Simulation and analysis of the coherent-dispersion spectrometer for exoplanet detection

2021 ◽  
Vol 503 (2) ◽  
pp. 3032-3043
Author(s):  
Yinhua Wu ◽  
Shasha Chen ◽  
Pengchong Wang ◽  
Shun Zhou ◽  
Yutao Feng ◽  
...  
Keyword(s):  
Data Processing ◽  
Michelson Interferometer ◽  
Energy Utilization ◽  
Design Stage ◽  
Inversion Method ◽  
Fringe Visibility ◽  
Common Path ◽  
Data Processing Method ◽  
Detection Instrument ◽  
Performance Research

ABSTRACT The coherent-dispersion spectrometer (CODES) is a new exoplanet detection instrument using the radial velocity (RV) method. This attempts mainly to improve environmental sensitivity and energy utilization by using an asymmetric, common-path Sagnac interferometer instead of a traditional Michelson interferometer. In order to verify its feasibility and to choose the appropriate key parameters to obtain the optimal performance, research on data processing for the design stage of the CODES is performed by systematic simulation and analysis. First, the instrument modelling is carried out for further data analysis according to the principle of the CODES, and the reliability of the model is verified by experiments. Second, the influence of key parameters on fringe visibility is analysed systematically, which provides a certain reference for the choice of the key parameters. Third, the RV inversion method for the CODES is proposed and optimized according to the related analysis results so as to promote RV inversion precision. Finally, the recommended values for the key parameters of the CODES are given. The experimental results show that the data processing error of RV inversion is less than 0.6 m s–1 within the recommended range of key parameters. This indicates that the scheme of the CODES is reasonable and feasible, and that the proposed data processing method is effective and well matched with the instrument design.

PENGARUH ETIKA PEGAWAI, PELAYANAN PUBLIK DAN REFORMASI BIROKRASI TERHADAP PENERAPAN ZONA INTEGRITAS PADA LAPAS MAKASSAR

2020 ◽  
Vol 2 (1) ◽  
pp. 13-15
Author(s):  
Adi Sucipto ◽  
Hasanuddin Remmang ◽  
Haeruddin Saleh
Keyword(s):  
Regression Analysis ◽  
Data Processing ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Random Sampling ◽  
Public Services ◽  
Simple Random Sampling ◽  
Processing Method ◽  
Data Processing Method

Penelitian ini bertujuan menguji pengaruh Etika Pegawai, Pelayanan Publik dan Reformasi Birokrasi terhadap Penerapan Zona Integritas. Pengaruh Etika Pegawai, Pelayanan Publik dan Reformasi Birokrasi terhadap Penerapan Zona Integritas pada Lembaga Pemasyarakatan Kelas I Makassar Responden dalam penelitian ini adalah Pengunjung dan keluarga nara-pidana Lembaga Pemasyarakatan Kelas I Makassar. Jumlah pengunjung yang menjadi sampel penelitian ini adalah 55 orang. Metode penentuan sampel yang digunakan dalam penelitian ini adalah Simple Random Sampling, sedangkan metode pengolahan data yang digunakan peneliti adalah analisis regresi berganda. Hasil penelitian ini menunjukkan bahwa Etika Pegawai dan Pelayanan Publik berpengaruh signifikan terhadap Penerapan Zona Integritas di Lembaga Pemasyarakatan Kelas I Makassar.     This study examines the effect of employee ethics and the improvement of public services on the implementation of the integrity zone. The effect of employee ethics, and improvement of public services on the implementation of integrity zone on Lembaga Pemasyarakatan Kelas 1 Makassar. Respondents in this study were Makassar class in penitentiary visitors. the number of visitors who sampled this study was 55 people. the method of determining the sample used in this study is simple random sampling, while the data processing method used by researchers is multiple regression analysis. the results of this study indicate that employee ethics and public services have a significant effect on the implementation of the integrity zone in Makassar class in penitentiary.

EAST-NBI experimental data processing method based on improved OPTICS algorithm

2021 ◽  
Vol 172 ◽  
pp. 112737
Author(s):  
Jinxin Wang ◽  
Zhimin Liu ◽  
Yuanzhe Zhao ◽  
Yahong Xie ◽  
Yuanlai Xie
Keyword(s):  
Experimental Data ◽  
Data Processing ◽  
Processing Method ◽  
Experimental Data Processing ◽  
Data Processing Method

Detection of cardiovascular drugs and marine toxins using a multifunctional cell-based impedance biosensor system

2015 ◽  
Vol 7 (18) ◽  
pp. 7715-7723 ◽  
Author(s):  
Hongbo Li ◽  
Quchao Zou ◽  
Ling Zou ◽  
Qin Wang ◽  
Kaiqi Su ◽  
...  
Keyword(s):  
Data Processing ◽  
Cardiovascular Drugs ◽  
System Structure ◽  
Marine Toxins ◽  
System P ◽  
Biosensor System ◽  
Processing Module ◽  
Detection Instrument

The system structure of the CIB detection instrument: cell-based impedance biosensor units, hardware module, and data processing module.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

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