• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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• pp.1-10
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• 2011

In this paper, we implemented the Android NDK porting application with Eclipse(JDK) ADT and TinyOS 2.0. TinyOS and Cygwin are component based embedded system and an Open-source basis for interfacing with sensor application from H-mote. Cygwin is a collection of tools for using the Linux environment for commercially released with x86 32 bit and 64 bit versions of Windows. TinyOS-2. x is a component based embedded OS by UC Berkeley and is an Open-source OS designed for interfacing the sensor application with specific C-language. The results of Android porting experiment are described to show the improvement of sensor interfacing functionality under the PXA320 embedded RTOS platform. We will further more develop the software programming of Android porting under Embedded platform and enhance the functionality of the Android SDK with mobile gaming and kernel programming under sensor interfacing activity.

• Journal of Digital Convergence
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• v.7 no.1
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• pp.149-156
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• 2009

Enterprise Resource Planning (ERP) system is one of key information technology to shape doing business. ERP adoption characteristics like IS planning and change management before ERP implementation are rising in importance, because of gaining competitive advantage. The purpose of this study is to analyze the impact of the characteristics of ERP adoption on ERP implementation success. From previous researches on ERP adoption and implementation, two characteristics of ERP adoption such as IS planning and change management, and 2 dependent variables such as process innovation and business performance, are identified. From data collection processes, 122 samples are collected. The results of hypothesis testing show that organizations with IS plan have higher implementation performance than organizations without IS plan. Also, organizations with the process of change management have higher implementation performance than organizations without the process of change management. Also, The interaction effect between IS planning and change management shows bigger impact in ERP implementation success.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.77-85
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• 2017

Due to the expansion of accommodation space caused by increase of population along with lifestyle changes, most of people spend their time indoor except for the travel time. Because of this, environmental change of indoor is very important, and it affects people's health and economy in resources. But, most of people don't acknowledge the importance of indoor environment. Thus, monitoring system for sustaining and managing indoor environment systematically is needed, and big data clusters should be used in order to save and manage numerous sensor data collected from many spaces. In this paper, we design a big data cluster for the indoor environment monitoring in order to store the sensor data and monitor unit of the huge building Implementation design big data cluster-based system for the analysis, and a distributed file system and building a Hadoop, HBase for big data processing. Also, various sensor data is saved for collection, and effective indoor environment management and health enhancement through monitoring is expected.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.57-59
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• 2003

Digital surface model generation from IKONOS stereo imagery is a new challenge in photogrammetric community, especially when the satellite company does not provide the raw data as well as their ancillary ephemeris data. In this paper we utilized an estimated relief displacement azimuth and the nominal collection elevation data included in the metadata file to correct the relief displacement of GCPs, together with a linear transformation for geometric modeling of IKONOS imagery. Space intersection is performed by the trigonometric intersection assuming a parallel projection of IKONOS imagery due to its small FOV and frame size. In the experiment, less than 2-meters of RMSE in orbit modeling is achieved denoting the potential positioning accuracy of the IKONOS stereo imagery.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.95-102
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• 2021

As social interest in the metaverse increases, various metaverse platforms and services are appearing, and various security issues are emerging accordingly. In particular, since all activities are performed in a variety of virtual spaces, and the metaverse utilizes sensing data using various hardware devices, more information is accumulated than other Internet services, and more damage can occur if information security is not guaranteed. Therefore, in this paper, we propose a metaverse security model that considers the major issues mentioned in previous papers and the necessary evaluation factors for the security functions required in the metaverse platform. As a result of performing the performance evaluation of the proposed model and the existing attribute information collection model, the proposed model can provide security functions such as anonymity and source authentication, which were not provided by the existing models.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.19-28
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• 2020

Vehicle license plate recognition system is not generalized in Malaysia due to the loose character layout rule and the varying number of characters as well as the mixed capital English characters and italic English words. Because the italic English word is hard to segmentation, a separate method is required to recognize in Malaysian license plate. In this paper, we propose a mixed character level and word level English license plate recognition algorithm using deep learning neural networks. The difference of Gaussian method is used to segment character and word by generating a black and white image with emphasized character strokes and separated touching characters. The proposed deep learning neural networks are implemented on the LPR system at the gate of a building in Kuala-Lumpur for the collection of database and the evaluation of algorithm performance. The evaluation results show that the proposed Malaysian English LPR can be used in commercial market with 98.01% accuracy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.233-244
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• 2023

Although artificial intelligence and machine learning technologies have been used in various fields, problems with personal information protection have arisen based on centralized data collection and processing. Federated learning has been proposed to solve this problem. Federated learning is a process in which clients who own data in a distributed data environment learn a model using their own data and collectively create an artificial intelligence model by centrally collecting learning results. Unlike the centralized method, Federated learning has the advantage of not having to send the client's data to the central server. In this paper, we quantitatively present the performance improvement when federated learning is applied using the building change detection learning data. As a result, it has been confirmed that the performance when federated learning was applied was about 29% higher on average than the performance when it was not applied. As a future work, we plan to propose a method that can effectively reduce the number of federated learning rounds to improve the convergence time of federated learning.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.1-12
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• 2023

With the rapid increase of user generated data on digital platforms, the task of categorizing and classifying theses huge data has become difficult. Topic modeling is an unsupervised machine learning technique that can be used to get a summary from a large collection of documents. Topic modeling has been widely used in English content, yet the application of topic modeling in Arabic language is limited. Therefore, the aim of this paper is to provide a systematic review of the application of topic modeling algorithms in Arabic content. Using a well-known and trusted databases including ScienceDirect, IEEE Xplore, Springer Link, and Google Scholar. Considering the publication date from 2012 to 2022, we got 60 papers. After refining the papers based on predefined criteria, we resulted in 32 papers. Our result show that unfortunately the application of topic modeling techniques in Arabic content is limited.

• Journal of Information Technology Services
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• v.23 no.3
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• pp.65-89
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• 2024

This study aims to enhance the continuous utilization of community mapping by structurally understanding the intentions behind its ongoing use and the impacts related to it, grounded in prior research. By analyzing existing community mapping cases through literature studies on community mapping, this research identifies the characteristics of community mapping that motivate its sustained use. Moreover, based on the Uses and Gratifications Theory and the Participatory Communication Model (PCM), this study proposes a research model to investigate the factors influencing the intention to continue using community mapping. Unlike previous studies, which primarily focused on developing and applying community mapping processes for case studies, this research not only develops and applies a community mapping process but also aims to apply the findings related to the intention of continued use in future community mapping activity processes for sustained engagement.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.427-433
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• 2024

This study aims to develop a personalized music digital therapeutic based on MBTI personality types and apply it to depression treatment. In the data collection stage, participants' MBTI personality types and music preferences were surveyed to build a database, which was then preprocessed as input data for the KNN model. The KNN model calculates the distance between personality types using Euclidean distance and recommends music suitable for the user's MBTI type based on the nearest K neighbors' data. The developed system was tested with new participants, and the system and algorithm were improved based on user feedback. In the final validation stage, the system's effectiveness in alleviating depression was evaluated. The results showed that the MBTI personality type-based music recommendation system provides a personalized music therapy experience, positively impacting emotional stability and stress reduction. This study suggests the potential of nonpharmacological treatments and demonstrates that a personalized treatment experience can offer more effective and safer methods for treating depression.