Sharif Digital Repository

Reviewing the various Partial Discharges (PD data mining researches which have been reported so far, this study compares the performance of different feature spaces and different classifiers employed for PD classification in insulation condition monitoring of power transformers. In this process, first a knowledge basis is developed through construction of 4 different types of PD models in the high voltage laboratory. Background noise is considered as one class in this knowledge basis. The high frequency time domain current signals of high voltage equipment are captured over one power frequency cycle. The single PD activities within this captured signal are extracted by application of a... 

Background and aims: Among different types of occupational accidents in the construction industry, falls and falling objects accidents (group I) account for 44% of construction accidents. Hit by vehicle, electric shock, collapse in the excavation and fire or explosion accidents (group II), while are only 7% frequent, make up about 26% of all fatalities and total disabling accidents. The aim of this study is to investigate these two groups of accidents and to discuss the obtained results in order to identify the potential hazards of construction industry. Methods: Data mining methods are employed to analyze data in this research. Hence, 21864 data records which were provided by Social... 

Data streams are endless flow of data produced in high speed, large size and usually non-stationary environments. The main property of these streams is the occurrence of concept drifts. Using decision trees is shown to be a powerful approach for accurate and fast learning of data streams. In this paper, we present an incremental regression tree that can predict the target variable of newly incoming instances. The tree is updated in the case of occurring concept drifts either by altering its structure or updating its embedded models. Experimental results show the effectiveness of our algorithm in speed and accuracy aspects in comparison to the best state-of-the-art methods  

In this paper, the joint problem of project selection and project scheduling under uncertain environment is formulated, analyzed, and solved by multistage stochastic programs. First of all, a general mathematical formulation which can address several versions of the problem is presented. A multi-period project selection and scheduling problem is introduced and modeled by multistage stochastic programs, which are effective for solving long-term planning problems under uncertainty. A set of scenarios and corresponding probabilities is applied to model the multivariate random data process (costs or revenues, available budget, chance of success). Then, due to computational complexity, a scenario... 

Trip destination and mode choice are highly influenced by travelers' perceptions and behaviors; selecting a destination and a vehicle for a trip are two interdependent problems. This paper presents and applies a disaggregate joint model for traveler destination and mode choice. The choice model uses fuzzy set and probability theory to deal with the uncertainty embedded in travelers' perceptions and behaviors. The model is structured as a decision tree in which the fuzzy and non-fuzzy classification of influential variables regarding destination selection and mode choice expand the tree. The most influential explanatory variables among all the variables categorized for travelers' household,... 

Aim of study: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. Methodology/Approach: The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for... 

In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian  

This paper introduces a novel approach for power system fault diagnosis based on synchronized phasor measurements during the fault. The synchronized measurements are obtained in real time from Phasor Measurement Units (PMUs) and compared with offline thresholds determined by decision trees (DTs) to diagnose the fault. The DTs have already been trained offline using detailed power system analysis for different fault cases. While the traditional methods for fault diagnosis use the status of protective relays (PRs) and circuit breakers (CBs) to infer the fault section in the power system, the proposed method uses the available signals following the fault and thus can be trusted even in case of... 

An adaptive wide-area damping controller (WADC) scheme based on gain scheduling (GS) technique and decision tree (DT) approach is presented. For the predefined operating scenarios, several robust controllers are designed, so that each controller performs well within a certain range of load/generation variation and contingencies. Considering the best acting controller in each operating condition as the target of classification, several DTs are trained using stage-wise additive modeling by multiclass exponential loss function (SAMME). The weighted combination of DTs is referred to as additive model DT (AMDT) that is used to interpolate the controller coefficients from phasor measurement unit... 

In this paper, we propose an adaptive object tracking algorithm in crowded scenes. The amplitudes of of Undecimated Wavelet Packet Tree coefficients for some selected pixels at the object border are used to create a Feature Vector (FV) corresponding to that pixel. The algorithm uses these FVs to track the pixels of small square blocks located at the vicinity of the object boundary. The search window is adapted through the use of texture information of the scene by finding the direction and speed of the object motion. Experimental results show a good object tracking performance in crowds that include object translation, rotation, scaling and partial occlusion. Copyright - World Automation... 

Evaporative condensers are regarded as highly-efficient and eco-friendly heat exchangers in refrigeration systems. Data-driven methods can play a key role in performance prediction of evaporative condensers, conducted without the complexity of theoretical analysis. In this study, four machine learning models including multi-layer perceptron artificial neural network (ANNMLP), support vector regression (SVR), decision tree (DT), and random forest (RF) models have been employed to predict heat transfer rate and overall heat transfer coefficient of a small-scale evaporative condenser functioning under a wide range of working conditions. A set of experimental tests were conducted, where inlet...