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The increasing VM density in cloud hosting services makes careful management of physical resources such as CPU, memory, and I/O bandwidth within individual virtualized servers a priority. To maximize cost-efficiency, resource management needs to be coupled with the revenue generating mechanisms of cloud hosting: the service level agreements (SLAs) of hosted client applications. In this paper, we develop...
As the number of cooking recipes posted on the Web increases, it becomes difficult to find a cooking recipe that a user needs. Moreover, even if it can be done, it is still difficult for users to arrange the cooking recipe, for example, by replacing ingredients with different ones. To deal with such problems, we propose a framework for typicality analysis of the combination of ingredients. The framework...
A key development in the design of visual object recognition systems is the combination of multiple features. In recent years, various popular optimization based feature combination methods have been proposed in the literatures. However, those methods obtain tiny performance improvement at the cost of enormous computation consumption. In this paper, we propose an improved averaging combination (IAC)...
The upcoming arrival of high dynamic range image and video applications to consumer electronics will force the utilization of floating-point numbers on them. This paper shows that introducing a slight modification on classical floating-point number systems, the implementation of those circuits can be highly improved. For a 16-bit numbers, by using the proposed format, the area and power consumption...
This paper presents an improved parameter extraction algorithm for photovoltaic (PV) panels based on only datasheet value, which is very useful in the development phase of power conditioning system (PCS). In order to increase the accuracy of PV circuit model especially in the maximum power point (MPP), optimization method with objective function incorporating only the MPP conditions was suggested...
The integration of distributed generation causes an increase of the voltage magnitude in low voltage (LV) grids. In addition to classical grid expansion distribution system operators have various options at their hands to reduce the voltage rise, e.g. by installing distribution transformers with on-load tap changers or voltage regulators. But for efficient control the network operators need information...
Apache Hadoop system is a software framework with the capability to process large-scale datasets across a cluster of distributed machines using MapReduce programming model. However, there are two main challenges for system administrators to manage the Hadoop system, (1) system administrators are difficult to tune the parameters appropriately since the behaviors and characteristics of large-scale distributed...
Dynamic Traffic Assignment (DTA) has become a main component of modern traffic control centres. To calibrate a DTA model the observations from the field are required. There has been increasing number of sensors and technologies which can provide these data. In this paper we briefly describe these sensors and elaborate on the various traffic data types that are used in dynamic demand calibration. The...
Service functionality can be provided by more than one service consumer. In order to choose the service which creates the most benefit before its consumption, a selection based on previous measurable experiences by other consumers is beneficial. In this paper, we present the results of our analysis of two machine learning approaches to predict the best service within this selection problem. The first...
In computer vision tasks such as action recognition and image classification, combining multiple visual feature sets is proven to be an effective strategy. However, simply combing these features may cause high dimensionality and lead to noises. Feature selection and fusion are common choices for multiple feature representation. In this paper, we propose a multi-view feature selection and fusion method...
In this study, optimal jamming of wireless localization systems is investigated. Two optimal power allocation schemes are proposed for jammer nodes in the presence of total and peak power constraints. In the first scheme, power is allocated to jammer nodes in order to maximize the average Cramér-Rao lower bound (CRLB) of target nodes whereas in the second scheme the power allocation is performed for...
Ultra-wide bandwidth (UWB) systems allow for accurate localization to tackle and complement the GPS-aided solutions, which are impractical in weak signal environments. We consider the problem of fast link scheduling in the medium access control (MAC) layer for UWB localization. We present an optimization strategy to perform robust ranging scheduling with localization constraints. Given the complexity...
We propose an efficient linear similarity metric learning method for face verification called Triangular Similarity Metric Learning (TSML). Compared with relevant state-of-the-art work, this method improves the efficiency of learning the cosine similarity while keeping effectiveness. Concretely, we present a geometrical interpretation based on the triangle inequality for developing a cost function...
A strictly defined notion of an approximate solution for a multicriteria optimization problem with functional constraints and a deterministic method for obtaining such approximations are presented. Unlike traditional algorithms for constrained multicriteria optimization the proposed method not only generates an approximation but also proves its accuracy.
Matching cost curves contribute more informative cues than disparity maps to accurate occlusion detection. Inspired by the fact that the human perceived depth of occluded pixels increases with increasing horizontal separation from an occluding edge, this paper proposes a matching cost curve based occlusion detection scheme. An asymmetric occlusion detection method is designed based on the fact that...
We deal with the processing of multiview video acquired by the use of practical thus relatively simple acquisition systems that have a limited number of cameras located around a scene on independent tripods. The real-camera locations are nearly arbitrary as it would be required in the real-world Free-Viewpoint Television systems. The appropriate test video sequences are also reported. We describe...
Subspace segmentation is one of the hottest issues in computer vision and machine learning fields. Generally, data (e.g. face images) are lying in a union of multiple linear subspaces, therefore, it is the key to find a block diagonal affinity matrix, which would result in segmenting data into different clusters correctly. Recently, graph construction based segmentation methods attract lots of attention...
This paper deals with new technologies being applied in Smart Asset Management field, namely in field of predictive maintenance. Multi-criteria and multi-parameter models are being assessed to leverage predictive abilities of predictive models. Ways of integration into distribution system operators' asset management infrastructure are being determined as well.
The presence of a large number of irrelevant features degrades the classifier accuracy, reduces the understanding of data, and increases the overall time needed for training and classification. Hence, Feature selection is a critical step in the machine learning process. The role of feature selection is to select a subset of size ‘d’ (d<n) from the given set of ‘n’ features that leads to the smallest...
To alleviate the loads of tracking web log file by human effort, machine learning methods are now commonly used to analyze log data and to identify the pattern of malicious activities. Traditional kernel based techniques, like the neural network and the support vector machine (SVM), typically can deliver higher prediction accuracy. However, the user of a kernel based techniques normally cannot get...
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