• The KIPS Transactions:PartA
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• v.13A no.4 s.101
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• pp.295-304
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• 2006

Java has been established as one of the most widely-used languages owing to its support of object-oriented concepts, safety, and flexibility. Garbage collection in the Java virtual machine is a core component that relieves application programmers of difficulties related to memory management. In this paper, we propose a memory manager and a garbage collector that is implemented on a embedded java virtual machine. The memory manager divide a heap into various-sized cells and manages it as blocks of same-sized cells. So it is possible to allocate and free memory fast. We adopted the 3-color based Mark & Sweep garbage collector as our base algorithm and we propose 4-color based Mark & Sweep garbage collector for supporting multi-threaded program. The proposed garbage collector occurs memory fragmentation but we show through a experiment that the fragmentation ratio is almost fixed according as we create objects continuously.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.245-252
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• 2008

We present a classification algorithm based on ant colony system(ACS) for classifying digital images. The ACS has been recently emerged as a useful tool for the pattern recognition, image extraction, and edge detection. The classification algorithm of digital images is very important in the application areas of digital image coding, image analysis, and image recognition because it significantly influences the quality of images. The conventional procedures usually classify digital images with the fixed value for the associated parameters and it requires postprocessing. However, the proposed algorithm utilizing randomness of ants yields the stable and enhanced images even for processing the rapidly changing images. It is also expected that, due to this stability and flexibility of the present procedure, the digital images are stably classified for processing images with various noises and error signals arising from processing of the drastically fast moving images could be automatically compensated and minimized.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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• pp.83-90
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• 2012

In this paper, an intelligent visual surveillance system based on a NIOS II embedded platform is implemented. By this time, embedded based visual surveillance systems were restricted for a special purpose because of high dependence upon hardware. In order to improve the restriction, we implement a flexible embedded platform, which is available for various purpose of applications. For high speed processing of software based programming, we improved performance of the system which is integrated the SOPC type of NIOS II embedded processor and image processing algorithms by using software programming and C2H(The Altera NIOS II C-To-Hardware(C2H) Acceleration Compiler) compiler in the core of the hardware platform. Then, we constructed a server system which globally manage some devices by the NIOS II embedded processor platform, and included the control function on networks to increase efficiency for user. We tested and evaluated our system at the designated region for visual surveillance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.1954-1960
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• 2008

Modem Robot is used in all industries. Previous Robot was used by simplicity work, at recent times, robot is developed in form that can do action such as a person. Robot's action runs according to command repeat or in the every moment according to sensor's output value, achieve other action. In this raper, we studied about self-regulation transfer robot that follow Object autonomously. This robot can be used by purpose that carry heavy burden instead of human. Robot's composition is drive part which run object's position awareness Sensor, Processor that control action and Motor part. After robot is connects with Network, we did robot remote control and monitor the action situation of robot. For the methode to reduce drive error, we developed algorithm for outside environment. For an experiment we made the self-regulation robot. We showed the directivity of sensor, error of directivity and soft moving of robot. We showed the monitoring system and the execution screen for communication between robot and PC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.643-650
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• 2008

If existing system need to expand security part, the security was established after paying much cost, processing of complicated installation and being patient with inconvenience at user's view because of closed structure. In this thesis, those defects could be overcome by using open security tools and constructing security server, which is firewall of 'bastion' form including proxy server, certification server and so on. Also each security object host comes to decide acceptance or denial where each packet comes from, then determines security level each hosts. Precisely it is possible choosing the packets from bastion host or following at the other policies. Although an intruder enter into inside directly, it is constructed safely because encryption algorithm is applied at communication with security object host. This thesis suggests more flexible, independent and open security system, which improves existing security through systematic linkage between system security and network security.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.187-194
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• 2019

Traditional demand forecasting methods are difficult to meet the needs of companies due to rapid changes in the market and the diversification of individual consumer needs. In a diversified production environment, the right demand forecast is an important factor for smooth yield management. Many of the existing predictive models commonly used in industry today are limited in function by little. The proposed model is designed to overcome these limitations, taking into account the part where each model performs better individually. In this paper, variables are extracted through Gray Relational analysis suitable for dynamic process analysis, and statistically predicted data is generated that includes characteristics of historical demand data produced through ARIMA forecasts. In combination with the LSTM model, demand forecasts can then be calculated by reflecting the many factors that affect demand forecast through an architecture that is structured to avoid the long-term dependency problems that the neural network model has.

• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.97-105
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• 2019

The tactical mobile ad-hoc network(MANET) consists of distributed autonomous networks between individual ground nodes, which is effective in terms of network survivability and flexibility. However, due to constraints such as limited power, terrain, and mobility, frequent link disconnection and shadow area may occur in communication. On the other hand, the satellite network has the advantage of providing a wide-area wireless link overcoming terrain and mobility, but has limited bandwidth and high-latency characteristic. In the future battlefield, an integrated network architecture for interworking multi-layer networks through a heterogeneous network gateway (HNG) is required to overcome the limitations of the existing individual networks and increase reliability and efficiency of communication. In this paper, we propose a new HNG architecture and detailed algorithm that integrates satellite network and the tactical MANET and enables reliable data transfer based on flow characteristics of traffic. The simulations validated the proposed architecture using Riverbed Modeler, a network-level simulator.

• The Journal of Bigdata
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• v.3 no.2
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• pp.35-49
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• 2018

It seems unrealistic to say that fried chicken, often known as the American soul food, has one of the biggest markets in South Korea. Yet, South Korea owns more numbers of fried chicken restaurants than those of McDonald's franchise globally[4]. Needless to say not all these fast-food commerce survive in such small country. In this study, we propose a predictive model that could potentially help one's decision whilst deciding to open a store. We've extracted all fried chicken restaurants registered at the Korean Ministry of the Interior and Safety, then collected a number of features that seem relevant to a store's closure. After comparing the results of different algorithms, we conclude that in order to best predict a store's survival is FDA(Flexible Discriminant Analysis). While Neural Network showed the highest prediction rate, FDA showed better balanced performance considering sensitivity and specificity.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.605-610
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• 2020

Software Defined Networking (SDN) is setting the standard for the management of networks due to its scalability, flexibility and functionality to program the network. The Distributed Denial of Service (DDoS) attack is most widely used to attack the SDN controller to bring down the network. Different methodologies have been utilized to detect DDoS attack previously. In this paper, first the dataset is obtained by Kaggle with 84 features, and then according to the rank, the 20 highest rank features are selected using Permutation Importance Algorithm. Then, the datasets are trained and tested with Convolution Neural Network (CNN) classifier model by utilizing deep learning techniques. Our proposed solution has achieved the best results, which will allow the critical systems which need more security to adopt and take full advantage of the SDN paradigm without compromising their security.