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Despite the rapid development of support vector regression (SVR), it costs unacceptable training time in large-scale datasets and is hard to fit complex, high frequency oscillating, and non-stationary time series data. SVRs are still perplexed by the selection of critical parameters and hidden noise in input data. This work proposes a hybrid model to overcome these issues that need to be resolved, namely EEMD-GRU-TWSVRCSSA. The proposed model utilizes twin support vector regression (TWSVR) to overcome the shortcomings of the SVR in terms of training time and fitting accuracy. A novel meta -heuristic algorithm, cloud salp swarm algorithm (CSSA), is employed to automatically select the optimal hyper parameters for the TWSVR. The ensemble empirical mode decomposition (EEMD) reduces the influences of hidden noise in the input data, meanwhile splitting the high-frequency and low-frequency sub-datasets and feeding them to the gated recurrent unit (GRU) and TWSVR-based model, respectively. The forecasting of the proposed algorithm and other alternative algorithms are conducted on three real-world electric load datasets from the National Electricity Market (NEM), Queensland and New South Wales regions, Australia, and the well-known National Grid UK. Experimental results demonstrate the superiority and competitiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Noncontact vital sign detection is widely used in finding victims in post-disaster search and rescue, through-wall surveillance (TWS), and medical diagnosis and monitoring. Human subject can be remotely sensed by extracting life activities such as respiration and heartbeat. However, the signal to noise ratio (SNR) is often low, particularly in complex environments, which results in errors in both range and respiration frequency (RF) estimation. To improve the accuracy, an improved system for the vital sign detection is presented which is based on impulse ultra-wideband (UWB) radar. The range is determined based on the short-time Fourier transform (STFT) of the standard deviation of the received signals. Further, the ensemble empirical mode decomposition (EEMD) based frequency accumulation (FA) technique is presented to determine RF. Performance results are presented which show that the proposed method is superior to several well-known techniques. [ABSTRACT FROM AUTHOR]