In emotion recognition based on physiological signals, collecting enough labeled data of a single subject for training is time-consuming and expensive. The physiological signals’ individual differences and the inherent noise will significantly affect emotion recognition accuracy. To overcome the difference in subject physiological signals, we propose a joint probability domain adaptation with the bi-projection matrix algorithm (JPDA-BPM). The bi-projection matrix method fully considers the source and target domain’s different feature distributions. It can better project the source and target domains into the feature space, thereby increasing the algorithm’s performance. We propose a substructure-based joint probability domain adaptation algorithm (SSJPDA) to overcome physiological signals’ noise effect. This method can avoid the shortcomings that the domain level matching is too rough and the sample level matching is susceptible to noise. In order to verify the effectiveness of the proposed transfer learning algorithm in emotion recognition based on physiological signals, we verified it on the database for emotion analysis using physiological signals (DEAP dataset). The experimental results show that the average recognition accuracy of the proposed SSJPDA-BPM algorithm in the multimodal fusion physiological data from the DEAP dataset is 63.6 and 64.4% in valence and arousal, respectively. Compared with joint probability domain adaptation (JPDA), the performance of valence and arousal recognition accuracy increased by 17.6 and 13.4%, respectively.
Junggar Basin is a typical superimposed oil and gas bearing basin in western China, where Mahu Depression is the largest oil-gas accumulation zone and exploration area. On April 23rd, 2013, the peak daily oil production of Mahu No.1 well reached 52 tons, achieving a significant breakthrough. On August 23rd of the same year, the high yield of Mahu No.18 well opened a new chapter of oil and gas exploration in the slope belt of the Mahu Depression. Since then, CNPC (China National Petroleum Corporation) has increased the exploration and development process of the Mahu Depression, establishing the exploration theory and technology system of the conglomerate reservoir in the depression area. Finally, the exploration and discovery of a supergiant conglomerate oil field (1 billion tons) were achieved. However, a series of development problems were exposed, which was due to the lack of in-depth research on reservoir geomechanics and distribution law for natural fractures: 1) The Hydraulic fracture propagation law is not clear; 2) In the process of fracturing, there exists an obvious phenomenon of non-uniform fluid inflow and expansion in various clusters of fractures; 3) Serious interference occurs between wells during the fracturing operation. Therefore, it is necessary to strengthen the study of geomechanics and distribution law for natural fractures in unconventional oil reservoirs, so as to provide technical support for the optimization of the horizontal well fracturing scheme, improvement of well pattern deployment parameters, and adjustment of oil reservoir development scheme, which further leads to cost reduction and development efficiency improvement while increasing single well production in unconventional oil reservoirs.
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