Simulation of ductile grain deformation and the porosity loss predicted model of sandstone during compaction based on grain packing texture

2021 ◽  
pp. 109583
Author(s):  
Yiming Yan ◽  
Liqiang Zhang ◽  
Xiaorong Luo ◽  
Keyu Liu ◽  
Bin Yang ◽  
...  
Keyword(s):  
Grain Packing ◽  
Predicted Model

Manufacturing a Mechanical Unit for Grain Packing

2016 ◽  
Vol 7 (11) ◽  
pp. 837-846
Author(s):  
A. El-Sayed ◽  
M. Megahed ◽  
Y. Ramadan ◽  
A. El-Beba
Keyword(s):  
Mechanical Unit ◽  
Grain Packing

scholarly journals A new evaluation and prediction model of sound quality of high-speed permanent magnet motor based on genetic algorithm-radial basis function artificial neural network

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110311
Author(s):  
Kai Hu ◽  
Guangming Zhang ◽  
Wenyi Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Basis Function ◽  
High Speed ◽  
Objective Evaluation ◽  
Sound Quality ◽  
Average Error ◽  
Permanent Magnet Motor ◽  
Predicted Model

Sound quality (SQ) has become an important index to measure the competitiveness of motor products. To better evaluate and optimize SQ, a novelty SQ evaluation and prediction model of high-speed permanent magnet motor (HSPMM) with better accuracy is presented in this research. Six psychoacoustic parameters of A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, fluctuation strength (FS), and perferred-frequency speech interference (PSIL) were adopted to objectively evaluate the SQ of HSPMM under multiple operating conditions and subjective evaluation was also conducted by the combination of semantic subdivision method and grade scoring method. The evaluation results show that the SQ is poor, which will have a certain impact on human psychology and physiology. The correlation between the objective evaluation parameters and the subjective scores is analyzed by coupling the subjective and objective evaluation results. The average error of multiple linear regression (MLR) model is 7.10%. It has good accuracy, but poor stability. In order to improve prediction accuracy, a new predicted model of radial basis function (RBF) artificial neural network was put forward based on genetic algorithm (GA) optimization. Compared with MLR, its average error rate is reduced by 3.16% and the standard deviation is reduced by 1.841. In addition, the weight of each objective parameter was analyzed. The new predicted model has a better accuracy. It can evaluate and optimize the SQ exactly. The research methods and conclusions of this paper can be extended to the evaluation, prediction, and optimization of SQ of other motors.

scholarly journals A Proposed Model of Jazz Theory Knowledge Acquisition

2014 ◽  
Vol 62 (3) ◽  
pp. 291-301 ◽  
Author(s):  
Charles R. Ciorba ◽  
Brian E. Russell
Keyword(s):  
Academic Achievement ◽  
Knowledge Acquisition ◽  
Latent Variables ◽  
Path Model ◽  
Confirmatory Factor Analyses ◽  
Root Mean Square Residual ◽  
Proposed Model ◽  
Confirmatory Factor ◽  
Predicted Model ◽  
Jazz Theory

The purpose of this study was to test a hypothesized model that proposes a causal relationship between motivation and academic achievement on the acquisition of jazz theory knowledge. A reliability analysis of the latent variables ranged from .92 to .94. Confirmatory factor analyses of the motivation (standardized root mean square residual [SRMR] = .067) and jazz theory (SRMR = .063) measures indicated a good fit of the predicted model to the observed data. Results of the latent path model indicated good fit (χ2 = 20.08, p = .692, df = 24, N = 102) and large, positive, and statistically significant direct effects of motivation (β = 0.65) and academic achievement (β = 0.56) on jazz theory knowledge acquisition. The successful identification of this proposed model lends enough support for continued investigation into the process surrounding the acquisition of jazz theory knowledge.

scholarly journals Analisis Preferensi Ketidaknyamanan Konsumen Terhadap Promosi Dan Personal Selleryang Mempengaruhi Minat Beli Konsumen Serta Pemilihan Merek Pada Asuransi (Studi Kasus di PT. Prudential Life Assurance)

2020 ◽  
Vol 2 (1) ◽  
pp. 16-29
Author(s):  
Putu Dyah Permatha Korry ◽  
Ayu Wendy Widhia Pramesti
Keyword(s):  
Structural Equation ◽  
Direct Marketing ◽  
A Value ◽  
Sense Of Security ◽  
Analysis Technique ◽  
Insurance Agent ◽  
Predicted Model ◽  
The Right ◽  
Insurance Data ◽  
A Company

Health is an important thing to be noticed in everyday life, because humans will not be separated from the dangers that arise suddenly by an event. Insurance is one way to guarantee a sense of security in handling the risks that arise suddenly. No wonder various promotions are carried out by companies to generate consumer buying interest, but one of the promotional techniques of direct marketing through telemarketing is inconvenience, because promotion through this technique consumers feel disturbed when contacted because according to them telemarketers do not think of the right time when offering a product. Personal Sales (Personal Seller) is an insurance agent from a company that can deal directly with consumers so that later there will be buying interest in insurance. To increase consumer buying interest, promotional inconvenience, and seller personalities are expected to be able to influence consumers so that after buying interest arises, consumers will be able to decide on the brand selection on insurance. Data was collected through questionnaires to 85 respondents. The data analysis technique used is structural equation (SEM) with PLS. The results of this study indicate that promotional discomfort has a negative and significant effect on consumer buying interest, while a personal seller has a positive and significant effect on consumer buying interest, and consumer buying interest has a positive and significant effect on brand selection. Based on the results of testing the Q2 model gets a value of 0.774, which shows the predicted model is appropriate.

scholarly journals Linear Regression Algorithm in Machine Learning through MATLAB

2021 ◽  
Vol 9 (12) ◽  
pp. 989-995
Author(s):  
Kalva Sindhu Priya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Critical Part ◽  
Squared Error ◽  
Master Level ◽  
Predicted Model ◽  
The Given

Abstract: In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error

Analytical Modeling of Machining Forces and Friction Characteristics in Ultrasonic Assisted Turning Process

2021 ◽  
pp. 1-13
Author(s):  
Jay Airao ◽  
Chandrakant Kumar Nirala
Keyword(s):  
Analytical Model ◽  
Work Piece ◽  
Contact Ratio ◽  
Friction Characteristics ◽  
Machining Forces ◽  
Ultrasonic Assisted ◽  
Predicted Model ◽  
Ss 304 ◽  
Intermittent Cutting ◽  
Conventional Machining

Abstract Intermittent cutting characteristics of Ultrasonic assisted turning (UAT), Compared to conventional turning (CT), has shown a significant enhancement in the machinability of hard-to-cut materials. The enhancement in machinability is associated with machining forces and friction characteristics of the process. The present article covers an analytical approach to predict the output responses such as machining forces and friction characteristics in UAT and CT processes. Specific cutting energy (SCE) for a particular work-piece material was considered to predict the output responses. The predictions were made by considering the conventional machining theories. Experiments for the UAT and the CT of SS 304 were carried out to validate the predicted model. The results from the analytical model showed that the shear angle increases and the tool-workpiece contact ratio (TWCR) decrease with an increase in amplitude and frequency of vibration. The results obtained from the analytical model were found to be in close agreement with the experimental ones, with an approximate error of 2-20%.

Deep Learning-Based Classification of Malignant and Benign Cells in Dermatoscopic Images via Transfer Learning Approach

2021 ◽  
Author(s):  
V. Akash Kumar ◽  
Vijaya Mishra ◽  
Monika Arora
Keyword(s):  
Skin Cancer ◽  
Transfer Learning ◽  
Human Life ◽  
Skin Pigmentation ◽  
Automatic Identification ◽  
Learning Approach ◽  
Predicted Model ◽  
Classification Tasks ◽  
Cancerous Cells

The inhibition of healthy cells creating improper controlling process of the human body system indicates the occurrence of growth of cancerous cells. The cluster of such cells leads to the development of tumor. The observation of this type of abnormal skin pigmentation is done using an effective tool called Dermoscopy. However, these dermatoscopic images possess a great challenge for diagnosis. Considering the characteristics of dermatoscopic images, transfer learning is an appropriate approach of automatically classifying the images based on the respective categories. An automatic identification of skin cancer not only saves human life but also helps in detecting its growth at an earlier stage which saves medical practitioner’s effort and time. A newly predicted model has been proposed for classifying the skin cancer as benign or malignant by DCNN with transfer learning and its pre-trained models such as VGG 16, VGG 19, ResNet 50, ResNet 101, and Inception V3. The proposed methodology aims at examining the efficiency of pre-trained models and transfer learning approach for the classification tasks and opens new dimensions of research in the field of medicines using imaging technique which can be implementable in real-time applications.

scholarly journals A Highly Conserved Epitope (RNNQIPQDF) of Porcine teschovirus Induced a Group-Specific Antiserum: A Bioinformatics-Predicted Model with Pan-PTV Potential

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1225
Author(s):  
Tung-Hsuan Tsai ◽  
Chia-Yi Chang ◽  
Fun-In Wang
Keyword(s):  
In Silico ◽  
Neutralizing Antibodies ◽  
Age Groups ◽  
Epitope Prediction ◽  
Toxin B ◽  
Swine Pathogen ◽  
Porcine Teschovirus ◽  
A Subunit ◽  
Predicted Model ◽  
Conserved Epitope

Porcine teschovirus (PTV) is an OIE-listed pathogen with 13 known PTV serotypes. Heterologous PTV serotypes frequently co-circulate and co-infect with another swine pathogen, causing various symptoms in all age groups, thus highlighting the need for a pan-PTV diagnostic tool. Here, a recombinant protein composed of a highly conserved “RNNQIPQDF” epitope on the GH loop of VP1, predicted in silico, and a tandem repeat of this epitope carrying the pan DR (PADRE) and Toxin B epitopes was constructed to serve as a PTV detection tool. This recombinant GST-PADRE-(RNNQIPQDF)n-Toxin B protein was used as an immunogen, which effectively raised non-neutralizing or undetectable neutralizing antibodies against PTV in mice. The raised antiserum was reactive against all the PTV serotypes (PTV–1–7) tested, but not against members of the closely related genera Sapelovirus and Cardiovirus, and the unrelated virus controls. This potential pan-PTV diagnostic reagent may be used to differentiate naturally infected animals from vaccinated animals that have antibodies against a subunit vaccine that does not contain this epitope or to screen for PTV before further subtyping. To our knowledge, this is the first report that utilized in silico PTV epitope prediction to find a reagent broadly reactive to various PTV serotypes.

The Predicted Model of Surface Roughness in Resharpening End-Mill for Tool Grinder

2006 ◽  
Vol 505-507 ◽  
pp. 523-528
Author(s):  
H.S. Lu ◽  
B.Y. Lee ◽  
C.T. Chung ◽  
Y.L. Liu
Keyword(s):  
Surface Roughness ◽  
Computer Program ◽  
Cutting Parameters ◽  
Training Data ◽  
Depth Of Cut ◽  
End Mill ◽  
Factors Affecting ◽  
Predicted Model ◽  
Predicted Values ◽  
Major Factors

This paper presents a predicted model of surface roughness of radial relief for resharpening end-mill. This model is constructed using a polynomial network. The major factors affecting grinding parameters are considered to be wheel spindle speed, feedrate, and grinding depth of cut. Experiments under specified conditions are deliberately designed and conducted to obtain the corresponding tested data for surface roughness that are used for training data of the proposed polynomial network. Consequently, a predicted model for surface roughness is established. Furthermore, a computer program in VB language is written based on this model. It can quickly calculate predicted values of surface roughness by simply inputting required cutting parameters. According to the experimental results, the developed polynomial network model shows high predicting capability on surface roughness of radial relief, and possesses promising potential in the application of predicting surface roughness in resharpening end-mill operation.

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