Existing work on unsupervised time series anomaly detection relies on clean training datasets (free of anomalies), which is often violated in practice. In this work, we propose an unsupervised time series anomaly dete...
Existing work on unsupervised time series anomaly detection relies on clean training datasets (free of anomalies), which is often violated in practice. In this work, we propose an unsupervised time series anomaly detection method based on adversarial interpolation and pseudo-anomaly calibration for training an anomaly detector under data con-tamination. Specifically, the normality description learned from the model is improved by using the following two proposed calibration methods: (1) by enforcing adversarial interpolation to learn an effective normality description from the typical samples rather than the edge or abnormal samples, and (2) by discriminating the original samples from the simulated pseudo-anomaly samples to learn the exact normality description boundary. These two calibration ways result in the normality description that prevents data contamination. Experimental results compared with baselines on public time series datasets demonstrate the effectiveness of our proposed method.
Generally,dams serve as reservoirs for water *** Colorado River Basin is currently in a state of continuous drought,and if the five states of AZ,CA,WY,NM and CO maintain the agreements made hundreds of years ago,there...
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Generally,dams serve as reservoirs for water *** Colorado River Basin is currently in a state of continuous drought,and if the five states of AZ,CA,WY,NM and CO maintain the agreements made hundreds of years ago,there will not be enough water to supply in the near *** propose a multi-objective programming algorithm under inequality constraints,under the condition that the sum of the competition coefficients is minimized,we solve the proportion of water resources that should be cut for general water usage and hydropower production at different water scarcity *** develop some policies to deal with water *** use the univariate analysis method to rationally analyze and reconstruct our model according to three different scenarios,and finally predict different situations,like additional water supply is needed or whether there will be more water flowing to the Gulf of California.
To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and ...
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Dear editor,Reduction of finite automata (FA) is of great importance because of its practical applications in engineering; for example the memory space of hardware realization grows exponentially with the number of st...
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Dear editor,Reduction of finite automata (FA) is of great importance because of its practical applications in engineering; for example the memory space of hardware realization grows exponentially with the number of states of FSMs. Existing results for reducing FA can roughly be classified into four categories:merging of states [1], refining of the state
The use of magnetic resonance (MR) Image has become more significant when treating rectal cancer. Rectal cancer can be staged more accurately with MRI, which serves as a great tool for choosing the most suitable cours...
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An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three altern...
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This paper is focused on uncertain fractional order systems. In this context, a new modelling approach of uncertain fractional order systems represented by an explicit fractional order interval transfer function is pr...
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In this paper, we study the energy-efficient unmanned aerial vehicle (UAV) and low earth orbital (LEO) satellite assisted mobile edge computing (MEC) in space-air-ground integrated networks (SAGINs). The key challenge...
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Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an...
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Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer and process trillions and zillions of bytes using the current cloud-device architecture.
Gas leaks are the main cause of industrial fires and accidents. These cause countless fatalities, equipment damage, and other severe environmental effects. In this paper, we provide a framework for the monitoring and ...
Gas leaks are the main cause of industrial fires and accidents. These cause countless fatalities, equipment damage, and other severe environmental effects. In this paper, we provide a framework for the monitoring and detection of methane leakage using a diffusion model based on the gas diffusion theory. Given that centralized Least Square methods are not efficient and robust as they require the gathering and processing of large-scale measurements on a central node. We propose a detection technique which makes use of the distributed (Non-linear) least squares method to overcome this problem. Then, a network of connected methane sensors is used to detect gas leaks. In order to estimate the parameters of the diffusive model for the gas leakage on each sensor node, a distributed recursive estimator of the consensus plus an innovation type technique is used. The characteristics being estimated include the gas source’s distance, which will be effectively triangulated to determine the source’s precise location. The targeted location is subsequently estimated using a location dispersed algorithm-based LS.
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