• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.196-203
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• 2020

In this study, we tried to explore the actual direction for the play culture by looking at the social perception of the change of play culture due to the Corona 19 using big data analysis. For this research, we used Textom, a website specializing in collecting big data, and collected 10,216 data using keywords of "Corona + Play," "Play Culture" and "Leisure" from January 19, 2020 to September 30, 2020, when the first confirmed case of Corona 19 occurred in Korea on various portal sites at home and abroad. The results of this paper showed that the social perception of the play culture in Corona 19 was 51.61%, not much different from the negative image of 48.15%. It is necessary to develop a play culture program that can identify people's various desires and emotions under the premise that situations similar to the current With Corona period and Corona19 can occur at any time, and find mental and physical stability and vitality in unstable situations. In addition, the results of this study can be used as basic data for the development of play culture policies or programs, with the significance that this study helped vitalize big data utilization research in the fields of play, leisure, and culture.

• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.27-36
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• 2003

Rccommender systems are being used by an ever-increasing number of E-Commerce sites to help consumers find products to purchase. Recommender Systems offer a technology that allows personalized recommendations of items of potential interest to users based on information about similarities and dissimilarities among different user' tastes. However, despite enormous interest in recommender systems, both the number of available published techniques and information about their performance are limited. In this paper. we design and implement an M-Commerce recommendation systems using the past buying behavior of the consumer, consumer information, and association rule mining.

• Geomechanics and Engineering
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• v.13 no.4
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• pp.685-699
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• 2017

In order to determine the rate of penetrability, water pressure test is used before the grouting. One of the parameters which have the highest effect is pressure. Mathematical modeling is used for the first time in this study to determine the optimum pressure. Thus, the joints that exist in the rock mass are simulated using cylindrical shell model. The joint surroundings are also modeled through Pasternak environment. In order to validate the modeling, pressure values obtained by the model were used in the sites of Seymareh and Aghbolagh dams and the relative error rates were measured considering the differences between calculated and actual pressures recorded in these operations. In water pressure test, in Seymareh dam, the error values were equal to 4.75, 3.93, 4.8 percent and in the Aghbolagh dam, were 22.43, 5.22, 2.6 percent and in grouting operation in Seymareh dam were equal to 9.09, 32.50, 21.98, 5.57, 29.61 percent and in the Aghbolagh dam were 2.96, 5.40, 4.32 percent. Due to differences in rheological properties of water and grout and based on the overall results, modeling in water pressure test is more accurate than grouting and this error in water pressure test is 7.28 percent and in grouting is 13.92 percent.

• Journal of Ecology and Environment
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• v.29 no.2
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• pp.143-149
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• 2006

This study was conducted to identify native ectomycorrhizal (ECM) fungi colonizing Pinus densiflora for revegetation of abandoned coal mines in Korea. Seedlings of P. densiflora growing on coal mining spoils of a study site in Samcheok were collected. ECM roots were observed under stereomicroscope and their DNA were extracted from each root tip for a seedling for molecular identification. A PCR primer pair specific to fungi, ITS1F and ITS4, was used to amplify fungal DNA. Restriction enzymes, Alul and Hinfl were used for restriction fragment length polymorphism (RFLP). Combined with RFLP profiles and sequence analysis, total twenty one taxa were identified from the ECM root tips. Basidiomycetous fungi including Thelephoraceae, Pezizales, Laccaria, Pisolithus and Ascomycetous fungi including ericoid mycorrhizal fungi were identified from this study. Results showed that the most frequently found in the study sites was a species in Thelephoraceae. A possible use of ECM fungi identified in this study for the revegetation of abandoned coal mines with P. densiflora was discussed.

• Journal of Ecology and Environment
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• v.41 no.5
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• pp.142-144
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• 2017

In recent decades, as it becomes increasingly important to monitor and research long-term ecological changes, worldwide attempts are being conducted to integrate and manage ecological data in a unified framework. Especially domestic ecological data in South Korea should be first standardized based on predefined common protocols for data integration, since they are often scattered over many different systems in various forms. Additionally, foreign ecological data should be converted into a proper unified format to be used along with domestic data for association studies. In this study, our interest is to integrate ECN data with Korean domestic ecological data under our unified framework. For this purpose, we employed our semi-automatic data conversion tool to standardize foreign data and utilized ground beetle (Carabidae) datasets collected from 12 different observatory sites of ECN. We believe that our attempt to convert domestic and foreign ecological data into a standardized format in a systematic way will be quite useful for data integration and association analysis in many ecological and environmental studies.

• Computers and Concrete
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• v.19 no.2
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• pp.171-177
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• 2017

Water pressure test operation is used before the grouting to determine the rate of penetrability, the necessity and estimations related to grouting, by the penetration of water into the borehole. One of the parameters which have the highest effect is pressure of water penetration since the application of excessive pressure causes the hydraulic fracture to occur in the rock mass, and on the other hand, it must not be so small that prevents from seeing mechanical weaknesses and the rate of permeability. Mathematical modeling is used for the first time in this study to determine the optimum pressure. Thus, the joints that exist in the rock mass are simulated using cylindrical shell model. The joint surroundings are also modeled through Pasternak environment. To obtain equations governing the joints and the surroundings, energy method is used accompanied by Hamilton principle and an analytical solution method is used to obtain the maximum pressure. In order to validate the modeling, the pressure values obtained by the model were used in the sites of Seymareh and Aghbolagh dams and the relative error rates were measured considering the differences between calculated and actual pressures. Modeling in the sections of Seymareh dam showed 4.75, 3.93, 4.8 percent error rates and in the sections of Aghbolagh dam it rendered the values of 22.43, 5.22, 2.6 percent. The results indicate that this modeling can be used to estimate the amount of pressure for hydraulic fracture in water pressure test, to predict it and to prevent it.

• Journal of the Korean Wood Science and Technology
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• v.50 no.6
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• pp.375-391
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• 2022

Improving public awareness of wood is crucial for expanding the use of wood and establishing a wood culture. In this study, the environment and trends of the public's wood utilization were analyzed using a questionnaire survey and online text mining at a time when eco-friendly practices are gaining popularity. As a result of reducing climatic and environmental concerns and its positive physical and psychological effects, the use of wood is predicted to rise in areas intimately connected to everyday living, such as wood furniture, wooden structures, and interior materials. Nonetheless, there was a negative awareness that wood was expensive, difficult to maintain, and associated with deforestation. The correlations between wood-related search terms on major Korean portal sites were analyzed and categorized into five groups: Wooden architecture, cultural education, woodworking, wood industry, and wood policy. As a building material, wood was seen as more traditional and friendly than reinforced concrete and stone. Eighty-six percent of respondents expected to utilize wood as a building material in the future, regardless of whether the wood is domestically produced or imported. Sixty-five percent responded favorably about the effects of wood on the health of wooden home inhabitants. It is believed that both active publicity and quantifiable value analysis of human and environmental friendliness are required to increase pro-environment awareness of wood utilization.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.164-170
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• 2023

The purpose of this study is to analyze the big data of Adidas brand participating in the Qatar World Cup in 2022 as a FIFA partner to understand useful information, semantic connection and context from unstructured data. Therefore, this study collected big data generated during the World Cup from Adidas participating in sponsorship as a FIFA partner for the 2022 Qatar World Cup and collected data from major portal sites to understand its meaning. According to text mining analysis, 'Adidas' was used the most 3,340 times based on the frequency of keyword appearance, followed by 'World Cup', 'Qatar World Cup', 'Soccer', 'Lionel Messi', 'Qatar', 'FIFA', 'Korea', and 'Uniform'. In addition, the TF-IDF rankings were 'Qatar World Cup', 'Soccer', 'Lionel Messi', 'World Cup', 'Uniform', 'Qatar', 'FIFA', 'Ronaldo', 'Korea', and 'Nike'. As a result of semantic network analysis and CONCOR analysis, four groups were formed. First, Cluster A named it 'Qatar World Cup Sponsor' as words such as 'Adidas', 'Nike', 'Qatar World Cup', 'Sponsor', 'Sponsor Company', 'Marketing', 'Nation', 'Launch', 'Official', 'Commemoration' and 'National Team' were formed into groups. Second, B Cluster named it 'Group stage' as words such as 'Qatar', 'Uruguay', 'FIFA' and 'group stage' were formed into groups. Third, C Cluster named it 'Winning' as words such as 'World Cup Winning', 'Champion', 'France', 'Argentina', 'Lionel Messi', 'Advertising' and 'Photograph' formed a group. Fourth, D Cluster named it 'Official Ball' as words such as 'Official Ball', 'World Cup Official Ball', 'Soccer Ball', 'All Times', 'Al Rihla', 'Public', 'Technology' was formed into groups.