混合式教学模式:内涵、意义与实施要求

随着信息技术的发展与MOOC热潮的兴起,以线上教学和线下教学相结合的混合式教学模式得到了快速的发展。文章通过分析混合式教学模式的内涵,指出混合式教学模式的实施是高等教育改革的趋势,能有效促进学生深度学习和优质教学资源的开发。实施混合式教学模式,要有成熟技术的支持,还要注意各个要素的合理分配与整合。此外,要完善教师培训体系,强化教师的相应能力,教学评价方面也要切实有效。     

烤在烹调中的变化与发展

烤,是一种古老的烹调方法,能风行至今,全是因其容易推广运用。本文对烤进行了概括地叙述和介绍,以利于拓展思路,从而获得新的启发,增强创新意识,促进此技法的发展。     

Method for discrimination of false targets in multistation radar systems based on the deep neural network

For the existing jamming discrimination methods for multistation radar systems,only the single feature of target echo space correlation is utilized as the metric,which leads to insufficient comprehensiveness of feature extraction,so that effectiveness and universality are insufficient for the discrimination algorithm.In this paper,an identification method in multistatic radar systems based on the deep neural network is proposed.This method combines the characteristics of multistatic radar systems cooperative detection technology,which has many available resources and strong scheduling ability in space,time and frequency domain,with the strong model learning and feature representation ability in the process of information processing on the deep neural network,so that it can effectively apply to the field of anti-deception jamming.Full use is made of unknown information about echo data to obtain more multi-dimensional,more comprehensive,more complete and deeper feature differences besides correlation,so as to achieve a better jamming discrimination effect.Simulation results show that the proposed method can effectively reduce the influence of noise and pulse number on the jamming discrimination performance.At the same time,the limitation of the target echo correlation coefficient on anti-jamming technology under nonideal conditions is alleviated,which broadens the boundary conditions of the application process.     

全光网络及其上传送IP的研究

讨论了网络的 IP化及 IP网络全光化趋势 ;讨论了全光网络的四种关键技术 ,包括光分插复用器 (OADM)、光交叉连接设备 (OXC)、高速路由器和全光路由 ;阐述了全光网络的三种网络结构以及在全光网络中传送 IP分组的三种方法。     

Multiobjective immune algorithm with nondominated neighbor-based selection

Abstract Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.     

深冷处理对低碳高合金马氏体轴承钢力学性能及组织的影响

利用洛氏硬度计、X射线衍射仪、扫描电子显微镜及透射电子显微镜等研究了低碳高合金马氏体轴承钢深冷处理后的硬度变化及组织演化。结果表明:深冷处理促使部分残留奥氏体转变为马氏体,导致深冷处理后实验钢的硬度较淬火态硬度有所升高。经深冷处理后实验钢在0~100 h回火过程中的硬度均比未深冷处理实验钢的硬度高。深冷处理促使钢中碳原子偏聚并在回火过程中以碳化物的形式析出,与未经深冷处理的实验钢相比,经深冷处理的实验钢回火后马氏体基体中的含碳量更低,表明实验钢经深冷处理后在回火过程中析出更多的碳化物。透射电镜分析表明,实验钢在回火过程中析出的大量弥散分布的纳米级M2C和M6C型碳化物是实验钢长时间回火后保持高硬度的主要原因。     

ADAPTIVE MULTI-OBJECTIVE OPTIMIZATION BASED ON NONDOMINATED SOLUTIONS

An adaptive hybrid model (AHM) based on nondominated solutions is presented in this study for multi-objective optimization problems (MOPs). In this model, three search phases are devised according to the number of nondominated solutions in the current population: 1) emphasizing the dominated solutions when the population contains very few nondominated solutions; 2) maintaining the balance between nondominated and dominated solutions when nondominated ones become more; 3) when the population consists of adequate nondominated solutions, dominated ones could be ignored and the isolated nondominated ones are allocated more computational budget by their crowding distance values for heuristic search. To exploit local information efficiently, a local incremental search algorithm, LISA, is proposed and merged into the model. This model maintains the adaptive mechanism between the optimization process by the online discovered nondominated solutions. The proposed model is validated using five ZDT and five DTLZ problems. Compared with three other state-of-the-art multi-objective algorithms, namely NSGA-II, SPEA2, and PESA-II, AHM achieves comparable results in terms of convergence and diversity metrics. Finally, the sensitivity of introduced parameters and scalability to the number of objectives are investigated.     

ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

Low-rank tensor completion via combined Tucker and Tensor Train for color image recovery

Applied Intelligence - In recent years, low-rank tensor completion has been widely used in color image recovery. Tensor Train (TT), as a balanced tensor rank minimization method, has achieved good...     

一种改进的多任务级联网络人脸检测算法研究

传统人脸检测算法在复杂环境背景下一直存在着检测准确率及效率低等问题.近年来,得益于人脸数据集的增长以及计算机硬件的极速发展,使用深度神经网络的人脸检测算法在准确度方面已有很大提升,但使用的模型结构越来越复杂,检测速度也相对变慢.本文提出一种改进的多任务卷积神经网络(Multi-task convolutional neural networks,MTCNN)算法.在制造数据集时更改IOU阈值参数,来获取更多、更精确的人脸样本;对与置信度损失有关的交叉熵损失函数和与偏移量损失有关的均方差函数求均值,使得整个网络收敛得更加平稳.经在AFW、PASCAL以及FDDB数据集上实验,与传统算法相比,该算法在保证实时性的同时提升了检测准确率,可应用于追求更高准确率的人脸检测系统.