基于仿人足球机器人协作定位技术研究

本文综述多机器人协作定位的研究现状,分析比较当前该领域的主要研究方向,介绍实现机器人协作定位的主要研究方法和关键技术,并总结多机器人协作定位面临的挑战和发展趋势.     

中医方剂与图集的转换方法

中药复方数据挖掘研究是将我国丰富的中药信息资源和现代最新信息技术相结合的重要内容,它意味着基于中医理论,利用知识发现技术,对传统中药新药、中医组方理论及规律、中药作用机制、有效成分构效关系等多个方面进行全面、系统的研究。图作为一种经典的数据结构,被广泛用于复杂数据结构分析与建模。文章探讨了图建模技术并实现了中医方剂与图集的转换。     

基于Transformer实现文本导向的图像编辑

为方便非专业用户修图,提出一种基于Transformer的图像编辑模型TMGAN,使用户可通过自然语言描述自动修改图像属性。TMGAN整体框架采用生成对抗网络,生成器采用Transformer编码器结构提取全局上下文信息,解决生成图像不够真实的问题;判别器包含基于Transformer的多尺度判别器和词级判别器两部分,给生成器细粒度的反馈,生成符合文本描述的目标图像且保留原始图像中与文本描述无关的内容。实验表明,此模型在CUB Bird数据集上,IS（inception score）、FID（Fréchet inception distance）以及MP（manipulation precision）度量指标分别达到了9.07、8.64和0.081。提出的TMGAN模型对比现有模型效果更好,生成图像既满足了给定文本的属性要求又具有高语义性。     

大通湖区水系连通工程改善水环境的效果评估

大通湖是洞庭湖区的重要组成部分,近年其水质状况呈现恶化态势,正通过实施水系连通工程,以期改善其水环境。基于MIKE21构建大通湖区水系连通工程的二维水动力-水质数学模型,选取总氮和总磷作为水质指标,模拟不同连通调度方案下大通湖的氮磷浓度变化,采用滞水区面积比例、浓度变化指数、换水率和水质浓度改善率,评估6个连通方案下大通湖水环境的改善效果。结果表明:通过实施引水调度方案能够有效改善大通湖水环境,当引水前期流量取30 m~3/s,出口水位控制在25.48 m时和引水后期流量保持为30 m~3/s不变,出口水位调整至25.88 m时,大通湖水环境改善效果最佳。本研究可为实施水系连通工程,改善类似湖泊水环境和提高引水调度效率提供参考。     

Parallel optimization of three-dimensional wedge-shaped underwater acoustic propagation based on MPI+OpenMP hybrid programming model

The three-dimensional wedge-shaped underwater acoustic propagation model exists analytical solution, which provides verification for models like FOR3D propagation model under certain situation. However, the solving process of a three-dimensional complex underwater sound field problem is hindered by intensive computing and long calculation times. In this paper, we exploit a hybrid parallel programing model, such as MPI and OpenMP, to accelerate the computation, design various optimization methods to improve the overall performance, and then carry out the performance and optimization analysis on the Tianhe-2 platform. Experiments show that the optimized implementation of the three-dimensional wedge-shaped underwater acoustic propagation model achieves a 46.5 speedup compared to the original serial program, thereby illustrating a substantial performance improvement. We also carried out scalability tests and parallel optimization experiments for large-scale practical examples.

     

Globally Optimal Point Set Registration by Joint Symmetry Plane Fitting

The present work proposes a solution to the challenging problem of registering two partial point sets of the same object with very limited overlap. We leverage the fact that most objects found in man-made environments contain a plane of symmetry. By reflecting the points of each set with respect to the plane of symmetry, we can largely increase the overlap between the sets and therefore boost the registration process. However, prior knowledge about the plane of symmetry is generally unavailable or at least very hard to find, especially with limited partial views. Finding this plane could strongly benefit from a prior alignment of the partial point sets. We solve this chicken-and-egg problem by jointly optimizing the relative pose and symmetry plane parameters. We present a globally optimal solver by employing the branch-and-bound paradigm and thereby demonstrate that joint symmetry plane fitting leads to a great improvement over the current state of the art in globally optimal point set registration for common objects. We conclude with an interesting application of our method to dense 3D reconstruction of scenes with repetitive objects.

     

A method of credit evaluation modeling based on block-wise missing data

Missing data is a common problem in credit evaluation practice and can obstruct the development and application of an evaluation model. Block-wise missing data is a particularly troublesome issue. Based on multi-task feature selection approach, this paper proposes a method called MMPFS to build a model for credit evaluation that primarily includes two steps: (1) dividing the dataset into several nonoverlapping subsets based on missing patterns, and (2) integrating the multi-task feature selection approach using logistic regression to perform joint feature learning on all subsets. The proposed method has the following advantages: (1) missing data do not need to be managed in advance, (2) available data can be fully used for model learning, (3) information loss or bias caused by general missing data processing methods can be avoided, and (4) overfitting risk caused by redundant features can be reduced. The implementation framework and algorithm principle of the proposed method are described, and three credit datasets from UCI are investigated to compare the proposed method with other commonly used missing data treatments. The results show that MMPFS can produce a better credit evaluation model than data preprocessing methods, such as sample deletion and data imputation.

     

Business process design: Process comparison and integration

This paper proposes a framework for business process design, which is based on process comparison and integration. This framework supports process stakeholders to model business processes collaboratively, which can greatly enhance the efficiency and effectiveness of business process design. By applying this framework, each process stakeholder can create his or her own model of the business process. The different models of the same business process can be automatically compared to find the conflicts between them. Based on the conflicts, process stakeholders can collaborate to improve the models. Then the models can be integrated to form a more complete model.     

基于全数字锁相环的步进电机控制系统

研究步进电机优化控制问题,步进电机控制系统存在着控制功能单一和控制精度不高的缺陷。针对上述问题,提出了一种用全数字锁相环的控制系统设计方案。锁相环能够在较大的频率范围内快速跟踪和锁定输入信号的频率和相位,应用脉冲分配控制器,可提高整个系统的同步性能,增强步进脉冲信号频率的稳定性。采用超高速集成电路硬件描述语言(VHDL)进行电路系统设计,利用计算机仿真技术对该系统进行了仿真验证,并给出了布局布线后时序仿真的结果。仿真结果表明,该系统具有控制灵活、响应速度快、稳定性能好等特点,改进的设计方案可实现对步进电机转速、转向和定位的一体化控制,能够显著提高系统的控制精度,并可应用于其它不同工作方式的步进电机控制系统设计。     

PSONN在可编程控制系统PID参数优化中的应用

研究可编程控制系统优化问题,可编程控制系统具有非线性、时变性等特点,传统PID控制器优化方法难以建立精确的数学模型,使得系统参数设定困难,导致可编程控制系统的控制效果不理想。为了解决传统的PID算法所带来的问题,利用RBF神经网络非线性、自学习能力,提出一种基于粒子群神经网络的PID参数优化算法。将粒子群和神经网络相结合,形成了一种智能控制算法,并将应用于可编程控制系统。测试结果表明,粒子群神经网络提高了PID控制参数优化速度,提高了可编程控制系统可靠性和鲁棒性,具有一定的理论和实用价值。