• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1738-1756
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• 2020

To improve the accuracy and realism of the virtual surgical simulation system, this paper proposes an optimized mass-spring model with shape restoration ability based on volume conservation to simulate soft tissue deformation. The proposed method constructs a soft tissue surface model that adopts a new flexion spring for resisting bending and incorporates it into the mass-spring model (MSM) to restore the original shape. Then, we employ the particle swarm optimization algorithm to achieve the optimal solution of the model parameters. Besides, the volume conservation constraint is applied to the position-based dynamics (PBD) approach to maintain the volume of the deformable object for constructing the soft tissue volumetric model base on tetrahedrons. Finally, we built a simulation system on the PHANTOM OMNI force tactile interaction device to realize the deformation simulation of the virtual liver. Experimental results show that the proposed model has a good shape restoration ability and incompressibility, which can enhance the deformation accuracy and interactive realism.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1127-1136
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• 2004

This paper proposes a novel multicasting algorithm, called ANMAS (Ad hoc Network Multicasting with Ant System), for Mobile Ad hoc Network (MANET). The algorithm utilizes the indirect communication method of the ants via 'pheromone' to effectively obtain dynamical topology change information, generating safer multicasting paths, and adapts the well-known CBT (Core Based Tree) multicasting algorithm into the ANMAS framework with proper modificiations to make 'tolerable' multicasting group in the MANET environment. We show the efficiency and the effectiveness of ANMAS via simulation studies.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.1863_1864
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• 2009

본 연구에서는 복잡한 비선형 모델링 방법인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 PNN(Polynomial Neural Network)을 접목한 새로운 형태의 Radial Basis Function Polynomial Neural Network(RPNN)를 제안한다. RBF 뉴럴 네트워크는 빠른 학습 시간, 일반화 그리고 단순화의 특징으로 비선형 시스템 모델링 등에 적용되고 있으며, PNN은 생성된 노드들 중에서 우수한 결과값을 가진 노드들을 선택함으로써 모델의 근사화 및 일반화에 탁월한 효과를 가진 비선형 모델링 방법이다. 제안된 RPNN모델의 기본적인 구조는 PNN의 형태를 이루고 있으며, 각각의 노드는 RBF 뉴럴 네트워크로 구성하였다. 사용된 RBF 뉴럴 네트워크에서의 커널 함수로는 FCM 클러스터링을 사용하였으며, 각 노드의 후반부는 다항식 구조로 표현하였다. 또한 입력개수, 입력변수, 클러스터의 개수를 PSO알고리즘(Particle Swarm Optimization)을 사용하여 최적화 시켰다. 제안한 모델의 적용 및 유용성을 비교 평가하기 위하여 비선형 데이터를 이용하여 그 우수성을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4228-4247
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• 2018

Many methods have been developed for the vehicles to create clusters in vehicular ad hoc networks (VANETs). Usually, nodes are vehicles in the VANETs, and they are dynamic in nature. Clusters of vehicles are made for making the communication between the network nodes. Cluster Heads (CHs) are selected in each cluster for managing the whole cluster. This CH maintains the communication in the same cluster and with outside the other cluster. The lifetime of the cluster should be longer for increasing the performance of the network. Meanwhile, lesser the CH's in the network also lead to efficient communication in the VANETs. In this paper, a novel algorithm for clustering which is based on the social behavior of Gray Wolf Optimization (GWO) for VANET named as Intelligent Clustering using Gray Wolf Optimization (ICGWO) is proposed. This clustering based algorithm provides the optimized solution for smooth and robust communication in the VANETs. The key parameters of proposed algorithm are grid size, load balance factor (LBF), the speed of the nodes, directions and transmission range. The ICGWO is compared with the well-known meta-heuristics, Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) for clustering in VANETs. Experiments are performed by varying the key parameters of the ICGWO, for measuring the effectiveness of the proposed algorithm. These parameters include grid sizes, transmission ranges, and a number of nodes. The effectiveness of the proposed algorithm is evaluated in terms of optimization of number of cluster with respect to transmission range, grid size and number of nodes. ICGWO selects the 10% of the nodes as CHs where as CLPSO and MOPSO selects the 13% and 14% respectively.

• Geomechanics and Engineering
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• 제28권6호
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• International Journal of Advanced Culture Technology
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• 제12권2호
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• pp.249-257
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• 2024

This study analyzes the current status and prospects of Iran's air defense network, focusing on the Russian-made S-300 system, and derives implications for the development of South Korea's air defense network. Iran's air defense network exhibits strengths such as long-range detection and interception capabilities, multi-target processing, high-altitude interception, and electronic warfare response. However, it also reveals weaknesses, including lack of mobility, difficulty in detecting low-altitude targets, obsolescence, training level of operating personnel, and vulnerability to electronic warfare. Real-world cases confirm these weaknesses, making the system susceptible to enemy evasion tactics, swarm drone attacks, and electronic warfare. Drawing from Iran's case, South Korea should establish a multi-layered defense system, strengthen low-altitude air defense and electronic warfare capabilities, foster the domestic defense industry for technological self-reliance, and enhance international cooperation. By addressing these aspects, South Korea can establish a robust air defense network and firmly protect its national security. Future research should aim to secure and analyze materials from the Iranian perspective for a more objective evaluation of Iran's air defense network and continuously track Iran's efforts to improve its air defense network and the trend of strengthening drone forces to predict changes in the Middle East security situation.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• 한국시뮬레이션학회논문지
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• 제28권4호
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• pp.21-32
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• 2019

군집에 대한 사회적 행동 모델에 영감을 받은 군집 최적화 알고리즘은 복잡한 최적화 문제 해결에서부터 인공 신경망의 학습에까지 활용되는 대표적인 메타휴리스틱 최적화 알고리즘 중의 하나이다. 하지만 이 알고리즘은 기본적으로 확률적 노이즈가 존재하지 않는 결정적인 환경에서 개발되었기 때문에, 많은 경우 확률적 노이즈가 존재하는 실제 문제에 적용하기에 어려움이 있었다. 본 논문에서는 이를 개선하기 위하여 불확실 평가 기법이라고 정의되는 통계적 가설 검정 기반의 리샘플링 기법을 적용한다. 이 기법을 통하여 입자 군집 최적화 알고리즘의 성능에 가장 큰 영향을 미치는 입자들의 전역 최적을 정확하게 찾으므로 노이즈 환경에서 입자들이 최적해로 보다 정확하고 빠르게 수렴하도록 한다. 다양한 벤치마크 문제들에 대한 기존 알고리즘들과의 비교 실험 결과는 제안하는 알고리즘의 개선된 성능을 입증하고, 사례 연구의 결과는 본 연구의 필요성을 강조한다. 본 연구 결과가 4차 산업혁명 시대에 디지털 트윈 등을 통한 시뮬레이션 기반 시스템 최적화에 효과적으로 적용될 수 있을 것이라 기대한다.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2001년도 The Seoul International Simulation Conference
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• pp.9-10
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• 2001

Simulation has been evolved with the advance of computer and technique of modeling application systems. Early simulations were numerical analysis of engineering models known as continuous simulation, analysis of random events using various random number generators thus named as Monte Carlo simulation, iud analysis o(\\\\`queues which are prevalent in many real world systems including manufacturing, transportation, telecommunication. Discrete-event simulation has been used far modeling and analyzing the systems with waiting lines and inefficient delays. These simulations, either discrete-event, continuous, or hybrid, have played a key role in industrial age by helping to design and implement the efficient real world systems. In the information age which has been brought up by the advent of Internet, e-business has emerged. E-business, any business using Internet, can be characterized by the network of extended enterprises---extended supply and demand chains. The extension of value chains spans far reaching scope in business functions and space globally. It also extends to the individual customer, customer preferences and behaviors, to find the best service and product fit for each individual---mass customization. Simulation should also play a key role in analyzing and evaluating the various phenomena of e-business where the phenomena can be characterized by dynamics, uncertainty, and complexity. In this tutorial, applications of simulation to e-business phenomena will be explained and illustrated. Examples are the dynamics of new economy, analysis of e-business processes, virtual manufacturing system, digital divide phenomena, etc. Partly influenced by e-business, a new trend of simulation has emerged called agent-based simulation, Agent-based simulation is a technique of simulation using software agent that have autonomy and proactivity which are useful in analyzing and integrating numerous individual customer's behavior. One particular form of agent-based simulation is swarm. This tutorial concludes with the illustration of swarm or swarm Intelligence applied to various e-business applications, and future directions and implications of this new trend of simulation.

• Structural Engineering and Mechanics
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• 제61권3호
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• pp.347-357
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• 2017

The applications of active control is being more popular nowadays. Several control algorithms have been developed to determine optimum control force. In this paper, a Chaotic Particle Swarm Optimization (CPSO) technique, based on Logistic map, is used to compute the optimum control force of active tendon system. A chaotic exploration is used to search the solution space for optimum control force. The response control of Multi-Degree of Freedom (MDOF) shear buildings, equipped with active tendons, is introduced as an optimization problem, based on Instantaneous Optimal Active Control algorithm. Three MDOFs are simulated in this paper. Two examples out of three, which have been previously controlled using Lattice type Probabilistic Neural Network (LPNN) and Block Pulse Functions (BPFs), are taken from prior works in order to compare the efficiency of the current method. In the present study, a maximum allowable value of control force is added to the original problem. Later, a twenty-story shear building, as the third and more realistic example, is considered and controlled. Besides, the required Central Processing Unit (CPU) time of CPSO control algorithm is investigated. Although the CPU time of LPNN and BPFs methods of prior works is not available, the results show that a full state measurement is necessary, especially when there are more than three control devices. The results show that CPSO algorithm has a good performance, especially in the presence of the cut-off limit of tendon force; therefore, can widely be used in the field of optimum active control of actual buildings.