In recent years, the need for low power electronic circuits like sensors and wireless systems has been considered by many researchers. Excessive weight, limited lifetime of the batteries and also the problem of replacing them, are the main reasons for harvesting energy from ambient vibrations. Among the various sources of environmental energy, mechanical vibrations have gained in popularity due to their availability. Between the different METHODs of ambient vibration energy harvesting, piezoelectric METHOD, is one of the good ways to harvest energy due to the favorable effects of electromechanical coupling. The most common means of harvesting energy from vibrations is a cantilevered beam with one or more piezoelectric layers. In the present paper, electrical energy harvesting from Euler-Bernoulli trapezoidal cantilevered unimorph beam with base excitation using DISTRIBUTED parameter METHOD has been considered. First, equations of motion are analytically obtained and then using Assumed modes METHOD (for rectangular beam), system’s natural frequencies are calculated and output voltage, current and power diagrams are presented. For verifying results, presented voltage, current and power output diagrams for trapezoidal configuration close to rectangular configuration, the results of which are published in references, will be compared. Then, functional PARAMETERS for trapezoidal energy harvester with resistance value changes for energy consumer have been analyzed.

In the past, Geomorphologic Instantaneous Unit Hydrograph (GIUH) had been proposed as a tool to generation of flood hydrographs from storm rainfall data. Nowadays, Spatial Unit Hydrograph is used instead, with making use of Geographic Information Systems (GIS). First, watershed is divided into small square cells, and then routing of runoff from each cell to the basin outlet is calculated using the first passage time response function based on the mean and variance of the flow time distribution, which is derived from advection- dispersion transport equation. The flow velocity for ground surface cells is calculated by Overton’s equation and for channel flow cells by Manning’s equation based on the local slope, roughness coefficient and hydraulic radius. The total direct runoff at the basin outlet is obtained by superimposing all contributions from every grid cell. The model is tested on kameh catchment in Khorasan Razavi state. Results are in excellent agreement with the measured hydrograph at the basin outlet. Sensitivity analysis shows that PARAMETERS of channel roughness coefficient and minimum slope threshold have larger influence than parameter of thershold of drainage area in delineating channel networks on peak discharge, time to peak and on the outflow hydrograph, Since the model accounts for spatially DISTRIBUTED hydrologic and geophysical characteristics of the catchments, it has great potential for study of the influence of changes in land use or soil cover on the hydrologic behavior of a river basin.

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.

Distance-based clustering METHODs categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these METHODs have several hyper-PARAMETERS, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free PARAMETERS. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by adaptively updating the PARAMETERS during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra PARAMETERS. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the METHOD. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD METHOD compared to the other well-known clustering METHODs.

Special conditions of wireless sensor networks, such as energy limitation, make it essential to accelerate the convergence of algorithms in this field, especially in the DISTRIBUTED compressive sensing (DCS) scenarios, which have a complex reconstruction phase. This paper presents a DCS reconstruction algorithm that provides a higher convergence rate. The proposed algorithm is a DISTRIBUTED primal-dual algorithm in a bidirectional incremental cooperation mode where the PARAMETERS change with time. The PARAMETERS are changed systematically in the convex optimization problems in which the constraint and cooperation functions are strongly convex. The proposed METHOD is supported by simulations, which show the higher performance of the proposed algorithm in terms of convergence rate, even in stricter conditions such as the small number of measurements or the lower degree of sparsity.

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.