جستجوی مقالات مرتبط با کلیدواژه "همه گیری" در نشریات گروه "برق"

One of the biggest challenges in this field is classification problems which refers to the number of different samples in each class. If a data set includes two classes, imbalance distribution occurs when one class has a large number of samples while the other is represented by a small number of samples. In general, the methods of solving these problems are divided into two categories: under-sampling and over-sampling. In this research, it is focused on under-sampling and the advantages of this method will be analyzed by considering the efficiency of classifying imbalanced data and it’s supposed to provide a method for sampling a majority data class by using subtractive clustering and fuzzy similarity measure. For this purpose, at first the subtractive clustering is conducted and the majority data class is clustered. Then, using fuzzy similarity measure, samples of each cluster will be ranked and appropriate samples are selected based on these rankings. The selected samples with the minority class create the final dataset. In this research, MATLAB software is used for implementation, the results are evaluated by using AUC criterion and analyzing the results has been performed by standard statistical tools. The experimental results show that the proposed method is superior to other methods of under-sampling.

Recently, the frequency and severity of natural and man-made disasters (extreme events), which have a high-impact low-frequency (HILF) property, are increased. These disasters can lead to extensive outages, damages, and costs in electric power systems. A power system must be built with “resilience” against disasters, which means its ability to withstand disasters efficiently while ensuring the least possible interruption in the supply of electricity, sustaining critical social services, and enabling a quick recovery and restoration to the normal operation state. Quantifying the power system resilience is a complicated and controversial problem. However, this is necessary for the evaluation and comparison of different resilience enhancement strategies. The resilience metrics are mathematical tools to measure the resilience level of a power system, which are normally employed for resilience cost-benefit in the planning and operation domains. Numerous resilience metrics have been presented in the power system literature. However, there is a lack of a comprehensive conceptual framework regarding the different types of resilience metrics in electric power systems, and existing frameworks have essential shortcomings. In this paper, after introducing and criticizing the existing frameworks, a conceptual framework is suggested to classify different types of resilience metrics in the power system literature. In this conceptual framework, power system resilience metrics are divided into “non-performance-based” and “performance-based” groups. The “performance-based” resilience metrics are also divided into “performance” and “consequence (outcome)” groups. The “performance” resilience metrics consist of five groups including “power”, “duration”, “frequency”, “probability” and “curve”. The “consequence (outcome)” resilience metrics consist of four groups including “economic”, “social”, “geographic” and “safety and health”. In addition, both of the “performance” and “consequence (outcome)” groups have a distinct group naming “general”. In order to verify and validate the comprehensiveness and inclusivity of the proposed conceptual framework, two actions are accomplished. Firstly, the existing power system resilience metrics are allocated to the framework’s groups. Secondly, the proposed conceptual framework is compared with the existing frameworks. These actions show that the proposed conceptual framework can cover and classify different types of power system resilience metrics in the literature, is more comprehensive comparing the existing frameworks, and lacks the essential shortcoming of those frameworks. Thus, the proposed conceptual framework is comprehensive and useful. The proposed conceptual framework can be used by academic and industrial researchers. Academic researchers can concentrate on groups that need further research to propose new resilience metrics, whereas industrial researchers can choose the appropriate resilience metric according to their needs.

In recent years, the sampling problem in massive graphs of social networks has attracted much attention for fast analyzing a small and good sample instead of a huge network. Many algorithms have been proposed for sampling of social network’ graph. The purpose of these algorithms is to create a sample that is approximately similar to the original network’s graph in terms of properties such as degree distribution, clustering coefficient, internal density and community structures, etc. There are various sampling methods such as random walk-based methods, methods based on the shortest path, graph partitioning-based algorithms, and etc. Each group of methods has its own pros and cones. The main drawback of these methods is the lack of attention to the high time complexity in making the sample graph and the quality of the obtained sample graph. In this paper, we propose a new sampling method by proposing a new equation based on the structural properties of social networks and combining it with bee colony algorithm. This sampling method uses an informed and non-random approach so that the generated samples are similar to the original network in terms of features such as network topological properties, degree distribution, internal density, and preserving the clustering coefficient and community structures. Due to the random nature of initial population generation in meta-heuristic sampling methods such as genetic algorithms and other evolutionary algorithms, in our proposed method, the idea of ​​consciously selecting nodes in producing the initial solutions is presented. In this method, based on the finding hub and semi-hub nodes as well as other important nodes such as core nodes, it is tried to maintain the presence of these important nodes in producing the initial solutions and the obtained samples as much as possible. This leads to obtain a high-quality final sample which is close to the quality of the main network. In this method, the obtained sample graph is well compatible with the main network and can preserve the main characteristics of the original network such as topology, the number of communities, and the large component of the original graph as much as possible in sample network. Non-random and conscious selection of nodes and their involvement in the initial steps of sample extraction have two important advantages in the proposed method. The first advantage is the stability of the new method in extracting high quality samples in each time. In other words, despite the random behavior of the bee algorithm, the obtained samples in the final phase mostly have close quality to each other. Another advantage of the proposed method is the satisfactory running time of the proposed algorithm in finding a new sample. In fact, perhaps the first question for asking is about time complexity and relatively slow convergence of the bee colony algorithm. In response, due to the conscious selection of important nodes and using them in the initial solutions, it generates high quality solutions for the bee colony algorithm in terms of fitness function calculation. The experimental results on real world networks show that the proposed method is the best to preserve the degree distribution parameters, clustering coefficient, and community structure in comparison to other method.

The core of every intelligent system is its ability to adapt to environmental changes, but there is not enough attention to the compatibility issue in these systems. Hence, the aim of this study is to provide a method in adaptive business intelligence system that dynamically segment customers and update this segmentation by monitoring purchasing behavior and analysis of "Recency", "Frequency" and "Monetary" of each one of them. In this research, data mining techniques have been applied on the adaptive modulus of the adaptive business intelligence system so that the current clients of the organization are classified and over time and with learning from the environment, this classification is improved to provide customized services to customers. This method can modify initial clustering with respect to environmental changes in less than 0.5 seconds and reduce the number of execution of conventional clustering algorithms (Approximately 22% of cases). The proposed method, considering different customer segments, their current RFM values and changes made by repeating purchases at these values, provides a better and more comprehensive clustering of the organization's customers that can be useful in improving the performance of an adaptive business intelligent system.

In industrial environments, a large amount of data is generated which in turn stores the database and data from all relevant areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, error detection, shutdown, relationship management. Collects with the customer, etc. Data mining has become the tool used to gain knowledge of the industrial process of iron and steel making. Due to the rapid growth of data mining, various industries have been using data mining technology to search for hidden patterns that may be more relevant to the new windshield system, which will introduce new models to improve production quality, optimal cost of productivity and maintenance, and so on. Continuous improvement of the entire steel production process due to the avoidance of quality deficiencies and associated production improvement is an essential task of the steel producer. Therefore, the zero defect strategy is popular today and several quality assurance techniques are used to maintain it. This article attempts to identify the effective state-of-the-art sensors in the system using data mining and then to obtain a suitable model using a support vector machine to predict the system status in this article, the accuracy of more than 95% of the error states is detected.

Near-real time data warehouse gives the end users the essential information to achieve appropriate decisions. Whatever the data are fresher in it, the decision would have a better result either. To achieve a fresh and up-to-date data, the changes happened in the side of source must be added to the data warehouse with little delay. For this reason, they should be transformed in to the data warehouse format. One of the famous algorithms in this area is called X-HYBRIDJOIN. In this algorithm the data characteristics of real word have been used to speed up the join operation. This algorithm keeps some partitions, which have more uses, in the main memory. In the proposed algorithm in this paper, disk-based relation is joined with input data stream. The aim of such join is to enrich stream. The proposed algorithm uses clustered index for disk-based relation and join attribute. Moreover, it is assumed that the join attribute is exclusive throughout the relation. This algorithm has improved the mentioned algorithm in two stages. At the first stage, some records of source table which are frequently accessible are detected. Detection of such records is carried out during the algorithm implementation. The mechanism is in the way that each record access is counted by a counter and if it becomes more than the determined threshold, then it is considered as the frequently used record and placed in the hash table. The hash table is used to keep the frequently used records in the main memory. When the stream is going to enter in to join area, it is searched in this table. At the second stage, the choice method of the partition which is going to load in the main memory has been changed. One dimensional array is used to choose the mentioned partition. This array helps to select a partition of source table with highest number of records for the join among all partitions of source table. Using this array in each iteration, always leads to choose the best partition loading in memory. To compare the usefulness of the suggested algorithm some experiments have been done. Experimental results show that the service rate acquired in suggested algorithm is more than the existing algorithms. Service rate is the number of joined records in a time unit. Increasing service rate causes the effectiveness of the algorithm.

In this paper, a new image processing technique has been proposed to measure the contact angle of symmetric and asymmetric drops. In our proposed method, the contact angle is calculated by analyzing the side view image of the drop without needing any liquid parameters. The procedure of the technique is according to the secant technique coupled with a Gaussian weighted function, as follows: at the beginning the exact location of the contact points is obtained by Harris corner detector function, then a series of points are selected on the drop boundary near the contact points. In the third step, a set of contact angles are estimated by passing secant lines through the contact point and each of the boundary points. In the final step, a gaussian weighted average function is applied to the calculated angles to estimate the main contact angle. The contact angle measurement algorithm is verified by comparing its results with the obtained measurements of a recent standard study. High accuracy, usability for all ranges of contact angles, applicability to both symmetric and asymmetric drops and being fully automatic are other advantages of the presented method.

Today, most energy consumption in industry is related to induction motors. Evaluation of induction motor’s efficiency is an important issue for life estimation, extend the life and energy saving managements. Using the estimated efficiency of the induction motor, its performance can be judged and replacing the existing low efficiency motor by a high efficiency motor could be decided. In this paper, a novel and efficient method based on Modified Artificial Bee Colony (MABC) Algorithm is presented for efficiency estimation in the induction motors. The main advantage of the proposed method is efficiency evaluation of induction motor without any intrusive test. In order to demonstrate the capabilities of the proposed method, a comparison with other intelligent optimization algorithms is performed. Then, one of the important applications of efficiency estimation, which replaces the high efficiency induction motors instead of conventional motors, is discussed. The results of the calculation of energy savings show that if a standard motor is replaced with a high efficiency motor, energy savings will be significant.

Consumers seek an economic way to procure electrical energy in the competitive electricity market. These ways includes: bilateral contract, participating in the pool market and generating the energy by the self-generating units. Electrical energy should be bought in appropriate time with the best price. In the other hand, effects of increasing the renewable energies have been investigated in the recent years. Considering the uncertainty of these sources increases the complexity of the decision making process as well as un-favorite decision making situations. In this paper, decision making to procure the electrical energy has been investigated in the presence of wind turbine uncertainty. The electricity energy prices and the electricity load demand have also uncertainty. Considering three sources of uncertainty increases the decision making risk. In this regard, an appropriate risk index has been selected to decrease the risk in the system. A simulation is also conducted to demonstrate the wind generation effects. Efficiency of the proposed method has been verified with various simulations.

Self-adaptive software systems by eliminating human intervention and replacing closed loop structures for opened loop ones, are able to adapt to runtime changes in the context. In this regard, a three-stage process consisting of detecting, decision-making and implementation is a suitable mechanism. Despite the importance of the decision stage on the appropriate implementation of a self-adaptive software system and given models for decision making, yet the development of self-adaptive systems is associated with challenges, and these models cannot have the full protection of the current environment Therefore, in this study, we propose a model based on the concepts related to the goal-based ways to support decision-making in self-adaptive systems. This model not only results in flexibility increase, but also covers some challenges facing self-adaptive systems. A case study is used in order to assess the proposed model, and the assessment results in the reduction of decision making time and increase of workload.

In this paper a new structure of learning automata which is called as extended distributed learning automata (eDLA) is introduced. A new eDLA-based iterative sampling method for finding optimal sub-graph in stochastic graphs is proposed. Some mathematical analysis of the proposed algorithm is presented and the convergence property of the algorithm is studied. Our study shows that the proposed algorithm can be converge to the optimal sub-graph.

Nowadays, the optimal application of phasor measurement units (PMUs) in power systems is increasing because of their advantages such as the capability for dynamical state estimation and improvements in the speed of control and protection systems. In this paper, a new method is presented using binary integer linear programming for the optimal placement of PMUs to guarantee full observability of a power system. Moreover, the problem of the optimal placement of these units in the case of a single line outage is investigated. In all of the investigations, the effect of zero-injection buses in the power system was considered. The ability of the proposed method in solving any of these problems was demonstrated by applying to the 14-, 30-, 39-, 57- and 118-bus IEEE standard systems, 42-bus transmission network of Esfahan Regional Electric and also two very large-scale systems with 2383 and 2746 buses. The simulation results verified the effective and appropriate performance of the proposed method in comparison with other methods.

In this work the inverse transfer function of the measurement room is obtained using a linear and RLS (recursive least-squares algorithm) adaptive filter. At first, an antenna with known pattern (reference antenna) is placed in the test zone to obtain the optimal tap weights of the adaptive filter. Once the filter converges, the learning stage finishes. After that the reference antenna is replaced with the test antenna and its pattern is measured and the data of the measurement is filtered by the optimal tap weights adaptive filter. The filter cancels the stray signals (reflected and scattered signal) and produces the radiation pattern of the test antenna. This technique is superior to that of others because it has better accuracy and it is less costly.