This paper describes rubble recognition using a depth image sensor and an automatic rubble crushing system using a construction machine for automatic rubble crushing at a building demolition site. Depth Distribution Split Labeling (DDSL) is proposed to recognize irregularly shaped rubble using depth images and to identify the largest rubble in the workspace. In DDSL, we focused on the fact that the depth of the contact area of adjacent rubble is lower, and labeling by dividing the depth makes it possible to identify the rubble. The automatic rubble crushing system enables to avoid excessive pushing and to grasp objects based on the cylinder driving force calculated from the hydraulic cylinder internal pressure of the construction machine. Through evaluation experiments simulating the crushing environment, it was confirmed that the proposed system can identify and automatically crush rubble.

This paper describes a octo-rotor unmanned aerial vehicle (UAV) with one degree of freedom (DoF) manipulator for bridge inspection. The UAV has an ability to contact bridge pier to conduct hammering test. To control contact force applied by the UAV to the bridge pier, one DoF is enough for the manipulator implemented on the UAV. A preliminary experiment shows that the force is proportional to the pitch angle of the UAV when it contacts the bridge pier. A bridge pier contact experiment shows that our developed UAV can control contact force between its end-effector and the bridge pier; mean error of the contact force is 4.27 ± 3.64 [N] against 20[N] desired force.

This paper describes a magnetic resonance imaging (MRI) compatibility assessment of our lower-extremity motion simulator called LoMS which provides gait-like motion for a wearer within his/her lying posture during functional MRI (fMRI) imaging. We confirmed that the existence and the movement of LoMS do not decrease the fMRI image quality when the distance between LoMS and the head coil of MRI is practical distance (more than 400 mm). We also confirmed that LoMS can operate properly in MRI environment, which is to measure own joint angle avoiding the noise from fMRI imaging. Then we show that the brain activity related to gait motion can be imaged during gait-like motion.

This paper describes a novel motion simulator for the lower extremities of human in a magnetic resonance imaging (MRI) environment. This motion simulator provides a wearer with physical supports to move their lower extremities or physical constrains as well as floor reactive force on bottoms of their feet during gait-like motion in an MRI room so that brain activities could be simultaneously measured. An MRI is one of the most powerful tools to measure activities in any part of brain but a device attached on a subject is limited because the material used for the device should be nonmagnetic. This paper shows the compatibility of the motion simulator that consists of Mckibben-type pneumatic artificial muscles and nonmagnetic materials. Also this paper shows the performance of the floor-reactive-force generator mounted on the soles of the motion simulator.

We propose a new text mining system which extracts characteristic contents from given documents. We define Key semantics as characteristic sub-structures of syntactic dependencies in the given documents, and consider the following three tasks in this paper: 1)Key semantics extraction: extracting characteristic syntactic dependency structures not only as ordered trees but also as unordered trees and free trees, 2)Redundancy reduction: from the result of extraction, deleting redundant dependency structures such as sub-structures or equivalent structures of the others, and 3)Phrase/sentence reconstruction: generating a phrase or sentence in a natural language corresponding to the extracted structure.Our system is a combination of natural language processing techniques and tree mining techniques. The system consists of the following five units: 1) syntactic dependency analysis unit, 2) input filters, 3) characteristic ordered subtree extraction unit, 4) output filters, and 5) phrase/sentence reconstruction unit. Although ordered trees are extracted in the third unit, the overall behavior of the system can be switched into the extraction of ordered trees, unordered trees, or free trees depending on which of the input filters is/are applied in the second step. The output filters delete redundant trees from the extraction result for efficient knowledge discovery. Finally, phrases or sentences corresponding to the extracted subtrees are reconstructed by utilizing the input documents.We demonstrate the validity of our system by showing experimental results using real data collected at a help desk and TDT pilot corpus.

The paper describes an on-line manual page retrieval system activated by spoken queries for multimodal cellular phones. The system recognizes a user's naturally spoken queries by telephone LVCSR and searches an on-line manual with a retrieval module on a server. The user can view the retrieved data on the screen of the phone via Web access. The LVCSR module consists of a telephone acoustic model and an n-gram language model derived from a task query corpus. The adaptation method using the target manual is also presented. The retrieval module utilizes pairs of words with dependency relations and also distinguishes affirmative and negative expressions to improve precision. The proposed system gives 82.6% keyword recognition accuracy and 77.5% task achievement rate. The field trial of the system is now underway.