39 research outputs found
Evaluation of TLR4 expression and chosen parameters of oxidative-antioxidative balance in young children with food allergy.
Neuronal modeling of power system development. Part 2. Models of IEEE RTS system
W pracy zamieszczono wybrane wyniki bada艅 dotycz膮ce modelowania neuralnego rozwoju systemu elektroenergetycznego na bazie danych testowych IEEE RTS 96., m.in.: spos贸b tworzenia macierzy danych wej艣ciowych oraz wyj艣ciowych, spos贸b doboru parametr贸w sieci, itp. W wyniku projektowania i uczenia SSN uzyskano modele rozwoju SEE, kt贸re poddano badaniom wra偶liwo艣ci m.in. na zmian臋 liczby warstw ukrytych oraz liczby neuron贸w w warstwie.The paper presents selected results of research on the modeling of neural development of the power system test data based on the IEEE RTS 96, m.in .: how to create a matrix of data input and output, how to select the network parameters and the like. As a result of learning design and development of the ANN models were obtained SEE, which has been tested sensitivity among to change the number of hidden layers and the number of neurons in a layer
Planning in production in coal mining with using information collected in GIS systems
Planuj膮c produkcj臋 w臋gla kamiennego, musimy przygotowywa膰 z wieloletnim wyprzedzeniem informacje o przewidywanych do realizacji zadaniach zwi膮zanych z robotami g贸rniczymi, zakupami wyposa偶enia czy te偶 w艂a艣ciw膮 produkcj膮. Wiarygodno艣膰 informacji dotycz膮cej wielko艣ci zasob贸w oraz jako艣ci w臋gla, kt贸ry ma by膰 eksploatowany, stanowi jedn膮 z kluczowych informacji, jakie s膮 niezb臋dne dla prawid艂owego funkcjonowania kopalni w臋gla kamiennego.While planning production of coal we have to prepare information connected with planned mining works, buying of proper equipement or proper production of coal with advance of many years. Credibility of information containing the size of resources or the quality of coal, which is to be exploited, is vital for coal mine to function properly
Design and research on artificial neural networks as electrical power system development models based on IEEE RTS data
The paper presents selected results of research on the design of artificial neural networks and training them using the electrical power system development model (EPS or EP system) based on IEEE RTS 96 test data, i.a. creation of training and test files, development of architecture of the artificial neural network, selection of parameters of the network, selection of appropriate training and testing method, etc. As a result of the development and training an ANN, the following EP system development models were obtained, which were examined for sensitivity to changes of the number of hidden layers, number of neurons in a layer, activation function, training method, etc. Subsequently, simulation models for studying fitness of the obtained models to the real systems. Interesting results were obtained, e.g. the method of the neural modelling of the system, the optimal architecture of the ANN that is a model of the system, possibilities and directions to improve a neural model of the system, etc
Paradigms development models power system. Part 2 Comparative methods for the identification
W pracy zamieszczono wybrane wyniki przeprowadzonych bada艅 por贸wnawczych metod i modeli identyfikacji rozwoju krajowego systemu elektroenergetycznego (KSE lub system KSE) na wybranym przyk艂adzie danych liczbowych z lat 1980-2010 [7], Zaproponowano algorytmy identyfikacji, a nast臋pnie przeprowadzono identyfikacj臋 z wykorzystaniem m.in metody arx, armax, ar, bj uzyskuj膮c modele rozwoju KSE. Por贸wnano te偶 metody z punktu widzenia wykorzystania otrzymanych modeli do projektowania rozwoju systemu KSE. Do przeprowadzenia identyfikacji wykorzystano 艣rodowisko MATLABAi Simulinka z System Identification Toolboxem stosuj膮c metody identyfikacji takie jak: arx (ang. AutoRegressive with eXogenous input) model autoregresji z zewn臋trznym wymuszeniem, armax (ang. AutoRegressive Moving Average with eXogeneus input), oe (ang. output error) model b艂臋du wej艣ciowego oraz bj model Box-Jenkinsa. Identyfikacj臋 przeprowadzono w 8 eksperymentach, a uzyskane wyniki wykorzystano do bada艅 por贸wnawczych, kt贸re przeprowadzono w Simulinku buduj膮c odpowiednie modele w postaci schemat贸w blokowych.The paper presents some results of comparative studies on the identification of methods and models applied to build a model for development of the power system (or system EEE EE) on the selected numerical example. Identification algorithms have been developed and were identified using the method m.in arx, ARMAX, ar, bj obtain models of KSE. It was also an attempt to compare the methods in terms of the use of models to design received the development of the EE. In order to carry out the experiments, identification numbers, data from the years 1980-2010 published including in the annals of Polish Electrical Power Engineering Statistics and the Central Statistical Office. Used to carry out the identification of the MATLAB environment Identification System Toolbox. EE system identification was performed using the following identification methods such as arx (autoregressive with exogenous called input) autoregressive model with external forcing, armax (autoregressive called Moving Average with eXogeneus input), for which a substitute in the equation nf = nd = 0, oe (called output error) model input error on = nc = nd = 0, bj Box-Jenkins model, at = 0 The identification was carried out in five experiments, and the results were used for comparative studies that were conducted in Simulink building appropriate models
Systemimical [!] evolutionary algorithm model for improving the system electric power exchange. Part 2, The implementation and some results
Artyku艂 jest kontynuacj膮 pracy o tym samym tytule g艂贸wnym i podtytu艂em Cz臋艣膰 1. Istota i mo偶liwo艣ci metody. W niniejszym artykule pokazano w jaki praktyczny spos贸b mo偶na utworzy膰 Populacj臋 Pocz膮tkow膮 (PP) na bazie modelu parametrycznego arx Towarowej Gie艂dy Energii Elektrycznej (TGEE) otrzymanego w wyniku identyfikacji z wykorzystaniem danych liczbowych notowanych na Rynku Dnia Nast臋pnego (RDN). Pokazano te偶 systemowy spos贸b konstruowania funkcji krzepko艣ci jak te偶 systemowych operator贸w krzy偶owania i mutacji, a tak偶e metody selekcji. Algorytm zaimplementowano w j臋zyku Matlab i przetestowano z wykorzystaniem danych TGEE. Uzyskano wiele interesuj膮cych wynik贸w bada艅, w tym w zakresie przebiegu algorytmu jak te偶 wizualizacji wybranych wyniki bada艅.The paper is a continuation of the article under the same title and subtitle the main part 1 The essence and the possibility of implementing. This article shows how a practical way to create initial population (PP) based on parametric model arx Power Exchange Electricity (TGEE) obtained as a result of identification using the figures listed on the Day Ahead Market (DAM). It also shows a process for designing a system as well as robustness features of system the crossover and mutation and selection methods. The algorithm is implemented in Matlab and tested using data TGEE. They obtained many interesting results, including the course of the algorithm as well as the visualization of selected results
Qualitative risk analysis in private sector projects
Jako艣ciowa analiza ryzyka jest procesem, kt贸ry polega na oszacowaniu wielko艣ci prawdopodobie艅stwa oraz skutk贸w wyst膮pienia czynnik贸w ryzyka, okre艣lonych na etapie identyfikacji. Ma ona umo偶liwi膰 dokonanie hierarchizacji zidentyfikowanych czynnik贸w wed艂ug ich potencjalnego wp艂ywu na osi膮gni臋cie cel贸w projektu, wskazuj膮c kierownikowi projektu ryzyko priorytetowe (ze wzgl臋du na przyj臋te kryterium, np. poziom ryzyka, prawdopodobie艅stwo lub dotkliwo艣膰 skutk贸w), przeznaczone do dalszej analizy. W artykule zaprezentowano hierarchi臋 czynnik贸w ryzyka w projektach planowanych i realizowanych w sektorze prywatnym, utworzon膮 na podstawie przeprowadzonych bada艅 empirycznych oraz przedstawiono rekomendacje dla poprawy obecnej sytuacji.Qualitative risk analysis is the estimation of the probability and impact of risks that have been identified during identification phase. It should allow to make a hierarchy of identified risks according to their potential impact on the achievement of the project objectives. It should also show to the project manager the priority risk (in accordance with criteria such as the level of risk, the likelihood or severity of effects) for further analysis. The article presents the hierarchy of risks in private sector projects that is based on empirical research and makes recommendations for improving the current situation
Self-Organizing Wireless Ad-Hock Sensor Networks
The main target of this article is to review the main items connected with Smart Dust and their resolving proposals by research workers. In chapter 1 contains hardware description, chapter 2 contains software description divided Into positioning problems, routing, description of TinyOS, tools used for building working environment and security, chapter 3 contains conclusions and proposals for future development
Dealing with Non-Convexity in Geographic Routing in Smart Dust Networks
The paper proposes a new approach to greedy geographic routing for sensor networks with non-convex covering structure