• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5425-5448
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• 2018

Wireless sensor network has been widely used in Internet of Things (IoT) applications to support large and dense networks. As sensor nodes are usually tiny and provided with limited hardware resources, the existing multiple access methods, which involve high computational complexity to preserve the protocol performance, is not available under such a scenario. In this paper, we propose an intelligent Medium Access Control (MAC) protocol selection scheme based on machine learning in wireless sensor networks. We jointly consider the impact of inherent behavior and external environments to deal with the application limitation problem of the single type MAC protocol. This scheme can benefit from the combination of the competitive protocols and non-competitive protocols, and help the network nodes to select the MAC protocol that best suits the current network condition. Extensive simulation results validate our work, and it also proven that the accuracy of the proposed MAC protocol selection strategy is higher than the existing work.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.629-632
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• 2020

현대사회는 4차 산업혁명 시대가 도래하면서 초연결, 초지능, 초융합 사회로 변화되고 있다. 최근 교육부는 많은 변화가 요구되고 있는 교육분야, 교육정책 방안으로 SW(소프트웨어)교육에 AI(인공지능) 교육까지 추가되야 한다고 제안하고 2024년까지 첨단 기술을 활용한 지능형 과학실을 구축한다고 밝혔다. 이에 본 논문에서는 정부의 교육정책 방안이 원활하게 진행될 수 있고 융합 교육 분야에서 활용될 수 있는 "미래 지능형 과학실 활용을 위한 화학원소기호 이미지 기계학습 AI·SW교육 프로그램"을 제안하고자 한다.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.225-232
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• 2023

Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machine-learning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson's heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.149-149
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• 2003

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1887-1892
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• 2005

In the future, a machine-tool will be more improved in the form of a knowledge evolution based device. In order to develop the knowledge evolution based machine-tool, this paper proposes the structure of standard M2M(Machine To Machine) and the scheme of agent communication in environment. The communication agent such as dialogue agent has a role of interfacing with another machine for cooperation. To design of the communication agent module in M2M environment, FIPA(Foundation of Intelligent Physical Agent) and ping agent based on JADE(Java Agent Development Framework) or FIPA-OS(Open Source) are analyzed in this study. Through this, it is expected that the agent communication can be more efficiently designed and the knowledge evolution based machine-tool can be hereafter more easily implemented.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.433-438
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• 2003

Recently, some studies of a web-based information recommendation technique which provides users with the most necessary information through websites like a web-based shopping mall have been conducted vigorously. In most cases of web information recommendation techniques which rely on a user profile and a specific feedback from users, they require accurate and diverse profile information of users. However, in reality, it is quite difficult to acquire this related information. This paper is aimed to suggest an information prediction technique for a web information service without depending on the users'specific feedback and profile. To achieve this goal, this study is to design and implement a Dynamic Web Information Prediction System which can recommend the most useful and necessary information to users from a large volume of web data by designing and embodying Ensemble Support Vector Machine and hybrid SOM algorithm and eliminating the scarcity problem of web log data.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.70-75
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• 2016

Nowadays, there are many research for recognizing face of people using the machine vision. the machine vision is classification and analysis technology using machine that has sight such as human eyes. In this paper, we propose algorithm for classifying human face using this machine vision system. This algorithm consist of Convolutional Neural Network and cascade face detector. And using this algorithm, we classified the face of subjects. For training the face classification algorithm, 2,000, 3,000, and 4,000 images of each subject are used. Training iteration of Convolutional Neural Network had 10 and 20. Then we classified the images. In this paper, about 6,000 images was classified for effectiveness. And we implement the system that can classify the face of subjects in realtime using USB camera.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.512-517
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• 2014

This paper describes the development of new sweeping machine based on Cyclone Technology, which maintains constant suction power and uses it in a industrial applications as a method for dust removed from grinding work. The performance of a cyclone separator is determined by the turbulence characteristics and particle-particle interaction. To achieve this goal, we design cyclone technology based dust separator for sweeping machine has been proposed as a system which is suitable to work utilizing dust suction alternative to conventional manual system. and Numerical analysis with computational fluid dynamics(CFD) was carried out to investigate the working fluid that flow into cyclone dust separator in order to design optimal structure of the sweeping machine. The validation of cyclone model with CFD is carried out by comparing with experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.336-342
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• 2007

Industrial HMI(Human Machine Interface) system is the main element among the factory automation processes and have been used to monitor and control operation and status of machine in factory with PLC. This HMI often brings heavy loads to the system development and difficult decreasing the system because it tends to use a specific system per each manufacturer. Therefore, in this thesis, we have developed an embedded linux-based embedded industrial HMI software modules which can be used for touch panel embedded system to solve these problem. In this module, we have used the Qt/Embedded software component because it can be used by all systems which support C++ compiler without modifying the existing codes. We can design more flexible system and network configuration because we have used the wireless communication module. In this thesis, we implement linux-based HMI software modules which are capable of wireless communication as well as bringing the mobility to the overall system and finally decreasing the system development loads by using the general purpose OS with competitive price.