Sharif Digital Repository

Data collection is an essential element of reliability assessment and many utilities throughout the world have established comprehensive procedures for assessing the performance of their electric power systems. Data collection is also a constituent part of quantitative power system reliability assessment in which system past performance and prediction of future performance are evaluated. This paper presents an overview of the Iran electric power system data collection scheme and the procedure to its reliability analysis. The scheme contains both equipment reliability data collection procedure and structure of reliability assessment. The former constitutes generation, transmission and... 

One of the most important applications of wireless sensor networks is data collection. This paper proposes as efficient approach for data collection in wireless sensor networks by introducing Member Forward List. This list includes the nodes with highest priority for forwarding the data. When a node fails or dies, this list is used to select the next node with higher priority. The benefit of this node is that it prevents the algorithm from repeating when a node fails or dies. The results show that Member Forward List decreases power consumption and latency in wireless sensor networks  

With the advent of cloud computing and Internet of Things and delegation of data collection and aggregation to third parties, the results of the computations should be verified. In distributed models, there are multiple sources. Each source creates authenticators for the values and sends them to the aggregator. The aggregator combines the authenticated values and creates a verification object for verifying the computation/aggregation results. In this paper, we propose two constructions for verifying the results of countable and window-based countable functions. These constructions are useful for aggregate functions such as median, max/min, top-k/first-k, and range queries, where the... 

With the widespread access of people to the Internet and the increasing usage of social networks in all nations, social networks have become a new source to study cultural similarities and differences. We identified major issues in traditional methods of data collection in cross-cultural studies: Difficulty in access to people from many nations, limited number of samples, negative effects of translation, positive self-enhancement illusion, and a few unreported problems. These issues are either causing difficulty to perform a cross-cultural study or have negative impacts on the validity of the final results. In this paper, we propose a framework that aims to calculate cultural distance among... 

In this paper, we investigate the CO emission from an oil refinery gas incinerator both theoretically and experimentally. At the beginning of this research, our collected data from this incinerator showed that the CO contamination would be far exceeding the permissible environmental standards at the stack exhaust. Therefore, we decided to perform a combined theoretical-experimental study to find a reasonable solution to reduce the CO pollution suitably. Our theoretical study showed that a reliable solution would be to increase the incinerator operating temperature. However, we needed to collect some data from this incinerator to examine if our achieved analytical solution would work... 

A limited number of studies have tried to apply behavioral models to freight policy analysis, but due to the lack of data, most have not produced satisfactory results. Many decision-makers are unwilling to participate in surveys that inquire about their shipping decisions, since such information is an important part of their business strategies, and understandably, they fear jeopardizing their competitive edge by participating. This results in generally poor participation rates for freight surveys and makes them very expensive in many cases. However, recent empirical findings suggest that the linkage between non-response rates and non-response biases is often nonexistent. This paper examines... 

Internet-of-Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning models. However, this approach introduces several privacy and efficiency challenges, as the service operator can perform unwanted inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger and more complicated models. In this article, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, and... 

Direct marketing identifies customers who buy, more probable, a specific product to reduce the cost and increase the response rate of a marketing campaign. The advancement of technology in the current era makes the data collection process easy. Hence, a large number of customer data can be stored in companies where they can be employed to solve the direct marketing problem. In this paper, a novel Bayesian method titled correlation-augment naïve Bayes (CAN) is proposed to improve the conventional naïve Bayes (NB) classifier. The performance of the proposed method in terms of the response rate is evaluated and compared to several well-known Bayesian networks and other well-known classifiers... 

Observatory-scale data collection efforts allow unprecedented opportunities for integrative, multidisciplinary investigations in large, complex watersheds, which can affect management decisions and policy. Through the National Science Foundation-funded REACH (REsilience under Accelerated CHange) project, in collaboration with the Intensively Managed Landscapes-Critical Zone Observatory, we have collected a series of multidisciplinary data sets throughout the Minnesota River Basin in south-central Minnesota, USA, a 43,400-km2 tributary to the Upper Mississippi River. Postglacial incision within the Minnesota River valley created an erosional landscape highly responsive to hydrologic change,... 

Estimanon of the origin-destination trip danad matrix (O-D) plays a key role in travel analysis and transportation planning and operations. Many researchers have developed different O-D maths estimation mediods using traffic counts, which allow simple data collection as opposed to die costly traditional direct estimation methods based on home and roadside interviews. In mis papet, a new fuzzy O-D matrix estimation model (FODMEM) is proposed to estimate die O-D matrix from traffic count. A gradient-based aigoridnn. containing 3 fuzzy rule based approach to control die estimated O-D matrix changes, is proposed to solve FODMEM Since link data only represents a snapshot situation, resulting in... 

Losing eight meters of water level over a 20-year period from 1996 to 2016 marked the Lake Urmia (LU) as one of the regional environmental crises. This condition has threatened biota life, intensified desertification around the lake, and raised social concerns by adversely impacting the inhabitants’ health and economy. In 2013, the Urmia Lake Restoration National Committee (ULRNC) started implementing certain management practices to stop the drying trend of LU, resulted in the cease of water level drop and stabilization of LU condition in 2016. Nevertheless, the restoration actions have not yet raised the lake to the water level as planned by the roadmap. This paper aims to describe and to...