• Journal of Drive and Control
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• v.14 no.4
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• pp.1-8
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• 2017

The Down-the-Hole hammer is one of the pneumatic drill equipment used for grinding, drilling, and mining. One the advantages of which is that a reduction work efficiency at deep site are relatively small compared to other drilling methods. Due to the large vibration in the underground area, it is difficult to measure the performance of the hammer, and hammer testing requires substantial production cost and operating expenses so research on the development of the hammer is insufficient. Therefore, this study has developed a dynamic simulation model that apprehends the operating principles of an 8-inch DTH hammer and calculates performance data such as performance impact force, piston speed, and BPM. By using the simulation model, design factors related to strike force and BPM were selected, and the influence of each design factors on performance was analyzed through ANOVA analysis. As a result, be the most important for BPM and the strike force are position of upper port that push the piston in the direction of the bit and in BPM, the size of the empty space between the bits and the piston is the second most important design factor.

• Geomechanics and Engineering
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• v.18 no.2
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• pp.189-203
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• 2019

Brine leakage is a common phenomenon during construction facilitated by artificial freezing technique, threatening the stability of frozen wall due to the continual thawing of already frozen domain. This paper takes the frequently encountered soft clay in Wujiang District as the study object, and remolded specimens were prepared by mixing calcium chloride solutions at five levels of concentration. Both the deformation and pore water pressure of frozen specimens during thawing were investigated by two-stage loading tests. Three sections were noted from the changes in the strain rate of specimens during thawing at the first-stage load, i.e., instantaneous, attenuated, and quasi-stable sections. During the second-stage loading, the deformation of post-thawed soils is closely correlated with the dissipation of pore water pressure. Two characteristic indexes were obtained including thaw-settlement coefficient and critical water content. The critical water content increases positively with salt content. The higher water content of soil leads to a larger thaw-settlement coefficient, especially at higher salt contents, based on which an empirical equation was proposed and verified. The normalized pore water pressure during thawing was found to dissipate slower at higher salt contents, with a longer duration to stabilize. Three physical indexes were experimentally determined such as freezing point, heat conductivity and water permeability. The freezing point decreases at higher salt contents, especially as more water is involved, like the changes in heat conductivity. The water permeability maintains within the same order at the considered range of salt contents, like the development of the coefficient of consolidation. The variation of the pore volume distribution also accounts for this.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.79-87
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• 2020

Under the background of regional cultural development and cities' diversity, various tourist destinations attract tourists' attention and visit by mining their unique folk culture. As a part of sustainable tourism, gastronomy tourism can provide residents and tourists services only by improving the residents' facilities without damaging the environment. However, the existing gastronomy tourism only makes tourists in the folk scene, and tourists can not overstep the cultural differences caused by intersubjectivity to experience the core of folk culture. This paper attempts to use the observation method, cross-subject study, and case study to study the role of food docent-guided tours in understanding folk culture. Moreover, the docent-guided tour studies how the docent can help tourists go deep into the core of folk culture better to realize the sustainable development goal of gastronomy tourism.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.1-8
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• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.27-33
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• 2021

With increasing interest in key areas of the 4th industrial revolution such as artificial intelligence, deep learning and big data, scientific approaches have developed in order to overcome the limitations of traditional decision-making methodologies. These scientific techniques are mainly used to predict the direction of financial products. In this study, the factors of apartment prices, which are of high social interest, were analyzed through SOM. For this analysis, we extracted the real prices of the apartments and selected a total of 16 input variables that would affect these prices. The data period was set from 1986 to 2021. As a result of examining the characteristics of the variables during the rising and faltering periods of the apartment prices, it was found that the statistical tendencies of the input variables of the rising and the faltering periods were clearly distinguishable. I hope this study will help us analyze the status of the real estate market and study future predictions through image learning.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.313-320
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• 2023

The cumulative registration status of dogs is from 1.07 million in 2016 to 2.32 million in 2020. Animal registration is increasing by more than 10% every year, and accordingly, a name must be decided when registering a dog. We want to give a name that fits the characteristics of a dog's appearance, but there are many difficulties in naming it. This paper explains the development of a system for recognizing dog images and recommends dog names based on similar objects or food. This system extracts similarities with dogs' images through models that learn images of various objects and foods, and recommends dog names based on similarities. In addition, by recommending additional related words based on the image data of the result value, it was possible to provide users with various options, increase convenience, and increase interest and fun. Through this system, it is expected that users will be able to solve their concerns about naming their dogs, check names that suit their dogs comfortably, and give them various options through various recommended names to increase satisfaction.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.251-265
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• 2023

In urban construction projects, it is crucial to evaluate the impacts of excavation-induced ground movements in order to protect surrounding structures. These ground movements resulting in damages to the neighboring structures and facilities (i.e., parking basement) are of main concern for the geotechnical engineers. Even more, the danger exists if the nearby structure is an ancient or masonry brick building. The formations of cracks are indicators of structural damage caused by excavation-induced ground disturbances, which pose issues for excavation-related projects. Although the effects of deep excavations on existing brick masonry walls have been thoroughly researched, the impact of twin excavations on a brick masonry wall is rarely described in the literature. This work presents a 3D parametric analysis using an advanced hypoplastic model to investigate the responses of an existing isolated brick masonry wall to twin perpendicular excavations in dry sand. One after the other, twin perpendicular excavations are simulated. This article also looks at how varying sand relative densities (D_r = 30%, 50%, 70%, and 90%) affect the masonry wall. The cracks at the top of the wall were caused by the hogging deformation profile caused by the twin excavations. By raising the relative density from 30% to 90%, excavation-induced footing settlement is greatly minimized. The crack width at the top of the wall reduces as a result of the second excavation in very loose to loose sand (D_r = 30% and 50%). While the crack width on the top of the wall increases owing to the second excavation in medium to very dense sand (D_r = 70% and 90%).

• Geomechanics and Engineering
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• v.31 no.1
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• pp.53-69
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• 2022

In densely built areas, the development of underground transportation systems often involves twin excavations, which are sometimes unavoidably constructed adjacent to existing piled foundations. Because soil stiffness degrades with induced stress release and shear strain during excavation, it is vital to investigate the piled raft responses to subsequent excavation after the first tunnel in a twin-excavation system. The effects of deep excavations on existing piled foundations have been extensively investigated, but the influence of twin excavations on a piled raft is seldom reported in the literature. In this study, three-dimensional numerical analyses were carried out to investigate the influence of sand density on an existing piled raft (with a working load on top of the raft) due to twin excavations. A wide range of relative density (D_r) from loosest (30%), loose to medium (50% and 70%), and densest (90%) were selected to investigate the effects on settlement and load transfer mechanism of the piled raft during twin excavations. An advanced hypoplastic sand model (which can capture small-strain stiffness and stress-state dependent dilatancy of sand) was adopted. The model parameters are calibrated against centrifuge test results in sand reported in the literature. From the computed results, it is found that twin excavations in loose sand (D_r=30%) caused the most significant settlement. This is because of the higher stiffness of denser sand (D_r=90%) than that of loose sand. In contrast, a much larger tilting (maximum magnitude=0.18%) was computed in dense sand than in loose sand after the completion of the first excavation. As far as the load transfer mechanism along the piles is concerned, an upward load transfer to mobilize shaft resistance is observed in loose sand. On the contrary, a downward load transfer is observed in dense sand.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3218-3241
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• 2023

Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.31-42
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• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.