• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6A
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• pp.420-425
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• 2012

A forward error correction (FEC) coding techniques is one of time diversity techniques with which the effect of channel impairments due to noise and fading are spreaded over independently, and thus the performance could be improved. Therefore, the performance of the FEC scheme can be maximized if we minimize the correlation of channel information across over a codeword. In this paper, we propose a block turbo code with the maximized time diversity effect which may be reduced due to utilization of high order modulation schemes and due to transmission over a comparatively fast fading environment. Especially, we propose a very simple formula to calculate the address of coded bit allocation, and thus we do not need any additional outer interleavers, i.e., inter-codeword interleavers. The simulation resuts investigated in this paper reveal that the proposed scheme can provide the performance gain of more than a few decibels compared to the conventional schemes.

• Journal of Digital Contents Society
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• v.15 no.5
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• pp.595-602
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• 2014

Cloud computing is already well known paradigm that a support computing resource flexible and scalable to users as the want in distributed computing environment. Actually, cloud computing can be implemented and provided by virtualization technology. Also, various products are released or under development. In this paper, we built the teaching practice system using cloud computing and evaluated practical environment which constructed over a virtual machine. Virtualization-based cloud computing provides optimized computing resources, as well as easy to manage practical resource and result. Therefore, we can save the time for configuration of practice environment. In the view of faculty, they can easily handle the practice result. Also, those practice condition reuse comfortably and apply to various configuration simply. And then we can increase capabilities and availabilities of limited resources. Additionally, we measure the performance requirements for educational applications through evaluation of virtual-based teaching practical system in advance.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.257-262
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• 2010

SOA (Service-Oriented Architecture), which has become very popular recently, is a new paradigm for software development and application. In this paper, we propose an integrated architecture which is able to effectively manage and control a variety of services for U-City projects focusing on the importance of service integration. SOA has a number of important features such as loose coupling, standard bases, and distributed computing, all of which are the essential elements for merging and providing various services in U-City projects. We exploit the ESB (Enterprise Service Bus) for reflecting those features, which is a core module linking mutually heterogeneous components so that the communication of services can be implemented. In this paper, we discuss the necessity of SOA in U-City services and a possible scenario and method for the implementation. Finally, we propose an integrated architecture for the U-City Integration and Management Center.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1015-1024
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• 2003

In [1] we proposed a method to generate a test model (GFSM) from a set of scenarios of embedded software. Each scenario describes the interaction sequences for an external input event. Although these external events are generated and accepted alternatively and concurrently by embedded software, we considered only the alternative relations. In this paper, we describe an improved algorithm to generate GFSM from concurrent scenarios, and propose methods to reduce the number of transitions in the GFSM. The first is the synchronous interpretation of message passing instead of asynchronous one considering the real behavior of tasks in embedded software. The others apply the partial order techniques to the GFSM using independent regions. We apply the method to generate a reduced GFSM of embedded software running on a digital TV.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.173-178
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• 2019

Recently, the types and amount of data generated, collected, and measured in IoT such as smart home, security, and factory are increasing. The technologies for IoT service include sensor devices to measure desired data, embedded software to control the devices such as signal processing, wireless network protocol to transmit and receive the measured data, and big data and AI-based analysis. In this paper, we focused on developing a gateway for interfacing heterogeneous sensor network protocols that are used in various IoT devices and propose a heterogeneous network interface IoT gateway. We utilized a OpenWrt-based wireless routers and used 6LoWAN stack for IP-based communication via BLE and IEEE 802.15.4 adapters. We developed a software to convert Z-Wave and LoRa packets into IP packet using our Python-based middleware. We expect the IoT gateway to be used as an effective device for collecting IoT big data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.691-705
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• 2012

Buffer overflow vulnerability is the most representative one that an attack method and its countermeasure is frequently developed and changed. This vulnerability is still one of the most critical threat since it was firstly introduced in middle of 1990s. Shellcode is a machine code which can be used in buffer overflow attack. Attackers make the shellcode for their own purposes and insert it into target host's memory space, then manipulate EIP(Extended Instruction Pointer) to intercept control flow of the target host system. Therefore, a lot of research to defend have been studied, and attackers also have done many research to bypass security measures designed for the shellcode defense. In this paper, we investigate shellcode defense and attack techniques briefly and we propose our new methodology which can hide shellcode in the 24bit BMP image. With this proposed technique, we can easily hide any shellcode executable and we can bypass the current detection and prevention techniques.

• Smart Media Journal
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• v.12 no.2
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• pp.9-14
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• 2023

In this paper, we proposed a face recognition network which attempts to use more facial features awhile using smaller number of training sets. When combining the neural network together for face recognition, we want to use networks that use different part of the facial features. However, the network training chooses randomly where these facial features are obtained. Other hand, the judgment basis of the network model can be expressed as a saliency map through gradCAM. Therefore, in this paper, we use gradCAM to visualize where the trained face recognition model has made a observations and recognition judgments. Thus, the network combination can be constructed based on the different facial features used. Using this approach, we trained a network for small face recognition problem. In an simple toy face recognition example, the recognition network used in this paper improves the accuracy by 1.79% and reduces the equal error rate (EER) by 0.01788 compared to the conventional approach.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.19-32
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• 2009

In this paper, we design thin-client framework capable of application sharing & data access on the Internet, and apply related skills, such as X windows system, pseudo server, CODA file system, MPI(Message Passing Interface). We suggest a framework for the thin client to access data produced by working on a server optimally as well as to run server side application, even in the case of network down. Additionally, it needed to reflect all local computing changes to remote server when network is restored. To design thin client framework with these characteristics, in this paper, we apply distributed pseudo server and CODA file system to our framework, also utilize MPI for the purpose of more efficient computing & management. It allows for implementation of network independent computing environment of thin client, also provide scalable application service to numerous user through the elimination of bottleneck on caused by server overload. In this paper, we discuss the implementing method of thin client framework in detail.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.739-754
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• 2022

Usually, text data consists of many variables, and some of them are closely correlated. Such multi-collinearity often results in inefficient or inaccurate statistical analysis. For supervised learning, one can select features by examining the relationship between target variables and explanatory variables. On the other hand, for unsupervised learning, since target variables are absent, one cannot use such a feature selection procedure as in supervised learning. In this study, we propose a word selection procedure that employs topic models to find latent topics. We substitute topics for the target variables and select terms which show high relevance for each topic. Applying the procedure to real data, we found that the proposed word selection procedure can give clear topic interpretation by removing high-frequency words prevalent in various topics. In addition, we observed that, by applying the selected variables to the classifiers such as naïve Bayes classifiers and support vector machines, the proposed feature selection procedure gives results comparable to those obtained by using class label information.

• The Journal of Bigdata
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• v.5 no.2
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• pp.77-84
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• 2020

In this study, we conducted a study on the win-loss predicton analysis of korean professional baseball by artificial intelligence models. Based on the model, we predicted the winner as well as each team's final rank in the league. Additionally, we developed a website for viewers' understanding. In each game's first, third, and fifth inning, we analyze to select the best model that performs the highest accuracy and minimizes errors. Based on the result, we generate the rankings. We used the predicted data started from May 5, the season's opening day, to August 30, 2020 to generate the rankings. In the games which Kia Tigers did not play, however, we used actual games' results in the data. KNN and AdaBoost selected the most optimized machine learning model. As a result, we observe a decreasing trend of the predicted results' ranking error as the season progresses. The deep learning model recorded 89% of the model accuracy. It provides the same result of decreasing ranking error trends of the predicted results that we observe in the machine learning model. We estimate that this study's result applies to future KBO predictions as well as other fields. We expect broadcasting enhancements by posting the predicted winning percentage per inning which is generated by AI algorism. We expect this will bring new interest to the KBO fans. Furthermore, the prediction generated at each inning would provide insights to teams so that they can analyze data and come up with successful strategies.