Preventive Maintenance of a Two-Unit Standby Redundant System with a Good State

A preventive maintenance policy is proposed for a two-unit standby redundant system, each of which has good, degraded and failed states. The maen time to first system down is derived by the theory of Semi-Markov process. Further, the condition under which the policy is effective is obtained

PHASE DIFFERENCE BETWEEN FORWARD SWINGS OF

In previous observations of the lower extremity movement in long distance running, a seemingly ineffective action, was noticed. It was observed that the upper leg swung to its most forward angle once, then returned backward before the lower leg swung to its most forward angle, prior to the foot touching the floor (Kawai & Hiki, 1999). In this study, the relationship between the phase difference and the amount of work around the hip joint was investigated, by using a computer simulation, in order to determine the meaning of the phase difference. From the results, it was reasoned that the upper leg swung forward in the advanced phase before the lower leg. This was in order to decelerate the mass of the lower leg smoothly, by raising the knee and to minimize the change of work for decelerating the whole leg before the foot touching the floor

CrossMap Transformer: A Crossmodal Masked Path Transformer Using Double Back-Translation for Vision-and-Language Navigation

Navigation guided by natural language instructions is particularly suitable for Domestic Service Robots that interacts naturally with users. This task involves the prediction of a sequence of actions that leads to a specified destination given a natural language navigation instruction. The task thus requires the understanding of instructions, such as ``Walk out of the bathroom and wait on the stairs that are on the right''. The Visual and Language Navigation remains challenging, notably because it requires the exploration of the environment and at the accurate following of a path specified by the instructions to model the relationship between language and vision. To address this, we propose the CrossMap Transformer network, which encodes the linguistic and visual features to sequentially generate a path. The CrossMap transformer is tied to a Transformer-based speaker that generates navigation instructions. The two networks share common latent features, for mutual enhancement through a double back translation model: Generated paths are translated into instructions while generated instructions are translated into path The experimental results show the benefits of our approach in terms of instruction understanding and instruction generation.Comment: 8 pages, 5 figures, 5 tables. Submitted to IEEE Robotics and Automation Letter

Transducer-based language embedding for spoken language identification

The acoustic and linguistic features are important cues for the spoken language identification (LID) task. Recent advanced LID systems mainly use acoustic features that lack the usage of explicit linguistic feature encoding. In this paper, we propose a novel transducer-based language embedding approach for LID tasks by integrating an RNN transducer model into a language embedding framework. Benefiting from the advantages of the RNN transducer's linguistic representation capability, the proposed method can exploit both phonetically-aware acoustic features and explicit linguistic features for LID tasks. Experiments were carried out on the large-scale multilingual LibriSpeech and VoxLingua107 datasets. Experimental results showed the proposed method significantly improves the performance on LID tasks with 12% to 59% and 16% to 24% relative improvement on in-domain and cross-domain datasets, respectively.Comment: This paper was submitted to Interspeech 202

Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR

Due to the modality discrepancy between textual and acoustic modeling, efficiently transferring linguistic knowledge from a pretrained language model (PLM) to acoustic encoding for automatic speech recognition (ASR) still remains a challenging task. In this study, we propose a cross-modality knowledge transfer (CMKT) learning framework in a temporal connectionist temporal classification (CTC) based ASR system where hierarchical acoustic alignments with the linguistic representation are applied. Additionally, we propose the use of Sinkhorn attention in cross-modality alignment process, where the transformer attention is a special case of this Sinkhorn attention process. The CMKT learning is supposed to compel the acoustic encoder to encode rich linguistic knowledge for ASR. On the AISHELL-1 dataset, with CTC greedy decoding for inference (without using any language model), we achieved state-of-the-art performance with 3.64% and 3.94% character error rates (CERs) for the development and test sets, which corresponding to relative improvements of 34.18% and 34.88% compared to the baseline CTC-ASR system, respectively.Comment: Submitted to ICASSP 202