Deep autoencoder-based methods are the majority of deep anomaly detection. An autoencoder learning on training data is assumed to produce higher reconstruction error for the anomalous samples than the normal samples and thus can distinguish anomalies from normal data. However, this assumption does not always hold in practice, especially in unsupervised anomaly detection, where the training data is anomaly

To deal with the problem of resource integration and optimal scheduling in cloud manufacturing, based on the analyzation of the existing literatures, multitask oriented virtual resource integration and optimal scheduling problem is presented from the perspective of global optimization based on the consideration of sharing and correlation among virtual resources. The correlation models of virtual resources in a task and among tasks are established. According to the correlation model and characteristics of resource sharing, the formulation in which resource time-sharing scheduling strategy is employed is put forward, and then the formulation is simplified to solve the problem easily. The genetic algorithm based on the real number matrix encoding is proposed. And crossover and mutation operation rules are designed for the real number matrix. Meanwhile, the evaluation function with the punishment mechanism and the selection strategy with pressure factor are adopted so as to approach the optimal solution more quickly. The experimental results show that the proposed model and method are feasible and effective both in situation of enough resources and limited resources in case of a large number of tasks.

The considerable risk of falls and the substantial increase in the elderly population make the automatic fall detection system become very important. Existing fall detection systems using accelerometer as the detector are often designed based on an empirical acceleration threshold to differentiate falls from normal activities. In this paper, we design the detection method under the Neyman-Pearson detection framework. An optimal detection threshold can be obtained which meets the specified false alarm rate while maximizing the detection probability. We use TelosW mote with accelerometer as the detector, which is attached to the waist of the old people to capture the movement data. Extensive experiments are conducted to evaluate the effectiveness of our method and the accuracy of the detection system.

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