Статті в журналах з теми "Direction-driven"

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1

Zhang, Yifan, Cheng Peng, Bin Cui, Zhengfei Wang, Xibin Pang, Renmin Ma, Feng Liu, Yanke Che, and Jincai Zhao. "Direction-Controlled Light-Driven Movement of Microribbons." Advanced Materials 28, no. 38 (August 15, 2016): 8538–45. http://dx.doi.org/10.1002/adma.201602411.

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2

Kiran, Raj, Anuruddh Kumar, Rajeev Kumar, and Rahul Vaish. "Poling direction driven large enhancement in piezoelectric performance." Scripta Materialia 151 (July 2018): 76–81. http://dx.doi.org/10.1016/j.scriptamat.2018.03.029.

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3

Janzen, D., and H. Saiedian. "Test-driven development concepts, taxonomy, and future direction." Computer 38, no. 9 (September 2005): 43–50. http://dx.doi.org/10.1109/mc.2005.314.

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4

Hinata, Hirofumi, Nobuyoshi Kanatsu, and Satoshi Fujii. "Dependence of Wind-Driven Current on Wind Stress Direction in a Small Semienclosed, Homogeneous Rotating Basin." Journal of Physical Oceanography 40, no. 7 (July 1, 2010): 1488–500. http://dx.doi.org/10.1175/2010jpo4363.1.

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Abstract The dependence of wind-driven current (WDC) on wind stress direction in a small semienclosed, homogeneous rotating basin is investigated using a linear steady-state analytical model based on Ekman solutions. The model is applicable to the middle of the basin (midbasin), and the current is driven by a constant wind stress of an arbitrary direction. The WDC is made up of wind stress–driven current (WSDC) and pressure-driven current (PDC) components. The laterally varying water depth of the basin confines the total volume transport in the longitudinal direction while the wind stress–driven volume transport changes direction according to the wind stress direction. Therefore, the pressure-driven volume transport or, equivalent, the pressure gradient depends on the wind stress direction: the relationship between the pressure gradient and the wind stress is anisotropic. As a result, the midbasin WDC is also dependent on the wind stress direction. The dependence varies according to the lateral position and Ekman number E. For large E (small rotation), the longitudinal volume transport is generally proportional to the longitudinal wind stress component. Hence, the ratio of the volume transport driven by the wind stress of direction θ (θ > 0) to that driven by the longitudinal wind stress (θ = 0) becomes cosθ. For small E (large rotation), the ratio becomes larger than cosθ. The extent to which each component of wind stress contributes to the generation of the pressure gradient to satisfy no-net-longitudinal and no-lateral transports is determined by a wind stress–pressure gradient transformation matrix, whose components depend on the lateral position and E.
5

MISAKA, Takashi, and Shigeru OBAYASHI. "New Direction of Engineering Simulation Driven by Data Assimilation." Proceedings of Mechanical Engineering Congress, Japan 2017 (2017): F011004. http://dx.doi.org/10.1299/jsmemecj.2017.f011004.

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6

Clarke, Brendan P., Kevin F. MacDonald, and Nikolay I. Zheludev. "Direction-division multiplexed holographic free-electron-driven light sources." Applied Physics Letters 112, no. 2 (January 8, 2018): 021109. http://dx.doi.org/10.1063/1.5008985.

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7

Davies, Gareth R., and Ian Roberts. "Is road safety being driven in the wrong direction?" International Journal of Epidemiology 43, no. 5 (May 6, 2014): 1615–23. http://dx.doi.org/10.1093/ije/dyu103.

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8

Jeong, Jinwon, Sangkug Chung, Jeong-Bong Lee, and Daeyoung Kim. "Electric Field-Driven Liquid Metal Droplet Generation and Direction Manipulation." Micromachines 12, no. 9 (September 20, 2021): 1131. http://dx.doi.org/10.3390/mi12091131.

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A gallium-based liquid metal got high attention recently, due to the excellent material properties that are useful in various research areas. We report here on electric field-induced liquid metal droplet generation and falling direction manipulation. The well-analyzed electro-hydrodynamic method is a selectable way to control the liquid metal, as the liquid metal is conductive. The electric field-induced liquid metal manipulation can be affected by the flow rate (0.05~0.2 mL/min), voltage (0~7 kV), and distance (15 and 30 mm) between electrodes, which changes the volume of the electric field-induced generated liquid metal droplet and the number of the generated droplets. When the electric field intensity increases or the flow rate increases, the generated droplet volume decreases, and the number of droplets increases. With the highest voltage of 7 kV with 15 mm between electrodes at the 0.2 mL/min flow rate, the lowest volume and the largest number of the generated droplets for 10 s were ~10 nL and 541, respectively. Additionally, we controlled the direction of the generated droplet by changing the electric field. The direction of the liquid metal droplet was controlled with the maximum angle of ~12°. Moreover, we exhibited a short circuit demonstration by controlling the volume or falling direction of the generated liquid metal droplet with an applied electric field.
9

Milton, Stewart Murray. "Changing Strategic Direction: Practical Insights into Opportunity Driven Business Development." Long Range Planning 33, no. 5 (October 2000): 733–34. http://dx.doi.org/10.1016/s0024-6301(00)00067-4.

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10

Ruangsupapichat, Nopporn, Michael M. Pollard, Syuzanna R. Harutyunyan, and Ben L. Feringa. "Reversing the direction in a light-driven rotary molecular motor." Nature Chemistry 3, no. 1 (October 31, 2010): 53–60. http://dx.doi.org/10.1038/nchem.872.

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11

Britto, Mishan, Kanwal Zehra, Adeline Goulet, Carolyn Moores, and Robert A. Cross. "Cut7-Driven Microtubule Sliding Reverses Direction Depending on Motor Density." Biophysical Journal 106, no. 2 (January 2014): 779a—780a. http://dx.doi.org/10.1016/j.bpj.2013.11.4273.

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12

Li, Shaoying, Shangqin Yuan, Jihong Zhu, Chuang Wang, Jiang Li, and Weihong Zhang. "Additive manufacturing-driven design optimization: Building direction and structural topology." Additive Manufacturing 36 (December 2020): 101406. http://dx.doi.org/10.1016/j.addma.2020.101406.

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13

Liu, Jinliang, Jun-Hong Liang, James C. McWilliams, Peter P. Sullivan, Yalin Fan, and Qin Chen. "Effect of Planetary Rotation on Oceanic Surface Boundary Layer Turbulence." Journal of Physical Oceanography 48, no. 9 (September 2018): 2057–80. http://dx.doi.org/10.1175/jpo-d-17-0150.1.

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AbstractA large-eddy simulation (LES) model is configured to investigate the effect of the horizontal (northward) component of Earth’s rotation on upper-ocean turbulence. The focus is on the variability of the effect with latitude/hemisphere in the presence of surface gravity waves and when capped by a stable stratification beneath the surface layer. When is included, the mean flow, turbulence, and vertical mixing depend on the wind direction. The value and effect of are the largest in the tropics and decrease with increasing latitudes. The variability in turbulent flows to wind direction is different at different latitudes and in opposite hemispheres. When limited by stable stratification, the variability in turbulence intensity to wind direction reduces, but the entrainment rate changes with wind direction. In wave-driven Langmuir turbulence, the variability in mean current to wind direction is reduced, but the variability of turbulence to wind direction is evident. When there is wind-following swell, the variability in the mean current to wind direction is further reduced. When there is strong wind-opposing swell so that the total wave forcing is opposite to the wind, the variability in the mean current to wind direction is reduced, but the variability of turbulence to wind direction is enhanced, compared to in Ekman turbulence. The profiles of eddy viscosity, including its shape and its value, show a strong wind direction dependence for both stratified wind-driven and wave-driven Langmuir turbulence. Our study demonstrates that wind direction is an important parameter to upper-ocean mixing, though it is overlooked in existing ocean models.
14

Germano, Jennifer M., Kimberleigh J. Field, Richard A. Griffiths, Simon Clulow, Jim Foster, Gemma Harding, and Ronald R. Swaisgood. "Mitigation-driven translocations: are we moving wildlife in the right direction?" Frontiers in Ecology and the Environment 13, no. 2 (March 2015): 100–105. http://dx.doi.org/10.1890/140137.

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15

Curran, William, Colin W. G. Clifford, and Christopher P. Benton. "The direction aftereffect is driven by adaptation of local motion detectors." Vision Research 46, no. 25 (November 2006): 4270–78. http://dx.doi.org/10.1016/j.visres.2006.08.026.

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16

Sasaki, M., K. Itoh, T. Kobayashi, N. Kasuya, A. Fujisawa, and S. I. Itoh. "Propagation direction of geodesic acoustic modes driven by drift wave turbulence." Nuclear Fusion 58, no. 11 (October 3, 2018): 112005. http://dx.doi.org/10.1088/1741-4326/aad251.

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17

Hiratsuka, Yuichi, Tetsuya Tada, Kazuhiro Oiwa, Toshihiko Kanayama, and Taro Q. P. Uyeda. "Controlling the Direction of Kinesin-Driven Microtubule Movements along Microlithographic Tracks." Biophysical Journal 81, no. 3 (September 2001): 1555–61. http://dx.doi.org/10.1016/s0006-3495(01)75809-2.

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18

Daou, Andrea, Jean-Baptiste Pothin, Paul Honeine, and Abdelaziz Bensrhair. "Indoor Scene Recognition Mechanism Based on Direction-Driven Convolutional Neural Networks." Sensors 23, no. 12 (June 17, 2023): 5672. http://dx.doi.org/10.3390/s23125672.

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Indoor location-based services constitute an important part of our daily lives, providing position and direction information about people or objects in indoor spaces. These systems can be useful in security and monitoring applications that target specific areas such as rooms. Vision-based scene recognition is the task of accurately identifying a room category from a given image. Despite years of research in this field, scene recognition remains an open problem due to the different and complex places in the real world. Indoor environments are relatively complicated because of layout variability, object and decoration complexity, and multiscale and viewpoint changes. In this paper, we propose a room-level indoor localization system based on deep learning and built-in smartphone sensors combining visual information with smartphone magnetic heading. The user can be room-level localized while simply capturing an image with a smartphone. The presented indoor scene recognition system is based on direction-driven convolutional neural networks (CNNs) and therefore contains multiple CNNs, each tailored for a particular range of indoor orientations. We present particular weighted fusion strategies that improve system performance by properly combining the outputs from different CNN models. To meet users’ needs and overcome smartphone limitations, we propose a hybrid computing strategy based on mobile computation offloading compatible with the proposed system architecture. The implementation of the scene recognition system is split between the user’s smartphone and a server, which aids in meeting the computational requirements of CNNs. Several experimental analysis were conducted, including to assess performance and provide a stability analysis. The results obtained on a real dataset show the relevance of the proposed approach for localization, as well as the interest in model partitioning in hybrid mobile computation offloading. Our extensive evaluation demonstrates an increase in accuracy compared to traditional CNN scene recognition, indicating the effectiveness and robustness of our approach.
19

Song, Chengkun, Chendong Jin, Jianbo Wang, and Qingfang Liu. "Dynamics of Dzyaloshinskii Domain Walls Driven by Spin Hall Effect in the Presence of Magnetic Fields." SPIN 07, no. 01 (March 2017): 1740004. http://dx.doi.org/10.1142/s2010324717400045.

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Current-induced domain wall motion (CIDWM) in perpendicularly magnetized materials exhibits large potential in spintronic device applications. The Dzyaloshinskii domain walls (DWs) are nucleated in ultrathin ferromagnetic/heavy-metal bilayers with high perpendicular magnetocrystalline anisotropy (PMA) in the presence of interfacial Dzyaloshinskii–Moriya interaction (DMI). Here, we investigate the effect of magnetic fields on Dzyaloshinskii DWs driven by spin Hall effect (SHE) by means of micromagnetic simulations. We find that magnetic fields can modify the dynamics of Dzyaloshinskii DW. When applying out-of-plane magnetic fields, the velocity of Dzyaloshinskii DWs increases when the field-driven and current-driven DW motion are in same direction, while it decreases with opposite direction. In the case of in-plane longitudinal magnetic fields, Dzyaloshinskii DW velocity increases when the direction of the magnetic field and Dzyaloshinskii DW propagation direction are same, and it decreases when applying opposite in-plane magnetic fields. These manifestations may offer a new method for manipulating Dzyaloshinskii DWs and promise applications in DW-based nanodevices.
20

Kukuljan, Lovel, Franci Gabrovsek, and Matthew Covington. "The relative importance of wind-driven and chimney effect cave ventilation: Observations in Postojna Cave (Slovenia)." International Journal of Speleology 50, no. 3 (September 2021): 275–88. http://dx.doi.org/10.5038/1827-806x.50.3.2392.

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Density-driven chimney effect airflow is the most common form of cave ventilation, allowing gas exchange between the outside and the karst subsurface. However, cave ventilation can also be driven by other mechanisms, such as barometric changes or pressure differences induced by the outside winds. We discuss the mechanism and dynamics of wind-driven ventilation using observations in Postojna Cave, Slovenia. We show how seasonal airflow patterns driven by the chimney effect are substantially modified by outside winds. Wind flow over irregular topography forms near-surface air pressure variations and thus pressure differences between cave entrances at different locations. These pressure differences depend on wind speed and direction and their relationship to surface topography and the location of cave entrances. Winds can act in the same or opposite direction as the chimney effect and can either enhance, diminish or even reverse the direction of the density-driven airflows. To examine the possibility of wind-driven flow, we used a computational fluid dynamics model to calculate the wind pressure field over Postojna Cave and the pressure differences between selected points for different configurations of wind speed and direction. We compared these values with those obtained from airflow measurements in the cave and from simple theoretical considerations. Despite the simplicity of the approach and the complexity of the cave system, the comparisons showed satisfactory agreement. This allowed a more general assessment of the relative importance of wind pressure for subsurface ventilation. We are certain that this example is not unique and that the wind-driven effect needs to be considered elsewhere to provide better insights into the dynamics of cave climate, air composition or dripwater geochemistry.
21

Engel, Kevin C., John H. Anderson, and John F. Soechting. "Oculomotor Tracking in Two Dimensions." Journal of Neurophysiology 81, no. 4 (April 1, 1999): 1597–602. http://dx.doi.org/10.1152/jn.1999.81.4.1597.

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Oculomotor tracking in two dimensions. Results from studies of oculomotor tracking in one dimension have indicated that saccades are driven primarily by errors in position, whereas smooth pursuit movements are driven primarily by errors in velocity. To test whether this result generalizes to two-dimensional tracking, we asked subjects to track a target that moved initially in a straight line then changed direction. We found that the general premise does indeed hold true; however, the study of oculomotor tracking in two dimensions provides additional insight. The first saccade was directed slightly in advance of target location at saccade onset. Thus its direction was related primarily to angular positional error. The direction of the smooth pursuit movement after the saccade was related linearly to the direction of target motion with an average slope of 0.8. Furthermore the magnitude and direction of smooth pursuit velocity did not change abruptly; consequently the direction of smooth pursuit appeared to rotate smoothly over time.
22

Grasse, Keith L. "Pharmacological isolation of visual cortical input to the cat accessory optic system: Effects of intravitreal tetrodotoxin on DTN unit responses." Visual Neuroscience 6, no. 2 (February 1991): 175–83. http://dx.doi.org/10.1017/s0952523800010555.

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AbstractDirection-selective responses were recorded from neurons in the dorsal terminal nucleus (DTN) of the cat accessory optic system before and after intravitreal injections of tetrodotoxin (TTX) into the contralateral eye. After approximately 100 min, direction-selective responses driven through stimulation of the contralateral, injected eye were reduced on average by 90%, while direction-selective responses driven through stimulation of the ipsilateral, uninjected eye were not significantly reduced. By 200 min postinjection, ipsilateral direction-selective responses were either equal to or sometimes greater than control values. In the final stages of these experiments (i.e. between 390–830 min after contralateral eye injections), ipsilateral eye responses were on average 30% higher than control. The effects of retinal blockade of the contralateral eye by TTX show that input from the ipsilateral eye alone is sufficient to mediate direction-selective responses in DTN cells. These results and those observed following bicuculline eye injections reported previously (Grasse et al. 1990) demonstrate that direction-selective responses in the DTN driven through stimulation of the contralateral and ipsilateral eyes arise from independent neural mechanisms located in the retina and visual cortex, respectively. Moreover, these findings also suggest that the contralateral eye exerts an inhibitory influence over ipsilateral eye responses which is diminished by TTX injections into the contralateral eye.
23

Wei, Xin, Wei Du, Huan Wan, and Weidong Min. "Feature Distribution Fitting with Direction-Driven Weighting for Few-Shot Images Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 10315–23. http://dx.doi.org/10.1609/aaai.v37i9.26228.

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Few-shot learning has received increasing attention and witnessed significant advances in recent years. However, most of the few-shot learning methods focus on the optimization of training process, and the learning of metric and sample generating networks. They ignore the importance of learning the ground-truth feature distributions of few-shot classes. This paper proposes a direction-driven weighting method to make the feature distributions of few-shot classes precisely fit the ground-truth distributions. The learned feature distributions can generate an unlimited number of training samples for the few-shot classes to avoid overfitting. Specifically, the proposed method consists of two optimization strategies. The direction-driven strategy is for capturing more complete direction information that can describe the feature distributions. The similarity-weighting strategy is proposed to estimate the impact of different classes in the fitting procedure and assign corresponding weights. Our method outperforms the current state-of-the-art performance by an average of 3% for 1-shot on standard few-shot learning benchmarks like miniImageNet, CIFAR-FS, and CUB. The excellent performance and compelling visualization show that our method can more accurately estimate the ground-truth distributions.
24

Andrée, Elin, Martin Drews, Jian Su, Morten Andreas Dahl Larsen, Nils Drønen, and Kristine Skovgaard Madsen. "Simulating wind-driven extreme sea levels: Sensitivity to wind speed and direction." Weather and Climate Extremes 36 (June 2022): 100422. http://dx.doi.org/10.1016/j.wace.2022.100422.

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25

Galajda, Péter, and Pál Ormos. "Rotors produced and driven in laser tweezers with reversed direction of rotation." Applied Physics Letters 80, no. 24 (June 17, 2002): 4653–55. http://dx.doi.org/10.1063/1.1480885.

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26

SUGITA, Shukei, Sota SETOWAKI, Naoya SAKAMOTO, Toshiro OHASHI, and Masaaki SATO. "327 Dynamic control of direction of kinesin-driven microtubules utilizing electric fields." Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2007.20 (2008): 317–18. http://dx.doi.org/10.1299/jsmebio.2007.20.317.

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27

Perdamaian, L. G., and Z. J. Zhai. "Enhanced wind-driven infiltration model by incorporating wind direction for design purpose." IOP Conference Series: Earth and Environmental Science 164 (June 2018): 012017. http://dx.doi.org/10.1088/1755-1315/164/1/012017.

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28

HARAGUCHI, Makoto, Takafumi HAYASHI, and Fumiya MIKI. "Development of a Walker Which Can be Driven With Vertical Direction Stepping." Proceedings of Conference of Hokuriku-Shinetsu Branch 2017.54 (2017): N041. http://dx.doi.org/10.1299/jsmehs.2017.54.n041.

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29

Chen, Hui-Yu, Renfan Shao, Eva Korblova, David Walba, Noel A. Clark, and Wei Lee. "Bistable SmA liquid-crystal display driven by a two-direction electric field." Journal of the Society for Information Display 16, no. 6 (2008): 675. http://dx.doi.org/10.1889/1.2938869.

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30

Kuusela, Pasi, Thomas Keil, and Markku V. J. Maula. "Driven by Aspirations, but in what Direction? Aspirations, strategic transactions, and slack." Academy of Management Proceedings 2012, no. 1 (July 2012): 17588. http://dx.doi.org/10.5465/ambpp.2012.17588abstract.

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31

Haas, Clemens, Katja Kräling, Michaela Cichon, Nicole Rahe, and Thomas Carell. "Excess Electron Transfer Driven DNA Does Not Depend on the Transfer Direction." Angewandte Chemie International Edition 43, no. 14 (March 26, 2004): 1842–44. http://dx.doi.org/10.1002/anie.200353067.

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32

Haas, Clemens, Katja Kräling, Michaela Cichon, Nicole Rahe, and Thomas Carell. "Excess Electron Transfer Driven DNA Does Not Depend on the Transfer Direction." Angewandte Chemie International Edition 43, no. 18 (April 26, 2004): 2321. http://dx.doi.org/10.1002/anie.200490053.

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33

Haas, Clemens, Katja Kräling, Michaela Cichon, Nicole Rahe, and Thomas Carell. "Excess Electron Transfer Driven DNA Does Not Depend on the Transfer Direction." Angewandte Chemie 116, no. 14 (March 26, 2004): 1878–80. http://dx.doi.org/10.1002/ange.200353067.

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34

Haas, Clemens, Katja Kräling, Michaela Cichon, Nicole Rahe, and Thomas Carell. "Excess Electron Transfer Driven DNA Does Not Depend on the Transfer Direction." Angewandte Chemie 116, no. 18 (April 26, 2004): 2373. http://dx.doi.org/10.1002/ange.200490053.

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35

Takahashi, Wataru, Hiroyuki Sasahara, Hiromasa Yamamoto, and Yuji Takagi. "FEM Simulation on the Effect of Cutting Parameters in the Driven Rotary Cutting." Key Engineering Materials 625 (August 2014): 564–69. http://dx.doi.org/10.4028/www.scientific.net/kem.625.564.

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In this paper, the influence of machining parameter in the driven rotary cutting was examined by using finite element simulation. Three dimensional modeling of rotary cutting of Inconel 718 was conducted and then cutting force, temperature distribution of chip and tool, chip thickness and its flow direction were analyzed. Then, the effect of the tool rotation speed was mainly focused on. When peripheral speed of the rotating tool increased, resultant cutting force decreased and the chip flow direction inclined to the tool rotating direction. Then high temperature region of chip became large. It was also shown that tool temperature on the driven rotary cutting was lower than that of the conventional turning. FEM simulation results were compared with the experimental results. As a result, the resultant cutting force, chip flow direction and the tool temperature of the experimental results and the analysis results showed the same trend.
36

Suzuki, Aya, and Minoru Hashimoto. "Development of a PVC Gel Actuator with a Particulate Structure." Journal of Robotics and Mechatronics 34, no. 2 (April 20, 2022): 273–75. http://dx.doi.org/10.20965/jrm.2022.p0273.

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Actuators are usually driven in a uniaxial direction, which limits their ability to be driven with multiple degrees of freedom. In this study, we propose an actuator that is not limited to a uniaxial direction. We developed a polyvinyl chloride gel actuator with a particulate structure. The actuator can change its surface shape by displacing each particle in the structure. As the first step in this experiment, each particle was displaced independently, by applying a voltage to the anode to change the actuator’s surface into an uneven shape.
37

Lu, Weiyong, and Bingxiang Huang. "Mathematical model of methane driven by hydraulic fracturing in gassy coal seams." Adsorption Science & Technology 38, no. 3-4 (April 21, 2020): 127–47. http://dx.doi.org/10.1177/0263617420919247.

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During hydraulic fracturing in gassy coal, methane is driven by hydraulic fracturing. However, its mathematical model has not been established yet. Based on the theory of ‘dual-porosity and dual-permeability’ fluid seepage, a mathematical model is established, with the cleat structure, main hydraulic fracture and methane driven by hydraulic fracturing considered simultaneously. With the help of the COMSOL Multiphysics software, the numerical solution of the mathematical model is obtained. In addition, the space–time rules of water and methane saturation, pore pressure and its gradient are obtained. It is concluded that (1) along the direction of the methane driven by hydraulic fracturing, the pore pressure at the cleat demonstrates a trend of first decreasing and later increasing. The pore pressure gradient exhibits certain regional characteristics along the direction of the methane driven by hydraulic fracturing. (2) Along the direction of the methane driven by hydraulic fracturing, the water saturation exhibits a decreasing trend; however, near the cleat or hydraulic fracture, the water saturation first increases and later decreases. The water saturation in the central region of the coal matrix block is smaller than that of its surrounding region, while the saturation of water in the entire matrix block is greater than that in the cleat or hydraulic fracture surrounding the matrix block. The water saturation at the same space point increases gradually with the time progression. The space–time distribution rules of methane saturation are contrary to those of the water saturation. (3) The free methane driven by hydraulic fracturing includes the original free methane and the free methane desorbed from the adsorption methane. The reduction rate of the adsorption methane is larger than that of free methane.
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Zhao, Yu Han. "A Study on Magnetism-Driven Gliding Arc Discharge." Advanced Materials Research 860-863 (December 2013): 2199–202. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2199.

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The diameter of non-thermal arc plasma is one of the main parameters that determine the current density of arc, so as to the temperature and the density of electrons. The imagines of gliding arc discharge driven by magnetism at atmosphere are captured observed and its dimension is measured by commercial CCD. The diameter projecting in the direction of motion of the arc (diameter in positive direction) is attained. And the relationship between diameter and current, magnetic field is analyzed, too.
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Chen, Zhong, Shihyuan Yeh, Jean-Francois Chamberland, and Gregory H. Huff. "A Sensor-Driven Analysis of Distributed Direction Finding Systems Based on UAV Swarms." Sensors 19, no. 12 (June 12, 2019): 2659. http://dx.doi.org/10.3390/s19122659.

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This paper reports on the research of factors that impact the accuracy and efficiency of an unmanned aerial vehicle (UAV) based radio frequency (RF) and microwave data collection system. The swarming UAVs (agents) can be utilized to create micro-UAV swarm-based (MUSB) aperiodic antenna arrays that reduce angle ambiguity and improve convergence in sub-space direction-of-arrival (DOA) techniques. A mathematical data model is addressed in this paper to demonstrate fundamental properties of MUSB antenna arrays and study the performance of the data collection system framework. The Cramer–Rao bound (CRB) associated with two-dimensional (2D) DOAs of sources in the presence of sensor gain and phase coefficient is derived. The single-source case is studied in detail. The vector-space of emitters is exploited and the iterative-MUSIC (multiple signal classification) algorithm is created to estimate 2D DOAs of emitters. Numerical examples and practical measurements are provided to demonstrate the feasibility of the proposed MUSB data collection system framework using iterative-MUSIC algorithm and benchmark theoretical expectations.
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YOSHIMURA, Yasuo, Shin KUBO, Takashi SHIMOZUMA, Hiroe IGAMI, Hiromi TAKAHASHI, Masaki NISHIURA, Satoru SAKAKIBARA, et al. "Dependence of EC-Driven Current on the EC-Wave Beam Direction in LHD." Plasma and Fusion Research 6 (2011): 2402073. http://dx.doi.org/10.1585/pfr.6.2402073.

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41

Vu-Quoc, Loc, and Xiang Zhang. "An elastoplastic contact force–displacement model in the normal direction: displacement–driven version." Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 455, no. 1991 (November 8, 1999): 4013–44. http://dx.doi.org/10.1098/rspa.1999.0488.

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42

Bangalee, Md Zavid Iqbal. "Effects of window position in vertical direction on wind driven natural cross ventilation." Progress in Computational Fluid Dynamics, An International Journal 15, no. 3 (2015): 177. http://dx.doi.org/10.1504/pcfd.2015.069578.

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43

Pas, Maciej, Kimihiro Nakamura, Nobukatsu Sawamoto, Toshihiko Aso, and Hidenao Fukuyama. "Stimulus-driven changes in the direction of neural priming during visual word recognition." NeuroImage 125 (January 2016): 428–36. http://dx.doi.org/10.1016/j.neuroimage.2015.10.063.

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44

TSUJI, Tomohiro, and Shigeomi CHONO. "Development of microactuators driven by liquid crystals (6th report, control of driving direction)." Transactions of the JSME (in Japanese) 81, no. 823 (2015): 14–00627. http://dx.doi.org/10.1299/transjsme.14-00627.

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45

Nagy, Henrietta, Balázs Illés, and József Káposzta. "New direction to knowledge and technology driven development according for some European regions." Journal of Process Management. New Technologies 5, no. 2 (2017): 25–35. http://dx.doi.org/10.5937/jouproman5-13667.

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46

Roseboom, W., T. Kawabe, and S. Nishida. "Direction of visual apparent motion driven by perceptual organization of cross-modal signals." Journal of Vision 13, no. 1 (January 4, 2013): 6. http://dx.doi.org/10.1167/13.1.6.

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47

Freeman, Elliot, and Jon Driver. "Direction of Visual Apparent Motion Driven Solely by Timing of a Static Sound." Current Biology 18, no. 16 (August 2008): 1262–66. http://dx.doi.org/10.1016/j.cub.2008.07.066.

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48

Beriwal, Sushil, and Junzo Chino. "Time-Driven Activity-Based Costing in Oncology: A Step in the Right Direction." International Journal of Radiation Oncology*Biology*Physics 100, no. 1 (January 2018): 95–96. http://dx.doi.org/10.1016/j.ijrobp.2017.10.017.

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49

Wang, Jingui, Dongjuan Cheng, Lihui Wu, and Xueyuan Yu. "Remote-Sensing Inversion Method for Evapotranspiration by Fusing Knowledge and Multisource Data." Scientific Programming 2022 (August 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/2076633.

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Evapotranspiration (ET) is the main process parameter of the land surface heat and water balance. Evapotranspiration remote-sensing inversion can be divided into two types of methods, process-driven and data-driven, according to the model power. This paper presents a comprehensive and systematic review of the research progress of data-driven ET remote-sensing inversion methods and their products; reviews the basic principles, advantages, and disadvantages of related methods/products from three perspectives: empirical regression, machine learning, and data fusion; and finally indicates the development direction of data-driven ET remote-sensing inversion research.
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O¨lc¸men, M. S., and R. L. Simpson. "Perspective: On the Near Wall Similarity of Three-Dimensional Turbulent Boundary Layers (Data Bank Contribution)." Journal of Fluids Engineering 114, no. 4 (December 1, 1992): 487–95. http://dx.doi.org/10.1115/1.2910059.

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The possible existence of a “law-of-the-wall” similarity velocity profile for 3-D boundary layers was investigated using nine different proposed relations with the data from nine experiments carried out in 3-D turbulent boundary layers. Both for pressure driven and shear-driven flows, the “law-of-the-wall” relation of Johnston for the local freestream velocity direction component best applies. Although not well described by any relation, the crosswise velocity component of pressure-driven flows and shear-driven flows is best represented by Mager’s relation and Chandrashekhar and Swamy equation, respectively.

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