Prony-Based Optimal Bayes Fault Classification of Overcurrent Protection

The development of deregulation and demand for high-quality electrical energy has lead to a new requirement in different fields of power systems. In the protection field, this means that high sensitivity and fast operation during the fault are required while maltripping of relay protection is not acceptable. One case that may lead to a maltrip of the high-sensitive overcurrent relay is the starting current of the induction motor or inrush current of the transformer. This transient current has the potential to affect the correct operation of protection relays close to the component being switched. In the case of switching events, such transients must not lead to overcurrent relay operation; therefore, a reliable and secure relay response becomes a critical matter. Meanwhile, proper techniques must be used to prevent maltripping of such relays, due to transient currents in the network. In this paper, the optimal Bayes classifier is utilized to develop a method for discriminating the fault from nonfault events. The proposed method has been designed based on extracting the modal parameters of the current waveform using the Prony method. By feeding the fundamental frequency damping and ratio of the 2nd harmonic amplitude over the fundamental harmonic amplitude to the classifier, the fault case is discriminated from the switching case. The suitable performance of this algorithm is demonstrated by simulation of different faults and switching conditions on a power system using PSCAD/EMTDC software.     

Robust decentralized neural networks based LFC in a deregulated power system

In this paper, a decentralized radial basis function neural network (RBFNN) based controller for load frequency control (LFC) in a deregulated power system is presented using the generalized model for LFC scheme according to the possible contracts. To achieve decentralization, the connections between each control area with the rest of system and effects of possible contracted scenarios are treated as a set of input disturbance signals. The idea of mixed H₂/H_∞ control technique is used for the training of the proposed controller. The motivation for using this control strategy for training the RBFNN based controller is to take large modeling uncertainties into account, cover physical constraints on control action and minimize the effects of area load disturbances. This newly developed design strategy combines the advantage of the neural networks and mixed H₂/H_∞ control techniques to provide robust performance and leads to a flexible controller with simple structure that is easy to implement. The effectiveness of the proposed method is demonstrated on a three-area restructured power system. The results of the proposed controllers are compared with the mixed H₂/H_∞ controllers for three scenarios of the possible contracts under large load demands and disturbances. The resulting controller is shown to minimize the effects of area load disturbances and maintain robust performance in the presence of plant parameter changes and system nonlinearities.     

Incidence of Listeria spp. in fish and environment of fish markets in Northern Greece

The incidence of Listeria spp. in the salt-water edible fish and in the environment of fish markets in Thessaloniki, Northern Greece was studied. One hundred and twenty raw/fresh fish bought at the fish markets of Thessaloniki (Northern Greece), were sampled and tested for presence of Listeria species using a two step enrichment procedure, followed by plating on two selective agars and subsequent biochemical identification of the isolates. Five fish samples were positive for Listeria spp. and in only one sample L. monocytogenes was detected. Also, 100 samples of knives, hands, boxes etc were sampled and 18 samples were positive for Listeria spp. and five for L. monocytogenes. L. innocua was more common being detected in four fish samples and 13 environmental samples. L. seeligeri was detected only in one environmental sample. Our findings indicate that only a few fish were contaminated with Listeria spp., while the level of contamination of the environment of fish markets was higher.     

Hodgkin disease in Iranian children

Troxevasin vacuum-phonophoresis and low-intensity laser irradiation on reflexogenic collar zones demonstrated statistically significant effectiveness. The treatment was used in 94 patients with spondylogenic discirculatory encephalopathy stage I-II.     

Statistical analysis of fiber-optic CDMA communication systems. I. Device modeling

In this paper, we present a new methodology for analysis of all-optical fiber-optic code-division multiple-access (FO-CDMA) networks. In this analysis, we propose statistical models, based on photon-counting techniques, for some basic elements of the network, such as splitters, combiners, star couplers, and FO-CDMA passive encoders/decoders. By following the statistical variation of the photon-count of the string of pulses that constitutes the address sequence in an FO-CDMA network, we will be able to reveal the quantum-limited optical signal-to-noise ratio (OSNR) required at the transmitter output to meet the performance limits. Moreover, considering receiver thermal noise and source extinction ratio, we explore the role of using an optical preamplifier before the detector and the dependence of the performance on the transmitted power and the weight of the employed optical orthogonal codes (OOCs).     

Optimum degeneration for minimum mismatch in bipolar currentsources

Statistical analysis of mismatch in bipolar degenerated current sources reveals the existence of an optimum degeneration for minimum mismatch. Extra degeneration does not improve matching but degrades it and forces unnecessary DC restrictions on the circuit     

A Web-based flood forecasting system for Shuangpai region

Traditional flood forecasting and operation of reservoirs in China are based on manual calculations by hydrologists or through standalone computer programs. The main drawbacks of these methods are long forecasting time due to time-consuming nature, individual knowledge, lack of communication, absence of experts, etc. A Web-based flood forecasting system (WFFS), which includes five main modules: real-time rainfall data conversion, model-driven hydrologic forecasting, model calibration, precipitation forecasting, and flood analysis, is presented in this paper. The WFFS brings significant convenience to personnel engaged in flood forecasting and control and allows real-time contribution of a wide range of experts at other spatial locations in times of emergency. The conceptual framework and detailed components of the proposed WFFS, which employs a multi-tiered architecture, are illustrated. Multi-tiered architecture offers great flexibility, portability, reusability and reliability. The prototype WFFS has been developed in Java programming language and applied in Shuangpai region with a satisfactory result.     

Critical success factors for a customer relationship management strategy

Most organizations have perceived the customer relationship management (CRM) concept as a technological solution for problems in individual areas, accompanied by a great deal of uncoordinated initiatives. Nevertheless, CRM must be conceived as a strategy, due to its human, technological, and processes implications, at the time an organization decides to implement it. On this basis, the main goal stated in this research is to propose, justify, and validate a model based on critical success factors (CSFs) that will constitute a guide for companies in the implementation and diagnosis of a CRM strategy. The model is conformed by a set of 13 CSFs with their 55 corresponding metrics, which will serve as a guide for organizations wishing to apply this type of strategy. These factors cover the three key aspects of every CRM strategy (human factor, processes, and technology); giving a global focus and propitiating success in the implementation of a CRM strategy. These CSFs – and their metrics – were evaluated by a group of internationally experts allowing determining guidelines for a CRM implementation as well as the probable causes of the deficiencies in past projects.     

A new approach for constructing the concept map

In recent years, e-learning system has become more and more popular and many adaptive learning environments have been proposed to offer learners customized courses in accordance with their aptitudes and learning results. For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance for learners. However, it is difficult and time consuming to create the concept map of a course. Thus, how to automatically create a concept map of a course becomes an interesting issue. In this paper, we propose a Two-Phase Concept Map Construction (TP-CMC) approach to automatically construct the concept map by learners’ historical testing records. Phase 1 is used to preprocess the testing records; i.e., transform the numeric grade data, refine the testing records, and mine the association rules from input data. Phase 2 is used to transform the mined association rules into prerequisite relationships among learning concepts for creating the concept map. Therefore, in Phase 1, we apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic data, apply Education Theory to further refine it, and apply Data Mining approach to find its grade fuzzy association rules. Then, in Phase 2, based upon our observation in real learning situation, we use multiple rule types to further analyze the mined rules and then propose a heuristic algorithm to automatically construct the concept map. Finally, the Redundancy and Circularity of the concept map constructed are also discussed. Moreover, we also develop a prototype system of TP-CMC and then use the real testing records of students in junior high school to evaluate the results. The experimental results show that our proposed approach is workable.