• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.339-349
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• 2010

The objective of this paper is to develop a structural equations model system for the purpose of tourists' excursion behaviour analysis with their information usage. A tourist' excursion behavior defined as activities in the sightseeing region between arriving and leaving time includes indexes with respect to activity' time use and the number of spots. Additionally, such indexes can be modeled as endogenous variables in the structural equation model system on the assumption that they are influenced by the degree of familarity on the sightseeing region, the degree of information usage, and individual attributes. The case study application involves excursion behaviour data such as one-day excursion activity and information usage diary that are observed in the Fuji Five Lakes, Japan. Since the models presented in the paper are available to statistically analyze the covariance among the endogenous variables, they have the advantage of effectiveness analysis on information usage in the excursion area, compared to the prior approaches such as discrete choice models.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Annual Conference of KIPS
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• 2024.10a
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• pp.290-293
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• 2024

Software vulnerabilities impose a significant burden on developers, particularly in debugging and maintenance. Automated Software Vulnerability Repair has emerged as a promising solution to mitigate these challenges. Recent advances have introduced learning-based approaches that take vulnerable functions and their Common Weakness Enumeration (CWE) types as input and generate repaired functions as output. These approaches typically fine-tune large pre-trained language models to produce vulnerability patches, with performance evaluated using Exact Match (EM) and CodeBLEU metrics to assess similarity to ground-truth patches. However, current methods rely on teacher forcing during fine-tuning, where the model is trained with ground-truth inputs, but during inference, inputs are generated by the model itself, leading to exposure bias. Additionally, while models are trained using the cross-entropy loss function, they are evaluated using discrete, non-differentiable metrics, resulting in a mismatch between the training objective and the test objective. This mismatch can yield inconsistent results, as the model is not directly optimized to improve test-time performance metrics. To address these discrepancies, we propose the use of reinforcement learning (RL) to optimize patch generation. By directly using the CodeBLEU score as a reward signal during training, our approach encourages the generation of higher-quality patches that align more closely with evaluation metrics, thereby improving overall performance.

• Structural Engineering and Mechanics
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• v.39 no.6
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• pp.795-812
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• 2011

Estimation of damage probability of buildings under a future earthquake is an essential issue to ensure the seismic reliability. Fragility curves are useful tools for showing the probability of structural damage due to earthquakes as a function of ground motion indices. The purpose of this study is to compare the damage probability of R/C buildings with low and high level of strength and ductility through fragility analysis. Two different types of sample buildings have been considered which represent the building types mentioned above. The first one was designed according to TEC-2007 and the latter was designed according to TEC-1975. The pushover curves of sample buildings were obtained via pushover analyses. Using 60 ground motion records, nonlinear time-history analyses of equivalent single degree of freedom systems were performed using bilinear hysteretic model and peak-oriented hysteretic model with stiffness - strength deterioration for each scaled elastic spectral displacement. The damage measure is maximum inter-story drift ratio and each performance level considered in this study has an assumed limit value of damage measure. Discrete damage probabilities were calculated using statistical methods for each considered performance level and elastic spectral displacement. Consequently, continuous fragility curves have been constructed based on the lognormal distribution assumption. Furthermore, the effect of hysteresis model parameters on the damage probability is investigated.

• Korean Journal of Remote Sensing
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• v.30 no.3
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• pp.399-415
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• 2014

The intensity of stormwater runoff is particularly acute across cities located in arid climates. During flash floods loose sediment and pollutants are typically transported across sun-hardened surfaces contributing to widespread degradation of water quality. Rapid, dense urbanization exacerbates the problem by creating continuous areas of impervious surfaces, perforated only by a few green patches. Our work demonstrates how the latest techniques in remote sensing can be used to routinely measure urban land cover types, impervious cover, and vegetated areas. In addition, multiple regression models can then infer relationships between urban land use and land cover types with stormwater quality data, initially sampled at discrete monitoring sites, and then extrapolated annually across an arid city; in our case, the city of Phoenix in Arizona, USA. Results reveal that from 30 storm event samples, solids and heavy metal pollutants were found to be highly related with general impervious surfaces; in particular, with industrial and commercial land use types. Repercussions stemming from this work include support for public policies that advocate environmental sustainability and the more recent focus on urban livability. Also, advocacy for new urban construction and re-development that both steer away from vast unbroken impervious surfaces, in place of more fragmented landscapes that harmonize built and green spaces.

• Korean Journal of Computational Design and Engineering
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• v.20 no.2
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• pp.193-209
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• 2015

For reasons such as global warming, depletion of fossil fuels and the danger of nuclear energy the research and development of renewable energy is actively underway. Wind energy has advantages over another renewable energy in terms of location requirements, energy efficiency and reliability. Nowadays the research and development area is expanded to offshore because it can supply more wind reliability and free from noise pollution. In this study, the monopile offshore wind turbine transportation and installation (T&I) process are investigated. In addition, the schedule and cost for the process are estimated by discrete event simulation. For the simulation, simulation models for various means of T&I are developed. The optimum T&I execution plan with shortest duration and lowest cost can be found by using different mission start day and T&I means.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2330-2336
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• 2013

Since many man-made systems consist of autonomous and interactive components, it is intrinsically difficult to analyze their abnormal behavior. The logical analysis of such a system is an indispensable process for high quality and reliable system development. In this paper, we propose an analysis method using two algebraic operations, named composition and minimization. Repetitive composition and minimization of component models with respect to a set of important events produces a new analysis model that has the same input output responses to an environment. An analysis example of the alternating bit protocol demonstrates the effectiveness of the proposed method showing that each message generated at the sender side eventually arrives to a receiver.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.390-397
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• 2013

This paper presents a consensus algorithm for uMAS (uncertain Multi-Agent Systems). Unlike previous results in which only nominal models for agents are considered, it is assumed that the uncertain agent model belongs to a known polytope set. In the middle of deriving the proposed algorithm, a convex set is found which includes all uncertainties in the problem using convexity of the polytope set. This set plays an important role in designing the consensus algorithm for uMAS. Based on the set, a consensus condition for uMAS is proposed and the corresponding consensus design problem is solved using LMI (Linear Matrix Inequality). Simulation result shows that the proposed consensus algorithm successfully leads to consensus of the state of uMAS.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.879-884
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• 2004

This paper describes the development of a synthetic multi-agent called KMITL Cadence 2003. KMITL Cadence 2003 is a robotic soccer simulation team consisting of eleven autonomous software agents. Each agent operates in a physical soccer simulation model called Robocup Soccer Server which provides fully distributed and real-time multi-agent system environment. All teammates have to cooperate to achieve the common goal of winning the game. The simulation models many aspects of the football field such as noise in ball movements, noisy sensors, unreliable communication channel between teammates and actuators, limited physical abilities and restricted communication. This paper addresses the algorithm to develop the soccer agents to perform basic actions which are scoring, passing ball and blocking the opponents effectively. The result of this development is satisfactory because the successful scoring attempts is increased from 11.1% to 33.3%, successful passing ball attempts is increased from 22.08% to 63.64%, and also, successful intercepting attempts is increased from 88% to 97.73%.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.295-300
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• 2015

In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.