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Performance regressions, such as a higher CPU utilization than in the previous version of an application, are caused by software application updates that negatively affect the performance of an application.Although a plethora of mining software repository research has been done to detect such regressions, research tools are generally not readily available to practitioners. Application Performance...
The issue of the workforce management in the field is crucial for infrastructure operators. A key role is played by workforce management systems that assign workers with the right skills at the optimal time in the right place, so long-term compliance with the required quality parameters is met at the lowest possible costs. Historical development and theoretical basics of workforce management systems...
Unattended falls can lead to serious medical issues among the elderly, especially when motor functions may become inactive. Motion sensors like accelerometer can aid in automatic characterization and classification of human motion. Un-Classified motion can be accounted for anomaly that when reported to the online knowledge builder can correct the existing model or estimate additional classes into...
Patient deterioration in the hospital ward is typically preceded by several hours of deranged physiology, as measured by the patient's vital signs. Estimation of the expected trajectory of a patient's future vital signs can allow us determine the degree of risk of physiological deterioration for that patient. Gaussian processes (GPs) offer a principled means of estimating vital-sign trajectories within...
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...
Affected by the special geographical environment and climate factors, some cities waterlogging occurred frequently, causing serious economic losses and social impacts. Because of certain topographic factors, once the heavy rain coming suddenly in the city, many of the major streets will be flooded by the water, how to better prevent the occurrence of waterlogging, or predict the depth of waterlogging...
This paper addresses the problem of damage detection technique of structural health monitoring (SHM). Kernel principal components analysis (KPCA)-based generalized likelihood ratio (GLR) technique is developed to enhance the damage detection of SHM processes. The data are collected from the complex three degree of freedom spring-mass-dashpot system in order to calculate the KPCA model. The developed...
As model-based methods have difficulty to solve more and more complex processes fault detection problems today, data-driven based techniques have been wildly used in industrial systems monitoring because of its ability to process unknown physical model. However, conventional static data-driven fault detection method have problems in processing nonlinear systems fault detection with deterministic disturbances...
In recent years, trust has emerged with the development of cloud computing. It is a critical step to select a trusted cloud provider before the service begins, which is related to the interests of cloud consumers themselves and the quality of the service. A SLA trust model based on behavior evaluation is proposed in this paper. User's subjective evaluations are abandoned, the provider who is trusted...
This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between...
Reliable information processing is an indispensable task in Smart City environments. Heterogeneous sensor infrastructures of individual information providers and data portal vendors tend to offer a hardly revisable information quality. This paper proposes a correlation model-based monitoring approach to evaluate the plausibility of smart city data sources. The model is based on spatial, temporal,...
Due to limited resource, noise and unreliable link, data loss and sensor faults are common in medical body sensor networks (BSN). Most available works used data reconstruction to improve data quality in traditional wireless sensor networks (WSN). However, existing data reconstruction schemes using redundant information of WSN can not provide a satisfactory accuracy for BSN. In light of this, a Bayesian...
In recent years, air quality has become a severe environmental problem in China. Since bad air quality brought significant influences on traffic and people's daily life, how to predict the future air quality precisely and subtly, has been an urgent and important problem. In this paper, a Spatio-Temporal Extreme Learning Machine (STELM) method is proposed for air quality prediction. STELM considers...
Tremendous amounts of data can be recorded during software execution. This provides valuable information on software runtime analysis. Many crashes and exceptions may occur, and it is a real challenge to understand how software is behaving. Software is usually composed of various components. A component is a nearly independent part of software that full-fills a clear function. Process mining aims...
Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale...
The performance of Smart Data Pricing (SDP) highly depends on the accuracy and reliability of measuring network bandwidth usage. The existing Internet protocol uses the distributed control, and the transport network protocols run together inside the routers and switches. However, it is hard to manage and retrieve online and precise measurements from the networks due to the large number of traffic...
Today's dynamic computing deployment for commercial and scientific applications is propelling us to an era where minor inefficiencies can snowball into significant performance and operational bottlenecks. Data center operations is increasingly relying on sensors based control systems for key decision insights. The increased sampling frequencies, cheaper storage costs and prolific deployment of sensors...
Validation of Sensors has very important effects on the consequences of structural experiments and subsequent analyzing works. This article focus on the problem that if the data collected from the sensors are valid or not. It tested the validation of an target acceleration sensor on a truss structure by using Tree Augmented Naive Bayesian Classifier which is based on machine learning technology whose...
We present a city-scale crowd simulation model based on a large data set (25 million GPS data points from 28'000 volunteers recorded during a 3-day city-wide festival held in Zurich in 2013). The model is based on a spatio-temporal abstraction of the festival, focusing on event sites and event times. Thus, we assume a certain number of events (concerts, shows, etc. as it's typical at such festivals)...
We examine the use of the Fourier transform to discriminate dynamic behavior differences between congested and uncongested systems. Simulation continuous time statistic ‘trajectories’ are converted to time series for Fourier analysis. The pattern of Fourier component magnitudes across frequencies differs for congested versus uncongested systems. We use this knowledge to explore statistical process...
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