This research is in the field of machine translation and in relation to extraction of Persian-English chunks. In this regard, the most important challenge is that the operation must be carried out on large corpus; therefore, it requires Distributed computing along with big data analysis techniques and tools. In fact, when translating text, we are usually confronted with a lot of chunks that we need to find the corresponding chunks of each one in the target language and insert it in our translation; this is accomplished by locating it in a corpus that contain the chunks and their corresponding translations. The existing methods, perform this operations in a non-Distributed way, therefore while they run slowly, they cannot use a very large corpus. To overcome this shortcoming, in this research a Distributed method has been presented, which also takes distance between the sections of chunks into account, and it lemmatization the words in the corpus as well. The proposed method extracts all possible chunks from the input sentences in the monolingual corpus and uses the correlation coefficient to translate those chunks using the bilingual corpus. We implemented the proposed algorithm in a platform consisting of a computing cluster with sixty-four GB of memory and a twenty-four-core processor in Spark. The incorporated experimental data was a Persian and an English monolingual corpus along with an English-Persian bilingual corpus, each of which containing 100, 000 sentences. Experimental results show that run time could greatly be reduced, and the quality of translation is also significantly improved.

Semantic web has been one of the most important research areas of computer science in recent years. The concept of ontology as one of the most elements of semantic web is used to formally describe the domain knowledge and to enable the reasoning capability. Semantic web is a Distributed system and ontologies may be developed on many different nodes, so centralized reasoning is very hard or even impossible in many cases. On the other hand, since the majority of information in semantic web is uncertain, considering the notion of uncertainty in ontological reasoning is crucial. In this paper a method for Distributed reasoning in uncertain ontologies is proposed. For this purpose the Distributed description logic (DDL) framework and the certainty theory are considered for Distributed reasoning and modeling the uncertainty respectively. To evaluate the functionality and performance of the algorithm, we developed a case study on application of the proposed method in purifying the mappings between ontologies. The results show that our algorithm makes the mappings more precise than other similar methods.

Due to the increasing need to Distributed energy resources in power systems, their problems should be studied. One of the main problem of Distributed energy resources is unplanned islanding. The unplanned islanding has some dangers to the power systems and the repairman which are works with the incorrect devices. In this paper, a passive local method is proposed. The proposed method is based on wavelet transform and a new classifier named as wavenet. The wavelet transform is used to extract features from the current waveform of current at the point of common coupling (PCC) point. PCC is assumed as the connection point of Distributed generation to the distribution system. The proposed method is implemented on a 15 bus grid in MATLAB/SIMULINK software. The results show the high accuracy of islanding detection of the proposed method. In this paper, one wind turbine is assumed as a Distributed resource.

In recent years, due to the increase in internet usage, the energy consumption of communication networks' infrastructures has been significantly increased, resulting in more monetary costs and carbon pollution. To address this challenge, green communication is investigated in order to reduce economic costs and environment pollution of ICT section. In this paper, a Distributed sleep-scheduling method is proposed that switches off some network links and nodes in order to save energy during low traffic periods. The proposed approach selects switching-off nodes based on vertex cover problem and the occurrence of their related links. Then, some low-occurrent links in the new topology are switched off if the network performance remains satisfied. The proposed approach does not depend on any central controller and imposes minimum computing and network traffic overload since it does not use the network matrix and profits from the information already provided by link-state protocols. Simulation results performed on real network scenarios show that our proposed technique allows higher network performance compared to other green alternatives, while a significant amount of energy conservation is achieved.

This paper presents a new fault location method for radial distribution networks with Distributed generations.The proposed fault location algorithm uses the voltage and current data obtained by digital fault recorder installed at the head of the network main feeder, with or without the data from any digital fault recorders installed at DG connection points. The algorithm is based on fundamental frequency calculations and includes novel subroutines of current calculation of DG contained laterals and average loading factor and power factor estimation of the network transformers to solve the fault location problem. The method has been tested by simulation studies using EMTP on a 205-node 20kV radial distribution network containing three DG's for different data acquisition scenarios. The results verify accuracy of the method under different fault conditions even with only one measuring point at the head of the main feeder.

In this paper, an online optimal coordinated solution for the voltage regulation of a distribution smart grids including an onload tap changer (OLTC) and multiple Distributed generators (DG) is presented. The proposed strategy sets reactive power points of Distributed generators as well as tap position of the OLTC hierarchically. In the first stage, a Distributed sub-gradient method in order to find the optimal reactive powers for the DGs is implemented to minimize the power losses subject to voltage limits. If the first stage could not regulate voltage profile inside acceptable limits, in the second stage tap position of the OLTC will change optimally. The proposed solution is verified by studying suddenly load change and DGs’ active power output. An IEEE 123-bus unbalanced test system is used for the verification of the proposed method. Fast response, responsibility to different suddenly changes in the operating condition, and reduction in the number of needed taps are the results of this study. Moreover, analogous steady state results in comparison with another centralized method i. e. interior point algorithm demonstrate the ability and efficiency of the proposed solution.

Demand for money is an important part of the macroeconomic models and the monetary policy. In this paper, we estimate the Iranian demand for money for the period 1958-2003using the Autoregressive Distributed Lag (ARDL) method. The results show that the real money balance, gross domestic product, inflation, foreign exchange rate, and government budget deficit have been co-integrated with each other. We also use the error correction model for short-run dynamic analysis. The result shows the speed of adjustment toward the long-run balance is slow.

Plastic hinge properties play a crucial role in predicting the nonlinear response of structural elements. The plastic hinge region of reinforced concrete normal beams has been previously studied experimentally and analytically. The main objective of this research is to evaluate the behavior of the plastic hinge region of reinforced concrete deep beams and its comparison with normal beams through finite element simulation. To do so, ten beams contain six deep beams, and four normal beams, under concentrated and uniformly Distributed loading, are investigated. Lengths in the plastic hinge region involving curvature localization, rebar yielding, and concrete crushing zones are studied. The results indicate that the curvature localization zone is not suitable for the prediction of plastic hinge length in reinforced concrete deep beams. Based on the results it can be stated that in simply supported normal beams the concrete crushing zone is focused on the middle span, but in simply supported deep beams by creating a compression strut between loading place and support, the concrete crushing zone spreads along the compression trajectory. The rebar yielding zone of simply supported beams increases as the loading type is changed from the concentrated load at the middle to the uniformly Distributed load.