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17 pages, 19512 KiB

Open AccessArticle

Semantic Reference Model for Individualization of Information Processes in IoT Heterogeneous Environment

by Dmitry Mouromtsev

Electronics 2021, 10(20), 2523; https://doi.org/10.3390/electronics10202523 - 16 Oct 2021

Cited by 5 | Viewed by 2763

The individualization of information processes based on artificial intelligence (AI), especially in the context of industrial tasks, requires new, hybrid approaches to process modeling that take into account the novel methods and technologies both in the field of semantic representation of knowledge and machine learning. The combination of both AI techniques imposes several requirements and restrictions on the types of data and object properties and the structure of ontologies for data and knowledge representation about processes. The conceptual reference model for effective individualization of information processes (IIP CRM) proposed in this work considers these requirements and restrictions. This model is based on such well-known standard upper ontologies as BFO, GFO and MASON. Evaluation of the proposed model is done on a practical use case in the field of precise agriculture where IoT-enabled processes are widely used. It is shown that IIP CRM allows the construction of a knowledge graph about processes that are surrounded by unstructured data in soft and heterogeneous domains. CRM also provides the ability to answer specific questions in the domain using queries written with the CRM vocabulary, which makes it easier to develop applications based on knowledge graphs. Full article

(This article belongs to the Special Issue Ambient Intelligence in IoT Environments)

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17 pages, 3764 KiB

Open AccessArticle

A Matrix Approach for Analyzing Signal Flow Graph

by Shyr-Long Jeng, Rohit Roy and Wei-Hua Chieng

Information 2020, 11(12), 562; https://doi.org/10.3390/info11120562 - 30 Nov 2020

Cited by 5 | Viewed by 7305

Abstract

Mason’s gain formula can grow factorially because of growth in the enumeration of paths in a directed graph. Each of the (n − 2)! permutation of the intermediate vertices includes a path between input and output nodes. This paper presents a novel method for analyzing the loop gain of a signal flow graph based on the transform matrix approach. This approach only requires matrix determinant operations to determine the transfer function with complexity O(n³) in the worst case, therefore rendering it more efficient than Mason’s gain formula. We derive the transfer function of the signal flow graph to the ratio of different cofactor matrices of the augmented matrix. By using the cofactor expansion, we then obtain a correspondence between the topological operation of deleting a vertex from a signal flow graph and the algebraic operation of eliminating a variable from the set of equations. A set of loops sharing the same backward edges, referred to as a loop group, is used to simplify the loop enumeration. Two examples of feedback networks demonstrate the intuitive approach to obtain the transfer function for both numerical and computer-aided symbolic analysis, which yields the same results as Mason’s gain formula. The transfer matrix offers an excellent physical insight, because it enables visualization of the signal flow. Full article

(This article belongs to the Special Issue Selected Papers from IIKII 2020 Conferences)

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