• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.17-22
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• 2022

With the spread of web services, data types are becoming more diversified. In addition to the form of storing data such as images, videos, and texts, the number and form of properties and metadata expressing the data are different for each data. In order to efficiently process such unstructured data, a key-value store is widely used for state-of-the-art applications. LSM-Tree (Log Structured Merge Tree) is the core data structure of various commercial key-value stores. LSM-Tree is optimized to provide high performance for small writes by recording all write and delete operations in a log manner. However, there is a problem in that the delay time and processing speed of user requests are lowered as batches of deletion operations for expired data are inserted into the LSM-Tree as special key-value data. This paper presents a Filtered LSM-Tree (FLSM-Tree) that solves the above problem by separating the deleted key from the main tree structure while maintaining all the advantages of the existing LSM-Tree. The proposed method is implemented in LevelDB, a commercial key-value store and it shows that the read performance is improved by up to 47% in performance evaluation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.139-147
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• 2023

The compact tracking radar system is a pulsed radar tracking system that searches, detects, and tracks targets in real time against aircraft targets with a small RCS(Radar Cross Section) maneuvering at high speed. This paper describes the development of comprehensive performance test equipment to verify the performance of the radar system in a anechoic chamber environment. Describes the design and manufacture of comprehensive performance test equipment to meet requirements, including the generation of simulated target signals to simulate aircraft target signals to verify performance in the laboratory environment of radar systems. It also describes a GUI(Graphic User Interface) program to check performance through a test in conjunction with the tracking radar system. Verify the comprehensive performance test equipment implemented through the performance test.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.119-129
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• 2009

In The increasing use of computers and high-speed Internet network has greatly influenced education, causing a veering away from the typical and traditional way of delivering instruction. Specifically, the various kinds of Web-based multimedia technology, the interactive activities on the Internet, and satellite broadcasting technology are accelerating the emergence of a virtual-lectures-based educational model, which transcends time and space. Such virtual lectures make it possible for the entire teaching-learning process to be done in a virtual learning environment, thus giving rise to problem regarding learning guidance, feedback, and appraisal. In this paper, we propose a system for attendance appraisal for learner participation degree based virtual lecture, an appraisal element in virtual learning environments. This appraisal model can set the elements of virtual learning environments in such a way as to reflect in the attendance appraisal of the opened virtual learning environment information regarding the learner's participation in class. In addition, this model motivates the learners to actively participate in the virtual learning environment and to support instructors by accomplishing the activities that are needed for attendance appraisal.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.193-198
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• 2023

We study a learner-customized lecture recommendation project using deep learning. Recommendation systems can be easily found on the web and apps, and examples using this feature include recommending feature videos by clicking users and advertising items in areas of interest to users on SNS. In this study, the sentence similarity Word2Vec was mainly used to filter twice, and the course was recommended through the Surprise library. With this system, it provides users with the desired classification of course data conveniently and conveniently. Surprise Library is a Python scikit-learn-based library that is conveniently used in recommendation systems. By analyzing the data, the system is implemented at a high speed, and deeper learning is used to implement more precise results through course steps. When a user enters a keyword of interest, similarity between the keyword and the course title is executed, and similarity with the extracted video data and voice text is executed, and the highest ranking video data is recommended through the Surprise Library.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.26-36
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• 2022

Recently, studies have been conducted to cluster vehicles and disseminate safety messages in a VANET environment for driver safety and smoothy traffic. This paper proposes cluster management scheme for safety message dissemination through V2V communication and V2I communication in a VANET environment with high vehicle density and mobility. The proposed scheme reduces packet loss by selecting CH considering reception quality, total data owned by vehicles, moving speed, and connected vehicles, and maintaining cluster head candidates, which are the main agents of message dissemination, considering frequent cluster departures and subscriptions. In addition, the proposed scheme reduces duplicate messages by utilizing clusters by collaborating with a Road side unit(RSU). To prove the excellence of the proposed scheme, various performance evaluations are performed in terms of message packet loss and the number of RSU processing requests. As a result of performance evaluation, the cluster management scheme proposed in this paper shows better performance than the existing scheme.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.267-277
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• 2024

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.15-20
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• 2024

Path planning for unmanned aerial vehicles (UAV) is crucial in avoiding collisions with obstacles in complex environments that include both static and dynamic obstacles. Path planning algorithms like RRT and A^* are effectively handle static obstacle avoidance but have limitations with increasing computational complexity in high-dimensional environments. Reinforcement learning-based algorithms can accommodate complex environments, but like traditional path planning algorithms, they struggle with training complexity and convergence in higher-dimensional environment. In this paper, we proposed a reinforcement learning model utilizing a cell decomposition algorithm. The proposed model reduces the complexity of the environment by decomposing the learning environment in detail, and improves the obstacle avoidance performance by establishing the valid action of the agent. This solves the exploration problem of reinforcement learning and improves the convergence of learning. Simulation results show that the proposed model improves learning speed and efficient path planning compared to reinforcement learning models in general environments.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.41-49
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• 2012

Wireless network support and extended mobile network environment with exponential growth of smart phone users allow us to utilize the network anytime or anywhere. Malicious attacks such as distributed DOS, internet worm, e-mail virus and so on through high-speed networks increase and the number of patterns is dramatically increasing accordingly by increasing network traffic due to this internet technology development. To detect the patterns in intrusion detection systems, an existing research proposed an efficient algorithm called the jumping window algorithm and analyzed approximately 2,000 patterns in Snort 2.1.0, the most famous intrusion detection system. using the algorithm. However, it is inappropriate from the number of TCAM lookups and TCAM memory efficiency to use the result proposed in the research in current environment (Snort 2.9.0) that has longer patterns and a lot of patterns because the jumping window algorithm is affected by the number of patterns and pattern length. In this paper, we simulate the number of TCAM lookups and the required TCAM size in the jumping window with approximately 8,100 patterns from Snort-2.9.0 rules, and then analyse the simulation result. While Snort 2.1.0 requires 16-byte window and 9Mb TCAM size to show the most effective performance as proposed in the previous research, in this paper we suggest 16-byte window and 4 18Mb-TCAMs which are cascaded in Snort 2.9.0 environment.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.9
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• pp.35-46
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• 2011

Future Internet should support more efficient mobility management, flexible traffic engineering and various emerging new services. So, lots of traffic engineering techniques have been suggested and developed, but it's impossible to apply them on the current running commercial Internet. To overcome this problem, OpenFlow protocol was proposed as a technique to control network equipments using network controller with various networking applications. It is a software defined network, so researchers can verify their own traffic engineering techniques by applying them on the controller. In addition, for high-speed packet processing in the OpenFlow network, programmable NetFPGA card with four 1G-interfaces and commercial Procurve OpenFlow switches can be used. In this paper, we implement an OpenFlow test-bed using hardware-accelerated NetFPGA cards and Procurve switches on the KREONET, and implement CSPF (Constraint-based Shortest Path First) algorithm, which is one of popular QoS routing algorithms, and apply it on the large-scale testbed to verify performance and efficiency of multimedia traffic engineering scheme in Future Internet.

• The Transactions of the Korea Information Processing Society
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• v.5 no.12
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• pp.3275-3284
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• 1998

In this paper, we deseribe a Medical Image Information System. Our system stores and manages 5 dimensional medical image data and provides the 3 dimensional medical data via the Internet. The Internet standard VR format. VRML(Virtual Reality Modeling Language) is used to represent the 3I) medical image data. The 3D images are reconstructed from medical image data which are enerated by medical imaging systems such ans CT(Computerized Tomography). MRI(Magnetic Resonance Imaging). PET(Positron Emission Tomograph), SPECT(Single Photon Emission Compated Tomography). We implemented the medical image information system shich rses a surface-based rendering method for the econstruction of 3D images from 2D medical image data. In order to reduce the size of image files to be transfered via the Internet. The system can reduce more than 50% for the triangles which represent the surfaces of the generated 3D medical images. When we compress the 3D image file, the size of the file can be redued more than 80%. The users can promptly retrieve 3D medical image data through the Internet and view the 3D medical images without a graphical acceleration card, because the images are represented in VRML. The image data are generated by various types of medical imaging systems such as CT, MRI, PET, and SPECT. Our system can display those different types of medical images in the 2D and the 3D formats. The patient information and the diagnostic information are also provided by the system. The system can be used to implement the "Tele medicaine" systems.