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Human motion recognition plays a key role in various fields including health monitoring. Radar is a type of sensor that has shown remarkable success in the classification of human motions. Different data representation domains have been used for the analysis of radar returns. Each domain provides one aspect of the observed motion not readily discernible in other domains. In this letter, we propose...
Cyber Defense Exercises have received much attention in recent years, and are increasingly becoming the cornerstone for ensuring readiness in this new domain. Crossed Swords is an exercise directed at training Red Team members for responsive cyber defense. However, prior iterations have revealed the need for automated and transparent real-time feedback systems to help participants improve their techniques...
This work considers the problem of fault localization in transparent optical networks. The aim is to localize single-link failures by utilizing statistical machine learning techniques trained on data that describe the network state upon current and past failure incidents. In particular, a Gaussian Process (GP) classifier is trained on historical data extracted from the examined network, with the goal...
In Vietnam, environmental data collected from ground-based stations may contain abnormal or missing values due to several problems during operation, i.e. sensor's problems. This paper proposes a standardization procedure which try to detect unusual values and fill in missing data. Experiments were conducted for PM10 data. Two datasets measured in 01/2011 and 01/2012 at Nguyen Van Cu station in Hanoi,...
Ankle edema an important symptom for monitoring patients with chronic systematic diseases. It is an important indicator of onset or exacerbation of a variety of diseases that disturb cardiovascular, renal, or hepatic system such as heart, liver, and kidney failure, diabetes, etc. The current approaches toward edema assessment are conducted during clinical visits. In-clinic assessments, in addition...
In automated video surveillance applications, detection of suspicious human behaviour is of great practical importance. However due to random nature of human movements, reliable classification of suspicious human movements can be very difficult. Defining an approach to the problem of automatically tracking people and detecting unusual or suspicious movements in Closed Circuit TV (CCTV) videos is our...
The first step to deal with the significant issue of air pollution in China and elsewhere in the world is to monitor it. While more physical monitoring stations are built, current coverage is limited to large cities with most other places undermonitored. In this paper we propose a complementary approach to monitor Air Quality Index (AQI): using machine learning models to estimate AQI from social media...
The random distribution of sensors and the irregularity of routing paths lead to unordered sensory data which are difficult to deal with in Wireless Sensor Networks (WSNs). However, for simplicity, most existing researches ignore those characteristics in the designs of Compressive Sensing based Data Aggregation Schemes (CSDAS). Since conventional sparsification bases (e.g., DCT, Wavelets) are inefficient...
In this paper, a modified partial least-squares (PLS) regression modeling method is proposed. The proposed method can build a modified regression model to extract the useful information in residual subspace, which is helpful to predict the output variables. With this method, more accurate quality variables are predicted. In simulation experiment, penicillin fermentation process is used to test the...
Mobile and pervasive ECG monitoring systems require continuous connectivity with server-side ECG analyser for instantaneously detecting abnormal cardiac situations. Normally, these systems generate a large amount of data, resulting in a high energy expenditure with data transmission on pervasive ECG platform. In this context, data reduction mechanisms can be applied for saving transmission energy...
A multivariate process monitoring and fault identification model using decision tree (DT) learning techniques is proposed. We Use one DT classifier for process monitoring and other p (p is the number of the variables) DT classifiers for fault identification. The Mahalanobis distance contours based method for selecting model training samples is proposed to decrease the number of training samples. Numerical...
Generally, rule-based systems work to make sense of a large volume of alerts generated by the intrusion detection systems (IDSs) every minute. Hence, it is very significant to verify that these systems are error-free and that the rules are suitable for the current network. This topic is addressed by Rule Adjustment, which automatically adjusts the rules based on the current network environment. The...
It is much more important for manufacturing products to accurately and quickly recognizing/monitoring quality problems in a complex manufacturing process. Back Propagation Neural Network (BPNN) is receiving increased attention in the process monitoring because of their universal function approximate. In this study, Cascade Correlation Neural Network and Back Propagation Neural Network simultaneously...
Predicting the life cycle and the short-term popularity of a Web object is important for network architecture optimization. In this paper, we attempt to predict the popularity of a Web object given its historical access records using a novel neural network technique, reservoir computing (RC). The traces of popular videos at YouTube for five continuous months are taken as a case study. We compare RC...
Artificial neural network (ANN)-based recognizers have been developed for monitoring and diagnosis bivariate process mean shift in multivariate statistical process control (MSPC). They have better average run lengths (ARLs) performance in monitoring process mean shifts and gave an useful diagnosis information compared to the traditional MSPC schemes such as T2, multivariate cumulative sum (MCUSUM)...
Tracking the spread of an epidemic disease like seasonal or pandemic influenza is an important task that can reduce its impact and help authorities plan their response. In particular, early detection and geolocation of an outbreak are important aspects of this monitoring activity. Various methods are routinely employed for this monitoring, such as counting the consultation rates of general practitioners...
In this paper, a neural network-based identification model is proposed for both mean and variance shifts in correlated processes. The proposed model uses a selective network ensemble approach named DPSOEN to obtain the improved generalization performance. The model is capable of on-line monitoring mean and variance shifts, and classifying the types of shifts without considering the occurrence of both...
This paper proposes a video-quality estimation method which does not need video decoding. It takes into account error-concealment effectiveness and the location of video-quality degradation to estimate video-quality from only packet-header information. Error-concealment effectiveness is evaluated on the basis of motion-level information estimated by the data-size of each frame. The quality-degraded...
Distributed systems have been developing rapidly in the past few years and their automatic control is a real challenge being a very active research field. In order to assure the load balancing and to optimize the resource utilization, a distributed system is using different software components, such as management tools, schedulers or monitoring tools. Considering the prediction of future behavior...
Quality characteristics are subject of both manufacturing and service industries, which include not only the variables but the attributes as well. In Quality Control area substantial research has been done for Auto-correlated variables; however, no attempt was done for Auto-correlated attributes. Ignoring the autocorrelation structure in constructing control charts cause the in-control run length...
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