• The Korean Journal of Applied Statistics
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• v.37 no.6
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• pp.703-719
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• 2024

In this paper, we propose a procedure for identifying sports game highlights using online live comments. Highlights in sports games can be defined as important events that capture the viewers' attention, often involving scoring. Internet portals and many other platforms may edit game videos and provide highlight scenes. Since reviewing each game and identifying highlights manually can be labor-intensive and time-consuming, statistical learning procedures are beneficial for the automatic identification of highlights. In this paper, we extract words from preprocessed live comments, and divide videos into one-minute intervals to create a document-term matrix summarizing the frequency of word occurrences at each timestamp. Next, employing the log-odds ratios as weights, the weighted sum of the term frequencies at each timestamp is calculated to generate an event score. Then, the nonparametric kernel regression model is applied to event scores to estimate the underlying trend of event scores. The residual for each timestamp is obtained as the difference between underlying trend and the actual event score. Finally, we predict events as those instances whose residuals are widely deviated from the estimated trend levels. Applying this approach to real soccer games, we could predict actual highlights more accurately compared to simply using comment frequencies.

• Journal of Internet of Things and Convergence
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• v.10 no.5
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• pp.165-171
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• 2024

The colored road surface guide lines have been proven to significantly reduce the occurrence of traffic accidents. However, these guide lines have been currently applied sequentially only on roads that meet the installation criteria designated by the Ministry of Land, Infrastructure, and Transport. Furthermore, existing navigation systems such as legacy navigation and HUD (Head-Up Display) have demonstrated errors or ambiguities in delivering route information, highlighting the need for a clearer method of guiding drivers. In this paper, we propose a lane detection and masking technique that integrates the features of AR HUD (Augmented Reality Head-Up Display), a technology gaining attention in vehicle convenience systems, with the characteristics of colored road surface guide lines. The proposed technique detects and classifies lanes based on their color and lane detection results, then performs road masking using the classified lanes. Experimental results show that the proposed technique masked approximately 70% of the lane area. This enables drivers to receive real-time images of the masked route they should follow, offering an experience similar to constantly visualizing colored road surface guide lines. We expect that this approach will provide more intuitive information and improve driver convenience. Future advancements in this masking technique will further contribute to enhancing both convenience and safety for drivers.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.6
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• pp.633-645
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• 2024

To maintain the safety of aging infrastructure, continuous management is essential, and this applies equally to concrete structures inside tunnels. The health of tunnel structures is managed through regular inspections and precise examination techniques. Traditional inspection methods are manpower-based, where workers visit the site in person to visually assess and record the condition. As a result, the condition of the structure is often determined based on subjective and experiential judgment. To address these issues and enhance the objectivity and reliability of inspection results, methods using high-resolution cameras and deep learning are being actively researched. Neural network model-based algorithms for detecting cracks in tunnel structures, in particular, have demonstrated high accuracy. However, such deep learning technology relies on the premise that a large amount of training image data is available. In reality, damage images such as cracks are not easily found, and collecting them involves significant costs and time. To address this challenge, this study proposes a method for augmenting crack image data using generative AI. Additionally, parameter analysis was conducted to generate crack images resembling real ones, resulting in a generative model with a performance of 31.73 Fréchet Inception Distance. This method is expected to be applied in conjunction with crack detection training methods, contributing to the improved accuracy and reliability of maintenance inspections.

• Smart Media Journal
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• v.13 no.11
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• pp.22-29
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• 2024

The rise in sea temperatures due to global warming has accelerated the migration of marine species, leading to the frequent discovery of toxic marine organisms in domestic waters. The blue-ringed octopus in particular is very dangerous because it contains a deadly poison called tetrodotoxin. Therefore, early detection of these toxic species and minimizing the risk to human life is crucial. This study evaluates the effectiveness of using the latest object detection technology, the YOLO model, to detect toxic marine species, aiming to provide valuable information for the development of a smart fisheries system. The analysis results showed that YOLOv8 achieved the highest precision at 0.989, followed by YOLOv7 at 0.775 and YOLOv5m at 0.318. In terms of recall, YOLOv8 scored 0.969, YOLOv5l scored 0.845, and YOLOv7 scored 0.783. For mAP50 and mAP50-95 metrics, YOLOv8 also performed the best with scores of 0.978 and 0.834, respectively. Overall, YOLOv8 demonstrated the highest performance, indicating its strong suitability for real-time detection of toxic marine organisms. On the other hand, the YOLOv5 series showed lower performance, revealing limitations in detection under complex conditions. These findings suggest that the use of the latest YOLO model is essential for establishing an early warning system for toxic marine species.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.59-88
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• 2008

Digital Convergence means integration between industry, technology, and contents, and in marketing, it usually comes with creation of new types of product and service under the base of digital technology as digitalization progress in electro-communication industries including telecommunication, home appliance, and computer industries. One can see digital convergence not only in instruments such as PC, AV appliances, cellular phone, but also in contents, network, service that are required in production, modification, distribution, re-production of information. Convergence in contents started around 1990. Convergence in network and service begins as broadcasting and telecommunication integrates and DMB(digital multimedia broadcasting), born in May, 2005 is the symbolic icon in this trend. There are some positive and negative expectations about DMB. The reason why two opposite expectations exist is that DMB does not come out from customer's need but from technology development. Therefore, customers might have hard time to interpret the real meaning of DMB. Time is quite critical to a high tech product, like DMB because another product with same function from different technology can replace the existing product within short period of time. If DMB does not positioning well to customer's mind quickly, another products like Wibro, IPTV, or HSPDA could replace it before it even spreads out. Therefore, positioning strategy is critical for success of DMB product. To make correct positioning strategy, one needs to understand how consumer interprets DMB and how consumer's interpretation can be changed via communication strategy. In this study, we try to investigate how consumer perceives a new product, like DMB and how AD strategy change consumer's perception. More specifically, the paper segment consumers into sub-groups based on their DMB perceptions and compare their characteristics in order to understand how they perceive DMB. And, expose them different printed ADs that have messages guiding consumer think DMB in specific ways, either cellular phone or personal TV. Research Question 1: Segment consumers according to perceptions about DMB and compare characteristics of segmentations. Research Question 2: Compare perceptions about DMB after AD that induces categorization of DMB in direction for each segment. If one understand and predict a direction in which consumer perceive a new product, firm can select target customers easily. We segment consumers according to their perception and analyze characteristics in order to find some variables that can influence perceptions, like prior experience, usage, or habit. And then, marketing people can use this variables to identify target customers and predict their perceptions. If one knows how customer's perception is changed via AD message, communication strategy could be constructed properly. Specially, information from segmented customers helps to develop efficient AD strategy for segment who has prior perception. Research framework consists of two measurements and one treatment, O1 X O2. First observation is for collecting information about consumer's perception and their characteristics. Based on first observation, the paper segment consumers into two groups, one group perceives DMB similar to Cellular phone and the other group perceives DMB similar to TV. And compare characteristics of two segments in order to find reason why they perceive DMB differently. Next, we expose two kinds of AD to subjects. One AD describes DMB as Cellular phone and the other Ad describes DMB as personal TV. When two ADs are exposed to subjects, consumers don't know their prior perception of DMB, in other words, which subject belongs 'similar-to-Cellular phone' segment or 'similar-to-TV' segment? However, we analyze the AD's effect differently for each segment. In research design, final observation is for investigating AD effect. Perception before AD is compared with perception after AD. Comparisons are made for each segment and for each AD. For the segment who perceives DMB similar to TV, AD that describes DMB as cellular phone could change the prior perception. And AD that describes DMB as personal TV, could enforce the prior perception. For data collection, subjects are selected from undergraduate students because they have basic knowledge about most digital equipments and have open attitude about a new product and media. Total number of subjects is 240. In order to measure perception about DMB, we use indirect measurement, comparison with other similar digital products. To select similar digital products, we pre-survey students and then finally select PDA, Car-TV, Cellular Phone, MP3 player, TV, and PSP. Quasi experiment is done at several classes under instructor's allowance. After brief introduction, prior knowledge, awareness, and usage about DMB as well as other digital instruments is asked and their similarities and perceived characteristics are measured. And then, two kinds of manipulated color-printed AD are distributed and similarities and perceived characteristics for DMB are re-measured. Finally purchase intension, AD attitude, manipulation check, and demographic variables are asked. Subjects are given small gift for participation. Stimuli are color-printed advertising. Their actual size is A4 and made after several pre-test from AD professionals and students. As results, consumers are segmented into two subgroups based on their perceptions of DMB. Similarity measure between DMB and cellular phone and similarity measure between DMB and TV are used to classify consumers. If subject whose first measure is less than the second measure, she is classified into segment A and segment A is characterized as they perceive DMB like TV. Otherwise, they are classified as segment B, who perceives DMB like cellular phone. Discriminant analysis on these groups with their characteristics of usage and attitude shows that Segment A knows much about DMB and uses a lot of digital instrument. Segment B, who thinks DMB as cellular phone doesn't know well about DMB and not familiar with other digital instruments. So, consumers with higher knowledge perceive DMB similar to TV because launching DMB advertising lead consumer think DMB as TV. Consumers with less interest on digital products don't know well about DMB AD and then think DMB as cellular phone. In order to investigate perceptions of DMB as well as other digital instruments, we apply Proxscal analysis, Multidimensional Scaling technique at SPSS statistical package. At first step, subjects are presented 21 pairs of 7 digital instruments and evaluate similarity judgments on 7 point scale. And for each segment, their similarity judgments are averaged and similarity matrix is made. Secondly, Proxscal analysis of segment A and B are done. At third stage, get similarity judgment between DMB and other digital instruments after AD exposure. Lastly, similarity judgments of group A-1, A-2, B-1, and B-2 are named as 'after DMB' and put them into matrix made at the first stage. Then apply Proxscal analysis on these matrixes and check the positional difference of DMB and after DMB. The results show that map of segment A, who perceives DMB similar as TV, shows that DMB position closer to TV than to Cellular phone as expected. Map of segment B, who perceive DMB similar as cellular phone shows that DMB position closer to Cellular phone than to TV as expected. Stress value and R-square is acceptable. And, change results after stimuli, manipulated Advertising show that AD makes DMB perception bent toward Cellular phone when Cellular phone-like AD is exposed, and that DMB positioning move towards Car-TV which is more personalized one when TV-like AD is exposed. It is true for both segment, A and B, consistently. Furthermore, the paper apply correspondence analysis to the same data and find almost the same results. The paper answers two main research questions. The first one is that perception about a new product is made mainly from prior experience. And the second one is that AD is effective in changing and enforcing perception. In addition to above, we extend perception change to purchase intention. Purchase intention is high when AD enforces original perception. AD that shows DMB like TV makes worst intention. This paper has limitations and issues to be pursed in near future. Methodologically, current methodology can't provide statistical test on the perceptual change, since classical MDS models, like Proxscal and correspondence analysis are not probability models. So, a new probability MDS model for testing hypothesis about configuration needs to be developed. Next, advertising message needs to be developed more rigorously from theoretical and managerial perspective. Also experimental procedure could be improved for more realistic data collection. For example, web-based experiment and real product stimuli and multimedia presentation could be employed. Or, one can display products together in simulated shop. In addition, demand and social desirability threats of internal validity could influence on the results. In order to handle the threats, results of the model-intended advertising and other "pseudo" advertising could be compared. Furthermore, one can try various level of innovativeness in order to check whether it make any different results (cf. Moon 2006). In addition, if one can create hypothetical product that is really innovative and new for research, it helps to make a vacant impression status and then to study how to form impression in more rigorous way.

• Progress in Medical Physics
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• v.25 no.2
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• pp.72-78
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• 2014

The aim of the study is to test a hypothesis that quasi-breath-hold (QBH) biofeedback improves the residual respiratory motion management in gated 3D thoracic MR imaging, reducing respiratory motion artifacts with insignificant acquisition time alteration. To test the hypothesis five healthy human subjects underwent two gated MR imaging studies based on a T2 weighted SPACE MR pulse sequence using a respiratory navigator of a 3T Siemens MRI: one under free breathing and the other under QBH biofeedback breathing. The QBH biofeedback system utilized the external marker position on the abdomen obtained with an RPM system (Real-time Position Management, Varian) to audio-visually guide a human subject for 2s breath-hold at 90% exhalation position in each respiratory cycle. The improvement in the upper liver breath-hold motion reproducibility within the gating window using the QBH biofeedback system has been assessed for a group of volunteers. We assessed the residual respiratory motion management within the gating window and respiratory motion artifacts in 3D thoracic MRI both with/without QBH biofeedback. In addition, the RMSE (root mean square error) of abdominal displacement has been investigated. The QBH biofeedback reduced the residual upper liver motion within the gating window during MR acquisitions (~6 minutes) compared to that for free breathing, resulting in the reduction of respiratory motion artifacts in lung and liver of gated 3D thoracic MR images. The abdominal motion reduction in the gated window was consistent with the residual motion reduction of the diaphragm with QBH biofeedback. Consequently, average RMSE (root mean square error) of abdominal displacement obtained from the RPM has been also reduced from 2.0 mm of free breathing to 0.7 mm of QBH biofeedback breathing over the entire cycle (67% reduction, p-value=0.02) and from 1.7 mm of free breathing to 0.7 mm of QBH biofeedback breathing in the gated window (58% reduction, p-value=0.14). The average baseline drift obtained using a linear fit was reduced from 5.5 mm/min with free breathing to 0.6 mm/min (89% reduction, p-value=0.017) with QBH biofeedback. The study demonstrated that the QBH biofeedback improved the upper liver breath-hold motion reproducibility during the gated 3D thoracic MR imaging. This system can provide clinically applicable motion management of the internal anatomy for gated medical imaging as well as gated radiotherapy.

• Journal of the Korean earth science society
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• v.29 no.2
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• pp.189-203
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• 2008

Microcomputer Based Laboratory (MBL), by offering accurate and effective data collection and real-time graphs, enables students to reduce experiment time and, thereby, have deeper discussions concerning experimental results. This helps to emphasis the essential aspect of scientific inquiry; the process communication. Therefore, this study examined secondary school earth science MBL lessons with regards to the five basic aspects of scientific inquiry: "Asking", "Evidencing", "Explaining", "Evaluating" and "Communicating". It then investigated the level of argumentative communication between the students and teachers and also among the students themselves. For this study, three classroom activities were observed and videotaped, and teaching materials, textbooks and students' notes were collected. The transcribed data were analyzed from the perspective of scientific inquiry level and argument frames. The results showed that the scientific inquiry levels of the three classes were similar, except for the "Communicating" aspect, which appeared in only one episode. "Asking" was carried out by the teacher and then students were directed to collect certain data in the "Evidencing" stage. Furthermore, students were given possible ways to use evidence to formulate explanations and connections through the "Explaining" and "Evaluating" stages. In the argumentation analysis, most argumentative communication was identified as being associated with a given procedure, rather than with any scientific phenomena. In only one episode, did "Communicating" relate directly to any scientific phenomena. It can be concluded, that although MEL places emphasis on communication for authentic scientific inquiry, the environment required for such inquiry and argumentative communication can not be easily created in the classroom. Therefore, in order for authentic inquiry to take place in the MBL classroom, teachers should provide students with the opportunity to develop meaningful argumentation and scaffolding abilities.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.