Experimental validation with field circuit by changing start voltage of salient pole synchronous motor

This paper reports on the experimental validation characteristics of a salient pole synchronous motor with the starting field circuit in contact. The starting characteristics were experimentally evaluated at voltages of 100%, 75%, and 50% using a 1.5 kVA salient pole synchronous motor. The signal for pulling into step was applied to the field circuit by using the control module for all test conditions. The results confirmed the proper pulling into step at 100% of voltage, and the results of the tests at 50% and 75% of voltage revealed that the pulling into step under these conditions becomes stable at the suitable slip condition.     

Robust comprehension of natural language instructions by a domestic service robot

We present a method through which domestic service robots can comprehend natural language instructions. For each action type, a variety of natural language expressions can be used, for example, the instruction, ‘Go to the kitchen’ can also be expressed as ‘Move to the kitchen.’ We are of the view that natural language instructions are intuitive and, therefore, constitute one of the most user-friendly robot instruction methods. In this paper, we propose a method that enables robots to comprehend instructions spoken by a human user in his/her natural language. The proposed method combines action-type classification, which is based on a support vector machine, and slot extraction, which is based on conditional random fields, both of which are required in order for a robot to execute an action. Further, by considering the co-occurrence relationship between the action type and the slots along with the speech recognition score, the proposed method can avoid degradation of the robot’s comprehension accuracy in noisy environments, where inaccurate speech recognition can be problematic. We conducted experiments using a Japanese instruction data-set collected using a questionnaire-based survey. Experimental results show that the robot’s comprehension accuracy is higher in a noisy environment using our method than when using a baseline method with only a 1-best speech recognition result.     

Terrain classification and identification of tree stems using ground‐based LiDAR

To operate autonomously in forested terrain, unmanned ground vehicles must be able to identify the load‐bearing surface of the terrain (i.e., the ground) and obstacles in the environment. To travel long distances, they must be able to track their position even when the forest canopy obstructs GPS signals, e.g., by tracking progress relative to tree stems. This paper presents a novel, robust approach for modeling the ground plane and tree stems in forests from a single viewpoint using a lightweight LiDAR scanner. Ground plane identification is implemented using a two‐stage approach. The first stage, a local height‐based filter, discards most nonground points. The second stage, based on a support vector machine classifier, identifies which of the remaining points belong to the ground. Main tree stems are modeled as cylinders or cones to estimate the diameter 130 cm above the ground plane. To fit these models, candidate main stem data are selected by finding points approximately 130 cm above the ground. These points are clustered into separate point clouds for each stem. Cylinders and cones are fit to each point cloud, and heuristic filters identify which fits correspond to tree stems. Experimental results from five forested environments demonstrate the effectiveness of this approach. For ground plane estimation, the overall classification accuracy was 86.28% with a mean error for the ground height of approximately 4.7 cm. For stem estimation, up to 50% of the main stems were accurately modeled using cones, with a root mean square diameter error of 13.2 cm.© 2012 Wiley Periodicals, Inc.     

Abstract picture generation and zooming user interface for intuitive music browsing

Today many people store music media files in personal computers or portable audio players, thanks to recent evolution of multimedia technologies. The more music media files these devices store, the messier it is to search for tunes that users want to listen to. We propose MusCat, a music browser to interactively search for the tunes according to features, not according to metadata (e.g. title, artist name). The technique firstly calculates features of tunes, and then hierarchically clusters the tunes according to the features. It then automatically generates abstract pictures, so that users can recognize characteristics of tunes more instantly and intuitionally. It finally visualizes the tunes by using abstract pictures. MusCat enables intuitive music selection with the zooming user interface.     

Data-efficient learning of robotic clothing assistance using Bayesian Gaussian process latent variable model

ABSTRACT

Motor-skill learning for complex robotic tasks is a challenging problem due to the high task variability. Robotic clothing assistance is one such challenging problem that can greatly improve the quality-of-life for the elderly and disabled. In this study, we propose a data-efficient representation to encode task-specific motor-skills of the robot using Bayesian nonparametric latent variable models. The effectivity of the proposed motor-skill representation is demonstrated in two ways: (1) through a real-time controller that can be used as a tool for learning from demonstration to impart novel skills to the robot and (2) by demonstrating that policy search reinforcement learning in such a task-specific latent space outperforms learning in the high-dimensional joint configuration space of the robot. We implement our proposed framework in a practical setting with a dual-arm robot performing clothing assistance tasks.     

Estimation of anisotropic properties of nano-structured arrays by modal vibration control at microscale

Nano-structured arrays are engineered to meet the requirements of a variety of applications such as microfilters, sensors, and structural interface due to their unique mechanical characteristics, which cannot be achieved by conventional solid materials. However, it is hard to evaluate the elastic properties of nano-structured arrays owing to the discrete structure, sample size, and availability of suitable techniques. To facilitate this, we develop an advanced three-dimensional microscale vibration testing process. In the test, a specially designed three-dimensional microspecimen with tuned mass is excited by a piezoelectric actuator, and the resonance frequencies are detected by a laser device successfully. The anisotropic elastic moduli of nano-structured array composed of helical nano-springs are identified from a single spectrum. This array shows so strong characteristic anisotropy that the solid one hardly can attain. The microscale testing technique can be extended to other materials and microstructures.     

Neuroprotection,Growth Factors and BDNF-TrkB Signalling in Retinal Degeneration

Neurotrophic factors play key roles in the development and survival of neurons. The potent neuroprotective effects of neurotrophic factors, including brain-derived neurotrophic factor (BDNF), ciliary neurotrophic factor (CNTF), glial cell-line derived neurotrophic factor (GDNF) and nerve growth factor (NGF), suggest that they are good therapeutic candidates for neurodegenerative diseases. Glaucoma is a neurodegenerative disease of the eye that causes irreversible blindness. It is characterized by damage to the optic nerve, usually due to high intraocular pressure (IOP), and progressive degeneration of retinal neurons called retinal ganglion cells (RGCs). Current therapy for glaucoma focuses on reduction of IOP, but neuroprotection may also be beneficial. BDNF is a powerful neuroprotective agent especially for RGCs. Exogenous application of BDNF to the retina and increased BDNF expression in retinal neurons using viral vector systems are both effective in protecting RGCs from damage. Furthermore, induction of BDNF expression by agents such as valproic acid has also been beneficial in promoting RGC survival. In this review, we discuss the therapeutic potential of neurotrophic factors in retinal diseases and focus on the differential roles of glial and neuronal TrkB in neuroprotection. We also discuss the role of neurotrophic factors in neuroregeneration.