Functional Regression

This chapter deals with the main theoretical fundamentals and practical issues of using functional regression in the context of genomic prediction. We explain how to represent data in functions by means of basis functions and considered two basis functions: Fourier for periodic or near-periodic data and B-splines for nonperiodic data. We derived the functional regression with a smoothed coefficient function under a fixed model framework and some examples are also provided under this model. A Bayesian version of functional regression is outlined and explained and all details for its implementation in glmnet and BGLR are given. The examples take into account in the predictor the main effects of environments and genotypes and the genotype × environment interaction term. The examples are done with small data sets so that the user can run them on his/her own computer and can understand the implementation process.

A Spatial–Temporal Similar Graph Attention Network for Cyber Physical System Perception via Traffic Forecasting

Traffic flow forecasting is the basic challenge in intelligent transportation system (ITS). The key problem is to improve the accuracy of model and capture the dynamic temporal and nonlinear spatial dependence. Using real data is one of the ways to improve the spatial–temporal correlation modeling accuracy. However, real traffic flow data are not strictly periodic because of some random factors, which may lead to some deviations. This study focuses on capturing and modeling the temporal perturbation in real periodic data and we propose a spatial–temporal similar graph attention network (STSGAN) to address this problem. In STSGAN, the spatial–temporal graph convolution module is to capture local spatial–temporal relationship in traffic data, and the periodic similar attention module is to treat the nonlinear traffic flow information. Experiments on three datasets demonstrate that our model is best among all methods.

Analysis and synthesis of function data of human movement

The article proposes a human motion capture method based on operational data. The thesis first uses the human body wear system to perform functional processing on the captured periodic motion data, and then extracts the data sequence for the few motions. Thereafter, the classification of the vector calculation method is carried out according to the characteristics of periodic data. Through experimental research, it is found that the functional data analysis (FDA) algorithm proposed in the thesis can accurately identify human motion behaviour, and the automatically collected data has a recognition rate that is as high as 98.9%. Therefore, we have concluded that the human body data functional analysis algorithm has higher recognition accuracy than the traditional optical capture system. Thus, it is worthy of further research and discussion.

Performance of Two Different Flight Configurations for Drone-Borne Magnetic Data

The compensation of magnetic and electromagnetic interference generated by drones is one of the main problems related to drone-borne magnetometry. The simplest solution is to suspend the magnetometer at a certain distance from the drone. However, this choice may compromise the flight stability or introduce periodic data variations generated by the oscillations of the magnetometer. We studied this problem by conducting two drone-borne magnetic surveys using a prototype system based on a cesium-vapor magnetometer with a 1000 Hz sampling frequency. First, the magnetometer was fixed to the drone landing-sled (at 0.5 m from the rotors), and then it was suspended 3 m below the drone. These two configurations illustrate endmembers of the possible solutions, favoring the stability of the system during flight or the minimization of the mobile platform noise. Drone-generated noise was filtered according to a CWT analysis, and both the spectral characteristics and the modelled source parameters resulted analogously to that of a ground magnetic dataset in the same area, which were here taken as a control dataset. This study demonstrates that careful processing can return high quality drone-borne data using both flight configurations. The optimal flight solution can be chosen depending on the survey target and flight conditions.

DAS/SOV: Rotary seismic sources with fiber-optic sensing facilitates autonomous permanent reservoir monitoring

With new developments of fiber-optics sensing and rotary sources, continuous active seismic monitoring for onshore applications has now the opportunity to be fully realized and applied to enhance utilization and resource extraction from the subsurface. To date, conventional seismic monitoring deployments consist primarily of receiver arrays, either fixed or not, with periodic data acquisition campaigns using temporarily deployed sources, resulting in time-lapse data with poor temporal resolution. Only a few niche efforts have demonstrated continuous acquisition using fixed source-receiver networks. Herein, we present initial results of a network of fixed rotary seismic sources, referred to as surface orbital vibrators (SOVs), coupled with a permanent distributed acoustic sensing (DAS) network at the CO2CRC Otway Field Site. While rotary seismic sources are not new, our development of the SOV focused on simplifying the cost and complexity of the source hardware while delivering broad frequency spectrum of the source signal. The upgraded hardware is aligned with a robust methodology for autonomous operation and data processing. At the Otway Site we deployed SOVs at nine locations, monitoring seismic response in seven DAS instrumented wells. Baseline operation of the DAS/SOV sensor array and source system demonstrates its capability with near offsets attaining a signal-to-noise ratio approaching 100 dB with an NRMS of 10%. Furthermore, analyses of travel-time repeatability indicate that the DAS/SOV system can deliver time resolution of +/- 500 µs.

PERIODISASI PERAWI HADIS: Studi Komparasi dan Korelasi Konsep Thabaqat al-Ruwat Masa Klasik dan Modern

<p><span>Periodisasi perawi mulai dikaji sejak abad ke 2 H. untuk keperluan penelitian sanad Hadits dan kritik perawi. Buku-buku indeks periodik paling awal telah terbit di awal abad ke 3 H. Penentuan periodik saat itu masih sangat asumtif dan subjektif dari setiap penulis, belum ada kajian metodologis yang menjadi patokan umum. Embrio teoritis paling awal baru muncul sejak abad ke 7 H oleh Ibn Shalah, yang kemudian menjadi teori utuh pada abad ke 9 H oleh Ibn Hajar. Dalam penelitian ini, originalitas teori Ibn Hajar dalam <em>Taqrib at-Tahdzib</em> ini menjadi konsep klasik dalam periodisasi perawi. Kajian setelah abad itu belum menunjukkan pengembangan yang signifikan, hingga abad ke 15 H saat ini. sehingga pengembangan teori dari konsep klasik oleh Abu Ibrahim berupa tabulasi periodik kemudian menjadi konsep modern. Melalui studi komparasi dan korelasi, peneliti menemukan titik perbedaan dan superioritas kedua konsep tersebut. Konsep klasik adalah hasil analisis data historis, biografi, data pribadi dan data pertemuan perawi yang dirumuskan berdasarkan 6 indikator ke dalam bentuk Buku Indeks Periodik. Sementara konsep modern adalah hasil kalkulasi matematis dari 6 indikator ke dalam bentuk Tabulasi Periodik. Keunggulan konsep klasik adalah kekuatan data historis yang valid, sehingga indeks periodik menjadi prioritas data dalam penetuan periode perawi tertentu. Sementara keunggulan konsep modern terletak pada pendekatan matematis, yang memungkinkan tabulasi menjadi perangkat penentuan periode perawi yang tidak ditemukan data periodiknya sama sekali. </span></p><p>[<strong><span>Periodization of Hadith Narrators: Comparative and Correlation Studies of the Classical and Modern <em>Thabaqat al-Ruwat</em> Concepts</span></strong><span>. The study of the Periodization of Rawi germinated in the 2nd century of Hijriyah, for research on Hadith sanad and Rawi criticism. The earliest periodic index books were published in the early 3rd century of Hijriyah. The earliest theoretical embryos appeared only in the 7th century of Hijriyah by Ibn Salah, which later became a complete theory in the 9th century of Hijriyah by Ibn Hajar. The originality of Ibn Hajar's theory in Taqrib at-Tahdzib became a classical concept in the periodization of Rawi. Studies after that century have not shown significant development, until the 15th century of Hijriyah. The development of the classical concept by Abu Ibrahim, in the form of periodic tabulations, is regarded as a modern concept. Through comparative and correlation studies, we have managed to find a point of difference and superiority between the two concepts. The classic concept is the result of analysis of historical data, biographies, Rawis’ data, and data of their encounters, which are formulated based on 6 indicators into the form of a Periodic Index Book. Meanwhile, the modern concept is the result of mathematical calculations from 6 indicators into the form of Periodic Tabulations. The advantage of the classical concept is the strength of valid historical data, so that the periodic index is the priority of the data in determining the Rawi certain periods. Meanwhile, the advantage of the modern concept is in the mathematical approach, which allows tabulation to be a tool for determining the period of Rawi whose periodic data can’t be found.</span>]</p>

Sampling Error of Continuous Periodic Data and its Application for Geodesy

Data acquisition for geoinformatics cannot be done continuously, but by discrete sampling of the object or phenomenon. The sampling involves errors on the knowledge of the continuous signal due to the loss of information in the sampling procedure. In the present study, an analytical formulation of the sampling error is provided, which embodies the amplitude, phase, bias and periodicity of the sampling error. The analysis is then subsequently applied for case studies: for the GRACE and GRACE-FO monthly solutions, and for different realizations of the Hungarian Gravimetric Network.