207 research outputs found
KINEMATIC ANALYSIS OF THE RUN UP FINAL STRIDE AND TAKE-OFF TECHNIQUE IN CHINESE FEMALE FOSBURY FLOP JUMPERS
The flop high-jump technique consists of a run up, take-off, flight and landing. Among these four phases, the take-off is the key to performance. The path that a jumper's center of gravity (CG) follows during flight is determined by the height of center of gravity before take-off, velocity that the athlete is propelled upward at take-off, and take-off angle. The run up will influence take-off technique and body movement in flight. Therefore, the final stride of the run up is the transitional phases connecting the run up and take-off. The purpose of this study was analyzed and estimated the final stride of the run up and kinematics of take-off technique used by Chinese female jumpers
Online Kernel Sliced Inverse Regression
Online dimension reduction is a common method for high-dimensional streaming
data processing. Online principal component analysis, online sliced inverse
regression, online kernel principal component analysis and other methods have
been studied in depth, but as far as we know, online supervised nonlinear
dimension reduction methods have not been fully studied. In this article, an
online kernel sliced inverse regression method is proposed. By introducing the
approximate linear dependence condition and dictionary variable sets, we
address the problem of increasing variable dimensions with the sample size in
the online kernel sliced inverse regression method, and propose a reduced-order
method for updating variables online. We then transform the problem into an
online generalized eigen-decomposition problem, and use the stochastic
optimization method to update the centered dimension reduction directions.
Simulations and the real data analysis show that our method can achieve close
performance to batch processing kernel sliced inverse regression
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression
Federated learning has become a popular tool in the big data era nowadays. It
trains a centralized model based on data from different clients while keeping
data decentralized. In this paper, we propose a federated sparse sliced inverse
regression algorithm for the first time. Our method can simultaneously estimate
the central dimension reduction subspace and perform variable selection in a
federated setting. We transform this federated high-dimensional sparse sliced
inverse regression problem into a convex optimization problem by constructing
the covariance matrix safely and losslessly. We then use a linearized
alternating direction method of multipliers algorithm to estimate the central
subspace. We also give approaches of Bayesian information criterion and
hold-out validation to ascertain the dimension of the central subspace and the
hyper-parameter of the algorithm. We establish an upper bound of the
statistical error rate of our estimator under the heterogeneous setting. We
demonstrate the effectiveness of our method through simulations and real world
applications
HandRefiner: Refining Malformed Hands in Generated Images by Diffusion-based Conditional Inpainting
Diffusion models have achieved remarkable success in generating realistic
images but suffer from generating accurate human hands, such as incorrect
finger counts or irregular shapes. This difficulty arises from the complex task
of learning the physical structure and pose of hands from training images,
which involves extensive deformations and occlusions. For correct hand
generation, our paper introduces a lightweight post-processing solution called
. HandRefiner employs a conditional inpainting approach
to rectify malformed hands while leaving other parts of the image untouched. We
leverage the hand mesh reconstruction model that consistently adheres to the
correct number of fingers and hand shape, while also being capable of fitting
the desired hand pose in the generated image. Given a generated failed image
due to malformed hands, we utilize ControlNet modules to re-inject such correct
hand information. Additionally, we uncover a phase transition phenomenon within
ControlNet as we vary the control strength. It enables us to take advantage of
more readily available synthetic data without suffering from the domain gap
between realistic and synthetic hands. Experiments demonstrate that HandRefiner
can significantly improve the generation quality quantitatively and
qualitatively. The code is available at
https://github.com/wenquanlu/HandRefiner
Spatial and Temporal Variation of Soil Salinity During Dry and Wet Seasons in the Southern Coastal Area of Laizhou Bay, China
260-270The southern coastal area of Laizhou Bay is subjected to severe soil salinization due to saline groundwater. The degree of spatial variability is strongly affected by seasonal changes during an annual cycle. In this paper, the spatio-temporal variability of soil salinity in Laizhou Bay, China, was examined to ascertain the current situation of soil salinization in the study area and to reveal the characteristics of seasonal variation of soil salinity. The classical statistical methods and geostatistical methods were applied to soil salinity data collected from four soil layers, i.e., 0-30, 30-60, 60-90, and 0-100 cm, during summer and autumn in 2014. The results indicated that the variation of soil salinity of all the soil layers in summer and autumn was moderate. The soil salinity in the 0-30 cm layer showed a moderate spatial autocorrelation, whereas the spatial autocorrelations of soil salinity in other layers were strong. The overall spatial distribution of soil salinity showed a clear banding distribution and the degree of salinization in the eastern area was lower than that in the western and northern regions.A high ratio of evaporation/precipitation is one of the important reasons for the soil salinity in July is significantly higher than that in November. The rank of soil salinity under different land-use types was: salt pan > orchard > weeds > soybean > woods > cotton > maize > ginger > sweet potato. The research findings can provide theoretical guidance for accurate assessment and soil partition management of regional soil salinization
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted
increasing attention. Infrastructure sensors play a critical role in this
research field; however, how to find the optimal placement of infrastructure
sensors is rarely studied. In this paper, we investigate the problem of
infrastructure sensor placement and propose a pipeline that can efficiently and
effectively find optimal installation positions for infrastructure sensors in a
realistic simulated environment. To better simulate and evaluate LiDAR
placement, we establish a Realistic LiDAR Simulation library that can simulate
the unique characteristics of different popular LiDARs and produce
high-fidelity LiDAR point clouds in the CARLA simulator. Through simulating
point cloud data in different LiDAR placements, we can evaluate the perception
accuracy of these placements using multiple detection models. Then, we analyze
the correlation between the point cloud distribution and perception accuracy by
calculating the density and uniformity of regions of interest. Experiments show
that when using the same number and type of LiDAR, the placement scheme
optimized by our proposed method improves the average precision by 15%,
compared with the conventional placement scheme in the standard lane scene. We
also analyze the correlation between perception performance in the region of
interest and LiDAR point cloud distribution and validate that density and
uniformity can be indicators of performance. Both the RLS Library and related
code will be released at
https://github.com/PJLab-ADG/LiDARSimLib-and-Placement-Evaluation.Comment: 7 pages, 6 figures, accepted to the IEEE International Conference on
Robotics and Automation (ICRA'23
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