• Tunnel and Underground Space
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• v.12 no.1
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• pp.19-30
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• 2002

Rock joint orientation is one of important geometric attributes that have an influence on the stability of rock structures such as rock slopes and tunnels. Especially, statistical models of the geometric attributes of rock joints can provide a probabilistic approach of rock engineering problems. The result from probabilistic modeling relies on the choice of statistical model. Therefore, it is critical to define a representative statistical model for joint orientation data as well as joint size and intensity and build up a series of modeling procedure including analytical validation. In this paper, we have examined a theoretical methodology for the statistical estimate and hypothesis analysis based upon Fisher distribution and bivariate normal distribution. In addition, we have proposed the algorithms of random number generator which is applied to the simulation of rock joint networks and risk analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.447-456
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• 2007

The ant colony optimization (ACO) is a probabilistic Meta-heuristic algorithm which has been developed in recent years. Originally ACO was used for solving the well-known Traveling Salesperson Problem. More recently, ACO has been used to solve many difficult problems. In this paper, we develop an ant colony optimization method to solve the maximum independent set problem, which is known to be NP-hard. In this paper, we suggest a new method for local information of ACO. Parameters of the ACO algorithm are tuned by evolutionary operations which have been used in forecasting and time series analysis. To show the performance of the ACO algorithm, the set of instances from discrete mathematics and computer science (DIMACS)benchmark graphs are tested, and computational results are compared with a previously developed ACO algorithm and other heuristic algorithms.

• Communications of the Korean Mathematical Society
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• v.17 no.2
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• pp.321-330
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• 2002

Consider the random assignment (or bipartite matching) problem with iid uniform edge costs t(i, j). Let $A_{n}$ be the optimal assignment cost. Just recently does Aldous [2] give a rigorous proof that E $A_{n}$ longrightarrowζ(2). In this paper we establish the upper and lower bounds for Var $A_{n}$ , i.e., there exist two strictly positive but finite constants $C_1$ and $C_2$ such athat $C_1$ $n^{(-5}$2)/ (log n)$^{(-3}$2)/ $\leq$ Var $A_{n}$ $\leq$ $C_2$ $n^{-1}$ (log n)$^2$.EX>.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.81-98
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• 2013

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.23-39
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• 2019

The use of drone-bots is demanded in times regarding the reduction of military force, the spread of the life-oriented thought, and the use of innovative technology in the defense through the fourth industrial revolution. Especially, the drone's surveillance and reconnaissance are expected to play a big role in the future battlefield. However, there are not many cases in which the concept of operation is studied scientifically. In this study, We propose search algorithms for reconnaissance drone through simulation analysis. In the simulation, the drone and target move linearly in continuous space, and the target is moving adopting the Random-walk concept to reflect the uncertainty of the battlefield. The research investigates the effectiveness of existing search methods such as Parallel and Spiral Search. We analyze the probabilistic analysis for detector radius and the speed on the detection probability. In particular, the new detection algorithms those can be used when an enemy moves toward a specific goal, PS (Probability Search) and HS (Hamiltonian Search), are introduced. The results of this study will have applicability on planning the path for the reconnaissance operations using drone-bots.

• Korean Journal of Cognitive Science
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• v.7 no.1
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• pp.37-56
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• 1996

In this paper,we design a verifier for unconstrained handwritten numerals using structural feature analysis,and use it as a comnination algorithm for multiple recognizers.The existing combination algorithms mainly use learnings,statistical methods,or probabilistic methods without considering structural features of numerals.That is why they cannot recognize some numerals which human can identify clearly.To overcome the shortcomings,we design one-to-one verifiers which compare and analyze the relative structural features between frequently confused numeral pairs,and apply them to combine multiple recongnizers.Structural features for verification consist of contour,direction al chain code,polygonal approximation,and zero crossing number of horizontal/vertical projections. We gained a 97.95% reliability with CENPARMI numeral data,and showed that some misconceived factors generated from typical combination algorithms can be removed.

• Structural Engineering and Mechanics
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• v.85 no.3
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• pp.369-379
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• 2023

Reliability-Based Design Optimization (RBDO) is an appropriate framework for obtaining optimal designs by taking uncertainties into account. Large-scale problems with implicit limit state functions and problems with discrete design variables are two significant challenges to traditional RBDO methods. To overcome these challenges, this paper proposes a hybrid method to perform RBDO of structures that links Firefly Algorithm (FA) as an optimization tool to advanced (finite element) reliability methods. Furthermore, the Genetic Algorithm (GA) and the FA are compared based on the design cost (objective function) they achieve. In the proposed method, Weighted Simulation Method (WSM) is utilized to assess reliability constraints in the RBDO problems with explicit limit state functions. WSM is selected to reduce computational costs. To performing RBDO of structures with finite element modeling and implicit limit state functions, a First-Order Reliability Method (FORM) based on the Direct Differentiation Method (DDM) is utilized. Four numerical examples are considered to assess the effectiveness of the proposed method. The findings illustrate that the proposed RBDO method is applicable and efficient for RBDO problems with discrete and continuous design variables and finite element modeling.

• Journal of Communications and Networks
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• v.6 no.3
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• pp.235-244
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• 2004

There have been a lot of approaches to evaluate and predict transmission control protocol (TCP) performance in a numerical way. Especially, under the recent advance in wireless transmission technology, the issue of TCP performance over wireless links has come to surface. It is because TCP responds to all packet losses by invoking congestion control and avoidance algorithms, resulting in degraded end-to-end performance in wireless and lossy systems. By several previous works, although it has been already proved that overall TCP performance is largely dependent on its loss recovery performance, there have been few works to try to analyze TCP loss recovery performance with thoroughness. In this paper, therefore, we focus on analyzing TCP's loss recovery performance and have developed a simple model that facilitates to capture the TCP sender's behaviors during loss recovery period. Based on the developed model, we can derive the conditions that packet losses may be recovered without retransmission timeout (RTO). Especially, we have found that TCP Reno can retransmit three packet losses by fast retransmits in a specific situation. In addition, we have proved that successive three packet losses and more than four packet losses in a window always invoke RTO easily, which is not considered or approximated in the previous works. Through probabilistic works with the conditions derived, the loss recovery performance of TCP Reno can be quantified in terms of the number of packet losses in a window.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.4
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• pp.267-277
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• 2024

The Sortie Generation Rate (SGR) is a critical performance indicator for carrier-based aircraft and is a key factor for the carrier design process. This study aims to analyze the factors that affect SGR and establish a representative Sortie Generation Process (SGP) along with simulation results to calculate SGR for a naval ship equipped to carry aircraft. Detailed SGR factors are identified from the perspectives of the aircraft, aviation personnel, and aircraft carrier during the flight preparation stage, and the SGP is established accordingly. As a representative, Korean Navy's CVX basic design is chosen for detailed analysis. The physical dimension and spots for the deck design with time and probabilistic data of SGP are considered to develop a queueing network model for SGR calculation. To consider the specific probabilistic features, the model was solved with discrete event simulation tools(SimPy and AnyLogic) where the results show great agreement. Such findings on SGR factors and calculation are expected to be incorporated in the future development of SGR calculation algorithms and also present guidelines for proper design of aircraft carrier based on concrete operation concept.