scholarly journals Wood Surface Roughness Prediction Modeling Depending on Influential Cutting Variables

2021 ◽  
Vol 1208 (1) ◽  
pp. 012005
Author(s):  
Atif Hodžić ◽  
Elmasa Aldžić ◽  
Damir Hodžić
Keyword(s):  
Design Of Experiment ◽  
Roughness Parameter ◽  
Solid Wood ◽  
High Accuracy ◽  
R Software ◽  
Design Expert ◽  
Different Types ◽  
Roughness Prediction ◽  
Central Composite ◽  
Cutting Speeds

Abstract Paper presents the design of experiment and determining mathematical model to calculate roughness parameter of wood planned surface. For design of experiment three different types of solid wood were taken and processed on the planner with three different displacements and three different cutting speeds. After measuring the roughness parameter Rz, experimental results were obtained on the basis of which the central composite plan of the experiment was made. Based on that, a model of roughness parameter Rz was made, which is adequate and with high accuracy. The significance of the model coefficients was determined using the R software and the results were presented using the Design Expert software.

scholarly journals Acid Pretreatment of Sago Wastewater for Biohydrogen Production

2018 ◽  
Vol 34 ◽  
pp. 02012
Author(s):  
Noor Illi Mohamad Puad ◽  
Nurainin Farhan Abd Rahim ◽  
Azlin Suhaida Azmi
Keyword(s):  
Renewable Energy Sources ◽  
Enterobacter Aerogenes ◽  
Dark Fermentation ◽  
Maximum Amount ◽  
Acid Pretreatment ◽  
Design Expert ◽  
Different Types ◽  
Greenhouse Effects ◽  
Central Composite ◽  
Raw Wastewater

Biohydrogen has been recognized to be one of the future renewable energy sources and has the potential in solving the greenhouse effects. In this study, Enterobacter aerogenes (E. aerogenes) was used as the biohydrogen producer via dark fermentation process using sago wastewater as the substrate. However, pretreatment of sago wastewater is required since it consists of complex sugars that cannot be utilized directly by the bacteria. This study aimed to use acid pretreatment method to produce high amount of glucose from sago wastewater. Three different types of acid: sulfuric acid (H2SO4); hydrochloric acid (HCl) and nitric acid (HNO3) were screened for the best acid in producing a maximum amount of glucose. H2SO4 gave the highest amount of glucose which was 9.406 g/L. Design of experiment was done using Face-centred Central Composite Design (FCCCD) tool under Response Surface Methodology (RSM) in Design Expert 9 software. The maximum glucose (9.138 g/L) was recorded using 1 M H2SO4 at 100 °C for 60 min. A batch dark fermentation using E. aerogenes was carried out and it was found that pretreated sago wastewater gave a higher hydrogen concentration (1700 ppm) compared to the raw wastewater (410 ppm).

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

A Review of Different Types of DOE Methods as a Useful Platform for Improving the Performance of Nano Adsorbents in Removal Systems of Pollutants

2020 ◽  
Vol 10 (3) ◽  
pp. 219-227
Author(s):  
Ali Behmaneshfar ◽  
Abdolhossein Sadrnia ◽  
Hassan Karimi-Maleh
Keyword(s):  
Response Surface Methodology ◽  
Design Of Experiments ◽  
Web Of Science ◽  
Computer Software ◽  
Coefficient Of Determination ◽  
Design Expert ◽  
Number Of Factors ◽  
Different Types ◽  
Matlab Programming ◽  
Nano Adsorbent

Background: In recent years, the Design of Experiments (DOE) is used for removing pollutant from wastewater by nano-adsorbent. Some methods are Taguchi, Response Surface Methodology (RSM) and factorial design. The aim of this paper is to review different used methods of DOE in removing pollutant to suggest some notations to scholars. Methods: The reviewed papers were searched in Google Scholar, Scopus, and Web of Science randomly and categorized based on DOE methods. Results: Number of factors and responses in DOE for removing pollutants from wastewater are between 2-6 and 1-4, respectively. There are several computer software programs that provide simple use of these methods, such as Qualitek, Design Expert, Minitab, R and Matlab Programming. All models have a coefficient of determination R-sq more than 0.9. Conclusion: All the mentioned methods are appropriate because of the high R-sq value. Since the largest number of runs are used in RSM, it is not suitable for the experiments which are conducted by expensive materials and process. Furthermore, Design Expert and Minitab are the most popular software used by scholars in DOE methods for the removal of pollutant.

Fuzzified time-frequency method for identification and localization of power system faults

2021 ◽  
pp. 1-13
Author(s):  
Pullabhatla Srikanth ◽  
Chiranjib Koley
Keyword(s):  
Power System ◽  
High Accuracy ◽  
Fuzzy Decision ◽  
Frequency Method ◽  
Time Frequency ◽  
Novel Approach ◽  
S Transform ◽  
Different Types ◽  
Power System Faults ◽  
Frequency Magnitude

In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM.

Effects of Resin Coated Sand Mixture on Bending Strength and Cost

2018 ◽  
Vol 765 ◽  
pp. 255-259 ◽  
Author(s):  
Puvadol Sirivimonpan ◽  
Napassavong Osothsilp
Keyword(s):  
Bending Strength ◽  
Optimization Technique ◽  
Total Cost ◽  
Sand Mixture ◽  
Backward Elimination ◽  
Different Types ◽  
Coated Sand ◽  
The Relationship ◽  
Central Composite ◽  
Experimental Runs

This research proposed a method to find out the relationship between bending strength of resin coated sand and the proportion of different types of sand and resin. It was figured out that Central Composite Design (CCD) was suitable to be used to save the number of experimental runs. Then, backward elimination regression analysis was used to determine the relationship equation of bending strength and proportion of different types of sand and resin. Next, optimization technique was applied to determine the optimal new setting, which provided any targeted level of bending strength with the minimal total cost of sand and resin. The results showed that the experimental results obtained from the CCD experiments provided the regression model, which had less than 6% error from the actual bending strength value. With this proposed method the total cost of sand and resin was reduced by 28.6% on average and it also provided the bending strength on any required target level.

scholarly journals Data-bearing halftone image alignment and assessment on 3D surface

2020 ◽  
Vol 2020 (15) ◽  
pp. 196-1-196-7 ◽  
Author(s):  
Ziyi Zhao ◽  
Yujian Xu ◽  
Robert Ulichney ◽  
Matthew Gaubatz ◽  
Stephen Pollard ◽  
...  
Keyword(s):  
Data Retrieval ◽  
High Accuracy ◽  
Image Alignment ◽  
Original Image ◽  
Halftone Image ◽  
3D Surface ◽  
Different Types ◽  
Alignment Problem ◽  
Halftone Images

An alignment approach for data-bearing halftone images, which are a visually pleasant alternative to barcodes, is proposed in this paper. In this paper, we address the alignment problem of data-bearing halftone images on a 3D surface. Different types of surfaces have been tested , using our proposed approach, and high accuracy results have been achieved. Additionally, we also develop a data retrieval tool from an aligned image, in order to decode the data embedded in the original image. A system to assess the accuracy of alignment is introduced to quantify the effectiveness of the proposed alignment approach.

scholarly journals Image Compression Based on Deep Learning: A Review

2021 ◽  
pp. 62-76
Author(s):  
Hajar Maseeh Yasin ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Compression ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Review Paper ◽  
High Accuracy ◽  
Machine Learning Methods ◽  
Digital Era ◽  
Different Types

Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.

scholarly journals Pengaruh Konsentrasi CaCl2 Dan Alginat Terhadap Karakteristik Analog Bulir Jeruk Dari Alginat

2015 ◽  
Vol 10 (2) ◽  
pp. 163
Author(s):  
Rosmawaty Peranginangin ◽  
Anna Mardiana Handayani ◽  
Dina Fransiska ◽  
Djagal W. Marseno ◽  
Supriyadi Supriyadi
Keyword(s):  
Response Surface Methodology ◽  
Central Composite Design ◽  
Response Surface ◽  
Software Design ◽  
Design Expert ◽  
Central Composite

Alginat memiliki sifat dapat membentuk gel dengan adanya ion Ca2+, yang dapat dimanfaatkan sebagai bahan dasar pembuatan bulir jeruk analog. Tujuan penelitian ini adalah untuk mendapatkan konsentrasi alginat dan CaCl2 yang optimum dalam pembuatan bulir jeruk analog  dengan menggunakan response surface methodology (RSM) dan mempelajari karakteristik bulir jeruk analog yang dihasilkan. RSM dengan central composite design (CCD) pada software Design Expert 7 (DX 7) digunakan dengan variasi konsentrasi alginat dan konsentrasi CaCl2 sebagai variabel. Parameter yang diamati pada analog bulir jeruk meliputi kekuatan gel, viskositas, sineresis, dan pH. Selain itu juga diamati kadar air, kadar abu, kadar serat, dan uji sensoris (hedonik skala 5). Analog bulir jeruk disimpan dalam larutan sari jeruk  selama 1 bulan dengan pengamatan berat dan warna periode per minggu. Optimasi dilakukan dengan menggunakan program DX 7 (RSM) dan 5 kali ulangan pada bulir jeruk yang dibuat dari alginat 0,8% dan CaCl2 0,5%.  Analog bulir jeruk yang dihasilkan memiliki kekuatan gel 130,29 g/cm2; viskositas larutan 118,6 cPs; sineresis 43,47% dan pH 3,99; sedangkan kadar air 94,05%; kadar abu 0,35%; kadar serat 2,46%. Hasil uji hedonik skala 5 pada analog bulir jeruk  memiliki nilai yaitu mendekati suka untuk tekstur (3,73), suka untuk kenampakan (4) dan antara agak suka hingga suka untuk rasa (3,53). 

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