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Available sensing measurements in modern industrial process include two significant characteristics: distribution and autocorrelation. Different types of sensing measurements exhibit different characteristics. Moreover, different feature extraction methods are suitable for data with corresponding characteristics. This paper proposes a novel dual-step subspace partition method in order to establish...
In this paper, we address the problem of fault detection (FD) of chemical processes using improved generalized likelihood ratio test. The improved GLRT is the method that combines the advantages of the exponentially weighted moving average (EWMA) filter with those of the GLRT method. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data...
To keep up with the growing demand for customized software solutions that are tailored to specific customer requirements, techniques like Software Product Line Engineering (SPLE) or the more ad-hoc clone-and-own (where engineers do not build each product from anew, but instead maximize the reuse of the available assets in building product families) have been devised. However testing such highly variable...
The types and numbers of benthic macroinvertebrates found in a water body reflect water quality. Therefore, macroinvertebrates are routinely monitored as a part of freshwater ecological quality assessment. The collected macroinvertebrate samples are identified by human experts, which is costly and time-consuming. Thus, developing automated identification methods that could partially replace the human...
The paper concerns the detection of fall events based on human silhouette shape variations. The detection of fall events is addressed from the statistical point of view as an anomaly detection problem. Specifically, the paper investigates the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy...
In this paper, we propose a novel intrusion detection technique using a deep neural network (DNN). In the proposed technique, in-vehicle network packets exchanged between electronic control units (ECU) are trained to extract low- dimensional features and used for discriminating normal and hacking packets. The features perform in high efficient and low complexity because they are generated directly...
Cardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this work. The features are obtained from a large dataset consisting of 2126 records in UCI Machine Learning...
This paper presents the methodology of the elderly people's fall detection using the k Nearest Neighbors (kNN) as the decision making module. The problem of the data acquisition by the depth sensors and feature selection for this task is introduced. The classification problem is discussed. The decision making algorithm and its parameters are briefly described. Experimental results based on data collected...
In order to detect the health status of high-speed railway, various studies have been examined by Acoustic Emission (AE) method. However, little work has been done on studying the relationship between rail status and features of AE signals, and this relationship can be used to establish a detection criterion for rail health monitoring. This paper presents a methodology on rail health monitoring by...
In this paper, non-intrusive load monitoring using a single point sensing and wavelet-based classification is presented and applied to a test system feeding two dynamic and two static three-phase loads. The features in the three-phase voltage and current signals are extracted by wavelet transform to decompose the original signals. The energy of the obtained wavelet coefficients at the detail levels...
Manufacturing data is an important source of knowledge that can be used to enhance the production capability. The detection of the causes of defects may possibly lead to an improvement in production. However, the production records generally contain an enormous set of features. It is almost impossible in practice to monitor all features at once. This research proposes the feature reduction technique,...
Structural Health Monitoring (SHM) is a process of continuous monitoring of the physical condition of a structure for purpose of ensuring the integrity of the structure. SHM techniques have been employed to reduce maintenance and repair costs while maintaining safety and reliability of aircrafts. In this paper we have investigated the benefits provided by integrating decision fusion algorithms to...
A primary difficulty in physiological monitoring is detecting changes of health status for patients. In order to address this difficulty, we propose a new framework in patient-specific physiological monitoring by defining a density ratio using the training density and testing density to denote the changes of patient status, such as health, sub-health and abnormalities. We use a Least Square-based...
In order to detect and rank sensitive topic in campus network and assure health and security of campus network culture, this paper proposes a sensitive topic detection model. Different from traditional TDT (topic detection and tracking) technologies, the model is based on collaboration of dynamic case knowledge base and multi-domain cooperative computing method. Dynamically nature of topic causes...
The identification of Internet applications is important for ISPs and network administrators to protect the network from unwanted traffic and prioritize some major applications. Statistical methods are widely used since they allow to classify applications according to their statistical signatures. They combine the statistical analysis of flow parameters, such as packet size and inter-packet time,...
Structural Health Monitoring (SHM) is the process of continuous and autonomous monitoring of the physical condition of a structure by means of sensors. It is a mean of Non-Destructive-Inspection for monitoring and ensuring the structural integrity of aircraft. SHM techniques have been explored to reduce air vehicle maintenance and repair costs while maintaining safety and reliability. This research...
Nowadays with the dramatic growth in communication and computer networks, security has become a critical subject for computer systems. A good way to detect the illegal users is to monitoring these user's packets. Different algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize...
In this paper, a new Wavelet-AR feature for activity recognition from a tri-axial acceleration signals has been proposed. We use Wavelet Transform to decompose the raw accelerometer signals and obtain the decomposed signals that can efficiently discriminate the different activities. After that we build autoregressive (AR) model for the decomposed signals and extract the AR coefficients as features...
Thanks to Rich Internet Applications (RIAs) with their enhanced interactivity, responsiveness and dynamicity, the user experience in the Web 2.0 is becoming more and more appealing and user-friendly. The dynamic nature of RIAs and the heterogeneous technologies, frameworks, communication models used for implementing them negatively affect their analyzability and understandability. Consequently, specific...
The activity of babies and more specifically the posture of babies is an important aspect in their safety and development. In this paper, we studied the automatic classification of baby posture using a pressure-sensitive mat. The posture classification problem is formulated as the design of features that describe the pressure patterns induced by the child in combination with generic classifiers. Novel...
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