Remaining useful life prediction of lubrication oil by integrating multi-source knowledge and multi-indicator data

Disjunctive belief rule-based reasoning for decision making with incomplete information

With the support of advanced information and communication technologies and open innovative design platforms, the emerging and blooming paradigm of mass personalization drives the process of engineering design to include knowledge with higher heterogeneity and more complex modalities. To this end, Multi-Modal Knowledge Graphs (MMKG), evolved from semantic networks and knowledge graphs, provide a powerful technology system for more effectively organizing and utilizing this knowledge. To understand the state-of-the-art of key aspects that enables MMKG, and recognize the potential challenges for forefront applications in engineering design, a literature review of MMKG-related publications is conducted. With selected 131 representative papers together with other 32 supplementary studies (up to 11/11/2023), this article summarizes the technical and practical efforts of multi-modal knowledge extraction, fusion technology, and specific applications in the engineering design process. Meantime, the challenges that MMKG faces and its foreseeable development potentials are discussed, which is hoped to provide a basis for the futuristic explorations and implementations of MMKG-enhanced availability and productivity in engineering design. 

Evolving to multi-modal knowledge graphs for engineering design: state-of-the-art and future challenges

Uncertain information processing is a key problem in classification. Dempster-Shafer evidence theory (D-S evidence theory) is widely used in uncertain information modelling and fusion. For uncertain information fusion, the Dempster's combination rule in D-S evidence theory has limitation in some cases that it may cause counterintuitive fusion results. In this paper, a new correlation belief function is proposed to address this problem. The proposed method transfers the belief from a certain proposition to other related propositions to avoid the loss of information while doing information fusion, which can effectively solve the problem of conflict management in D-S evidence theory. The experimental results of classification on the UCI dataset show that the proposed method not only assigns a higher belief to the correct propositions than other methods, but also expresses the conflict among the data apparently. The robustness and superiority of the proposed method in classification are verified through experiments on different datasets with varying proportion of training set. 

/pdf/a-new-correlation-belief-function-in-dempster-shafer-2q04dfkl.pdf

A new correlation belief function in Dempster-Shafer evidence theory and its application in classification

The combination rule explosion problem of belief rule base (BRB) is a difficult problem to solve in complex systems and has attracted wide attention. A new interval-constructed belief rule base with rule reliability (IBRB-r) is proposed to solve the problem of combination rule explosion in belief rule base. This model not only proposes a new interval rule construction method, but also designs a new interval rule inference process with rule reliability. This approach can not only clearly indicate the contribution degree of each rule to the model, but also solve the problem of combination rule explosion. This is because combining rules in interval addition form avoids the exponential growth in the number of rules caused by combining rules in Cartesian product form. Therefore, IBRB-r is more suitable for complex system modeling. In the case study section, the structural safety assessment of liquid launch vehicle is introduced to conduct a concrete example analysis. Experimental results show that the proposed model achieves over 95% accuracy under the liquid rocket dataset and has relatively higher accuracy under other datasets as well. 

/pdf/a-new-interval-constructed-belief-rule-base-with-rule-37sglbp5.pdf

A new interval constructed belief rule base with rule reliability

Advancement and application of rule-based expert systems have been a key research area in explainable artificial intelligence (XAI) because the rule-base is one of the most common and natural explainable frameworks for knowledge representation. The present work aims to design a novel rule-based system, called Cumulative Belief Rule-Based System (CBRBS), by establishing efficient rule-base modeling and inference procedures, where the rule-base modeling procedure includes the generation of cumulative belief rules via numeric data transformation and extended belief rule integration, and the rule-base inference procedure includes the inference of cumulative belief rules via consistent rule activation and activated rule integration. All these procedures enable CBRBS to better achieve the balance of explainability, high-efficiency (or computing complexity), and accuracy to fit with different application scenarios, as well as overcome the limitations of classical rule-based systems. Extensive experiments based on the well-known pipeline leak detection problem and open-source classification problems are conducted to illustrate the feature and advantage of the CBRBS over other classical rule-based systems and some commonly used classifiers. 

/pdf/highly-explainable-cumulative-belief-rule-based-system-with-3huqpg0d.pdf

Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme

https://link.springer.com/content/pdf/10.1007/s13042-022-01504-y.pdf

Editorial: New trends on machine learning applied to information processing under uncertainty

Industrial cost is a comprehensive indicator to reflect industrial behaviors, whereas industrial data with annotation are rare because the annotation process is very expensive. Data increment transformation is a feasible solution to enrich annotated industrial data, but it bring a new challenge in system modeling because the size of transformational data is the quadratical relationship with that of collected data, and even turn into big data problem. Hence, a novel rule-based system proposed for handling big data problems, called micro-extended belief rule-based system (Micro-EBRBS), is introduced for industrial cost prediction. Firstly, the Micro-EBRBS is improved by (1) the use of activation factor to revise the calculation of individual matching degrees; (2) the use of parameter optimization to determine the optimal value of basic parameters. Afterwards, on the basis of data increment transformation, a novel industrial cost prediction model, called data increment-based Micro-EBRBS (DIME) model, is developed to accurately predict industrial costs. In case study, 13 state-own holding industries with historical data from 1999 to 2019 in China are used to illustrate the effectiveness of the DIME model. Comparative results show that the DIME model is more accurate than some existing models in industrial cost prediction. 

/pdf/micro-extended-belief-rule-based-system-with-activation-7n696kqg.pdf

Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction

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Papers

Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme

Editorial: New trends on machine learning applied to information processing under uncertainty

Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction