Constructing and evaluating alternative frames of discernment

We construct alternative frames of discernment from input belief functions. We assume that the core of each belief function is a subset of a so far unconstructed frame of discernment. The alternative frames are constructed as different cross products of unions of different cores. With the frames constructed the belief functions are combined for each alternative frame. The appropriateness of each frame is evaluated in two ways: (i) we measure the aggregated uncertainty (an entropy measure) of the combined belief functions for that frame to find if the belief functions are interacting in interesting ways, (ii) we measure the conflict in Dempster’s rule when combining the belief functions to make sure they do not exhibit too much internal conflict. A small frame typically yields a small aggregated uncertainty but a large conflict, and vice versa. The most appropriate frame of discernment is that which minimizes a probabilistic sum of the conflict and a normalized aggregated uncertainty of all combined belief functions for that frame of discernment.     

基于CMAC网络强化学习的电梯群控调度

电梯群控调度是一类开放、动态、复杂系统的多目标优化问题.目前应用于群控电梯调度的算法主要有分区算法、基于搜索的算法、基于规则的算法和其他一些自适应的学习算法.但已有方法在顾客平均等待时间等目标上并不能够达到较好的优化性能.本文采用强化学习技术应用到电梯群控调度系统中,使用CMAC神经网络函数估计模块逼近强化学习的值函数,通过Q-学习算法来优化值函数,从而获得优化的电梯群控调度策略.通过仿真实验表明在下行高峰模式下,本文所提出的基于CMAC网络强化学习的群控电梯调度算法,能够有效地减少平均等待时间,提高电梯运行效率.     

阻抗劈散射的MM-PO混合算法研究

本文系统地研究了各向异性阻抗劈绕射的矩量法-物理光学(MM-PO)混合算法.首先研究了任意各向异性阻抗面的物理光学模型,推导出表面物理光学等效电磁流计算式.其次,提出了一种有效的含Hankel函数的弱振荡被积函数无穷积分处理方法.最后,将作者已公开发表的修正绕射电流基函数用于各向异性阻抗劈散射场研究,数值结果和已知的一致性绕射理论结果高度吻合.     

多谐波失真模型应用研究

基于非线性大信号散射函数理论,对一种用来线性化散射函数的新方法进行了研究.另外,在弱非线性条件下,与相位归一化和线性化技术相结合,分析了极坐标系下线性化表达式的各部分功能,并预测了输出散射波所应该具有的特性.最后,利用实际HEMT晶体管的测量数据说明了应用多谐波失真模型对精确分析非线性器件的重要意义.     

BOC(10,5)信号的调制与解调

讨论了卫星导航中BOC调制解调的原理和实现方法,并着重研究了接收机的码跟踪技术。首先阐述了BOC信号体制出现的国际背景及信号特点,并介绍了BOC（10,5）信号形式和频谱特性,然后简要介绍了信号调制的方法,最后重点讨论信号解调方法。针对BOC信号多峰结构的自相关特性,分别研究并比较了两种解调方案,最后综合两者特点提出了组合式解调恢复方法。     

噪声环境下语音信号的基音检测

研究和改进了一种基于归一化互相关系数的基音检测方法(NCCFPDA)。NCCFPDA算法利用归一化互相关系数、短时归一化能量和自适应门限来区分清/浊音和选取基音周期,计算量小,精度高。在NCCFPDA算法的基础上,采用动态规划的方法对检测出的基音周期进行平滑,并在噪声环境下与自相关法检测基音周期的检测结果进行了比较。实验表明NCCFPDA算法在基音轨迹的平滑和清/浊判别方面都具有令人满意的效果,能实现基音周期的鲁棒性检测。     

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.     

Stochastic Theory of Discrete Binary Fragmentation—Kinetics and Thermodynamics

We formulate binary fragmentation as a discrete stochastic process in which an integer mass k splits into two integer fragments j, k−j, with rate proportional to the fragmentation kernel Fj,k−j. We construct the ensemble of all distributions that can form in fixed number of steps from initial mass M and obtain their probabilities in terms of the fragmentation kernel. We obtain its partition function, the mean distribution and its evolution in time, and determine its stability using standard thermodynamic tools. We show that shattering is a phase transition that takes place when the stability conditions of the partition function are violated. We further discuss the close analogy between shattering and gelation, and between fragmentation and aggregation in general.