2,605 research outputs found
Noh Creation of Shakespeare
This article contains select comments and reviews on Noh Hamlet and Noh Othello in English and Noh King Lear in Japanese. The scripts from these performances were arranged based on Shakespeare’s originals and directed on stage and performed in English by Kuniyoshi Munakata from the early 1980s until 2014. Also, the whole text of Munakata’s Noh Macbeth in English (Munakata himself acted as Macbeth and Lady Macbeth in one play) is for the first time publicized. The writers of the comments and reviews include notable people such as John Fraser, Michael Barrett, Upton Murakami, Donald Richie, Rick Ansorg, James David Audlin, Jesper Keller, Jean-Claude Saint-Marc, Jean-Claude Baumier, Judy Kendall, Allan Owen, Yoshio ARAI, Yasumasa OKAMOTO, Tatsuhiko TAIRA, Hikaru ENDO, Kazumi YAMAGATA, Hanako ENDO, Yoshiko KAWACHI, Mari Boyd, and Daniel Gallimore
Global Dynamics: a new concept for design of dynamical behavior
The global dynamics, a novel concept for design of human/humanoid behavior is proposed. The principle of this concept is to exploit the body dynamics and apply control input only where it is necessary.
Within the phase space of the body dynamics, there are many stable and unstable mani-folds coexist. Then if we analysed its structure and obtained a map in sufficient resolution, it may be possible to realise a motion by exploiting stable regions for reducing control input and unstable regions for switching between stable regions.
Also, we expect an emergence of symbols within the dynamics, as the series of points where control input should be adopted. This feature realises higher level description and makes adaptation behavior easier. We are studying from two aspects, the motion capture experiment and dynamical simulation of simple elastic robot. The former supports that above assumption and the latter supports the exploiting the dynamical stability is useful
Field Telemetry of Blade-rotor Coupled Torsional Vibration at Matuura Power Station Number 1 Unit
The quasi-modal reduction technique and finite element model (FEM) were used to construct an analytical model for the blade-rotor coupled torsional vibration of a steam turbine generator of the Matuura Power Station. A single rotor test was executed in order to evaluate umbrella vibration characteristics. Based on the single rotor test results and the quasi-modal procedure, the total rotor system was analyzed to predict coupled torsional frequencies. Finally, field measurement of the vibration of the last stage buckets was made, which confirmed that the double synchronous resonance was 124.2 Hz, meaning that the machine can be safely operated. The measured eigen values are very close to the predicted value. The single rotor test and this analytical procedure thus proved to be a valid technique to estimate coupled torsional vibration
Dual pathway spindle assembly increases both the speed and the fidelity of mitosis
Roughly half of all animal somatic cell spindles assemble by the classical prophase pathway, in which the centrosomes separate ahead of nuclear envelope breakdown (NEBD). The remainder assemble by the prometaphase pathway, in which the centrosomes separate following NEBD. Why cells use dual pathway spindle assembly is unclear. Here, by examining the timing of NEBD relative to the onset of Eg5-mEGFP loading to centrosomes, we show that a time window of 9.2 ± 2.9 min is available for Eg5-driven prophase centrosome separation ahead of NEBD, and that those cells that succeed in separating their centrosomes within this window subsequently show .3-fold fewer chromosome segregation errors and a somewhat faster mitosis. A longer time window would allow more cells to complete prophase centrosome separation and further reduce segregation errors, but at the expense of a slower mitosis. Our data reveal dual pathway mitosis in a new light, as a substantive strategy that increases both the speed and the fidelity of mitosis
Japanese Management in Singapore(<Partly Special Issue>Japanese Management in Southeast Asia)
この論文は国立情報学研究所の学術雑誌公開支援事業により電子化されました
Nutrient-mediated interaction between above- and below-ground herbivores of poinsettias
Understanding the relationship between above and below ground herbivores and how this interaction is mediated by nutrition can lead to the development of more efficient insect management methods. We evaluated the effect of two nitrogen levels on the interaction between darkwinged fungus gnats (DWFG), Bradysia difformis, and silverleaf whitefly (SLWF), Bemisia tabaci biotype B, on poinsettias and their combined effect on plant performance. Plants were infested three days after transplant with whiteflies, and ten days after transplant with fungus gnats. The effects on SLWF were measured by evaluating developmental time and F1 oviposition. Pupae numbers remaining in the soil and adult emergence were measured for DWFG. Herbivore impact on plant performance was evaluated by measuring total plant dry weight and photosynthesis. Our results show that SLWF was positively impacted by the presence of DWFG as SLWF F1 oviposition was higher when DWFG were present. This relationship was mediated by nutrition as we found stronger effects when the plants were grown at the high nitrogen level (150 mg/L). Conversely, DWFG were negatively impacted by SLWF presence. There were fewer DWFG pupae found in the soil when SLWF were present. However, DWFG emergence was not influenced by SLWF presence. Overall, our data supports the hypothesis of competition effects between SLWF and DWFG. SLWF benefited from the competition while DWFG were negatively impacted. The presence of both herbivores affected total dry weight of poinsettias, especially when they were grown at the low nitrogen level (50 mg/L) but photosynthesis rate was not affected
Emergent Spontaneous Movements Based on Embodiment: Toward a General Principle for Early Development
We investigate whether spontaneous movements, which initiate and guide early development in animals, can be accounted for by the properties underlying embodiment. We constructed computer and robotic models of several biological species with biologically plausible musculoskeletal bodies and nervous systems, and extracted the embodied and motor networks based on inter-muscle connectivities. In computer simulations and robot experiments, we found that the embodied and motor networks had similar global and local topologies, suggesting the key role of embodiment in generating spontaneous movements in animals
Can Transformers Predict Vibrations?
Highly accurate time-series vibration prediction is an important research
issue for electric vehicles (EVs). EVs often experience vibrations when driving
on rough terrains, known as torsional resonance. This resonance, caused by the
interaction between motor and tire vibrations, puts excessive loads on the
vehicle's drive shaft. However, current damping technologies only detect
resonance after the vibration amplitude of the drive shaft torque reaches a
certain threshold, leading to significant loads on the shaft at the time of
detection. In this study, we propose a novel approach to address this issue by
introducing Resoformer, a transformer-based model for predicting torsional
resonance. Resoformer utilizes time-series of the motor rotation speed as input
and predicts the amplitude of torsional vibration at a specified quantile
occurring in the shaft after the input series. By calculating the attention
between recursive and convolutional features extracted from the measured data
points, Resoformer improves the accuracy of vibration forecasting. To evaluate
the model, we use a vibration dataset called VIBES (Dataset for Forecasting
Vibration Transition in EVs), consisting of 2,600 simulator-generated vibration
sequences. Our experiments, conducted on strong baselines built on the VIBES
dataset, demonstrate that Resoformer achieves state-of-the-art results. In
conclusion, our study answers the question "Can Transformers Forecast
Vibrations?" While traditional transformer architectures show low performance
in forecasting torsional resonance waves, our findings indicate that combining
recurrent neural network and temporal convolutional network using the
transformer architecture improves the accuracy of long-term vibration
forecasting
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