Mathematical Model Of Meaning Research Articles

In the Circle of Sense and Nonsense: Through a Mathematical Model of Meaning

Abstract In this article, the authors explore the relations and interactions between sense and nonsense with a threefold goal: (1) elaborations of formal definitions of these concepts; (2) investigation of the processes of making sense from nonsense; (3) establishment of the mathematical foundations for a sense-nonsense theory. The authors thus extend the dyad sense-nonsense by introducing the concept no-sense, situated between sense and nonsense, and by extending the concept sense to the concept poly-sense. The authors construct a mathematical model of these concepts and related processes using structures such as logical calculus, logical variety and logical prevariety. This approach accords with Leonardo da Vinci’s opinion that “no human investigation can claim to be scientific if it doesn’t pass the test of mathematical proof.”

A Global Environment Analysis and Visualization System with Semantic Computing for Multi-Dimensional World Map

Humankind, the dominant species on Earth, faces the most essential and indispensable mission; we must endeavor on a global scale to perpetually restore and improve our natural and social environments. The essential computation in environmental study is context-dependent-differential computation to analyze the changes of various situations (temperature, color, CO 2 , places of livings, sea level, coral area, etc.). It is important to realize global environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way with a large amount of information resources in terms of global environments. It is also significant to memorize those situations and compute environment change in various aspects and contexts, in order to discover what is happening in the nature of our planet. We have various (almost infinite) aspects and contexts in environmental changes in our planet, and it is essential to realize a new analyzer for computing differences in those situations for discovering actual aspects and contexts existing in the nature. We propose a new method for Differential Computing in our Multi-dimensional World map. We utilize a multi-dimensional computing model, the Mathematical Model of Meaning (MMM), and a multi-dimensional space filtering method with an adaptive axis adjustment mechanism to implement differential computing. Computing environmental changes in multi-aspects and contexts using differential computing, important factors that change natural environment are highlighted. We also present a method to analyze and visualize the highlighted factors using our Multi-dimensional World Map (5-Dimensional World Map) System. We also introduce the concept of “SPA (Sensing, Processing and Analytical Actuation Functions)” for realizing a global environmental system, to apply it to Multi-dimensional World Map (5-Dimensional World Map) System. This concept is effective and advantageous to design environmental systems with Physical-Cyber integration to detect environmental phenomena as real data resources in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences.

Hierarchical detection of wildfire flame video from pixel level to semantic level

The importance of flame detection cannot be ignored in a wildfire video surveillance system due to disturbance of heavy fog and challenging of smoke detection. In this paper a novel method for hierarchical detection of wildfire flame video is presented. Specifically, wildfire flame images are gradually recognized from low level visual features of pixel based to high level semantics of video clip based. For all the pixels of one image, the pixels which meet color rules and motion characteristics are labeled as flame colored pixels. The candidate flame region roughly generated by flame-like pixels is divided into non-overlapped image blocks. The sparse representation of the blocks are defined and recognized by learned dictionaries to more accurately segment candidate flame region and exclude some non-flame regions. To reduce the cost of computation, the proposed method detects one Frate (Frate denotes one frame rate) frames instead of one frame at a time by using a sliding time window. Flicker features and spatiotemporal features extracted from video clips of the size Frate are used to build semantic model of wildfire flame video recognition based on mathematical model of meaning. Experimental results show that the proposed approach can effectively segment flame region and significantly improve the performance of wildfire flame detection.

Dangerous Smoke Classification Using Mathematical Model of Meaning

Fire accident remains a problem in modern society. This leads great efforts in finding ways to prevent, detect and control it. Conventional fire detection systems are mostly point detectors, which have limitation for early smoke detection, especially in a high-ceiling atrium. A video-based smoke detection system is an interesting alternative approach. It has better area coverage and detecting smoke faster. In this work, a video-based smoke detection system was developed with two main processes, i.e. moving objects segmentation with Gaussian Mixture Models (GMM) and smoke classifications with Mathematical Model of Meaning (MMM). In the MMM model, the interpretation of dangerous smoke is based on the context provided. Then the classification results are compared with conventional smoke detector. The results show that MMM can recognize the dangerous smoke faster than conventional smoke detectors.

意味検索可能なアート・アーカイヴを用いたレニ・リーフェンシュタール, 及び同時代の表象の分析

意味検索可能なアート・アーカイヴを用いたレニ・リーフェンシュタール, 及び同時代の表象の分析

A metadatabase system for semantic image search by a mathematical model of meaning

In the design of multimedia database systems, one of the most important issues is to extract images dynamically according to the user's impression and the image's contents. In this paper, we present a metadatabase system which realizes the semantic associative search for images by giving keywords representing the user's impression and the image's contents.This metadatabase system provides several functions for performing the semantic associative search for images by using the metadata representing the features of images. These functions are realized by using our proposed mathematical model of meaning. The mathematical model of meaning is extended to compute specific meanings of keywords which are used for retrieving images unambiguously and dynamically. The main feature of this model is that the semantic associative search is performed in the orthogonal semantic space. This space is created for dynamically computing semantic equivalence or similarity between the metadata items of the images and keywords.