• 한국철도학회논문집
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• 제12권5호
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• pp.806-810
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• 2009

병원, 호텔 및 철도역사 등과 같은 대형의 건물 및 차량내부에 사용되는 공조 시스템은 일반적으로 높은 소음을 발생시킨다. 이러한 공조용 발생소음을 저감시키기 위해서 흡음재가 부착된 쳄버를 공기 시스템에 설치한다. 그러나 이러한 쳄버에 설치된 흡음재는 유리섬유 혹은 폴리우레탄 폼이 많이 사용되기 때문에 최근에는 환경적 문제를 야기한다. 이러한 문제를 해결하기 위해서 환경친화적인 다공판 시스템이 내장된 소음쳄버를 개발하였다. 이 소음쳄버는 기존의 소음쳄버에 비해서 중고주파수 영역에서 통일한 소음저감 성능을 나타내지만, 200Hz~400Hz 영역에서는 최고 8dB(A)까지 소음저감 효과가 있다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2004년도 추계학술대회 논문집
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• pp.1353-1359
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• 2004

There are many massive components added on the railway overhead-line. These components cause larger fluctuations of contact forces, which are due to wave reflections and Doppler effects when a high-speed train passes those. In this paper, mathematical formula are derived for the relation between the added mass and contact force fluctuations. Using the derived formula, we calculate a added mass on the overhead-line which cause amplification factor to become 2.5. German design practice requires that amplification factor due to the wave reflection should be less than 2.5 to obtain good current collection performance. To show the validity of the formula, simulation results are compared with the calculation results. Simulation results showed that contact force fluctuations grow rapidly when an added mass is larger than the calculation result. Therefore, the simple form of formula can be used for estimating maximum added mass not to cause large fluctuations of contact forces in early design phase.

• Journal of information and communication convergence engineering
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• 제13권2호
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.101-106
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• 2006

The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There are several methods for geoidal undulation determination. The paper presents a method employing a simple architecture Two Layer Multiquadric-Biharmonic Artificial Neural Network (TLMB-ANN) to approximate an area of 4200 square kilometres quasigeoid surface with GPS-levelling data. Hardy’s Multiquadric-Biharmonic functions is used as the hidden layer neurons’ activation function and Levenberg-Marquardt algorithm is used to train the artificial neural network. In numerical examples five surfaces were compared: the gravimetric geometry hybrid quasigeoid, Support Vector Machine (SVM) model, Hybrid Fuzzy Neural Network (HFNN) model, Traditional Three Layer Artificial Neural Network (ANN) with tanh activation function and TLMB-ANN surface approximation. The effectiveness of TLMB-ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are similar with those obtained by gravity and geometry hybrid method. Importantly, TLMB-ANN surface approximation model possesses good extrapolation performance with high precision.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4126-4142
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• 2015

Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.

• 한국항만경제학회지
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• 제18권2호
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• pp.109-133
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• 2002

The China entered World Trade Oganization(WTO) last year, thus opening its border to more - and freer - trade. With its foreign trade rapidly expanding and with economic growth continuing at a substantial -rate, China will be the largest container traffic generating country in the world. In the light of this potential trade bonanza, regional ports in North-East Asia strive to gain a competitive-edge. The Port of Busan, the world's third largest container port, wants to capture a significant share of the china's container cargoes. In this circumstance, development strategies of the Port of Busan are suggested as follows. First, to cope with increasing volumes, the New Busan Port on Gaduk island should be constructed without failure. Second, it is necessary to add modernized high-performance gantry cranes and to train crane operators' skill. Third, it needs to apply Dwell Time- Sliding Scale System for transshipment cargoes. Fourth, it needs to develop the EDI network in terminal areas or adjacent hub ports to exchange trustworthy and satisfactory informations Fifth, port authority -needs to enlarge designated Free Trade Zone to facilitate the free flow of cargoes. Sixth, the restoration of rail links between North and South Korea is abundantly clear. Thus it needs to enlarge railroad facilities in advance. Seventh, it needs to establish the Port Authority of Busan immediately. Finally, it needs to strengthen port sales and to open events like 'Marine Week 2001' regularly to attract potential canters or big shippers.

• 한국지반환경공학회 논문집
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• 제22권11호
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• pp.39-47
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• 2021

우리나라는 국토 전체 면적 중 70% 이상 산악지형으로 철도공사 시 주행성능 확보를 위해 터널 공사가 매년 증가하고 있다. 터널공사가 증가함에 따라 터널굴착 방법 또한 다양해지고 있다. 굴착부의 지반이 풍화암으로 구성되어있으면 다양한 터널굴착 공법이 적용될 수 있지만 굴착부가 파쇄대를 지나거나 저토피의 계곡부를 지나는 경우 터널 굴착 시 붕괴의 위험성을 지니고 있다. 따라서 본 연구에서는 다양한 지반에서의 보강공법을 제시하고자 저토피 및 암질불량구간의 굴착 중인 대표터널을 선정하였다. 수치해석은 암질불량구간일 때 강관을 미적용한 경우와 적용한 경우, 저토피 구간일 때 터널 상부에 고화토를 성토한 경우 강관보강을 적용하여 수치해석을 통해 안정성 분석을 수행하였다.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Smart Structures and Systems
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• 제29권3호
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.