With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. ...With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.展开更多
This study proposes an effective method to enhance the accuracy of the Differential Quadrature Method(DQM)for calculating the dynamic characteristics of functionally graded beams by improving the form of discrete node...This study proposes an effective method to enhance the accuracy of the Differential Quadrature Method(DQM)for calculating the dynamic characteristics of functionally graded beams by improving the form of discrete node distribution.Firstly,based on the first-order shear deformation theory,the governing equation of free vibration of a functionally graded beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam axial displacement,transverse displacement,and cross-sectional rotation angle by considering the effects of shear deformation and rotational inertia of the beam cross-section.Then,ignoring the shear deformation of the beam section and only considering the effect of the rotational inertia of the section,the governing equation of the beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam transverse displacement.Based on the differential quadrature method theory,the eigenvalue problem of ordinary differential equations is transformed into the eigenvalue problem of standard generalized algebraic equations.Finally,the first several natural frequencies of the beam can be calculated.The feasibility and accuracy of the improved DQM are verified using the finite element method(FEM)and combined with the results of relevant literature.展开更多
随着工业化进程的加快和城市化的发展,大量污染物排入黄河流域,并被频繁检出,威胁生态系统和人类健康。为获取潜在生态环境风险污染物,该研究通过调研2000年1月1日−2022年12月31日Web of Science(WoS)和中国知网(CNKI)数据库中黄河流域...随着工业化进程的加快和城市化的发展,大量污染物排入黄河流域,并被频繁检出,威胁生态系统和人类健康。为获取潜在生态环境风险污染物,该研究通过调研2000年1月1日−2022年12月31日Web of Science(WoS)和中国知网(CNKI)数据库中黄河流域已报道的288篇污染物相关文献,使用多指标综合评分法筛选黄河流域的特征污染物,采用风险商值法获取水样和沉积物中的风险污染物。结果表明:①黄河流域共检出10类144种污染物,采用9类共13个筛选指标构建多指标综合评分法,对污染物各项指标进行评分,然后进行K-means聚类分析,按得分高低分为Ⅰ~Ⅵ级,选取得分较高的33种Ⅰ级和Ⅱ级高分值污染物作为黄河流域特征污染物,包括12种有机氯农药、10种多环芳烃、10种多氯联苯和1种邻苯二甲酸酯。②水样污染物浓度和沉积物含量前5种都是重金属、有机氯农药、邻苯二甲酸酯、多环芳烃以及药品和个人护理产品,而且二者顺序完全一致,且多数污染物的浓度之间存在显著相关性。③根据风险最大化原则,使用风险商值法(RQ)分别对水样和沉积物进行风险评估,将RQ≥0.1的污染物列为风险污染物,水样中共筛选出21种风险污染物,其中RQ≥1的高风险污染物有5种,包括硒、铅、苯并[a,h]蒽、苯并[a]蒽和邻苯二甲酸二丁酯。④沉积物中共筛选出19种风险污染物,其中有13种高风险污染物,包括8种多环芳烃(芘、蒽、荧蒽、苊、萘、芴、苯并[a]蒽、苯并[a,h]蒽)、4种重金属(汞、铅、硒、砷)和1种邻苯二甲酸酯(邻苯二甲酸二丁酯)。该研究对相关部门拟定黄河流域污染物监测方案和管控措施有重要参考意义。展开更多
基金supported in part by The Science and Technology Development Fund, Macao SAR, China (0108/2020/A3)in part by The Science and Technology Development Fund, Macao SAR, China (0005/2021/ITP)the Deanship of Scientific Research at Taif University for funding this work。
文摘With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.
基金Anhui Provincial Natural Science Foundation(2308085QD124)Anhui Province University Natural Science Research Project(GrantNo.2023AH050918)The University Outstanding Youth Talent Support Program of Anhui Province.
文摘This study proposes an effective method to enhance the accuracy of the Differential Quadrature Method(DQM)for calculating the dynamic characteristics of functionally graded beams by improving the form of discrete node distribution.Firstly,based on the first-order shear deformation theory,the governing equation of free vibration of a functionally graded beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam axial displacement,transverse displacement,and cross-sectional rotation angle by considering the effects of shear deformation and rotational inertia of the beam cross-section.Then,ignoring the shear deformation of the beam section and only considering the effect of the rotational inertia of the section,the governing equation of the beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam transverse displacement.Based on the differential quadrature method theory,the eigenvalue problem of ordinary differential equations is transformed into the eigenvalue problem of standard generalized algebraic equations.Finally,the first several natural frequencies of the beam can be calculated.The feasibility and accuracy of the improved DQM are verified using the finite element method(FEM)and combined with the results of relevant literature.
文摘随着工业化进程的加快和城市化的发展,大量污染物排入黄河流域,并被频繁检出,威胁生态系统和人类健康。为获取潜在生态环境风险污染物,该研究通过调研2000年1月1日−2022年12月31日Web of Science(WoS)和中国知网(CNKI)数据库中黄河流域已报道的288篇污染物相关文献,使用多指标综合评分法筛选黄河流域的特征污染物,采用风险商值法获取水样和沉积物中的风险污染物。结果表明:①黄河流域共检出10类144种污染物,采用9类共13个筛选指标构建多指标综合评分法,对污染物各项指标进行评分,然后进行K-means聚类分析,按得分高低分为Ⅰ~Ⅵ级,选取得分较高的33种Ⅰ级和Ⅱ级高分值污染物作为黄河流域特征污染物,包括12种有机氯农药、10种多环芳烃、10种多氯联苯和1种邻苯二甲酸酯。②水样污染物浓度和沉积物含量前5种都是重金属、有机氯农药、邻苯二甲酸酯、多环芳烃以及药品和个人护理产品,而且二者顺序完全一致,且多数污染物的浓度之间存在显著相关性。③根据风险最大化原则,使用风险商值法(RQ)分别对水样和沉积物进行风险评估,将RQ≥0.1的污染物列为风险污染物,水样中共筛选出21种风险污染物,其中RQ≥1的高风险污染物有5种,包括硒、铅、苯并[a,h]蒽、苯并[a]蒽和邻苯二甲酸二丁酯。④沉积物中共筛选出19种风险污染物,其中有13种高风险污染物,包括8种多环芳烃(芘、蒽、荧蒽、苊、萘、芴、苯并[a]蒽、苯并[a,h]蒽)、4种重金属(汞、铅、硒、砷)和1种邻苯二甲酸酯(邻苯二甲酸二丁酯)。该研究对相关部门拟定黄河流域污染物监测方案和管控措施有重要参考意义。
