Web前端开发技术在在线学习平台中的研究与应用

文章着眼于Web前端开发技术特点,基于在线学习平台的运转、使用实际,转变技术应用思路,创新技术应用方法,推动We b前端开发技术的合理化使用,打造成熟的桌面端、移动端学习平台,促进在线学习平台功能的完善与丰富.     

运用英语流程图诊断数控机床故障问题

英语已成为数控专业高技能人才的基本素质要求之一,而诊断数控机床的故障问题是数控专业学生常见的工作任务,尝试基于“工学一体化”教学模式,以期为培养数控专业学生英语运用能力提供一些参考。     

Modeling of spiking analog neural circuits using organic semiconductor thin film transistors with silicon oxide nitride semiconductor gates

This paper uses the results of the characterization of amorphous semiconductor thin film transistors (TFTs) with the quasi-permanent memory structure referred to as silicon oxide nitride semiconductor (SONOS) gates, to model spiking neural circuits. SONOS gates were fabricated and characterized. In addition, MOSFETs using organic copper phthalocyanine (CuPc) were fabricated with these SONOS gates to demonstrate proof of concept performance. Analog spiking circuits were then modeled using these low performance TFTs to demonstrate the general suitability of organic TFTs in neural circuits. The basic circuit utilizes a standard comparator with charge and discharge circuits. A simple Hebbian learning circuit was added to charge and discharge the SONOS device. The use of these elements allows for the design and fabrication of high-density 3-dimensional circuits that can achieve the interconnect density of biological neural systems.     

仿真软件Multisim10在桥式整流滤波电路理论课教学中的应用探讨

针对桥式整流滤波电路理论课教学特点,通过即时仿真手段,创设电路仿真情境,结合递进性情境操作,唤醒学生探究未知的主动性.     

计算机应用设计3DMAX的学习感受

3DMAX软件是一款专业服务艺术设计的三维设计软件,由美国Autdesk传媒娱乐部开发,运行在Win32和Win64平台的计算机图形软件。当前3DMAX软件运用在多个领域,并由于高性价比、易上手、用户多易交流等优势得到广泛认可,业界甚至可以通过对3DMAX软件能力,去衡量个人或企业的水准。鉴于此,研究3DMAX的运用和学习能够深刻读者对该软件的认识,进一步提升相关学者的软件学习能力。     

Selective body biasing for post-silicon tuning of sub-threshold designs: A semi-infinite programming approach with Incremental Hypercubic Sampling

Sub-threshold designs have become a popular option in many energy constrained applications. However, a major bottleneck for these designs is the challenge in attaining timing closure. Most of the paths in sub-threshold designs can become critical paths due to the purely random process variation on threshold voltage, which exponentially impacts the gate delay. In order to address timing violations caused by process variation, post-silicon tuning is widely used through body biasing technology, which incurs heavy power and area overhead. Therefore, it is imperative to select only a small group of the gates with body biasing for post-silicon-tuning. In this paper, we first formulate this problem as a linear semi-infinite programming (LSIP). Then an efficient algorithm based on the novel concept of Incremental Hypercubic Sampling (IHCS), specially tailored to the problem structure, is proposed along with the convergence analysis. Compared with the state-of-the-art approach based on adaptive filtering, experimental results on industrial designs using 65 nm sub-threshold library demonstrate that our proposed IHCS approach can improve the pass rate by up to 7.3× with a speed up to 4.1×, using the same number of body biasing gates with about the same power consumption.     

Kernel propagation strategy: A novel out-of-sample propagation projection for subspace learning

Kernel matrix optimization (KMO) aims at learning appropriate kernel matrices by solving a certain optimization problem rather than using empirical kernel functions. Since KMO is difficult to compute out-of-sample projections for kernel subspace learning, we propose a kernel propagation strategy (KPS) based on data distribution similar principle to effectively extract out-of-sample low-dimensional features for subspace learning with KMO. With KPS, we further present an example algorithm, i.e., kernel propagation canonical correlation analysis (KPCCA), which naturally fuses semi-supervised kernel matrix learning and canonical correlation analysis by means of kernel propagation projections. In KPCCA, the extracted correlation features of out-of-sample data not only incorporate integral data distribution information but also supervised information. Extensive experimental results have demonstrated the superior performance of our proposed method.     

“工科电路分析”课程教学方法的研究

"工科电路分析"是工科各类专业,特别是电类专业的一门重要的基础理论课,它为后续课程提供了必要的理论基础。本文通过对课程教学方法的具体研究,指出灵活、科学地运用多种教学方法,加强实践教学环节,对激发学生学习兴趣,提高教学质量起到良好的作用。     

Design of a Temporal Learning Chip for Signal Generation and Classification

The design and analog VLSI implementation of a recurrent neural network with integrated temporal learning is presented. The learning algorithm is forward in time, and is implemented strictly as instantaneous, local weight updates. PSpice simulations of networks with 4 to 6 neurons demonstrate robust learning of trajectory generation and classification tasks. A scalable 2-D VLSI architecture is described and a prototupe 4-neuron recurrent neural network with learning has subsequently been fabricated in MOSIS TinyChip 2 micron technology. Experimental results of the chip validate the learning performance with convergence in the millisecond range. Specific experimental results of learning circular and figure-8 dynamic trajectories are included.     

基于体验学习的“过程控制”实验教学设计

"过程控制"是自动化专业的主要专业课程之一,具有专业性和实践性较强的特点。课程不仅涉及许多控制理论知识,而且与工业生产实际紧密联系。实验环节在整个课程中起着十分重要的作用。本文从体验学习理论出发,从知识获取过程的角度对过程控制实验教学环节进行了重新设计和安排,使实验教学过程更符合人获取知识过程的规律。