Band Structures of Quasi-One-Dimensional Incommensurate Helical Systems: A Case Study of Infinite Chromium Extended Metal Atom Chain

2018 ◽  
Vol 122 (47) ◽  
pp. 9199-9208 ◽  
Author(s):  
Jen-Hao Ou ◽  
Bih-Yaw Jin
Keyword(s):  
Metal Atom ◽  
Band Structures ◽  
One Dimensional ◽  
Atom Chain

One-dimensional metal atom chain [Ru(CO)4]n as a catalyst precursor—Hydroformylation of 1-hexene using carbon dioxide as a reactant

2009 ◽  
Vol 365 (1) ◽  
pp. 130-134 ◽  
Author(s):  
Maija-Liisa Kontkanen ◽  
Larisa Oresmaa ◽  
M. Andreina Moreno ◽  
Janne Jänis ◽  
Elina Laurila ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Metal Atom ◽  
Catalyst Precursor ◽  
One Dimensional ◽  
Atom Chain

scholarly journals ANALISIS JENIS LAYANAN PENDIDIKAN DAN NON PENDIDIKAN YANG PENTING BAGI PENINGKATAN KEPUASAN MAHASISWA (STUDI KASUS: FAKULTAS TEKNIK UNIVERSITAS DIPONEGORO)

2018 ◽  
Vol 13 (1) ◽  
pp. 7
Author(s):  
Aries Susanty ◽  
Haryo Santoso ◽  
Pramudiastuti Nursyachbani
Keyword(s):  
Taguchi Method ◽  
Student Satisfaction ◽  
Educational Services ◽  
Optimal Result ◽  
One Dimensional ◽  
The Right ◽  
Right Order ◽  
Item Item

AbstrakPenelitian ini memiliki dua tujuan. Pertama, penelitian ini bertujuan untuk mengindentifikasi item layanan pendidikan dan non pendidikan yang dianggap penting untuk peningkatan kepuasan mahasiswa Fakultas Teknik Univesitas Diponegoro (UNDIP). Kedua, penelitian ini bertujuan untuk menyusun sejumlah rekomendasi untuk perbaikan atas item layanan pendidikan  dan non pendidikan yang dianggap penting tersebut. Terdapat 7 dimensi dan 28 item layanan yang digunakan untuk mengindentifikasi jenis layanan pendidikan dan non Pendidikan yang diterima oleh mahasiswa Fakultas Teknik UNDIP. Penelitian ini menggunakan Metode Kano dan Taguchi untuk mengindentifikasi item layanan pendidikan dan non pendidikan yang dianggap paling penting. Dalam hal ini, Metode Kano digunakan untuk memilih sejumlah item layanan pendidikan dan non pendidikan yang termasuk dalam kelompok attractive dan one-dimentional. Adapun Metode Taguchi digunakan untuk dua hal, yang pertama yaitu memverifikasi hasil pengelompokan dari Metode Kano sehingga diperoleh hasil yang lebih optimal dan mengurutkan  prioritas perbaikan dari item-item layanan yang termasuk dalam kelompok  attractive dan one-dimentional. Data untuk penelitian ini diperoleh dari hasil pengisian kuesioner oleh 120 responden untuk kuesioner Kano dan 60 responden untuk kuesioner Taguchi. Hasil pengolahan data dengan menggunakan Metode Kano menunjukkan bahwa terdapat 6 item layanan yang termasuk dalam kategori one-dimensional dan terdapat 2 item layanan .yang termasuk dalam kategori attractive. Selanjutnya, pengolahan data dengan menggunakan  Metode Taguchi diperoleh bahwa terdapat 2 item layanan yang perpindah dari one-dimensional ke attractive dan 1 item layanan yangberpindah dari attractive ke one-dimensional. AbstractAnalysis of the type of educational and non-educational services that are important for the enhancement of student satisfaction (case study Faculty of Engineering, Diponegoro University)] This research has two objective. First this study aims to identify the type of educational and non-educational services that are important for the satisfaction’s enhancement of the student of Faculty Engineering, Diponegoro University. Second, this study aims to formulate some recommendation for improving the type of educational and non-educational services that are important for the satisfaction’s enhancement of the student of Faculty Engineering. There are 7 dimensions and 28 indicators used to identify the type of educational and non-educational received by the student. This research uses the Kano and Taguchi method to identify the type of educational and non-educational services that are important for student. In this case, the Kano method is used to identify educational and non-educational services that are include attractive and one-dimensional categories. Whereas the Taguchi method is used to verify Kano’s categorize result for getting more optimal result than Kano method and to put priorities in the right order of those services that are include attractive and one-dimensional categories. Data for this research is got from questionnaires that were distributed to 120 respondents for Kano method and 60 respondents for Taguchi method. Kano method’s result showed that there are 6 type of services that are include in one-dimensional category and 2 type of services that are include in attractive category.  However, based on validation result that is using Taguchi method showed that there are 5 type of services that are include in one-dimensional category and 3 type of services that are include in attractive category.Keywords: Educational and Non-educational Services; Satisfaction’s Enhancement of Student; Faculty of Engineering Diponegoro Univesity; Kano Method; Taguchi Method.

scholarly journals Dependence of the sliding distance of a one-dimensional atom chain on initial velocity

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Jian-Wen Li ◽  
Tong-Biao Wang ◽  
Nian-Hua Liu ◽  
Tianbao Yu
Keyword(s):  
Initial Velocity ◽  
One Dimensional ◽  
Atom Chain ◽  
Sliding Distance

scholarly journals Generalized thermoelastic band structures of Rayleigh wave in one-dimensional phononic crystals

Meccanica ◽  
2017 ◽  
Vol 53 (4-5) ◽  
pp. 923-935 ◽  
Author(s):  
Ying Wu ◽  
Kaiping Yu ◽  
Linyun Yang ◽  
Rui Zhao
Keyword(s):  
Rayleigh Wave ◽  
Phononic Crystals ◽  
Band Structures ◽  
One Dimensional ◽  
Generalized Thermoelastic

scholarly journals D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5286 ◽  
Author(s):  
Ugochukwu Ejike Akpudo ◽  
Jang-Wook Hur
Keyword(s):  
Neural Network ◽  
Feature Learning ◽  
Ease Of Use ◽  
Fault Detection And Isolation ◽  
Vibration Signals ◽  
One Dimensional ◽  
Real Time Applications ◽  
Equipment State ◽  
High Level

This paper develops a novel hybrid feature learner and classifier for vibration-based fault detection and isolation (FDI) of industrial apartments. The trained model extracts high-level discriminative features from vibration signals and predicts equipment state. Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (D-dCNN) which automatically extracts high-level discriminative features from vibration signals for FDI. Via Softmax averaging at the output layer, the model mitigates the limitations of the standalone classifiers. A diagnostic case study demonstrates the efficiency of the model with a significant accuracy of 92% (F1 score) and extensive comparative empirical validations.

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