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Relevant bibliographies by topics / Auto-picking

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Journal articles

Academic literature on the topic 'Auto-picking'

Author: Grafiati

Published: 1 April 2023

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Journal articles on the topic "Auto-picking"

1

Jin, Xin, Shu Jun Li, Xue Jun Yang, Jian Min Wu, Zhong Jun Liu, and Han Kun Liu. "Developments in Research on Seedling Auto-Picking Device of Vegetable Transplanter." Applied Mechanics and Materials 364 (August 2013): 375–79. http://dx.doi.org/10.4028/www.scientific.net/amm.364.375.

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Seedling auto-picking device is one of the most important working parts of a vegetable automatic transplanter. Its performance determines the transplanting quality, reliability and operation efficiency. The current developments of the seedling auto-picking device were discussed, and several typical devices were introduced in this paper. The existing problems of seedling auto-picking devices of different picking methods were pointed out. With the advantages of accurate location, high efficiency and easy picking, the mandril-type seedling picking was thought as the better transplanting mode of v

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2

Yuan, Cheng, Mingjun Su, Changkuan Ni, Xingye Liu, Yunze Xu, and Xiangli Cui. "Horizon auto-picking with quantitative uncertainty evaluation by using a modified VQ-VAE framework." Journal of Geophysics and Engineering 19, no. 4 (2022): 788–806. http://dx.doi.org/10.1093/jge/gxac051.

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Abstract In this paper, we propose a novel approach of deep-learning-based seismic horizon auto-picking that introduces a modified vector quantized variational autoencoder (VQ-VAE) framework to improve the accuracy of seismic horizon interpretation and, for the first time, quantitatively evaluate the uncertainty of the auto-picked horizon by exploiting the concept of entropy. Compared with the conventional VQ-VAE approach, the proposed method not only modifies the VQ-VAE model with more deep-learning channels at each layer of the network to enhance the performance of horizon auto-picking withi

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3

Tanaka, Masahiro, and Hideki Fujiwara. "Visualization of Categorical Data by Hybridization of Two Types of Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 1 (2000): 3–11. http://dx.doi.org/10.20965/jaciii.2000.p0003.

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The sandglass neural network is often used for nonlinear auto-association, where the principal information can be extracted by picking up the values of the middle layer. However, the boundary of the classes on this 2-1) surface tends to be complicated because no class information is used. In this paper, the hybridization of auto-associative network and the multi-layer perceptron for classification is proposed. The usefulness of this method is demonstrated by using clinical data.

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4

Yu, Jun Hua, Li Jia Xu, Ke Fan Ren, Wei Peng Zhang, Zhi Gang Lu, and Pei Da Wang. "Design and Test of a Mechanical Device for the Manipulator of the Watermelon Picking Machine." Applied Mechanics and Materials 442 (October 2013): 291–97. http://dx.doi.org/10.4028/www.scientific.net/amm.442.291.

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This Paper designs a mechanical device for the manipulator of the watermelon picking machine against the low mechanical degree of watermelon picking machine. The mechanical device utilizes a mechanical arm to drive the end effector to run and the end effector is responsible for clamping and shearing watermelon vines, which avoids vine disturbance and sorts out vines to be easily cut down through the process design of clamping, promoting, and re-shearing. In addition, this Paper applies Pro/E modeling, finite element analysis, and simulation analysis to complete the 3D model design of the mecha

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5

WU, L., Z. LUO, J. ZHOU, and H. WANG. "AN AUTO-PICKING STACKING VELOCITY ALGORITHM BASED ON AN AI AND PR APPROACH." International Journal of Pattern Recognition and Artificial Intelligence 03, no. 01 (1989): 103–11. http://dx.doi.org/10.1142/s0218001489000097.

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Stacking velocity is a very important parameter in seismic data processing. Until now the determination of stacking velocity has been done manually. This article proposes an automatic algorithm for picking stacking velocity. The algorithm uses artificial intelligence and pattern recognition techniques.

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6

Ren, Guo Qiang, Bo Zhang, Ya Fei Lian, and Zi Sheng Zhang. "The Auto-Control of pH Value Based on Labview." Advanced Materials Research 466-467 (February 2012): 79–83. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.79.

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Grounded on Labview platform, the grid of technology of solution pH value contributes to a new device----the auto-control technology of solution pH value. This device is easier to operate just by the parameter input in the interface. And it breaks the limit of single admeasuring apparatus, meanwhile adopts the method of picking the average value by multi-metering, which avoids the delay effect caused by solution mixing link. The Acid precipitation-Flocculation process in paper making sewage sets an example for the pH value auto-control and supports PAC control of pH value with software concept

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7

Ren, Guo Qiang, Chang Xie, Wen Zhao Li, Zi Sheng Zhang, and Zhi Qiang Liu. "The Auto-Control of pH Value Based on Labview." Applied Mechanics and Materials 235 (November 2012): 181–85. http://dx.doi.org/10.4028/www.scientific.net/amm.235.181.

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Grounded on Labview platform, the grid of technology of solution pH value contributes to a new device----the auto-control technology of solution pH value. This device is easier to operate just by the parameter input in the interface. And it breaks the limit of single admeasuring apparatus, meanwhile adopts the method of picking the average value by multi-metering, which avoids the delay effect caused by solution mixing link. The Acid precipitation-Flocculation process in paper making sewage sets an example for the pH value auto-control and supports PAC control of pH value with software concept

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8

Lu, Jianqi, Shanyou Li, Peiyang He, et al. "Energy- and Predominant-Period-Dependent P-Wave Onset Picker (EDP-Picker)." Seismological Research Letters 91, no. 4 (2020): 2355–67. http://dx.doi.org/10.1785/0220190260.

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Abstract An energy- and predominant-period-dependent (EDP) P-wave onset automatic picking (EDP-picker) algorithm is proposed to deal with the problem of inaccurate P-wave onset picking in cases in which the P-wave onset is hidden in high-amplitude ambient noise or the energy difference between the seismic P-wave and ambient noise is indistinguishable. The algorithm evaluates the energy change using a characteristic variable ΔE, which describes the energy increment of the P wave above ambient noise. The period change is evaluated using two variables with respect to the predominant period, namel

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9

Chen, Jiqing, Hongdu Zhang, Zhikui Wang, et al. "An image restoration and detection method for picking robot based on convolutional auto-encoder." Computers and Electronics in Agriculture 196 (May 2022): 106896. http://dx.doi.org/10.1016/j.compag.2022.106896.

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10

Buyurgan, Nebil, and Paiman Farrokhvar. "Supply Chain-Related Adverse Events and Patient Safety in Healthcare." International Journal of Healthcare Information Systems and Informatics 10, no. 2 (2015): 14–33. http://dx.doi.org/10.4018/ijhisi.2015040102.

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This research investigates adverse events and patient safety in healthcare due to poor supply chain management practices, and inadequate and disorganized product validation procedures. Focusing on commodity medical and surgical products, this research investigates correct product validation points for maximum patient safety. This study also explores benefits of standard product identifying technologies such as HIBC or GS1 data standards as well as automated validation systems such as barcode or Auto ID to minimize workflow interruptions. Site visits and phone interviews are conducted with six

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