• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.344-351
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• 2016

In the point of view of a train operator, the main concern with a train operation is not only to maintain a time schedule, but also to decrease the energy consumption as much as possible. Generally for a manual drive, a train conductor controls the train acceleration and deceleration by controlling the notches not to exceed the regulation velocity by considering the given maximum velocity profile for an operation route. For this case, the guideline for a conductor is needed to choose the proper notches by applying the notch optimization so as to drive at the regulation velocity and minimize energy consumption simultaneously. In this paper, the real-time notch optimization plan is suggested using a genetic algorithm that optimizes the notches for the remaining route in real time when the event occurs that track information or regulation velocity profile of the remaining route changes during train operation as well as a normal operation situation. An energy saving effect and the convergence behavior of the optimal solution obtained was analyzed in a genetic algorithm.

• The KIPS Transactions:PartC
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• v.14C no.1 s.111
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• pp.73-80
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• 2007

In conventional on-demand mobile ad hoc routing algorithms, an alternate path is sought only after an active path is broken. It incurs a significant cost in terms of money and time in detecting the disconnection and establishing a new route. In this thesis, we propose proactive route selection and maintenance to conventional mobile ad hoc on-demand routing algorithms. The key idea for this research is to only consider a path break to be likely when the signal power of a received packet drops below an optimal threshold value and to generate a forewarning packet. In other words, if a path is lost with high probability, the neighboring node that may easily be cut off notifies the source node by sending a forewarning packet. Then the source node can initiate route discovery early and switched to a reliable path potentially avoiding the disconnection altogether. For the simulational study, network simulator(NS2) is used. The result of simulation shows that the algorithm significantly improves the performance of networks comparing to conventional on-demand routing protocols based on DSR and AODV in terms of packet delivery ratio, packet latency and routing overhead.

• Journal of Internet Computing and Services
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• v.9 no.2
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• pp.35-42
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• 2008

Mobile IP is a solution to support mobile nodes, however, it does not handle NEtwork MObility (NEMO). The NEMO Basic Support (NBS) protocol ensures session continuity for all the nodes in the mobile network. Since the protocol is based on Mobile IP, it inherits the same fundamental problem such as tunnel convergence, when supporting the multicast for NEMO. In this paper, we propose the multicast route optimization scheme for NEMO environment. We assume that the Mobile Router (MR) has a multicast function and the Nested Mobile Router Information (NeMRI) table. The NeMRI is used to record o list of the CoAs of all the MRs located below it. And it covers whether MRs desire multicast services. Any Route Optimization (RO) scheme can be employed here for pinball routing. Therefore, we achieve optimal routes for multicasting based on the given architecture. We also propose cost analytic models to evaluate the performance of our scheme. We observe significantly better multicast cost in NEMO compared with other techniques such as Bi-directional Tunneling, Remote Subscription, and Mobile Multicast based on the NBS protocol.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.1-8
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• 2021

In Mobile Ad-Hoc Network (MANET) Application, routing protocol is essential to ensure successful data transmission to all nodes. Ad-hoc On-demand Distance Vector (AODV) Protocol is a reactive routing protocol that is mostly used in MANET applications. However, the protocol causes Route Request (RREQ) message flooding issue due to the broadcasting method at the route request stage to find a path to a particular destination, where the RREQ will be rebroadcast if no Request Response (RREP) message is received. A scalable neighbor-based routing (SNBR) protocol was then proposed to overcome the issue. In the SNBR protocol, the RREQ message is only rebroadcast if the number of neighbor nodes less than a certain fix number, known as drop factor. However, since a network always have a dynamic characteristic with a dynamic number of neighbor nodes, the fix drop factor in SNBR protocol could not provide an optimal flooding problem solution in a low dense network environment, where the RREQ message is continuously rebroadcast RREQ message until reach the fix drop factor. To overcome this problem, a new broadcasting method as Dynamic SNBR (DSNBR) is proposed, where the drop factor is determined based on current number of neighbor nodes. This method rebroadcast the extra RREQ messages based on the determined dynamic drop factor. The performance of the proposed DSNBR is evaluated using NS2 and compared with the performance of the existing protocol; AODV and SNBR. Simulation results show that the new routing protocol reduces the routing request overhead, energy consumption, MAC Collision and enhances end-to-end delay, network coverage ratio as a result of reducing the extra route request messages.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.1-6
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• 2023

The purpose of this study is to research and develop so that users who are gradually progressing in the popularization of smartphones and the calculation of agricultural quality can use more active and flexible applications than existing application fields. People use event management applications to remember what they need to do, and maps applications to get to their appointments on time. You will need to build a glue-delivered application that leverages the Maps API to be able to recommend the glove's path for events so that the user can use the application temporarily. By comparing and analyzing currently used calendar, map, and schedule applications, several Open Maps APIs were compared to supplement the weaknesses and develop applications that converge the strengths. The results of application development by applying the optimal algorithm for recommending traffic routes according to time and place for the schedule registered by the user are described.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.1 s.24
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• pp.21-28
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• 2003

This study was aimed at suggest a proper planning method to select a optimal forest road network in mountains forest using GIS(Geographic Information System). To examine the field applicability, the method was applied to the National Forest at Mt. Kumsan in Namhae-gun, Gyungsangnam-do. The main results from altogether six alternative road route plans were derived from these criteria obtained the alternative route plan No.2 has two layout criteria, longitudinal gradient and earth work volume, and it showed similar pattern of existing forest road network which was designed mainly ground slope and longitudinal gradient. The alternative route plan No.6 has four criteria, longitudinal gradient, earth work volume, investment effect and landscape impact. It was different for the lowest forest road density among the alternatives and the pattern of the forest road layout was radial form, which was also quite different to other alternatives. For optimal forest road network planning, GIS provide the efficient and resonable solutions for decision making to provide the support for evaluation about various alternative road networks. If detailed inventory and relevant data are provided and also clear and objective indicators for evaluations are set up, it could be applied to preliminary analysis and detail planning stage to prevent undesirable effect such the land slide and soil erosion due to inadequate planning for forest road network.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.870-874
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• 2018

In the worst maritime accidents, people should abandon ship, but ship structures are narrow and complex and operation takes place on rough seas, so escape is not easy. In particular, passengers on cruise ships are untrained and varied, making evacuation prospects worse. In such a case, the evacuation management of the crew plays a very important role. If a rescuer enters a ship at distress and conducts rescue activities, which zones represent the most effective entry should be examined. Generally, crew and rescuers take the shortest route, but if an accident occurs along the shortest route, it is necessary to select the second-best alternative. To solve this situation, this study aims to calculate evacuation routes using Q-Learning of Reinforcement Learning, which is a machine learning technique. Reinforcement learning is one of the most important functions of artificial intelligence and is currently used in many fields. Most evacuation analysis programs developed so far use the shortest path search method. For this reason, this study explored optimal paths using reinforcement learning. In the future, machine learning techniques will be applicable to various marine-related industries for such purposes as the selection of optimal routes for autonomous vessels and risk avoidance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.1
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• pp.31-46
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• 1978

Consider a time interval during which the demand for trips is fixed (e.g. the rush hour period). The traveller has a choice between various public modes, whose travel times and fares are fixed, and the automobile mode, for which travel time and cost depend on the volume of traffic flow on those roads, which are subject to congestion. We consider the equilibrium in terms of a representative travellerm, who choses for any trip the mode and route with the least combined money and time cost. When several (parallel) model or routes are chosen, then the combined cost of money and time must be equal among these. Our problem is first, to find the optimal flows of cars and of public mode carriers on the various links of their networks and second the optimal fares for trips by the variousmodes. The object is to minimize the total operating costs of the carriers and car plus the total time costs to travellers. The optimal fares are related to, but not identical with the dual variables of the underlying Nonlinear Program. They are equal to these dual variables only in the case, when congestion tolls on trips or on the use of specific roads are collected from automobile users. When such tolls are not collected, they must be passed on as subsidies to travellers using competing modes. The optimal fares of public modes are then reduced by the amounts of these subsidies. Note that subsidies are not a flat payment to public carriers, but are calculated on the basis of tickets sold. Fares and subsidies depend in general on tile period considered. They will be higher during periods of higher demand. When the assumption of fixed trip demand is relaxed, this tare system is no longer best, but only second best since too much traffic will, in general, be generated. The Free Rider Theorem states the following : Suppose road tolls can be charged, so that a best pricing system for public modes is posssible. Then there may exist free rides on some routes and modes, but never on a complete round trip.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.4
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• pp.413-428
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• 2013

In this study, Ant Colony Algorithm(ACO) was used for optimal model. ACO which are metaheuristic algorithm for combinatorial optimization problem are inspired by the fact that ants are able to find the shortest route between their nest and food source. For applying the model to water distribution systems, pipes, tanks(reservoirs), pump construction and pump operation cost were considered as object function and pressure at each node and reservoir level were considered as constraints. Modified model from Ostfeld and Tubaltzev(2008) was verified by applying 2-Looped, Hanoi and Ostfeld's networks. And sensitivity analysis about ant number, number of ants in a best group and pheromone decrease rate was accomplished. After the verification, it was applied to real water network from S water treatment plant. As a result of the analysis, in the Two-looped network, the best design cost was found to $419,000 and in the Hanoi network, the best design cost was calculated to $6,164,384, and in the Ostfeld's network, the best design cost was found to $3,525,096. These are almost equal or better result compared with previous researches. Last, the cost of optimal design for real network, was found for 66 billion dollar that is 8.8 % lower than before. In addition, optimal diameter for aged pipes was found in this study and the 5 of 8 aged pipes were changed the diameter. Through this result, pipe construction cost reduction was found to 11 percent lower than before. And to conclusion, The least cost design model on water distribution system was developed and verified successfully in this study and it will be very useful not only optimal pipe change plan but optimization plan for whole water distribution system.