• Science of Emotion and Sensibility
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• v.11 no.4
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• pp.575-588
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• 2008

This study is to inquire respective patterns of mental models caused by wrongful usages which can be experienced when a user who is used to a keypad-based mobile phone starts using a touch screen mobile phone and to find out the features of the user's logical process of correcting such wrongful usages to a new mental model. In addition, design improvement to be considered for easy generation of the mental model regarding touch screen mobile phones was reviewed in this study. We set up test subjects for the most frequently used seven high priority functions among touch screen phone functions and carried out the subject assessment together with interview surveys after the video observation experiment. Our test results show that test subjects who were used to keypad-based mobile phones tend to use operation knowledge related to the computer operational system(Window) or the web browse, navigation including Tap or Double Tap in order to correct the mental model when a wrongful usage is made. In addition, the result of comparison and analysis of the subject assessment and the video observation experiment data shows that wrongful usages of touch screen mobile phones mostly occurred in the field of 'information feedback' and 'navigation' among mobile phone components.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.263-271
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• 2016

Localizing an object in image is a common task in the field of computer vision. As the existing methods provide a detection for the single object in an image, they have an utilization limit for the use of the application, due to similar objects are in the actual picture. This paper proposes an efficient method of object localization for image recognition. The new proposed method uses color correlation back-projection in the YCbCr chromaticity color space to deal with the object localization problem. Using the proposed algorithm enables users to detect and locate primary location of object within the image, as well as candidate regions can be detected accurately without any information about object counts. To evaluate performance of the proposed algorithm, we estimate success rate of locating object with common used image database. Experimental results reveal that improvement of 21% success ratio was observed. This study builds on spatially localized color features and correlation-based localization, and the main contribution of this paper is that a different way of using correlogram is applied in object localization.

• Journal of The Korean Association of Information Education
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• v.18 no.1
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• pp.13-23
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• 2014

For this paper, we developed a game-addiction therapy program for students with gaming addiction based on multiple intelligences (MI) and verified its effects. The participants were 54 elementary students selected through a game addiction test in Incheon City, Korea. We tested the students' MI to facilitate program development. the students with gaming addiction showed strengths in the Bodily-kinesthetic, Naturalist, and Spatial intelligences whilst showing weaknesses in the Logical-mathematical, Intrapersonal, and Interpersonal intelligences as opposed to normal students who had opposite results. We arranged the program around various gaming and playing activities to engage their stronger intelligences; we added activities to address their weakness (i.e., Logical-mathematical, Intrapersonal, and Interpersonal intelligences). This study has shown that this program lowered the game immersion level of the students and was helpful in turning their attention to other activities. There were significant differences between pretest and posttest game addiction scores (p<0.001). Their weekly gaming time and computer usage decreased rapidly. Satisfaction with the game addiction therapy program based on MI was very high.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.256-264
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• 2020

Industrial Wireless Sensor Networks (IWSNs) is exploited to achieve various objectives such as improving productivity and reducing cost in the diversity of industrial application, and it has requirements such as low-delay and high reliability packet transmission. To accomplish the requirement, the network manager performs graph construction and resource allocation about network topology, and determines the transmission cycle and path of each node in advance. However, this network management scheme cannot treat mobile devices that cause continuous topology changes because graph reconstruction and resource reallocation should be performed as network topology changes. That is, despite the growing need of mobile devices in many industries, existing scheme cannot adequately respond to path failure caused by movement of mobile device and packet loss in the process of path recovery. To solve this problem, a network management scheme is required to prevent packet loss caused by mobile devices. Thus, we analyse the location and movement cycle of mobile devices over time using machine learning for predicting the mobility pattern. In the proposed scheme, the network manager could prevent the problems caused by mobile devices through performing graph construction and resource allocation for the predicted network topology based on the movement pattern. Performance evaluation results show a prediction rate of about 86% compared with actual movement pattern, and a higher packet delivery ratio and a lower resource share compared to existing scheme.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.239-246
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• 2020

In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.

• Journal of The Korean Association of Information Education
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• v.24 no.2
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• pp.157-165
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• 2020

As interest in software education has increased, discussions on teaching, learning, and evaluation method it have also been active. One of the problems of software education teaching method is that the instructor cannot grasp the content of coding in progress in the learner's computer in real time, and therefore, instructors are limited in providing feedback to learners in a timely manner. To overcome this problem, in this study, we developed a software education support system that grasps the real-time learner coding situation under block-based programming environment by applying a learning analysis technique and delivers it to the instructor, and visualizes the data collected during learning through the Hadoop system. The system includes a presentation layer to which teachers and learners access, a business layer to analyze and structure code, and a DB layer to store class information, account information, and learning information. The instructor can set the content to be learned in advance in the software education support system, and compare and analyze the learner's achievement through the computational thinking components rubric, based on the data comparing the stored code with the students' code.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.283-288
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• 2012

3D is the one of the current world in the spotlight as part of the future earnings of the business sector. Existing flat 2D and stereoscopic 3D to change the 3D shape and texture make walking along the dimension of the real world and the virtual reality world by making it feel contemporary reality of coexistence good show. 3D for the interest of the people has been spreading throughout the movie which is based on a 3D Avata. 3D TV market of the current conglomerate of changes in the market pioneer in the 3D market, further leap into the era of the upgrade was. At the same time, however, the modern man of the world, if becoming a necessity in the smartphone craze new innovation in the IT market mobile phone market and also has made. A small computer called a smartphone enough, the ripple velocity and the aftermath of the innovation of the telephone, the Internet as much as to leave many issues. Smartphone smart phone is a mobile phone that can be several functions. The current iPhone, Android. In addition to a large number of Windows Phone smartphones are released. Above the overall prospects of the future and a business service model for 3D facial expression as input avatar virtual 3D character on camera on your smartphone camera to recognize a user's emotional expressions on the face of the person is able to synthetic synthesized avatars in real-time to other mobile phone users matching, transmission, and be able to communicate in real-time sensibility fused video communication services to the development of applications.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.333-338
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• 2012

Human resource management is bringing the various changes with the IT technology. In particular, if HRM is non-scientific method such as group management, physical plant, working hours constraints, personal contacts, etc, the current enterprise human resources management(e-HRM) appeared in the individual dimension management, virtual workspace (for example: smart work center, home work, etc.), working time flexibility and elasticity, computer-based statistical data and the scientific method of analysis and management has been a big difference in the sense. Therefore, depending on changes in the environment, companies have introduced a variety of techniques as RFID card, fingerprint time & attendance systems in order to build more efficient and strategic human resource management system. In this paper, time and attendance, access control management system was developed using multi camera for 2D and 3D face recognition technology-based for efficient enterprise human resource management. We had an issue with existing 2D-style face-recognition technology for lighting and the attitude, and got more than 90% recognition rate against the poor readability. In addition, 3D face recognition has computational complexities, so we could improve hybrid video recognition and the speed using 3D and 2D in parallel.