基于电子媒介的驻地训练支撑系统设计

本文设计了一种新型的驻地支撑系统,此系统可实现训练数据的集成和电子媒介发布,以及驻地的自主训练,有效的解决了部队驻地训练资源短缺,训练保障人员多,费用昂贵的难题,为提高部队复杂电磁环境基础训练奠定了基础。     

电子学习

一、新纪元的挑战 Ｉｎｔｅｒｎｅｔ的高速发展产生了因特网经济的游戏规则，终生的学习，变化是永恒，执行速度是决胜的关键，信息的价值来自分享，人才＋信息＝竞争优势。新纪元的挑战是，快速的变化，经济与社会转型，大量的新信息，Ｉｎｔｅｒｎｅｔ的挑战。 教育面临空前的挑战，人口的老化，老化的劳动力市场，技能要求的不断提高。因此教育是企业面对的极大挑战，要在竞争激烈的市场中生存，企业必须致力重塑组织文化，创造自己的竞争优势。 现在商学院所传授的，教科书里所描述的，总经理们所认同的管理学已经过时了，未来的成功依…     

电子学习系统对计算机教学改革的促进与作用

本文介绍了我院在教学改革中引入电子学习系统平台作为教学手段的扩充，充分体现了以教师为主导，学生为主体的现代教学理念。实践表明，扩大了课堂和实验教学效果，学生的认知能力和创新能力得到了明显的提高。     

电子学习语义网

语义网上的一个重要问题是存储在远程或本地数据库中的学习对象的重用和方便二次开发;另一个司题是本体的架构;第3个问题是使学习对象更具智慧,以便在语义网上充当更有意义的角色.讨论的这些问题及提出的基于语义网的学习模型将影响语义网上学习对象的开发.     

电子学习中的情感计算

电子学习(E-learning)也称在线学习,是指利用互联网技术来设计、执行、选择、管理、支持和扩展的学习活动,包括电子阅读、多媒体远程教育、虚拟教室、数字图书馆和电子出版等网络学习的各种形式。众所周知,人随时随地都会有喜怒哀乐等情感的起伏变化,而人的情绪状态及其变化会对学习活动的绩效有重要影响。那么,在电子学习过程中,计算机是否能觉察学习者的情绪状态,并见机行事呢?所谓的情感计算(affective computing)就是试图赋予计算机像人一样的观察、理解和生成各种情感特征的能力。     

一种儿童电子读物的程序设计

阐述了一种儿童电子读物的程序设计方法与代码实现,并给出了一个基于Delphi的程序设计和实现实例。     

新一代电子阅读器与移动学习简析

电子阅读,从大众最初的不适应到现在新生代数以原住民的诞生,已经形成了第二种阅读模式。这种阅读模式的诞生,除了方便大众进行娱乐、信息获取、沟通与交流外,借助阅读器本身电子媒体的特点,同样也能与移动学习结合起来     

电子书包在开放学习中的应用探究

为了构建学习型社会,开放学习成为世界各国关注的焦点,成人的继续教育在学习型社会建设过程中发挥着突出的作用,将电子书包这一新兴的学习资源应用于开放学习过程中将取得更好的效果。本文结合电子书包的特点,探讨电子书包在成人开放学习过程中存在的优势,并尝试着提出了电子书包在成人开放学习过程中的应用模式。     

Windows Media技术在网络教学中的应用

本文介绍了Windows Media技术的要点,设计并构建了采用Windows Media技术的网络多媒体教学系统,以较低的代价实现了多媒体在网络教学中的应用。     

基于集成分类算法的自动图像标注

基于语义的图像检索技术中,按照图像的语义进行自动标注是一个具有挑战性的工作. 本文把图像的自动标注过程转化为图像分类的过程,通过有监督学习对每个图像区域分类并得到相应关键字,实现标注. 采用一种快速随机森林(Fast random forest, FRF)集成分类算法,它可以对大量的训练数据进行有效的分类和标注. 在基于Corel数据集的实验中,相比经典算法, FRF改善了运算速度,并且分类精度保持稳定. 在图像标注方面有很好的应用.     

ASL Recognition by the Layered Learning Model Using Clustered Groups

American Sign Language (ASL) images can be used as a communication tool by determining numbers and letters using the shape of the fingers. Particularly, ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons, because sign language is their only channel of expression. Representative ASL recognition methods primarily adopt images, sensors, and pose-based recognition techniques, and employ various gestures together with hand-shapes. This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers. In the proposed model, the collected ASL images were clustered based on similarities in shape, and clustered group classification was first performed, followed by reclassification within the group. The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition. After selecting the optimized group, we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing. The proposed model exhibited an improved performance compared with the general classification model.     

Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders

Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors' knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract Log-Mel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multi-layer perceptron (MLP) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory (LSTM) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine (OC-SVM) algorithm. The performance has been evaluated in terms area under curve (AUC) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OC-SVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11%      

基于代价敏感多标记学习的开源软件分类

随着开源软件数量的增多,从开源软件社区中有效检索到所需的开源软件是具有挑战性的工作.现有方法通常是:首先,人工给每个软件赋予多个描述其功能、用途的标注;然后,通过关键词匹配寻找用户所需的软件.由于其简单、方便,基于标注进行软件检索得到了广泛的应用.然而,用户通常不愿意主动为其上载的开源软件提供标注,这使得根据用户上载软件的文字描述信息,从众多备选软件标注中为其自动选择能够表征其功能、用途的标注,成为了有效检索该软件的关键.把开源软件自动标注形式化为一个代价敏感多标记学习问题,并提出了一种新型代价敏感多标记学习方法ML-CKNN.该方法通过在多标记学习中引入代价信息,有效缓解了对每一个标注而言具有该标注的示例与不具有该标注的示例分布非均衡性给多标记学习造成的影响.在3个开源软件社区上的实验结果表明:所提出的ML-CKNN方法能够为新上载的开源软件提供高质量的标注,其标注性能显著优于现有方法.     

使用增强学习训练多焦点聚焦模型

聚焦模型（Attention model,AM）将计算资源集中于输入数据特定区域,相比卷积神经网络,AM具有参数少、计算量独立输入和高噪声下正确率较高等优点.相对于输入图像和识别目标,聚焦区域通常较小;如果聚焦区域过小,就会导致过多的迭代次数,降低了效率,也难以在同一输入中寻找多个目标.因此本文提出多焦点聚焦模型,同时对多处并行聚焦.使用增强学习（Reinforce learning,RL）进行训练,将所有焦点的行为统一评分训练.与单焦点聚焦模型相比,训练速度和识别速度提高了25%.同时本模型具有较高的通用性.     

Learning Binary Relations Using Weighted Majority Voting

In this paper we demonstrate how weighted majority voting with multiplicative weight updating can be applied to obtain robust algorithms for learning binary relations. We first present an algorithm that obtains a nearly optimal mistake bound but at the expense of using exponential computation to make each prediction. However, the time complexity of our algorithm is significantly reduced from that of previously known algorithms that have comparable mistake bounds. The second algorithm we present is a polynomial time algorithm with a non-optimal mistake bound. Again the mistake bound of our second algorithm is significantly better than previous bounds proven for polynomial time algorithms.A key contribution of our work is that we define a non-pure or noisy binary relation and then by exploiting the robustness of weighted majority voting with respect to noise, we show that both of our algorithms can learn non-pure relations. These provide the first algorithms that can learn non-pure binary relations.The first author was supported in part by NSF grant CCR-91110108 and NSF National Young Investigator Grant CCR-9357707 with matching funds provided by Xerox Corporation, Palo Alto Research Center and WUTA. The second author was supported by ONR grant NO0014-91-J-1162 and NSF grant IRI-9123692.     

Using Genetic Algorithms for Concept Learning

In this article, we explore the use of genetic algorithms (GAs) as a key element in the design and implementation of robust concept learning systems. We describe and evaluate a GA-based system called GABIL that continually learns and refines concept classification rules from its interaction with the environment. The use of GAs is motivated by recent studies showing the effects of various forms of bias built into different concept learning systems, resulting in systems that perform well on certain concept classes (generally, those well matched to the biases) and poorly on others. By incorporating a GA as the underlying adaptive search mechanism, we are able to construct a concept learning system that has a simple, unified architecture with several important features. First, the system is surprisingly robust even with minimal bias. Second, the system can be easily extended to incorporate traditional forms of bias found in other concept learning systems. Finally, the architecture of the system encourages explicit representation of such biases and, as a result, provides for an important additional feature: the ability todynamically adjust system bias. The viability of this approach is illustrated by comparing the performance of GABIL with that of four other more traditional concept learners (AQ14, C4.5, ID5R, and IACL) on a variety of target concepts. We conclude with some observations about the merits of this approach and about possible extensions.     

采用深度学习方法的非正面表情识别综述

2017年人工智能正式升级为中国国家战略,作为人工智能领域中重要的研究方向,人脸表情识别受到了国内外研究者们的广泛关注.然而传统的人脸表情识别技术无法适应自然环境下的表情识别需求.因此非正面人脸表情识别方法成为实现表情识别技术实用化突破的重点.但是现有的非正面表情识别研究面临很多困难:头部偏转不仅造成了识别图像的扭曲,...