scholarly journals Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm

2019 ◽  
Vol 9 (7) ◽  
pp. 1493 ◽  
Author(s):  
Changfang Guo ◽  
Zhen Yang ◽  
Shuai Chang ◽  
Ting Ren ◽  
Wenli Yao
Keyword(s):  
Hybrid Approach ◽  
Propagation Distance ◽  
Inversion Result ◽  
Proposed Model ◽  
Optimal Value ◽  
Channel Wave ◽  
Precise Prediction ◽  
Value Effect ◽  
Reconstructive Techniques ◽  
Search Capability

Precise prediction of coal thickness is of the utmost importance in realizing intelligent and unmanned mining. As the channel wave is characterized by an easily recognizable waveform, a long propagation distance, and strong energy, it is widely used for coal thickness inversion. However, most traditional inversion methods are local in nature, and the inversion result is probably not optimal in the global scope. This paper introduces the GA-SIRT hybrid approach, which combines Genetic Algorithms (GA) and Simultaneous Iterative Reconstructive Techniques (SIRT) in order to deal with the above problem and to improve the accuracy of coal thickness inversion. The proposed model takes full advantage of the strong global search capability of GA and of the fast local convergence rate of the SIRT. Moreover, it inhibits the poor local search ability and the local optimal value effect of the GA and the SIRT respectively. The application of the GA-SIRT in the Guoerzhuang coal mine has significantly enhanced its accuracy, stability, and overall computational efficiency. Hence, the introduced novel hybrid model can precisely resolve and identify the coal thickness according to the channel wave. It can also be extended to other geophysical tomographic inversion problems towards the reduction of potential local optimal solutions.

scholarly journals KGPMH: A hybrid approach for solving a novel facility location problem

2021 ◽  
Author(s):  
Maryam DehghanChenary ◽  
Arman Ferdowsi ◽  
Fariborz Jolai ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Mathematical Model ◽  
Location Problem ◽  
Hybrid Approach ◽  
Facility Location Problem ◽  
Evaluation Function ◽  
Genetic Approach ◽  
Hub Location ◽  
Hub Location Problem ◽  
Proposed Model ◽  
The Impact

<pre>The focus of this paper is to propose a bi-objective mathematical model for a new extension of a multi-period p-mobile hub location problem and then to devise an algorithm for solving it. The developed model considers the impact of the time spent traveling at the hubs' network, the time spent at hubs for processing the flows, and the delay caused by congestion at hubs with specific capacities. Following the unveiled model, a hybrid meta-heuristic algorithm will be devised that simultaneously takes advantage of a novel evaluation function, a clustering technique, and a genetic approach for solving the proposed model.</pre>

Investigation of the volitional, non-volitional, emotional, motivational and automatic processes in determining golfers’ intention

2014 ◽  
Vol 26 (7) ◽  
pp. 1118-1135 ◽  
Author(s):  
Heesup Han ◽  
Jinsoo Hwang
Keyword(s):  
Conceptual Framework ◽  
Structural Equation ◽  
Equation Modeling ◽  
Content Type ◽  
Proposed Model ◽  
Decision Making Processes ◽  
Precise Prediction ◽  
Golf Industry ◽  
The Impact ◽  
Metric Invariance

Purpose – This study was designed with the aim to examine the formation of golfers’ intentions to play golf on traditional golf courses by considering the moderating impact of their outcome beliefs regarding the playing of screen golf. Other goals in this research were to test the mediating impact of desires and to identify the relative importance of study variables in generating intention within the proposed conceptual framework. The Model of Goal-directed Behavior (MGB) was utilized to make a precise prediction of golfers’ intentions. Design/methodology/approach – The dataset was developed by distributing surveys in person at screen-golf cafés. A structural equation modeling (SEM) was used to evaluate the fit of the proposed model and assess the hypothesized relationships. Tests for metric invariance were used to examine the moderating impact of outcome beliefs. Findings – Results from the SEM revealed that the proposed model predicted golfers’ intentions well, explaining significant amounts of variance. Desire acted as a significant mediator in the proposed conceptual framework. Compared to other study variables, both positive anticipated emotions and subjective norms had superior ability in generating golfers’ intentions to play real golf. Moreover, results from the test for metric invariance indicated that the intensity of golfers’ perceived benefits of playing screen golf affected their decision formation as a moderator, decreasing their intention to play real golf. Originality/value – Research considering the impact of screen golf on golfers’ decision-making processes is rare in the golf industry. Filling this gap, the present study successfully demonstrated that golfers’ decision formation is sufficiently explained by the MGB, and their perceived outcomes from playing screen golf represent a possible threat to the traditional golf industry.

Kinetic Model with Effect of Water Content for Enzyme-Catalyzed Citronellyl Laurate Esterification Process

2013 ◽  
Vol 284-287 ◽  
pp. 423-428 ◽  
Author(s):  
Siti Asyura Zulkeflee ◽  
Suhairi Abd Sata ◽  
Norashid Aziz
Keyword(s):  
Experimental Data ◽  
Kinetic Model ◽  
Water Content ◽  
Model Development ◽  
Initial Water Content ◽  
Initial Water ◽  
Effect Of Water ◽  
Proposed Model ◽  
Optimal Value ◽  
Influence Of Water

A kinetic model with effect of water content for enzyme-catalyzed citronellyl laurate was developed. These models incorporate the combined influences of established kinetics model with the function model on the effect of initial water content with kinetic parameters. The model development was carried out by performing a linear and nonlinear regression based on the behavior of the kinetic parameter profiles and validated with experimental data. Using the developed models, the influence of water content towards the enzyme-catalyzed initial rate of reaction was theoretically explained. It has been shown that the proposed model have good agreement between experimental data and intends to capture the effect of water content towards the conversion of ester. With this model, the optimal value of initial water content for this process could be estimated.

scholarly journals Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

2021 ◽  
Vol 15 (4) ◽  
pp. 1-18
Author(s):  
Gururaj T. ◽  
Siddesh G. M.
Keyword(s):  
Processing Time ◽  
Gene Expression Analysis ◽  
Gene Sequence ◽  
Hybrid Approach ◽  
Basic Operation ◽  
Sequence Matching ◽  
Hadoop Mapreduce ◽  
Proposed Model ◽  
Performance Issues ◽  
Memory Utilization

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig & Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.

scholarly journals On the compressibility effects in mixing layers

2016 ◽  
Vol 20 (5) ◽  
pp. 1473-1484
Author(s):  
Hechmi Khlifi ◽  
Taieb Lili
Keyword(s):  
High Speed ◽  
Compressible Flows ◽  
Mixing Layers ◽  
The Other ◽  
Compressible Turbulence ◽  
The Past ◽  
Main Indicator ◽  
Proposed Model ◽  
Precise Prediction ◽  
Compressibility Effects

Previous studies of compressible flows carried out in the past few years have shown that the pressure-strain is the main indicator of the structural compressibility effects. Undoubtedly, this terms plays a key role toward strongly changing magnitude of the turbulent Reynolds stress anisotropy. On the other hand, the incompressible models of the pressure-strain correlation have not correctly predicted compressible turbulence at high speed shear flow. Consequently, a correction of these models is needed for precise prediction of compressibility effects. In the present work, a compressibility correction of the widely used incompressible Launder Reece and Rodi model making their standard coefficients dependent on the turbulent and convective Mach numbers is proposed. The ability of the model to predict the developed mixing layers in different cases from experiments of Goebel and Dutton is examined. The predicted results with the proposed model are compared with DNS and experimental data and those obtained by the compressible model of Adumitroiae et al. and the original LRR model. The results show that the essential compressibility effects on mixing layers are well captured by the proposed model.

scholarly journals A Theoretical Investigation of Channel Wave Multipath Propagation in a Coal Seam

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Yinjing Guo ◽  
Jianhua Zhang ◽  
Yuanyuan Ju ◽  
Xiaohan Guo
Keyword(s):  
Coal Seam ◽  
Time Delays ◽  
Multipath Propagation ◽  
Multipath Fading ◽  
Wave Pattern ◽  
Good Match ◽  
Multipath Fading Channel ◽  
Proposed Model ◽  
Channel Wave ◽  
Constant Distance

This study investigates the mechanism of channel wave multipath propagation to determine features of a channel wave in a coal seam. For this purpose, deduction formulas were used for different time delays and propagation paths and, subsequently, a multipath fading channel wave pattern was built. MATLAB software was used to simulate the model. To this end, the characteristics of the coal seam and the surrounding rock were considered. The simulation results were compared with results obtained from the model contained in COMSOL software to verify the accuracy of the model. According to the results obtained from the simulation, the proposed model shows a good match with the COMSOL model, which is a common simulation comparison standard. In addition, the channel wave energy approximates to exponential decay at a constant distance from the source, and the multipath propagation considerably affects its energy attenuation. Furthermore, the multipath reflections’ travel time derivation is accomplished properly in the coal seam.

scholarly journals A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities

2020 ◽  
Vol 10 (23) ◽  
pp. 8326
Author(s):  
Juan Jesús Ruiz-Aguilar ◽  
José Antonio Moscoso-López ◽  
Daniel Urda ◽  
Javier González-Enrique ◽  
Ignacio Turias
Keyword(s):  
Support Vector Regression ◽  
Linear Models ◽  
Hybrid Approach ◽  
Accurate Prediction ◽  
Support Vector ◽  
Self Organizing Map ◽  
Support Vector Regression Model ◽  
Proposed Model ◽  
Key Factor ◽  
Predicted Values

An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a three-step procedure, combining the strengths of linear and non-linear models and the capability of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages (SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of each cluster. These values are finally used as inputs of a support vector regression (SVR) model together with the historical data of the cluster. The final prediction result integrates the prediction results of each cluster. The experimental results showed that the proposed model provided accurate prediction results and outperforms the rest of the models tested. The proposed model can be used as an automatic decision-making tool by seaport management due to its capacity to plan resources in advance, avoiding congestion and time delays.

scholarly journals A hybrid approach to forecast the COVID-19 epidemic trend

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0256971
Author(s):  
Saqib Ali Nawaz ◽  
Jingbing Li ◽  
Uzair Aslam Bhatti ◽  
Sibghat Ullah Bazai ◽  
Asmat Zafar ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Prediction Models ◽  
Hybrid Approach ◽  
Peak Time ◽  
The Novel ◽  
Tuning Parameters ◽  
Epidemic Size ◽  
Proposed Model ◽  
Novel Coronavirus ◽  
Self Protection

Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model’s inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic.

scholarly journals Machine Learning Based Rainfall Analysis

2021 ◽  
pp. 208-212
Author(s):  
Pratap Ganachari ◽  
Vijetha ◽  
Ruchi Kumari
Keyword(s):  
Machine Learning ◽  
Meteorological Parameters ◽  
Rainfall Analysis ◽  
Rainfall Prediction ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Precise Prediction ◽  
The Mean ◽  
Complex Phenomena ◽  
Machine Learning Models

Rainfall is one of the most vital components of agriculture and also predicting it is the most challenging task. In general, weather and rainfall are highly non-linear and complex phenomena, which require progressive computer modeling and simulation for their precise prediction. Numerous and diverse machine learning models are used to predict the rainfall which are Multiple Linear Regression, Neural networks, K-means, Naive Bayes and more. This paper proposes a rainfall prediction model using Conventual Neural Network (CNN) for Indian dataset. The input data is having multiple meteorological parameters and to predict the rainfall in more precise. The Mean Square Error (MSE), accuracy, correlation are the parameters used to validate the proposed model. From the results, the proposed machine learning model provides better results than the other algorithms in the literature.

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