503 research outputs found
固体酸化物燃料電池の開発に向けた製造から発電までの全工程におけるセル発生応力の数値的および実験的評価法に関する研究
Tohoku University博士(工学)要約のみthesi
Multi-channel Sampling on Graphs and Its Relationship to Graph Filter Banks
In this paper, we consider multi-channel sampling (MCS) for graph signals. We
generally encounter full-band graph signals beyond the bandlimited one in many
applications, such as piecewise constant/smooth and union of bandlimited graph
signals. Full-band graph signals can be represented by a mixture of multiple
signals conforming to different generation models. This requires the analysis
of graph signals via multiple sampling systems, i.e., MCS, while existing
approaches only consider single-channel sampling. We develop a MCS framework
based on generalized sampling. We also present a sampling set selection (SSS)
method for the proposed MCS so that the graph signal is best recovered.
Furthermore, we reveal that existing graph filter banks can be viewed as a
special case of the proposed MCS. In signal recovery experiments, the proposed
method exhibits the effectiveness of recovery for full-band graph signals
Dynamic Sensor Placement Based on Graph Sampling Theory
In this paper, we consider a dynamic sensor placement problem where sensors
can move within a network over time. Sensor placement problem aims to select M
sensor positions from N candidates where M < N. Most existing methods assume
that sensors are static, i.e., they do not move, however, many mobile sensors
like drones, robots, and vehicles can change their positions over time.
Moreover, underlying measurement conditions could also be changed that are
difficult to cover the statically placed sensors. We tackle the problem by
allowing the sensors to change their positions in their neighbors on the
network. Based on a perspective of dictionary learning, we sequentially learn
the dictionary from a pool of observed signals on the network based on graph
sampling theory. Using the learned dictionary, we dynamically determine the
sensor positions such that the non-observed signals on the network can be best
recovered from the observations. Furthermore, sensor positions in each time
slot can be optimized in a decentralized manner to reduce the calculation cost.
In experiments, we validate the effectiveness of the proposed method via the
mean squared error (MSE) of the reconstructed signals. The proposed dynamic
sensor placement outperforms the existing static ones both in synthetic and
real data
Graph Signal Sampling Under Stochastic Priors
We propose a generalized sampling framework for stochastic graph signals.
Stochastic graph signals are characterized by graph wide sense stationarity
(GWSS) which is an extension of wide sense stationarity (WSS) for standard
time-domain signals. In this paper, graph signals are assumed to satisfy the
GWSS conditions and we study their sampling as well as recovery procedures. In
generalized sampling, a correction filter is inserted between sampling and
reconstruction operators to compensate for non-ideal measurements. We propose a
design method for the correction filters to reduce the mean-squared error (MSE)
between original and reconstructed graph signals. We derive the correction
filters for two cases: The reconstruction filter is arbitrarily chosen or
predefined. The proposed framework allows for arbitrary sampling methods, i.e.,
sampling in the vertex or graph frequency domain. We also show that the graph
spectral response of the resulting correction filter parallels that for
generalized sampling for WSS signals if sampling is performed in the graph
frequency domain. Furthermore, we reveal the theoretical connection between the
proposed and existing correction filters. The effectiveness of our approach is
validated via experiments by comparing its MSE with existing approaches
Lumbar motor control & perceptual awareness
Purpose : The purpose of this study was to clarify the differences in lumbar spine and hip joint motor control ability (MCA) in prone hip extension (PHE) between individuals with and without low back pain (LBP). It also aimed to determine the relationship between lumbar spine and hip joint MCA and lumbar perceptual awareness in individuals with LBP. Methods : In total, 78 university students (20 with LBP and 58 without) were included in the study. The MCA of the lumbar spine and hip joint in PHE and perceptual awareness were evaluated. The MCA of the lumbar spine and hip joint was measured using a wearable sensor. Subsequently, a comparison of the MCA of the lumbar spine and hip joints of the participants and the relationship between MCA and lumbar perceptual awareness were examined. Results : The MCA of the LBP group was higher than that of the non-LBP group in motion on the sagittal plane. In addition, perceptual awareness was negatively correlated with MCA in the sagittal plane in the lumbar spine. Conclusion : People with LBP had higher lumbar spine and hip joint MCA than those without LBP. Perceptual awareness was associated with lumbar spine and hip joint MCA in people with LBP
Lossy Compression of Adjacency Matrices by Graph Filter Banks
This paper proposes a compression framework for adjacency matrices of
weighted graphs based on graph filter banks. Adjacency matrices are widely used
mathematical representations of graphs and are used in various applications in
signal processing, machine learning, and data mining. In many problems of
interest, these adjacency matrices can be large, so efficient compression
methods are crucial. In this paper, we propose a lossy compression of weighted
adjacency matrices, where the binary adjacency information is encoded
losslessly (so the topological information of the graph is preserved) while the
edge weights are compressed lossily. For the edge weight compression, the
target graph is converted into a line graph, whose nodes correspond to the
edges of the original graph, and where the original edge weights are regarded
as a graph signal on the line graph. We then transform the edge weights on the
line graph with a graph filter bank for sparse representation. Experiments on
synthetic data validate the effectiveness of the proposed method by comparing
it with existing lossy matrix compression methods
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