Sharif Digital Repository

Online voltage security assessment is necessary to choose appropriate remedial and corrective actions in order to prevent a large-scale blackout. This paper presents a new online voltage security assessment method based on wide-area measurements and decision-tree algorithm. For the predicted load and generation variation scenarios (i.e., a day ahead), the database is obtained using continuation power flow. Moreover, each operating point is labeled as 'secure' or 'insecure' from voltage stability points of view based on WECC voltage security criteria. Decision trees are trained on the subset of the existing data by applying two famous splitting rules and various predictors. Bagging and... 

Accidents involving falls and falling objects (group I) are highly frequent accidents in the construction industry. While being hit by a vehicle, electric shock, collapse in the excavation and fire or explosion accidents (group II) are much less frequent, they make up a considerable proportion of severe accidents. In this study, multiple-correspondence analysis, decision tree, ensembles of decision tree and association rules methods are employed to analyse a database of construction accidents throughout Iran between 2007 and 2011. The findings indicate that in group I, there is a significant correspondence among these variables: time of accident, place of accident, body part affected, final... 

The biophysical and ultrasonographic properties of the skin change in papulosquamous diseases. Aims: To identify biophysical and ultrasonographic properties for the differentiation of five main groups of papulosquamous skin diseases. Methods: Fifteen biophysical and ultrasonographic parameters were measured by multiprobe adapter system and high-frequency ultrasonography in active lesions and normal control skin in patients with chronic eczema, psoriasis, lichen planus, pityriasis rosea and parapsoriasis/mycosis fungoides. Using histological diagnosis as a gold standard, a decision tree analysis was performed based on the mean percentage changes of these parameters [(lesion-control/control)... 

In this paper, we show that by considering the factor of usage in instruction bundles in VLIW processors and using the slots filled with NOPs in bundles, we can improve the overall performance by reducing the total execution time of the program. By our proposed scheme, Combined Bundle Scheduling (CBS), we have gained better performance compared to that for the PDT scheme (Predicted Decision Tree scheduling) which is the best scheduling strategy known so far. © 2006 IEEE  

A subgraph in an edge-colored graph is multicolored if all its edges receive distinct colors. In this paper, we prove that a complete graph on 2m (m ≠ 2) vertices K2m can be properly edge-colored with 2m - 1 colors in such a way that the edges of K2m can De partitioned into m multicolored isomorphic spanning trees. © 2006 Society for Industrial and Applied Mathematics  

Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship between cost overrun risks during the risk assessment process is one of the primary reasons construction projects go over budget. Conversely, recent studies have failed to account for potential interrelationships between risk factors in their machine learning (ML) models. Additionally, the presented ML models are not interpretable. Thus, this study contributes to the entire ML process using a Bayesian network (BN) classifier model by considering the possible interactions between predictors, which are cost overrun risks, to predict cost overrun and assess cost overrun risks. Furthermore, this study... 

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... 

There are several approaches to emotions in AI, most of which are inspired by human emotional states and their arousal mechanisms. These approaches usually use high-level models of human emotions that are too complex to be directly applicable in simple artificial systems. It seems that a new approach to emotions, based on their functional role in information processing in mind, can help us to construct models of emotions that are both valid and simple. In this paper, we will try to present a model of emotions based on their role in controlling the attention. We will evaluate the performance of the model and show how it can be affected by some structural and environmental factors. © 2008 The... 

Background: Brucellosis is a common zoonotic infection of humans from livestock. This bacterial infection is acquired from infected animals and their products. The pathogen of this disease is a genus of bacilli called Brucella, and no effective vaccine has been discovered yet for the prevention of human brucellosis. Objectives: The present study is mainly conducted to diagnose brucellosis accurately and timely, using Data Mining techniques. Based on the knowledge discovered with Data Mining and opinions of specialist physicians, this study aims to propose instructions for diagnosing brucellosis. Materials and methods: The dataset used in this study contains 340 samples and is extracted from... 

Decision tree-clustered context-dependent hidden semi-Markov models (HSMMs) are typically used in statistical parametric speech synthesis to represent probability densities of acoustic features given contextual factors. This paper addresses three major limitations of this decision tree-based structure: (i) The decision tree structure lacks adequate context generalization. (ii) It is unable to express complex context dependencies. (iii) Parameters generated from this structure represent sudden transitions between adjacent states. In order to alleviate the above limitations, many former papers applied multiple decision trees with an additive assumption over those trees. Similarly, the current... 

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,... 

This paper proposes the use of a new binary decision tree, which we call a soft decision tree, to improve generalization performance compared to the conventional ‘hard’ decision tree method that is used to cluster context-dependent model parameters in statistical parametric speech synthesis. We apply the method to improve the modeling of fundamental frequency, which is an important factor in synthesizing natural-sounding high-quality speech. Conventionally, hard decision tree-clustered hidden Markov models (HMMs) are used, in which each model parameter is assigned to a single leaf node. However, this ‘divide-and-conquer’ approach leads to data sparsity, with the consequence that it suffers... 

This study presents a passive model to detect islanding conditions of synchronous distributed generation resources in a distribution network or a microgrid. The proposed approach uses the classification and regression tree algorithm for distinguishing between islanding and non-islanding situations. It utilises the rate of change of frequency (ROCOF) and harmonic content of the equivalent reactance seen at the location of distributed generation as input features for decision tree construction. Indeed the thresholds of the proposed input features are extracted by the decision tree algorithm. The output if-then rules of the decision tree algorithm are then utilised to make a new relay logic for... 

Electricity demand forecast is necessary for power systems' operation scheduling and management. However, power consumption is uncertain and depends on several factors. Moreover, since the onset of covid-19, the electricity consumption pattern went through significant changes across the globe, which made the forecasting demand more challenging. This is mainly due to the fact that pandemic-driven restrictions changed people's lifestyles and work activities. This calls for new forecasting algorithms to more effectively handle these conditions. In this paper, ensemble-based machine learning models are utilized for this task. The lockdown temporal policies are added to the feature set in order... 

Under smart grid environment, islanding detection plays an important role in reliable operation of distributed generation (DG) units. In this paper an intelligent-based islanding detection algorithm for PV and DFIG units is proposed. Decision tree algorithm is used to classify islanding detection instances. This algorithm is rapid, simple, intelligible and easy to interpret. The error rate of this method is reduced by Adaptive Boosting (AdaBoost) technique. The proposed method is tested on a distribution system including PV, DFIG and synchronous generator. Probable events in the system are simulated under diverse operating states to generate classification data set. First and second order... 

Speaker adaptive speech synthesis based on Hidden Semi-Markov Model (HSMM) has been demonstrated to be dramatically effective in the presence of confined amount of speech data. However, we could intensify this effectiveness by training the average voice model appropriately. Hence, this study presents a new method for training the average voice model. This method guarantees that data from every speaker contributes to all the leaves of decision tree. We considered this fact that small training data and highly diverse contexts of training speakers are considered as disadvantages which degrade the quality of average voice model impressively, and further influence the adapted model and synthetic... 

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  

In this paper, we describe LHTNDT, an algorithm that learns the preconditions of HTN methods by examining plan traces produced by another planner. LHTNDT extracts conditions for applying methods by using decision tree based algorithm. It considers the state of relevant domain objects in both current and goal states. Redundant training samples are removed using graph isomorphism. Our experiments, LHTNDT converged. It can learn most of preconditions correctly and quickly. 80% of our test problems were solved by preconditions extracted by 3/4 of plan traces needed for full convergence  

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... 

Accurate unfolding of the energy spectrum of a neutron source gives important information about unknown neutron sources. The obtained information is useful in many areas like nuclear safeguards, nuclear nonproliferation, and homeland security. In the present study, the energy spectrum of a poly-energetic fast neutron source is reconstructed using the developed computational codes based on the Group Method of Data Handling (GMDH) and Decision Tree (DT) algorithms. The neutron pulse height distribution (neutron response function) in the considered NE-213 liquid organic scintillator has been simulated using the developed MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif...