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.

     

An improved generative adversarial network for translating clothes from the human body to tiled image

Neural Computing and Applications - Nowadays, purchasing the products similar to the styles of stars’ products has become a new trend in e-commerce platforms. Clothing transaction constitutes...     

μC／OS—Ⅲ在Cortex—M3处理器上的移植

为了将μC／OS-Ⅲ移植到Cortex—M3处理器上,选用RealView MDK作为软件开发平台,针对Cortex—M3处理器特性编写了移植所需的C语言和汇编语言源代码,并验证了移植的正确性。移植后的μC／OS-Ⅲ能够稳定运行于Cortex～M3处理器上。该移植对大部分Cortex—M3处理器具有通用性,对其他架构处理器的μC／OS-Ⅲ移植具有参考作用。     

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。     

基于Android平台的无线WiFi控制方法

提出了一种基于Android手机的无线WiFi控制系统。首先采用嵌入式ARM9为硬件控制模块,手机终端以Android为应用程序的开发,编写了一个手机客户端软件,完成了手机端与控制器端之间的数据传输,实现了手机远程对PWM调速等的控制。系统中Android手机可利用周围无线网络资源与其他设备进行交互并实施控制,不仅为现有智能控制系统提供了新的控制方法,也为实现机器与人的信息交换提供了新的交互手段。     

多媒体教学中的问题与对策

随着计算机技术及多媒体技术的发展,多媒体在教学中的应用越来越广泛,多媒体有着传统教学媒体不具备的独特优势,但在实际教学中也有不足之处,在教学中必须根据实际情况灵活运用多媒体才能取得理想的教学效果。