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At present, e-commerce and e-services system can provide trust information about services provider performance to the customers in the near future. Such information is an accumulation value by long-term evaluation of quality of service, but it does not reflect the Service's future trends. This paper presents an approach using a cubic fitting curve to(More)
The simple trust evaluation methods that result in a final trust level (FTL) value can't be able to evaluate service trust trend (STT). Furthermore, statistical trust values include some unfair and questionable trust values caused by subjective or objective factors. In response to these problems in trust management, an approach called two-way trust(More)
Uncertain data exist in many application fields, and there are numerous recent efforts in processing uncertain data to get more reliable results, especially uncertainty processing in clustering method. However, it is one of the urgent challenges to discover clusters with specific shape features. So we present a clustering method for data with uncertainties,(More)
Trust is a critical issue in e-commerce and e-service applications. Usually, the value of trust is in a dynamic process of change. At present, most of studies and applications regarding trust are focus on methods that result in a single final trust level (FTL) value to represent the trust level of sellers or service providers. Such simple trust evaluation(More)
When estimating the distance for wireless sensor networks (WSNs), we always suppose that a fixed curve model exists between the received signal strength indicator (RSSI) and communication distance. But there exist some negative factors in practice, which makes this assumption to contradict with the situation in real communication environment. It results in(More)
Telemetry data, containing the data of multiple subsystems such as power system, implies the on-orbit operation status information of the satellite. We can obtain performance characteristics and fault symptom of the satellite subsystems through analyzing these data. Using classification algorithm we can provide normal data for anomaly detection and find the(More)
In this paper, to combine the advantage of both polynomial kernel and the Mahalanobis distance metric learning (DML) methods, we propose a Mahalanobis DML based polynomial kernel for the classification of hyperspectral images. To ensure the method is computing-saving, we adapt a fast iterative method to learn the Mahalanobis matrix. Simulation experiment is(More)
A target of interest may exhibit significant appearance variations because of its complex maneuvers, ego-motion of the camera platform, etc. Currently, target tracking in forward-looking infrared (FLIR) sequences is still a challenging problem in the field of computer vision. Although many efforts have been devoted, there are still some issues to be(More)