• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.5
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• pp.684-688
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• 2002

One of the Problems, which the Intrusion Detection System has, is a False Positive. This False make to low condition of the Intrusion Detection System. The cause of the False Positive is that the learning is not enough during audit data learning steps. Therefore, in this paper, 1 propose the method of the Intrusion Detection System that be learnt audit data to agent with association rule.

• Electronics and Telecommunications Trends
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• v.37 no.6
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• pp.32-42
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• 2022

Recently, decentralized approaches to artificial intelligence (AI) development, such as federated learning are drawing attention as AI development's cost and time inefficiency increase due to explosive data growth and rapid environmental changes. Collaborative AI technology that dynamically organizes collaborative groups between different agents to share data, knowledge, and experience and uses distributed resources to derive enhanced knowledge and analysis models through collaborative learning to solve given problems is an alternative to centralized AI. This article investigates and analyzes recent technologies and applications applicable to the research of multi-agent collaboration of AI bots, which can provide collaborative AI functionality autonomously.

• ETRI Journal
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• v.45 no.5
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• pp.862-873
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• 2023

Determining whether an autonomous self-driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three-camera model, which would enable us to more easily compile a variety of training data to endow our model with improved generalizability. In this work, we provide three separate fusion methods (feature, early, and late) of combining the information from three cameras. Extensive pedestrian-view intersection classification experiments show that our feature fusion model provides an area under the curve and F1-score of 82.00 and 46.48, respectively, which considerably outperforms contemporary three- and one-camera models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1367-1370
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• 2011

u-learning은 학습자들의 개별화 욕구에 따라 다양한 학습이 가능하며 학습자의 수준 정보나 주변의 상황 정보를 결합하여 학습자에게 필요한 학습상황과 내용을 추정하고, 최적의 학습 환경 및 학습 콘텐츠를 제공할 수 있다. 본 논문에서는 기존의 e-learning과 m-learning의 특징을 추출하여 유비쿼터스 교육에 적합한 환경을 분석하여 u-learning을 효율적으로 지원하기 위한 에이전트를 식별 설계하고 학습자로 하여금 최상의 학습 콘텐츠를 제공 받을 수 있도록 퍼지 기반 지능형 에이전트 시스템을 설계 구현한다. 또한 학습의 효율성을 향상시키기 위한 개념으로 이들 모바일 지능 에이전트 시스템 통합하여 응용 예를 제시한다.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.21-25
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• 2004

The proposed QoS Agent System for distance video learning is used unicast and P2P. This method is developed for decreased the traffic of server by control agent. First step capture from stream server's camera, and transmitted streaming data to the first CSplayer(c/s), and outrank CSplayer transmitted streaming data to the subordinate CSplayer controled by QoS Control Agent.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.650-657
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• 2003

We describe a scheme for developing character-based interface within the context of an agent-based tutoring system in the Web environment. The ideas in this paper stem from original work representing aspects of human emotion in tutoring computer models, where may provide mote natural ways for students to communicate with digital learning materials. The proposed system model is a set of software services that enable developers to incorporate interactive animated characters into their Web pages designed for on-line lectures. The prototypical application is developed and shown for validating the applicability and the effectiveness of this model in real tutoring settings.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.255-263
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• 2003

A multi-agent system designed to represent newly deregulated electricity markets in the USA is aimed at testing the capability of the multi-agent model to replicate the observed price behavior in the wholesale market and developing a smart business intelligence which quickly searches the optimum offer strategy responding to the change in market environments. Simulation results show that the optimum offer strategy is to withhold expensive generating units and submit relatively low offers when demand is low, regardless of firm size; the optimum offer strategy during a period of high demand is either to withhold capacity or speculate for a large firm, while it is to be a price taker a small firm: all in all, the offer pattern observed in the market is close to the optimum strategy. From the firm's perspective, the demand-side participation as well as the intense competition dramatically reduces the chance of high excess profit.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.63-70
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• 2018

Safety management agent manages the risk behavior of the worker with the naked eye, but there is a real difficulty for one the agent to manage all the workers. In this paper, IoT device is attached to a harness safety belt that a worker wears to solve this problem, and behavior data is upload to the cloud in real time. We analyze the upload data through the deep learning and analyze the risk behavior of the worker. When the analysis result is judged to be dangerous behavior, we designed and implemented a system that informs the manager through monitoring application. In order to confirm that the risk behavior analysis through the deep learning is normally performed, the data values of 4 behaviors (walking, running, standing and sitting) were collected from IMU sensor for 60 minutes and learned through Tensorflow, Inception model. In order to verify the accuracy of the proposed system, we conducted inference experiments five times for each of the four behaviors, and confirmed the accuracy of the inference result to be 96.0%.