分类： 心理学 >> 生理心理学 提交时间： 2021-03-23
摘要： 皮层-基底节-丘脑网络与脑岛网络属于感觉运动相关网络，这两个网络的改变可能是导致精神分裂症的重要原因。目前主流研究与临床干预聚焦于患者的高级脑区异常，对感觉运动系统的关注不足。对健康个体的研究发现舞蹈训练对感觉运动相关脑网络具有显著提升作用，并自下而上地促进高级功能。以上研究提示舞蹈训练可能是干预精神分裂症，改善其认知功能的新途径。本研究拟借助多模态磁共振成像技术，以精神分裂症感觉运动相关网络为着力点，通过分析精神分裂症患者在舞蹈训练前后的脑影像、临床及认知行为的改变，揭示舞蹈训练临床干预的神经机制。
分类： 信息科学与系统科学 >> 信息科学与系统科学基础学科 提交时间： 2021-03-23
摘要： " Dempster-Shafer evidence theory, as an extension of Probability theory, is widely used in the field of information fusion due to it satisfies weaker conditions than probability theory in dealing with uncertain information. Nevertheless , the description space of the current evidence theory is only a real space, and it cannot effectively describe and process the uncertain information in the face of multidimensional characteristic data and periodic data with phase angle changes. Based on this gap , in this paper, Dempster-Shafer evidence theory is extended to the complex Dempster-Shafer evidence theory. In complex Dempster-Shafer evidence theory, mass function that used to describe the uncertain information extends from the real space to the complex space, named as complex mass function, and the modulus of the mass function indicates the degree of support for the proposition. On this basis, other basic concepts used to describe uncertainty information are also defined and discussed, such as complex belief function, complex plausibility function, etc. In order to perfect the complex Dempster-Shafer evidence theory, the complex Dempster combination rule (CDCR) is supplemented. CDCR is an extension of Dempster combination rule (CDR), which satisfies the commutative and associative laws just as CDR does, and it can degenerate into CDR under certain condition. In addition, we propose a method to generate complex mass function and apply it to target recognition. The recognized results show that compared with the mass function of the real plane, the target recognition rate can be larger by using complex mass function to describe the uncertain information.
分类： 物理学 >> 普通物理:统计和量子力学,量子信息等 提交时间： 2021-03-23
摘要： In real life, occurrences of a series of things are supposed to come in an order. Therefore, it is necessary to regard sequence as a crucial factor in managing different kinds of things in fuzzy environment. However, few related researches have been made to provided a reasonable solution to this demand. Therefore, how to measure degree of uncertainty of ordinal fuzzy sets is still an open issue. To address this issue, a novel ordinal relative fuzzy entropy is proposed in this paper taking orders of propositions into consideration in measuring level of uncertainty in fuzzy environment. Compared with previously proposed entropies, effects on degrees of fuzzy uncertainty brought by sequences of sequential propositions are embodied in values of measurement using proposed method in this article. Moreover, some numerical examples are offered to verify the correctness and validity of the proposed entropy.
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