• 대한조선학회논문집
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• 제56권3호
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• pp.273-280
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• 2019

Predicting ship manoeuvrability is attracting widespread interest in the field of analyzing maritime accident to simulate a highly accurate track of a ship in abnormal accident situations. This study investigated the manoeuvrability of a ship in abnormally heeled condition. Free Running Model Tests (FRMT) with 1/65.83 scaled KCS (KRISO container ship) were conducted in three heeled conditions; $35^{\circ}$ turning circle tests and 20/20 zigzag manoeuvring tests were conducted in $0^{\circ}$, $-10^{\circ}$, and $-20^{\circ}$ conditions. The test results showed that the heeled to port condition significantly affected starboard turning and zigzag characteristics; the tactical diameters in the turning circle tests decreased, and the first overshoot angles in the zigzag tests increased when the ship was in the larger heeled condition. These results indicate that the roll angle of the ship considerably affects yaw rate and speed decrease of the ship. The turning and zigzag indices from trajectory and navigation data in the study were provided for benchmark data sets.

• Nuclear Engineering and Technology
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• 제50권7호
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• pp.1043-1050
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• 2018

For an efficient Monte Carlo (MC) burnup analysis, an accurate high-order depletion scheme to consider the nonlinear flux variation in a coarse burnup-step interval is crucial accompanied with an accurate depletion equation solver. In a Seoul National University MC code, McCARD, the high-order depletion schemes of the quadratic depletion method (QDM) and the linear extrapolation/quadratic interpolation (LEQI) method and a depletion equation solver by the Chebyshev rational approximation method (CRAM) have been newly implemented in addition to the existing constant extrapolation/backward extrapolation (CEBE) method using the matrix exponential method (MEM) solver with substeps. In this paper, the quadratic extrapolation/quadratic interpolation (QEQI) method is proposed as a new high-order depletion scheme. In order to examine the effectiveness of the newly-implemented depletion modules in McCARD, four problems in the VERA depletion benchmarks are solved by CEBE/MEM, CEBE/CRAM, LEQI/MEM, QEQI/MEM, and QDM for gadolinium isotopes. From the comparisons, it is shown that the QEQI/MEM predicts ${k_{inf}}^{\prime}s$ most accurately among the test cases. In addition, statistical uncertainty propagation analyses for a VERA pin cell problem are conducted by the sensitivity and uncertainty and the stochastic sampling methods.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.203-211
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• 2021

Image tampering detection and localization have become an active area of research in the field of digital image forensics in recent times. This is due to the widespread of malicious image tampering. This study presents a new method for image tampering detection and localization that combines the advantages of dilated convolution, residual network, and UNET Architecture. Using the UNET architecture as a backbone, we built the proposed network from two kinds of residual units, one for the encoder path and the other for the decoder path. The residual units help to speed up the training process and facilitate information propagation between the lower layers and the higher layers which are often difficult to train. To capture global image tampering artifacts and reduce the computational burden of the proposed method, we enlarge the receptive field size of the convolutional kernels by adopting dilated convolutions in the residual units used in building the proposed network. In contrast to existing deep learning methods, having a large number of layers, many network parameters, and often difficult to train, the proposed method can achieve excellent performance with a fewer number of parameters and less computational cost. To test the performance of the proposed method, we evaluate its performance in the context of four benchmark image forensics datasets. Experimental results show that the proposed method outperforms existing methods and could be potentially used to enhance image tampering detection and localization.

• ETRI Journal
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• 제42권6호
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• pp.886-898
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• 2020

Unusual data patterns or outliers can be generated because of human errors, incorrect measurements, or malicious activities. Detecting outliers is a difficult task that requires complex ensembles. An ideal outlier detection ensemble should consider the strengths of individual base detectors while carefully combining their outputs to create a strong overall ensemble and achieve unbiased accuracy with minimal variance. Selecting and combining the outputs of dissimilar base learners is a challenging task. This paper proposes a model that utilizes heterogeneous base learners. It adaptively boosts the outcomes of preceding learners in the first phase by assigning weights and identifying high-performing learners based on their local domains, and then carefully fuses their outcomes in the second phase to improve overall accuracy. Experimental results from 10 benchmark datasets are used to train and test the proposed model. To investigate its accuracy in terms of separating outliers from inliers, the proposed model is tested and evaluated using accuracy metrics. The analyzed data are presented as crosstabs and percentages, followed by a descriptive method for synthesis and interpretation.

• 한국게임학회 논문지
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• 제20권6호
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• pp.31-42
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• 2020

MMO 게임 서버는 수천 명 이상의 대량 동시접속 시 성능저하를 막기 위해 운영체제에서 제공하는 고성능 I/O 모델을 사용해 구현해야한다. 하지만 운영체제에서 제공하는 I/O 모델들이 계속 발전하고 있음에도 불구하고 기존 어플리케이션들에 대한 적용은 즉각적으로 이루어지지 않고 있다. 이에 본 연구에서는 Linux의 새로운 I/O 모델인 io_uring을 MMO 게임 서버에 적용하였고, 이를 위해 기존의 서버 구조를 io_uring에 맞추어 최적화하였다. 이를 통해 개선된 성능을 대용량 접속 벤치마크 프로그램을 통해 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제26권1호
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• pp.11-17
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• 2021

본 논문에서는 순수 함수형 언어로 작성된 쌍방시뮬레이션 알고리듬의 성능을 멀티코어 프로세서 컴퓨터에서 병렬화로 향상시키는 방법을 연구한다. 이 병렬화에 있어 핵심 아이디어는 순수 함수형 프로그램의 참조 투명성을 십분 활용하면 병렬화가 전혀 고려되지 않고 작성된 초기 구현으로부터 최소한의 수정만으로 성능 개선 효과를 기대할 수 있다는 것이다. 초기 구현과 병렬화 구현 둘 다 순수 함수형 언어인 하스켈로 작성되었다. 초기 구현을 병렬화할 때 변화는 아주 적어서 병렬화된 구현에서도 초기 구현의 프로그램 구조가 거의 그대로 유지되었다. 벤치마크를 통해 제시된 간단한 병렬화만으로도 초기 구현과 비교해 두 배 이상의 성능 개선을 확인했다. 또한, 병렬화와는 별개의 최적화 기법인 메모이제이션이 적용된 버전의 쌍방시뮬레이션 구현에도 같은 방식의 병렬화를 적용함으로써 마찬가지로 성능을 개선할 수 있음을 확인하였다.

• ETRI Journal
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• 제44권5호
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.261-273
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• 2022

Over the last decade, the scientific community has been actively developing technologies for automated software bug fixes called Automated Program Repair (APR). Several APR techniques have recently been proposed to effectively address multiple classroom programming errors. However, little attention has been paid to the advances in effective APR techniques for software bugs that are widely occurring during the software life cycle maintenance phase. To further enhance the concept of software testing and debugging, we recommend an optimized automated software repair approach based on hybrid technology (OAPR-HOML'1). The first contribution of the proposed OAPR-HOML'1 technique is to introduce an improved grasshopper optimization (IGO) algorithm for fault location identification in the given test projects. Then, we illustrate an opposition learning based artificial neural network (OL-ANN) technique to select AST node-level transformation schemas to create the sketches which provide automated program repair for those faulty projects. Finally, the OAPR-HOML'1 is evaluated using Defects4J benchmark and the performance is compared with the modern technologies number of bugs fixed, accuracy, precession, recall and F-measure.

• 한국정밀공학회지
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• 제11권4호
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• pp.58-68
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• 1994

An equilibrium approach is suggested as an effective tool for the analysis of sheet metal forming processes on the basis of force balance together with geometric relations and plasticity theroy. In computing a force balance equation, it is required to define a geometric curve approximating the shape of the sheet metal at any step of deformation from the geometric interaction between the die and the deforming sheet. Then the geometic informations for contacting and non-contacting sections of the sheet metal such as the number and length of both non-contact region, contact angle, and die radius of contact section are known from the geometric forming curve and utilized for optimization by force balance equation. In computation, the sheet material is assumed to be of normal amisotropy and rigid-phastic workhardening. It has been shown that there are good agreements between the equilibrium approach and FEM computation for the benchmark test example and auto-body panels whose sections can be assumed in plane-strain state. The proposed equilibrium approach can thus be used as a robust computational method in estimating the forming defects and forming severity rather quickly in the die design stage.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.