A folksonomy is a classification system in which volunteers collaboratively create and manage tags to annotate and categorize content. The folksonomy has several problems in retrieving music using tags, including problems related to synonyms, different tagging levels, and neologisms. To solve the problem posed by synonyms, we introduced a mood vector with 12 possible moods, each represented by a numeric value, as an internal tag. This allows moods in music pieces and mood tags to be represented internally by numeric values, which can be used to retrieve music pieces. To determine the mood vector of a music piece, 12 regressors predicting the possibility of each mood based on acoustic features were built using Support Vector Regression. To map a tag to its mood vector, the relationship between moods in a piece of music and mood tags was investigated based on tagging data retrieved from Last.fm, a website that allows users to search for and stream music. To evaluate retrieval performance, music pieces on Last.fm annotated with at least one mood tag were used as a test set. When calculating precision and recall, music pieces annotated with synonyms of a given query tag were treated as relevant. These experiments on a real-world data set illustrate the utility of the internal tagging of music. Our approach offers a practical solution to the problem caused by synonyms.

There have been two major streams for the motion control of mobile robots. The first is the model-based deliberate control and the second is the sensor-based reactive control. Since two schemes have complementary advantages and disadvantages, one cannot completely replace the other. There are a variety of environmental conditions which affect the navigation performances. The main idea of this paper is to design discrete navigation behaviors and to integrate behaviors by an appropriate selection framework. In this paper, we propose a behavior selection framework using the GSPN (Generalized Stochastic Petri Nets). We have designed two navigation behaviors which show completely different performances. In order to define behavior selection criteria, two kinds of navigation statuses are defined to monitor navigation performances of the robot. The proposed navigation strategy is simulated using the open source simulator Player/Stage to investigate the performances in a variety of conditions. Through the simulations, it was made clear that different behaviors show remarkably different performances. Moreover, the average navigation time of the proposed behavior selection framework is significantly decreased than that of any single navigation scheme DWA or tracking.

A mobile service robot should have the ability to carry out multiple practical tasks. However, most mobile mobile service robots have been designed to carry out a specific task. Hence it has been difficult to design a navigation framework for multiple navigation tasks. In this paper, we will design and implement a navigation framework for multiple tasks using the GSPN (Generalized Stochastic Petri-Nets). The GSPN-based navigation framework has several advantages, such as performance analysis and dynamic system analysis. Furthermore, such a design can process system events in realtime. Also, the typical problem of a hybrid control system, namely the synchronization and scheduling between reactive layer and high-level planner can be solved using the GSPNpsilas event monitoring ability using the timed transition and immediate transition. Finally, the idle time and activation time of the tasks can be predicted under the GSPN-based navigation framework. In this paper, firstly, the transportation task and patrol task are implemented. Then, a navigation framework is designed using the GPSN. The proposed GSPN-based navigation framework is verified in the human co-existing environments by experiments. The results showed that the proposed navigation scheme can be used practically in real environments.

In this paper, we present one approach to achieve safe navigation in indoor dynamic environment. So far, there have been various useful collision avoidance algorithms and path planning schemes. However, those algorithms have a fundamental limitation that the robot can avoid only "visible" obstacles. In real environment, it is not possible to detect all the dynamic obstacles around the robot. There exist a lot of "invisible" regions due to the limitation of field of view. In order to avoid possible collisions, it is desirable to consider visibility information. Then a robot can reduce the speed or modify a path. This paper proposes a safe navigation scheme to reduce the risk of collision due to unexpected dynamic obstacles. The robot's motion is controlled according to a hybrid control scheme. The possibility of collision is dually reflected to a path planning and a speed control. The proposed scheme clearly indicates the structural procedure how to model and to exploit the risk of navigation. The proposed scheme is experimentally tested in a real office building. The presented result shows that the robot moves along the safe path to obtain sufficient field of view, while appropriate speed control is carried out.