Delayless acceleration measurement method for elevator control

Accurate sensing of acceleration could provide improved performance for elevator control. Acceleration control is, however, seldom implemented, due to the unsatisfactory results of most acceleration measurement methods. We propose a polynomial predictive differentiator which delaylessly differentiates the available velocity signal. Polynomial prediction is justified, because the acceleration curve can be modeled accurately by a piecewise polynomial     

Genetic Algorithm Training of Elman Neural Network in Motor Fault Detection

Fault detection methods are crucial in acquiring safe and reliable operation in motor drive systems. Remarkable maintenance costs can also be saved by applying advanced detection techniques to find potential failures. However, conventional motor fault detection approaches often have to work with explicit mathematic models. In addition, most of them are deterministic or non-adaptive, and therefore cannot be used in time-varying cases. In this paper, we propose an Elman neural network-based motor fault detection scheme to address these difficulties. The Elman neural network has the advantageous time series prediction capability because of its memory nodes, as well as local recurrent connections. Motor faults are detected from the variants in the expectation of feature signal prediction error. A Genetic Algorithm (GA) aided training strategy for the Elman neural network is further introduced to improve the approximation accuracy, and achieve better detection performance. Experiments with a practical automobile transmission gearbox with an artificial fault are carried out to verify the effectiveness of our method. Encouraging fault detection results have been obtained without any prior information on the gearbox model.     

Digital filtering for robust 50/60 Hz zero-crossing detectors

An improved digital filtering method for line frequency zero-crossing detectors is proposed. The multistage filter efficiently attenuates harmonics, wide-band noise, commutation notches, and other impulsive disturbances without causing any phase shift on the primary sinusoidal waveform. Our novel signal-processing system is a cascade of a median filter and an adaptive sinusoid predictor, followed by up-sampling and interpolation. The three-point median filter effectively removes impulses, and the predictor provides wide-band noise attenuation while compensating for delays in the other processing steps. The predictor adapts to possible line frequency variations within the specified range by changing the set of coefficients, based on an estimate of the instantaneous line frequency. The adaptive approach allows the use of highly selective IIR bandpass predictors     

Delayless method to generate current reference for active filters

Active power filters are used to eliminate AC harmonic currents by injecting equal but opposite compensating currents. Successful control of active filters requires, among other things, an accurate current reference. In this paper, we introduce a multistage adaptive filtering system which generates the current reference delaylessly and accurately. Our filter structure combines a low-pass prefilter and an adaptive predictive filter, making it possible to extract the sinusoidal active current from the distorted waveform without harmful phase shift, even when the frequency and amplitude alter simultaneously. Although active filters are typically used to compensate for the supply harmonics, where the fundamental frequency remains almost constant, we will show that our filter structure can also be applied in applications where the frequency alters rapidly     

Periodical switching between related goals for improving evolvability to a fixed goal in multi-objective problems

Evolutionary computation plays a principal role in intelligent design automation. Evolutionary approaches have discovered novel and patentable designs. Nonetheless, evolutionary techniques may lead to designs that lack robustness. This critical issue is strongly connected to the concept of evolvability. In nature, highly evolvable species tend to be found in rapidly changing environments. Such species can be considered robust against environmental changes. Consequently, to create robust engineering designs it could be beneficial to use variable, rather than fixed, fitness criteria. In this paper, we study the performance of an evolutionary programming algorithm with periodical switching between goals, which are selected randomly from a set of related goals. It is shown by a dual-objective filter optimization example that altering goals may improve evolvability to a fixed goal by enhancing the dynamics of solution population, and guiding the search to areas where improved solutions are likely to be found. Our reference algorithm with a single goal is able to find solutions with competitive fitness, but these solutions are results of premature convergence, because they are poorly evolvable. By using the same algorithm with switching goals, we can extend the productive search length considerably; both the fitness and robustness of such designs are improved.     

A survey of autonomic computing methods in digital service ecosystems

Service engineering of digital service ecosystems can be associated with several challenges, such as change and evolution of requirements; gathering of quality requirements and assessment; and uncertainty caused by dynamic nature and unknown deployment environment, composition and users. Therefore, the complexity and dynamics in which these digital services are deployed call for solutions to make them autonomic. Until now there has been no up-to-date review of the scientific literature on the application of the autonomic computing initiative in the digital service ecosystems domain. This article presents a review and comparison of autonomic computing methods in digital service ecosystems from the perspective of service engineering, i.e., requirements engineering and architecting of services. The review is based on systematic queries in four leading scientific databases and Google Scholar, and it is organized in four thematic research areas. A comparison framework has been defined which can be used as a guide for comparing the different methods selected. The goal is to discover which methods are suitable for the service engineering of digital service ecosystems with autonomic computing capabilities, highlight what the shortcomings of the methods are, and identify which research activities need to be conducted in order to overcome these shortcomings. The comparison reveals that none of the existing methods entirely fulfills the requirements that are defined in the comparison framework.     

An adaptive window function method for power measurement

An adaptive window function for distorted power measurement is introduced in this paper. It can automatically provide the optimal window function according to the measured signal. Thus, the applied window function is always optimal in the sense of the zeros of its frequency response corresponding to the undesired measured signal spectrum components which will result in the harmful truncation errors. As a result, the harmful truncation errors of asynchronous sampling and the influence of interharmonics in the measured signal spectrum can be totally eliminated. The correctness, accuracy, and applicability of the proposed method have been verified both theoretically and by extensive simulations     

A linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) with learning algorithm

In this study, the linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) proposed earlier by the authors is first introduced. The principles of α-level sets and backpropagation through time approach are also briefly discussed. We next employ these two methods to derive an explicit learning algorithm for the feedback parameters of the LIFBDFS. With this training algorithm, our LIFBDFS indeed becomes a potential candidate in solving real-time modeling and prediction problems.     

Evolutionary-programming-based optimization of reduced-rank adaptive filters for reference generation in active power filters

We introduce an evolutionary-programming-based method for designing robust and computationally efficient adaptive bandpass filters. These predictive filters are optimized for generating current references in active power filters (APFs). The accuracy (phase/amplitude) of the reference current is crucial in current-injection-type systems, because it directly affects the harmonics reduction ability of the APF. Our digital filtering approach has the following advantages: selective bandpass response, efficient attenuation of specific harmonic components, capability to handle typical frequency alteration, small number of multiplications, and structural simplicity. In addition, practically no prior knowledge of the electricity distribution network and its loading characteristics is needed for designing the current reference generator. In an illustrative example, the total harmonic distortion of an artificial current waveform was reduced from 36.7% to less than 3.7% within the line frequency range 49-51 Hz. The proposed scheme is a combination of the hard-computing (HC)-type multiplicative general parameter method and evolutionary programming that, on the other hand, is a constituent of soft computing (SC). Such open-minded fusion thinking is emerging among researchers and engineers, and it can potentially lead to efficient combinations of HC and SC methodologies*both on the algorithm level and on the system level.