• Advances in nano research
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• v.15 no.4
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• pp.315-328
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• 2023

The main goal of the present study was to assess the effects of eggshell powder (ESP) and halloysite nanotubes (HNTs) on the mechanical properties of abaca fiber (AF)-reinforced natural composites. For this purpose, a limited number of indentation tests were first performed on the AF/polypropylene (PP) composites for different HNT and ESP loadings (0 wt.% ~ 6 wt.%), load amplitudes (150, 200, and 250 N), and two types of indenters (Vickers or conical). The Young's modulus, hardness and plasticity index of each specimen were calculated using the indentation test results and Oliver-Pharr method. The accuracy of the experimental results was confirmed by comparing the values of the Young's modulus obtained from the indentation test with the results of the conventional tensile test. Then, a feed-forward shallow artificial neural network (ANN) with high efficiency was trained based on the obtained experimental data. The trained ANN could properly predict the variations of the mentioned mechanical properties of AF/PP composites incorporated with different HNT and ESP loadings. Furthermore, the trained ANN demonstrated that HNTs increase the elastic modulus and hardness of the composite, while the incorporation of ESP reduces these properties. For instance, the Young's modulus of composites incorporated with 3 wt.% of ESP decreased by 30.7% compared with the pure composite, while increasing the weight fraction of ESP up to 6% decreased the Young's modulus by 34.8%. Moreover, the trained ANN indicated that HNTs have a more significant effect on reducing the plasticity index than ESP.

• Animal Bioscience
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• v.37 no.2_spc
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• pp.370-384
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• 2024

Rumen microbiota play a central role in the digestive process of ruminants. Their remarkable ability to break down complex plant fibers and proteins, converting them into essential organic compounds that provide animals with energy and nutrition. Research on rumen microbiota not only contributes to improving animal production performance and enhancing feed utilization efficiency but also holds the potential to reduce methane emissions and environmental impact. Nevertheless, studies on rumen microbiota face numerous challenges, including complexity, difficulties in cultivation, and obstacles in functional analysis. This review provides an overview of microbial species involved in the degradation of macromolecules, the fermentation processes, and methane production in the rumen, all based on cultivation methods. Additionally, the review introduces the applications, advantages, and limitations of emerging omics technologies such as metagenomics, meta-transcriptomics, metaproteomics, and metabolomics, in investigating the functionality of rumen microbiota. Finally, the article offers a forward-looking perspective on the new horizons and technologies in the field of rumen microbiota functional research. These emerging technologies, with continuous refinement and mutual complementation, have deepened our understanding of rumen microbiota functionality, thereby enabling effective manipulation of the rumen microbial community.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.489-509
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• 2024

Slope stability analysis and prediction are of critical importance to geotechnical engineers, given the severe consequences associated with slope failure. This research endeavors to forecast the factor of safety (FOS) for slopes through the implementation of six distinct ML techniques, including back propagation neural networks (BPNN), feed-forward neural networks (FFNN), Takagi-Sugeno fuzzy system (TSF), gene expression programming (GEP), and least-square support vector machine (Ls-SVM). 344 slope cases were analyzed, incorporating a variety of geometric and shear strength parameters measured through the PLAXIS software alongside several loss functions to assess the models' performance. The findings demonstrated that all models produced satisfactory results, with BPNN and GEP models proving to be the most precise, achieving an R² of 0.86 each and MAE and MAPE rates of 0.00012 and 0.00002 and 0.005 and 0.004, respectively. A Pearson correlation and residuals statistical analysis were carried out to examine the importance of each factor in the prediction, revealing that all considered geomechanical features are significantly relevant to slope stability. However, the parameters of friction angle and slope height were found to be the most and least significant, respectively. In addition, to aid in the FOS computation for engineering challenges, a graphical user interface (GUI) for the ML-based techniques was created.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.

• Safety and Health at Work
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• v.15 no.3
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• pp.317-326
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• 2024

Background: Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. Methods: The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. Results: The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. Conclusion: This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.28 no.5
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• pp.179-184
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• 2018

To lessen oxygen concentrations in a wafer through modifying the length of graphite heaters, we investigated the influence of relative distance from heater to quartz crucible on temperature profile of hot-zone in Czochralski silicon-crystal growth by simulation. In particular, ATC temperature and power profiles as a function of different ingot body positions were investigated for five different heater designs; (a) typical side heater (SH), (b) short side heater-up (SSH-up), (c) short side heater-low (SSH-low), (d) bottom heater without side heater (Only-BH), and (e) side heater with bottom heater (SH + BH). It was confirmed that lower short side heater exhibited the highest ATC temperature, which was attributed to the longest distance from triple point to heater center. In addition, for the viewpoint of energy efficiency, it was observed that the typical side heater showed the lowest power because it heated more area of quartz crucible than that of others. This result provides the possibility to predict the feed-forward delta temperature profile as a function of various heater designs.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.1
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• pp.129-137
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• 2016

In the present study we verified performance of feed-forward control algorithm using short term prediction of ship motion information by taking advantage of developed numerical simulation model of FPSO motion. Up until now, various studies have been conducted about thrust control and allocation for dynamic positioning systems maintaining positions of ships or marine structures in diverse sea environmental conditions. In the existing studies, however, the dynamic positioning systems consist of only feedback control gains using a motion of vessel derived from environmental loads such as current, wind and wave. This study addresses dynamic positioning systems which have feedforward control gain derived from forecasted value of a motion of vessel occurred by current, wind and wave force. In this study, the future motion of vessel is forecasted via Brown's Exponential Smoothing after calculating the vessel motion via a selected mathematical model, and the control force for maintaining the position and heading angle of a vessel is decided by the feedback controller and the feedforward controller using PID theory and forecasted vessel motion respectively. For the allocation of thrusts, the Lagrange Multiplier Method is exploited. By constructing a simulation code for a dynamic positioning system of FPSO, the performance of feedforward control system which has feedback controller and feedforward controller was assessed. According to the result of this study, in case of using feedforward control system, it shows smaller maximum thrust power than using conventional feedback control system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.62-70
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• 2007

In this paper, the boost Power Factor Correction(PFC) technique for Direct Torque Control(DTC) of brushless DC motor drive in the constant torque region is implemented on a TMS320F2812DSP. Unlike conventional six-step PWM current control, by properly selecting the inverter voltage space vectors of the two-phase conduction mode from a simple look-up table at a predefined sampling time, the desired quasi-square wave current is obtained, therefore a much faster torque response is achieved compared to conventional current control. Furthermore, to eliminate the low-frequency torque oscillations caused by the non-ideal trapezoidal shape of the actual back-EMF waveform of the BLDC motor, a pre-stored back-EMF versus position look-up table is designed. The duty cycle of the boost converter is determined by a control algorithm based on the input voltage, output voltage which is the dc-link of the BLDC motor drive, and inductor current using average current control method with input voltage feed-forward compensation during each sampling period of the drive system. With the emergence of high-speed digital signal processors(DSPs), both PFC and simple DTC algorithms can be executed during a single sampling period of the BLDC motor drive. In the proposed method, since no PWM algorithm is required for DTC or BLDC motor drive, only one PWM output for the boost converter with 80 kHz switching frequency is used in a TMS320F2812 DSP. The validity and effectiveness of the proposed DTC of BLDC motor drive scheme with PFC are verified through the experimental results. The test results verify that the proposed PFC for DTC of BLDC motor drive improves power factor considerably from 0.77 to as close as 0.9997 with and without load conditions.