Sharif Digital Repository

This paper presents a new inference algorithm for active learning method (ALM). ALM is a pattern-based algorithm for soft computing, which uses the ink drop spread (IDS) algorithm as its main engine for feature extraction. In this paper, a fuzzy number is extracted from each IDS plane rather than from the narrow path and the spread, as in previous approaches. This leads to a significant reduction in the hardware required to implement the inference part of the algorithm and real-time computation of the implemented hardware. A modified version of the memristor crossbar structure is used to solve the problem of varying shapes of the ink drops, as reported in previous studies. In order to... 

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of... 

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of... 

In this study we will show that the variation rate of the memristance of the memristive device depends completely on its current memristance which means that it can change significantly with time during the learning phase. This phenomenon can degrade the performance of learning methods like Spike Timing-Dependent Plasticity (STDP) and cause the corresponding neuromorphic systems to become unstable  

Fuzzy techniques can be used for accurate and high-speed modeling as well as for the control of complex systems, but various challenging problems are usually encountered during their actual implementation. For example, the variable parameters need to be optimized iteratively during the training phase, where this process is inspired by crisp domain algorithms. However, in recent years, memristor-based structures have emerged as another promising method for implementing neural network structures and fuzzy algorithms. In this study, we propose a novel adaptive and real-time fuzzy modeling algorithm, which employs the active learning method concept to mimic the functionality of the brain's right... 

This paper presents a comparative study of various soft computing algorithms for reconstruction of neutron noise sources in the nuclear reactor cores. To this end, the computational code for reconstruction of neutron noise source is developed based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Decision Tree (DT), Radial Basis Function (RBF) and Support Vector Machine (SVM) algorithms. Neutron noise source reconstruction process using the developed computational code consists of three stages of training, testing and validation. The information of neutron noise sources and induced neutron noise distributions are used as output and input data of training stage, respectively. As input... 

Accurate unfolding of the energy spectrum of a neutron source gives important information about unknown neutron sources. The obtained information is useful in many areas like nuclear safeguards, nuclear nonproliferation, and homeland security. In the present study, the energy spectrum of a poly-energetic fast neutron source is reconstructed using the developed computational codes based on the Group Method of Data Handling (GMDH) and Decision Tree (DT) algorithms. The neutron pulse height distribution (neutron response function) in the considered NE-213 liquid organic scintillator has been simulated using the developed MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif... 

The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi-Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code... 

The neutron noise is defined as the stationary fluctuation of the neutron flux around its mean value due to the induced perturbation in the reactor core. The neutron noise analysis may be useful in many applications like noise source reconstruction. To identify the noise source, calculated neutron noise distribution of the detectors is used as input data by the considered unfolding algorithm. The neutron noise distribution of the VVER-1000 reactor core is calculated using the developed computational code based on Galerkin Finite Element Method (GFEM). The noise source of type absorber of variable strength is considered in the calculation. The computational code developed based on An Adaptive... 

Within the "pseudo-Dirac" scenario for massive neutrinos the existence of sterile neutrinos which are almost degenerate in mass with the active ones is hypothesized. The presence of these sterile neutrinos can affect the flavor composition of cosmic neutrinos arriving at Earth after traveling large distances from astrophysical objects. We examine the prospects of neutrino telescopes such as IceCube to probe the very tiny mass squared differences 10-12eV2>Δm2>10-19eV2, by analyzing the ratio of μ-track events to showerlike events. Considering various sources of uncertainties which enter this analysis, we examine the capability of neutrino telescopes to verify the validity of the pseudo-Dirac... 

We discuss the prospect of extracting the values of the mixing parameters δ and θ13 through the detection of cosmic neutrinos in the planned and forthcoming neutrino telescopes. We take the ratio of the μ-track to shower-like events, R, as the realistic quantity that can be measured in the neutrino telescopes. We take into account several sources of uncertainties that enter the analysis. We then examine to what extent the deviation of the initial flavor composition from we : w μ : wτ 1 : 2 : 0 can be tested  

The annihilation of dark matter particles captured by the Sun can lead to a neutrino flux observable in neutrino detectors. Considering the fact that these dark matter particles are nonrelativistic, if a pair of dark matter annihilates to a neutrino pair, the spectrum of neutrinos will be monochromatic. We show that in this case, even after averaging over the production point inside the Sun, the oscillatory terms of the oscillation probability do not average to zero. This leads to interesting observable features in the annual variation of the number of muon track events. We show that smearing of the spectrum due to thermal distribution of dark matter inside the Sun is too small to wash out... 

We examine the feasibility of deriving neutrino mixing parameters δ and θ13 from the cosmic neutrino flavor composition under the assumption that the flavor ratios of the cosmic neutrinos at the source were Fνe + Fover(ν, ̄)e : Fνμ + Fover(ν, ̄)μ : Fντ + Fover(ν, ̄)τ = 1 : 2 : 0. We analyze various uncertainties that enter the derivation of δ and θ13 from the ratio of the shower-like to μ-tracks events which is the only realistic source of information on the flavor composition at neutrino telescopes such as ICECUBE. We then examine to what extent the deviation of the initial flavor ratio from 1 : 2 : 0 can be tested by measurement of this ratio at neutrino telescopes taking into account... 

We construct a new set of combinations from the mass matrices of the charged leptons and neutrinos that are invariant under basis transformation, hereafter the invariants. We use these invariants to study various symmetries and neutrino mass textures in a basis independent way. In particular, we show that by using these invariants the ansatz such as μ - τ exchange and reflection symmetries, various texture zeros and flavor symmetries can be expressed in a general basis. © 2008 Elsevier B.V. All rights reserved  

Semantic web search engines are evolving and many prototype systems and some implementation have been developed. However, there are some different views on what a semantic search engine should do. In this paper, a categorization scheme for semantic web search engines are introduced and elaborated. For each category, its components are described according to a proposed general architecture and various approaches employed in these components are discussed. We also propose some factors to evaluate systems in each category. © 2006 IEEE