• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.354-359
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• 2016

Power plants maintenance data is to be sent to management server system via a communication network. In this case, reliable communication network is required. Transmission of the power plants maintenance data is used in the wired communication network or wireless communication network. PLC communication network is a kind of wired communication network. However PLC communication network is easily affected by noise. On the vulnerable areas in power line system, such as a mountain or rural areas, it is difficult to form a power line communication network. For a wireless communication, environment are also influenced factors in wireless communication. Harsh environmental factors are bring the communication characteristic degradation. In such areas it can be used a combination of two networks and in this way the complementary function can be achieved. Power plants are distributed in various regions across the country. The appropriate communication network is needed to maintain the power plant.This study investigated the effect of environment on the wired communication and wireless communication. It would examine a variable factor which is affect to the communication characteristic. We used PLC communication for wired communication network and ZigBee communication for wireless communication network. We investigated the characteristics of a single communication network and it raised the need for a complex communication technology to complement a single communication network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.553-555
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• 2015

In this paper, we proposed the blue-green algae monitoring system using the HSV(Hue Saturation Value) color model. The proposed system is to extract the image data from the camera of raspberry pie server by an wireless network, and it is analyzed through the HSV color model. We implemented a web server to provide the information of the XML data which was analyzed from the raspberry pie server. Also, the mobile app was developed to view the XML data on smart devices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.1
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• pp.65-72
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• 2007

This paper aims to provide information to generate a statistical load model of an educational server by analyzing workload of an e-Learning sewer at Dankook University. The result of the analysis shows file size distribution, access frequency and transmission volume for each file type, access interval, changes in preference and clients access rate by networks. In particular, it had different results from previous studies about video file's size distribution and file distribution based on access frequency. This is because the characteristics of e-learning are influenced by using authoring tools for making into video file and by freeing the number of students who register for a course. The result in this paper can be used as a basic data for studies designed to improve e-learning system architecture and server performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.153-161
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• 2009

In this paper, we designed and implemented realtime DID(digital information display) system based on web server that displayed multimedia contents. The contents are weather, news information on the internet web sites and public relations or advertisements data on local systems. The DID system has client/server architecture that the server send to client that schedule informations and multimedia contents received form web server and the client displayed the contents though scheduled information. Therefore the systems overcome network fault for the mean time. Also, the system has realtime services of web page filtering function that extract the partial information of specific web pages.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.143-151
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• 2008

In this paper, we have implemented a ubiquitous healthcare system that can measure and check human's health in anytime and anywhere. The implemented prototype are composed of both front-end and back-end. The front-end have several groups: environment sensor group such as temperature, humidity, photo, voice sensor, health sensor group such as blood pressure, heart beat, electrocardiogram, spo2 sensor, gateway for wired/wireless communication, and RFID reader to identify personal. The back-end has a serial forwarder to propagate measurment results, monitor program, and medical information server The implemented sensor node constructs a sensor network using the Zigbee protocol and is ported the tinyOS. The data gathering base node is linux-based terminal that can transfer a sensed medial data through wireless LAN. And, the medical information server stores the processed medical data and can promptly notify the urgent status to the connected medical team. Through our experiments, we've confirmed the possibility of ubiquitous healthcare system based on sensor network using the Zigbee.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.407-410
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• 2012

Recently, wireless network services are widely used, depending on the development of wireless network module technologies and the utilization gradually expanded, and thus is a trend that appears a lot of IT convergence industries. For this study, the OBD-II communication to Import your vehicle information, and other external devices in high-speed driving condition of the vehicle to verify the information system was developed to transfer data to an external server. From various sensors inside the vehicle using the OBD-II connector easily convert all users to read the information, then, Sent to the external server using the wireless network module, high-speed vehicle status check system was implemented. It was to test the performance of the system was developed using the actual circuit in a high-speed road racing vehicles. Transfer data generated from high-speed driving vehicles through the OBD-II scanner and check the status of a high-speed vehicle system was confirmed that this data is normally received. In the future, these new cars convergence of IT technology will grow as a new field of research.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.289-298
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• 2006

Many researches are going on with regard to issues and problems related to mobile database systems, which are caused by the weak connectivity of wireless networks, the mobility and the portability of mobile clients. Mobile computing satisfies user's demands for convenience and performance to use information at any time and in any place, but it has many problems to be solved in the aspect of data management. The purpose of our study is to design Mobile Continuous Query Processing System(MCQPS) to solve problems related to database hoarding, the maintenance of shared data consistency and the optimization of logging, which are caused by the weak connectivity and disconnection of wireless networks inherent in mobile database systems under mobile client server environments. We proved the superiority of the proposed MCQPS by comparing its performance to the C I S(Client-Intercept-Slaver) model. In Addition, we experiment on proposed index structure and methodology in various methods.

• The KIPS Transactions:PartD
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• v.12D no.4 s.100
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• pp.521-530
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• 2005

Many researches are going on with regard to issues and problems related to mobile database systems, which are caused by the weak connectivity of wireless networks, the mobility and the Portability of mobile clients. Mobile computing satisfies user's demands for convenience and Performance to use information at any time and in any Place, but it has many Problems to be solved in the aspect of data management. The purpose of our study is to design Mobile Query Processing System(MQPS) to solve problems related to database hoarding, the maintenance of shared data consistency and the optimization of logging, which are caused by the weak connectivity and disconnection of wireless networks inherent in mobile database systems under mobile client-server environments. In addition, we proved the superiority of the proposed MQPS by comparing its performance to the C-I-S(Client-Intercept- Server) model.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.239-245
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• 2000

This study is aimed to develop the historical data management system named "ACON"(Actual COst Network system) which can be used for collecting, analysis and modification of the historical data. ACON_Local is used for collecting the historical data at each spot place. It redueces the work potential of managers, spending time and cost. ACON_Analysis is to analyze the collection , modifies the data′s error and updates the historical database. ACON_Network is developed to transfer data from ACON to Web server or opposites. The proposed model, ACON, provide the easy way for the cost estimate system, "HiCOMS" to collect, analyze and modify the historical cost evaluated data, which is used to compose the historical data base.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.