Conventionally when diagnosing diabetes, experts diagnose using infrared spectroscopy of blood in hospital. Recently, however Blood testing apparatus that are easily available at home are attracting a lot of attention as a preventive medicine. To realize this, it must be easy to measure by an ordinary person at any time. Therefore, I aim to develop a system to analyze infrared spectroscopy acquired from blood by machine learning. Neural networks are also used for pattern recognition problems such as biological signals. However, to discriminate the infrared spectrum, the input becomes high-dimensional, and in a device such as those used at home, large errors are included in the data, so the accuracy drops remarkably. In this study, as a basic study of the analysis of infrared spectroscopy by machine learning, I experimented to extract characteristic peaks from artificially generated data by a new machine learning. In the proposed machine learning, learning is performed by an error back propagation using a correlation coefficient for the error function to extract a waveform more than the value magnitude as a feature value. In this paper, I show the result of the experiment of extracting feature value from artificial data simulating infrared spectroscopy.

The goal of this research is to construct conversational robots that can stimulate users’ motivation to talk with them in non-task-oriented dialogue, where it is required to keep up the dialogue. The non-task-oriented dialogue involves exchanging subjective opinions between speakers. This paper aims at investigating how the user’s dialogue motivation is influenced by the attribution of opinions to the conversational android. We examined the influence by testing various kinds of the android’s opinions in a questionnaire survey. As the result, it is clarified that not only the users’ interest in the android’s opinions but the attribution of the subjective opinions to the android influence their motivation for dialogue. This result suggests that there is a problem when the conversational robot makes the utterances based on human-human dialogue database that includes the opinions which are hardly attributed to it. In a design of conversational robot, it is necessary to take account of whether users can attribute the subjective opinions included in the dialogue contents to the robot in order to promote their motivation of dialogue.

Ontological considerations about part-of relations have been extensively investigated because they are basic and important relationships for ontology building. Although there are various discussions on kinds of part-of and their ontological characteristics, there remains some room for discussing a couple of fundamental issues such as “What is a part?” and “When is a part-of relation composed?” This paper discusses ontology patterns of descriptions of part-of relationships on the basis of ontological theories in order to provide practitioners with useful guidelines for descriptions of part-of structurers. This paper focuses on ontology patterns which capture commonality and special characteristics of parts so that complicated structures of physical objects are described appropriately. We discuss four problems related to descriptions of parts. 1) interdependence between the whole and its parts, 2) kinds of parts such as components, portions and materials, 3) multiple inheritance according to substance and properties of parts, 4) the commonality and specificity of parts. To cope with these problems, this paper introduces a part representation model based on ontological theory of roles. The main idea of the part representation model is to distinguish between a part dependent on its whole and the context-independent properties of the part. The former is defined as the role-holder which plays roles and the latter is defined as the player of the role. The role defines properties of the part which is dependent on its whole. These three kinds of definitions enable to describe differences of various properties of parts according to their context dependence. We show how this model is used to describe various parts through practical examples of the anatomical structure of human body developed in the medical ontology project in Japan.

We propose a novel driving policy for self-driving vehicles to reduce traffic jams. Although the driving policy in previous research was empirically designed according to a given traffic situation, which meant that the driving policy needed to be reconfigured for every traffic situation and every change in traffic, we proposed the driving policy that is learned by a learner agent that learns the driving policy through reinforcement learning using data collected on the self-driving vehicles in simulation. The driving policy is relayed to the smart vehicles, which in turn, are directed by the driving policy, we conducted traffic flow simulations with manually driven vehicle and self-driving vehicles in several scenarios where the two key parameters, vehicle density and self-driving vehicle penetration rate, are assigned different values. Our findings show that a driving policy for self-driving vehicles does reduce traffic jams in such conditions as (1) when the vehicle density is 42 vehicles/km and the penetration of the self-driving vehicle is 10% of the total traffic, and (2) when the vehicle density is 50 vehicles/km and the penetration of the self-driving vehicle is 70% of the total traffic (at which point traffic flow is nearly optimized). In addition, we found that intervehicle communication among self-driving vehicles provides real-time information that reduce traffic jam even more effectively.