• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.396-402
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• 2016

Recently, as the performance of smart phones has advanced and their distribution has increased, various functions in existing devices are accumulated. In particular, functions in smart devices have matured through improvement of diverse sensors. Various applications with the development of smart phones get fleshed out. As a result, services from applications promoting physical activity in users have gotten attention from the public. However, these services are about diet alone, and because these have no exercise motion recognition capability to detect movement in the correct position, the user has difficulty obtaining the benefits of exercise. In this paper, we develop exercise motion-recognition software that can sense the user's motion using a sensor built into a smart phone. In addition, we implement a system to offer exercise with friends who are connected via web server. The exercise motion recognition utilizes a Kalman filter algorithm to correct the user's motion data, and compared to data that exist in sampling, determines whether the user moves in the correct position by using a DTW algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.45-55
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• 2022

Currently, the Collective Control Algorithm is the most popular elevator algorithm. The Collective Control Algorithm allows the user to use the elevator when the direction of movement of the elevator and the direction of the user's destination are the same. However, the algorithm has a problem in that only one elevator responds to a user's call when the user's waiting time and using multiple elevators. To solve this problem, this paper proposes a new hybrid HRN algorithm based on the highest response ratio next (HRN) algorithm. In general, HRN Algorithm requires a user's boarding time and getting off time, but due to the nature of the elevator, it is difficult to predict the user's call in advance. Therefore, to overcome these limitations, this paper proposes Hybrid HRN Algorithm that considers the distance between the user's call location and the arrival location. This paper shows that Hybrid HRN Algorithm, proposed through experiments, has an average waiting time of 23.34 seconds, a standard deviation of 11.86, a total moving distance of 535.2m, a total operating time of 84sec, and a driving balance between the two elevators is 92m, which is superior to the previously suggested Collective Control, Zoning, and 3-Passage Algorithm.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.31-43
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• 2022

Recently, metaverse is rapidly expanding its area as a platform that can be applied to various fields. In particular, the function that allows many users to interact in a three-dimensional space allows VR/AR-based educational content to be used as a more advanced concept. Due to the nature of engineering education, it is often based on three-dimensional objects. In the case of a three-dimensional object, it is difficult to explain through two-dimensional videos or documents, and it becomes more difficult to express when the process of changing the object is included. The three-dimensional space of the metaverse can improve this difficulty based on real-time rendering. Another characteristic of engineering education is that there are many invisible elements. Although it is involved in the movement of objects due to electromagnetic fields, magnetic fields, and forces, it is the main reason for increasing learning difficulty because it is invisible. These problems can also help learning because they can be visually represented in the metaverse space. In this paper, the results of the establishment of the metaverse platform for engineering education and the real-time lecture contents produced based on it are described, and the applied results and lecture evaluation are discussed. Lectures using a total of 9 metaverse contents were conducted, and 90% of the positive lecture evaluation results were obtained.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.67-79
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• 2023

The bounce rate is the rate at which a user leaves immediately after visiting, and this study aimed to find out what attributes of a website affect the bounce rate. Web site evaluation items were defined as a total of 4 items and 27 evaluation attributes, including usability, information, service interaction, and technology, so that they can be commonly applied to venture companies in various industries through prior research. As a result of the study, 6 website attributes that affect the bounce rate were verified to be significant by discriminant analysis and decision tree analysis. Suggestions to reduce the bounce rate of venture business websites through this study are as follows. First, the path name of the website is displayed as mandatory and a pull-down menu function is added to facilitate movement to other pages. Second, it is good to expose core content that can attract users' attention in the form of a banner, and place internal link banners in the right place on sub-pages. Third, external links should be linked to a new window so that they do not leave the current page immediately so that they can be re-entered. Lastly, it is recommended to expose the contact information of the person in charge and consultation function as direct information for communication with customers, but if individual response is difficult, at least the consultation function must be added. These suggestions are expected to be of practical help in various fields such as website development, operation, and marketing. However, in special cases, a high bounce rate may be normal, so it should be considered according to the situation.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.35-42
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• 2016

Recently many researches have been focused on the use of the markerless augmented reality system using face, foot, and hand of user's body to alleviate many disadvantages of the marker based augmented reality system. In addition, most existing augmented reality systems have been utilized rigid objects since they just desire to insert and to basic interaction with virtual object in the augmented reality system. In this paper, unlike restricted marker based augmented reality system with rigid objects that is based in display, we designed and implemented the markerless augmented reality system using deformable objects to apply various fields for interactive situations with a user. Generally, deformable objects can be implemented with mass-spring modeling and the finite element modeling. Mass-spring model can provide a real time simulation and finite element model can achieve more accurate simulation result in physical and mathematical view. In this paper, the proposed markerless augmented reality system utilize the mass-spring model using tetraheadron structure to provide real-time simulation result. To provide plausible simulated interaction result with deformable objects, the proposed method detects and tracks users hand with Kinect SDK and calculates the external force which is applied to the object on hand based on the position change of hand. Based on these force, 4th order Runge-Kutta Integration is applied to compute the next position of the deformable object. In addition, to prevent the generation of excessive external force by hand movement that can provide the natural behavior of deformable object, we set up the threshold value and applied this value when the hand movement is over this threshold. Each experimental test has been repeated 5 times and we analyzed the experimental result based on the computational cost of simulation. We believe that the proposed markerless augmented reality system with deformable objects can overcome the weakness of traditional marker based augmented reality system with rigid object that are not suitable to apply to other various fields including healthcare and education area.

• The Korean Journal of Archival Studies
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• no.41
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• pp.175-212
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• 2014

In 2001 a large amount of labor record have been donated from Jeontaeil Labor Archive-Institute to SungKongHoe University(SKHU). Institute of Labor History in SKHU was established in the wake of the installation of the labor archive. Development of oral archive raised the awareness of the various relationships between the use and production of labor record. Interviewees of oral testimony expressed dissatisfaction and the role of the researchers was not sufficiently exhibited. Examining the main cases of Korea union movement history, we can find contradictions between the use and production of labor record clearly. Interval of interpretation and memory was too big between the parties of 'democratic' union movement in the 1970s. While among the parties who took part in Guro Alliance Strike of 1985, there is a group that remains in the "winner" in history on the one hand, but "loser" on the other without any reasonable criterion. Active intervention of the record users(researchers) is very limited. Among citizens or workers how will be resolved such "struggle of memory" in due process can not be seen. This is one of the reasons why labor archive is not rooted in the region. In this paper, I present a methodological alternatives for the production and use of records through the construction of participatory labor archive. Further, the reconstituted contents of the "documenting locality" strategy by complementing the theoretical part of the method of participation. The study of local and locality requires a "scale" dimension that will make up the identity recognition space, a memory and identity, a social relationship rather than the dimension of the physical space. Alternative "documenting locality" strategy will be able to contribute to solve the problems that occur between the production and use of the recording in labor archive.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Korean Journal of Cognitive Science
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• v.11 no.2
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• pp.37-51
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• 2000

The present study compared computer users target-selection response patterns when the targets were varied in terms of their relative location and distance from the current position of the cursor. In Experiment 1, where the mouse was used as an input device, the effects of different directions and distances of simple target(small rectangle) on target-selection response were investigated. The results of Experiment 1 can be summarized as follows: (1) Overshooting was more frequent than either undershooting or correct movement and (2) this tendency was more prominent when the targets were presented in the oblique direction or in farther location from the current cursor position. (3) Although the overshooting and undershooting were more frequent in the oblique direction, the degree of deviation was larger in horizontal and vertical direction. (4) Time spent in moving the mouse rather than that spent in planning, calibrating or clicking was found to be the most critical factor in determining total response time. In Experiment 2, effects of the font size and line-height of the target on target-selection response were compared with regard to two types of input devices(keyboard vs. mouse). The results are as follows: (1) Mouse generally yielded shorter target-selection time than keyboard. but this tendency was reversed when the targets were presented in horizontal and vertical directions. (2) In general, target-selection time was the longest in the condition of font size of 10 and line-height of 100%, and the shortest in the condition of font size of 12 and line-height of 150%. (3) When keyboard was used as the input device, target-selection time was shortest in the 150% line-height condition, whereas in the mouse condition, target-selection time tended to be increased as the line-height increased. which resulted in the significant interaction effect between input device and line-height. Finally, several issues relating to human-computer interaction were discussed based on the results of the present study.