Ontological reasoning in the design space exploration of advanced cyber–physical systems

Cyber–physical systems are becoming increasingly complex. In these advanced systems, the different engineering domains involved in the design process become more and more intertwined. Therefore, a traditional (sequential) design process becomes inefficient in finding good design options. Instead, an integrated approach is needed where parameters in multiple different engineering domains can be chosen, evaluated, and optimized to achieve a good overall solution. However, in such an approach, the combined design space becomes vast. As such, methods are needed to mitigate this problem.In this paper, we show a method for systematically capturing and updating domain knowledge in the context of a co-design process involving different engineering domains, i.e. control and embedded. We rely on ontologies to reason about the relationships between parameters in the different domains. This allows us to derive a stepwise design space exploration workflow where this domain knowledge is used to quickly reduce the design space to a subset of likely good candidates. We illustrate our approach by applying it to the design space exploration process for an advanced electric motor control system and its deployment on embedded hardware.     

MicroPort: A general simulation platform for seaport container terminals

Seaport container terminals are essential nodes in sea cargo transportation networks. As such, the operational efficiency of container terminals in handling containers passing through them plays a critical role in a globalized world economy. Many models and algorithms have been developed to address various decision problems in container terminals to help improve operational efficiency. These decision support tools are usually used separately for specific purposes. However, the problems they are trying to tackle are often interrelated. Therefore, in this regard, an evaluation tool which can capture as many operational conditions as possible for different decision problems is necessary. This paper introduces a general simulation platform, named MicroPort, which aims to provide an integrated and flexible modeling system for evaluating the operational capability and efficiency of different designs of seaport container terminals. The software structure of MicroPort comprises three programming layers: (1) the Functions layer; (2) the Applications layer; and (3) the Extensions layer. Different layers are bound by Application Programming Interfaces (APIs). Basic functions built in the Functions layer support the Applications layer in which major operation processes can be modeled by an agent-based method. External modules and decision support tools in the Extensions layer then use APIs to adjust the system to produce suitable simulation models for specific purposes.     

Why does language matter to artificial intelligence?

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored.     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.     

一个基于实例推理的软件过程知识复用方法

过程资产库的建立和基于过程资产的项目过程定义是一个组织的软件能力成熟度达到已定义级的关键标志,传统的过程资产库中存储的过程往往是通过文字描述和使用者判断其适用性,用于项目过程的建立。本文提出了一种基于实例推理(CBR)的过程知识复用方法,通过过程特征的刻画和实例相似度计算,实现过程实例的提取和复用。实践证明,该方法有助于提高过程知识复用的准确度和自动化程度,为软件过程改进中的过程建立提供有效手段。     

中国耕地细碎化的理论解析与研究框架

基于演绎推理法和文献研究法,探索耕地细碎化的概念、过程、机制和效应,并构建耕地细碎化研究框架,为耕地细碎化研究提供理论指导。结果表明:①耕地细碎化是耕地在自然-人为双重因素驱动下,耕地地块在形态上不断分割、变小,耕地产权在主体上不断细分、多元的过程,并分别表现为相互关联又有所区别的耕地景观细碎化和耕地权属细碎化; ②耕地细碎化研究应遵循“诊断-机制-效应-治理”的理论框架,耕地细碎化诊断需从形态和权属两方面构建评价指标体系,形成机制可从自然切割力、利用切割力、设施切割力和管理切割力4个方面揭示不同因素的作用机制,效应需揭示不同尺度下耕地细碎化形态和产权的趋势性转折对经济、社会和生态的影响,治理研究应按照“问题识别治理潜力治理路径保障措施”的逻辑顺序构建。     

D-2-DenseNet噪音鲁棒的城市音频分类模型

为了提高噪音环境下城市音频分类系统的鲁棒性,提出了一种双特征2阶密集卷积神经网络（D-2-DenseNet）噪音鲁棒的城市音频分类模型.首先介绍了噪音添加和噪音鲁棒处理,阐述了一种双特征互补偿的算法;然后结合2阶密集卷积神经网络与自适应机制提出了一种噪音鲁棒音频分类模型：双特征2阶密集卷积神经网络.模型采用双特征互补偿自适应算法,可在特征提取与模型训练中更有针对性地提取有效音频信息,降低噪音干扰,以提高噪音鲁棒性.最后,基于Dcase2016数据集开展噪音环境下城市音频分类测试.实验结果表明,模型分类准确率分别可达77.12%、75.52%,与基线模型相比,平均分类准确率分别提高了8.51%和10.38%,验证了模型良好的噪音鲁棒性.     

CBR技术在自动配色系统中的应用研究

为提高复杂多变条件下的配色准确率,提出了基于案例推理（case-based reasoning,CBR）的新型自动配色技术,并给出了原型--基于案例推理的配色系统(CBRCMS). CBRCMS对配方库组织结构、配方检索、配方复用、学习调整等关键技术进行了探讨.CBRCMS基于大量的历史优良配方,以色度迭代逼近技术作为配色精度的基本保证,并将其作为CBR配方复用的主要方法.实验数据显示,CBR的引入可明显提高配色准确率,CBR技术的自适应、自学习特点对传统配色技术是很好的补充.     

面向口语统计语言模型建模的自动语料生成算法

在资源相对匮乏的自动语音识别(Automatic speech recognition, ASR)领域, 如面向电话交谈的语音识别系统中, 统计语言模型(Language model, LM)存在着严重的数据稀疏问题. 本文提出了一种基于等概率事件的采样语料生成算法, 自动生成领域相关的语料, 用来强化统计语言模型建模. 实验结果表明, 加入本算法生成的采样语料可以缓解语言模型的稀疏性, 从而提升整个语音识别系统的性能. 在开发集上语言模型的困惑度相对降低7.5%, 字错误率(Character error rate, CER)绝对降低0.2个点; 在测试集上语言模型的困惑度相对降低6%, 字错误率绝对降低0.4点.