Sharif Digital Repository

Correctly detecting the position where a concept begins to drift is important in mining data streams. In this paper, we propose a new method for detecting concept drift. The proposed method, which can detect different types of drift, is based on processing data chunk by chunk and measuring differences between two consecutive batches, as drift indicator. In order to evaluate the proposed method we measure its performance on a set of artificial datasets with different levels of severity and speed of drift. The experimental results show that the proposed method is capable to detect drifts and can approximately find concept drift locations  

In the present study, two-phase refrigerant flow is simulated using drift flux model for straight and helical capillary tubes. The conservation equations of mass, energy and momentum are solved using the 4th order Runge-Kutta method. This model is validated by previously published experimental and numerical results and also by experimental results presented in this work. The effect of various parameters such as inlet pressure, inlet temperature, sub-cooling degree, and geometric dimensions are studied. The results of the present study show that for the same length and under similar conditions, mass flux through helical capillary tube with coil diameter of 40 mm are about 11% less than that... 

It is well-known that the behavior of soil-structure systems can be well described using a limited number of nondimensional parameters. This is the outcome of researches based on the premise that the foundation is bonded to the ground. Here, it is shown the concept can be extended to systems with foundation uplift. A set of non-dimensional parameters are introduced which controls the main features of uplifting systems. The effect of foundation uplift on response of soil-structure systems are investigated parametrically through time history analysis for a wide range of systems subjected to ground motions recorded on different soil types. In particular, the effects of uplift on displacement... 

Classifying streams of data, for instance financial transactions or emails, is an essential element in applications such as online advertising and spam or fraud detection. The data stream is often large or even unbounded; furthermore, the stream is in many instances non-stationary. Therefore, an adaptive approach is required that can manage concept drift in an online fashion. This paper presents a probabilistic non-parametric generative model for stream classification that can handle concept drift efficiently and adjust its complexity over time. Unlike recent methods, the proposed model handles concept drift by adapting data-concept association without unnecessary i.i.d. assumption among the... 

Effect of openings' dimensions on the relative flexural behavior of adjacent piers (independent or conjugate) in perforated shear walls is addressed. 384 designed models were made and exposed to lateral loads. For middle openings, in addition to the alpha parameter in the literature, the relative flexural behavior of piers in medium-rise buildings can be predicted as function of thickness-to-length ratio of the coupling beam and the ratio of the coupling beam length to the pier length; but in high-rise buildings, it is always conjugate. For corner openings, the alpha parameter must be modified with respect to the number of stories  

A reaction-diffusion equation on [0, 1]d with the heat conductivity k > 0, a polynomial drift term and an additive noise, fractional in time with H > 1/2, and colored in space, is considered. We have shown the existence, uniqueness and uniform boundedness of solution with respect to k Also we show that if k tends to infinity, then the corresponding solutions of the equation converge to a process satisfying a stochastic ordinary differential equation  

Classification of data streams has become an important area of data mining, as the number of applications facing these challenges increases. In this paper, we propose a new ensemble learning method for data stream classification in presence of concept drift. Our method is capable of detecting changes and adapting to new concepts which appears in the stream  

Concept drift, change in the underlying distribution that data points come from, is an inevitable phenomenon in data streams. Due to increase in the number of data streams' applications such as network intrusion detection, weather forecasting, and detection of unconventional behavior in financial transactions; numerous researches have recently been conducted in the area of concept drift detection. An ideal method for concept drift detection should be able to rapidly and correctly identify changes in the underlying distribution of data points and adapt its model as quickly as possible while the memory and processing time is limited. In this paper, we propose a novel explicit method based on... 

Phase Change Memory (PCM) is an emerging memory technology that has the capability to address the growing demand for memory capacity and bridge the gap between the main memory and the secondary storage. As a resistive memory, PCM is able to store data based on its resistance values. The wide resistance range of PCM makes it possible to store even multiple bits per cell (MLC) rather than a single bit per cell (SLC). Unfortunately, PCM cells suffer from short lifetime. That means PCM cells could tolerate a limited number of write operations, and afterward they tend to permanently stick at a constant value. Limited lifetime is an issue related to PCM memory; hence, in recent years, many studies... 

Shear wall panels, including cold-formed steel frames and its attached sheathing, are common lateral load resisting systems of cold-formed steel structures. In this paper, the finite element method is used to study the lateral performance of shear wall panels. The finite element model is validated against experimental results of other researchers. Using the validated model, a parametric study is described to determine strength, drift and seismic behavior of the shear wall panels. Based on the results, it is concluded that the initial stiffness and ultimate lateral strength are dramatically affected by the thickness of the frame members, type of sheathing material, edge screw spacing, height... 

In this work we prove Malliavin differentiability for the solution to an SDE with locally Lipschitz and semi-monotone drift. To prove this formula, we construct a sequence of SDEs with globally Lipschitz drifts and show that the p-moments of their Malliavin derivatives are uniformly bounded  

Recent advances in storage and processing have provided the possibility of automatic gathering of information, which in turn leads to fast and continuous flows of data. The data which are produced and stored in this way are called data streams. Data streams are produced in large size, and much dynamism and have some unique properties which make them applicable to model many real data mining applications. The main challenge of streaming data is the occurrence of concept drift. In addition, regarding the costs of labeling of instances, it is often assumed that only a small fraction of instances are labeled. In this paper, we propose an ensemble algorithm to classify instances of non-stationary... 

We have entered the era of networked communications where concepts such as big data and social networks are emerging. The explosion and profusion of available data in a broad range of application domains cause data streams to become an inevitable part of the most real-world applications. In the classification of data streams, there are four major challenges: infinite length, concept drift, recurring and evolving concepts. This paper proposes a novel method to address the mentioned challenges with a focus on the last one. Unlike the existing methods for detection of evolving concepts, we cast joint classification and detection of evolving concepts into optimizing an objective function by... 

We have entered the era of networked communications where concepts such as big data and social networks are emerging. The explosion and profusion of available data in a broad range of application domains cause data streams to become an inevitable part of the most real-world applications. In the classification of data streams, there are four major challenges: infinite length, concept drift, recurring and evolving concepts. This paper proposes a novel method to address the mentioned challenges with a focus on the last one. Unlike the existing methods for detection of evolving concepts, we cast joint classification and detection of evolving concepts into optimizing an objective function by... 

In most of researches on soil-structure systems, it is assumed that the foundation is bonded to the ground where no foundation uplift is allowed. Uplifting makes changes in force–displacement behavior of the soil-structure systems, which in turn alters structural demands. In this research, a set of non-dimensional parameters is considered which controls the behavior of uplifting systems. The effects of foundation uplift on response of soil-structure system are investigated parametrically through time history analysis for a wide range of systems subjected to harmonic excitation and also ground motions recorded on soft soil with predominant period. It is seen that the response of systems with... 

In this paper, we study the problem of resistance drift in an MLC Phase Change Memory (PCM) and propose a solution to circumvent its thermally-affected accelerated rate in 3D CMPs. Our scheme is based on the observation that instead of alleviating the problem of resistance drift by using large margins or error correction codes, the PCM read circuit can be reconfigured for tolerating most of the resistance drift errors in a dynamic manner. Through detailed characterization of memory access patterns for 22 applications, we propose an efficient mechanism to facilitate such reliable read scheme via tolerating (a) early-cycle resistance drifts by using narrow margins so that considerably saving... 

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