The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Missing values becomes one of the problems that frequently occur in the data observation or data recording process. The needs of data completeness of the observation data for the uses of advanced analysis becomes important to be solved. Conventional method such as mean and mode imputation, deletion, and other methods are not good enough to handle missing values as those method can caused bias to the...
A cyber-physical system (CPS) is a system featuring a tight combination of, and coordination between, the system's computational and physical elements. System reliability is a critical requirement of cyber-physical systems. An unreliable CPS often leads to system malfunctions, service disruptions, financial losses and even human life. Improving CPS reliability requires an objective measurement, estimation...
Numerous studies have generated cost estimating relationships (CERs) for transportation projects via data analysis. Some studies collected data from databases, while others sourced data from conventional paper-based formats. When cost data were not in a consistent format, many studies failed to discuss the streamlining of pattern recognition. This work adopts a standard procedure for identifying CERs...
Sequential pattern mining is a process of extracting useful patterns in data sequences. Existing works on mining Top-K patterns on data streams are mostly for non-sequential patterns. In our framework, we focus on the topic of Top-K sequential pattern mining, where users can obtain adequate amount of interesting patterns. The proposed method can automatically adjust the minimum support during mining...
In this paper, we study the statistical features of Chinese foreign exchange market data. Furthermore, we mainly fit the sample tail data employed generalized pareto distribution (GPD), when building VaR model based on fat-tail distribution of extreme value theory (EVT), we show that the model can be appropriate to be applied to Chinese foreign exchange market data. We have also proposed a procedure...
An explosion of new research is vastly changing the medical sciences understanding of the cancer biology and giving new clues about how to attack it. This research tries to use a new impact analysis approach to investigating how the age, year and sex affect the probability of getting breast cancer. The impact analysis is an approach that is used to investigate the association of the impact factor...
Tempo is a common criterion by which humans describe and categorize music, and this has spawned a large amount of research in the field of automatic tempo estimation. Most tempo estimation systems focus mainly on detecting the temporal repetition and periodicity present within a signal, and represent tempo as a count of beats-per-minute (BPM). However, in real-world music retrieval applications such...
Traditional statistical data processing approach needs to know the distribution regularity of samples. But in the antiradiation missiles (ARM), when aerial defense radar uses active-decoying, the sample distribution regularity usually can't be known or has many likelihoods. To improve the precision and the stabilization of the angle-measure in the active-decoying environment, a grey processing approach...
Referring to the possible problem of gyro random drift effect on the Line of Sight (LOS) stabilization accuracy, a method for the LOS stabilization based on visual attitude estimation is proposed. A monocular vision system is established by four coplanar reference points on the ground and a double-focus CCD camera in the carrier. The quaternion is used to represent the transform relation between geodetic...
This paper proposes an algorithm for estimating available bandwidth of network nodes. The algorithm, based on statistical information of the network nodes, calculates link utilization and estimates the available bandwidth through the exponentially weighed moving average (EWMA) filter, and can thereby estimate precisely the available bandwidth of the link in the next time interval. Compared with conventional...
Illumination estimation for color constancy is an important problem in computer vision. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, the advantages of the two kinds are integrated. At first, a novel statistic-based algorithm called Illumination Estimation using K-nearest-neighbor (IE-KNN) is proposed. And then the physics-based...
The paper proposes a new 3D-kriging system based on Microsoft Visual Studio.NET 2003. The system was module designed and has high quality of 3D visualization and friendly user interface by using some 3D dynamic display such as OpenGL. Compared with other popular geostatistical softwares, the system has its own advantages.
This paper proposes a way to allow more effective use of speculation control techniques by combining multiple confidence estimators into a {em composite confidence estimator}. This new class of confidence estimators provides improved performance and finer speculation control. This paper makes three contributions. First, we describe techniques for building efficient composite confidence estimators...
This paper considers statistical issues of source localization from received signal strength (RSS) measurements affected by log-normal shadowing with focus on bias and variance. We characterize the class of all unbiased estimates of the source position and show that their error variances grow exponentially with the noise power. Finally, we propose an estimate the bias and error variance of which are...
Localization is an essential problem in wireless sensor networks (WSNs). Many localization algorithms have been proposed, but few efforts have been paid on theoretical analysis on the accuracy of these algorithms. Because it is naturally to formalize range-based localization problems as deterministic parameter estimation problems, for range-based localization algorithms Crameacuter-Rao lower bound...
Estimating entropies is important in many fields including statistical physics, machine learning and statistics. While the Shannon logarithmic entropy is the most fundamental, other Reacutenyi entropies are also of importance. In this paper, we derive a bias corrected estimator for a subset of Renyi entropies. The advantage of the estimator is demonstrated via theoretical and experimental considerations.
Music mood estimation (MME) is a key technology in mood-based music recommendation. While mainstream MME research nowadays relies on audio music analysis, exploring the significance of lyrics text in predicting song emotion is gaining attention in recent years. One major impediment to MME research is the lack of a clearly labeled and publicly available dataset annotating the emotion ratings of lyrics...
Characterizing user churn has become an important topic in studying P2P networks, both in theoretical analysis and system design. Recent work has shown that direct sampling of user lifetimes may lead to certain bias (arising from missed peers and round-off inconsistencies) and proposed a technique that estimates lifetimes based on sampled residuals. In this paper, however, we show that under non-stationary...
This paper gives a new analytical solution for assessment level of experts in multi-attribute group decision-making. A new consistent estimator is made by the method on basis of mathematical statistic theory, according to experts' information provided in decision-making matrices, assessment level of experts is estimated and judged adversely, that is difference of assessment level and estimation of...
In this paper, we address the problem of unsupervised detection of anomalies in hyperspectral images. Our proposed method is based on a novel statistical background modeling approach that combines local and global approaches. The local-global background model has the ability to adapt to all nuances of the background process like local approaches but avoids over-fitting due to a too high number of...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.