• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.201-209
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• 2009

According to previous researches about online evaluation in many e-Learning contents, it took too much time and effort to generate test questions for formative or achievement tests using a database as an item pool. Furthermore, it is hard to measure accomplishment of learners for each unit through overall tests provided by existing e-learning contents. In this paper, to efficiently cope with problems described above, the item pool based on Item Form was transformed into Interaction Date Model in Run-Time Environment of SCORM2004. And the contents for the math concepts and principles that students would learn from regular classroom were developed in accordance with SCORM. In addition, Confidence Factor Function was used to take an objective view in measuring the accomplishment of learners through the items automatically generated by LMS(Learning Management System).

• Smart Structures and Systems
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• v.32 no.5
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.611-619
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• 2010

This paper presents an efficient modeling system of virtual pottery in which the user can deform a body of virtual clay with a haptic tool for E-learning. We propose a Circular Sector Element Method (CSEM) which represents the virtual pottery with a set of circular sector elements based on the cylindrical symmetry of pottery. Efficient algorithms for collision detection and response, interactions between adjacent elements, and GPU-based visual-haptic synchronization are designed and implemented for the CSEM. Empirical evaluation showed that the modeling system is computationally efficient with finer details and provides convincing model deformation and force feedback. The developed system, if combined with educational contents, is expected to be used as an effective E-learning platform for elementary school students.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.3
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• pp.227-234
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• 2017

In this paper, we propose a system whereby one can automatically classifies categories based on image data of the products for a shopping mall platform. Many products sold within internet shopping malls are classified their category defined by the same use of product names and products. However, it is difficult to search by category classification when the classification of the product is uncertain and the product classified by the shopping mall seller judgment is different from the purchasing user judgment. We proposes classification and retrieval method by Deep Learning technique solely using product image. The system can categorize products by using their images and its speed and accuracy are quantified using test data. The performance is evaluated with the test data. In addition, its usability is tested with the participants.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.13-28
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• 2021

Reinforcement Learning (RL) is a machine learning algorithm that repeat the closed-loop process that agents perform actions specified by the policy, the action is evaluated with a reward function, and the policy gets updated accordingly. The key benefit of RL is the ability to optimze the policy with action evaluation. Hence, it can effectively be applied to developing advanced intelligent systems and autonomous systems. Conventional RL incoporates a single policy, a reward function, and relatively simple policy update, and hence its utilization was limited. In this paper, we propose an extended RL model that considers multiple instances of RL elements. We define a formal model of the key elements and their computing model of the extended RL. Then, we propose design methods for applying to system development. As a case stud of applying the proposed formal model and the design methods, we present the design and implementation of an advanced car navigator system that guides multiple cars to reaching their destinations efficiently.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.526-537
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• 2019

This study researched the effect of application systems for media industry by using machine learning method focusing on industrial organization theory. First, for applying the system successfully, formation of sympathy about needs is required. The introduction of machine learning can bring change in each stage of value chain especially, decision making process of investment and production process. In investment side, objective performance prediction data can enhance efficiency, and content diversity can decrease with concentrated investment phenomenon to secured content by the system. In production side, if the system support to make creators decrease simple repeat works, production efficiency will increase.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.433-441
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• 2021

Android applications are released across various categories, including productivity apps and games, and users are exposed to various applications and even malware depending on their usage patterns. On the other hand, most analysis engines train using existing datasets and do not reflect user patterns even if periodic updates are made. Thus, the detection rate for known malware is high, while types of malware such as adware are difficult to detect. In addition, existing engines incur increased service provider costs due to the cost of server farm, and the user layer suffers from problems where availability and real-timeness are not guaranteed. To address these problems, we propose an analysis system that performs on-device malware detection through transfer learning, which requires only one-time communication with the server. In addition, The system has a complete process on the device, including decompiler, which can distribute the load of the server system. As an evaluation result, it shows 90.3% accuracy without transfer learning, while the model transferred with adware catergories shows 95.1% of accuracy, which is 4.8% higher compare to original model.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.25-32
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• 2022

In this paper, we propose a pet monitoring system based on deep learning using an activity recognition device. The system consists of a pet's activity recognition device, a pet owner's smart device, and a server. Accelerometer and gyroscope data were collected from an Arduino-based activity recognition device, and the number of steps was calculated. The collected data is pre-processed and the amount of activity is measured by recognizing the activity in five types (sitting, standing, lying, walking, running) through a deep learning model that hybridizes CNN and LSTM. Finally, monitoring of changes in the activity, such as daily and weekly briefing charts, is provided on the pet owner's smart device. As a result of the performance evaluation, it was confirmed that specific activity recognition and activity measurement of pets were possible. Abnormal behavior detection of pets and expansion of health care services can be expected through data accumulation in the future.

• Journal of Digital Convergence
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• v.7 no.2
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• pp.51-62
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• 2009

Most studies of organizational effectiveness previously done, have measured financial performance such as productivity, efficiency or subjective performance such as a commitment, satisfaction, and turnover intention of employees. However, these measurements have limitations to evaluate a organizational effectiveness in recent knowledge information era because evaluation of organizational effectiveness in knowledge-based society needs measurements in various aspects such as financial, customer, internal business process, and learning and growth. The purpose of this study is to overcome these problems and to introduce an appropriate system for evaluation of organizational effectiveness under knowledge management paradigm. In this paper, we suggest a Balanced Scorecard(BSC) as a new measurement standard of organizational effectiveness in a knowledge information era. The balanced scorecard is designed to help firms that have historically overemphasized short-term financial performance. When measuring organizational effectiveness through Balanced Scorecard(BSC) suggested by Kaplan and Norton[31], it is to present the measurement indices that can cover the limitation of the past evaluation indices of organizational effectiveness.

• International Journal of Contents
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• v.14 no.3
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• pp.69-74
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• 2018

A new breed of universities equipped with student-centered education programs and advanced digital technologies is changing the face of higher education. "Flipped learning" is heralded as a new model of education, yet its effect is underexplored. The purpose of this study is to provide evaluation criteria to assess and understand the merit of student-centered education programs and apply them to actual cases. Discussion on the nature of knowledge, its production mechanism and system, and possible contribution of digital technology to user-centered programs are discussed to produce five key criteria; initiative of students, interaction in class, interaction in field, customization of courses, and automated personal service. They are applied to evaluation of Minerva and Ecole 42.