Two types of learning effects on maintenance activities

This paper studies a manufacturer with a system prone to failure. The manufacturer performs two types of maintenance activities: preventive maintenance (PM), performed periodically, resets the system, and Minimal Repair (MR), performed after breakdowns, restores the system to working condition. It is assumed that two different types of learning take place: (i) repetition learning: due to the repetitive nature of PM, the manufacturer gains experience and learns to perform the PM activities faster and at a lower cost and (ii) failure learning: each failure gives the manufacturer the opportunity to find the root causes, to learn from mistakes and to improve the system. This paper, the first one to quantify failure learning in maintenance literature, assumes that such learning can then be applied during the next PM activity, which brings down the failure rate for the next PM cycle. For the increasing failure rate case, repetition learning increases the PM frequency, whereas failure learning causes the manufacturer to reduce the optimal number of PM activities. However, for the constant failure rate, repetition learning has no effect on the PM frequency, whereas failure learning may actually increase it.     

Self‐directed Learning Readiness Among Engineering Undergraduate Students

Two studies related to readiness for self‐directed learning of engineering students were performed using the Self‐directed Learning Readiness Scale (SDLRS). A cross‐sectional study of students in the first through final years of study showed that their SDLRS scores are significantly correlated with academic year of study and with grade point average, but not with gender. However, neither academic year of study nor grade point average is a good predictor of SDLRS scores; together they account for less than 5 percent of the observed variance. A second study investigated the effect of a problem‐based learning experience on students' readiness for self‐directed learning. It showed that the average readiness for self‐directed learning increased significantly for students in the problem‐based learning courses. However, investigation of the changes for individual students revealed that only nine of eighteen students showed significant increases in their SDLRS scores, and two showed significant decreases. Potential underlying causes are explored.     

基于深度强化学习的图像去模糊方法

目的为了有效地去除多种图像模糊,提高图像质量,提出基于深度强化学习的图像去模糊方法。方法选用GoPro与DIV2K这2个数据集进行实验,以峰值信噪比(PSNR)和结构相似性(SSIM)为客观评价指标。通过卷积神经网络获得模糊图像的高维特征,利用深度强化学习结合多种CNN去模糊工具建立去模糊框架,将峰值信噪比(PSNR)作为训练奖励评价函数,来选择最优修复策略,逐步对模糊图像进行修复。结果通过训练与测试,与现有的主流算法相比,文中方法有着更好的主观视觉效果,且PSNR值与SSIM值都有更好的表现。结论实验结果表明,文中方法能有效地解决图像的高斯模糊和运动模糊等问题,并取得了良好的视觉效果,在图像去模糊领域具有一定的参考价值。     

美国大学电子化教育的考察

美国大学电子化教育的应用面已经相当广泛。电子化教育注重以人为本，满足人们终身学习的需要；重视师生的教学互动，并采用多种媒体混合的形式，取得了良好的效果。本文根据作者对美国大学教育信息化的考察，对美国大学实行电子化教育的基本依据和主要做法做了介绍。     

MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance (MR) images of healthy individuals and epileptic patients was built. Spatial Version of the Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Stego (HUGO), and Minimizing the Power of Optimal Detector (MIPOD) techniques used in spatial image steganalysis were adapted to the problem, and various payloads of confidential data were hidden in medical images. The architectures of medical image steganalysis networks were transferred separately from eleven Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet), and Inception-based models. The steganalysis outputs of these networks were determined by assembling models separately for each spatial embedding method with different payload ratios. The study demonstrated the success of pre-trained ResNet, DenseNet, and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads. Due to the high detection accuracy achieved, the proposed model has the potential to lead to the development of novel medical image steganography algorithms that existing deep learning-based steganalysis methods cannot detect. The experiments and the evaluations clearly proved this attempt.     

未来1000年中的企业业务进修：工业实践中多媒体教学方法的潜力

企业已经在全球性竞争中认识进行变革和继续学习的必要性，而这只可能通过受过相应教育并受激励的企业员工来实现。企业成为集中的学习场所。根据调查，明确了工业界所要求的生产工程师的培训目标。另一方面，实际知识的半期在缩短，这样导致在工作岗位上持续培训和进修的必要性，虽然培训活动应予于加强，而由于成本压力不断增加、其培训费用又必须减少。     

A generic framework of intelligent adaptive learning systems: from learning effectiveness to training transfer

This paper provides guidance for adaptive learning systems designers by reviewing the evolution of learning theories and their associated progress, which is enabled by computers, artificial intelligence and augmented cognition. A generic conceptual framework of intelligent adaptive learning systems is discussed in detail for individualised learning. The utility of such design methodologies is illustrated through an example of a customised intelligent tutoring system for improvised explosive device disposal operator training. This testbed approach is the first phase of truly determining the utility and effectiveness of intelligent adaptive learning systems for multiple fields of use in the ever complicated spectrum of society's education and training needs.     

Analysis of Semi-Supervised Text Clustering Algorithm on Marine Data

Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning. This paper implements and compares unsupervised and semi-supervised clustering analysis of BOAArgo ocean text data. Unsupervised K-Means and Affinity Propagation (AP) are two classical clustering algorithms. The Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable range. Semi-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition, and use this data for semi-supervised cluster analysis. Several semi-supervised clustering algorithms were chosen for comparison of learning performance: Constrained-K-Means, Seeded-K-Means, SAP (Semi-supervised Affinity Propagation), LSAP (Loose Seed AP) and CSAP (Compact Seed AP). In order to adapt the single label, this paper improves the above algorithms to SCKM (improved Constrained-K-Means), SSKM (improved Seeded-K-Means), and SSAP (improved Semi-supervised Affinity Propagationg) to perform semi-supervised clustering analysis on the data. A DSAP (Double Seed AP) semi-supervised clustering algorithm based on compact seeds is proposed as the experimental data shows that DSAP has a better clustering effect. The unsupervised and semi-supervised clustering results are used to analyze the potential patterns of marine data.     

Understanding Student Differences

Students have different levels of motivation, different attitudes about teaching and learning, and different responses to specific classroom environments and instructional practices. The more thoroughly instructors understand the differences, the better chance they have of meeting the diverse learning needs of all of their students. Three categories of diversity that have been shown to have important implications for teaching and learning are differences in students' learning styles (characteristic ways of taking in and processing information), approaches to learning (surface, deep, and strategic), and intellectual development levels (attitudes about the nature of knowledge and how it should be acquired and evaluated). This article reviews models that have been developed for each of these categories, outlines their pedagogical implications, and suggests areas for further study.     

水声通信中的信道估计与机器学习交叉研究进展

近年来,以深度学习为代表的机器学习技术飞速发展,凭借其出色的学习能力,在复杂环境条件下的建模问题中展现出了独特的优势。当前,基于机器学习的水声通信技术研究方兴未艾,在信道估计及均衡、典型通信系统应用等方面取得了一定的进展,但是针对实际水声环境约束条件下的研究较少。为此,文章围绕信道估计这一水声通信关键技术,针对水声信道估计中存在的样本不足,标签标定困难以及水声环境时空变导致的源域、目标域失配等问题,讨论了水声信道估计与数据增强、无标签学习、少样本学习等模型和方法交叉研究的发展思路,并给出了初步的仿真和试验结果。文章是对水声通信中的信道估计与机器学习交叉领域研究重难点问题的初步探索,为水下各类平台自主智能化的通信技术发展提供了参考。     

The Relationships between Students' Conceptions of Learning Engineering and their Preferences for Classroom and Laboratory Learning Environments

This study developed a survey entitled Conceptions of Learning Engineering (CLE), to elicit undergraduate engineering students' conceptions of learning engineering. The reliability and validity of the CLE survey were confirmed through a factor analysis of 321 responses of undergraduate students majoring in electrical engineering. A series of ANOVA analyses revealed that students who preferred a classroom setting tended to conceptualize learning engineering as “testing” and “calculating and practicing,” whereas students who preferred a laboratory setting expressed conceptions of learning engineering as “increasing one's knowledge,” “applying,” “understanding,” and “seeing in a new way.” A further analysis of student essays suggested that learning environments which are student‐centered, peer‐interactive, and teacher‐facilitated help engineering students develop more fruitful conceptions of learning engineering.     

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction. For temporal sequence, this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short Term Memory (BiLSTM) to capture long-term dependencies. Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes. In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters. Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data. In contrast, the other uses optical flow images. Finally, the proposed ensemble approach using max hard voting outperforms state-of-the-art methods with 96.30% and 90.07% accuracies on the UCF101 and HMDB51 datasets.     

工业应用专家系统学习模块的设计

学习功能是提高专家系统（ＥｘｐｅｒｔＳｙｓｔｅｍ）自我更新、自我完善能力的重要途径。本文作者在实践的基础上提出了工业应用ＥＳ学习模块的设计思想，并简单介绍了在该思想下建立的焊接工艺制定专家系统（ＷＰＤＥＳ）的学习功能。     

企业敏捷学习要素体系模型构建及实证研究

基于敏捷制造理论,提出敏捷学习的概念,并认为,敏捷学习是敏捷制造的基础和保证.进一步构建了敏捷学习的要素体系模型.以我国企业为样本进行了实证研究,发现对企业敏捷学习影响最大的因素是学习过程因子,特别是信息获取;其次是战略参与的内部驱动因子;此外,人力资源管理实践、团队工作形式、内部信任、开发式学习方式、人际关系、网络信息化水平也有较大影响.