scholarly journals Factors that determine directional constraint in ipsilateral hand-foot coordinated movements

2013 ◽  
Vol 1 (5) ◽  
Author(s):  
Kento Nakagawa ◽  
Tetsuro Muraoka ◽  
Kazuyuki Kanosue
Keyword(s):  
Directional Constraint

On an adaptive antenna array under directional constraint

1985 ◽  
Vol 33 (5) ◽  
pp. 1326-1328 ◽  
Author(s):  
M. Er ◽  
A. Cantoni
Keyword(s):  
Antenna Array ◽  
Adaptive Antenna ◽  
Adaptive Antenna Array ◽  
Directional Constraint

scholarly journals Directional constraint of endpoint force emerges from hindlimb anatomy

2010 ◽  
Vol 213 (12) ◽  
pp. 2131-2141 ◽  
Author(s):  
N. E. Bunderson ◽  
J. L. McKay ◽  
L. H. Ting ◽  
T. J. Burkholder
Keyword(s):  
Directional Constraint

Geometric point interpolation method in space with tangent directional constraint

2012 ◽  
Vol 44 (12) ◽  
pp. 1217-1228 ◽  
Author(s):  
Xiao-Diao Chen ◽  
Weiyin Ma
Keyword(s):  
Interpolation Method ◽  
Geometric Point ◽  
Point Interpolation Method ◽  
Point Interpolation ◽  
Directional Constraint

Design and fabrication of a laterally-driven inertial micro-switch with multi-directional constraint structures for lowering off-axis sensitivity

2016 ◽  
Vol 26 (5) ◽  
pp. 055008 ◽  
Author(s):  
Qihuan Zhang ◽  
Zhuoqing Yang ◽  
Qiu Xu ◽  
Yang Wang ◽  
Guifu Ding ◽  
...  
Keyword(s):  
Directional Constraint

Experiment of a hybrid-type implementation of an adaptive array under directional constraint

1981 ◽  
Vol 64 (2) ◽  
pp. 72-81
Author(s):  
Kazuaki Takao ◽  
Osamu Kawamura ◽  
Koji Komiyama ◽  
Kozo Hashimoto ◽  
Iwane Kimura
Keyword(s):  
Adaptive Array ◽  
Hybrid Type ◽  
Directional Constraint

scholarly journals Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 526
Author(s):  
Xiaoqian Cheng ◽  
Chengming Li ◽  
Weibing Du ◽  
Jianming Shen ◽  
Zhaoxin Dai
Keyword(s):  
Clustering Algorithm ◽  
New Method ◽  
Correct Identification ◽  
Hotspot Detection ◽  
Trajectory Data ◽  
Time Duration ◽  
Contextual Constraint ◽  
Two Factors ◽  
Directional Constraint ◽  
Electric Bike

Trajectory data include rich interactive information of humans. The correct identification of trips is the key to trajectory data mining and its application. A new method, multi-rule-constrained homomorphic linear clustering (MCHLC), is proposed to extract trips from raw trajectory data. From the perspective of the workflow, the MCHLC algorithm consists of three parts. The first part is to form the original sub-trajectory moving/stopping clusters, which are obtained by sequentially clustering trajectory elements of the same motion status. The second part is to determine and revise the motion status of the original sub-trajectory clusters by the speed, time duration, directional constraint, and contextual constraint to construct the stop/move model. The third part is to extract users’ trips by filtering the stop/move model using the following rules: distance rule, average speed rule, shortest path rule, and completeness rule, which are related to daily riding experiences. Verification of the new method is carried out with the shared electric bike trajectory data of one week in Tengzhou city, evaluated by three indexes (precision, recall, and F1-score). The experiment shows that the index values of the new algorithm are higher (above 93%) than those of the baseline methods, indicating that the new algorithm is better. Compared to the baseline velocity sequence linear clustering (VSLC) algorithm, the performance of the new algorithm is improved by approximately 10%, mainly owing to two factors, directional constraint and contextual constraint. The better experimental results indicate that the new algorithm is suitable to extract trips from the sparse trajectories of shared e-bikes and other transportation forms, which can provide technical support for urban hotspot detection and hot route identification.

An adaptive antenna array under directional constraint

1976 ◽  
Vol 24 (5) ◽  
pp. 662-669 ◽  
Author(s):  
K. Takao ◽  
M. Fujita ◽  
T. Nishi
Keyword(s):  
Antenna Array ◽  
Adaptive Antenna ◽  
Adaptive Antenna Array ◽  
Directional Constraint

scholarly journals DIRECTED SPIRAL PERCOLATION HULL ON THE SQUARE AND TRIANGULAR LATTICES

2005 ◽  
Vol 16 (08) ◽  
pp. 1251-1268 ◽  
Author(s):  
SANTANU SINHA ◽  
S. B. SANTRA
Keyword(s):  
Fractal Dimension ◽  
Critical Exponents ◽  
Percolation Cluster ◽  
Size Distribution Function ◽  
Two Dimensions ◽  
Critical Properties ◽  
Percolation Clusters ◽  
Error Bars ◽  
Triangular Lattices ◽  
Directional Constraint

Critical properties of hulls of directed spiral percolation clusters are studied on the square and triangular lattices in two dimensions (2D). The hull fractal dimension (dH) and some of the critical exponents associated with different moments of the hull size distribution function of the anisotropic DSP clusters are reported here. The values of dH and other critical exponents are found the same within error bars on both the lattices. The universality of the hull's critical exponents then holds true between the square and triangular lattices in 2D unlike the cluster's critical exponents which exhibit a breakdown of universality. The hull fractal dimension (dH ≈ 1.46) is also found close to 4/3 and away from 7/4, that of ordinary percolation cluster hull. A new conjecture is proposed for dH in terms of two connectivity length exponents (ν‖ and ν⊥) of the anisotropic clusters generated here. The values of dH and other critical exponents obtained here are very close to that of the spiral percolation cluster hull. The hull properties of the DSP clusters are then mostly determined by the rotational constraint and almost independent of the directional constraint present in the model.

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