Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Intention Estimation of Adversarial Spatial Target Based on Fuzzy Inference

Estimating the intention of space objects plays an important role in air-craft design,aviation safety,military and otherfields,and is an important refer-ence basis for air situation analysis and command decision-making.This paper studies an intention estimation method based on fuzzy theory,combining prob-ability to calculate the intention between two objects.This method takes a space object as the origin of coordinates,observes the target’s distance,speed,relative heading angle,altitude difference,steering trend and etc.,then introduces the spe-cific calculation methods of these parameters.Through calculation,values are input into the fuzzy inference model,andfinally the action intention of the target is obtained through the fuzzy rule table and historical weighted probability.Ver-ified by simulation experiment,the target intention inferred by this method is roughly the same as the actual behavior of the target,which proves that the meth-od for identifying the target intention is effective.

Knowledge Representation and Semantic Inference of Process Based on Ontology and Semantic Web Rule Language

The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.

Mapping of Freshwater Lake Wetlands Using Object-Relations and Rule-based Inference

Inland freshwater lake wetlands play an important role in regional ecological balance.Hongze Lake is the fourth biggest freshwater lake in China.In the past three decades,there has been significant loss of freshwater wetlands within the lake and at the mouths of neighboring rivers,due to disturbance,primarily from human activities.The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them.In the paper,an integrated method,which combined per-pixel and per-field classification,was used for mapping wetlands of Hongze Lake and their neighboring upland types.Firstly,Landsat ETM+ imagery was segmented and classified by using spectral and textural features.Secondly,ETM+ spectral bands,textural features derived from ETM+ Pan imagery,relative relations between neighboring classes,shape features,and elevation were used in a decision tree classification.Thirdly,per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context.The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies,but also has improved the accuracy of identification by nearly 5%.

Incorporating Fuzzy Inference in Active Database Rules

Active databases react to stimulation, or event from inside or outside the system without user or application interference through Events Conditions Actions(ECA) rules (triggers). ECA rule is defined as: ON event IF condition THEN action, which means when an event happens, if the condition is satisfied then the corresponding action is executed. The nature of ECA rule makes it an appropriate means to model dynamic character of systems, as gained much studies during recent years. Traditional ECA rule is crisp, which means their events, condition (s) and action(s) are accurate. As indicate that ECA rules can only represent precise knowledge. But knowledge is usually fuzzy in engineering. A concept of fuzzy ECA rules characterized with fuzzy event, fuzzy condition and fuzzy action is proposed in this article.The realization avenues of fuzzy triggers are discussed. The work we have done blazes a way in representing approximate syntax in active database application systems. At last a case of 'overheating alarm' is given to illustrate the approach.

The Jena-Based Ontology Model Inference andRetrieval Application

Ontology as an important representation model of semantic web has valuable application. A new ontology model on the basis of Computer Graphics (CG) knowledge is proposed, called CG ontology model. The protégé is used to build this ontology model conveniently. The Jena API is applied to store CG owl documents in MySQL, set inference rule and achieve search queries on the ontology database. Finally, the Jena-based ontology model retrieval system is developed.

Fuzzy Logic Inference Applications in Road Traffic and Parking Space Management

In modern motoring, many factors are considered to realize driving convenience and achieving safety at a reasonable cost. A drive towards effective management of traffic and parking space allocation in urban centres using intelligent software applications is currently being developed and deployed as GPS enabled service to consumers in automobiles or smartphone applications for convenience, safety and economic benefits. Building a fuzzy logic inference for such applications may have numerous approaches such as algorithms in Pascal or C-languages and of course using an effective fuzzy logic toolbox. Referring to a case report based on IrisNet project analysis, in this paper Matlab fuzzy logic toolbox is used in developing an inference for managing traffic flow and parking allocation with generalized feature that is open for modification. Being that modifications can be done within any or all among the tool’s universe of discourse, increment in the number of membership functions and changing input and output variables etc, the work here is limited within changes at input and output variables and bases of universe of discourse. The process implications is shown as plotted by the toolbox in surface and rule views, implying that the inference is flexibly open for modifications to suit area of application within reasonable time frame no matter how complex. The travel time to the parking space being an output variable in the current inference is recommended to be substituted with distance to parking space as the former is believed to affect driving habits among motorist, whom may require the inference to as well cover other important locations such as nearest or cheapest gas station, hotels, hospitals etc.

面向溢流与井漏监测的钻井液池体积趋势校正方法

钻井液池体积是溢流和井漏监测的常用参数。在钻井正常施工时,随着井筒容积的变化以及井筒内钻井液与地层流体的交换,钻井液池体积会产生与溢流和井漏发生时相似的“类风险趋势”,导致现有的溢流与井漏监测方法易产生误报,降低了溢流与井漏监测方法在现场应用时的有效性。针对此问题,文章首先分析了导致池体积产生“类风险趋势”的原因,建立了钻进与起下钻工况下池体积的正常变化模型,利用建立的模型校正池体积的“类风险趋势”,降低溢流与井漏监测的误报率;然后,建立了基于规则推理的溢流与井漏监测模型,用于测试池体积的“类风险趋势”对溢流与井漏监测的影响。利用现场实测的4500组钻井数据,采用基于规则推理的溢流与井漏监测方法开展了风险监测实验,结果表明,在进行池体积趋势校正后,溢流与井漏风险监测的误报率由10.03%降低至3.06%。

煤矿事故智能应急预案生成方法研究

为了实现应急预案数字智能化管理,快速生成精准有效的救援方案,采用网络爬虫技术从事故案例及事发后状况中获取基础数据,并对数据进行结构化处理;构建一种分层结构化应急预案生成模型,结合模糊综合评价法与层次分析法对应急预案进行有效性评估,形成了煤矿事故数字智能化应急预案系统。选取煤矿瓦斯爆炸事故,验证“8·29”肖家湾煤矿瓦斯爆炸事故和“10·31”金山沟煤业瓦斯爆炸事故子事件集合,证明了智能应急预案生成结果和实际情况基本相符。研究结果表明:实时添加事发信息,快速生成可视化预案,自动分析结构化存储,可提高应急预案生成效率与精准性,降低事故灾害风险和损失,提升矿井应急救援保障能力。

基于改进概率软逻辑模型的高校学生状态推理方法

随着高等教育的普及,各类管理决策需求增加,基于数据驱动和知识驱动的决策方法成为新的发展趋势。基于此,提出了一种通过改进概率软逻辑模型挖掘知识规律和推理学生状态的方法,该方法通过引入规则挖掘算法,赋予概率软逻辑模型自动挖掘规则的能力。在真实学生状态数据上的实验结果表明,该方法在多项目标关系的推理上,准确率能达到80%以上,并且多层次推理结构的实验效果优于非层次推理结构的实验效果。对于学生状态类非完整知识库数据的挖掘和推理,该方法减少了人工工作量,降低了因人为认知偏差带来的不确定性。

Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction

Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a particularly challenging task. This paper presents a novel neural fuzzy method for the hourly wind speed prediction. Firstly, a neural structure is proposed for the functional-type single-input-rule-modules(FSIRMs) connected fuzzy inference system(FIS) to combine the merits of both the FSIRMs connected FIS and the neural network. Then, in order to achieve both the smallest training errors and the smallest parameters, a least square method based parameter learning algorithm is presented for the proposed FSIRMs connected neural fuzzy system(FSIRMNFS). Further,the proposed FSIRMNFS and its parameter learning algorithm are applied to the hourly wind speed prediction. Experiments and comparisons are also made to show the effectiveness and advantages of the proposed approach. Experimental results verified that our study has presented an effective approach for the hourly wind speed prediction. The proposed approach can also be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.

Improving Rule Base Quality to Enhance Production Systems Performance

Production systems have a special value since they are used in state-space searching algorithms and expert systems in addition to their use as a model for problem solving in artificial intelligence. Therefore, it is of high importance to consider different techniques to improve their performance. In this research, rule base is the component of the production system that we aim to focus on. This work therefore seeks to investigate this component and its relationship with other components and demonstrate how the improvement of its quality has a great impact on the performance of the production system as a whole. In this paper, the improvement of rule base quality is accomplished in two steps. The first step involves re-writing the rules having conjunctions of literals and producing a new set of equivalent rules in which long inference chains can be obtained easily. The second step involves augmenting the rule base with inference short-cut rules devised from the long inference chains. These inference short-cut rules have a great impact on the performance of the production system. Finally, simulations are performed on randomly generated rule bases with different sizes and goals to be proved. The simulations demonstrate that the suggested enhancements are very beneficial in improving the performance of production systems.

A NEW EVIDENCE UPDATING RULE BASED ON CONDITIONAL EVENT

Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.

Evolving Fuzzy Neural Networks for Extracting Rules

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基于置信规则的村镇应急避难场所暴雨灾害链风险诊断

为解决村镇应急避难场所面临暴雨灾害时的风险问题,基于故障树和置信规则库推理方法,提出暴雨灾害链和村镇应急避难场所功能破坏链相结合的场所避难功能失效风险诊断模型。根据事故致因理论推理灾害节点变量,通过故障树描述灾害链,运用关联规则从历史灾害数据中挖掘规则,建立置信规则库系统,构建村镇应急避难场所功能失效风险诊断模型,并以四川省某寄宿制学校为例进行模型验证。研究结果表明:该模型可实现不同证据组合下村镇应急避难场所功能失效风险的诊断推理;实例的模型诊断结果与实际情况吻合,证实该模型能够科学地诊断村镇应急避难场所面临暴雨等恶劣自然条件时存在的风险,可为村镇应急避难场所规划设计和应急管理提供理论支撑。

多层次传递型内幕交易责任认定的实证考察与理论反思

信息传递与互联网技术的耦合带来传递型内幕交易中传播链条的延长,造成现行推定规则在追究各信息受领主体责任时的局限性,也导致了法律责任在交易端与泄露端间的严重不匹配。应当认识到,泄密人与受密人责任是成立内幕交易责任的两个方面,现行推定规则实则是在泄密人责任成立的基础上对受密人责任的推定。将泄密人违法性具体化为从泄密中获益,受密人交易行为的违法性具体化为对受密人违法的认知,方能揭示内幕交易以获益为核心的违法性本质。身份关系所蕴含的人际利益是信息传递的原动力,在坚持一次推定的基础上,通过分析信息传递过程中各主体间的身份关系,以信息获取便利性与利益一致性为实质审查标准,可以将形式上处于不同传播层级的主体实质认定为同一层级知情人,从而在确保推定事实效力的基础上对内幕交易责任主体实现精准覆盖。

自治故障管理系统推理规则的智能学习技术

随着高性能计算机系统规模急剧增加,系统整体的固有可靠性逐步降低,产生了“可靠性墙”问题。为了应对这一挑战,天河高性能计算机系统设计了自治故障管理系统,通过该系统实时监控、分析、管理全系统的报警、故障和错误。自治故障管理系统所收集的故障消息垂直涵盖系统的各个逻辑层次,水平覆盖系统的全部功能模块,因此故障消息之间存在逻辑上的因果关系,即一个故障源会导致后续一系列的故障事件。提出了一种针对于故障信息的推理规则学习算法EMRL,把故障信息的推理规则建模为一个概率模型,通过该模型自动从故障信息中挖掘故障推理规则,并且根据挖掘结果自动生成最小的故障推理图。采用天河系统的部分运行数据,验证了EMRL算法的有效性,结果表明EMRL能有效挖掘故障信息的推理关系。

基于Protégé的矿床成因类型本体推理判别方法——以中低温岩浆热液型金矿床为例

中低温岩浆热液型金矿床分布范围广,规模大,数量约占中国金矿床的65%,是中国矿产金的主要来源。在找矿过程中,不同矿床类型对应不同的找矿方法,确定矿床成矿模式能够为找矿提供理论支撑,并且能为矿产勘查和预测提供理论指导。鉴于此,结合勘查区找矿预测理论,对中低温岩浆热液型金矿床的本体构建和推理机制进行研究。利用Protégé本体建模软件,完成对矿床的本体建模,基于SWRL推理规则语言,定义矿床成因类型的推理规则,设计了一套面向矿床类型判别的本体推理机制,利用HermiT推理机完成推理。经大量试验,提出了矿床成因类型本体推理判别方法,其在金矿床成因类型判别方面具有较好应用效果,并可广泛应用于其他矿床成因类型的判别。

改进DARMmcr模型的输电线路安全运行数据比较分析

针对现有故障预测模型无法兼顾不同类型数据的特性,预测精度和效率较低等问题,提出了一种将计及罕见变量的动态关联规则挖掘模型和模糊推理系统相结合用于输电线路故障预测。使用外部环境数据(连续和离散特征)用作输入数据,连续特征由集成模型处理,离散特征由计及罕见变量的动态关联规则挖掘模型处理。通过算例对预测方法的性能进行比较分析,验证了该方法的优越性。结果表明,与传统的预测方法相比,该预测模型考虑了不同类型输入数据的特征,可以进一步提高预测效果,有一定的参考价值。

基于正则推断的数据格式规则生成方法

为解决手工制定数据质量规则费时费力且容易出错的问题,基于正则推断理论,研究从正样本推断自动生成数据格式规则的方法,提出多尺度样本增强、循环模式和公共子序列抽取的样例泛化策略,构造格式规则候选空间,证明多尺度样本增强的合理性,分析公共子序列对格式规则质量的影响;基于编码成本构造目标函数,利用整数规划方法对候选规则的组合优化问题建模,推荐较优数据质量规则给数据治理者。真实数据集和模拟数据集实验结果表明:该方法生成的规则质量比同类方法平均提高70%,验证算法的可行性和有效性。该方法可以提升制定和管理数据格式规则的效率。