Sinteza backstepping regulatora za praćenje maksimalne proizvodnje energije u fotonaponskim sustavima

This work presents a new control method to track the maximum power point of a grid-connected photovoltaic (PV) system. A backstepping controller is designed to be applied to a buck-boost DC-DC converter in order to achieve an optimal PV array output voltage. This nonlinear control is based on Lyapunov functions assuring the local stability of the system. Control reference voltages are initially estimated by a regression plane, avoiding local maximum and adjusted with a modified perturb and observe method (P&O). Thus, the maximum power extraction of the generating system is guaranteed. Finally, a DC-AC converter is controlled to supply AC current in the point of common coupling (PCC) of the electrical network. The performance of the developed system has been analyzed by means a simulation platform in Matlab/Simulink helped by SymPowerSystem Blockset. Results testify the validity of the designed control method.Ovaj rad predstavlja novu metodu upravljanja za slije.enje točke maksimalne snage fotonaponskog (PV) sustava. Dana je sinteza backstepping regulatora za primjenu u silazno-uzlaznom DC-DC pretvaraču za postizanje optimalnog izlaznog napona PV-a. Ova je nelinearna metoda upravljanja zasnovana na Ljapunovim funkcijama osiguravajući tako lokalnu stabilnost sustava. Upravljačke reference napona prvo su estimirane korištenjem regresijske ravnine izbjegavajući lokalne maksimume, a zatim podešene tzv. modificiranom perturbiraj i uoči metodom (P&O). Prema tome, zagarantirano je maksimalno izvlačenje energije iz sustava proizvodnje. Naposlijetku, DC-AC pretvaračem upravlja se na način da osigurava željena izmjenična struja u točki zajedničkog spoja (PCC) elektroenergetske mreže. Ponašanje razvijenog sustava analizirano je kroz simulacije provedene u Matlab/Simulink okruženju uz korištenje SymPowerSystem biblioteke

LIVAS: A 3-D multi-wavelength aerosol/cloud database based on CALIPSO and EARLINET

We present LIVAS (LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies), a 3-D multi-wavelength global aerosol and cloud optical database, optimized to be used for future space-based lidar end-to-end simulations of realistic atmospheric scenarios as well as retrieval algorithm testing activities. The LIVAS database provides averaged profiles of aerosol optical properties for the potential spaceborne laser operating wavelengths of 355, 532, 1064, 1570 and 2050 nm and of cloud optical properties at the wavelength of 532 nm. The global database is based on CALIPSO observations at 532 and 1064 nm and on aerosol-type-dependent backscatter- and extinction-related Ångström exponents, derived from EARLINET (European Aerosol Research Lidar Network) ground-based measurements for the UV and scattering calculations for the IR wavelengths, using a combination of input data from AERONET, suitable aerosol models and recent literature. The required spectral conversions are calculated for each of the CALIPSO aerosol types and are applied to CALIPSO backscatter and extinction data corresponding to the aerosol type retrieved by the CALIPSO aerosol classification scheme. A cloud optical database based on CALIPSO measurements at 532 nm is also provided, neglecting wavelength conversion due to approximately neutral scattering behavior of clouds along the spectral range of LIVAS. Averages of particle linear depolarization ratio profiles at 532 nm are provided as well. Finally, vertical distributions for a set of selected scenes of specific atmospheric phenomena (e.g., dust outbreaks, volcanic eruptions, wild fires, polar stratospheric clouds) are analyzed and spectrally converted so as to be used as case studies for spaceborne lidar performance assessments. The final global data set includes 4-year (1 January 2008–31 December 2011) time-averaged CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) data on a uniform grid of 1° × 1° with the original high vertical resolution of CALIPSO in order to ensure realistic simulations of the atmospheric variability in lidar end-to-end simulations

Supramolecularly directed rotary motion in a photoresponsive receptor

Stimuli-controlled motion at the molecular level has fascinated chemists already for several decades. Taking inspiration from the myriad of dynamic and machine-like functions in nature, a number of strategies have been developed to control motion in purely synthetic systems. Unidirectional rotary motion, such as is observed in ATP synthase and other motor proteins, remains highly challenging to achieve. Current artificial molecular motor systems rely on intrinsic asymmetry or a specific sequence of chemical transformations. Here, we present an alternative design in which the rotation is directed by a chiral guest molecule, which is able to bind non-covalently to a light-responsive receptor. It is demonstrated that the rotary direction is governed by the guest chirality and hence, can be selected and changed at will. This feature offers unique control of directional rotation and will prove highly important in the further development of molecular machinery

Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

BACKGROUND: We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox's proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. RESULTS: Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers. CONCLUSIONS: The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers

A Constrained Fuzzy Knowledge-Based System for the Management of Container Yard Operations

The management of container yard operations is considered by yard operators to be a very challenging task due to the many uncertainties inherent in such operations. The storage of the containers is one of those operations that require proper management for the efficient utilisation of the yard, requiring rapid retrieval time and a minimum number of re-handlings. The main challenge is when containers of a different size, type, or weight need to be stored in a yard that holds a number of pre-existing containers. This challenge becomes even more complex when the date and time for the departure of the containers are unknown, as is the case when the container is collected by a third-party logistics company without any prior notice being given. The aim of this study is to develop a new system for the management of container yard operations that takes into consideration a number of factors and constraints that occur in a real-life situation. One of these factors is the duration of stay for the topmost containers of each stack, when the containers are stored. Because the duration of stay for containers in a yard varies dynamically over time, an ‘ON/OFF’ strategy is proposed to activate/deactivate the duration of stay factor constraint if the length of stay for these containers varies significantly over time. A number of tools and techniques are utilised for developing the proposed system including: discrete event simulation for the modelling of container storage and retrieval operations, a fuzzy know ledge-based model for the stack allocation of containers, and a heuristic algorithm called ‘neighbourhood’ for the container retrieval operation. Results show that by adopting the proposed ‘ON/OFF’ strategy, 5% of the number of re-handlings, 2.5% of the total retrieval time, 6.6% of the total re-handling time and 42% of the average waiting time per truck are reduced

Electrons, Photons, and Force: Quantitative Single-Molecule Measurements from Physics to Biology

Single-molecule measurement techniques have illuminated unprecedented details of chemical behavior, including observations of the motion of a single molecule on a surface, and even the vibration of a single bond within a molecule. Such measurements are critical to our understanding of entities ranging from single atoms to the most complex protein assemblies. We provide an overview of the strikingly diverse classes of measurements that can be used to quantify single-molecule properties, including those of single macromolecules and single molecular assemblies, and discuss the quantitative insights they provide. Examples are drawn from across the single-molecule literature, ranging from ultrahigh vacuum scanning tunneling microscopy studies of adsorbate diffusion on surfaces to fluorescence studies of protein conformational changes in solution