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PM2.5 is one of the major indicators of ambient air quality which has become a focus of public attention. Urban PM2.5 can be measured by air quality monitoring stations which are costly and not sufficiently installed in a city. In this paper, we aim to infer the PM2.5 information at the place where there is no air quality monitoring station. As PM2.5 concentration varies over time and space domains,...
The Cusp Catastrophe Model provides a promising approach for health and behavioral researchers to investigate both continuous and quantum changes in one modeling framework. However, application of the model is hindered by unresolved issues around a statistical model fitting to the data. This paper reports our exploratory work in developing a new approach to statistical cusp catastrophe modeling. In...
With more and more high rise buildings in modern cities, people flow and occupancy estimation has gained lots of attention in recent years. Most previous works on this topic are based on the Kalman filter (KF) due to its high efficiency. However, the Kalman filter requires good people flow models which are usually hard to get due to the complexity of people behavior. People simulation softwares such...
Event detection through sensors data recording human activities is an aspect to learn human behaviors. In this paper, counted numbers from a sensor installed on a building entrance recording the number of people entering the building, will be processed to find the anomaly time interval when there are more people going through the entrance, which is viewed as event. An approach is adopted having two...
Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in a sequence. This is a fundamental problem in data mining with diversified applications in many science and business fields, such as multimedia analysis (motion gesture/video sequence recognition), marketing analytics (buying path prediction), and financial modelling (trend of...
Based on the well established model-based fault detection techniques, in this paper, a data-driven fault detection approach for static processes with deterministic disturbances is proposed. The basic idea behind this approach is, first identify the maximum influence of the unknown input on the measurement using the fault-free recorded data, and then apply the existing model-based schemes to solve...
When two Web services work together, they exchange messages in a predefined interface process. Two interface processes should be compatible when they can work properly. Our idea to fix incompatibility problem in service processes is to change an incompatible process so that the new process can simulate a compatible process. We consider not only the control flow but also the data flow in modeling the...
Linearly constrained discriminant analysis (LCDA) and orthogonal subspace projection (OSP) are both explored in hyperspectral image classification and have shown promise in signature detection, discrimination and classification. However, the two subspace projection approaches cannot directly estimate the signature abundance. The OSP has been extended by a least squares orthogonal subspace projection...
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