• Explosives and Blasting
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• v.37 no.1
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• pp.24-33
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• 2019

Recently, Electronic Detonators have gradually increased their performance for various purposes such as vibration control and improved Fragmentation. This study analyzed the vibration estimation equations of electric and electronic detonator blast by comprehensive analysis of the vibration data collected during electric and electronic detonator blast waves at the comparison sites of urban areas, geology and soil conditions, stone quarries and mines in different areas of Korea from June 2017 to December 2018. It has been confirmed that electronic detonator blast can meet the criteria for allowing vibration even if maximum charge weight per delay is increased by 1.5 times compared to the electric detonator blast.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.614-627
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• 2022

Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generated natural language. So far, most of the methods only use the implicit position information of the aspect in the context, instead of directly utilizing the position relationship between the aspect and the sentiment terms. In fact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of given aspects, and proposes a position embedding interactive attention network based on a long short-term memory network. Firstly, it uses the position information of the context simultaneously in the input layer and the attention layer. Secondly, it mines the importance of different context words for the aspect with the interactive attention mechanism. Finally, it generates a valid representation of the aspect and the context for sentiment classification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurant dataset and 1% on the laptop dataset.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• Geomechanics and Engineering
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• v.28 no.3
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• pp.313-324
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• 2022

Rock properties are important in the design of mines and civil engineering excavations to prevent the imminent failure of slopes and collapse of underground excavations. However, the time, cost, and expertise required to perform experiments to determine those properties are high. Therefore, empirical models have been developed for estimating the mechanical properties of rock that are difficult to determine experimentally from properties that are less difficult to measure. However, the inherent variability in rock properties makes the accurate performance of the empirical models unrealistic and therefore necessitate the use of soft computing models. In this study, Gaussian process regression (GPR), artificial neural network (ANN) and response surface method (RSM) have been proposed to predict the static and dynamic rock properties from the P-wave and rock density. The outcome of the study showed that GPR produced more accurate results than the ANN and RSM models. GPR gave the correlation coefficient of above 99% for all the three properties predicted and RMSE of less than 5. The detailed sensitivity analysis is also conducted using the RSM and the P-wave velocity is found to be the most influencing parameter in the rock mechanical properties predictions. The proposed models can give reasonable predictions of important mechanical properties of sedimentary rock.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.120-124
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• 2022

With advancements in underwater stealth technology for naval vessels, new sensor configurations for detecting targets have been attracting increased attention. Latest underwater mines adopt multiple sensor configurations that include electric field sensors to detect targets and to help acquire accurate ignition time. An underwater electric field sensor consists of a pair of electrodes, signal processing unit, and preamplifier. For detecting underwater electric fields, the preamplifier requires low-noise amplification at ultra-low frequency bands. In this paper, the specific requirements for low-noise preamplifiers are discussed along with the experimental results of various setups of matched transistors and chopper stabilized operational amplifiers. The results showed that noise characteristics at ultra-low frequency bands were affected significantly by the voltage noise density of the chopper amplifier and the number of matched transistors used for differential amplification. The fabricated preamplifier was operated within normal design parameters, which was verified by testing its gain, phase, and linearity.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.59-66
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• 2010

We present a personalized clothing recommender system - one that mines association rules from transaction described in ontologies and infers a recommendation from the rules. The recommender system can forecast frequently changing trends of clothing using the Onto-Apriori algorithm, and it makes appropriate recommendations for each users possible through the inference marked as meta nodes. We simulates the rule generator and the inferential search engine of the system with focus on accuracy and efficiency, and our results validate the system.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Journal of the Korean Geotechnical Society
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• v.23 no.10
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• pp.33-45
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• 2007

In this study, the blast-induced vibration effects on the structural stability of the adjacent tunnel were estimated with respect to the allowable peak particle velocity (PPV). The blasting distance from the tunnel satisfying the allowable PPV was estimated based on the analytical solutions, United States Bureau of Mines (USBM) suggestions, and the equations used in the subway in Seoul. The allowable blasting distance was estimated by using finite difference analysis (FDA) and the behavior of the concrete lining and rock bolts was examined and the stability of those was estimated during the blast. Research results show that the blast-induced vibration effects on the structural stability are negligible for the concrete lining but relatively large for the rock bolts.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.1
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• pp.31-40
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• 2023

This paper investigates the effect of independent directorship on the firm performance of Chinese listed companies under the impact of the global COVID-19 epidemic. The study starts by assessing the relationship between independent director-related characteristics and firm performance, then mines independent director characteristics variables, collects variable data, proposes reasonable hypotheses, and constructs a data model. 1597 companies listed on Shanghai and Shenzhen stock index, China, from 2020 to 2021 has been selected as the research sample. An empirical study on the relationship between independent directors' characteristics and firm performance was conducted using SPSS25. The results show that under the impact of the global COVID-19 epidemic, the proportion of independent directors on the board of directors, the age of independent directors, the remuneration of independent directors, and the overseas background of independent directors in Chinese listed companies have a negative relationship with the current firm performance, while the proportion of female independent directors and the part-time rate of independent directors do not have a positive effect on firm performance. The findings of this study strongly imply that independent directors' characteristics play a significant role in corporate governance and firm performance in Chinese listed companies and that the external environment has an impact on how well independent directors can carry out their duties.