• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.185-190
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• 2005

In IS-95 and IMT-2000 systems using variable code rates and constraint lengths, this paper limits code rate 1/2 and constraint length 3 and realizes forward real-time decoder using Sliding-Window with decoding length 6 and PVSL(Prototype Vector Selecting Logic), LVQ(Learning Vector Quantization) in Neural Network. In comparison condition to theoretically constrained AWGN channel environment at $S/(N_{0}/2)=1$ I verified the superiority of forward real-time decoder through hard-decision and soft-decision comparison between Viterbi decoder and forward real-time decoder such as BER and Secure Communication and H/W Structure.

• Journal of Korea Multimedia Society
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• v.3 no.5
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• pp.506-515
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• 2000

In this paper a web-based real-time education system, which is able to support education through multimedia, is suggested for the expansion of learner's creative ability in the school. This system is designed so that it can support three things: 1) a real time interaction between interaction between instructors and learners, 2) individual learning through such an interaction, and 3) a coercive distribution of display by instructions for preventing the deviation of learners from learning. Also, the system, which UML is applied to, makers efficient interaction possible through the module for the real-time exchange and management of messages even in the multi-user environment. Through this system, not only the simulation by learners can be made for experiments and practices, but also questions and respondence can be supported on the procedure of experiments and the analysis of their results. This system is bulit on constructivism, and aimed at helping the learning progress and knowledge formation of learners.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2801-2815
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• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.204-206
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• 2021

Real-time drilling parameters prediction is a considerably important study from the viewpoint of maximizing drilling efficiency. Among the methods of maximizing drilling, the method of improving the drilling speed is common, which is related to the rate of penetration, drillstring rotational speed, weight on bit, and drilling mud flow rate. This study proposes a method of predicting the drilling rate, one of the real-time drilling parameters, using a recurrent neural network-based deep learning model, and compares the existing physical-based drilling rate prediction model with a prediction model using deep learning.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

• International journal of advanced smart convergence
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• v.7 no.1
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• pp.24-32
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• 2018

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.649-658
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• 2005

Since the Network based attack Is extensive in the real state of damage, It is very important to detect intrusion quickly at the beginning. But the intrusion detection using supervised learning needs either the preprocessing enormous data or the manager's analysis. Also it has two difficulties to detect abnormal traffic that the manager's analysis might be incorrect and would miss the real time detection. In this paper, we propose a traffic attributes correlation analysis mechanism based on self-organizing maps(SOM) for the real-time intrusion detection. The proposed mechanism has three steps. First, with unsupervised learning build a map cluster composed of similar traffic. Second, label each map cluster to divide the map into normal traffic and abnormal traffic. In this step there is a rule which is created through the correlation analysis with SOM. At last, the mechanism would the process real-time detecting and updating gradually. During a lot of experiments the proposed mechanism has good performance in real-time intrusion to combine of unsupervised learning and supervised learning than that of supervised learning.

• Journal of Korea Game Society
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• v.11 no.3
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• pp.3-8
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• 2011

The learning by computer games, is in the spotlight as an effective education method for the game generation. The educational games need to provide additionally an omniscient view of the learning as an objective viewpoint for storytelling of learning. The omniscient viewpoint needs good readability in real-time, and to provide systematically overall and detailed informations of learning. In this paper, we propose the visual learning map as the omniscient view for storytelling of learning. The proposed learning map is composed of the district map, the topic map, and the progress map for the omniscient view of the learning. The proposed learning map is represented by several visual diagrams for real-time readability. The proposed learning map can apply to all the educational games which provide the game minimaps.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.143-152
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• 2021

It is very important to learn behavioral policies that allow multiple agents to work together organically for common goals in various real-world applications. In this multi-agent reinforcement learning (MARL) environment, most existing studies have adopted centralized training with decentralized execution (CTDE) methods as in effect standard frameworks. However, this multi-agent reinforcement learning method is difficult to effectively cope with in a dynamic environment in which new environmental changes that are not experienced during training time may constantly occur in real life situations. In order to effectively cope with this dynamic environment, this paper proposes a novel multi-agent reinforcement learning system, C-COMA. C-COMA is a continual learning model that assumes actual situations from the beginning and continuously learns the cooperative behavior policies of agents without dividing the training time and execution time of the agents separately. In this paper, we demonstrate the effectiveness and excellence of the proposed model C-COMA by implementing a dynamic mini-game based on Starcraft II, a representative real-time strategy game, and conducting various experiments using this environment.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.8
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• pp.94-100
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• 2003

This paper presents the experiments on the approximation of a hydraulic servo system using a real time recurrent neural networks (RTRN) with time varying weights. In order to verify the effectiveness of the RTRN algorithm in hydraulic servo system, we design the experimental hydraulic system and implemented the real time approximation of system output. Experimental results show that approximated output of the RTRN well follows the position trajectory of the electro-hydraulic servo system. And also it is verified that the 2-D RNN can be implemented in sampling time even though high sampling frequency experimentally.