145 research outputs found
A Method for Generating Random Vibration Using Acceleration Kurtosis and Velocity Kurtosis
Random vibration tests for packaging are conducted to confirm safety during shipping by truck. However, there is a difference between the traditional random vibration tests and the real vibrations on the truck bed. One reason for this difference is the shock caused by road roughness. Hence, many studies have been conducted to improve random vibration testing. In these studies, the root mean square, power spectral density, kurtosis, and probability density of acceleration are considered. In this study, we show that the kurtosis and probability density of velocity are also important factors for such tests and propose a new method for generating vibrations with arbitrary kurtosis of acceleration and velocity. By bringing the kurtosis and probability density of velocity closer to those of real vibration, it is possible to conduct more accurate vibration tests
Experimental verification on the effectiveness of random vibration testing with controlling acceleration and velocity kurtosis
Random vibration tests for packages are conducted to confirm the safety of these packages during shipping. In our previous study, a method of generating the random vibration controlling the power spectral density and kurtosis of acceleration and the kurtosis of velocity was proposed. The aim of the present study is to verify the effectiveness of the proposed method. Three vibrations were generated in this study and compared with the real vibration, which replicates the truck bed. In the first case, control of the acceleration and velocity kurtosis was neglected. In the second case, the vibration controlling the acceleration kurtosis was considered. The third case corresponded to the vibration controlling both the acceleration and velocity kurtosis generated by the proposed method. In the present study, an aluminum plate simulating the product was fixed to a table for evaluating the vibrations. The natural frequency of the plate was varied by varying the mass of the weight placed on the plate. The relative displacement of the plate was calculated from the difference between the readings of two laser displacement meters. The vibrations were evaluated via the root mean square, kurtosis, and skewness of the relative displacements of the plate. Kurtosis and skewness of the relative displacement of the proposed method were similar to those of the real vibration. However, the kurtosis and skewness of the other generated vibrations were far from those of the real vibration. Results provided experimental verification that the kurtosis of velocity is an important factor for random vibration tests
岩塩型ネオジム単酸化物エピタキシャル薄膜とヘテロ構造における遍歴強磁性の研究
要約のみTohoku University福村知昭課
Polos: Multimodal Metric Learning from Human Feedback for Image Captioning
Establishing an automatic evaluation metric that closely aligns with human
judgments is essential for effectively developing image captioning models.
Recent data-driven metrics have demonstrated a stronger correlation with human
judgments than classic metrics such as CIDEr; however they lack sufficient
capabilities to handle hallucinations and generalize across diverse images and
texts partially because they compute scalar similarities merely using
embeddings learned from tasks unrelated to image captioning evaluation. In this
study, we propose Polos, a supervised automatic evaluation metric for image
captioning models. Polos computes scores from multimodal inputs, using a
parallel feature extraction mechanism that leverages embeddings trained through
large-scale contrastive learning. To train Polos, we introduce Multimodal
Metric Learning from Human Feedback (MLHF), a framework for developing
metrics based on human feedback. We constructed the Polaris dataset, which
comprises 131K human judgments from 550 evaluators, which is approximately ten
times larger than standard datasets. Our approach achieved state-of-the-art
performance on Composite, Flickr8K-Expert, Flickr8K-CF, PASCAL-50S, FOIL, and
the Polaris dataset, thereby demonstrating its effectiveness and robustness.Comment: CVPR 202
Object affordance as a guide for grasp-type recognition
Recognizing human grasping strategies is an important factor in robot
teaching as these strategies contain the implicit knowledge necessary to
perform a series of manipulations smoothly. This study analyzed the effects of
object affordance-a prior distribution of grasp types for each object-on
convolutional neural network (CNN)-based grasp-type recognition. To this end,
we created datasets of first-person grasping-hand images labeled with grasp
types and object names, and tested a recognition pipeline leveraging object
affordance. We evaluated scenarios with real and illusory objects to be
grasped, to consider a teaching condition in mixed reality where the lack of
visual object information can make the CNN recognition challenging. The results
show that object affordance guided the CNN in both scenarios, increasing the
accuracy by 1) excluding unlikely grasp types from the candidates and 2)
enhancing likely grasp types. In addition, the "enhancing effect" was more
pronounced with high degrees of grasp-type heterogeneity. These results
indicate the effectiveness of object affordance for guiding grasp-type
recognition in robot teaching applications.Comment: 12 pages, 11 figures. Last updated February 27th, 202
Constraint-aware Policy for Compliant Manipulation
Robot manipulation in a physically-constrained environment requires compliant
manipulation. Compliant manipulation is a manipulation skill to adjust hand
motion based on the force imposed by the environment. Recently, reinforcement
learning (RL) has been applied to solve household operations involving
compliant manipulation. However, previous RL methods have primarily focused on
designing a policy for a specific operation that limits their applicability and
requires separate training for every new operation. We propose a
constraint-aware policy that is applicable to various unseen manipulations by
grouping several manipulations together based on the type of physical
constraint involved. The type of physical constraint determines the
characteristic of the imposed force direction; thus, a generalized policy is
trained in the environment and reward designed on the basis of this
characteristic. This paper focuses on two types of physical constraints:
prismatic and revolute joints. Experiments demonstrated that the same policy
could successfully execute various compliant-manipulation operations, both in
the simulation and reality. We believe this study is the first step toward
realizing a generalized household-robot
MT neurons in the macaque exhibited two types of bimodal direction tuning as predicted by a model for visual motion detection
AbstractWe previously proposed a model for detecting local image velocity on the magnocellular visual pathway (Kawakami & Okamoto (1996) Vision Research, 36, 117–147). The model detects visual motion in two stages using the hierarchical network that includes component and pattern cells in area MT. To validate the model, we predicted two types of bimodal direction tuning for MT neurons. The first type is characteristic of component cells. The tuning is bimodal when stimulated with high-speed spots, but unimodal for low-speed spots or for bars. The interval between the two peaks widens as the spot’s speed increases. The second type is characteristic of pattern cells. The tuning is bimodal when stimulated with low-speed bars, but unimodal for high-speed bars or for spots. The interval widens as the bar’s speed decreases. To confirm this prediction, we studied the change of direction tuning curves for moving spots and bars in area MT of macaque monkeys. Out of 35 neurons measured at various speeds, six component cells and four pattern cells revealed the predicted bimodal tunings. This result provided neurophysiological support for the validity of the model. We believe ours is the first systematic study that records the two types of bimodality in MT neurons
Thioredoxin-1 maintains mechanistic target of rapamycin (mTOR) function during oxidative stress in cardiomyocytes
Thioredoxin 1 (Trx1) is a 12-kDa oxidoreductase that catalyzes thiol-disulfide exchange reactions to reduce proteins with disulfide bonds. As such, Trx1 helps protect the heart against stresses, such as ischemia and pressure overload. Mechanistic target of rapamycin (mTOR) is a serine/threonine kinase that regulates cell growth, metabolism, and survival. We have shown previously that mTOR activity is increased in response to myocardial ischemia-reperfusion injury. However, whether Trx1 interacts with mTOR to preserve heart function remains unknown. Using a substrate-trapping mutant of Trx1 (Trx1C35S), we show here that mTOR is a direct interacting partner of Trx1 in the heart. In response to H2O2 treatment in cardiomyocytes, mTOR exhibited a high molecular weight shift in non-reducing SDS-PAGE in a 2-mercaptoethanol-sensitive manner, suggesting that mTOR is oxidized and forms disulfide bonds with itself or other proteins. The mTOR oxidation was accompanied by reduced phosphorylation of endogenous substrates, such as S6 kinase (S6K) and 4E-binding protein 1 (4E-BP1) in cardiomyocytes. Immune complex kinase assays disclosed that H2O2 treatment diminished mTOR kinase activity, indicating that mTOR is inhibited by oxidation. Of note, Trx1 overexpression attenuated both H2O2-mediated mTOR oxidation and inhibition, whereas Trx1 knockdown increased mTOR oxidation and inhibition. Moreover, Trx1 normalized H2O2-induced down-regulation of metabolic genes and stimulation of cell death, and an mTOR inhibitor abolished Trx1-mediated rescue of gene expression. H2O2-induced oxidation and inhibition of mTOR were attenuated when Cys-1483 of mTOR was mutated to phenylalanine. These results suggest that Trx1 protects cardiomyocytes against stress by reducing mTOR at Cys-1483, thereby preserving the activity of mTOR and inhibiting cell death
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