素质教育下课堂教学互动平台的建立

教学过程的改革是教育改革的主要内容,课堂教学是教学活动的主要环节之一。课堂教学要适应素质教育的要求,建立民主平等合作的师生关系,达到教与学的互动,提升课堂教学的效果。     

Optimal production ramp-up in the smartphone manufacturing industry

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production ramp-up model that can be applied to economically satisfy nonstationary demand for short-life-cycle products by high-tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp-up has been increasingly critical to the success of a new smartphone. In the production ramp-up, the key challenge is to match the increasing capacity to nonstationary demand. The high-tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high-dimensional Markov decision process (MDP) model to characterize the production ramp-up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high-tech smartphone manufacturers to better plan the production ramp-up.     

基于深度神经网络的OFDM系统IQ不平衡补偿技术

正交频分复用(OFDM)是现代移动通信中一项重要的物理层通信技术,并且OFDM系统要求子载波间严格正交。然而在实际系统中,振荡器和滤波器等器件的非理想特性会导致同相正交(In-phase and Quadrature-phase,IQ)不平衡,从而破坏子载波的正交性,严重影响OFDM系统的性能。本文研究了IQ不平衡对OFDM系统的影响,提出了基于并联深度神经网络的IQ不平衡补偿技术。该算法利用了深度神经网络不依赖于模型的特点,直接从接收到的频域信号恢复原输入信号的二进制序列,并利用IQ不平衡的干扰信号来自镜像子载波的先验知识来初始化神经网络,加快其网络优化的收敛速度。仿真结果表明,该算法能有效地补偿IQ不平衡失真,并且在幅度和相位失真的补偿上,其性能都优于传统的导频的最小二乘（LS）估计补偿IQ不平衡的算法,证明了深度学习方法解决物理层问题的优越性。     

基于深度神经网络的无线覆盖预测算法*

为保证新一代移动无线网络能够根据实时覆盖情况动态地调节小区天线参数,需要实现高效且准确的无线覆盖预测。传统的求解方法通过精确的场强预测来判断天线参数的优劣,虽然精度很高但需要大量的计算资源,无法满足5G和后5G移动网络通过实时覆盖预测进行射频参数动态调整的实际需求。现采用基于深度神经网络的算法对给定天线参数的覆盖效果进行预测,以取代对目标区域的精确场强预测。数值结果表明,该方法能够在保持计算准确性的同时显著减少计算量,为5G动态网络规划提供基础性参考数据。     

基于深度学习的MPSK信号调制识别

为了有效实现信号调制方式的智能识别,提出基于深度学习的多进制相移键控(Multiple Phase Shift Keying, MPSK)信号调制识别方法。分析接收MPSK信号的循环谱,并通过提取MPSK信号循环谱的等高图获得二维特征信息,利用深度学习中的卷积神经网络对二维特征进行训练,使用测试样本对所设计的调制识别方法的有效性进行验证。仿真结果表明,所提方法具有良好的识别性能。     

采用区分性幅相联合字典学习的低截获概率信号分离方法

为解决采用字典学习的信号分离方法存在的相位信息缺失和子字典交叉表示问题,提出一种区分性幅相联合字典学习方法。该方法针对相位信息缺失问题,构建了幅相联合字典模型;针对混合信号在联合字典上投影时存在的交叉表示问题,基于区分性字典学习思想提出在字典学习过程目标函数中加入交叉表示抑制项。仿真结果表明：幅相联合字典能够充分表示典型低截获概率信号的幅相信息,交叉表示抑制项能有效抑制信号间的交叉表示,算法具有良好的分离性能。     

Lot sizing with learning and forgetting in setups: Analytical results and insights

This article considers the dynamic lot sizing problem when there is learning and forgetting in setups. Learning in setups takes place with repetition when additional setups are made and forgetting takes place when there is a break between two successive setups. We allow the amount forgotten over a break to depend both on the length of the break and the amount of learning at the beginning of the break. The learning and forgetting functions we use are realistic. We present several analytical results and use these in developing computationally efficient algorithms for solving the problem. Some decision/forecast horizon results are also developed, and finally we present managerial insights based on our computational results. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 93–108, 2016     

Strategy in the Age of Artificial Intelligence

ABSTRACT

We argue that Artificial Intelligence (AI) will, in the very near future, have a profound impact on the conduct of strategy and will be disruptive of existing power balances. To do so, we review the psychological foundations of strategy and explore the ways in which AI will impact human decision-making. We then review current and evolving capabilities in ‘narrow’, modular AI that is optimised to perform in a particular environment, and explore its military potential. Lastly, we look ahead to the more distant prospect of a general AI.     

公安现役院校英语学习环境的调查与思考

学习环境对于语言学习者至关重要,就外部环境和内部环境相互关系而言,外部环境影响人的学习态度和认知过程,内部环境控制外部环境的影响力。这些因素无疑能促进英语学习,但也可能在某种程度上阻碍英语学习的进步。在对几所公安现役部队院校的英语学习环境调查后发现,英语学习环境不容乐观,还有很多急需改进之处,应引起有关方面的重视。     

浅谈公安现役院校教育改革与学员个性发展

随着公安教育改革的不断深入,注重学员个性发展已成为公安教育改革的热点问题。公安现役院校学员个性化发展应从优化人才培养方案、创新管理体制、强化学员自主学习能力等方面进行探讨,以促进学员个性发展。