高速列车车体气密疲劳强度试验台控制软件界面设计

针对高速列车车体气密疲劳强度台设计了相应的控制软件界面。基于虚拟仪器技术,应用虚拟仪器开发环境LabVIEW完成该实验台控制软件的图形化用户界面的设计。界面系统由数据监控、波形生成、数据回放、高级设置4个子界面组成,在功能上可以满足高速列车车体气密疲劳强度试验的控制需求。在设计中引入人机工程学原理,使界面具有良好的可操作性,同时减轻用户对操作界面及气密疲劳强度试验流程的认知负担。     

基于用户反馈的搜索引擎排名算法

以Web 2.0中用户行为作为研究对象,通过发掘用户反馈方式,提出用户反馈分值的概念,对用户反馈影响搜索结果排名的具体方法以及相应实现进行研究,提出了一种基于神经网络的网页排序算法。该算法引入BP神经网络模型,根据用户反馈分值选择样本训练神经网络。将传统搜索结果输入到经过训练的神经网络进行计算,根据计算出的结果所表示的网页相关性强弱判断后进行二次排序。该算法利用了神经网络具有的模式识别能力,有效地将用户反馈和搜索引擎结合起来,使得搜索结果更加符合用户的搜索要求。     

A knowledge-based system for the prediction of surface roughness in turning process

In the present work, a knowledge-based system is developed for the prediction of surface roughness in turning process. Neural networks and fuzzy set theory are used for this purpose. Knowledge acquired from the shop floor is used to train the neural network. The trained network provides a number of data sets, which are fed to a fuzzy-set-based rule generation module. A large number of IF–THEN rules are generated, which can be reduced to a smaller set of rules by using Boolean operations. The developed rule base may be used for predicting surface roughness for given process variables as well as for the prediction of process variables for a given surface roughness. The concise set of rules helps the user in understanding the behavior of the cutting process and to assess the effectiveness of the model. The performance of the developed knowledge-based system is studied with the experimental data of dry and wet turning of mild steel with HSS and carbide tools.     

阔大货物装载方案中超限自动识别方法

为了改善阔大货物运输安全,提高铁路货物运输效率,开发了一种铁路阔大货物运输超限自动识别系统该自动识别系统利用阔大货物三视图,结合阔大货物运输车辆,在综合分析货物三维空间结构与铁路限界几何关系的基础上,实现阔大货物超限自动识别技术．该系统可以帮助货物运输前选车,并对货物超限等级做出预先判断,保证货物运输的安全性．     

基于SVM算法的用户行为认证方法

为提高手机安全性,提出一种基于SVM的用户操作行为认证方法.通过监听手机触摸屏设备,持续获取用户操作时的滑动轨迹、接触面积等原始数据.设计用户行为特征提取算法以建立用户特征样本,经SVM算法加以训练形成用户行为特征模型;综合用户访问目标及历史认证结果采用不同认证策略,达到重点保护敏感数据,方便用户访问非敏感数据的效果.在Android系统环境下的实验验证表明,该方法具有良好的认证效果.     

面向5G MEC基于行为的用户异常检测方案

5G边缘计算靠近用户侧提供服务,而边缘侧汇聚着用户的敏感信息,用户非法接入或合法用户自身的恶意行为威胁到整个边缘网络的安全。将机器学习算法应用于边缘计算架构,提出一种基于行为的用户异常检测方案。对用户行为进行建模,采用独热编码和互信息进行数据预处理和特征选择,并利用极限梯度提升算法训练一个多分类器分类识别进入园区的用户,根据识别结果与用户身份是否一致来判定用户是否异常。在此基础上,通过孤立森林算法对授权用户历史行为数据进行模型训练,从而检测可信任用户的行为是否异常,实现对小型固定园区内未授权用户的识别以及对授权用户异常行为的检测。实验结果表明,该方案可满足边缘计算场景的时间复杂度要求,并且能够有效区分不同用户,分类准确率达到0.953,而对异常行为样本的误报率仅为0.01。     

A hybrid expert system for investment advising

Abstract: Investor is an investment advising hybrid expert system which incorporates two of the most powerful techniques developed in artificial intelligence, the connectionist and the logic programming paradigms, in a unified model. Investor employs a neural network model to detect the interest rate trends and a rule-based component to classify the user into the appropriate investor model. The investor model and the interest rate information are then used by the knowledge base to infer the appropriate investment strategy. Genetic algorithms and classifier systems were employed to train and configure the architecture of Investor's neural network component.     

基于Web的化工原理虚拟仿真实验系统

为了提高实验安全性，实现化工原理课程的网络化教学，运用虚拟仿真技术和多种图像、动画表现形式构造了常规虚拟实验设备模型，设计了相关实验过程，开发了专用实验数据处理与绘图模块，结合CAT(Computer Aided Test)理论和人工智能方法研究了实验操作过程的自动评判技术。实践证明，虚拟设备能再现各种实验场景，仿真实验过程，实验系统能在人机交互条件下训练用户组装实验设备、进行实验操作和测取实验数据，并自动评判实验成绩。     

A survey of artificial neural network training tools

Artificial neural networks (ANN) are currently an additional tool which the engineer can use for a variety of purposes. Classification and regression are the most common tasks; however, control, modeling, prediction and forecasting are common tasks as well. For over three decades, the field of ANN has been the center of intense research. As a result, one of the outcomes has been the development of a large set of software tools used to train these kinds of networks, making the selection of an adequate tool difficult for a new user. This paper aims to help the ANN user choose the most appropriate tool for its application by providing a large survey of the solutions available, as well as listing and explaining their characteristics and terms of use. The paper limits itself to focusing on the tools which were developed for ANN and the relevant characteristics of these tools, such as the operating systems, hardware requirements, license types, architectures and algorithms available.     

Visualization of Delay Uncertainty and its Impact on Train Trip Planning: A Design Study

Uncertainty about possible train delays has an impact on train trips, as the exact arrival time is unknown during trip planning. Delays can lead to missing a connecting train at the transfer station, or to coming too late to an appointment at the destination. Facing this uncertainty, the traveler may wish to use an earlier train or a different connection arriving well before the appointment. Currently, train trip planning is based on scheduled times of connections between two stations. Information about approximate delays is only available shortly before train departure. Although several visualization approaches can show temporal uncertainty, we are not aware of any visual design specifically supporting trip planning, which can show delay uncertainty and its impact on the connections. We propose and evaluate a visual design which extends train trip planning with delay uncertainty. It shows the scheduled train connections together with their expected train delays as well as their impacts on both the arrival time, and the potential of missing a transfer. The visualization also includes information about alternative connections in case of these critical transfers. In this way the user is able to judge which train connection is suitable for a trip. We conducted a user study with 76 participants to evaluate our design. We compared it to two alternative presentations that are prominent in Germany. The study showed that our design performs comparably well for tasks concerning train schedules. The additional uncertainty display as well as the visualization of alternative connections was appreciated and well understood. The participants were able to estimate when they would likely arrive at their destination despite possible train delays while they were unable to estimate this with existing presentations. The users would prefer to use the new design for their trip planning.     

基于情感-时空信息的机器人服务自主认知及个性化选择

为了提高机器人的人机交互能力,针对家庭服务机器人在认知服务任务时往往忽略用户情感因素的弊端,提出了以用户情感为核心的机器人服务任务自主认知方法以及个性化服务选择策略.首先,利用智能空间本体技术结合用户情感状态与时间空间信息建立情感-时空本体模型,消除智能空间中的信息异构性.在此基础上,将与情感-时空相关的服务规则库编码并训练BP（逆向传播）神经网络构建推理机,将实时更新的智能空间信息与神经网络相匹配,推理出机器人需要执行的服务,实现机器人对以用户情感为核心的服务任务自主认知.最后,将用户情感状态作为执行服务的奖惩反馈信号,对服务集合中的子类服务进行动态的偏好度调节,完成有针对性的服务选择.仿真结果表明,基于该方法能够实现以用户情感为核心的机器人服务任务自主认知,同时可以根据用户偏好变化提供个性化的服务,有效提高了家庭服务机器人的智能性和灵活性,增强了用户的服务体验.     

融合注意力机制的增强受限玻尔兹曼机驱动的交互式分布估计算法

面向用户生成内容(User generated content, UGC)的进化搜索在大数据及个性化服务领域已引起广泛关注, 其关键在于基于多源异构用户生成内容构建用户认知偏好模型, 进而设计高效的进化搜索机制. 针对此, 提出融合注意力机制(Attention mechanism, AM)的受限玻尔兹曼机(Restricted Boltzmann machine, RBM)偏好认知代理模型构建机制, 并应用于交互式分布估计算法(Interactive estimation of distribution algorithm, IEDA), 设计含用户生成内容的个性化进化搜索策略. 基于用户群体提供的文本评论, 以及搜索物品的类别文本, 构建无监督受限玻尔兹曼机模型提取广义特征; 设计注意力机制, 融合广义特征, 获取对用户认知偏好高度相关特征的集成; 利用该特征再次训练受限玻尔兹曼机, 实现对用户偏好认知代理模型的构建; 根据用户偏好认知代理模型, 给出交互式分布估计算法概率更新模型以及物品适应度评价函数, 实现物品个性化进化搜索. 算法在亚马逊个性化搜索实例的应用验证了用户认知偏好模型的可靠性, 以及个性化进化搜索的有效性.     

Online annotation of faces in personal videos by sequential learning

This paper addresses semi-automatic annotation of faces in personal videos. Different from previous offline annotation systems, this paper studies online annotation of faces. During an annotation session, few annotations are requested from the user only for some part of the video online. These annotations are used to train a system that will perform annotation automatically for the rest of the video. The automatic annotation results are presented to the user during the same session and the user is allowed to correct any automatic annotation mistakes. Thus, only fast and accurate face recognition methods are considered. Instead of batch learning, which has been used in the existing annotation systems, this paper proposes sequential learning methods to be used as face classifiers. Different classification methods are tested with various feature extraction methods using the same database so that a fair comparison is made among them. The results are evaluated in terms of recognition accuracies and execution time requirements.     

基于BP神经网络的MRI分割

本文提出了基于ＢＰ神经网络的脑部多回波磁共振图像分割方法。输入图像包括Ｔ１,Ｔ２,ＰＤ三种加权特征图像,分割是基于各像素点灰度值进行的。首先通过一组已标号的训练样本训练神经网络分类器,不正确的分割结果通过交互方式修正训练点,然后再次训练,直至满意的分割结果。     

搜索引擎广告用户行为预测与特征分析

介绍了搜索引擎广告系统的基本运作模式。通过对广告四元组的特征提取、特征值平滑等操作, 将广告记录解析成为训练数据, 并将数据分为训练集和测试集, 使用支持向量机算法并利用训练集训练出的模型将测试集分类, 从而预测出用户的行为。通过对特征的分析, 得出对用户行为预测准确率影响最大的特征是点击率。实验证明, 在使用该模型中所有特征的情况下, 分类的准确率能够达到83. 17%。     

Surrogate model-assisted interactive genetic algorithms with individual’s fuzzy and stochastic fitness

We propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract a user’s cognition on evaluated individuals in order to alleviate user fatigue in interactive genetic algorithms with an individual’s fuzzy and stochastic fitness. We firstly present an approach to construct a directed fuzzy graph of an evolutionary population according to individuals’ dominance relations, cut-set levels and interval dominance probabilities, and then calculate an individual’s crisp fitness based on the out-degree and in-degree of the fuzzy graph. The approach to obtain training data is achieved using the fuzzy entropy of the evolutionary system to guarantee the credibilities of the samples which are used to train the surrogate model. We adopt a support vector regression machine as the surrogate model and train it using the sampled individuals and their crisp fitness. Then the surrogate model is optimized using the traditional genetic algorithm for some generations, and some good individuals are submitted to the user for the subsequent evolutions so as to guide and accelerate the evolution. Finally, we quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to find the satisfactory individuals, and also apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.     

A feasible timetable generator simulation modelling framework for train scheduling problem

An important problem in management of railway systems is the train scheduling/timetabling problem. This is the problem of determining a timetable for a set of trains that do not violate track capacities and satisfy some operational constraints. In this study, a feasible timetable generator framework for stochastic simulation modelling is developed. The objective is to obtain a feasible train timetable for all trains in the system. The feasible train timetable includes train arrival and departure times at all visited stations and calculated average train travel time. Although this study focuses on train scheduling/timetabling problem, the developed simulation framework can also be used for train rescheduling/dispatching problem if this framework can be fed by real time data. The developed simulation model includes stochastic events, and can easily cope with the disturbances that occur in the railway system.     

Self-training classifier of natural-language texts

A natural-language text classifier is developed using an artificial neural network. A model of the classifier and its implementation are proposed. The classification system consists of two main components, namely, a frequency analyzer and a neural network classifier. Before using the classifier, the user should first prepare a set of training texts and then train the classifier. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 169–176, May–June 2007.     

Neural network applications for automatic new topic identification of FAST and Excite search engine transaction logs

Abstract: Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms for more efficient search engines. Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. This study proposes an artificial neural network application in the area of search engine research to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals and query reformulation patterns. Sample data logs from the FAST and Excite search engines are selected to train the neural network and then the neural network is used to identify topic changes in the data log. As a result, almost all the performance measures yielded favourable results.     

Correlations of perceived post-editing effort with measurements of actual effort

Human rating of predicted post-editing effort is a common activity and has been used to train confidence estimation models. However, the correlation between human ratings and actual post-editing effort is under-measured. Moreover, the impact of presenting effort indicators in a post-editing user interface on actual post-editing effort has hardly been researched. In this study, ratings of perceived post-editing effort are tested for correlations with actual temporal, technical and cognitive post-editing effort. In addition, the impact on post-editing effort of the presentation of post-editing effort indicators in the user interface is also tested. The language pair involved in this study is English-Brazilian Portuguese. Our findings, based on a small sample, suggest that there is little agreement between raters for predicted post-editing effort and that the correlations between actual post-editing effort and predicted effort are only moderate, and thus an inefficient basis for MT confidence estimation. Moreover, the presentation of post-editing effort indicators in the user interface appears not to impact on actual post-editing effort.