• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.709-714
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• 2001

Recently, most vehicles has the Automatic transmission system as their transmission system. The automatic transmission system operates with fixed shift patterns. In the opposite of manual operation, it is easy and convenient for driving. Though these merit, the system can not evaluate the driver s intension because of usage of fixed shift pattern. To consider driver s intension, we must consider both the driving intensity of driver and the status of vehicle. In this paper, we developed flexible automatic transmission system by using the proposed moduled neural networks which can learn the status of th vehicle and driver s intensity. As a results, we compare the transmission system using fixed shift pattern and the proposed transmission system and show the good performance in the change of shift position.

• The Journal of Engineering Research
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• v.6 no.2
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• pp.11-22
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• 2004

A distributed constraint satisfaction problem (distributed CSP) is a constraint satisfaction problem(CSP) in which variables and constraints are distributed among multiple automated agents. ACSP is a problem to find a consistent assignment of values to variables. Even though the definition of a CSP is very simple, a surprisingly wide variety of AI problems can be formalized as CSPs. Similarly, various application problems in DAI (Distributed AI) that are concerned with finding a consistent combination of agent actions can be formalized as distributed CAPs. In recent years, many new backtracking algorithms for solving distributed CSPs have been proposed. But most of all, they have common drawbacks that the algorithm assumes the priority of agents is static. In this thesis, we establish a basic algorithm for solving distributed CSPs called dynamic priority search algorithm that is more efficient than common backtracking algorithms in which the priority order is static. In this algorithm, agents act asynchronously and concurrently based on their local knowledge without any global control, and have a flexible organization, in which the hierarchical order is changed dynamically, while the completeness of the algorithm is guaranteed. And we showed that the dynamic priority search algorithm can solve various problems, such as the distributed 200-queens problem, the distributed graph-coloring problem that common backtracking algorithm fails to solve within a reasonable amount of time. The experimental results on example problems show that this algorithm is by far more efficient than the backtracking algorithm, in which the priority order is static. The priority order represents a hierarchy of agent authority, i.e., the priority of decision-making. Therefore, these results imply that a flexible agent organization, in which the hierarchical order is changed dynamically, actually performs better than an organization in which the hierarchical order is static and rigid. Furthermore, we describe that the agent can be available to hold multiple variables in the searching scheme.

• Journal of Platform Technology
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• v.9 no.1
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• pp.3-14
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• 2021

Cognitive Radio network (CR) is a promising paradigm that helps the unlicensed user (Secondary User) to analyse the spectrum and coordinate the spectrum access to support the creation of common control channel (CCC). The cooperation of secondary users and broadcasting between them is done through transmitting messages in CCC. In case, if the control channels may get jammed and it may directly degrade the network's performance and under such scenario jammers will devastate the control channels. Hopping sequences may be one of the predominant approaches and it may be used to fight against this problem to confront jammer. The jamming attack can be alleviated using one of the game modelling approach and in this proposed scheme stochastic games has been analysed with more single users to provide the flexible control channels against intrusive attacks by mentioning the states of each player, strategies ,actions and players reward. The proposed work uses a modern player action and better strategic view on game theoretic modelling is stochastic game theory has been taken in to consideration and applied to prevent the jamming attack in CR network. The selection of decision is based on Q learning approach to mitigate the jamming nodes using the optimal MDP decision process

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.169-176
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• 1992

In parts assignment problem for FMS, both the parts and the tools required to process the parts are assigned to the machine tools for a specified planning period. This is an important decision-making problem for a short-term production planning of FMS. In this paper, parts assignment problem for FMS was analyzed to determine the optimal plan which has the adaptability to production fluctuation. A mathematical model was developed for determing the parts and the tools required for the machine tools which compose FMS, and the model was analyzed to obtain optimal solution. In addition, the vality and feasibility of the computational algorithm was examined by solving a numerical example.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.91-96
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• 2019

For a wireless sensor network in general, efficient routing decision is important because wireless connections are not stable, sensitive to external interference, and topology changes dynamically. RPL standard of IETF is not flexible to various environmental changes and causes packet loss and delay due to topological imbalance. Sending packets through multipath can partially remedy this problem. The multipath routing, however, can introduce significant delay overhead by allocating unnecessary timeslots. This paper proposes an RPL using multipath adaptively according to network conditions. We show by simulations that the proposed algorithm is more efficient than the basic RPL and the multipath RPL.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.285-292
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• 2021

In Sapiens it shows the reason that sapiens has conquered the earth for the invention of intersubjective reality. In Homodeus sapiens made the science revolution through the religion of humanism. The development of science showed that human free will and emotion is just made through the chemical interaction of neuron which can be manipulated and developed. Algorithm and data wlll be the sovereign which make decision in everything. Yuval Harari tells us the Homodeus appear and break down the order of equality. His anticipation proves right in competition law issues. The collusion through the algorithm makes it difficult to apply existing cartel logic. Agreement is the ground of responsibility but undertaking is not responsible in the market where sovereign algorithm decides eveything. Extreme price differentiation can appear and break down the existing market logic and competition dogma. Everything changes and it is necessary to have the flexible attitude and develop new logic.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.525-532
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• 2006

This paper proposes a new class of image restoration on the Ising modeled binary 'Takbon' image by the flexible line-fitting ICM(Iterated conditional modes) method. Basically 'Takbon' image need be divided into two extreme regions, information and background one due to its stroke combinations. The main idea is the line process, comparing with the conventional ICM approaches which were based on partially rectangular structured point process. For calculating geometrical mechanism, we have defined line-fitting functions at each current pixel array which form the set of linear lines with gradients and lengths. By applying the Bayes' decision to this set, the region of the current pixel is decided as one of the binary levels. In this case, their statistical reiteration for distinct tracking between intra and extra region offers a criterion to decide the attachment at each step. Finally simulations using the binary 'Takbon' image are provided to demonstrate the effectiveness of our new algorithm

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2805-2826
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• 2021

In most cases, the routing policy of networks shows a preference for a static one-to-one mapping of communication pairs to routing paths, which offers adversaries a great advantage to conduct thorough reconnaissance and organize an effective attack in a stress-free manner. With the evolution of network intelligence, some flexible and adaptive routing policies have already proposed to intensify the network defender to turn the situation. Routing mutation is an effective strategy that can invalidate the unvarying nature of routing information that attackers have collected from exploiting the static configuration of the network. However, three constraints execute press on routing mutation deployment in practical: insufficient route mutation space, expensive control costs, and incompatibility. To enhance the availability of route mutation, we propose an OpenFlow-based route mutation technique called Polymorphic Path Transferring (PPT), which adopts a physical and virtual path segment mixed construction technique to enlarge the routing path space for elevating the security of communication. Based on the Markov Decision Process, with considering flows distribution in the network, the PPT adopts an evolution routing path scheduling algorithm with a segment path update strategy, which relieves the press on the overhead of control and incompatibility. Our analysis demonstrates that PPT can secure data delivery in the worst network environment while countering sophisticated attacks in an evasion-free manner (e.g., advanced persistent threat). Case study and experiment results show its effectiveness in proactively defending against targeted attacks and its advantage compared with previous route mutation methods.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.58-63
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• 2017

Emergencies and disasters can happen any time without any warning, and things can change and escalate very quickly, and often it is swift and decisive actions that make all the difference. It is a responsibility of the building facility management to ensure that a proven evacuation plan in place to cover various worst scenario to handled automatically inside the facility. To mapping out optimal safe escape routes is a straightforward undertaking, but does not necessarily guarantee residents the highest level of protection. The emergency evacuation navigation approach is a state-of-the-art that designed to evacuate human livings during an emergencies based on real-time decisions using live sensory data with pre-defined optimum path finding algorithm. The poor decision on causalities and guidance may apparently end the evacuation process and cannot then be remedied. This paper propose a cloud connected emergency evacuation system model to react dynamically to changes in the environment in emergency for safest emergency evacuation using IoT based emergency exit sign system. In the previous researches shows that the performance of optimal routing algorithms for evacuation purposes are more sensitive to the initial distribution of evacuees, the occupancy levels, and the type and level of emergency situations. The heuristic-based evacuees routing algorithms have a problem with the choice of certain parameters which causes evacuation process in real-time. Therefore, this paper proposes an evacuee routing algorithm that optimizes evacuation by making using high computational power of cloud servers. The proposed algorithm is evaluated via a cloud-based simulator with different "simulated casualties" are then re-routed using a Dijkstra's algorithm to obtain new safe emergency evacuation paths against guiding evacuees with a predetermined routing algorithm for them to emergency exits. The performance of proposed approach can be iterated as long as corrective action is still possible and give safe evacuation paths and dynamically configure the emergency exit signs to react for real-time instantaneous safe evacuation guidance.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.2
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• pp.27-38
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• 2018

Environmental planning includes resource allocation and spatial planning process for the conservation and management of environment. Because the spatialization of the environmental planning is not specifically addressed in the relevant statutes, it actually depends on the qualitative methodology such as expert judgement. The results of the qualitative methodology have the advantage that the accumulated knowledge and intuition of the experts can be utilized. However, it is difficult to objectively judge whether it is enough to solve the original problem or whether it is the best of the possible scenarios. Therefore, this study proposed a methodology to quantitatively and objectively spatialize various environmental planning. At first, we suggested a quantitative spatial planning model based on an optimization algorithm. Secondly, we applied this model to two kinds of environmental planning and discussed about the model performance to present the applicability. Since the models were developed based on conceptual study site, there was a limitation in showing possibility of practical use. However, we expected that this study can contribute to the fields related to environmental planning by suggesting flexible and novel methodology.