• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1036-1043
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• 2022

This paper discusses about the development of a visual sensor that can be installed in an automatic mooring device to detect the berthing condition of a vessel. Despite controlling the ship's speed and confirming its location to prevent accidents while berthing a vessel, ship collision occurs at the pier every year, causing great economic and environmental damage. Therefore, it is important to develop a visual system that can quickly obtain the information on the speed and location of the vessel to ensure safety of the berthing vessel. In this study, a visual sensor was developed to observe a ship through an image while berthing, and to properly check the ship's status according to the surrounding environment. To obtain the adequacy of the visual sensor to be developed, the sensor characteristics were analyzed in terms of information provided from the existing sensors, that is, detection range, real-timeness, accuracy, and precision. Based on these analysis data, we developed a 3D visual module that can acquire information on objects in real time by conducting conceptual designs of LiDAR (Light Detection And Ranging) type 3D visual system, driving mechanism, and position and force controller for motion tilting system. Finally, performance evaluation of the control system and scan speed test were executed, and the effectiveness of the developed system was confirmed through experiments.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.13-24
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• 2022

SNS has become a very close service to our daily life. As marketing is done through SNS, places often called hot places are created, and users are flocking to these places. However, it is often crowded with a large number of people in a short period of time, resulting in a negative experience for both visitors and service providers. In order to improve this problem, it is necessary to recognize the congestion level, but the method to determine the congestion level in a specific area at an individual level is very limited. Therefore, in this study, we tried to propose a system that can identify the congestion level information and the characteristics of visitors to a specific tourist destination by using the data on the SNS. For this purpose, posting data uploaded by users and image analysis were used, and the performance of the proposed system was verified using the Naver DataLab system. As a result of comparative verification by selecting three places by type of tourist destination, the results calculated in this study and the congestion level provided by DataLab were found to be similar. In particular, this study is meaningful in that it provides a degree of congestion based on real data of users that is not dependent on a specific company or service.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.3
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• pp.348-356
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• 2007

Statistical and probabilistic method was used in the analysis of data, which is the most effective one in describing the various natures, and the methodology relating the results with the design was developed. Influents and effluents of three treatment plants were analyzed and the focus was made on BOD, COD, SS, IN, TP The fluctuations of influent such as BOD, COD, SS were extremely large and their standard deviations(st.dev) were more than 10 mg/L. but those of TN, TP were small; the st.dev was 6.6 mg/L for TN, 0.6 mg/L for TP, respectively. But, effluent concentration showed consistent pattern regardless of the influent fluctuations, the st.dev was ranged between 0.28 and 4.48 mg/L. Effluent distributional characteristics were as follows; BOD, COD were distributed normally, but SS, TN, and TP, log-normally; unsymmetric and skewed to the right. The coefficient of reliability(COR) based on the results of statistics of data was introduced to evaluate the process performance an4 to reflect the process performance to the process design. The coefficient of reliability relates the design value(the goal) with the standards and it can be used in operating treatment facilities under a certain reliability level and/or in evaluating the reliability of the treatment facilities on operation. Each treated water quality of effluent showed the half of water quality standards in the level of 50% percentile and all treatment plant was achieved 100% probability of water quality standards. It was concluded that the variability of the process performance should be reflected to the design procedure and the standards through the analysis based on the statistics and the probability.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

• Korean Journal of Applied Biomechanics
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• v.15 no.4
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• pp.43-53
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• 2005

This study is to define how the difference of athletic change influence on the last regrasp after somersault in Belle movement of parallel bars. For his study, the following conclusion was produced by analysis of athletic change by means of three dimensional visual image in three athlete of nation. 1. As the picture of S1, there are total used time(2.01 sec), S3(2.17 sec) and S2(2.19 sec). In case of a short needed time, it is difficult for them to perform the remaining movement of the vertical elevating flight easily and comfortably, it is judged as performing the small movement with restrict swing. 2 In the change of body center sped by each event, it is calculated as $-89.1^{\circ}$ the narrowest in S1, $-81.96^{\circ}$ the widest and then $86.34^{\circ}$ in S3. In E3 event, average compound speed is 4.07m/s, S2 showed the fastest speed of 4.14m/s whereas S1 the narrowest angle of 3.95m/s. 3. A shoulder joint and coxa are the period of mention in E3. In E4 which was pointed out the longest vertical distance, S2 that is indicated the highest vertical height as the period of detach in parallel bars. showed -3.91m. This is regarded as a preparatory movement for dynamic performance after using effectively elastic movement of shoulder joint and coxa while easily going up with turning back movement. In the 5th phrase, long airborne time and vertical change position is showed as the start while regrasping securely air flight movement from high position. 4. In E5, a long flight time and a long vertical displacement were shown as the regrasp after somersault efficiently in high position with stability from the point of the highest peak of the center of the body. Especially, S2 is marked as a little bit long position, while S1 is reversely indicated as performing somersault and unstable motion in a low position. 5. In E3, at the point of the largest extension of the shoulder joint and hip joint the shoulder joint is largely marked in $182^{\circ}$ and the hip point $182^{\circ}$ in S2. The shoulder joint is marked at the smallest angle in $177^{\circ}$ and the hip point $176^{\circ}$ in S1. And S1 is being judged by its performance of the less self - confident motion with lessening a breath of swing. S2 makes the most use of flexion and extension of the shoulder joint and the hip joint effectively. It was performed greatly with swinging and dropping the rotary movement and the rotary inertia naturally. 6. In E6, as the point of regrasp of the upper arm in parallel bars it is recognized by the that of components of vertical and horizontal velocity stably. During this study, the insufficient thing and the study on the parallel bars at a real game later are more activated than now. If it is really used as the basic materials by means of Belle Picked Study of Super E level after Bell movement, you may perceive the technique movement previously and perform without difficulty. Especially, such technique as crucifix is quite advantageous for oriental people thanks to small body shape condition. In conclusion we will nicely prepare for our suitable environment to gradually lessen trials and errors by analyzing and studying kinematically this movement.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1071-1085
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• 2015

The researchers attempted to develop a way to extract a near real-time online nation image using social media. Referring to previous studies about nation images and the categories defined in Wikipedia, an ontology considering the characteristics of nation image was constructed. Separately, data sets from various social media were compared and the click view of Wikipedia English-edition was selected. The ontology was applied to the recent six years of the data extracted of the three big exporting countries of the east Asia, China, Japan, and Korea. To compare the nation images, correspondence analysis was employed to show images in the area of politics, society, culture, and economy. The nation images extracted are indeed the reasonable representation of them. The researchers verified them to a few known government policies and confirmed that it could be used to help government officers to make foreign policies to boost nation's export and to employ as a key performance index for them.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.739-744
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• 2017

Elastic deformation and fatigue damage can cause the permanent deformation of a kart's frame during turning, affecting the kart's driving performance. A kart's frame does not contain any suspension or differential devices and, therefore, the dynamic behavior caused by torsional deformation when driving along a curve can strongly affect these two kinds of deformations. To analyze the dynamic behavior of a kart along a curved section, the GPS trajectory of the kart is obtained and the torsional stress acting on the kart-frame is measured in real time. The mechanical properties of leisure and racing karts are investigated by analyzing their material properties and conducting a tensile test. The torsional stress concentration and frame distortion are investigated through a stress analysis of the frame on the basis of the obtained results. Leisure and racing karts are tested in each driving condition using driving analysis equipment. The behavior of a kart when being driven along a curved section is investigated through this test. Because load movement occurs owing to centrifugal force when driving along a curve, torsional stress acts on the kart's steel frame. In the case of a leisure kart, the maximum torsional stress derived from the torsional fatigue limit was found to be 230 MPa, and the torsional fatigue limit coefficient was 0.65 when driving at a speed of 40 km/h. Furthermore, the driving elements during the cornering of a kart were measured based on an actual auto-test after installing a driving measurement system, and the driving behavior of the kart was analyzed by measuring its vertical displacement.

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.52-60
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• 2022

Purpose: For now, cognitive load is assessed based on survey-based methods, which can be difficult to track the amount of cognitive load in real-time. In this study, we investigated the difference in electrophysiological activation due to different levels of cognitive load not only at sensor-level but also at source-level using electroencephalogram that might be potentially used for quantitative cognitive load evaluation. Materials and Methods: In this study, ten healthy subjects (mean age 24.3 ± 2.1, three female) participated the experiment. All participants performed 4 sessions of n-back task in different difficulties: 0-, 1-, 2-, and 3-back during electroencephalogram recording. For sensor-level analysis, we calculated the event-related potential and event-related spectral perturbation while low resolution brain electromagnetic tomography (LORETA) to estimate the source activation. Each result was compared between different workload conditions using statistical analysis. Results: Statistical results revealed that the accuracy of the task performance was significantly different between different cognitive loads (p = 0.018). The post-hoc analysis confirmed that the accuracy of the 3-back task was significantly decreased compared to 1-back condition (p = 0.018), but not with 2-back condition (p = 0.180). ERP results showed that P300 target amplitude between 1-back and 3-back had a marginal difference in Cz (p = 0.059) and Pz(p = 0.093). A significant inhibition in Cz high-beta activation (p = 0.017) and decrease in source activation of right parahippocampal gyrus was found in 3-back condition compared to 1-back condition (p < 0.05). Conclusion: In this study, we compared the sensor- and source-level differences in electroencephalogram between different levels of cognitive load, that were found to be in line with the previous reports related to cognitive load evaluation. We expect that the outcome of the current study can be used as a feature to establish a quantitative cognitive load assessment system.

• 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.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.