• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.279-285
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• 2020

The study on the temporal and spatial monitoring of passing vessels is important in terms of protection and management the marine ecosystem in the coastal area. In this paper, we propose the automatic detection technique of passing vessel by utilizing an artificial intelligence technology and broadband striation patterns which are characteristic of broadband noise radiated by passing vessel. Acoustic measurements to collect underwater noise spectrum images and ship navigation information were conducted in the southern region of Jeju Island in South Korea for 12 days (2016.07.15-07.26). And the convolution neural network model is optimized through learning and validation processes based on the collected images. The automatic detection performance of passing vessel is evaluated by precision (0.936), recall (0.830), average precision (0.824), and accuracy (0.949). In conclusion, the possibility of the automatic detection technique of passing vessel is confirmed by using an artificial intelligence technology, and a future study is proposed from the results of this study.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.69-80
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• 2021

Neural machine translation using deep neural network has emerged as a mainstream research, and an abundance of investment and studies on model structure and parallel language pair have been actively undertaken for the best performance. However, most recent neural machine translation studies pass along decoding strategy to future work, and have insufficient a variety of experiments and specific analysis on it for generating language to maximize quality in the decoding process. In machine translation, decoding strategies optimize navigation paths in the process of generating translation sentences and performance improvement is possible without model modifications or data expansion. This paper compares and analyzes the significant effects of the decoding strategy from classical greedy decoding to the latest Dynamic Beam Allocation (DBA) in neural machine translation using a sequence to sequence model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.113-120
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• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.291-300
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• 2018

This descriptive study was conducted to determine the influence of stress coping styles of nursing students on problem solving ability. Data were collected from 142 nursing students in the M-area from September 20, 2016, to November 30 and analyzed using SPSS WIN 21.0. The average degree of stress coping styles was $3.36{\pm}.30$ (5 point-scale), problem solving abilities was $3.42{\pm}.38$ (5 point-scale). Among the subdomains of stress coping styles, problem focusing was highest, with a score of $3.60{\pm}.42$. Additionally, among the subdomains of problem solving ability, clarification was highest, with a score of $3.50{\pm}.51$. The problem solving abilities of nursing students showed a positive correlation with stress coping styles (r=0.53, p<.001) and was positively correlated with problem focusing (r=0.66, p<.001), positive point of view (r=0.53, p<.001), and social support navigation (r=0.42, p<.001). In addition, the factor with the greatest effect among subdomains of stress coping styles was problem focusing (${\beta}=0.416$, p<.001) and positive point of view (${\beta}=0.257$, p=.002). These two factors were found to explain 54.3% of the variance in problem solving ability. Therefore, the results of this study indicate that application of teaching-learning methods has the potential to improve students' problem-solving ability through problem-oriented and positive stress coping behavior.

• Journal of Gifted/Talented Education
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• v.24 no.2
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• pp.217-241
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• 2014

This study aimed to explore the direction and possibility of development of the integrated curriculum for the Gifted students in the discipline-centered curriculum perspective. To this end, the study analysed the Ontario interdisciplinary curriculum based on a Drake's Integration Model which is typical model of the discipline-centered curriculum and explored the applicability to Science Gifted Academy in Korea. Through showing the possibility of enrichment on the selected majors, integration with other disciplines and solving the future problems by the integrated curriculum centered on 'Big Idea', the Ontario interdisciplinary curriculum gave suggestions of curriculum integration within or through individual research and integrated curriculum for the Gifted. The application of the Ontario's "Introduction to Information studies" to "Information Science" in Science Gifted Academy in Korea could be obtained the conclusion that the Drake's Integration model is applied to the Gifted by the individualization of the navigation network, KDB(Knowledge-Do-Be) umbrella, and the final interdisciplinary task. From this result, we could suggest that the development of integrated curriculum for the Gifted should be considered the clarity of learning objectives for the Gifted, the plan of evaluation to demonstrate big understanding and big idea, the integration with other disciplines or real-world problem, as well as the need of teachers council for the integrated curriculum. This study is expected to be contribute to development of the integrated curriculum model for the gifted based on the their characteristics and to be utilized in Science Gifted Academy.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.780-790
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• 2022

Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.