• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.30-35
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• 2015

The most important factor within the wireless sensor network is to have effective network usage and increase the lifetime of the individual nodes in order to operate the wireless network more efficiently. Therefore, many routing protocols have been developed. The LEACH protocol presented by Wendi Hein Zelman, especially well known as a simple and efficient clustering based routing protocol. However, because LEACH protocol in an irregular network is the total data throughput efficiency dropped, the stability of the cluster is declined. Therefore, to increase the stability of the cluster head, in this paper, it proposes a stochastic cluster head selection method for improving the LEACH protocol. To this end, it proposes a SH-LEACH (Stochastic Cluster Head Selection Method-LEACH) that it is combined to the HEED and LEACH protocol and the proposed algorithm is verified through the simulation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.145-154
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• 1994

This paper proposes an efficient method for improving the training performance of the neural network by using a hybrid of a stochastic approximation and a backpropagation algorithm. The proposed method improves the performance of the training by appliying a global optimization method which is a hybrid of a stochastic approximation and a backpropagation algorithm. The approximate initial point for a stochastic approximation and a backpropagation algorihtm. The approximate initial point for fast global optimization is estimated first by applying the stochastic approximation, and then the backpropagation algorithm, which is the fast gradient descent method, is applied for a high speed global optimization. And further speed-up of training is made possible by adjusting the training parameters of each of the output and the hidden layer adaptively to the standard deviation of the neuron output of each layer. The proposed method has been applied to the parity checking and the pattern classification, and the simulation results show that the performance of the proposed method is superior to that of the backpropagation, the Baba's MROM, and the Sun's method with randomized initial point settings. The results of adaptive adjusting of the training parameters show that the proposed method further improves the convergence speed about 20% in training.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.210-216
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• 1999

Todays computer communication technology let people to do many unrealistic things possible and the use of those technologies is becoming increasingly prevalent throughout the military operation. Both DIS and ADS are welled defined computer aided military simulations. This study discusses a simulation of stochastic combat network modeling through Internet. We have developed two separate simulation models, one for clients and another for server, and validated for conducting studies with these two models. The object-oriented design was necessary to define the system entities and their relationship, to partition functionality into system entities, and to transform functional metrics into realizations derived from system component behaviors. Heterogeneous forces for each side are assumed at any battle node. The time trajectories for mean number of survivors at each node, some important combat measures, and relative difference computations between models were made. We observe and may conclude that the differences exist and some fo these are significant based on a limited number of experiments.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.276-276
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• 2012

일반적으로 기후변화영향에 관한 연구수행을 위해 전지구기후모형(GCM; Global Climate Model)이 사용되고 있다. 하지만 GCM은 공간해상도(Spatial resolution)가 거칠기 때문에 수문학 분야에서 주로 사용되는 유역규모의 지역적인 스케일특성과 물리적 특징을 표현하는데 한계가 있다. 또한 GCM 기후변수들 중 강수량의 경우 한반도 지역의 6월과 10월 사이에 연강수량의 67% 이상이 집중되는 계절성을 반영하지 못하고 있으며, 높은 불확실성을 보이고 있다. 본 연구에서는 GCM 기반의 다지점 인공신경망기법을 적용한 상세화(Downscaling)를 실시하였다. GCM의 24개 2D변수에 대한 주성분분석을 실시하여 신경망의 학습인자로 사용하였으며, 학습, 검증 및 예측기간은 각각 1981~1995년, 1996~2000년, 2011~2100년으로 A1B 시나리오를 대상으로 상세화를 실시하였다. 또한, 여름철 태풍사상을 모의하기 위한 Stochastic Typhoon Simulation기법과 Baseline과 Projection 사이의 강수량 보정을 위한 Dynamic Quantile Mapping 기법을 적용하여, 강수량의 불확실성을 최소화 하고자 하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.125-129
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• 2015

The most important factor within the wireless sensor network is to have effective network usage and increase the lifetime of the individual nodes in order to operate the wireless network more efficiently. Therefore, many routing protocols have been developed. The LEACH protocol presented by Wendi Heinzelman, especially well known as a simple and efficient clustering based routing protocol. However, because LEACH protocol in an irregular network is the total data throughput efficiency dropped, the stability of the cluster is declined. Therefore, to increase the stability of the cluster head, in this paper, it proposes a stochastic cluster head selection method for improving the LEACH protocol. To this end, it proposes a SH-LEACH(Stochastic Cluster Head Selection Method-LEACH) that it is combined to the HEED and LEACH protocol and the proposed algorithm is verified through the simulation.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.315-320
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• 2017

In this paper, I design a new scheme that is immune to malicious attack based on the weighted voting game. By using stochastic learning, the proposed scheme can revoke the certification of malicious node. Through the revocation process, the proposed scheme can effectively adapt the dynamic Mobile Ad hoc network situation. Simulation results clearly indicate that the developed scheme has better performance than other existing schemes under widely diverse network environments.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2296-2298
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• 1998

In this paper, an on-chip learning pulse-mode digital neural network with a massively parallel yet compact and flexible network architecture is suggested. Algebraic neural operations are replaced by stochastic processes using pseudo-random sequences and simple logic gates are used as basic computing elements. Using Back-propagation algorithm both feed-forward and learning phases are efficiently implemented with simple logical gates. RNG architecture using LFSR and barrel shifter are adopted to avoid some correlation between pulse trains. Suggested network is designed in digital circuit and its performance is verified by computer simulation.

• Economic and Environmental Geology
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• v.28 no.5
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• pp.503-512
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• 1995

Groundwater flow in fracture networks is simulated using a discrete fracture (DF) model which assume that groundwater flows only through the fracture network. This assumption is available if the permeability of rock matrix is very low. It is almost impossible to describe fracture networks perfectly, so a stochastic approach is used. The stochastic approach assumes that the characteristic parameters in fracture network have special distribution patterns. The stochastic model generates fracture networks with some characteristic parameters. The finite element method is used to compute fracture flows. One-dimensional line element is the element type of the finite elements. The simulation results are shown by dominant flow paths in the fracture network. The dominant flow path can be found from the simulated groundwater flow field. The model developed in this study provides the tool to estimate the influences of characteristic parameters on groundwater flow in fracture networks. The influences of some characteristic parameters on the frcture flow are estimated by the Monte Carlo simulation based on 30 realizations.

• Clean Technology
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• v.12 no.4
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• pp.224-231
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• 2006

Collection efficiencies and pressure drops for the removal of small particles from dilute liquid suspensions by granular bed filter were calculated using network model. The network model is composed of a number of nodes connected with cylindrical bond and particles are deposited on the bond surface. The collection efficiency of each cylindrical bond was predicted using unit cell model corresponding to the pore volume of cylindrical pore both at the initial and transient states. Deposited particles on the collector surface may act as additional collector and reduce the pore size of the collector. As a result, the collection efficiency was improved and pressure drop increased with deposition. Even though the stochastic nature of network requires a large number of simulation work, the model proposed in this study can be used in investigating collection efficiency and pressure drop.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3273-3308
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• 2017

Building appropriate trust evaluation models is an important research issue for security guarantee in social networks. Most of the existing works usually consider the trust values at the current time slot, and model trust as the stochastic variable. However, in fact, trust evolves over time, and trust is a stochastic process. In this paper, we propose a novel time-variant stochastic trust evaluation (TSTE) model, which models trust over time and captures trust evolution by a stochastic process. Based on the proposed model, we derive the time-variant bound of untrustworthy probability, which provides stochastic trust guarantee. On one hand, the time-variant trust level of each node can be measured by our model. Meanwhile, by tolerating nodes with relatively poor performance, our model can effectively improve the node resource utilization rate. Numerical simulations are conducted to verify the accuracy and consistency of the analytical bounds on distinguishing misbehaved nodes from normal ones. Moreover, simulation results on social network dataset show the tradeoff between trust level and resource utilization rate, and verify that the successful transmission rate can be improved by our model.