关键词：
*ERPs;*Face perception;*N170;*Own-race faces;*Spatial frequency
摘要：
In the present study we examined the influence of spatial filtering on the N170 elicited by own-race and other-race faces, a relatively early face-selective ERP difference associated with face detection. It was found that BB (broad band) faces elicited larger N170 than did LSF (low spatial frequency) faces and the latter larger than HSF (high spatial frequency) condition. Faces' races significantly modulated the N170 amplitudes, showing larger N170 for other-race than own-race faces for both BB and HSF conditions but not for LSF condition. For own-race faces, the N170 did not differ between BB and LSF conditions, whereas other-race faces elicited larger N170 for BB than LSF conditions. The present data provided new electrophysiological evidence for race perception of faces.
期刊：
Chinese Geographical Science,2014年24(1):60-70 ISSN：1002-0063
通讯作者：
Xu Weihua
作者机构：
[Xu Weihua; Ouyang Zhiyun] Chinese Acad Sci, Ecoenvironm Sci Res Ctr, State Key Lab Reg & Urban Ecol, Beijing 100085, Peoples R China.;[Liu Wei; Liu Jianguo; Vina, Andres] Michigan State Univ, Dept Fisheries & Wildlife, Ctr Syst Integrat & Sustainabil, E Lansing, MI 48823 USA.;[Qi Zengxiang] Univ South China, Coll Design & Art, Hengyang 421001, Peoples R China.;[Wan Hui] World Wide Fund Nat WWF Xian Programme Off, Xian 710075, Peoples R China.
通讯机构：
[Xu Weihua] Chinese Acad Sci, Ecoenvironm Sci Res Ctr, State Key Lab Reg & Urban Ecol, Beijing 100085, Peoples R China.
关键词：
giant panda;habitat suitability;Maximum Entropy (MAXENT);nature reserve network;surrogate species
摘要：
Many nature reserves are established to protect the habitat needs of particular endangered species of interest but their effectiveness for protecting other species is questionable. In this study, this effectiveness was evaluated in a nature reserve network located in the Qinling Mountains, Shaanxi Province, China. The network of reserves was established mainly for the conservation of the giant panda, a species considered as a surrogate for the conservation of many other endangered species in the region. The habitat suitability of nine protected species, including the giant panda, was modeled by using Maximum Entropy (MAXENT) and their spatial congruence was analyzed. Habitat suitability of these species was also overlapped with nature reserve boundaries and their management zones (i.e.,core, buffer and experimental zones). Results show that in general the habitat of the giant panda constitutes a reasonable surrogate of the habitat of other protected species, and giant panda reserves protect a relatively high proportion of the habitat of other protected species. Therefore, giant panda habitat conservation also allows the conservation of the habitat of other protected species in the region. However,a large area of suitable habitat was excluded from the nature reserve network. In addition, four species exhibited a low proportion of highly suitable habitat inside the core zones of nature reserves. It suggests that a high proportion of suitable habitat of protected species not targeted for conservation is located in the experimental and buffer zones, thus, is being affected by human activities. To increase their conservation effectiveness, nature reserves and their management zones need to be re-examined in order to include suitable habitat of more endangered species. The procedures described in this study can be easily implemented for the conservation of many endangered species not only in China but in many other parts of the world.
摘要：
Fire detection technology based on video images can avoid many flaws in conventional methods and detect fires. To achieve this, the support vector machine (SVM) method in machine learning theory has unique advantages, while rough set (RS) theory and SVM complement each other in application. Thus, a new classifier could be created by organically combining these methods to identify fires and provide fire warnings, yielding excellent noise suppression and promotion. Therefore, in this study, an RS is used as the front-end system for the SVM method, yielding improved performance than only SVM. Recognition time is reduced, and recognition efficiency is improved. Experiments show that the RS-SVM classifier model based on parameter optimization proposed in this paper mitigates deficiencies in overfitting and determining local extremum with excellent reliability and stability, and enhances the forecast accuracy of fires. The method also reduces false fire-detection alarms and uses fire feature selection in virtual reality (VR) video images and fire detection and recognition.
