Sharif Digital Repository

Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems. SISO systems as associative layer of algorithm capture partial spatial knowledge of sample data space, and enable a granular knowledge resolution tuning mechanism through the learning process. The... 

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 the narrow path and spread as in previous approaches. In order to compare performance of the proposed algorithm with the original one, two functions which are widely used in literature are modeled as the benchmark. Simulation results show that the proposed algorithm is as effective as previous one in the modeling task  

This paper presents a new and simple algorithm for partitioning the input domain for implementation of Active Learning Method (ALM) algorithm. 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 simple algorithm is introduced with a few computation cost. In order to evaluate the performance of the proposed algorithm, it is applied to two applications, system modeling and pattern recognition. Simulation results show the effectiveness of our algorithm in specifying the appropriate points for dividing the inputs domains  

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... 

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... 

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... 

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  

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... 

This paper introduces an innovative adaptive controller based on the actor-critic method. The proposed approach employs the ink drop spread (IDS) method as its main engine. The IDS method is a new trend in softcomputing approaches that is a universal fuzzy modeling technique and has been also used as a supervised controller. Its process is very similar to the processing system of the human brain. The proposed actor-critic method uses an IDS structure as an actor and a 2-dimensional plane, representing control variable states, as a critic that estimates the lifetime goodness of each state. This method is fast, simple, and away from mathematical complexity. The proposed method uses the... 

Here we analyze the effect of two relevant aspects related to cell preparation on quantum dot sensitized solar cells (QDSCs) performance: the architecture of the TiO 2 nanostructured electrode and the growth method of quantum dots (QD). Particular attention is given to the effect on the photovoltage, V oc, since this parameter conveys the main current limitation of QDSCs. We have analyzed electrodes directly sensitized with CdSe QDs grown by chemical bath deposition (CBD) and successive ionic layer adsorption and reaction (SILAR). We have carried out a systematic study comprising structural, optical, photophysical and photoelectrochemical characterization in order to correlate the material... 

Low-temperature geothermal heat sources have the highest share of geothermal energy in the world. Utilization of these heat sources for energy and freshwater generation can play an important role in meeting energy and freshwater demands. To do so, this study aims to propose a novel trigeneration cycle powered by low-temperature geothermal sources. The proposed system, which is an integration of Kalina and humidification-dehumidification (HDH) cycles, is used for the generation of electricity, heating, and freshwater. For the Kalina cycle, an evaporative condenser is used. It also acts as a humidifier and heater of the humidification-dehumidification desalination cycle, resulting in a... 

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... 

In this paper we present experimental results describing electrical readout of the mechanical vibratory response of graphene-doped fibers by employing electrical actuation. For a fiber resonator with an approximate radius of 850 nm and length of 100 m, we observed a resonance frequency around 580 kHz with a quality factor (Q) of about 2511 in air at ambient conditions. Through the use of finite element simulations, we show that the reported frequency of resonance is relevant. We also show that the resonance frequency of the fiber resonators decreases as the bias potential is increased due to the electrostatic spring-softening effect  

The respiratory system is a vital organ system which makes breathing and gas exchange possible for human's body. The loss of functionality in this system is due to either genetical problems or environmental issues like pollution or toxic gases which in many cases is inevitable. These circumstances may cause different respiratory diseases which may not be curable presently but their side effects can be partly controlled by careful and timely analysis improving the quality of patient's life. So, an appropriate device for conducting different analysis can be effective for people who suffer from respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, occupational lung... 

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... 

Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle detection and tracking, and discovers the motion patterns automatically in traffic scenes by using topic model. Then, likelihood value allocated to each video clip enables us to estimate its traffic density. Results on a standard dataset show high classification performance of our proposed... 

Traffic density estimation is one of the most challenging problems in Intelligent Transportation Systems. In this paper, we estimate the traffic flow density based on classification. Various new efficient features are introduced for distinguishing between different traffic states, including number of key-points, edges of difference-image and moving edges. These features describe the traffic flow without any need to individual vehicles detection and tracking. We experiment our proposed approach on a standard database and some real videos from Tehran roads. The results show high accuracy performance of our method, even in changes of environmental conditions (e.g., lighting), by using efficient...