Information Representation Model Research Articles

A Tolerance Specification Automatic Design Method for Screening Geometric Tolerance Types

At present, the automatic generation of tolerance types based on rule-based reasoning has an obvious characteristic: for the same assembly feature, tolerance items are recommended that satisfy all feature characteristics, with a large number of recommendations. For this reason, automatically selecting tolerance types and reducing designer autonomy remains a challenging task, especially for complex mechanical products designed using heterogeneous CAD systems. This article proposes a tolerance specification design method for the automatic selection of assembly tolerance types. Based on the construction of a hierarchical representation model of assembly tolerance information with tolerance-zone degrees of freedom (DOFs), a semantic model of geometric tolerance information with tolerance-zone DOFs and a meta-ontology model of assembly tolerance information representation are constructed. Descriptive logic is used to express the attribute relationships between different classes in the assembly tolerance information meta-ontology model, and screening inference rules are constructed based on the mechanism for selecting assembly tolerance types based on tolerance-zone DOFs. On this basis, a process for selecting assembly geometric tolerance types based on the ontology of tolerance-zone DOFs is formed. Finally, the effectiveness and feasibility of this method were verified through examples.

A planar dimension measurement optimization method with metric information compensation

Visual dimension measurement is commonly required in industrial detection. However, monocular vision measurements have a low accuracy with large field of view (FOV), while multi-vision measurement involves multi-source information fusion and incurs a large computation overhead. Hence, we propose a planar dimension measurement optimization method with metric information compensation (PMOM). This method constructs a linear representation model of pixel metric information using images at different heights along the camera’s optical axis; designs a multi-resolution multi-dimensional key point mapping mechanism (KPM) to realize the projection from a coarse resolution image point to a fine resolution image block and the secondary optimization of measurement results. By utilizing pixel-level and subpixel-level object extraction algorithms for both coarse resolution and fine resolution image blocks, the algorithm’s complexity and data processing requirements are notably reduced. The paper includes a theoretical proof of the method’s effectiveness, shown by analyzing the measurement errors of different resolution images. Comparative experiments on the measurement of spacing between planar patterns and workpiece machining accuracy validate the method meeting the requirements of industrial high-precision with a large FOV and low computational overhead.

A Knowledge Graph-Based Approach for Assembly Sequence Recommendations for Wind Turbines

There are various forms of assembly data sources for wind turbines, which contributes to the lack of a unified and standardized expression. Moreover, the reusability of historical assembly data is low, which leads to the poor reasoning ability of a new product assembly sequence. In this paper, we propose a knowledge graph-based approach for assembly sequence recommendations for wind turbines. First, for the multimodal data (text in process manual, image of tooling, and three-dimensional (3D) model) of assembly, a multi-process assembly information representation model is established to express assembly elements in a unified way. In addition, knowledge extraction methods for different modal data are designed to construct a multimodal knowledge graph for wind turbine assembly. Further, the retrieval of similar assembly process items based on the bidirectional encoder representation from transformers joint graph-matching network (BERT-GMN) is proposed to predict the assembly sequence subgraphs. Also, a Semantic Web Rule Language (SWRL)-based assembly process items inference method is proposed to automatically generate subassembly sequences by combining component assembly relationships. Then, a multi-objective sequence optimization algorithm for the final assembly is designed to output the optimal assembly sequences. Finally, taking the VEU-15 wind turbine as the object, the effectiveness of the assembly process information modeling and part multi-source information representation is verified. Sequence recommendation results are better quality compared to traditional assembly sequence planning algorithms. It provides a feasible solution for wind turbine assembly to be optimized from multiple objectives simultaneously.

Digital Image Representation Model Enriched with Semantic Web Technologies: Visual and Non-Visual Information

The types of content of digital images, visual (syntactic and semantic) and non-visual, cause the complexity of their representation. Considering these contents separately hinders digital image retrieval because this creates a gap between the contents of the image and its representation. Therefore, this work aims to present a representation model of visual and non-visual information of digital images, with semantic enrichment through the Semantic Web technologies. For that, a qualitative methodology with a bibliographical approach was used. Theoretical subsidies of the topics addressed were sought, and it has an applied focus since it proposes a model and its exemplification. The developed model depicts the representation image process and allows the semantic enrichment of the data. This enrichment facilitates the retrieval in multiple contexts with technologies that favor the use of the data through inferences. Also, a use case with digital medical images is presented, demonstrating the feasibility of the proposal. It is concluded that the representation of visual and non-visual content aims to improve the way images are retrieved in digital information environments. The junction of the content and the context of images should be considered, even though search mechanisms usually treat this separately due to the disaggregation of image representation itself.

A twin data-driven approach for user-experience based design innovation

Data-driven innovation has received increasing attention, which explores big data technologies to gain more insights and advantages for product design. In user experience (UX) based design innovation, user-generated data and archived design documents are two valuable resources for various design activities such as identifying opportunities and generating design ideas. However, these two resources are usually isolated in different systems. Additionally, design information typically represented based on functional aspects is limited for UX-oriented design. To facilitate experience-oriented design activities, we propose a twin data-driven approach to integrate UX data and archived design documents. In particular, we aim to extract UX concepts from product reviews and design concepts from patents respectively and to discover associations between the extracted concepts. First, a UX-integrated design information representation model is proposed to associate capabilities with key elements of UX at the concept, category, and aspect levels of information. Based on this model, a twin data-driven approach is developed to bridge experience information and design information. It contains three steps: experience aspect identification using an attention-based LSTM (Long short-term memory) network, design information categorization based on topic clustering using BERT (Bidirectional Encoder Representations from Transformers) and LAD (Latent Dirichlet allocation) model, and experience needs and design information integration by leveraging word embedding techniques to measure concept similarity. A case study using healthcare-related experience and design information has demonstrated the feasibility and effectiveness of this approach.

MARCOS approach based upon cubic Fermatean fuzzy set and its application in evaluation and selecting cold chain logistics distribution center

The evaluation and selection of cold chain logistics distribution center (CCLDC) possesses important strategic significance for enterprises to optimize logistics network. This paper firstly introduces an innovative uncertain information representation model called cubic Fermatean fuzzy set (CCFS) by integrating the cubic fuzzy set (CFS) and Fermatean fuzzy set (FFS) to portray the complicated indeterminacy and inaccuracy information. To begin with, the definition, score and accuracy functions, comparison laws, and generalized distance measure of CFFS are all defined successively. Then, based upon the defined Aczel-Alsina operations of CFFS, several cubic Fermatean fuzzy Aczel-Alsina aggregation operators are presented to flexibly integrate the cubic Fermatean fuzzy information, as well as some elegant properties of those operators are investigated at length. Again, two weight identification models are introduced based upon the defined score function and distance measure to calculate the weight of experts and criteria. In addition, an extended measurement alternatives and ranking based on the compromise solution (MARCOS) method by means of the presented operators, score function, and distance measure is brought forward within cubic Fermatean fuzzy circumstance. Consequently, an empirical study about the evaluation and selection of CCLDC is applied to demonstrate the efficacy and practicability of the presented method. Moreover, the comparative study and sensibility analysis are implemented to validate the rationality and superiority of the propounded method in addressing the problem of selecting the optimal CCLDC. The outcomes reflect that the developed approach provides a synthetic, robust and operable group decision framework for the evaluation and selection of CCLDC within an uncertain context.

Integrated Information Model of an Enterprise and Cybersecurity Management System

The paper analyzes the concepts of "data", "information", "knowledge", "intelligence", "activity", establishes their relationship in the context of integrating information about the enterprise with the contours of security management through the connection of information technologies, operation technologies, ecology technologies (IT, OT, ET). The authors propose an integrated model of information representation of the organization based on a chain of data, information, knowledge and activity / intelligence. The main feature of this model is that it explicitly includes activities that are implemented considering the laws of natural intelligence and forms a corresponding control loop. The security management system is based on identifying and tolerating threats on the stages of processing the data, information, knowledge and intelligence/activities and IT, OT and ET levels. Two industrial cases related to dialog enterprise management system and post-accident monitoring system are discussed.

INFORMATION MODELS FOR MANUFACTURING WORKSPACES IN ROBOTIC PROJECTS

The subject of research in the article are the workspace models for flexible integrated robotic systems. The goal of the work is in development of information models to represent workspaces for following application in the automated control systems of flexible integrated manufacturing. The article solves the next tasks: to analyze the representation of workspace to decide practical problems of robotic systems of different nature, to consider the development of informational models for representation on workspaces of intelligent control systems of integrated manufacturing, to consider the practical examples of information presentation on workspaces of production systems. Research methods are set theory and predicate theory. The following results were obtained: there were analysed the main features of informational models development to solve robotic tasks of different nature and were pointed the limitations of existing approaches of formal description, the need of integration of workspace models to decision-support systems and systems of graphical and mathematical simulation of integrated systems; the set theory-based model of information representation for problem-solving processes of flexible integrated robotic systems is proposed; the information-logic model of workspace for mobile robot applications, functioning in flexible integrated systems. is developed and contains the list of objects, includes their geometrical dimensions and supplies the preservation of parameters in time and space; information presentation for automated control system of flexible integrated manufacturing, which implements proposed models, is considered. Conclusions: application of models of information type for automated control systems makes to supply logical unification of flexible integrated manufacturing elements, to provide monitoring of states of technological equipment of production systems in space and time and formation of their digital twins, to promote functioning of intelligent decision-support systems for robotic systems of different types, that improves characteristics of production control.

PySBOL3: SBOL3 for Python Programmers.

The Synthetic Biology Open Language version 3 (SBOL3) provides a data model for representation of synthetic biology information across multiple scales and throughout the design-build-test-learn workflow. To support practical use of this data model, we have developed pySBOL3, a Python library that allows programmers to create and edit SBOL3 documents. Here we describe this library and key engineering decisions in its design. The resulting implementation is a compact and maintainable core that provides both a familiar, pythonic interface for manipulating SBOL3 objects as well as mechanisms for building additional extensions and representations on this base.

Low-carbon tourism destination selection by a thermodynamic feature-based method

Developing low-carbon tourism can not only improve the ecological environment, but also promote the development of economy in China. To select a desirable low-carbon tourism destination, we need to consider multiple conflicting and incommensurate criteria, simultaneously. However, traditional decision-making methods such as analytical hierarchy process and Delphi fail to model the complex cognition of decision makers. In this regard, this article proposes a novel multi-criteria decision making method called the thermodynamic feature-based method to aid decision makers to select the optional low-carbon tourism destination. The q-rung orthopair fuzzy set (q-ROFS), as a recently proposed information representation model, is used to express the imprecise information of decision makers. We first introduce new distance measures of q-ROFSs and investigate their desirable properties in detail. Afterwards, a thermodynamic feature-based method is developed from the aspects of energy, exergy and entropy of alternatives integrating both quantitative and qualitative decision information. Finally, a case study concerning the selection of low-carbon tourism destination is given to illustrate the applicability and superiority of the proposed method.

Information representation of blockchain technology: Risk evaluation of investment by personalized quantifier with cubic spline interpolation

With the applications of blockchain technology in various fields, the research on blockchain has attracted much attention. Different from the researches focusing on specific applications of blockchain technology in a certain field, this study devotes to capturing the attitudes of investors regarding different risk criteria in blockchain technology investment decision making. We use personalized quantifiers to extract investors’ preferences on each risk evaluation criterion. At present, the personalized quantifier that can reflect individual attitudes and behavior intentions have been fitted by various functions, but there are still limitations. In this regard, this paper introduces a cubic spline interpolation function to fit the personalized quantifier, and addresses the consistency of the personalized quantifier in the ordered weighted averaging aggregation. Moreover, we employ a qualitative information representation model called probabilistic linguistic term sets to express decision-makers' evaluations on each criterion. We give a case study to illustrate the usability of the proposed personalized quantifier in blockchain risk evaluation. The comparative analysis with other four types of personalized quantifiers shows that our proposed personalized quantifier with cubic spline interpolation has ideal geometric characteristics in terms of smooth curve and high fitting accuracy, thus having strong applicability. Further, we show that this method is relatively easy to operate.

A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data

The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet the needs of intelligent and personalized service. Based on this, this paper designs a cognitive information representation model based on a knowledge graph, which combines the perceptual information of industrial robot ontology with semantic description information such as functional attributes obtained from the Internet to form a structured and logically reasoned cognitive knowledge graph including perception layer and cognition layer. Aiming at the problem that the data sources of the knowledge base for constructing the cognitive knowledge graph are wide and heterogeneous, and there are entity semantic differences and knowledge system differences among different data sources, a multimodal entity semantic fusion model based on vector features and a system fusion framework based on HowNet are designed, and the environment description information such as object semantics, attributes, relations, spatial location, and context acquired by industrial robots and their own state information are unified and standardized. The automatic representation of robot perceived information is realized, and the universality, systematicness, and intuition of robot cognitive information representation are enhanced, so that the cognition reasoning ability and knowledge retrieval efficiency of robots in the industrial Internet environment can be effectively improved.

Customer-oriented product and service design by a novel quality function deployment framework with complex linguistic evaluations

Quality function deployment (QFD), as a widely-used quality management method, can effectively capture customers’ preferences of products and services by involving lots of evaluations including the weights of customer requirements, the performance of alternatives under different design requirements and the interactions of design requirements. However, there are challenges to use this tool to solve complex customer-oriented design problems because of the uncertain information regarding customer requirements, design requirements and alternatives’ performances. To address the inherent uncertainty of qualitative information in the framework of QFD, this paper incorporates the continuous interval-valued linguistic term set, a novel information representation model, to depict the complex linguistic evaluations of experts, and then introduces a novel QFD framework with complex linguistic evaluations. Such a QFD framework is constructed with three phases: the first step is to determine the importance degrees of customer requirements and additional requirements, which are important for product deployments, with the help of the best worst method; the second step is to determine the relative importance of design requirements; the third step is to calculate the interval weights of alternatives according to the uncertainty degrees of evaluations and the weights of design requirements. The inner-element correlations are considered in the whole procedure of the proposed QFD. The proposed method is applied to the aviation service development for Sichuan Airlines in China to illustrate its practicability. It is noted that this method can also be applied to various cases about product and service design.

A Model for Structured Information Representation in Neural Networks of the Brain.

Humans can reason at an abstract level and structure information into abstract categories, but the underlying neural processes have remained unknown. Recent experimental data provide the hint that this is likely to involve specific subareas of the brain from which structural information can be decoded. Based on this data, we introduce the concept of assembly projections, a general principle for attaching structural information to content in generic networks of spiking neurons. According to the assembly projections principle, structure-encoding assemblies emerge and are dynamically attached to content representations through Hebbian plasticity mechanisms. This model provides the basis for explaining a number of experimental data and provides a basis for modeling abstract computational operations of the brain.

A Modified Skip-Gram Algorithm for Extracting Drug-Drug Interactions from AERS Reports.

Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.

Facial Attribute Capsules for Noise Face Super Resolution

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.

Representation of Information about Part on The Basis of Its Engineering Features

Design stage of production processes for machining of parts is a nontrivial task, and technologists face considerable difficulties while selecting the best solution. Aiming at better selection of alternatives, it is important to apply intelligent systems to design production processes. Ontological model of information representation about production process is described using attribute graphs, which allows analyzing the form and spatial relationships between the structural elements of objects, and reduces the scope of interpretation of concepts and the number of interpretive functions.

A Multiple Attribute Group Decision-making Method Based on the Partitioned Bonferroni Mean of Linguistic Intuitionistic Fuzzy Numbers

As a more effective linguistic information representation model, the linguistic intuitionistic fuzzy number (LIFN), made up of the linguistic membership degree (LMD) and the linguistic non-membership degree (LNMD), plays an important role in describing uncertain-decision information in cognitive activity. The partitioned Bonferroni mean (PBM) operator is constructed based on real conditions, considering that some factors considered by decision-makers are interrelated and some are independent. The PBM operator can aggregate evaluation information under various attributes, which are divided into several independent groups; the factors interact with each other within the same group, but the factors are independent of each other when located in different groups. The classical PBM operators and their extensions can handle many decision-making problems but are unable to handle decision-making problems under a linguistic intuitionistic environment. To take full advantage of the PBM operator and LIFNs, this paper combines the PBM operator with LIFNs to propose several PBM operators for aggregating LIFNs. First, the relative theories about LIFNs are reviewed in brief. Then, linguistic intuitionistic fuzzy partitioned BM aggregation operators and partitioned geometric BM aggregation operators are discussed, including the linguistic intuitionistic fuzzy partitioned Bonferroni mean (LIFPBM) operator, the linguistic intuitionistic fuzzy weighted partitioned Bonferroni mean (LIFWPBM) operator, the linguistic intuitionistic fuzzy partitioned geometric Bonferroni mean (LIFPGBM) operator, and the linguistic intuitionistic fuzzy weighted partitioned geometric Bonferroni mean (LIFWPGBM) operator. Moreover, a novel method constructed on the developed operators is presented to address multiple attribute group decision-making (MAGDM) problems with the LIFNs. The feasibility of the proposed method is given by a numeric example, and the comparative analysis of our proposed method and other methods shows some advantages of these new methods. The developed method is constructed based on LIFNs and the PBM operator, and it is more practical and general than other existing methods and easily describes qualitative information that stems from a decision maker’s cognition. In addition, it can more effectively handle the complex relationship among multiple attributes in MAGDM problems.

CNN-VWII: An efficient approach for large-scale video retrieval by image queries

This paper aims to solve the problem of large-scale video retrieval by a query image. Firstly, we define the problem of top-k image to video query. Then, we combine the merits of convolutional neural networks(CNN for short) and Bag of Visual Word(BoVW for short) module to design a model for video frames information extraction and representation. In order to meet the requirements of large-scale video retrieval, we propose a visual weighted inverted index(VWII for short) and related algorithm to improve the efficiency and accuracy of retrieval process. Comprehensive experiments show that our proposed technique achieves substantial improvements (up to an order of magnitude speed up) over the state-of-the-art techniques with similar accuracy.

A novel multi-criteria decision making method for assessing health-care waste treatment technologies based on D numbers

Health-care waste (HCW) management is a complex problem influenced by many aspects, such as, economic, environment, technical, social, etc. A selection of the best treatment technology for HCW management can be regarded as a complex multi-criteria decision making problem where a number of alternatives and multiple evaluation criteria are need to be considered. In addition, decision makers often express their personal assessments by using multi-granularity linguistic term sets. Due to the involvement of human judgment, various uncertainties are introduced in the HCW process. One critical issue of the HCW treatment technology selection is the representation and handling of uncertain information. In response, a novel multi-criteria decision making method is proposed for the HCW treatment technology selection problem based on an effective representation model of uncertain information, called D numbers. In the proposal, the assessment results of HCW treatment are expressed and modeledby D numbers. It provides a new framework for the HCW treatment technology selection problem in which it was effective and feasible to handle MCDM problems under various uncertainties environment. Comparing with the existing method, the proposed method is clear and concise. Finally, an empirical case study in Shanghai, China is illustrated to validate the feasibility and applicability of the proposed method. As shown in the results, the proposed method selects steam sterilization as the optimal technology to deal with the health-care wastes which is consistent with the existing work where fewer pollutants are discharged and non-hazardous residues are produced so that it decreases more impacts on the environment and public health. The experimental results demonstrate that the proposed method can handle the HCW treatment technology selection problem effectively under complex and uncertain environments.