• Journal of Information Processing Systems
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• v.2 no.3 s.4
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• pp.159-162
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• 2006

Wireless LANs are becoming more widespread because of the rapid advance of wireless technologies and mobile computers. In this paper, we present the design and implementation of a system to help mobile users to select the most competent AP. By monitoring the network traffic of APs within the local LAN in real time, this system offers the mobile user the network utilizations, locations, and signal strengths of APs online. Based on the information, the user can select a competent AP with a high wireless bandwidth. Finally, we verified the accuracy of monitoring and calculating with regard to the utilizations of APs through real experiments.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.262-269
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• 2003

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involved reducing both production and distribution costs. The purpose of this study is to determine the optimum quantity of production and transportation with minimum cost in the supply chain network. We have presented a mathematical model that deals with real world factors and constructs. Considering the complexity of solving such model, we have applied the genetic algorithm approach for solving this model computational experiments using a commercial genetic algorithm based optimizer. The results show that the real size problems we encountered can be solved In reasonable time

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.64-68
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• 2016

These fields are integrated to visualize and finalize the proposed development, in simulation environment. SCADA (supervisory control and data acquisition) systems and distributed control systems (DCSs) are widely deployed in all over the world, which are designed to control the industrial infrastructures, in real ways. To supervise and control the various parts of designed systems; trends to require a deep knowledge to understand the overall functional needs of industries, which could be a big challenge. Industrial field devices (or network sensors) are usually distributed in many locations and are controlled from centralized site (or main control center); the communication provides various signs of security issues. To handle these issues, the research contribution will twofold: a method using cryptography is deployed in critical systems for security purposes and overall transmission is controlled from main controller site. At controller site, multimedia components are employed to control the overall transmission graphically, such as system communication, bytes flows, security embedded parameters and others, by the means of multimedia technology.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.162-164
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• 2006

This whitepaper is a research about the sensor network technology which enhance the performance of the blood management system. The problem of measuring and monitoring the real time temperature of a every point in a limited environment let us to develop a system which is able to monitor the temperature of a remote area using multi-hop networking technology. This whitepaper propose the error correction technologies, which were used to eliminate problems that might occur during real tests of the system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7B
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• pp.638-646
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• 2003

In this paper, we propose a group communication approach which can provide quality of service for soft real-time applications. This proposed scheme combining the explicit multicast, end-point measure-based admission control scheme(EMBAC), and diffserv, removes the need of maintaining state information in the network, so that it would be easily deployable in real networks. We propose the scheme of node configuration to measure the quality of the diffserv network, and that of managing EMBAC over group communication environment. By simulation, we validate the QoS of the proposed multicast service network.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.45-52
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• 2020

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Network(SCN). One of keys issues in the current SCN research area involves minimizing both production and distribution costs. This study deals with finding an optimal solution for minimizing the total cost of production and distribution problems in supply chain network. First, we presented an integrated mathematical model that satisfies the minimum cost in the supply chain. To solve the presented mathematical model, we used a genetic algorithm with an excellent searching ability for complicated solution space. To represent the given model effectively, the matrix based real-number coding schema is used. The difference rate of the objective function value for the termination condition is applied. Computational experimental results show that the real size problems we encountered can be solved within a reasonable time.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2088-2096
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• 2009

This paper deals with hybrid MAC protocol design for underwater acoustic networks. The proposed MAC protocol has the cluster structure with a master node and slave nodes, and the hybrid network structure that combines a contention free period based on TDMA(Time Division Multiple Access) with a contention period. The suggested MAC protocol has a beacon packet for supervising network, a guard period between time slots for packet collision, time tag for estimation of propagation delay with a master node, the time synchronization of nodes, entering and leaving of network, and the communication method among nodes. In this paper, we adapt the proposed hybrid MAC protocol to AUV network, that is the representative mobile device of underwater acoustic network, and verify this protocol is applicable in real underwater acoustic network environment.