880 resultados para Network-based
Resumo:
The Wireless Sensor Networks (WSN) methods applied to the lifting of oil present as an area with growing demand technical and scientific in view of the optimizations that can be carried forward with existing processes. This dissertation has as main objective to present the development of embedded systems dedicated to a wireless sensor network based on IEEE 802.15.4, which applies the ZigBee protocol, between sensors, actuators and the PLC (Programmable Logic Controller), aiming to solve the present problems in the deployment and maintenance of the physical communication of current elevation oil units based on the method Plunger-Lift. Embedded systems developed for this application will be responsible for acquiring information from sensors and control actuators of the devices present at the well, and also, using the Modbus protocol to make this network becomes transparent to the PLC responsible for controlling the production and delivery information for supervisory SISAL
Resumo:
In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development
Resumo:
In this work a modification on ANFIS (Adaptive Network Based Fuzzy Inference System) structure is proposed to find a systematic method for nonlinear plants, with large operational range, identification and control, using linear local systems: models and controllers. This method is based on multiple model approach. This way, linear local models are obtained and then those models are combined by the proposed neurofuzzy structure. A metric that allows a satisfactory combination of those models is obtained after the structure training. It results on plant s global identification. A controller is projected for each local model. The global control is obtained by mixing local controllers signals. This is done by the modified ANFIS. The modification on ANFIS architecture allows the two neurofuzzy structures knowledge sharing. So the same metric obtained to combine models can be used to combine controllers. Two cases study are used to validate the new ANFIS structure. The knowledge sharing is evaluated in the second case study. It shows that just one modified ANFIS structure is necessary to combine linear models to identify, a nonlinear plant, and combine linear controllers to control this plant. The proposed method allows the usage of any identification and control techniques for local models and local controllers obtaining. It also reduces the complexity of ANFIS usage for identification and control. This work has prioritized simpler techniques for the identification and control systems to simplify the use of the method
Resumo:
T'his dissertation proposes alternative models to allow the interconnectioin of the data communication networks of COSERN Companhia Energética do Rio Grande do Norte. These networks comprise the oorporative data network, based on TCP/IP architecture, and the automation system linking remote electric energy distribution substations to the main Operatin Centre, based on digital radio links and using the IEC 60870-5-101 protoco1s. The envisaged interconnection aims to provide automation data originated from substations with a contingent route to the Operation Center, in moments of failure or maintenance of the digital radio links. Among the presented models, the one chosen for development consists of a computational prototype based on a standard personal computer, working under LINUX operational system and running na application, developesd in C language, wich functions as a Gateway between the protocols of the TCP/IP stack and the IEC 60870-5-101 suite. So, it is described this model analysis, implementation and tests of functionality and performance. During the test phase it was basically verified the delay introduced by the TCP/IP network when transporting automation data, in order to guarantee that it was cionsistent with the time periods present on the automation network. Besides , additional modules are suggested to the prototype, in order to handle other issues such as security and prioriz\ation of the automation system data, whenever they are travesing the TCP/IP network. Finally, a study hás been done aiming to integrate, in more complete way, the two considered networks. It uses IP platform as a solution of convergence to the communication subsystem of na unified network, as the most recente market tendencies for supervisory and other automation systems indicate
Resumo:
In the past few years the interest is accomplishing a high accuracy positioning increasing. One of the methods that has been applied by the scientific community is the network based on positioning. By using multiple reference station data, it is possible to obtain centimetric positioning in a larger coverage area, in addition to gain in reliability, availability and integrity of the service. Besides, using this concept, it is possible to model the atmospheric effects (troposphere refraction and ionosphere effect). Another important question concerning this topic is related to the transmission of the network corrections to the users. There are some possibilities for this fact and an efficient one is the Virtual Reference Station (VRS) concept. In the VRS concept, a reference station is generated near to the rover receiver (user). This provides a short baseline and the user has the possibility of using a single frequency receiver to accomplish the relative positioning. In order to test this kind of positioning method, a software has been developed at São Paulo State University. In this paper, the methodology applied to generate the VRS data is described and the VRS quality is analyzed by using the Precise Point Positioning (PPP) method.
Resumo:
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Resumo:
This work identifies and analyzes literature about knowledge organization (KO), expressed in scientific journals communication of information science (IS). It performs an exploratory study on the Base de Dados Referencial de Artigos de Periodicos em Ciência da Informacio (BRAPCI, Reference Database of Journal Articles on Information Science) between the years 2000 and 2010. The descriptors relating to "knowledge organization" are used in order to recover and analyze the corresponding articles and to identify descriptors and concepts which integrate the semantic universe related to KO. Through the analysis of content, based on metrical studies, this article gathers and interprets data relating to documents and authors. Through this, it demonstrates the development of this field and its research fronts according to the observed characteristics, as well as noting the transformation indicative in the production of knowledge. The work describes the influences of the Spanish researchers on Brazilian literature in the fields of knowledge and information organization. As a result, it presents the most cited and productive authors, the theoretical currents which support them, and the most significant relationships of the Spanish-Brazilian authors network. Based on the constant key-words analysis in the cited articles, the co-existence of the French conception current and the incipient Spanish influence in Brazil is observed. Through this, it contributes to the comprehension of the thematic range relating to KO, stimulating both criticism and self-criticism, debate and knowledge creation, based on studies that have been developed and institutionalized in academic contexts in Spain and Brazil.
Resumo:
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Resumo:
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Resumo:
Thin films were prepared using glass precursors obtained in the ternary system NaPO(3)-BaF(2)-WO(3) and the binary system NaPO(3)-WO(3) with high concentrations of WO(3) (above 40% molar). Vitreous samples have been used as a target to prepare thin films. Such films were deposited using the electron beam evaporation method onto soda-lime glass substrates. Several structural characterizations were performed by Raman spectroscopy and X-ray Absorption Near Edge Spectroscopy (XANES) at the tungsten L(I) and L(III) absorption edges. XANES investigations showed that tungsten atoms are only sixfold coordinated (octahedral WO(6)) and that these films are free of tungstate tetrahedral units (WO(4)). In addition, Raman spectroscopy allowed identifying a break in the linear phosphate chains as the amount of WO(3) increases and the formation of P-O-W bonds in the films network indicating the intermediary behavior of WO(6) octahedra in the film network. Based on XANES data, we suggested a new attribution of several Raman absorption bands which allowed identifying the presence of W-O and W=O terminal bonds and a progressive apparition of W-O-W bridging bonds for the most WO(3) concentrated samples (above 40% molar) attributed to the formation of WO(6) clusters. (C) 2008 Elsevier B.V. All rights reserved.
Resumo:
Transparent glasses were synthesized in the NaPO3-BaF2 WO3 tertiary system and several structural characterizations were performed by X-ray absorption spectroscopy (XANES) at the tungsten L-I and L-III absorption edges and by Raman spectroscopy. Special attention was paid to the coordination state of tungsten atoms in the vitreous network.XANES investigations showed that tungsten atoms are only six-fold coordinated (octahedra WO6) and that these glasses are free of tungstate tetrahedra (WO4).In addition, Raman spectroscopy allowed to identify a break in the linear phosphate chains as the amount of WO3 increases and the formation of P-O-W bonds in the vitreous network indicating the modifier behavior of WO6 octahedra in the glass network. Based on XANES data, we suggested a new attribution of several Raman absorption bands which allowed to identify the presence of W-O- and W=O terminal bonds and a progressive apparition of W-O-W bridging bonds for the most WO3 concentrated samples (≥ 30% molar) due to the formation of WO6 clusters. © 2004 Elsevier B.V. All rights reserved.
Resumo:
Internal and external computer network attacks or security threats occur according to standards and follow a set of subsequent steps, allowing to establish profiles or patterns. This well-known behavior is the basis of signature analysis intrusion detection systems. This work presents a new attack signature model to be applied on network-based intrusion detection systems engines. The AISF (ACME! Intrusion Signature Format) model is built upon XML technology and works on intrusion signatures handling and analysis, from storage to manipulation. Using this new model, the process of storing and analyzing information about intrusion signatures for further use by an IDS become a less difficult and standardized process.
Resumo:
Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.
Resumo:
This paper aims to evaluate the quality of the pseudorange observables generated for a Virtual Reference Station (VRS). In order to generate the VRS data three different approaches were implemented and tested. In the first one, raw data from the reference station network were used while in the second it was based on double difference reference station corrections. Finally, in the third approach atmospheric models (ionosphere and troposphere) were used to create the VRS data. Sao Paulo State Network stations were used in all experiments. The VRS data were generated in a reference station position of known coordinates (real file). In order to validate the approaches, the VRS data were compared with the real data file. The results were quite similar, reaching the decimeter or centimeter level, depending on the approach applied.
Resumo:
Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.