逻辑系统中的拟对偶性与拟排中律

在经典逻辑和t-模基础逻辑中提出了排中律与拟排中律的新概念,说明经典逻辑(带对偶非的MTL)满足排中律(拟排中律),证明了Go?del模糊逻辑(Lukasiewicz(简称Luk)模糊逻辑)关于最小算子ù与最大算子(关于t-模与t-余模对补算子c)既不满足拟对偶性也不满足拟排中律,检验了Luk模糊逻辑关于Lukt-模与Lukt-余模对?算子满足拟对偶性和排中律。     

基于尺度自适应与增量式学习的人脸对齐方法

针对传统基于回归的人脸对齐算法在人脸尺度归一化时会造成纹理的损失,以及为了提升算法模型的泛化能力必须扩充数据集重新训练而导致训练时间增加,甚至出现不收敛、不可计算等问题,提出一种基于尺度自适应与增量式学习（IL）的人脸对齐方法来提高定位精度。首先,建立初始人脸形状与标准人脸形状的映射关系;然后,通过映射关系实现纹理特征在原图上的提取和人脸尺度的归一化;最后,利用算法模型在新的数据集上进行增量式的学习,快速提高原模型的泛化能力。实验结果表明,与传统回归方法相比,所提方法有更高的对齐精度,特别是在AFW数据集（68个特征点）上提高了2~4个百分点;在10万级别的大数据集（5个特征点）上,所提方法的鲁棒性比基于深度学习的方法高1~2个百分点。同时,所提的增量式学习方法不仅适用于人脸对齐场景下的回归模型求解,还适用于其他应用场景下回归模型的求解。     

基于自由空间中线法提取构建拓扑地图

首先利用激光传感器扫描数据,构建环境的以点阵表示的几何地图,然后在构建环境几何地图的基础上,利用提出的中线法抽取机器人所处环境的自由空间的拓扑结构,在拓扑节点构建时,加入机器人位姿信息,完成拓扑地图的构建。将该算法分别在Z型和T型环境中进行实验,构建环境几何地图,并抽取相应的拓扑结构。结果表明,该方法能够实时、安全、紧凑地表示环境。     

Interpreting and extracting fuzzy decision rules from fuzzy information systems and their inference

Information systems, which contain only crisp data, precise and unique attribute values for all objects, have been widely investigated. Due to the fact that in realworld applications imprecise data are abundant, uncertainty is inherent in real information systems. In this paper, information systems are called fuzzy information systems, and formalized by (objects; attributes; f), in which f is a fuzzy set and expresses some uncertainty between an object and its attribute values. To interpret and extract fuzzy decision rules from fuzzy information systems, the meta-theory based on modal logic proposed by Resconi et al. is modified. The modified meta-theory not only expresses uncertainty between objects and their attributes, but also uncertainty in the process of recognizing fuzzy information systems. In addition, according to perception computing (proposed by Zadeh), granules of fuzzy information systems can be represented by fuzzy decision rules, so that, fuzzy inference methods can be used to obtain the decision attribute of a new object. Finally, a novel way of combining evidences based on the modified meta-theory is introduced, which extends the concept of combining evidences based on Dempster-Shafer theory.     

Recognition of hand-printed characters based on structural description and inductive logic programming

Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over the large degree of variation between writing styles and recognition rules can be constructed by example.

The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the average correct recognitions rate obtained using cross-validation was 86.65%.     

An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms

This paper proposes an optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty (FOU) of the membership functions, considering three different cases to reduce the complexity problem of searching the parameter space of solutions. For the optimization method, we propose the use of a genetic algorithm (GA) to optimize the type-2 fuzzy inference systems, considering different cases for changing the level of uncertainty of the membership functions to reach the optimal solution at the end.     

动态增量式AOP框架的研究与实现

在研究AOP原理的基础上,提出一种面向aspect的编程框架AODF,采用基于日志技术的增量式编织机制,不仅提高了编织效率,而且基于Bigraph理论的AODF支持方面的动态演化,实现了行为的一致性与相容性。另外,AODF从语言层提供对方面的支持,无需改动语言本身任何结构及特性,而且可以把自行编制的aspect配置为标准组件入库,实现方面的重用。从而使得遗留系统无需变更就可以获得面向方面的扩展。     

基于证据理论的不确定性推理方法及其应用

针对民航无线电管理中遇到的干扰问题以及客观世界中描述客观现象的知识和信息具有不确定性的特点,结合民航无线电干扰查处的实际情况,提出基于一种产生式规则的知识表示方法,以区间确定因子描述不确定性。提出基于Dempster-Shafer证据理论和区间确定因子的不确定性推理方法。进一步根据领域专家经验建立民航无线电干扰查处规则,以基于Dempster-Shafer证据理论和区间确定因子的不确定性推理方法为推理机,给出民航无线电干扰查处专家系统的结构及工作流程,采用C#语言结合MySQL数据库开发民航无线电干扰查处专家系统,并通过案例分析说明该系统的有效性和实用性。     

A novel approach for edge detection based on the theory of universal gravity

This paper presents a new, simple and effective low-level processing edge detection algorithm based on the law of universal gravity. The algorithm assumes that each image pixel is a celestial body with a mass represented by its grayscale intensity. Accordingly, each celestial body exerts forces onto its neighboring pixels and in return receives forces from the neighboring pixels. These forces can be calculated by the law of universal gravity. The vector sums of all gravitational forces along, respectively, the horizontal and the vertical directions are used to compute the magnitude and the direction of signal variations. Edges are characterized by high magnitude of gravitational forces along a particular direction and can therefore be detected. The proposed algorithm was tested and compared with conventional methods such as Sobel, LOG, and Canny using several standard images, with and without the contamination of Gaussian white noise and salt & pepper noise. Results show that the proposed edge detector is more robust under noisy conditions. Furthermore, the edge detector can be tuned to work at any desired scale.     

基于模糊逻辑的雾天降质图像对比度增强算法

提出一种新颖的雾天降质图像增强算法。该算法通过对降质图像进行规范化预处理,降低不同雾况对灰度级分布范围的影响,并对规范化后的图像,根据大气散射对对比度衰减的影响规律,在模糊域内实现对比度增强处理。实验结果表明,该算法可以有效提高雾天降质图像的对比度,视觉效果改善明显。     

基于事件逻辑的改进Needham-Schroeder协议安全性证明

安全协议是现代网络安全的基础,密码协议的安全性证明是一个挑战性的问题。事件逻辑是一种描述分布式系统中状态迁移的形式化方法,用于刻画安全协议的形式化描述,是定理证明的基础。用事件序语言、事件类和一个表示随机数、密钥、签名和密文的原子类,给出身份认证协议可以被形式化定义和强认证性证明理论。利用该理论对增加时间戳的Needham-Schroeder协议安全性进行证明,证明改进的Needham-Schroeder协议是安全的。此理论适用于类似复杂协议形式化分析与验证。     

How flipped learning based on the cognitive theory of multimedia learning affects students' academic achievements

The study aims to identify the effect of a flipped classroom approach designed according to the cognitive theory of multimedia learning on the academic achievements of eighth‐grade students (aged 14 years) in Saudi Arabia in computer science. To this end, a quasi‐experimental design was used, with a sample of 67 students; 33 students were assigned to the experimental group, whereas 34 comprised the control group. The experimental group was subjected to the flipped classroom approach, whereas the control group was given direct instruction. To measure student achievements, an instrument that measures cognitive skills based on Revised Bloom's taxonomy levels was designed. Findings revealed a positive effect on the experimental group's achievement levels with respect to Bloom's higher order thinking skills, that is, applying, analysing, and evaluating. No difference was found between the two groups in terms of academic achievements at the remembering and understanding levels of Bloom's taxonomy. Moreover, learners with low prior knowledge showed a higher improvement in academic achievements compared with those with high prior knowledge. This corresponds to the assumption of the cognitive theory of multimedia learning that learners with low prior knowledge would benefit from its principles more than learners with high prior knowledge.     

A cross‐cultural study of the effect of a graph‐oriented computer‐assisted project‐based learning environment on middle school students' science knowledge and argumentation skills

The purpose of this mixed‐methods study was to explore how seventh graders in a suburban school in the United States and sixth graders in an urban school in Taiwan developed argumentation skills and science knowledge in a project‐based learning environment that incorporated a graph‐oriented, computer‐assisted application (GOCAA). A total of 42 students comprised the treatment condition and were engaged in a project‐based learning environment that incorporated a GOCAA. Of these 42 students, 21 were located in the United States and 21 were located in Taiwan. A total of 26 students comprised the control condition and were engaged in a project‐based learning environment without the GOCAA. Of these 26 students, 15 were in the United States and 11 were in Taiwan. In each country, verbal collaborative argumentation was recorded and the students' post‐essays were collected. A one‐way analysis of variance (ANOVA) was conducted for each measure of science knowledge about alternative energies. The results showed a significant treatment effect for the outcome of scientific explanation among U.S. students, while among Taiwanese students, a significant treatment effect on scientific facts was observed. A one‐way ANOVA was additionally conducted for each measure of argumentation skills and a significant treatment effect on counterarguments and rebuttals was observed among the U.S. students, while in Taiwan, a significant treatment effect on reasoning and rebuttals was observed. A qualitative analysis was conducted to examine how the GOCAA supported students' development of argumentation skills in different countries. This study found distinct argumentation patterns between the U.S. and Taiwanese intervention teams. Additionally, a distinct gender difference in the use of evidence and division of labour was noted when the Taiwanese teams were compared with the U.S. teams, which may be explained by cultural differences. This study concluded that, in both the United States and Taiwan, a project‐based learning environment incorporating a GOCAA was effective in improving students' science knowledge and developing their scientific argumentation skills.     

Fault detection and isolation for uncertain closed‐loop systems based on adaptive and switching approaches

This paper deals with the fault detection and isolation (FDI) problem for uncertain closed‐loop systems with external disturbances and nonlinear perturbations. To address the system uncertainties and the nonlinear perturbations in different faulty models, adaptive and switching techniques are introduced to construct a bank of FDI observers, such that one of them can match the current system, and the corresponding observer estimate errors can converge asymptotically to zero. An effective FDI scheme is then presented by introducing some model‐matching indexes. Moreover, the introduced switching laws liberate the equality constraints often used in the existing FDI approaches, which are hard to satisfy if the system matrices include uncertainties. Finally, a simulation example of F/A‐18A automatic carrier landing system is used to illustrate the effectiveness of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.     

Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle

Since learning English is very popular in non-English speaking countries, developing modern assisted-learning tools that support effective English learning is a critical issue in the English-language education field. Learning English involves memorization and practice of a large number of vocabulary words and numerous grammatical structures. Vocabulary learning is a principal issue for English learning because vocabulary comprises the basic building blocks of English sentences. Therefore, many studies have attempted to improve the efficiency and performance when learning English vocabulary. With the accelerated growth in wireless and mobile technologies, mobile learning using mobile devices such as PDAs, tablet PCs, and cell phones has gradually become considered effective because it inherits all the advantages of e-learning and overcomes limitations of learning time and space that limit web-based learning systems. Therefore, this study presents a personalized mobile English vocabulary learning system based on Item Response Theory and learning memory cycle, which recommends appropriate English vocabulary for learning according to individual learner vocabulary ability and memory cycle. The proposed system has been successfully implemented on personal digital assistant (PDA) for personalized English vocabulary learning. The experimental results indicated that the proposed system could obviously promote the learning performances and interests of learners due to effective and flexible learning mode for English vocabulary learning.     

激光遥感近期发展趋向

激光遥感就是用激光束作为光谱探头来探测大气成分。在过去的十五年中,这种激光雷达技术已被证明是一种测量大气中几种重要化学成分的有效探测方法,现在,它不但对我们了解大气起着重要的作用,而且正在     

基于AMSR2多频亮温的黑河流域中上游土壤水分估算研究

以黑河流域中上游为研究区,初步探究了利用AMSR2卫星的多频亮度温度数据估算土壤水分的方法。基于土壤水分和土壤发射率的统计关系,通过黑河流域上游的4个像元2013年7月至2014年6月内的实测土壤水分和土壤温度数据,采用了“四像元交叉拟合法”获得了统计系数,并用此方法估算出了黑河流域中上游的土壤水分。采用2014年7月至2014年10月内估算的土壤水分,连同与AMSR2的4个常用的土壤水分产品和GLDAS土壤水分产品在时间序列上,与八宝河流域WSN土壤水分地面观测展开了对比验证,结果表明估算土壤水分精度明显高于上述5种产品。同时借助高程和土地覆被辅助数据,与GLDAS土壤水分在空间格局上进行了比较,发现估算土壤水分时空分布特征更加合理。该方法可为流域尺度的土壤水分反演与监测提供了一种简而易行的思想方法和可行之路。     

On extracting recommendation knowledge for personalized web-based learning based on ant colony optimization with segmented-goal and meta-control strategies

Personalized web-based learning has become an important learning form in the 21st century. To recommend appropriate online materials for a certain learner, several characteristics of the learner, such as his/her learning style, learning modality, cognitive style and competency, need to be considered. An earlier research result showed that a fuzzy knowledge extraction model can be established to extract personalized recommendation knowledge by discovering effective learning paths from past learning experiences through an ant colony optimization model. Though that results revealed the theoretical potential of the proposed method in discovering effective learning paths for learners, critical limitations arose when considering its applications in real world situations, such as the requirement of a large amount of learners and a long period of training cycles in order to discover good learning paths for learners. These practical issues motivate this research. In this paper, the aim is to resolve the aforementioned issues by devising more efficient algorithms that basically run on the same ant colony model yet requiring only a reasonable number of learners and training cycles to find satisfactory good results. The key approaches to resolving the practical issues include revising the global update policy, an adaptive search policy and a segmented-goal training strategy. Based on simulation results, it is shown that these new ingredients added to the original knowledge extraction algorithm result in more efficient ones that can be applied in practical situations.     

基于ELM和FOA的股票价格预测

针对股票价格预测中应用极限学习机预测存在稳定性不理想的问题,提出了一种改进果蝇优化极限学习机（IFOA-ELM）预测模型的算法。在该算法中,果蝇群通过不断调整群半径来优化ELM的输入层与隐含层连接权值和隐含层阈值,并以优化后的结果为基础,构建ELM预测模型。将IFOA-ELM模型用于股票价格预测。实验表明,与ELM和FOA-ELM相比,IFOA-ELM在股票价格预测中具有更高的预测精度和更好的稳定性。     

关于对Oracle数据仓库应用技术的研究与实现

随着科学技术的不断发展,二十一世纪人类已经进入了信息化时代,将计算机、网络、通信以及数据库系统于一身的信息技术成为了社会发展强劲的主要动力。目前,中国化工企业是我国国民经济的主要经济支柱性产业,具有涉及面广、产量规模巨大、管理层面复杂等特点,一直以来都是我国建设的重点行业。由于随着世界能源的日趋紧缺,现阶段,我国的石油化工领域竞争变得越来越激烈,石油化工企业要想在竞争中占有强势地位,加强信息化管理的水平,提升企业管理素质和管理理念,最大程度的取得较好的经济效益是势在必行的发展方向。本文根据Oracle数据仓库开发工具,对石油化工企业管理信息化进行数据仓库的含义、设计原理、建模以及结构优化等技术的研究与探讨,供各位朋友鉴赏。