measurement." Journal of Educational and Behavioral Statistics 45, no. 3 (December 13, 2019): 316–38. http://dx.doi.org/10.3102/1076998619890589.
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Pairwise comparison is becoming increasingly popular as a holistic measurement method in education. Unfortunately, many comparisons are required for reliable measurement. To reduce the number of required comparisons, we developed an adaptive selection algorithm (ASA) that selects the most informative comparisons while taking the uncertainty of the object parameters into account. The results of the simulation study showed that, given the number of comparisons, the ASA resulted in smaller standard errors of object parameter estimates than a random selection algorithm that served as a benchmark. Rank order accuracy and reliability were similar for the two algorithms. Because the scale separation reliability (SSR) may overestimate the benchmark reliability when the ASA is used, caution is required when interpreting the SSR.
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Yang, Haiyan, Hongqiang Liu, Zhongliang Zhou, and An Xu. "A practical adaptive nonlinear tracking algorithm with range rate measurement." International Journal of Distributed Sensor Networks 14, no. 5 (May 2018): 155014771877686. http://dx.doi.org/10.1177/1550147718776863.
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It is difficult to answer the problem whether the range rate measurement should be adopted to track a target in a tracking scenario. A practical adaptive nonlinear tracking algorithm with the range rate measurement is proposed, which avoids this problem and achieves good accuracy of target state estimation. First, three popular nonlinear filtering algorithms only with the position measurement are surveyed. Second, three popular nonlinear filtering algorithms with the position and range rate measurements are surveyed. Then, a novel tracking algorithm with range rate measurement is proposed based on the cumulative sum detector and the above two kinds of nonlinear algorithms. The results of simulation experiment demonstrate that the range rate measurement could reduce accuracy of the target state estimation in mismatch tracking scenarios. The results of simulation experiment also verify that the performance of proposed algorithm is better than the current state and the art interacting multiple-model algorithm and can well follow the state estimation output of the measurement equation matching the tracking scenario.
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Roscoe, A. J., I. F. Abdulhadi, and G. M. Burt. "P and M Class Phasor Measurement Unit Algorithms Using Adaptive Cascaded Filters." IEEE Transactions on Power Delivery 28, no. 3 (July 2013): 1447–59. http://dx.doi.org/10.1109/tpwrd.2013.2238256.
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Münch, K., W. Vilser, and I. Senff. "Adaptive Algorithmen zur automatischen Messung retinaler Gefäßdurchmesser - Adaptive Algorithms for the Automatic Measurement of Retinal Vessel Diameters." Biomedizinische Technik/Biomedical Engineering 40, no. 11 (1995): 322–25. http://dx.doi.org/10.1515/bmte.1995.40.11.322.
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Alekseev, V. V., E. M. Antonyuk, and I. E. Varshavskiy. "Algorithmic Support of Adaptive Automatic Control Systems with Data Compression." Journal of the Russian Universities. Radioelectronics 23, no. 6 (December 29, 2020): 84–99. http://dx.doi.org/10.32603/1993-8985-2020-23-6-84-99.
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Introduction. The exponential growth of measurement information caused by ongoing complication of technical and production facilities necessitates the development of improved or brand new information and measurement systems, including those performing adaptive automatic control functions. Automatic criteria-based selection and reduction of measurement information continuously supplied by multi-parameter sources characterizing the objects under study require algorithms ensuring reconfiguration of automatic control systems during operation. In comparison with automatic control systems based on time-division channelling, the considered adaptive systems provide timely information on the pre-emergency and emergency operation of a facility.Aim. To develop an algorithmic support for adaptive automatic control systems using asynchronous-cyclic and parallel-sequential operating algorithms, as well as to compare the proposed algorithms in terms of their, control reliability, compression ratio, operation speed and the error associated with multi-channelling.Materials and methods. The algorithms proposed for supporting the operation of adaptive systems were developed on the basis of queuing theory and simulation modelling using the MatLab/Simulink programming languages, C++.Results. The developed algorithmic support for automatic control systems based on asynchronous-cyclic analysis of deviations allows the amount of redundant information to be reduced by more than 4 times and the operation speed to be increased by 1.5 times. The developed algorithmic support for automatic control systems based on parallel-sequential analysis of deviations allows the error associated with multi-channelling to be reduced by 1.4 times, thereby bringing the control reliability of such systems closer to that of continuous-control systems. An analysis of the graphs of the error associated with multi-channelling showed that the automatic control systems based on parallel-sequential operational algorithms are invariant to the law of distribution of input quantities, compared to the systems based on asynchronous-cyclic operational algorithms.Conclusions. The proposed algorithmic support can significantly decrease the redundancy of information and improve the metrological characteristics of automatic control systems. The use of the developed algorithms in automatic control systems based on time-division channelling render their control reliability comparable with that of continuous-control systems.
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Chabaud, Ulysse, Damian Markham, and Adel Sohbi. "Quantum machine learning with adaptive linear optics." Quantum 5 (July 5, 2021): 496. http://dx.doi.org/10.22331/q-2021-07-05-496.
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We study supervised learning algorithms in which a quantum device is used to perform a computational subroutine – either for prediction via probability estimation, or to compute a kernel via estimation of quantum states overlap. We design implementations of these quantum subroutines using Boson Sampling architectures in linear optics, supplemented by adaptive measurements. We then challenge these quantum algorithms by deriving classical simulation algorithms for the tasks of output probability estimation and overlap estimation. We obtain different classical simulability regimes for these two computational tasks in terms of the number of adaptive measurements and input photons. In both cases, our results set explicit limits to the range of parameters for which a quantum advantage can be envisaged with adaptive linear optics compared to classical machine learning algorithms: we show that the number of input photons and the number of adaptive measurements cannot be simultaneously small compared to the number of modes. Interestingly, our analysis leaves open the possibility of a near-term quantum advantage with a single adaptive measurement.
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Danilova, S. K., and N. N. Tarasov. "Adaptive Algorithm of Filtration with Integrated Residuals." Mekhatronika, Avtomatizatsiya, Upravlenie 20, no. 2 (February 13, 2019): 80–89. http://dx.doi.org/10.17587/mau.20.80-89.
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This paper proposes a filtering algorithm based on the use of not only the residuals between the measured and estimated coordinates, as in classical filtering algorithms, but also multiple integrals of these residuals. Classical filtering algorithms use reliable information about both the motion and measurement models and the statistical characteristics of the input random disturbances and measurement noise. The real control objects operate under conditions of action not only of highfrequency random disturbances, but also under the influence of low-frequency forces and moments from an aggressive environment, the characteristics of which are known with huge approximations. In this regard, the efficiency of using classical filtering algorithms for real systems is extremely low due to large errors. The algorithm proposed in the paper allows to eliminate these drawbacks by restoring external low-frequency disturbances in real time. Under external disturbances are understood not only external influences from the environment, but inaccurate knowledge about the motion model itself. For integral residuals, an algorithm is proposed for calculating the gains in the feedback in an analytical form. This algorithm is based on the processing of residuals, as well as estimates of external disturbances and their derivatives in the current time. A control algorithm is proposed that includes estimates of both phase coordinates, which are responsible for the quality of transients, and estimates of unknown disturbances, which is responsible for the compensation of external disturbances. Knowing the estimates of external disturbances in real time will, on the one hand, improve the quality of control, and, on the other hand, reduce the time and material costs associated with the study of the control object’s movement dynamics and the external environment. Using the example of an underwater vehicle model described by a linear system of differential equations under conditions of external disturbances (wave and hydrological forces and moments), the simulation was performed and the efficiency of the proposed algorithms for various numbers of integral residuals was shown.
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Russo, Daniel. "Simple Bayesian Algorithms for Best-Arm Identification." Operations Research 68, no. 6 (November 2020): 1625–47. http://dx.doi.org/10.1287/opre.2019.1911.
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This paper considers the optimal adaptive allocation of measurement effort for identifying the best among a finite set of options or designs. An experimenter sequentially chooses designs to measure and observes noisy signals of their quality with the goal of confidently identifying the best design after a small number of measurements. Just as the multiarmed bandit problem crystallizes the tradeoff between exploration and exploitation, this “pure exploration” variant crystallizes the challenge of rapidly gathering information before committing to a final decision. The paper proposes several simple Bayesian algorithms for allocating measurement effort and, by characterizing fundamental asymptotic limits on the performance of any algorithm, formalizes a sense in which these seemingly naive algorithms are the best possible.
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Zigic, Aleksandar. "Experimental verification of preset time count rate meters based on adaptive digital signal processing algorithms." Nuclear Technology and Radiation Protection 20, no. 2 (2005): 40–44. http://dx.doi.org/10.2298/ntrp0502040z.
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Experimental verifications of two optimized adaptive digital signal processing algorithms implemented in two pre set time count rate meters were per formed ac cording to appropriate standards. The random pulse generator realized using a personal computer, was used as an artificial radiation source for preliminary system tests and performance evaluations of the pro posed algorithms. Then measurement results for background radiation levels were obtained. Finally, measurements with a natural radiation source radioisotope 90Sr-90Y, were carried out. Measurement results, con ducted without and with radio isotopes for the specified errors of 10% and 5% showed to agree well with theoretical predictions.
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Sergeev, V. А., and S. Е. Reschikoff. "Adaptive algorithms for measuring low-frequency noise parameters of semiconductor devices under mass control." Izmeritel`naya Tekhnika, no. 11 (2020): 59–64. http://dx.doi.org/10.32446/0368-1025it.2020-11-59-64.
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The solution of the problem of increasing the confidence and efficiency of quality control of semiconductor devices is considered. The analysis of conditions for measuring the power spectral density of low – frequency noise of semiconductor devices with a spectrum of the form (γ – the spectrum shape indicator) under mass quality control is presented. The error in measuring the power spectral density under the specified measurement conditions strongly depends on the value of the spectrum shape indicator. Adaptive algorithms for measuring low-frequency noise parameters are proposed for cases of a given limit error in measuring the power spectral density and a given time for a single measurement. The proposed algorithms include a preliminary estimation of the value of the spectrum shape indicator and subsequent measurement of the noise power spectral density at the optimal filter bandwidth. The optimal filter bandwidth is determined based on the results of a preliminary assessment of the spectrum shape indicator. For both cases, we obtained estimates of the gain in the sense of the average for the set (ensemble) of controlled products. The possibility of adaptive or cognitive adjustment of the measurement system parameters in the control process based on the results of evaluating sample averages in the training sample is discussed.
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Li, Wang, and Zheng. "Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise." Sensors 19, no. 14 (July 11, 2019): 3069. http://dx.doi.org/10.3390/s19143069.
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Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.
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Taddia, Chiara, Gianluca Mazzini, and Riccardo Rovatti. "Duty Cycle Measurement Techniques for Adaptive and Resilient Autonomic Systems." International Journal of Adaptive, Resilient and Autonomic Systems 2, no. 3 (July 2011): 63–87. http://dx.doi.org/10.4018/jaras.2011070105.
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When systems are deployed in environments where change is the rule rather than the exception, adaptability and resilience play a crucial role in order to preserve good quality of service. This work analyses methods that can be adopted for the duty cycle measurement of sensor-originated waveforms. These methods start from the assumption that no regular sampling is possible and thus they are naturally thought for an adaptive coexistence with other heterogeneous and variable tasks. Hence, the waveform carrying the information from low-priority sensors can be sampled only at instants that are non-controlled. To tackle this problem, this paper proposes some algorithms for the duty cycle measurement of a digital pulse train signal that is sampled at random instants. The solutions are easy to implement and lightweight so that they can be scheduled in extremely loaded microcontrollers. The results show a fast convergence to the duty cycle value; in particular, a considerable gain with respect to other known solutions is obtained in terms of the average number of samples necessary to evaluate the duty cycle with a desired accuracy is obtained.
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Shangguan, Wentao, Qiurong Yan, Hui Wang, Chenglong Yuan, Bing Li, and Yuhao Wang. "Adaptive Single Photon Compressed Imaging Based on Constructing a Smart Threshold Matrix." Sensors 18, no. 10 (October 14, 2018): 3449. http://dx.doi.org/10.3390/s18103449.
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We demonstrate a single-photon compressed imaging system based on single photon counting technology and compressed sensing theory. In order to cut down the measurement times and shorten the imaging time, a fast and efficient adaptive sampling method, suited for single-photon compressed imaging, is proposed. First, the pre-measured rough images are transformed into sparse bases as a priori information. Then a smart threshold matrix is designed by using large sparse coefficients of the rough image in sparse bases. The adaptive measurement matrix is obtained by modifying the original Gaussian random matrix with the specially designed threshold matrix. Building the adaptive measurement matrix requires only one level of sparse representation, which means that adaptive imaging can be achieved quickly with very little computation. The experimental results show that the reconstruction effect of the image measured using the adaptive measurement matrix is obviously superior than that of the Gaussian random matrix under different measurement times and different reconstruction algorithms.
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Montgomery, Jacob M., and Josh Cutler. "Computerized Adaptive Testing for Public Opinion Surveys." Political Analysis 21, no. 2 (2013): 172–92. http://dx.doi.org/10.1093/pan/mps060.
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Survey researchers avoid using large multi-item scales to measure latent traits due to both the financial costs and the risk of driving up nonresponse rates. Typically, investigators select a subset of available scale items rather than asking the full battery. Reduced batteries, however, can sharply reduce measurement precision and introduce bias. In this article, we present computerized adaptive testing (CAT) as a method for minimizing the number of questions each respondent must answer while preserving measurement accuracy and precision. CAT algorithms respond to individuals' previous answers to select subsequent questions that most efficiently reveal respondents' positions on a latent dimension. We introduce the basic stages of a CAT algorithm and present the details for one approach to item selection appropriate for public opinion research. We then demonstrate the advantages of CAT via simulation and empirically comparing dynamic and static measures of political knowledge.
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Wang, Lijun, Sisi Wang, and Wenzhi Yang. "Adaptive federated filter for multi-sensor nonlinear system with cross-correlated noises." PLOS ONE 16, no. 2 (February 19, 2021): e0246680. http://dx.doi.org/10.1371/journal.pone.0246680.
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This paper presents an adaptive approach to the federated filter for multi-sensor nonlinear systems with cross-correlations between process noise and local measurement noise. The adaptive Gaussian filter is used as the local filter of the federated filter for the first time, which overcomes the performance degradation caused by the cross-correlated noises. Two kinds of adaptive federated filters are proposed, one uses a de-correlation framework as local filter, and the subfilter of the other one is defined as a Gaussian filter with correlated noises at the same-epoch, and much effort is made to verify the theoretical equivalence of the two algorithms in the nonlinear fusion system. Simulation results show that the proposed algorithms are superior to the traditional federated filter and Gaussian filter with same-paced correlated noises, and the equivalence between the proposed algorithms and high degree cubature federated filter is also demonstrated.
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Nazarahari, Milad, and Hossein Rouhani. "Adaptive Gain Regulation of Sensor Fusion Algorithms for Orientation Estimation with Magnetic and Inertial Measurement Units." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–13. http://dx.doi.org/10.1109/tim.2020.3033077.
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Zhou, Xin, Haishun Sun, Cong Zhang, and Qiangsheng Dai. "Optimal Placement of PMUs Using Adaptive Genetic Algorithm Considering Measurement Redundancy." International Journal of Reliability, Quality and Safety Engineering 23, no. 03 (June 2016): 1640001. http://dx.doi.org/10.1142/s0218539316400015.
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With the extensive use of PMUs in power system, how to implement the optimal PMU placement for complete system observability is becoming more and more significant. Against simple genetic algorithm, easy to fall into local minima, this paper proposed a simple and effective adaptive genetic algorithm, aimed at determining the optimal placement of PMUs. The proposed algorithm provides adaptive crossover and mutation probability in iterations and has better convergence and global search ability than simple genetic algorithm, confirmed in many practical applications. Generally speaking, minimum PMU placement problem has multiple solutions, and this paper proposed an index of observability redundancy to rank these multiple solutions. Moreover, several observability rules were also put forward and proved with great help in obtaining the optimal solution. Simulations using MATLAB are conducted on IEEE 7 bus, 14 bus, 24 bus, 30 bus, 57 bus, 118 bus and New England 39 bus test systems in order to verify the validity of this adaptive method. Results compared with other existing methods show that the proposed adaptive method is simple to realize and obtains the optimal solution with high redundancy in very few generations, which is much more effective than the results by major existing algorithms and shows a good reference value.
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Park, Seung Hyo, Sa Yong Chong, Hyung June Kim, and Taek Lyul Song. "Adaptive Estimation of Spatial Clutter Measurement Density Using Clutter Measurement Probability for Enhanced Multi-Target Tracking." Sensors 20, no. 1 (December 23, 2019): 114. http://dx.doi.org/10.3390/s20010114.
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The point detections obtained from radars or sonars in surveillance environments include clutter measurements, as well as target measurements. Target tracking with these data requires data association, which distinguishes the detections from targets and clutter. Various algorithms have been proposed for clutter measurement density estimation to achieve accurate and robust target tracking with the point detections. Among them, the spatial clutter measurement density estimator (SCMDE) computes the sparsity of clutter measurement, which is the reciprocal of the clutter measurement density. The SCMDE considers all adjacent measurements only as clutter, so the estimated clutter measurement density is biased for multi-target tracking applications, which may result in degraded target tracking performance. Through the study in this paper, a major source of tracking performance degradation with the existing SCMDE for multi-target tracking is analyzed, and the use of the clutter measurement probability is proposed as a remedy. It is also found that the expansion of the volume of the hyper-sphere for each sparsity order reduces the bias of clutter measurement density estimates. Based on the analysis, we propose a new adaptive clutter measurement density estimation method called SCMDE for multi-target tracking (MTT-SCMDE). The proposed method is applied to multi-target tracking, and the improvement of multi-target tracking performance is shown by a series of Monte Carlo simulation runs and a real radar data test. The clutter measurement density estimation performance and target tracking performance are also analyzed for various sparsity orders.
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HOLEVA, LEE F. "RANGE ESTIMATION FROM CAMERA BLUR BY REGULARIZED ADAPTIVE IDENTIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 06 (December 1994): 1273–300. http://dx.doi.org/10.1142/s0218001494000644.
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One of the fundamental problems of machine vision is the estimation of object depth from perceived images. This paper describes both an apparatus and the corresponding algorithms for the passive extraction of object depth. Here passive extraction implies the processing of images acquired using only the existing illumination, in this case roughly uniform white light. Depth from defocused algorithms are extremely sensitive to image variations. Regularization, the application of a priori constraints, is employed to improve the accuracy of the range measurements. When the camera’s point spread function is shift invariant, an adaptive algorithm is developed in the frequency domain. The constraints imposed upon the solution power spectrum vector vary temporally. When the camera’s point spread function is shift varying, an adaptive algorithm is developed in the spatial domain. The constraints imposed upon the solution point spread vector vary spatially. Data is acquired from line scan cameras. Only a single range measurement or a single depth profile is extracted. By relying upon the motion of the observed object on a conveyor belt, a complete range image may be generated.
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Guan, Binglei, and Xianfeng Tang. "Multisensor decentralized nonlinear fusion using adaptive cubature information filter." PLOS ONE 15, no. 11 (November 5, 2020): e0241517. http://dx.doi.org/10.1371/journal.pone.0241517.
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In nonlinear multisensor system, abrupt state changes and unknown variance of measurement noise are very common, which challenges the majority of the previously developed models for precisely known multisensor fusion techniques. In terms of this issue, an adaptive cubature information filter (CIF) is proposed by embedding strong tracking filter (STF) and variational Bayesian (VB) method, and it is extended to multi-sensor fusion under the decentralized fusion framework with feedback. Specifically, the new algorithms use an equivalent description of STF, which avoid the problem of solving Jacobian matrix during determining strong trace fading factor and solve the interdependent problem of combination of STF and VB. Meanwhile, A simple and efficient method for evaluating global fading factor is developed by introducing a parameter variable named fading vector. The analysis shows that compared with the traditional information filter, this filter can effectively reduce the data transmission from the local sensor to the fusion center and decrease the computational burden of the fusion center. Therefore, it can quickly return to the normal error range and has higher estimation accuracy in response to abrupt state changes. Finally, the performance of the developed algorithms is evaluated through a target tracking problem.
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Dong, Xiangxiang, Luigi Chisci, and Yunze Cai. "An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise." Sensors 20, no. 23 (November 26, 2020): 6757. http://dx.doi.org/10.3390/s20236757.
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Aiming towards state estimation and information fusion for nonlinear systems with heavy-tailed measurement noise, a variational Bayesian Student’s t-based cubature information filter (VBST-CIF) is designed. Furthermore, a multi-sensor variational Bayesian Student’s t-based cubature information feedback fusion (VBST-CIFF) algorithm is also derived. In the proposed VBST-CIF, the spherical-radial cubature (SRC) rule is embedded into the variational Bayes (VB) method for a joint estimation of states and scale matrix, degree-of-freedom (DOF) parameter, as well as an auxiliary parameter in the nonlinear system with heavy-tailed noise. The designed VBST-CIF facilitates multi-sensor fusion, allowing to derive a VBST-CIFF algorithm based on multi-sensor information feedback fusion. The performance of the proposed algorithms is assessed in target tracking scenarios. Simulation results demonstrate that the proposed VBST-CIF/VBST-CIFF outperform the conventional cubature information filter (CIF) and cubature information feedback fusion (CIFF) algorithms.
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Chiella, Antônio C. B., Bruno O. S. Teixeira, and Guilherme A. S. Pereira. "Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter." Sensors 19, no. 10 (May 23, 2019): 2372. http://dx.doi.org/10.3390/s19102372.
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This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature.
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Basturk, Halil Ibrahim. "Active Unmatched Disturbance Cancellation and Estimation by State--Derivative Feedback for Plants Modeled as an LTI System." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 8, no. 2 (July 20, 2018): 237–49. http://dx.doi.org/10.11121/ijocta.01.2018.00519.
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We design adaptive algorithms for both cancellation and estimation of unknown periodic disturbance, by feedback of state--derivatives ( i.e.,} without position information for mechanical systems) for the plants which are modeled as a linear time invariant system. We consider a series of unmatched unknown sinusoidal signals as the disturbance.The first step of the design consists of the parametrization of the disturbance model and the development of observer filters.The result obtained in this step allows us to use adaptive control techniques for the solution of the problem.In order to handle the unmatched condition, a backstepping technique is employed. Since the partial measurement of the virtual inputs is not available, we design a state observer and the estimates of these signals are used in the backstepping design.Finally, the stability of the equilibrium of the adaptive closed loop system with the convergence of states is proven.As a numerical example, a two-degree of freedom system is considered and the effectiveness of the algorithms are shown.
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Yang, Yang, Quan Li, Junnan Zhang, and Yangmin Xie. "Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle." Sensors 20, no. 2 (January 13, 2020): 439. http://dx.doi.org/10.3390/s20020439.
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Most path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage the measurement accuracy especially when turning at high speed. In this study, an adaptive iterative learning algorithm is proposed to optimize the turning parameters, which accounts for the dynamic characteristics of unmanned surface vehicles (USVs). The resulting optimal turning radius and speed are used to generate the path and speed profiles. The simulation results show that the proposed path-smoothing and speed profile design algorithms can largely increase the path-following performance, which potentially can help to improve the measurement accuracy of various activities.
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Franken, Arnoud R. C., and Paul C. Ivey. "Enhancing Flow Field Measurements Through Adaptive Multidimensional Data Sampling." Journal of Engineering for Gas Turbines and Power 128, no. 3 (September 6, 2005): 518–24. http://dx.doi.org/10.1115/1.2135822.
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A way to gain insight into the flow field conditions in turbomachinery is by carrying out a series of point measurements in a cross section of the flow, for example, with a miniature multihole pressure probe. A problem commonly encountered in situations like these is the selection of a suitable measurement grid layout and density for obtaining all essential information in a cost-effective and timely manner. In order to achieve the latter, a novel adaptive multidimensional data sampling technique has been developed at Cranfield University. This paper describes the underlying principles of this technique, the algorithms utilized, and the results obtained during its successful application to data sets of two different flow fields in a high-speed research compressor.
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Wang, Jie Gui. "New Method of Moving Targets Passive Tracking by Single Moving Observer Based on Measurement Data Fusion." Applied Mechanics and Materials 239-240 (December 2012): 942–45. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.942.
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Moving targets passive tracking by single moving observer is a difficult problem. A new location method based on measurement data fusion is proposed in this paper. Firstly, the adaptive passive tracking initiation algorithm is introduced. Secondly, a new data association algorithm is proposed, based on the data fusion of multiple measurements, the decision of synthetic data association is made. Finally, with the help of computer simulations, the proposed algorithms are proven to be correct and effective.
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Lu Zhou and B. Yazici. "Discretization Error Analysis and Adaptive Meshing Algorithms for Fluorescence Diffuse Optical Tomography in the Presence of Measurement Noise." IEEE Transactions on Image Processing 20, no. 4 (April 2011): 1094–111. http://dx.doi.org/10.1109/tip.2010.2083677.
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Mahr, Tristan J., Visar Berisha, Kan Kawabata, Julie Liss, and Katherine C. Hustad. "Performance of Forced-Alignment Algorithms on Children's Speech." Journal of Speech, Language, and Hearing Research 64, no. 6S (June 18, 2021): 2213–22. http://dx.doi.org/10.1044/2020_jslhr-20-00268.
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Purpose Acoustic measurement of speech sounds requires first segmenting the speech signal into relevant units (words, phones, etc.). Manual segmentation is cumbersome and time consuming. Forced-alignment algorithms automate this process by aligning a transcript and a speech sample. We compared the phoneme-level alignment performance of five available forced-alignment algorithms on a corpus of child speech. Our goal was to document aligner performance for child speech researchers. Method The child speech sample included 42 children between 3 and 6 years of age. The corpus was force-aligned using the Montreal Forced Aligner with and without speaker adaptive training, triphone alignment from the Kaldi speech recognition engine, the Prosodylab-Aligner, and the Penn Phonetics Lab Forced Aligner. The sample was also manually aligned to create gold-standard alignments. We evaluated alignment algorithms in terms of accuracy (whether the interval covers the midpoint of the manual alignment) and difference in phone-onset times between the automatic and manual intervals. Results The Montreal Forced Aligner with speaker adaptive training showed the highest accuracy and smallest timing differences. Vowels were consistently the most accurately aligned class of sounds across all the aligners, and alignment accuracy increased with age for fricative sounds across the aligners too. Conclusion The best-performing aligner fell just short of human-level reliability for forced alignment. Researchers can use forced alignment with child speech for certain classes of sounds (vowels, fricatives for older children), especially as part of a semi-automated workflow where alignments are later inspected for gross errors. Supplemental Material https://doi.org/10.23641/asha.14167058
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Shi, Gang, Xisheng Li, Zhe Wang, and Yanxia Liu. "A new measurement for yaw estimation of land vehicles using MARG sensors." Sensor Review 39, no. 5 (September 16, 2019): 636–44. http://dx.doi.org/10.1108/sr-10-2018-0276.
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Purpose The magnetometer measurement update plays a key role in correcting yaw estimation in fusion algorithms, and hence, the yaw estimation is vulnerable to magnetic disturbances. The purpose of this study is to improve the ability of the fusion algorithm to deal with magnetic disturbances. Design/methodology/approach In this paper, an adaptive measurement equation based on vehicle status is derived, which can constrain the yaw estimation from drifting when vehicle is running straight. Using this new measurement, a Kalman filter-based fusion algorithm is constructed, and its performance is evaluated experimentally. Findings The experiments results demonstrate that the new measurement update works as an effective supplement to the magnetometer measurement update in the present of magnetic disturbances, and the proposed fusion algorithm has better yaw estimation accuracy than the conventional algorithm. Originality/value The paper proposes a new adaptive measurement equation for yaw estimation based on vehicle status. And, using this measurement, the fusion algorithm can not only reduce the weight of disturbed sensor measurement but also utilize the character of vehicle running to deal with magnetic disturbances. This strategy can also be used in other orientation estimation fields.
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Zhou, Tao, Huiling Lu, Fuyuan Hu, Hongbin Shi, Shi Qiu, and Huiqun Wang. "A New Robust Adaptive Fusion Method for Double-Modality Medical Image PET/CT." BioMed Research International 2021 (February 4, 2021): 1–18. http://dx.doi.org/10.1155/2021/8824395.
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A new robust adaptive fusion method for double-modality medical image PET/CT is proposed according to the Piella framework. The algorithm consists of the following three steps. Firstly, the registered PET and CT images are decomposed using the nonsubsampled contourlet transform (NSCT). Secondly, in order to highlight the lesions of the low-frequency image, low-frequency components are fused by pulse-coupled neural network (PCNN) that has a higher sensitivity to featured area with low intensities. With regard to high-frequency subbands, the Gauss random matrix is used for compression measurements, histogram distance between the every two corresponding subblocks of high coefficient is employed as match measure, and regional energy is used as activity measure. The fusion factor d is then calculated by using the match measure and the activity measure. The high-frequency measurement value is fused according to the fusion factor, and high-frequency fusion image is reconstructed by using the orthogonal matching pursuit algorithm of the high-frequency measurement after fusion. Thirdly, the final image is acquired through the NSCT inverse transformation of the low-frequency fusion image and the reconstructed high-frequency fusion image. To validate the proposed algorithm, four comparative experiments were performed: comparative experiment with other image fusion algorithms, comparison of different activity measures, different match measures, and PET/CT fusion results of lung cancer (20 groups). The experimental results showed that the proposed algorithm could better retain and show the lesion information, and is superior to other fusion algorithms based on both the subjective and objective evaluations.
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Dichev, Dimitar, Hristofor Koev, Totka Bakalova, and Petr Louda. "А Меаsuring Method for Gyro-Free Determination of the Parameters of Moving Objects." Metrology and Measurement Systems 23, no. 1 (March 1, 2016): 107–18. http://dx.doi.org/10.1515/mms-2016-0001.
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Abstract The paper presents a new method for building measuring instruments and systems for gyro-free determination of the parameters of moving objects. To illustrate the qualities of this method, a system for measuring the roll, pitch, heel and trim of a ship has been developed on its basis. The main concept of the method is based, on one hand, on a simplified design of the base coordinate system in the main measurement channel so as to reduce the instrumental errors, and, on the other hand, on an additional measurement channel operating in parallel with the main one and whose hardware and software platform makes possible performing algorithms intended to eliminate the dynamic error in real time. In this way, as well as by using suitable adaptive algorithms in the measurement procedures, low-cost measuring systems operating with high accuracy under conditions of inertial effects and whose parameters (intensity and frequency of the maximum in the spectrum) change within a wide range can be implemented.
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Heng, Sovannarith, Phet Aimtongkham, Van Nhan Vo, Tri Gia Nguyen, and Chakchai So-In. "Fuzzy Adaptive-Sampling Block Compressed Sensing for Wireless Multimedia Sensor Networks." Sensors 20, no. 21 (October 31, 2020): 6217. http://dx.doi.org/10.3390/s20216217.
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The transmission of high-volume multimedia content (e.g., images) is challenging for a resource-constrained wireless multimedia sensor network (WMSN) due to energy consumption requirements. Redundant image information can be compressed using traditional compression techniques at the cost of considerable energy consumption. Fortunately, compressed sensing (CS) has been introduced as a low-complexity coding scheme for WMSNs. However, the storage and processing of CS-generated images and measurement matrices require substantial memory. Block compressed sensing (BCS) can mitigate this problem. Nevertheless, allocating a fixed sampling to all blocks is impractical since each block holds different information. Although solutions such as adaptive block compressed sensing (ABCS) exist, they lack robustness across various types of images. As a solution, we propose a holistic WMSN architecture for image transmission that performs well on diverse images by leveraging saliency and standard deviation features. A fuzzy logic system (FLS) is then used to determine the appropriate features when allocating the sampling, and each corresponding block is resized using CS. The combined FLS and BCS algorithms are implemented with smoothed projected Landweber (SPL) reconstruction to determine the convergence speed. The experiments confirm the promising performance of the proposed algorithm compared with that of conventional and state-of-the-art algorithms.
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Wang, Hongjian, Ying Wang, Cun Li, Juan Li, Qing Li, and Xicheng Ban. "Adaptive Weight Update Algorithm for Target Tracking of UUV Based on Improved Gaussian Mixture Cubature Kalman Filter." Complexity 2020 (June 4, 2020): 1–12. http://dx.doi.org/10.1155/2020/7828050.
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The Gaussian mixture filter can solve the non-Gaussian problem of target tracking in complex environment by the multimode approximation method, but the weights of the Gaussian component of the conventional Gaussian mixture filter are only updated with the arrival of the measurement value in the measurement update stage. When the nonlinear degree of the system is high or the measurement value is missing, the weight of the Gauss component remains unchanged, and the probability density function of the system state cannot be accurately approximated. To solve this problem, this paper proposes an algorithm to update adaptive weights for the Gaussian components of a Gaussian mixture cubature Kalman filter (CKF) in the time update stage. The proposed method approximates the non-Gaussian noise by splitting the system state, process noise, and observation noise into several Gaussian components and updates the weight of the Gaussian components in the time update stage. The method contributes to obtaining a better approximation of the posterior probability density function, which is constrained by the substantial uncertainty associated with the measurements or ambiguity in the model. The estimation accuracy of the proposed algorithm was analyzed using a Taylor expansion. A series of extensive trials was performed to assess the estimation precision corresponding to various algorithms. The results based on the data pertaining to the lake trial of an unmanned underwater vehicle (UUV) demonstrated the superiority of the proposed algorithm in terms of its better accuracy and stability compared to those of conventional tracking algorithms, along with the associated reasonable computational time that could satisfy real-time tracking requirements.
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Khadour, Tammam, Michel Al Saba, and Louay Saleh. "Improving bearings-only target state estimation tracking problem by using adaptive and nonlinear kalman algorithms." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (July 1, 2019): 190. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp190-198.
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<p><span style="font-family: 'Times New Roman'; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Finding the best estimate of the process state from noisy data is the main problem in tracking systems, many efforts and researches have been done to remove this noise. More useful information about the target’s state can be extracted from observations by using a more appropriate model for the target’s motion or using additional sensors. In this paper, we will introduce two methods to improve the estimation of bearing-only target tracking problem in two dimensions (2D). The first method is by adding a third sensor and making a good alignment of those sensors, and at the same time an extended Kalman filter (EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF) are implemented. The second method is by applying an adaptive nonlinear Kalman filter (ANKF) for two sensors to solve the problem of measurement variance uncertainty.</span></p>
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Alimuradov, Alan K., Alexander Yu Tychkov, Andrey V. Kuzmin, Pyotr P. Churakov, Alexey V. Ageykin, and Galina V. Vishnevskaya. "Improved CEEMDAN Based Speech Signal Analysis Algorithm for Mental Disorders Diagnostic System." International Journal of Embedded and Real-Time Communication Systems 10, no. 1 (January 2019): 22–47. http://dx.doi.org/10.4018/ijertcs.2019010102.
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An automated algorithm for pitch frequency measurement for diagnostic systems of borderline mental disorders is developed. It is based on decomposition of a speech signal into frequency components using an adaptive method for analyzing of non-stationary signals, improved complete ensemble empirical mode decomposition with adaptive noise (improved CEEMDAN), and isolating the component containing pitch. A block diagram for the developed algorithm and a detailed mathematical description are presented. A research of the algorithm using the formed verified signal base of healthy patients, and male and female patients with psychogenic disorders, aged from 18 to 60, is conducted. The research results are evaluated in comparison with the known algorithms for pitch frequency measurement. In accordance with the results of the study, the developed algorithm for pitch frequency measurement provides an accuracy increase in determination of borderline mental disorders: for the error of the first kind, on the average, it is more accurate by 10.7%, and for the second type error by 4.7%.
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Xie, Zheng Wen. "Study on Thermogravimetry Data of Cooking Oil Tar Based on Adaptive Wavelet Analysis." Applied Mechanics and Materials 423-426 (September 2013): 2486–90. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2486.
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Test of the combustion characteristics of cooking oil tar in pipe was conducted. Wavelet transform was introduced to the thermogravimetric data smoothing and differentiation analysis according to the experiment results, and the orthogonal test method was used to find the optimize wavelet parameter. Wavelet transform results were compared to the traditional Moving average,Gaussian Smoothing and Vondrak smoothing methods and it was proved that the signal-to-noise ratio () of the measurement is increased significantly. The kinetic parameters calculated from the original TG curves and smoothed DTG curves have excellent agreement,and thus the wavelet transform smoothing algorithms can be used directly and accurately in kinetic analysis.
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Orlov, Y. V. "Sliding Mode Observer-Based Synthesis of State Derivative-Free Model Reference Adaptive Control of Distributed Parameter Systems." Journal of Dynamic Systems, Measurement, and Control 122, no. 4 (January 20, 2000): 725–31. http://dx.doi.org/10.1115/1.1320447.
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This paper presents control laws for distributed parameter systems of parabolic and hyperbolic types which, on the one hand ensure robustness with respect to small dynamic uncertainties and disturbances, and on the other hand, permit on-line plant parameter estimation. The novelty of the algorithms proposed is (a) in the construction of a sliding mode-based state derivative observer and (b) in the inclusion of this observer into a model reference adaptive controller which thereby regularizes the ill-posed identification problem itself. Apart from this, the controllers constructed do not suffer from on-line computation of spatial derivatives of the measurement data, and hence they are of reduced sensitivity with respect to the measurement noise. [S0022-0434(00)02104-3]
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Kubal, Sharvaj, Elizabeth Lee, Chor Yong Tay, and Derrick Yong. "Multitrack Compressed Sensing for Faster Hyperspectral Imaging." Sensors 21, no. 15 (July 24, 2021): 5034. http://dx.doi.org/10.3390/s21155034.
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Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times.
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Battiston, Adrian, Inna Sharf, and Meyer Nahon. "Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle." International Journal of Robotics Research 38, no. 10-11 (August 8, 2019): 1286–306. http://dx.doi.org/10.1177/0278364919867397.
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An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, [Formula: see text] filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned filters is first evaluated in a simulation environment using modified sensor models to capture the effects of collision on inertial measurements. Simulation results help define the efficacy and use cases of the conventional and novel algorithms in a quadcopter collision scenario. An analogous evaluation is then conducted by post-processing logged sensor data from collision flight tests, to gain new insights into algorithms’ performance in the transition from simulated to real data. The post-processing evaluation compares each algorithm’s attitude estimate, including the stock attitude estimator of the PixHawk controller, to data collected by an offboard infrared motion capture system. Based on this evaluation, two promising algorithms, the MEKF and an adaptive [Formula: see text] filter, are selected for implementation on the physical quadcopter in the control loop of the collision recovery pipeline. Experimental results show an improvement in the metric used to evaluate experimental performance, the time taken to recover from the collision, when compared with the stock attitude estimator on the PixHawk (PX4) software.
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Miruthula, D., and Ramachandran Rajeswari. "Synchro Phasor Measurement Based Fault Analysis of a Parallel Transmission Line." Advanced Materials Research 984-985 (July 2014): 996–1004. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.996.
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This paper presents a new method to classify transmission line shunt faults and determine the fault location using phasor data of the transmission system. Most algorithms employed for analyzing fault data require that the fault type to be classified. The older fault-type classification algorithms are inefficient because they are not effective under certain operating conditions of the power system and may not be able to accurately select the faulted transmission line if the same fault recorder monitors multiple lines. An intelligent techniques described in this paper is used to precisely detect all ten types of shunt faults that may occur in an electric power transmission system (double-circuit transmission lines) with the help of data obtained from phasor measurement unit. This method is virtually independent of the mutual coupling effect caused by the adjacent parallel circuit and insensitive to the variation of source impedance. Thousands of fault simulations by MATLAB have proved the accuracy and effectiveness of the proposed algorithm. This paper includes the analysis of fault identification techniques using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System based protection schemes. The performances of the techniques are examined for different faults on the parallel transmission line and compared with the conventional relay scheme. The results obtained shows that ANFIS based fault identification gives better performance than other techniques.
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Guo, Yanbing, Lingjuan Miao, and Yusen Lin. "A Novel EM Implementation for Initial Alignment of SINS Based on Particle Filter and Particle Swarm Optimization." Mathematical Problems in Engineering 2019 (February 20, 2019): 1–12. http://dx.doi.org/10.1155/2019/6793175.
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For nonlinear systems in which the measurement noise parameters vary over time, adaptive nonlinear filters can be applied to precisely estimate the states of systems. The expectation maximization (EM) algorithm, which alternately takes an expectation- (E-) step and a maximization- (M-) step, has been proposed to construct a theoretical framework for the adaptive nonlinear filters. Previous adaptive nonlinear filters based on the EM employ analytical algorithms to develop the two steps, but they cannot achieve high filtering accuracy because the strong nonlinearity of systems may invalidate the Gaussian assumption of the state distribution. In this paper, we propose an EM-based adaptive nonlinear filter APF to solve this problem. In the E-step, an improved particle filter PF_new is proposed based on the Gaussian sum approximation (GSA) and the Monte Carlo Markov chain (MCMC) to achieve the state estimation. In the M-step, the particle swarm optimization (PSO) is applied to estimate the measurement noise parameters. The performances of the proposed algorithm are illustrated in the simulations with Lorenz 63 model and in a semiphysical experiment of the initial alignment of the strapdown inertial navigation system (SINS) in large misalignment angles.
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Wang, Di, Hua Liu, and Xiang Cheng. "A Miniature Binocular Endoscope with Local Feature Matching and Stereo Matching for 3D Measurement and 3D Reconstruction." Sensors 18, no. 7 (July 12, 2018): 2243. http://dx.doi.org/10.3390/s18072243.
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As the traditional single camera endoscope can only provide clear images without 3D measurement and 3D reconstruction, a miniature binocular endoscope based on the principle of binocular stereoscopic vision to implement 3D measurement and 3D reconstruction in tight and restricted spaces is presented. In order to realize the exact matching of points of interest in the left and right images, a novel construction method of the weighted orthogonal-symmetric local binary pattern (WOS-LBP) descriptor is presented. Then a stereo matching algorithm based on Gaussian-weighted AD-Census transform and improved cross-based adaptive regions is studied to realize 3D reconstruction for real scenes. In the algorithm, we adjust determination criterions of adaptive regions for edge and discontinuous areas in particular and as well extract mismatched pixels caused by occlusion through image entropy and region-growing algorithm. This paper develops a binocular endoscope with an external diameter of 3.17 mm and the above algorithms are applied in it. The endoscope contains two CMOS cameras and four fiber optics for illumination. Three conclusions are drawn from experiments: (1) the proposed descriptor has good rotation invariance, distinctiveness and robustness to light change as well as noises; (2) the proposed stereo matching algorithm has a mean relative error of 8.48% for Middlebury standard pairs of images and compared with several classical stereo matching algorithms, our algorithm performs better in edge and discontinuous areas; (3) the mean relative error of length measurement is 3.22%, and the endoscope can be utilized to measure and reconstruct real scenes effectively.
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Jiao, Hong, Junhui Liu, Kathleen Haynie, Ada Woo, and Jerry Gorham. "Comparison Between Dichotomous and Polytomous Scoring of Innovative Items in a Large-Scale Computerized Adaptive Test." Educational and Psychological Measurement 72, no. 3 (November 8, 2011): 493–509. http://dx.doi.org/10.1177/0013164411422903.
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This study explored the impact of partial credit scoring of one type of innovative items (multiple-response items) in a computerized adaptive version of a large-scale licensure pretest and operational test settings. The impacts of partial credit scoring on the estimation of the ability parameters and classification decisions in operational test settings were explored in one real data analysis and two simulation studies when two different polytomous scoring algorithms, automated polytomous scoring and rater-generated polytomous scoring, were applied. For the real data analyses, the ability estimates from dichotomous and polytomous scoring were highly correlated; the classification consistency between different scoring algorithms was nearly perfect. Information distribution changed slightly in the operational item bank. In the two simulation studies comparing each polytomous scoring with dichotomous scoring, the ability estimates resulting from polytomous scoring had slightly higher measurement precision than those resulting from dichotomous scoring. The practical impact related to classification decision was minor because of the extremely small number of items that could be scored polytomously in this current study.
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Pei, Wei, Yong Ying Zhu, S. X. Liu, and J. M. Wen. "Three-Dimensional Measurement System Based on Micro Stereovision." Key Engineering Materials 609-610 (April 2014): 1189–94. http://dx.doi.org/10.4028/www.scientific.net/kem.609-610.1189.
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With vision feedback the binocular micro stereovision system based on stereo light microscope (SLM) makes it possible to achieve 2D/3D high accuracy auto-positioning, 3D information extraction, 3D shape reconstruction and 3D measurement. Therefore, it is extensively used in micro robot navigation, micromanipulation, micro assembly and bioengineering, etc. To improve the key problems of low accuracy, refraction and occlusion in micro stereovision measurement, a novel micro stereo vision system is built based on optical theory and digital image processing. Then, the nonlinear correlation between optical paths of the micro stereo vision system is studied and the environment adaptive nonlinear model is established. On this basis, the coupling mechanism of multi-refraction and occlusion with micro-projection nonlinear model is proved, and multi-refraction variable refractive index correction and micro stereo occlusion reconstruction algorithms are developed. It realizes synchronization observation in larger field of view, rapid non-contact accuracy positioning and 3D measurement based on stereovision to solve the pivotal problems of accuracy positioning and measure in the chip encapsulation, micro assembly and the micro manipulation.
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Liu, Hongqiang, Zhongliang Zhou, and Lei Yu. "Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement." Mathematical Problems in Engineering 2018 (June 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/4984186.
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An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.
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Galvanauskas, Vytautas, Rimvydas Simutis, and Vygandas Vaitkus. "Adaptive Control of Biomass Specific Growth Rate in Fed-Batch Biotechnological Processes. A Comparative Study." Processes 7, no. 11 (November 4, 2019): 810. http://dx.doi.org/10.3390/pr7110810.
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This article presents a comparative study on the development and application of two distinct adaptive control algorithms for biomass specific growth rate control in fed-batch biotechnological processes. A typical fed-batch process using Escherichia coli for recombinant protein production was selected for this research. Numerical simulation results show that both developed controllers, an adaptive PI controller based on the gain scheduling technique and a model-free adaptive controller based on the artificial neural network, delivered a comparable control performance and are suitable for application when using the substrate limitation approach and substrate feeding rate manipulation. The controller performance was tested within the realistic ranges of the feedback signal sampling intervals and measurement noise intensities. Considering the efforts for controller design and tuning, including development of the adaptation/learning algorithms, the model-free adaptive control algorithm proves to be more attractive for industrial applications, especially when only limited knowledge of the process and its mathematical model is available. The investigated model-free adaptive controller also tended to deliver better control quality under low specific growth rate conditions that prevail during the recombinant protein production phase. In the investigated simulation runs, the average tracking error did not exceed 0.01 (1/h). The temporary overshoots caused by the maximal disturbances stayed within the range of 0.025–0.11 (1/h). Application of the algorithm can be further extended to specific growth rate control in other bacterial and mammalian cell cultivations that run under substrate limitation conditions.