Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

Multi-Domain Malicious Behavior Knowledge Base Framework for Multi-Type DDoS Behavior Detection

Due to the many types of distributed denial-of-service attacks(DDoS)attacks and the large amount of data generated,it becomes a chal-lenge to manage and apply the malicious behavior knowledge generated by DDoS attacks.We propose a malicious behavior knowledge base framework for DDoS attacks,which completes the construction and application of a multi-domain malicious behavior knowledge base.First,we collected mali-cious behavior traffic generated by five mainstream DDoS attacks.At the same time,we completed the knowledge collection mechanism through data pre-processing and dataset design.Then,we designed a malicious behavior category graph and malicious behavior structure graph for the characteristic information and spatial structure of DDoS attacks and completed the knowl-edge learning mechanism using a graph neural network model.To protect the data privacy of multiple multi-domain malicious behavior knowledge bases,we implement the knowledge-sharing mechanism based on federated learning.Finally,we store the constructed knowledge graphs,graph neural network model,and Federated model into the malicious behavior knowledge base to complete the knowledge management mechanism.The experimental results show that our proposed system architecture can effectively construct and apply the malicious behavior knowledge base,and the detection capability of multiple DDoS attacks occurring in the network reaches above 0.95,while there exists a certain anti-interference capability for data poisoning cases.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Study on Fault Diagnosis of Rotating Machinery with Hybrid Neural Networks

With the help of the feedforward neural network diagnostic method, the hybrid diagnostic networks corresponding to information in multiple symptom domains are built and the comprehensive judgment is carried out with weighted average method. Meanwhile, this method has the ability of self learning and self adaptation in order to adapt both the complexity of vibrations produced practically and the pluralistic potent of vibration symptoms induced really for large rotating machinery, especially for turbogenerators. The reliability and precision of diagnosis with this method is heightened. It seems that the method can take more practical value in engineering applications.

Knowledge-Based Systems for the Assessment and Management of Bridge Structures: A Review

It is becoming an important social problem to make maintenance and rehabilitation of existing infrastructures such as bridges, buildings, etc. in the world. The kernel of such structure management is to develop a method of safety assessment on items which include remaining life and load carrying capacity. The purpose of this paper is to summarize the finding of up-to-date research articles concerning the application of knowledge-based systems to assessment and management of structures and to illustrate the potential of such systems in the structural engineering. In here, knowledge-based systems include knowledge-based expert systems incorporation with artificial neural networks, fuzzy reasoning and genetic or immune algorithms. Specifically, two modern bridge management systems (BMS’s) are presented in the paper. The first is a BMS to assess the performance and derive optimal strategies for inspection and maintenance of concrete bridge structures using reliability based and knowledge-based systems. The second is the concrete bridge rating expert system (J-BMS BREX) to evaluate the performance of existing bridges by incorporating with artificial neural networks and fuzzy reasoning.

基于句法依赖增强图的方面级情感分析

方面级情感分析旨在分析句子中特定方面的情感极性,现有研究侧重于利用图神经网络建模上下文与方面的依赖信息,忽略了对上下文中情感词及其词性的挖掘和利用。为此,提出一种基于句法依赖的增强图(syntactic dependency enhancement graph, SDEG)模型,在原始句法依赖图上引入情感知识和词性信息,增强情感词权重和相关词性单词在上下文中的作用。使用双向长短期记忆网络和卷积神经网络捕捉句子的重点语义信息,通过图卷积神经网络建模句法依赖增强图,通过交互注意力机制生成特定方面的上下文语义和语法表示以进行情感极性分类。在多个公共基准数据集上的实验结果表明,所提模型在性能上有明显提升。

Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks

Question answering (QA) over knowledge base (KB) aims to provide a structured answer from a knowledge base to a natural language question. In this task, a key step is how to represent and understand the natural language query. In this paper, we propose to use tree-structured neural networks constructed based on the constituency tree to model natural language queries. We identify an interesting observation in the constituency tree: different constituents have their own semantic characteristics and might be suitable to solve different subtasks in a QA system. Based on this point, we incorporate the type information as an auxiliary supervision signal to improve the QA performance. We call our approach type-aware QA. We jointly characterize both the answer and its answer type in a unified neural network model with the attention mechanism. Instead of simply using the root representation, we represent the query by combining the representations of different constituents using task-specific attention weights. Extensive experiments on public datasets have demonstrated the effectiveness of our proposed model. More specially, the learned attention weights are quite useful in understanding the query. The produced representations for intermediate nodes can be used for analyzing the effectiveness of components in a QA system.

基于层次结构图的多跳知识图谱问答模型

知识图谱问答(KBQA)旨在理解用户的自然语言问句,在结构化的知识图谱中通过检索、推理等手段来获取答案实体。近年来,多跳KBQA备受关注,然而,复杂问句中通常存在多个关系意图,已有KBQA方法大多忽视了推理关系链的关系顺序问题。为此,提出一种基于层次结构图的多跳知识图谱问答模型(HSG-KBQA),建模自然语言问句的关系层次顺序,指导模型在每个推理步选择合理的关系意图。设计一种层次结构图,显式地体现问句中关系的层次距离,利用LSTM-BiGCN编码层将词语间的依存信息编码到问句中;提出虚拟节点的概念,利用图池化技术过滤不重要的节点,学习推理过程中知识图谱的状态;设计基于注意力机制和层次权重的解码器来优化指令生成,使推理指令更匹配问句中的关系链顺序。实验结果表明,HSG-KBQA在WebQuestionsSP数据集上取得了71.3%的Hits@1分数,在PathQuestions数据集上取得了97.3%(PQ-2H)和89.7%(PQ-3H)的Hits@1分数,均优于对照基准模型,表明HSG-KBQA模型在KBQA任务中具有更好的性能。

融合外部知识和图卷积神经网络的生物医学事件联合识别

利用自然语言处理技术从生物医学文本中抽取药物治疗、疾病诊断等事件以及事件中涉及的疾病、药物等实体,对于生物医学领域相关学术研究以及各类生物医学应用系统具有重要意义。针对生物医学文本中的缩略词及专业术语难以识别和生物医学语义关系难以嵌入的问题,提出了一种融合外部知识和图卷积神经网络的生物医学信息联合识别模型。图卷积神经网络构建了包含实体和语义关系的异构图,能够迭代地融合本地知识图和外部知识图中的交互信息,根据得到的交互信息来进行生物医学实体对之间关系的抽取任务。预训练编码后利用图卷积神经网络构建本地和外部知识两个知识图,获得两个图中每个节点的特征表示,并且通过注意力实体链接的方法将两个图进行融合与信息迭代,进而抽取其最后一层隐藏层来完成最终的分类识别。其中统一医学语言系统(unified medical language system,UMLS)被用作实体消歧的外部知识库,实体链接器根据注意力权重选择对应实体。通过在MLEE语料库上进行的实验表明,联合任务能够实现事件抽取和触发词、元素识别的综合性能。

Where Does AlphaGo Go: From Church-Turing Thesis to AlphaGo Thesis and Beyond

An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation. © 2014 Chinese Association of Automation.

A brief overview of traditional Chinese medicine prescription powered by artificial intelligence

Traditional Chinese medicine prescription is one of the treasures of traditional Chinese medicine(TCM).There are tens of thousands TCM prescriptions accumulated in the past thousands of years,corresponding to different diseases,symptoms and therapeutic goals.The correspondences are so complicated that there is an urgent need to leverage new technologies such as artificial intelligence(AI)to analyze,understand and utilize them effectively.In this paper,we present a brief overview of this direction,where current research progress on TCM prescription powered by AI is summarized.Our summarization focuses on three aspects,TCM prescription mining that aims at understanding the TCM prescription,TCM prescription or herb knowledge base construction that aims at extracting knowledge to support the TCM prescription-related study,and TCM prescription discovery that aims at utilizing AI technologies to further energize TCM.It is encouraging to see that steady progress in this direction has been made recently.Besides,a toy experiment on image-based TCM herb recognition by using convolutional neural networks is also conducted.It basically verifies that it is promising to use AI technologies to address challenging tasks in TCM.We also point out several research topics that could be cooperatively performed by researchers from the two disciplines.

知识增强的方面词交互图神经网络

现有的方面级情感分析方法对句法依存树蕴含信息使用不足,忽略多方面词之间的关联,并且缺少对外部知识的使用。针对这些问题,提出一种知识增强的方面词交互图神经网络(KEAIG)模型。首先利用融合领域知识的BERT-PT (Bidirectional Encoder Representation from Transformers with Post-Train)编码文本,并利用知识图谱增加句法树的情感信息。模型分两部分对句法依存树蕴含的信息进行提取:第一部分利用句法依存树中的关联关系和每个单词的词性标签提取句子特征,第二部分对融入知识图谱的句法依存树进行特征提取。之后使用融合门控单元将多方面词关联特征融合进提取到的特征中。最后将两部分句子表示拼接起来作为最终分类依据。在4个数据集上的实验结果表明,所提模型相较于基准模型关系图注意力网络(RGAT),在准确率上分别提升了2.17%、5.54%、2.60%和2.83%,在F1值(Macro-F1)上分别提升了2.69%、6.87%、8.77%和14.70%,充分表明了利用句法树、引入外部知识和提取多方面词关联的有效性。

知识增强的交互注意力方面级情感分析模型

方面级情感分析(ABSA)已经成为自然语言处理领域的研究热点,与传统的情感分析技术相比,基于方面的情感分析能够判断句子中多个方面的情感倾向,可以更加准确地挖掘用户对方面的情感极性。当前,将注意力机制和神经网络相结合的模型在解决方面级情感分析任务时大多仅考虑方面对上下文的影响,且时常忽略句子中的相关语法信息和背景知识。针对上述问题,提出一种借助知识图谱和图卷积网络的交互注意力神经网络模型,为评论文本注入背景信息和语言知识。首先,利用知识图谱解决词汇在不同语境下的一词多义性问题。其次,利用文本图卷积网络完善评论语句的语法结构信息。最后,通过交互注意力机制实现评论文本上下文与评价方面的协调优化。最终在五个公开数据集上的实验结果表明,合理利用外部知识是改善方面级情感分析模型性能的有效策略。

基于关联规则挖掘的电炉电能计量装置异常诊断系统

现有电炉电能计量装置异常诊断系统的数据处理方式比较有限,只能根据预设规则进行简单的故障判断,未利用机器学习等先进技术对大量数据进行分析和挖掘,难以发现潜在的异常模式和规律,导致诊断精度较低。为此,设计基于关联规则挖掘的电炉电能计量装置异常诊断系统。首先,设计系统功能模块,其中包括主控模块、远程通信模块和数据管理模块;然后,建立系统知识库,并在知识库内通过关联规则挖掘技术挖掘电炉电能计量装置异常数据;最后,将上述挖掘到的异常数据输入到卷积神经网络模型中,通过学习和训练完成电炉电能计量装置异常诊断,并输出异常诊断结果。实验结果表明,所设计系统的电炉电能计量装置异常诊断准确率较高,具有一定的技术水平与实用性。

面向多跳问答的多视图语义推理网络

由于多跳知识图谱问答任务的复杂性,现有研究大多通过堆叠多层图神经网络以捕捉更大范围的高阶邻居信息。这种做法将多阶信息融合在一起,以损失节点判别性为代价获取更全局的信息,存在过平滑问题;并且,由于离节点越近的邻居置信度越高,将多阶邻居信息融合在一起的做法会忽略邻居的置信度。此外,多跳知识图谱问答存在许多数据集通常没有给定中间路径的监督信息的弱监督问题,会使模型在进行路径推理时缺乏有效的指导信息,导致模型推理能力降低。为了解决以上问题,论文提出了一种多视图语义推理网络,该网络利用全局和局部两种视图的信息共同进行推理。全局视图信息是指节点的多阶邻居信息,能够为推理提供更丰富的证据;局部视图信息则只关注节点的1阶邻居信息,更具有判别性,能够缓解全局视图信息存在的过平滑问题。同时,该网络将问题分解为多个子问题作为中间路径推理的指导信息,并从问题语义构成的均匀性和一致性出发,设计了一种新颖的损失函数以提升问题分解的质量,以提高模型中间路径推理的能力。论文方法在3个真实数据集上进行了大量实验,实验结果表明,多视图的语义信息能够为推理提供更加全面的证据,将问题分解为子问题的做法能够提高中间路径推理的准确性,证明了论文方法的有效性。

“问题地图”智能检测发展现状与关键技术

为在保护国家领土主权和地理信息安全的前提下,促进测绘地理信息行业的发展,推进“问题地图”智能检测的研究进展,本文综述了“问题地图”检测的发展现状,分析了“问题地图”智能检测的痛点问题,提出通过地理空间大数据挖掘技术获取训练样本,构建统一的地图审查模型和计算模式等关键技术。本文对“问题地图”智能检测的进展有积极的借鉴和促进作用。

一种语音信源的语义压缩编码方法

语义压缩编码方法通过开辟信息表征新维度,能够显著提高压缩效率,因而受到广泛关注。旨在通过语义表征与编码实现语音信源的高效压缩:首先,面向语音信源内容、韵律、音调、音色四方面特性,提出多层语义表征框架,实现语音语义的综合表征;基于此框架,利用矢量量化方法与哈夫曼编码方法,构建语音语义知识库,进一步提高语音压缩效率。仿真结果表明,相较于经典低比特率语音压缩方法,所提方法能够显著提高信源压缩效率。

基于深度神经网络的干扰知识库构建与识别方法

随着各类无线终端数量的增加与不同无线网络的跨域融合,使得网络中的同频与外在干扰变得异常严峻。为了改善通信传输质量、抑制复杂干扰对无线终端通信质量的影响,文章针对复杂电磁环境下存在多种类型干扰信号导致实时通信质量和效率较差问题,提出了一种基于深度神经网络的干扰知识库构建与识别方法。仿真结果表明,该方法的识别准确率优于传统决策树识别方法 8%左右。

注塑模模架设计KBE系统及其智能关键技术

结合当前工程设计领域 KBE技术的发展和模架设计的数据流图 ,给出了注塑模模架设计KBE系统的基本框架 ,并对其关键技术进行了分析 :模架设计知识采用框架 -规则的方法表示 ,并给出了推理的流程图 ,通过面向子目标的方法提高了 KBE系统的知识表示和推理能力 ;将事例推理的方法结合在 KBE系统中 ,提高了系统对以往成功事例的参考能力 ;利用神经网络的自学习能力 ,解决了模架中镶块和前后模设计的计算问题 .通过实际的应用 。