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The article considers the problem of complex estimation of the product state, associated with the need to help decision-makers in managing the life cycle of space facilities. A system analysis of the subject area was conducted, which showed the presence of limitations in the existing information system of the technical state and reliability of space facilities. The article presents a new intellectual...
In this paper, anomaly symptom detection using ensemble prediction based on newly developed weighting method is presented for time series. This weighting method is characterized that weights are determined in proportion to the indices defined by the prediction errors in a certain time period in the past. Next, alarm prediction based on this prediction method is proposed. Prediction accuracy is considered...
In this paper, we address a wiener-process degradation model to estimate the remaining useful lifetime of aero-engines. The measurement error and unit-to-unit variability are taken into account in the proposed degradation model and the RUL estimation is derived by the concept of first hitting time. Typically, the unknown parameters of the degradation model were estimated based on historical degradation...
Air pollution poses disproportionate health hazard in developing countries, due to juxtaposition of industrial units and residences. While excessive emission is routinely detected, enforcement of emission norms remains rare due to present technological limitations in pinpointing sources. To fill this gap, we propose a method of source localization and emission rate estimation via AERMOD-based simulation...
The traffic monitoring system is an imperative tool for traffic analysis and transportation planning. In this paper, we present WiTraffic: the first WiFi-based traffic monitoring system. Compared with existing solutions, it is non-intrusive, cost- effective, and easy-to-deploy. Unique WiFi Channel State Information (CSI) patterns of passing vehicles are captured and analyzed to effectively perform...
We present a method for developing executable algorithms for quantitative cyber-risk assessment. Exploiting techniques from security risk modeling and actuarial approaches, the method pragmatically combines use of available empirical data and expert judgments. The input to the algorithms are indicators providing information about the target of analysis, such as suspicious events observed in the network...
In the paper are observed methods for synthesis of the information model of the systems' evolution for the variation properties of their parameters. It is proposed to carry out the variations description based on entropy potentials values. As a result, it is possible to obtain compact and representative models in various situations with the original data. The synthesis of these models requires the...
In this work, we propose to use anthropometrics and physiological data to estimate cardiorespiratory fitness (CRF) in free-living and analyze the relation between estimated CRF and running performance. In particular, we use the ratio between running speed and heart rate (HR) as predictor for CRF estimation in free-living. The ratio is representative of fitness as lower HR at a given speed is expected...
In this paper we introduce a new SVR (Support Vector Regression) based model for estimating air pollution at fine spatial granularity. Specifically we use historical data from (sparse) government monitoring sites and a (dense) wireless sensor network, along with SVR — a supervised regression learning method to estimate an air pollution surface for any given hour on any given day in Sydney. Further,...
Drilled well control is a process that incorporates the assessment of well status through the monitoring of its physical parameters. This management allows the detection of well anomalies such as gas kick and mud loss. In this paper is developed a gas kick/ well loss early detection model that determines the well condition and early predicts possible anomaly. The developed model insures the system...
This paper presents a method to estimate the loads on a distribution feeder in real-time with limited real-time customer load data from AMI. The method consists of two modules: a load clustering module and a load modeling module. Load clustering helps to identify customer loads with similar profiles thereby helping to determine the subset of customers that need to be monitored in real-time. The load...
Fireworks/firecrackers have always played an important role in traditional Chinese New Year. However, several pollutants are emitted while burning fireworks/firecrackers. Among them, fine particulate matter (PM2.5) draws extensive attention due to its negative effect on human body. Previous studies have proved fireworks/firecrackers displays can raise regional PM2.5 concentration, but studies are...
Power consumption is a big challenge in chip design. Decisions taken in early design phases have large impact on the power consumption. Generally, simulation-based Design Space Exploration (DSE) is computationally costly for large problems due the size of design space. Simulate the possible scenarios in a distributed fashion can decrease the time to find efficient solutions. In this paper we describe...
In the context of developing countries, buildings account for around one-third of aggregate energy consumption with decentralized air conditioners (AC) being the major contributor. The possibility of room-level control, together with buildings substandard thermal insulation make decentralized ACs, an attractive target for energy conservation. Our overall objective is to provide targeted feedback for...
The success of web services is changing the way in which software is designed, developed, and distributed. Services are in fact continuously re-designed and incrementally developed, released in heterogeneous and distributed environments. They are selected and integrated at runtime within external business processes. To ensure that a deployed service fulfils the QoS requirements, a Service Level Agreement...
In this paper, we consider the sliding window model and propose two different (on-line) algorithms that approximate the items frequency in the active window. More precisely, we determine a (ε, δ)-additive-approximation meaning that the error is greater than ε only with probability δ. These solutions use a very small amount of memory with respect to the size...
An approach to smooth pursuit eye movement’s analysis by means of stochastic anomaly detection is presented and applied to the problem of distinguishing between patients diagnosed with Parkinson’s disease and normal controls. Both parametric Wiener model-based techniques and nonparametric modeling utilizing a description of the involved probability density functions in orthonormal bases are considered...
Monitoring the status of ongoing sales opportunities in IT service engagements is important for sales teams to improve the win rate of deals. Existing approaches aim at predicting the final outcome, i.e., The eventual chance of winning or losing a deal, given a snapshot of the deal data. While this type of prediction indirectly advises on the deal status, it offers limited guidance and insights. During...
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...
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...
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