佐々木 史織

This paper describes about project “Data Sensorium” launched at the Asia AI Institute of Musashino University. Data Sensoriumis a conceptual framework of systems providing physical experience of content stored in database. Spatial immersive display is a key technology of Data Sensorium. This paper introduces prototype implementation of the concept and its application to environmental and architectural dataset.

In this paper, we propose a 'disaster-affected area estimation method', which is a simple and low-cost disaster-impact survey and risk assessment method using satellite multispectral images and GIS data for collecting disaster information in a wide area and in real-time. One of the features of our method is in the low-cost and low-technical barriers to implementation, as it utilizes open data satellite multispectral imagery to estimate the size of disaster-affected area with relatively high accuracy using an inexpensive and uncomplicated estimation method. This feature makes the method widely applicable to Least Developed Countries and small local government units. The method is particularly effective in disaster countermeasures, such as rapid confirmation of the disaster situation in wide-area disasters. In this paper, the basic concept and the implementation method of our disaster-affected area estimation is presented. By applying the method to several cases of landslide disaster in Japan, it is discussed that the method can detect and estimate the size of disaster-affected area with a certain level of accuracy. In addition, the feasibility of the method for the automation of disaster surveys is demonstrated, and the issues for its future implementation are identified.

© 2019 IEEE. For each movie, every person recognizes every event with Kansei which is unique to the individual. In this method, we focus on the point that people like about musical movies and then create a Kansei space for musical films using the idea of mathematical models of meaning. In this method, we calculate an emotional distance in Kansei space and provide the best recommendation for users with new user experiences.

It is becoming essentially significant to analyze and recognize environmental situations from various viewpoints on each purpose in environmental experts in governments, international organizations, universities, and research institutes. Visualizing environments in combination with relationships, reflections, and co-occurrences through multiple kinds of data is effective to interpret actual situations to analyze those data combinatory to see causality/resultant through multiple phenomena. This paper presents "A Multidimensional Visualization Method for Disaster Analysis on 5D World Map System". The 5D World Map System allows calculating in five dimensions, semantic in one dimension (1D), geographical world map in three dimensions (3D), and time in 1 dimension (1D), we have already presented. One of the essential features of our new method is to express one semantic in one Knowledge-Layer, and the selected Knowledge-Layers can be mapped simultaneously (transparency) to integrate/reduce multiple semantics into one visual dimension on the 5D World Map. Our method makes compatibility for multiple contexts of disaster phenomena. We also show several experimental results of our method for disaster analysis correspond to SDG11 has been proposed by United Nations, to clarify the feasibility of our method, and also clarify the applicability for the area of disaster analysis.

© 2019 The authors and IOS Press. All rights reserved. Semantic computing integration with deep-learning realizes a new artificial brain-memory system. We have presented a concept of 'MMM: Semantic Computing System' for analyzing and interpreting environmental phenomena and changes occurring in the oceans and rivers in the world. 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. This paper presents integration and semantic-analysis methods for KEIO-MDBL-UN-ESCAP Joint system for global ocean-water analysis with Coral-Image Analysis in two environmental-semantic spaces with water-quality and image databases. We have implemented an actual space integration system for accessing environmental information resources with water-quality and image analysis. We clarify the feasibility and effectiveness of our method and system by showing several experimental results for environmental medical document data Environmental-semantic space integration realizes deep analysis environmental phenomena and situations. The essential computation in environmental study is context-dependent-differential computation to analyze the changes of various situations (air, water, CO2, 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. In the design of environment-analysis systems, one of the most important issues is how to integrate several environmental aspects and analyze environmental data resources with semantic interpretations. In this paper, we present an environmental-semantic computing system. Our environmental-semantic computing system realizes integration and semantic-search among environmental-semantic spaces with waterquality and image databases.

This paper presents a preliminary visualization of surface water quality by 5D World Map (5DWM) system of three canals i.e. Bang Sue Canal, SamSen Canal, and Bang Krabue Canal in Bangkok Capital, Thailand. Seven sampling sites were selected and 13 parameters were analyzed i.e. temperature, pH, DO, BOD, COD, H2S, SS, TKN, NH3-N, NO2-N, NO3-N, TP, and Salinity. The previous data selected from 2007 to April 2017. Those parameters were analyzed and visualized by 5DWM system. The results indicated the 5DWM system visualized those parameters of each water sampling site in term of different colors and graphs that they indicated the water quality conditions change from past to present due to the system can show the water quality states in time series. (C) 2018 INT TRANS J ENG MANAG SCI TECH.

The most important aim of our study is to realize a multi-database for social sciences and environmental sciences. Our system connects heterogeneous databases about historical phenomena by using common spatiotemporal information and visualize the connected results onto 5D World Map (a set of chronologically ordered global maps). To actualize that, we created a news articles provision system using real-Time sensor data as a trigger and determined the effectiveness in the experiment. Here we found that we can get the information about the happening in the same atmospheric condition in the past by exhibiting news articles. These results provide new insight into our understanding of the relationship between real-Time situation and past occurrence with news articles.

Deforestation is still a major nature phenomenon in our society. For assessing deforestation effect, satellites remote sensing provides a fundamental data for observation. While new remote-sensing technologies are able to represent high-resolution forest mapping, the application is still limited only for detecting and mapping the deforestation area. In this paper, we proposed a new method for automatically extract features of Satellite Multispectral images for interpreting deforestation effect in the context of soil degradation. We proposed an idea to interpret reflected "substances (material)" of bare soil in deforested area in spectrum domain into human language. The objectives of this paper are to (1) recognize the deforestation activity automatically. (2) Identify deforestation causes and examines the deforestation effect based on deforestation causes. (3) Scrutinize deforestation effects on soil degradation. (4) Representing nature knowledge of deforestation effect in human language using semantic computing, to bring the clear, comprehensible knowledge even for people who are not familiar with forestry. As for the experimental study, Riau Tropical Forest has been selected as the study area, where the multispectral data was acquired by using Landsat 8 Satellite between 2013 and 2014 Where forest fire and logging activities are reported and detected.

The multi-dimensional analysis is a promising approach to a new interpreting of environments by ground of the value-information and language-information on intellectual activities in various environment meanings to society. This paper presents a new analysis-system with semantic computing for environments in water-quality areas by integrating the fundamental important parameters of water-quality for creating the new meaning to society. The multi-water-parameter-analysis in a multi-dimensional space is important for current research issues in some water-quality research fields, which are based on the values and meanings of each parameter for obtaining the meaningful words in the category of agriculture, aquatic life, fish, drinking, industrial and irrigation. The multi-dimensional semantic space is significantly utilized for various interpretations related to the water-quality.

In the design of multimedia data mining systems, one of the most important issues is how to search and analyze media data, according to contexts. We have introduced a semantic associative search method based on our "Mathematical Model of Meaning (MMM) [1, 2, 3]". This model is applied to compute semantic correlations between keywords, images, music and documents dynamically in a context-dependent way. We have constructed " A Multimedia Data Mining System for International and Collaborative Research in Global Environmental Analysis," as a new platform of a multimedia data mining environment between our research team and international organizations. This environment is constructed by creating the following subsystems: (1) Multimedia Data Mining System with semantic associative-search functions and (2) 5D Space Sharing and Collaboration System for cooperative creation and manipulation of multimedia objects. It is very important to memorize those situations and compute environment change in various aspects and contexts, in order to discover what are 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 [4, 5, 6]. We utilize a multi-dimensional computing model, the Mathematical Model of Meaning (MMM), and a multi-dimensional space filtering method with, 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 visualize the highlighted factors using our Multi-dimensional World Map. Semantic computing is an important and promising approach to semantic analysis for various environmental phenomena and changes in real world. This paper presents a new semantic computing method with multi-spectral images for analyzing and interpreting environmental phenomena and changes occurring in the physical world. We have presented a concept of "Semantic Computing System"for realizing global environmental analysis. This paper presents a new semantic computing method to realize semantic associative search for the multiple-colours-spectral images in the multi-dimensional semantic space, that is "multi-spectral semanticimage space"consisting of (a) Infra-Red filtered axis, (b) Red axis, (c) Green filtered axis, (d) Blue filtered axis, (e) NDVI axis, and (f) NDWI axis, with semantic projection functions. This space is created for dynamically computing semantic equivalence, similarity and difference between multi-spectral images and environmental situations. The most essential and significant point of our "multispectral-semantic computing method" is that it realizes "the interpretation of substances (materials)" appearing and reflected in the multi-spectrum images by using "6-dimensional multi-spectral semantic-image space" and "semantic projection functions". That is, this method interprets the substances appearing in the image into "the names of substances" by using "knowledge of substances" expressed in this semantic-image space. This is corresponding to the human-level interpretation when we look at an image and recognize the substances appearing in the image. This method realizes this human-level interpretation with "multi-spectral semantic-image space" and "semantic projection functions". We apply this system to global environmental analysis as a new platform of environmental computing. We have already presented the 5D World Map System, as an international research environment with spatio-temporal and semantic analysers. We also present several new approaches to global environmental-analysis for multi-spectrum images in "multi-spectral semantic-image space."

Human-microbiome-relations extraction is important for analyzing the effects on human gut microbiome from the difference of human attributes such as country, sex, age and so on. Human gut microbiome, a set of bacteria, provides various pathological and biological impacts on a hosting human body system. This paper presents a new analytical method for data resources that are difficult to understand such as human gut microbiome, by extracting the unknown relations with other adjunct metadata (e.g. human attributes data) with context-dependent clustering and semantic analysis. This method realizes the significant bacterial components acquisition for categorizing human attributes. The most important feature of our method is to analyze the unknown relations of human-microbiome with or without a correlation between a human attribute and bacteria that is found by related studies in bacteriology. With this method, an analyst is able to grasp the overview of bacteria data clustered by several clustering algorithms (k-means clustering / hierarchical clustering) using bacteria data selected by human attributes as a set of context. In addition, even without an association between a human attribute and bacteria as heuristic knowledge, an analyst is able to extract human-microbiome-relations focusing on a number of bacteria selected from all bacteria combinations by one-way analysis of variance (ANOVA) and our original criteria called the "degree of separation" of clustering. This paper also presents an experimental study about human-microbiome-relations extraction and the experimental results that show the feasibility and effectiveness of this method.

Multispectral image becomes widely used for environmental analysis to detect an object or phenomena that human eyes cannot capture. One of the main type of images acquired by remote sensing such as satellite or aircraft for earth observation. This paper presents a multispectral analysis for aerial images that captured by dual cameras (visible and infrared camera), which are mounted on an unmanned autonomous vehicle (UAV) or Drone. In our experiments, four spectral bands (three visible and one infrared band) were imaged, processed and analyzed to detect agricultural area and measure the health of vegetation. To interpret environmental phenomena and realize an environmental analysis, this study applies semantic analysis by creating a multispectral semantic image space, combined with three numerical indicators (the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI) and the soil adjusted vegetation index (SAVI)) that can be used to analyze plant health, photosynthetic activity and detect environmental object to determine an agricultural area. This paper also proposed the concept of multi-spectrum semantic-image space for agricultural monitoring by defining the correlation meaning from multi-dimensional parameters which related to agricultural analysis to realize and explain agriculture conditions. This paper presents the experimental study on a rice field, a cornfield, a salt farm and a coconut farm in Thailand.

This research proposes the multispectral image retrieval method by using spectral feature and semantic computing which is not many studies have focused. The main contributions are to enhance the effectiveness and advantageous of global environmental analysis system and realize semantic associative search and analysis. In this work, we study multispectral image retrieval using spectral feature computed in multispectral semanticimage space. The multispectral semantic-image space is supposing to realize the interpretation of substance (materials) on earth surface which can be provided the analyzed results as human-level interpretation. Our essential approach is utilizing the semantic computing to measure the similarity between multispectral image and the meaningful keywords which according to the user's contexts. Our research results found that this method possible to acquire the spectral feature from the multispectral image and could be used in multispectral image retrieval. In this study, a multispectral image is used as the image query according to user's query contexts. Moreover, the method performance of UAV-based multispectral aerial image retrieval using spectral feature and semantic computing is measured based on the queries with three contexts of multispectral image which is indicated by previous study on agricultural monitoring system and semantic interpretation model.

Human-microbiome-relations extraction is important for analyzing the effects on human gut microbiome from the difference of human attributes such as country, sex, age and so on. Human gut microbiome, a set of bacteria, provides various pathological and biological impacts on a hosting human body system. This paper presents a new analytical method for data resources that are difficult to understand such as human gut microbiome, by extracting the unknown relations with other adjunct metadata (e.g. human attributes data) with context-dependent clustering and semantic analysis. This method realizes the significant bacterial components acquisition for categorizing human attributes. The most important feature of our method is to analyze the unknown relations of human-microbiome with or without a correlation between a human attribute and bacteria that is found by related studies in bacteriology. With this method, an analyst is able to grasp the overview of bacteria data clustered by several clustering algorithms (k-means clustering / hierarchical clustering) using bacteria data selected by human attributes as a set of context. In addition, even without an association between a human attribute and bacteria as heuristic knowledge, an analyst is able to extract human-microbiome-relations focusing on a number of bacteria selected from all bacteria combinations by one-way analysis of variance (ANOVA) and our original criteria called the "degree of separation" of clustering. This paper also presents an experimental study about human-microbiome-relations extraction and the experimental results that show the feasibility and effectiveness of this method.

In the design of multimedia data mining systems, one of the most important issues is how to search and analyze media data, according to contexts. We have introduced a semantic associative search method based on our "Mathematical Model of Meaning (MMM) [1, 2, 3]". This model is applied to compute semantic correlations between keywords, images, music and documents dynamically in a context-dependent way. We have constructed " A Multimedia Data Mining System for International and Collaborative Research in Global Environmental Analysis," as a new platform of a multimedia data mining environment between our research team and international organizations. This environment is constructed by creating the following subsystems: (1) Multimedia Data Mining System with semantic associative-search functions and (2) 5D Space Sharing and Collaboration System for cooperative creation and manipulation of multimedia objects. It is very important to memorize those situations and compute environment change in various aspects and contexts, in order to discover what are 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 [4, 5, 6]. We utilize a multi-dimensional computing model, the Mathematical Model of Meaning (MMM), and a multi-dimensional space filtering method with, 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 visualize the highlighted factors using our Multi-dimensional World Map. Semantic computing is an important and promising approach to semantic analysis for various environmental phenomena and changes in real world. This paper presents a new semantic computing method with multi-spectral images for analyzing and interpreting environmental phenomena and changes occurring in the physical world. We have presented a concept of "Semantic Computing System"for realizing global environmental analysis. This paper presents a new semantic computing method to realize semantic associative search for the multiple-colours-spectral images in the multi-dimensional semantic space, that is "multi-spectral semanticimage space"consisting of (a) Infra-Red filtered axis, (b) Red axis, (c) Green filtered axis, (d) Blue filtered axis, (e) NDVI axis, and (f) NDWI axis, with semantic projection functions. This space is created for dynamically computing semantic equivalence, similarity and difference between multi-spectral images and environmental situations. The most essential and significant point of our "multispectral-semantic computing method" is that it realizes "the interpretation of substances (materials)" appearing and reflected in the multi-spectrum images by using "6-dimensional multi-spectral semantic-image space" and "semantic projection functions". That is, this method interprets the substances appearing in the image into "the names of substances" by using "knowledge of substances" expressed in this semantic-image space. This is corresponding to the human-level interpretation when we look at an image and recognize the substances appearing in the image. This method realizes this human-level interpretation with "multi-spectral semantic-image space" and "semantic projection functions". We apply this system to global environmental analysis as a new platform of environmental computing. We have already presented the 5D World Map System, as an international research environment with spatio-temporal and semantic analysers. We also present several new approaches to global environmental-analysis for multi-spectrum images in "multi-spectral semantic-image space."

The multi-dimensional analysis is a promising approach to a new interpreting of environments by ground of the value-information and language-information on intellectual activities in various environment meanings to society. This paper presents a new analysis-system with semantic computing for environments in water-quality areas by integrating the fundamental important parameters of water-quality for creating the new meaning to society. The multi-water-parameter-analysis in a multi-dimensional space is important for current research issues in some water-quality research fields, which are based on the values and meanings of each parameter for obtaining the meaningful words in the category of agriculture, aquatic life, fish, drinking, industrial and irrigation. The multi-dimensional semantic space is significantly utilized for various interpretations related to the water-quality.