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Dominant Meaning Method for Intelligent Topic-Based Information Agent towards More Flexible MOOCs 面向更灵活mooc的基于主题的智能信息代理主导意义方法
Pub Date : 2014-10-15 DOI: 10.4236/JILSA.2014.64015
M. A. Razek
The use of agent technology in a dynamic environment is rapidly growing as one of the powerful technologies and the need to provide the benefits of the Intelligent Information Agent technique to massive open online courses, is very important from various aspects including the rapid growing of MOOCs environments, and the focusing more on static information than on updated information. One of the main problems in such environment is updating the information to the needs of the student who interacts at each moment. Using such technology can ensure more flexible information, lower waste time and hence higher earnings in learning. This paper presents Intelligent Topic-Based Information Agent to offer an updated knowledge including various types of resource for students. Using dominant meaning method, the agent searches the Internet, controls the metadata coming from the Internet, filters and shows them into a categorized content lists. There are two experiments conducted on the Intelligent Topic-Based Information Agent: one measures the improvement in the retrieval effectiveness and the other measures the impact of the agent on the learning. The experiment results indicate that our methodology to expand the query yields a considerable improvement in the retrieval effectiveness in all categories of Google Web Search API. On the other hand, there is a positive impact on the performance of learning session.
agent技术作为一种强大的技术在动态环境中的应用正在迅速发展,从各个方面来看,将智能信息agent技术的优势提供给大规模在线开放课程是非常重要的,包括mooc环境的快速增长,以及对静态信息的关注多于对更新信息的关注。在这样的环境中,一个主要的问题是如何根据每时每刻都在互动的学生的需要来更新信息。使用这种技术可以确保更灵活的信息,减少浪费的时间,从而提高学习收益。本文提出了基于主题的智能信息代理,为学生提供包括各类资源在内的知识更新。智能体利用支配意义法对互联网进行搜索,对来自互联网的元数据进行控制,过滤并显示为分类的内容列表。对基于主题的智能信息代理进行了两个实验,一个是测量检索效率的提高,另一个是测量代理对学习的影响。实验结果表明,我们的扩展查询方法在b谷歌Web搜索API的所有类别的检索效率上都有很大的提高。另一方面,对学习过程的表现有积极的影响。
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引用次数: 0
Image-Based Methods for Interaction with Head-Worn Worker-Assistance Systems 与头戴式工人辅助系统交互的基于图像的方法
Pub Date : 2014-08-11 DOI: 10.4236/JILSA.2014.63011
Frerk Saxen, Omer Rashid, A. Al-Hamadi, S. Adler, A. Kernchen, R. Mecke
In this paper, a mobile assistance-system is described which supports users in performing manual working tasks in the context of assembling complex products. The assistance system contains a head-worn display for the visualization of information relevant for the workflow as well as a video camera to acquire the scene. This paper is focused on the interaction of the user with this system and describes work in progress and initial results from an industrial application scenario. We present image-based methods for robust recognition of static and dynamic hand gestures in realtime. These methods are used for an intuitive interaction with the assistance-system. The segmentation of the hand based on color information builds the basis of feature extraction for static and dynamic gestures. For the static gestures, the activation of particular sensitive regions in the camera image by the user’s hand is used for interaction. An HMM classifier is used to extract dynamic gestures depending on motion parameters determined based on the optical flow in the camera image.
本文描述了一种移动辅助系统,该系统支持用户在组装复杂产品的背景下执行手动工作任务。辅助系统包括一个头戴式显示器,用于可视化与工作流程相关的信息,以及一个摄像机来获取场景。本文的重点是用户与该系统的交互,并描述了正在进行的工作和来自工业应用场景的初步结果。我们提出了基于图像的方法来实时鲁棒识别静态和动态手势。这些方法用于与辅助系统的直观交互。基于颜色信息的手部分割为静态和动态手势的特征提取奠定了基础。对于静态手势,通过用户的手激活相机图像中的特定敏感区域来进行交互。使用HMM分类器根据相机图像中的光流确定的运动参数提取动态手势。
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引用次数: 3
Simulation Model Using Meta Heuristic Algorithms for Achieving Optimal Arrangement of Storage Bins in a Sawmill Yard 基于元启发式算法的锯木厂仓仓优化布置仿真模型
Pub Date : 2014-05-02 DOI: 10.4236/JILSA.2014.62010
Asif Rahman, Siril Yella, M. Dougherty
Bin planning (arrangements) is a key factor in the timber industry. Improper planning of the storage bins may lead to inefficient transportation of resources, which threaten the overall efficiency and thereby limit the profit margins of sawmills. To address this challenge, a simulation model has been developed. However, as numerous alternatives are available for arranging bins, simulating all possibilities will take an enormous amount of time and it is computationally infeasible. A discrete-event simulation model incorporating meta-heuristic algorithms has therefore been investigated in this study. Preliminary investigations indicate that the results achieved by GA based simulation model are promising and better than the other meta-heuristic algorithm. Further, a sensitivity analysis has been done on the GA based optimal arrangement which contributes to gaining insights and knowledge about the real system that ultimately leads to improved and enhanced efficiency in sawmill yards. It is expected that the results achieved in the work will support timber industries in making optimal decisions with respect to arrangement of storage bins in a sawmill yard.
垃圾箱规划(安排)是木材工业的一个关键因素。仓库规划不当可能导致资源运输效率低下,威胁到整体效率,从而限制了锯木厂的利润空间。为了应对这一挑战,开发了一个仿真模型。然而,由于有许多可用于排列箱子的替代方案,模拟所有可能性将花费大量时间,并且在计算上是不可行的。因此,本研究对包含元启发式算法的离散事件模拟模型进行了研究。初步研究表明,基于遗传算法的仿真模型取得了较好的结果,且优于其他元启发式算法。此外,本文还对基于遗传算法的最优安排进行了敏感性分析,这有助于深入了解实际系统,从而最终改善和提高锯木厂的效率。预计在工作中取得的结果将支持木材工业在锯木厂场地的存储箱安排方面做出最佳决策。
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引用次数: 5
Structural Analysis and Static Simulation of Coastal Planktonic Networks 沿海浮游网络的结构分析与静态模拟
Pub Date : 2014-05-02 DOI: 10.4236/JILSA.2014.62009
G. C. Pereira, L. P. Andrade, R. P. Espíndola, N. Ebecken
The coastal marine habitats are often characterized by high biological activity. Therefore, monitoring programs and conservation plans of coastal environments are needed. So, in order to contribute to decision making process of the Brazilian Information System of Coastal Management, this paper presents a preliminary analysis of the effects of simulated deletions of individual organisms within a planktonic network as knowledge acquisition platform. An in situ scanning flow cytometer was used to data acquisition. A static and undirected food web is generated and represented by a fuzzy graph structure. Our results show through a series of indices the main changes of these networks. It was also verified similar traits and properties with other food webs found in the literature.
沿海海洋生境往往具有高生物活性的特点。因此,海岸环境的监测和保护计划是必要的。因此,为了对巴西海岸管理信息系统的决策过程有所贡献,本文对浮游生物网络中个体生物模拟缺失的影响进行了初步分析,作为知识获取平台。数据采集采用原位扫描流式细胞仪。生成静态无向食物网,并用模糊图结构表示。我们的研究结果通过一系列指标显示了这些网络的主要变化。它也被证实与文献中发现的其他食物网具有相似的特征和特性。
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引用次数: 6
A Comparison of Neural Classifiers for Graffiti Recognition 用于涂鸦识别的神经分类器的比较
Pub Date : 2014-05-02 DOI: 10.4236/JILSA.2014.62008
A. H. Al-Fatlawi, S. Ling, H. Lam
Technological advances and the enormous flood of papers have motivated many researchers and companies to innovate new technologies. In particular, handwriting recognition is a very useful technology to support applications like electronic books (eBooks), post code readers (that sort mails in post offices), and some bank applications. This paper proposes three systems to discriminate handwritten graffiti digits (0 to 9) and some commands with different architectures and abilities. It introduces three classifiers, namely single neural network (SNN) classifier, parallel neural networks (PNN) classifier and tree-structured neural network (TSNN) classifier. The three classifiers have been designed through adopting feed forward neural networks. In order to optimize the network parameters (connection weights), the back-propagation algorithm has been used. Several architectures are applied and examined to present a comparative study about these three systems from different perspectives. The research focuses on examining their accuracy, flexibility and scalability. The paper presents an analytical study about the impacts of three factors on the accuracy of the systems and behavior of the neural networks in terms of the number of the hidden neurons, the model of the activation functions and the learning rate. Therefore, future directions have been considered significantly in this paper through designing particularly flexible systems that allow adding many more classes in the future without retraining the current neural networks.
技术的进步和大量的论文促使许多研究人员和公司创新新技术。特别是,手写识别是一种非常有用的技术,可以支持电子书、邮政编码阅读器(在邮局对邮件进行分类)和一些银行应用程序等应用程序。本文提出了三种识别手写涂鸦数字(0 ~ 9)的系统和一些具有不同架构和能力的命令。介绍了三种分类器,即单神经网络(SNN)分类器、并行神经网络(PNN)分类器和树状神经网络(TSNN)分类器。这三种分类器均采用前馈神经网络设计。为了优化网络参数(连接权值),采用了反向传播算法。本文应用并考察了几种架构,从不同的角度对这三种系统进行了比较研究。研究的重点是检验它们的准确性、灵活性和可扩展性。本文从隐藏神经元数量、激活函数模型和学习率三个方面分析研究了三个因素对系统精度和神经网络行为的影响。因此,本文通过设计特别灵活的系统来考虑未来的方向,这些系统允许在未来添加更多的类,而无需重新训练当前的神经网络。
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引用次数: 7
Design of Type-1 and Interval Type-2 Fuzzy PID Control for Anesthesia Using Genetic Algorithms 基于遗传算法的麻醉1型和区间2型模糊PID控制设计
Pub Date : 2014-05-02 DOI: 10.4236/JILSA.2014.62007
Hugo Araujo, Bo Xiao, Chuang Liu, Yanbin Zhao, H. Lam
This paper presents the automatic drug administration for the regulation of bispectral (BIS) index in the anesthesia process during the clinical surgery by controlling the concentration target of two drugs, namely, propofol and remifentanil. To realize the automatic drug administration, real clinical data are collected for 42 patients for the construction of patients’ models consisting of pharmacokinetic and pharmacodynamic models describing the dynamics reacting to the input drugs. A nominal anesthesia model is obtained by taking the average of 42 patients’ models for the design of control scheme. Three PID controllers are employed, namely linear PID controller, type-1 (T1) fuzzy PID controller and interval type-2 (IT2) fuzzy PID controller, to regulate the BIS index using the nominal patient’s model. The PID gains and membership functions are obtained using genetic algorithm (GA) by minimizing a cost function measuring the control performance. The best trained PID controllers are tested under different scenarios and compared in terms of control performance. Simulation results show that the IT2 fuzzy PID controller offers the best control strategy regulating the BIS index while the T1 fuzzy PID controller comes the second.
本文介绍了通过控制异丙酚和瑞芬太尼两种药物的浓度靶点,自动给药来调节临床手术麻醉过程中双谱(BIS)指数。为实现自动给药,收集42例患者的真实临床数据,构建患者模型,包括药代动力学模型和药效学模型,描述患者对输入药物的动力学反应。将42例患者的模型取平均值,得到名义麻醉模型,用于设计控制方案。采用线性PID控制器、1型(T1)模糊PID控制器和区间2型(IT2)模糊PID控制器三种PID控制器,利用标称患者模型对BIS指标进行调节。通过最小化测量控制性能的代价函数,利用遗传算法获得PID增益和隶属函数。在不同的场景下测试训练最好的PID控制器,并比较其控制性能。仿真结果表明,IT2模糊PID控制器对BIS指标的控制效果最好,T1模糊PID控制器的控制效果次之。
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引用次数: 18
Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion 网格界面海浪能量转换的自适应模糊滑模控制器
Pub Date : 2014-05-02 DOI: 10.4236/JILSA.2014.62006
Adel Elgammal
This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator- and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.
针对并网波能转换系统(WECS)驱动的自激感应发电机(SEIG),提出了一种基于自适应模糊滑模控制器(FL-SMC)的闭环矢量控制结构。该控制方法利用多目标遗传算法(MOGA)和多目标粒子群算法(MOPSO)对模糊控制器(FLC)的比例因子和隶属函数进行自动调整和优化。两个脉宽调制电压源PWM变换器与基于载波的正弦PWM调制为发电机和电网侧的变换器已经背靠背连接在发电机终端和公用电网之间通过公共直流链路。采用间接矢量控制方案,在变转子转速和变负载的情况下,保持发电机端电压和直流母线电压恒定。在MATLAB/Simulink环境下进行了仿真研究,验证了电力电子变换器在稳态和暂态以及机器参数不匹配情况下的鲁棒性和控制方法的有效性。该控制方案改善了波能方案在大范围工况下的电压调节和暂态性能。
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引用次数: 5
Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS) 入侵检测系统的支持向量机和随机森林建模
Pub Date : 2014-01-27 DOI: 10.4236/JILSA.2014.61005
Md. Al Mehedi Hasan, M. Nasser, B. Pal, Shamim Ahmad
The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with many features. To get rid of this problem, several types of intrusion detection methods have been proposed and shown different levels of accuracy. This is why the choice of the effective and robust method for IDS is very important topic in information security. In this work, we have built two models for the classification purpose. One is based on Support Vector Machines (SVM) and the other is Random Forests (RF). Experimental results show that either classifier is effective. SVM is slightly more accurate, but more expensive in terms of time. RF produces similar accuracy in a much faster manner if given modeling parameters. These classifiers can contribute to an IDS system as one source of analysis and increase its accuracy. In this paper, KDD’99 Dataset is used and find out which one is the best intrusion detector for this dataset. Statistical analysis on KDD’99 dataset found important issues which highly affect the performance of evaluated systems and results in a very poor evaluation of anomaly detection approaches. The most important deficiency in the KDD’99 dataset is the huge number of redundant records. To solve these issues, we have developed a new dataset, KDD99Train+ and KDD99Test+, which does not include any redundant records in the train set as well as in the test set, so the classifiers will not be biased towards more frequent records. The numbers of records in the train and test sets are now reasonable, which make it affordable to run the experiments on the complete set without the need to randomly select a small portion. The findings of this paper will be very useful to use SVM and RF in a more meaningful way in order to maximize the performance rate and minimize the false negative rate.
任何入侵检测系统都是一个复杂的问题,因为它的非线性和定量或定性的网络流量数据流具有许多特征。为了解决这一问题,人们提出了几种入侵检测方法,并显示出不同程度的准确性。这就是为什么选择有效的、鲁棒的入侵检测方法是信息安全中非常重要的课题。在这项工作中,我们建立了两个用于分类的模型。一种是基于支持向量机(SVM),另一种是随机森林(RF)。实验结果表明,两种分类器都是有效的。SVM稍微准确一些,但是在时间上花费更多。如果给定建模参数,射频以更快的方式产生类似的精度。这些分类器可以作为IDS系统的一个分析来源,并提高其准确性。本文以KDD ' 99数据集为研究对象,找出最适合该数据集的入侵检测器。对KDD ' 99数据集的统计分析发现了严重影响被评估系统性能的重要问题,并导致异常检测方法的评估非常差。KDD ' 99数据集中最重要的缺陷是大量的冗余记录。为了解决这些问题,我们开发了一个新的数据集,KDD99Train+和KDD99Test+,它不包括训练集中和测试集中的任何冗余记录,因此分类器不会偏向于更频繁的记录。训练和测试集中的记录数量现在是合理的,这使得在完整的集合上运行实验变得负担得起,而不需要随机选择一小部分。本文的研究结果对于更有意义地使用支持向量机和射频来最大化性能和最小化假阴性率是非常有用的。
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引用次数: 134
Fuzzy-Weighted Similarity Measures for Memory-Based Collaborative Recommender Systems 基于记忆的协同推荐系统的模糊加权相似度度量
Pub Date : 2014-01-27 DOI: 10.4236/JILSA.2014.61001
Mohammad Yahya H. Al-Shamri, N. Al-Ashwal
Memory-based collaborative recommender system (CRS) computes the similarity between users based on their declared ratings. However, not all ratings are of the same importance to the user. The set of ratings each user weights highly differs from user to user according to his mood and taste. This is usually reflected in the user’s rating scale. Accordingly, many efforts have been done to introduce weights to the similarity measures of CRSs. This paper proposes fuzzy weightings for the most common similarity measures for memory-based CRSs. Fuzzy weighting can be considered as a learning mechanism for capturing the preferences of users for ratings. Comparing with genetic algorithm learning, fuzzy weighting is fast, effective and does not require any more space. Moreover, fuzzy weightings based on the rating deviations from the user’s mean of ratings take into account the different rating scales of different users. The experimental results show that fuzzy weightings obviously improve the CRSs performance to a good extent.
基于记忆的协同推荐系统(CRS)是基于用户声明的评分来计算用户之间的相似度。然而,并不是所有的评级对用户来说都同样重要。根据每个用户的心情和品味,每个用户所权重的评分集会因用户而异。这通常反映在用户的评分量表上。因此,已经做了许多努力来为crs的相似性度量引入权重。本文提出了基于记忆的CRSs最常见的相似度度量的模糊加权。模糊加权可以被视为一种学习机制,用于捕获用户的偏好进行评级。与遗传算法学习相比,模糊加权具有快速、有效、不占用空间等优点。此外,基于评分偏离用户评分均值的模糊权重考虑了不同用户的不同评分尺度。实验结果表明,模糊加权能较好地改善CRSs的性能。
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引用次数: 23
Simulation and Optimization Techniques for Sawmill Yard Operations-A Literature Review 锯木厂堆场作业的模拟与优化技术——文献综述
Pub Date : 2014-01-27 DOI: 10.4236/JILSA.2014.61003
Asif Rahman, Siril Yella, M. Dougherty
Increasing costs and competitive business strategies are pushing sawmill enterprises to make an effort for optimization of their process management. Organizational decisions mainly concentrate on performance and reduction of operational costs in order to maintain profit margins. Although many efforts have been made, effective utilization of resources, optimal planning and maximum productivity in sawmill are still challenging to sawmill industries. Many researchers proposed the simulation models in combination with optimization techniques to address problems of integrated logistics optimization. The combination of simulation and optimization technique identifies the optimal strategy by simulating all complex behaviours of the system under consideration including objectives and constraints. During the past decade, an enormous number of studies were conducted to simulate operational inefficiencies in order to find optimal solutions. This paper gives a review on recent developments and challenges associated with simulation and optimization techniques. It was believed that the review would provide a perfect ground to the authors in pursuing further work in optimizing sawmill yard operations.
成本的增加和竞争的商业战略促使锯木厂企业努力优化其过程管理。组织决策主要集中在性能和降低运营成本,以保持利润率。尽管做出了许多努力,但如何有效利用资源、优化规划、实现生产效率最大化仍然是摆在锯木业面前的难题。许多研究者提出了将仿真模型与优化技术相结合的方法来解决综合物流优化问题。仿真与优化技术相结合,通过模拟系统在考虑目标和约束条件下的所有复杂行为来确定最优策略。在过去十年中,为了找到最佳解决方案,进行了大量的研究来模拟操作效率低下的情况。本文综述了与仿真和优化技术相关的最新发展和挑战。相信该综述将为进一步开展优化锯木厂堆场运营的工作提供一个良好的基础。
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引用次数: 11
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