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.     

Discovery of Five New Ethylene-Forming Enzymes for Clean Production of Ethylene in E. coli

Ethylene is an essential platform chemical with a conjugated double bond, which can produce many secondary chemical products through copolymerisation. At present, ethylene production is mainly from petroleum fractionation and cracking, which are unsustainable in the long term, and harmful to our environment. Therefore, a hot research field is seeking a cleaner method for ethylene production. Based on the model ethylene-forming enzyme (Efe) {"type":"entrez-protein","attrs":{"text":"AAD16440.1","term_id":"4323597","term_text":"AAD16440.1"}}AAD16440.1 (6vp4.1.A) from Pseudomonas syringae pv. phaseolicol, we evaluated five putative Efe protein sequences using the data derived from phylogenetic analyses and the conservation of their catalytic structures. Then, pBAD expression frameworks were constructed, and relevant enzymes were expressed in E. coli BL21. Finally, enzymatic activity in vitro and in vivo was detected to demonstrate their catalytic activity. Our results show that the activity in vitro measured by the conversion of α-ketoglutarate was from 0.21–0.72 μmol ethylene/mg/min, which varied across the temperatures. In cells, the activity of the new Efes was 12.28–147.43 μmol/gDCW/h (DCW, dry cellular weight). Both results prove that all the five putative Efes could produce ethylene.     

On accounting for screen resolution in adaptive video streaming: QoE-driven bandwidth sharing framework

Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link mean opinion score (MOS) to video bitrate for different screen types (e.g., Common Intermediate Format [CIF], Quarter Common Intermediate Format [QCIF], and High Definition [HD]). We leverage such studies and formulate a Quality of Experience (QoE)-driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and Karush Kuhn Tucker (KKT) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look-alike strategy implementing max-min fairness.     

Harmonic analysis and performance improvement of slip energyrecovery induction motor drives

Slip energy recovery induction motor drives are used in high power applications, in which by controlling the slip power a variable speed drive system is provided. However, poor power factor is a disadvantage of the system. Presence of sub-harmonics of the line frequency on the stator side is one of the special features of these drives. A phase-controlled inverter can absorb the reactive power from the supply and inject the low-order current harmonics with relatively high amplitudes to the supply. This paper simulates the harmonic content of waveforms in various points of drive at different speeds, based on the hybrid model (dqabc). Then the sinusoidal pulse-width-modulation (SPWM) control technique is used in order to improve the power factor of the drive and to weaken the injected low-order harmonics to the supply. Based on the frequency spectrum, total harmonic distortion, distortion factor and power factor, two phase control and SPWM techniques are compared and the advantage of the SPWM technique over the phase control technique is shown     

An Elastomeric Poly(Thiophene‐EDOT) Composite with a Dynamically Variable Permeability Towards Organic and Water Vapors

An interpenetrating polymer network (IPN) material with controllable nanoporosity is developed for applications such as chemical protection. The IPN material is based on a conducting polymer backbone consisting of thiophene and 3,4 ethylenedioxythiophene (EDOT) repeat units–poly(thiophene‐EDOT)–formed within a soft polyurethane support. The IPN demonstrates reversible, electrochemically switchable nanoporosity in the absence of standard liquid electrolyte, with the oxidized state being the open (high porosity) state and the reduced state being the closed (low porosity) state. The switching of the IPN between its oxidized (open) and reduced (closed) states is actuated using application of ±1.0 V. The variability in the IPN porosity, induced by the electrochemical switching, is revealed by large changes in water vapor diffusivity, as well as changes in the diffusivities of the chemical agent simulants chloroethyl ethyl sulfide (CEES) and methyl salicylate (MeS). The closed state of the IPN is able to decrease CEES diffusivity by ≈99% compared to expanded Teflon (ePTFE), while the open state allows high MVT rates comparable to ePTFE. The IPN's ability to allow high MVT under non‐threat conditions (open state) and high protection from agents under threat conditions (closed state) is a unique and desirable characteristic of this novel IPN material.     

Performance of Object Classification Using Zernike Moment

Moments have been used in all sorts of object classification systems based on image. There are lots of moments studied by many researchers in the area of object classification and one of the most preference moments is the Zernike moment. In this paper, the performance of object classification using the Zernike moment has been explored. The classifier based on neural networks has been used in this study. The results indicate the best performance in identifying the aggregate is at 91.4% with a ten orders of the Zernike moment. This encouraging result has shown that the Zernike moment is a suitable moment to be used as a feature of object classification systems.     

Development of Optical Code Division Multiple Access Based System Using Spectral Amplitude Coding via Fiber Bragg Gratings (FBGs)

Wireless Personal Communications - The multiple user’s based transmission systems are developed in the past using various multiple access techniques for fibre optic communications. Among...     

A Dual-Band Printed Antenna Design Based on Annular Koch Snowflake Slot Structure

Wireless Personal Communications - The design of a fractal based slot antenna, to serve the dual-band communication applications, is proposed in this paper. The structure of the proposed antenna is...     

Estimation of solar radiation components incident on Helwan site using neural networks

Field testing carried out for solar energy applications is costly, time consuming and depends heavily on prevailing weather conditions. Adequate security and weather protection must be provided at the test site. Measurements may also suffer from delays that can be caused by system failures and bad weather. To overcome these problems the need for accurate model becomes evermore important. To achieve such prediction task, an artificial neural network, ANN, model is regarded as a cost-effective technique superior to traditional statistical methods. In this paper, Levenberg optimization function is adopted to predict insolation data in different spectral bands for Helwan (Egypt) monitoring station. The predicted values were then compared with the actual values and presented in terms of usual statistics. The results hint that, the ANN model predicted infrared, ultraviolet, and global insolation with a good accuracy of approximately 95%, 93% and 96%, respectively. In addition, ANN model was tested to predict the same components for Aswan over an 11 month period. The predicted values of the ANN model compared to the actual values for Aswan produced an accuracy of 95%, 91% and 92%, respectively. Data for Aswan were not included as a part of ANN training set. Hence, these results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates.