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Monitoring of dynamic industrial process has been increasingly important due to more and more strict safety and reliability requirements. Popular methods like time lagged arrangement-based and subspace-based approaches exhibit good performance in fault detection, however, they suffer from difficulty in accurately isolating faulty variables and diagnosing fault types. To alleviate this difficulty,...
We address the problem of estimating a spatial field of signal strength from measurements of low accuracy. The measurements are obtained by users whose locations are inaccurately estimated. The spatial field is defined on a grid of nodes with known locations. The users report their locations and received signal strength to a central unit where all the measurements are processed. After the processing...
In this paper, the application of independent component analysis (ICA) to statistical process monitoring is studied. This paper mainly focuses on studying on the fault detection and isolation principle based on the data model of ICA. Contributions of this paper are: (1) for the purpose of fault detection, two monitoring statistics are designated by detailed analysis on the data model of ICA; (2) a...
The wisdom tourism is the important development direction of tourism which will realize the refinement and personalized of tourism. The wisdom tourism needs support of the data flow processing platform and framework because tourism data is dynamic, large number and multi-dimensions. This paper describes a two-stage modular data flow processing framework based on data view and data service. Taking...
Environmental sensors monitor supercomputing facility health, generating massive data in the largest facilities. Current state-of-the-art is for human operators to evaluate environmental data by hand. This approach will not be viable on Exascale machines, nor is it ideal on current systems. We evaluate effectiveness of the DBSCAN algorithm for identifying anomalies in supercomputing sensor data. We...
For the low-speed diesel engine monitoring with multi-sensor information on ship, the on-line anomaly detection as the fault symptom pre-warning is mainly considered in this paper. The stable operating condition is firstly identified by the ADF test. Then, the on-line anomaly detection with baseline deviation is modeled by the Auto associative Kernel Regression (AAKR) method, where the baseline is...
Most Wi-Fi based localization algorithms are cooperative as user device is required to associate with an AP. However, user may not associate with AP in scenarios such as supermarkets which calls for non-cooperative localization. In this paper, the probe request (PR) frame sent by device is analyzed and the weighted kernel density estimation assisted Bayes (w-KAB) algorithm is utilized for localization...
We experience changes in stationarity/time variance in many practical applications. Since changes modify the operational framework the application is working with, its accuracy performance is in turn affected. When changes can occur, we need to detect them as soon as possible, in general by inspecting features extracted from data, and afterwards intervene to mitigate their effects. In this paper,...
This paper presents a 10 years experience of data driven models for sensor validation applied for petroleum and natural gas industry. Auto-associative kernel regression has been used as the main modeling method. The models achieved were embedded in software called Sentinell, which is used for sensors diagnosis. The software is being used in a natural gas compression station, and it has been evaluated...
The production of Integrated Circuits (ICs) is subject to high quality standards, and many control steps are incorporated in manufacturing processes. In the same perspective, Statistical Process Control (SPC) methods are intensively used as decision tools for the sake of quality monitoring. However, these conventional SPC methods don't include spatial correlation in their analysis, which can limit...
Mobile network failures have occurred many times in recent years. Some network failures become “silent” failures that mobile carriers cannot detect because of incomplete rules concerning failure detection by the network operating system. However, the increasing number of services and devices, and the increasing complexity of the network make it hard to generate rules that cover all network failures...
Wireless sensor network (WSN) has become widely used in different applications. Fault detection of sensors is importance for maintaining a reliable WSN operation. And identification of faulty nodes in a WSN can be transformed into a pattern classification problem. In this paper, we introduce an effective label propagation procedure using semi-supervised local kernel density estimation. The proposed...
Anomaly detection has been an important topic in hyperspectral image analysis. This technique is sometimes more preferable than the supervised target detection because it requires no <bold>a priori</bold> information for the interested materials. Many efforts have been made in this topic; however, they usually suffer from excessive time cost and a high false-positive rate. There are two...
In this paper, we propose a method to distributively monitor a dynamic mobile network. For this purpose, we take advantage of the consensus theory to provide each node with a common view of the network. More specifically, we give a decentralized algorithm to estimate a time-varying distribution, where each node has a partial information on this distribution. Our algorithm allows a trade-off between...
This paper introduces a novel sensor-less, event-driven power analysis framework called FEPMA for providing highly accurate and nearly instantaneous estimates of power dissipation in an Android smartphone. The key idea is to collect and correctly record various events of interest within a smartphone as applications are running on the application processor within it. This is in turn done by instrumenting...
The paper describes a system for the human machine interaction that is able to identify users according how she looks at the monitor while using a given interface. The system does not need invasive measurements that could limit the naturalness of her actions and detects the eyes movement from the estimation provided by a kinect camera. The proposed approach clusters the sequences of user gaze on the...
Crowd density estimation is the fundamental content and central issue in most public video monitoring systems, and it is also a hot spot in computer vision area. Recent state-of-the-art method is based on image processing. Traditional methods can be divided into two main directions, one is based on pixel statistics, and the other is based on texture analysis. In our paper, we combine these two methods...
In recent years, there has been an increasing interest in applying kernel density estimation technique to estimate the transmission error rate, so as to improve the estimation reliability and the data efficiency. However, most of the previous studies on this topic are limited to binary channels. This paper demonstrates that, by selecting an appropriate decision metric, the kernel-based error rate...
This article proposes to monitor industrial process faults using Kullback Leibler (KL) divergence. The main idea is to measure the difference between the distributions of normal and faulty data. Sensitivity analysis on the KL divergence under Gaussian distribution assumption is performed, which shows that the sensitivity of KL divergence increases with the number of samples. For non-Gaussian data,...
This study presents a recursive Kernel Density Estimation model (r-KDE) based method for the segmentation of dynamic scenes. In the algorithm, local maximum in the density functions is approximated recursively via mean shift method firstly. Via the proposed thresholding scheme, components and parameters in the mixture Gaussian distributions can be determined adaptively. The coarse foreground is obtained...
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