• ETRI Journal
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• v.44 no.5
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• pp.733-745
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• 2022

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

• Korean Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.1-10
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• 1997

Finding the optimum route of ship's pipes is complicated and time-consuming process. Experience of designers is the main tool in this process. To reduce design man-hours and human errors a design expert system shell and a geometric modeler is used to automate the design process. In this paper, a framework of the intelligent CAD system for pipe auto-routing is suggested, which consists of general-purpose expert system shell and a geometric modeler. The design expert system and the geometric modeling kernel have been integrated. The CADDS5 of Computervision is used as the overall CAD environment. The Nexpert Object of Neuron Data is used as the expert system shell. The CADDS5 ISSM is used as the interface that creates and modifies geometric models of pipes. Existing algorithms for the routing problem have been analyzed. Most of them are to solve the 2-D circuit routing problems. Ship piping system, specially within the engine room, is a complicated, large scale 3-D routing problem. Methods of expert system have been used to find the route of ship pipes on the main deck.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.465-468
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• 2007

An intelligent home network system using low-power and low-cost sensor nodes was designed and implemented. In Intelligent Home Network System, active home appliances control is composed of RSSI (Received Signal Strength Indicator) based user indoor location tracking, dynamic multi-hop routing, and learning integration remote-control. Through the remote-control learning, home appliances can be controlled in wireless network environment. User location information for intelligent service is calculated using RSSI based Triangle measurement method, and then the received location information is passed to Smoothing Algorithm to reduce error rate. In order to service Intelligent Home Network, moreover, the sensor node is designed to be held by user. The gathered user data is transmitted through dynamic multi-hop routing to server, and real-time user location & environment information are displayed on monitoring program.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.50-58
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• 2001

Modern day networks are increasingly moving towards peer to peer architecture where routing tasks will not be limited to some dedicated routers, but instead all computers in a network will take part in some routing task. Since there are no specialized routers, each node performs some routing tasks and information passes from one neighbouring node to another, not in the form of dumb data, but as intelligent virtual agents or active code that performs some tasks by executing at intermediate nodes in its itinerary. The mobile agents can run, and they are free to d other tasks as the agent will take care of the routing tasks. The mobile agents because of their inherent 'intelligence'are better able to execute complex routing tasks and handle unexpected situations as compared to traditional routing techniques. In a modern day dynamic network users get connected frequently, change neighbours and disconnect at a rapid pace. There can be unexpected link failure as well. The mobile agent based routing system should be able to react to these situations in a fact and efficient manner so that information regarding change in topology propagates quickly and at the same time the network should not get burdened with traffic. We intend to build such a system.

• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.155-166
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• 2008

During last two decades the transportation system has developed into very intelligent system with GIS, GPS and ITS. The practical transportation management system provides real time response module to manage the customer's order. We have surveyed research papers on the real time vehicle routing problem in last two decades to figure out the dynamic vehicle routing problem. The papers are classified by basic routing algorithms and by managing the dynamic events which are the order management, the routing re-optimization, the routing post-optimization and the waiting strategy.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1113-1128
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• 2020

Ad hoc networks play an important role in mobile communications, and the performance of nodes has a significant impact on the choice of communication links. To ensure efficient and secure data forwarding and delivery, an intelligent routing protocol (IAODV) based on learning method is constructed. Five attributes of node energy, rate, credit value, computing power and transmission distance are taken as the basis of segmentation. By learning the selected samples and calculating the information gain of each attribute, the decision tree of routing node is constructed, and the rules of routing node selection are determined. IAODV algorithm realizes the adaptive evaluation and classification of network nodes, so as to determine the optimal transmission path from the source node to the destination node. The simulation results verify the feasibility, effectiveness and security of IAODV.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.31-39
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• 2012

In this paper, we propose an energy-aware source routing protocol for maximizing a network lifetime in mobile ad hoc network environments. The proposed routing protocol is based on the source routing and chooses a path that maximize the path lifetime, by considering both transmit/receive power consumption and residual battery power in the mobile nodes from the perspective of source-destination end-to-end. This paper proposes a new routing cost and designs a new routing protocol for minimizing the control packet overhead occurred during the route discovery. Simulation results show that the proposed scheme has similar performances to the conventional routing schemes in terms of the number of transmission hops, transmission rate and total energy consumption, but achieves the performance improvement of 20 percent with respect to the lifetime.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.666-668
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• 1998

In this paper, we consider a bi-objective vehicle routing problem to minimize total distance traveled and maximize minimum integrated satisfaction level of selecting desirable routes in an fuzzy graph. The fuzzy graph reflects a real delivery situation in which there are a depot, some demand points, paths linking them, and distance and integrated satisfaction level are associated with each route. For solving the vi-objective problem we introduce a concept of routing vector and define non-dominated solution for comparing vectors. An efficient algorithm involving a selection method of non-dominated solutions based on DEA is proposed for the vehicle routing problem with rigid distance and integrated satisfaction level.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.213-215
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• 2003

Multimedia has to carry data of heterogeous types. Multicast communication techniques can supply the most appropriate infrastructures to such multimedia. Of many multicast protocols, the core based tree (CBT) protocol is the most concentrative studies are conducted on. The CBT places a core router at center of the shared tree and transfers data through the tore router. However, the CBT has two problems due to centralizing all network traffics into a core router. First it can raise bottleneck effect at a core router. Second, it is possible to make an additive processing overhead when core router is distant from receivers. To cope with the problems, this paper proposes an intelligent anycast routing protocol. The anycast routing attempts to distribute the centralized traffic into plural core routers by using a knowledge-based algorithm. The anycast routing estimates the traffic characteristics of multimedia data far each multicast source, and achieves effectively the distributing that places an appropriate core router to process the incoming traffic based on the traffic information in the event that request of receivers are raised. This method prevent the additional overhead to distribute traffic because an individual core router uses the information estimated to multicast sources connected to oneself and the traffic processing statistics shared with other core neuters.

• Korean Management Science Review
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• v.21 no.3
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• pp.101-113
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• 2004

In the rapidly propagating Internet based electronic transaction environment. the importance of real time order confirmation has been more emphasized, In this paper, using data mining techniques, we develop intelligent operations decision model to allow real time order confirmation at the time the customer places an order with required delivery terms. Among various operation plannings used for order fulfillment. mill routing is the first interface decision point to link the order receiving at the marketing with the production planning for order fulfillment. Though linear programming based mathematical optimization techniques are mostly used for mill routing problems, some early orders should wait until sufficient orders are gathered for optimization. And that could effect longer order fulfillment lead-time, and prevent instant order confirmation of delivery terms. To cope with this problem, we provide the intelligent decision model to allow instant order based mill routing decisions. Data mining techniques of decision trees and neural networks. which are more popular in marketing and financial applications, are used to develop the model. Through diverse computational trials with the industrial data from the steel company. we have reported that the performance of the proposed approach is effective compared to the present heuristic only mill routing results. Various issues of data mining techniques application to the mill routing problems having linear programming characteristics are also discussed.