The development of digital signal processors and the increase in their computing capabilities bring opportunities to employ algorithms with multiple variable parameters in active noise control systems. Of particular interest are the algorithms based on artificial neural networks. This paper presents an active noise control algorithm based on a neural network and a nonlinear input-output system identification model. The purpose of the algorithm is an active noise control system with a nonlinear primary path. The algorithm uses the NARMAX system identification model. The neural network employed in the proposed algorithm is a multilayer perceptron. The error backpropagation rule with adaptive learning rate is employed to update the weight of the neural network. The performance of the proposed algorithm has been tested by numerical simulations. Results for narrow-band input signals and nonlinear primary path are presented below.

System identification is an approach for parameter detection and mathematical model determination using response signals of a dynamic system. Two degrees of freedom (2DOF) pendulum controlled by a QUBE-servo motor is a great experiment device to work with; though it is not easy to control this system due to its complex structure and multi-dimensional outputs. Hence, system identification is required for this system to analyze and evaluate its dynamic behaviors. This paper presents a methodology for identifying a 2DOF pendulum and its dynamic behaviors including noise from an encoder cable. Firstly, all parameters from both mechanical and electrical sides of the QUBE-servo motor are analyzed. Secondly, a mathematical model and identified parameters for the 2DOF pendulum are illustrated. Finally, disturbances from encoder cable of the QUBE-servo motor which introduce an unwanted oscillation or self-vibration in this system are introduced. The effect of itself on output response signals of the 2DOF QUBE-pendulum is also discussed. All identified parameters are checked and verified by a comparison between a theoretical simulation and experimental results. It is found that the disturbance from encoder cable of the 2DOF QUBE-pendulum is not negligible and should be carefully considered as a certain factor affecting response of system.

In the areas of acoustic research or applications that deal with not-precisely-known or variable conditions, a method of adaptation to the uncertainness or changes is usually necessary. When searching for an adaptation algorithm, it is hard to overlook the least mean squares (LMS) algorithm. Its simplicity, speed of computation, and robustness has won it a wide area of applications: from telecommunication, through acoustics and vibration, to seismology. The algorithm, however, still lacks a full theoretical analysis. This is probabely the cause of its main drawback: the need of a careful choice of the step size - which is the reason why so many variable step size flavors of the LMS algorithm has been developed.

This paper contributes to both the above mentioned characteristics of the LMS algorithm. First, it shows a derivation of a new necessary condition for the LMS algorithm convergence. The condition, although weak, proved useful in developing a new variable step size LMS algorithm which appeared to be quite different from the algorithms known from the literature. Moreover, the algorithm proved to be effective in both simulations and laboratory experiments, covering two possible applications: adaptive line enhancement and active noise control.

This article investigates unstable tiltrotor in hover system identification from flight test data. The aircraft dynamics was described by a linear model defined in Body-Fixed-Coordinate System. Output Error Method was selected in order to obtain stability and control derivatives in lateral motion. For estimating model parameters both time and frequency domain formulations were applied. To improve the system identification performed in the time domain, a stabilization matrix was included for evaluating the states. In the end, estimates obtained from various Output Error Method formulations were compared in terms of parameters accuracy and time histories. Evaluations were performed in MATLAB R2009b environment.

The main open-field producer regions of cucurbits (watermelon, squash, melon and cucumber) in Panama (Los Santos, Herrera and Coclé provinces) were surveyed for molecular identification, occurrence and distribution of Thrips palmi (the most important pest thrip species on cucurbits in Panama), Frankliniella intonsa and Frankliniella cephalica during the growing seasons of 2009 to 2013 and 2017 to 2018. Forty plots were surveyed and DNA extracts of 186 thrips (larvae and adults) were analyzed by multiplex PCR, using a set of T. palmi-specific primers in combination with a set of insect-universal primers. DNA extracts corresponding to 174 individual thrips (93.5%) rendered both PCR products of expected size with T. palmi-specific and insect-universal primers, whereas the remaining DNA extracts corresponding to 12 individual thrips (6.5%) only rendered the product of the expected size with insect-universal primers. Sequencing of those PCR products and BLAST analysis allowed for the identification of F. intonsa and F. cephalica. Thrips palmi was detected in all three provinces, while F. intonsa and F. cephalica were detected in Herrera and Los Santos provinces. To our knowledge, this is not only the first detection of F. intonsa in Panama, but also the first detection of F. cephalica in Panamanian cucurbit crops.

The use of fractional-order calculus for system modeling is a good alternative to well-known classic integer-order methods, primarily due to the precision with which the modeled object may be mapped. In this study, we created integer and fractional discrete models of a real object – a highspeed brushless micro-motor. The accuracy of the models was verified and compared.

In this paper, quanizted multisine inputs for a maneuver with simultaneous elevator, aileron and rudder deflections are presented. The inputs were designed for 9 quantization levels. A nonlinear aircraft model was exited with the designed inputs and its stability and control derivatives were identified. Time domain output error method with maximum likelihood principle and a linear aircraft model were used to perform parameter estimation. Visual match and relative standard deviations of the estimates were used to validate the results for each quantization level for clean signals and signals with measurement noise present in the data. The noise was included into both output and input signals. It was shown that it is possible to obtain accurate results when simultaneous flight controls deflections are quantized and noise is present in the data.

The article provides a sociological analysis of national identities of Polish children growing up in Nor-way. The research results presented are unique in the sense that the portrayals of national identifica-tions constructed in the process of migration are shown through direct experiences of children. The analysis is based on semi-structured interviews with children, observation in the research situation (children’s rooms) and Sentence Completion Method. Adopting Antonina Kłoskowska’s analytical framework of national identity and her terminology of the so called ‘cultural valence’ (adoption of cul-ture), we argue that identities are processual and constructed, a result of the fact that mobility took place at a certain moment in time and in a specific geographical space. In addition, we see identities as conditioned by a plethora of identifiable objective and subjective reasons. The intensified mobility of children due to labour migrations of their parents leads to multiple challenges within the (re)construc-tions of children’s identities in their new place of settlement.

The availability of cheap and widely applicable person identification techniques is essential due to a wide-spread usage of online services. The dynamics of typing is characteristic to particular users, and users are hardly able to mimic the dynamics of typing of others. State-of-the-art solutions for person identification from the dynamics of typing are based on machine learning. The presence of hubs, i.e., few instances that appear as nearest neighbours of surprisingly many other instances, have been observed in various domains recently and hubness-aware machine learning approaches have been shown to work well in those domains. However, hubness has not been studied in the context of person identification yet, and hubnessaware techniques have not been applied to this task. In this paper, we examine hubness in typing data and propose to use ECkNN, a recent hubness-aware regression technique together with dynamic time warping for person identification. We collected time-series data describing the dynamics of typing and used it to evaluate our approach. Experimental results show that hubness-aware techniques outperform state-of-the-art time-series classifiers.