Introduction to Statistics

Härdle, Wolfgang Karl; Klinke, Sigbert; Rönz, Bernd

doi:10.1007/978-3-319-17704-5

Cited by 16 publications

(9 citation statements)

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“…Again, the CNNs were trained, validated, and tested 10 times each to predict the values of the eight topography indicators described in Section 'Analysis of the topography' according to Hartl et al 21 Figure 16 shows the mean values and standard deviations of the PCC between the true topography indicators x and the predicted topography indicators y for the 10 computations. The PCC was calculated according to the following formula 42 PCC ¼…”

Section: Regressionmentioning

confidence: 99%

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Hartl

Praehofer

Zaeh

2020

Proceedings of the Institution of Mechanical Engineers, Part L:

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Friction stir welding is an advanced joining technology that is particularly suitable for aluminum alloys. Various studies have shown that welding quality depends significantly on the welding speed and the rotational speed of the tool. It is frequently possible to detect an unsuitable setting of these parameters by examining the resulting surface defects, such as increased flash formation or surface galling. In this work, Artificial Neural Networks were used to analyze process data in friction stir welding and predict the resulting quality of the weld surface. For this purpose, nine different variables were recorded during friction stir welding of EN AW-6082 T6 sheets: the forces and accelerations in three spatial directions, the spindle torque, and temperatures at the tool shoulder and tool probe. In Case 1, the welds were assigned to the classes good and defective on the basis of a human visual inspection of the weld surface. In Case 2, the welds were categorized into the two classes on the basis of a surface topography analysis. Subsequently, three different major Artificial Neural Network architectures were tested for their ability to predict the surface quality: Feed Forward Fully Connected Neural Networks, Recurrent Neural Networks and Convolutional Neural Networks. The highest classification accuracy was achieved when Convolutional Neural Networks were used. Thus, the evaluation of the force signal transverse to the welding direction yielded the highest accuracy of 99.1% for the prediction of the result of the human visual inspection. The result achieved for the prediction of the topography analysis was an accuracy of 87.4% when the spindle torque was evaluated. Using all nine different process variables to predict the topography analysis, the accuracy could be improved slightly to 88.0%. The sampling rate of the spindle torque was varied between 40 Hz and 9600 Hz and no significant influence was determined. The findings show that Convolutional Neural Networks are well suited for the interpretation of friction stir welding process data and can be used to predict the resulting surface quality. In future work, the results are to be used to develop a parameter optimization method for friction stir welding.

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Section: Regressionmentioning

confidence: 99%

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Hartl

Praehofer

Zaeh

2020

Proceedings of the Institution of Mechanical Engineers, Part L:

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“…Ada banyak metode telah digunakan untuk analisis perubahan dan kecenderungan variabel hidrologi dan iklim. Publikasi buku teks dan laporan dari , Helsel dan Hirsch (2002), Härdle et al (2015) merangkumkan prinsip-prinsip metode statistik yang dapat digunakan untuk analisis perubahan dan kecenderungan iklim.…”

Section: Pendahuluanunclassified

Kecenderungan Hujan Ekstrem di Unit Pelaksana Teknis Pengelolaan Sumberdaya Air di Pasuruan, Jawa Timur

Hidayat¹,

Indarto²,

Askin³

et al. 2020

J. Tanah dan Iklim

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Peningkatan curah hujan ekstrem dengan durasi lebih lama dapat meningkatkan frekuensi dan besar bencana hidro-meteorologi yang terjadi pada suatu wilayah. Hujan ekstrem dengan durasi lebih lama dan merata pada suatu wilayah telah menyebabkan kejadian banjir bandang pada beberapa kota di Indonesia. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis kecenderungan dan perubahan hujan ekstrem (2-harian) dan menggambarkan sebaran spasial kecenderungan hujan ekstrem ke dalam peta tematik. Data hujan dari 64 stasiun di wilayah Unit Pelaksana Teknis Pengelolaan Sumberdaya Air (UPT-PSDA) di Kabupaten Pasuruan (periode rekaman data dari 1980-2015), digunakan sebagai input utama. Data hujan 2harian diolah dari data hujan harian. Analisis kecenderungan menggunakan Uji Mann-Kendall, Rank-Sum, dan Median Crossing test. Hasil uji Mann-Kendall menunjukkan kecenderungan signifikan hujan 2-harian terjadi pada 12 stasiun. Hasil uji Rank-Sum menunjukkan delapan stasiun hujan mengalami perubahan hujan 2-harian signifikan. Berdasarkan uji tersebut secara keseluruhan wilayah UPT PSDA Pasuruan tidak mengalami kecenderungan perubahan hujan ektrim pada periode 1980 sampai dengan 2015.

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“…Analisis kecenderungan dan perubahan variabel hidrologi seperti hujan ekstrim dapat dilakukan pendekatan dengan metode statistik. Zbigniew (2000), Helsel dan Hirsch (2002) dan Härdle et al, (2015) telah merangkum beberapa prinsip metode statistik dapat digunakan untuk mendeteksi kecenderungan dan perubahan iklim yang termuat dalam publikasi buku teks serta laporan. Penelitian terkait kecenderungan telah banyak dilakukan dengan tujuan untuk mengetahui, membandingkan metode ataupun untuk mengetahui kecenderungan beberapa unsur iklim seperti curah hujan, temperatur dan evaporasi (Subarna, 2014).…”

Section: Pedoman Penulisan Artikel Bagi Penulisunclassified

Trend and spatial variability of 1-day extreme rainfall from 1980 - 2015: study at the adminisitrative area of UPT PSDA Pasuruan

Indarto

Askin²,

Hidayat³

2019

JTEP-L

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This study aims to analyze trends, shift and spatial variability of extreme-rainfall in the area of UPT PSDA Pasuruan. The daily rainfall data from 64 stations from 1980 until 2015 were used as main input. The 1-day extreem rainfall data is determined as the maximum annual of 24-hour rainfall events. The statistical analysis using Mann-Kendall, Rank-Sum, and Median Crossing Test using significance level α = 0,05. The spatial variability of extrem rainfall data is described using average annual 24-hour rainfall during the periods of record. Each station is represented by one value. The values are then interpolated using IDW interpolation methods to maps the spatial variability of extreem rainfall event. The results show the value of statistical test for each stations that show the existing trend, shift, or randomness of data. The result also produce thematic maps show the spatial variability of extreme rainfall and the value of each trend.

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Introduction to Statistics

Cited by 16 publications

References 0 publications

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Kecenderungan Hujan Ekstrem di Unit Pelaksana Teknis Pengelolaan Sumberdaya Air di Pasuruan, Jawa Timur

Trend and spatial variability of 1-day extreme rainfall from 1980 - 2015: study at the adminisitrative area of UPT PSDA Pasuruan

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