AASRI Procedia最新文献

Restructuring of Electricity supply industry introduced many issues such as transmission pricing, transmission loss allocation and congestion management. Many methodologies and algorithms were proposed for addressing these issues. In this paper a graph theory based method is proposed which involves Matrices methodology for the transmission usage, loss and transmission reliability margin (TRM) allocation for generators and demands. This method provides loss and TRM allocation in a direct way because all the computation is previously done for usage allocation. The proposed method is simple and easy to implement in a large power system. Further it is less computational because it requires matrix inversion only a single time. A comparison between proposed method and already exiting methods also presents. Results are shown for the sample 6 bus system and IEEE 14 bus system.

Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We show empirically that Dropout works better than DropConnect on ImageNet dataset.

Initial weight choice is an important aspect of the training mechanism for sigmoidal feedforward artificial neural networks. Usually weights are initialized to small random values in the same interval. A proposal is made in the paper to initialize weights such that the input layer to the hidden layer weights are initialized to random values in a manner that weights for distinct hidden nodes belong to distinct intervals. The training algorithm used in the paper is the Resilient Backpropagation algorithm. The efficiency and efficacy of the proposed weight initialization method is demonstrated on 6 function approximation tasks. The obtained results indicate that when the networks are initialized by the proposed method, the networks can reach deeper minimum of the error functional during training, generalize better (have lesser error on data that is not used for training) and are faster in convergence as compared to the usual random weight initialization method.

TMS320F2808 DSP chip as the core, combined with signal conditioning and keyboard scan peripheral circuits, made up the hardware circuit of the measuring system. As for software,DSP as the main controller, realized the data display,controlment and management about the whole system, Using the DSP internal integrated ADC module to achieve the data collection, while using its internal resources to complete the FFT algorithm and optimization. The field test results show that this measuring system of digital signal distortion realizes the amplitude and frequency display in time domain and frequency domain when the setting signal passed the test system, and the measuring error is less than 0.8 dB.

Spectrum sensing is used in cognitive radio systems to detect the availability of spectrum holes for secondary usage. The simplest and most famous spectrum sensing techniques are based either on energy detection or eigenspace analysis from Random Matrix Theory (RMT) such as using the Marchenko-Pastur law. These schemes suffer from uncertainty in estimating the noise variance which reduces their performance. In this paper we propose a new method to evaluate the noise variance that can eliminate the limitations of the aforementioned schemes. This method estimates the noise variance from a measurement set of noisy signals or noise-only signals. Extensive simulations show that the proposed method performs well in estimating the noise variance. Its performance greatly improves with increasing numbers of measurements and also with increasing numbers of samples taken per measurement.

Due to advancements in technology, many integrated circuits are fabricated to develop an artificial system that could perform “intelligent” tasks similar to those performed by the human brain. Many of them use off-chip learning method either by analog hardware or massively by parallel computers. This proposed work is about a trainable neural chip using Field Programmable Gate Array (FPGA) as this helps in learning capability by exploiting the inherent parallelism of neural network. By this fast prototyping is possible for real-time applications, such as speech recognition, speech synthesis, image processing, pattern recognition and classification. In this work on-chip learning method is designed for standard benchmark XOR problem using back propagation based multilayer perceptron and is implemented in VIRTEX-E FPGA using VHDL. The design works at 5.332 MHz and the total gate count is 4, 73,237.

Authors present a power flow tracing based method for usage and loss allocation along with FACTS devices. Graph theory is the basis for usage allocation. Modified Kirchhoff matrix is used for this purpose. FACTS devices are used in the system for various purposes such as reactive power compensation; voltage profile improvement etc. hence in this paper Thyristor controlled series controller (TCSC) with voltage source modeling is used for finding the effect of FACTS device on transmission usage and loss allocation. A sample 5 bus is used for this purpose.

This paper presents a proposal of digital mediation for enhancing the communication between sports media and its user community. The proposal is based on a conceptual model and a proof of concept in the form of a Web application. It is believed that the proposed application can contribute to enrich sports media Web portals with innovative features and to strengthen the relationship with their user community, exploiting the potential contributions of its members. This project also aims at fostering user participation and sharing of opinions in sports media Web portals.

In the paper we suggested a method for video sequence handling with the aim of producing a single noise- free image. An algorithm for the image alignment based on the approaches of dot matches search and the resolution capacity enhancement has been elaborated. A method for multiple images stacking has been proposed to produce a single noise-free picture. The proposed method has been successfully experimentally tested using developed software tools.

A-SMAC protocol based on sensor node data traffic dynamic adjusting node listen and sleep time. Based on A-SMAC protocol, sensor nodes sent out the underlying error decisions .In this paper, we propose an adaptive fault-tolerant algorithm (AFTA) of node. According to the results of fault tolerant judgment, use of A-SMAC protocol dynamic adjusting node listen and sleep time, realize the combination of data judgment and node dynamic listening. Our experimental results show that the AFTA outperforms the Bayesian algorithm and exhibits strong fault-tolerant capabilities and less energy consume.