基于多微处理器的智能超声探测系统

设计了一种基于多微处理器的智能超声探测系统.该系统采用由上位工作模式控制单元和下位智能超声传感器阵列组成的分布式结构.每个超声传感器均由独立的微处理器控制,并完成实时数据处理、抗干扰处理、故障报警以及数据通信等功能.上位控制单元根据移动机器人的运动状态采用不同的控制策略,使下位传感器阵列分组并行工作,提高了探测系统的实时性;下位传感器采用"阈值比较法"、"改进型递推均值滤波"算法和模糊信息处理技术,提高了探测系统的准确性、可靠性.将该探测系统安装于RIRA-Ⅱ移动机器人上,进行了基于主动视觉和超声信息的运动目标跟踪实验,实验表明超声探测系统运行可靠、稳定.     

基于多超声波传感器的自主移动机器人探测系统

超声波传感器在自主式移动机器人领域有着广泛的应用.本研究根据自主式移动机器人对实时探测未知环境的要求以及超声波传感器的特点,设计并实现了一种适用于自主式移动机器人的声纳环探测系统.首先,采用了多超声波分组循环发射的方法,极大地降低了多超声波传感器之间的回波干扰;然后在对原始数据进行初步处理的基础上,给出了多种距离信息表示形式;测试结果及其分析证明了本系统的鲁棒性及实用性.     

利用PIC12系列单片机实现火灾探测系统总线

本文提出采用PIC12C5xx系列单片机实现一种火灾探测系统总线的方法,用以构成无极性两总线,总线上为火灾传感器提供直流电源的同时又传送系统控制命令、传感器地址、传感器状态和类型以及传感器数据等多种信息。使总线具有高性能、低功耗和低成本,抗干扰能力强,传输距离远,组成灵活方便的特点。     

一种权系数两级自调整的融合定位精度提高方法

在分布式传感器网络节点定位技术中,使用数据融合方法以提高探测系统的检测与定位精度正成为研究的热点。提出了一种应用于分布式传感器网络中的数据融合定位算法,通过对各个传感器节点的定位信息的加权求和来进行数据融合,用来提高探测系统目标定位的精度。该算法采用两级自适应调整得到最优加权因子,首先利用线性最小均方差（LMSE）算法得到权系数的初始值,然后利用训练节点和递归最小二乘（RLS）算法自适应地调整达到最优。对静态和运动目标的定位数据融合算法进行了仿真,仿真结果表明：相比单节点定位,提出的融合算法的定位精度有约1—2个数量级的提高。     

激光测距在移动机器人自主导航中的应用

移动机器人在运动范围内需要有足够的传感器信息可供利用来进行自主导航,在完全未知的环境中,由机器人依靠其自身携带的传感器所提供的信息建立环境模型是机器人进行自主定位和导航的前提之一。介绍了激光测距在移动机器人自主导航中的应用研究;通过二维测距传感器对其周围环境进行扫描,提出了自主导航中地图创建、定位如何用二维扫描获得三维数据流的算法描述,并实验验证该算法的运用使机器人获得一个很好的三维视觉结构图。     

Kinect传感器的网络通信数据泄露自主感知系统设计

针对传统方法获取的网络通信深度信息不完整、数据传输安全性不高等问题,设计Kinect传感器的网络通信数据泄露自主感知系统.模糊推理方法结合专家经验构建Kinect传感器数据脆性评估体系,运用基于灰色关联分析及理想优基点法计算通信数据敏感度,获得敏感数据泄露漏洞挖掘目标;基于开源数据组件集,构建集数据收集整理、数据存储、态势展现等功能于一体的完整数据泄露自主感知系统.仿真结果表明,本文方法可有效增强通信数据安全性能,大幅降低数据泄露事件发生概率,为网络通信稳定运行提供扎实基础.     

多声传感器异步融合算法

声探测定位技术是利用声传感器接收特定声波信号以确定声源的一种无源定位技术,它具有隐蔽性好、不易受干扰等优点.本文研究了仅能提供方位角信息的异步多声传感器无源探测系统数据融合问题,提出了一种适用于声传感器的在线估计可变周期融合算法.该算法首先采用伪线性算法,将非线性量测方程线性化;然后通过采用参数在线估计和基于运动模型向前追溯确保时间同步的方法,解决了目标跟踪时信号传输时延和传感器异步的问题.通过Monte Carlo仿真表明,在参数在线估计中,步长选取适当时,该算法可以满足声探测系统的精度要求,并且具有收敛快、精度高、稳定的特点.     

基于多超声波传感器的移动机器人目标识别

移动机器人在不确定环境中的目标识别技术是自主导航及复杂任务分解的一瞎丶际?本文利用自行铑设计的多超声波传感器探测系统感知外界环境,提出了基于目标原型的目标识别和对感兴趣目标的主动探测方法.根据多超声波传感器的TOF(Time-of-Flight)信息,利用Dempster-Shfer证据理论,实现了移动机器人对室内特征环境的准确识别.测试结果及分析验证了该方法的可行性和识别准确性.并且该方法适用于室内机器人运动中的实时探测.     

光电式自主寻迹小车抗干扰技术研究

根据自主寻迹小车的运行要求,完成了光电式自主寻迹小车的整体设计。结合激光和红外传感器的布局方式及其采集信号时存在的干扰问题,提出了传感器采集路径信息的可行方法及处理检测数据的A/D采样自适应、边缘跟踪检测等滤波算法。最后,通过实测数据对比分析,证明采用文中所述的滤波算法来处理检测路径干扰信息是可行的。     

基于红外传感器的气体检测系统的设计

本文设计一种基于红外传感器的工业环境气体探测系统。该系统通过红外传感器实现对检测气体的测量,以MSP430为核心对数据进行处理,并以工业标准信号将探测数据传输。     

Autonomous exploration: driven by uncertainty

Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously, they will always be faced with this dilemma, and can only be successful if they play a much more active role. This paper presents such a machine. It deliberately seeks out those parts of the world which maximize the fidelity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm autonomous exploration, and the machine an autonomous explorer. This paper has two major contributions. The first is a theory that tells us how to explore, and which confirms the intuitive ideas we have put forward previously. The second is an implementation of that theory. In our laboratory, we have constructed a working autonomous explorer and here, for the first time, show it in action. The system is entirely bottom-up and does not depend on any a priori knowledge of the environment. To our knowledge, it is the first to have successfully closed the loop between gaze planning and the inference of complex 3D models     

Feature representation selection based on Classifier Projection Space and Oracle analysis

One of the main problems in pattern recognition is obtaining the best set of features to represent the data. In recent years, several feature extraction algorithms have been proposed. However, due to the high degree of variability of the patterns, it is difficult to design a single representation that can capture the complex structure of the data. One possible solution to this problem is to use a multiple-classifier system (MCS) based on multiple feature representations. Unfortunately, still missing in the literature is a methodology for comparing and selecting feature extraction techniques based on the dissimilarity of the feature representations. In this paper, we propose a framework based on dissimilarity metrics and the intersection of errors, in order to analyze the relationships among feature representations. Each representation is used to train a classifier, and the results are compared by means of a dissimilarity metric. Then, with the aid of Multidimensional Scaling, visual representations are obtained of each of the dissimilarities and used as a guide to identify those that are either complementary or redundant. We applied the proposed framework to the problem of handwritten character and digit recognition. The analysis is followed by the use of an MCS built on the assumption that combining dissimilar feature representations can greatly improve the performance of the system. Experimental results demonstrate that a significant improvement in classification accuracy is achieved due to the complementary nature of the representations. Moreover, the proposed MCS obtained the best results to date for both the MNIST handwritten digit dataset and the Cursive Character Challenge (C-Cube) dataset.     

The MurMur project: Modeling and querying multi-representation spatio-temporal databases

Successful information management implies the ability to design accurate representations of the real world of interest, in spite of the diversity of perceptions from the applications sharing the same database. Current database management systems do not provide representation schemes that preserve each perception while fully supporting their diversity and maintaining their consistency. This is a major hindrance for building an all-embracing view of the world while serving multiple applications, whether it is by developing a single database or by providing transparent access (e.g., via the Web) to several heterogeneous data sources (that would typically hold a great diversity of stored representations). This paper reports on results from the multiple representations and multiple resolutions in geographical databases project,¹ funded by the European Commission under the 5th Framework Programme. The objective of the project has been to enhance GIS (or DBMS) by adding functionality that supports multiple coexisting representations of the same real-word phenomena (semantic flexibility), including representations of geographic data at multiple resolutions (cartographic flexibility). The new functionality enables a semantically meaningful management of multi-scale, integrated, and temporal geo-databases.     

Realization by inspection

We investigate which first-order representations can be obtained from high-order representations of linear systems “by inspection”, that is, just by rearrangement of the data. Under quite weak conditions it is possible to obtain minimal realizations in the so-called pencil form; under stronger conditions one can obtain minimal realizations in standard state-space form by inspection. The development is based on a reformulation of the realization problem as a problem of finding a complete set of basis vectors for the nullspace of a given constant matrix. Since no numerical computation is needed, the realization method in particular is suitable for situations in which some of the coefficients are symbolic rather than numerical     

Strongly Typed Rewriting For Coupled Software Transformation

Coupled transformations occur in software evolution when multiple artifacts must be modified in such a way that they remain consistent with each other. An important example involves the coupled transformation of a data type, its instances, and the programs that consume or produce it. Previously, we have provided a formal treatment of transformation of the first two: data types and instances. The treatment involved the construction of type-safe, type-changing strategic rewrite systems. In this paper, we extend our treatment to the transformation of corresponding data processing programs.The key insight underlying the extension is that both data migration functions and data processors can be represented type-safely by a generalized abstract data type (GADT). These representations are then subjected to program calculation rules, harnessed in type-safe, type-preserving strategic rewrite systems. For ease of calculation, we use point-free representations and corresponding calculation rules.Thus, coupled transformations are carried out in two steps. First, a type-changing rewrite system is applied to a source type to obtain a target type together with (representations of) migration functions between source and target. Then, a type-preserving rewrite system is applied to the composition of a migration function and a data processor on the source (or target) type to obtain a data processor on the target (or source) type. All rewrites are type-safe.     

概率逻辑的研究

在人工智能科学中,不确定推理扮演着一个非常重要的角色,而其表示方法也很多,文中要讨论的概率逻辑便是其中之一,它是以逻辑表示为基础进行概率推理。首先,从知识表示和概率定义两个不同的角度系统地介绍了概率逻辑的产生及其发展,然后总结了它的一些基本概念,在此基础上给出了求得一致可能世界的一种逻辑系统,为概率逻辑的发展起到推动作用。最后将概率逻辑同与之容易混淆的模糊逻辑加以区分,且提出了概率逻辑的价值及其展望。     

Social Representations of High School Students About Mathematics Assessment

The perceptions of students about assessment in mathematics classes have been sparsely investigated. In order to fill this gap, this qualitative study aims to identify the social representations (understood as the system of values, ideas, and practices about a social object) of high school students regarding assessment in mathematics. We used semistructured focus group interviews to obtain data (N = 50). The data were examined using theoretical thematic analysis with the assistance of the specialized ATLAS.ti software. Eight themes or social representations emerged (e.g., assessment that measures the students' learning or assessment that measures the advances or what has been acquired). The results are consistent with literature that shows that students' representations about assessment in mathematics are closely linked to their representations of mathematics itself and the learning of it and also to their representation of what is fair and what is not.     

Conceptual representations of actions for autonomous robots

An autonomous robot involved in long and complex missions should be able to generate, update and process its own plans of action. In this perspective, it is not plausible that the meaning of the representations used by the robot is given from outside the system itself. Rather, the meaning of internal symbols must be firmly anchored to the world through the perceptual abilities and the overall activities of the robot. According to these premises, in this paper we present an approach to action representation that is based on a “conceptual” level of representation, acting as an intermediate level between symbols and data coming from sensors. Symbolic representations are interpreted by mapping them on the conceptual level through a mapping mechanism based on artificial neural networks. Examples of the proposed framework are reported, based on experiments performed on a RWI-B12 autonomous robot.     

The Need for Fuzzy AI

Artificial intelligence (AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, there is also increasing focus on the need for computerised systems to be able to explain their decisions, at least to some degree. It is also clear that data and knowledge in the real world are characterised by uncertainty. Fuzzy systems can provide decision support, which both handle uncertainty and have explicit representations of uncertain knowledge and inference processes. However, it is not yet clear how any decision support systems, including those featuring fuzzy methods, should be evaluated as to whether their use is permitted. This paper presents a conceptual framework of indistinguishability as the key component of the evaluation of computerised decision support systems. Case studies are presented in which it has been clearly demonstrated that human expert performance is less than perfect, together with techniques that may enable fuzzy systems to emulate human-level performance including variability. In conclusion, this paper argues for the need for " fuzzy AI” in two senses: (i) the need for fuzzy methodologies (in the technical sense of Zadeh’s fuzzy sets and systems) as knowledge-based systems to represent and reason with uncertainty; and (ii) the need for fuzziness (in the non-technical sense) with an acceptance of imperfect performance in evaluating AI systems.      

Adversarial Heterogeneous Network Embedding with Metapath Attention Mechanism

Heterogeneous information network (HIN)-structured data provide an effective model for practical purposes in real world. Network embedding is fundamental for supporting the network-based analysis and prediction tasks. Methods of network embedding that are currently popular normally fail to effectively preserve the semantics of HIN. In this study, we propose AGA2Vec, a generative adversarial model for HIN embedding that uses attention mechanisms and meta-paths. To capture the semantic information from multi-typed entities and relations in HIN, we develop a weighted meta-path strategy to preserve the proximity of HIN. We then use an autoencoder and a generative adversarial model to obtain robust representations of HIN. The results of experiments on several real-world datasets show that the proposed approach outperforms state-of-the-art approaches for HIN embedding.