• Journal of the Korean Magnetics Society
• /
• v.17 no.4
• /
• pp.166-171
• /
• 2007

We report the magneto-transport properties of an individual single crystalline Bi nanowire grown by a spontaneous growth method. We have successfully fabricated a four-terminal device based on an individual 400-nm-diameter nanowire using plasma etching technique to remove an oxide layer forming on the outer surface of the nanowire. The transverse MR (2496% at 110 K) and longitudinal MR ratios (38% at 2 K) for the Bi nanowire were found to be the largest known values in Bi nanowires. This result demonstrates that the Bi nanowires grown by the spontaneous growth method are the highest-quality single crystalline in the literatures ever reported. We find that temperature dependence of Fermi energy ($E_F$) and band overlap (${\triangle}_0$) leads to the imbalance between electron concentration ($n_e$) and hole concentration ($n_h$) in the Bi nanowire, which is good agreement with the calculated $n_e\;and\;n_h$ from the respective density of states, N(E), for electrons and holes. We also find that the imbalance of $n_e\;and\;n_h$ plays a crucial role in determining magnetoresistance (MR) at T<75 K for $R_T$ and at T<205 K for $R_L$, while mean-free path is responsible for MR at T>75 K for $R_T$ and T>205 K for $R_L$.

• Wind and Structures
• /
• v.21 no.4
• /
• pp.425-442
• /
• 2015

This paper discusses the processing of the wind loads measured in wind tunnel tests by means of multi-channel pressure scanners, in order to compute the response of 3D structures to atmospheric turbulence in the time domain. Data compression and the resulting computational savings are still a challenge in industrial contexts due to the multiple trial configurations during the construction stages. The advantage and robustness of the bi-orthogonal decomposition (BOD) is demonstrated through an example, a sail glass of the Fondation Louis Vuitton, independently from any tentative physical interpretation of the spatio-temporal decomposition terms. We show however that the energy criterion for the BOD has to be more rigorous than commonly admitted. We find a level of 99.95 % to be necessary in order to recover the extreme values of the loads. Moreover, frequency limitations of wind tunnel experiments are sometimes encountered in passing from the scaled model to the full scale structure. These can be alleviated using a spectral extension of the temporal function terms of the BOD.

• Structural Engineering and Mechanics
• /
• v.78 no.3
• /
• pp.333-350
• /
• 2021

Motivated by the demand of seismic protection of museum collections and development of performance-based seismic design guidelines, this paper investigates the seismic fragility of sliding artifacts based on incremental dynamic analysis and three-dimensional finite element model of the artifact-showcase-museum system considering nonlinear behavior of the structure and contact interfaces. Different intensity measures (IMs) for seismic fragility assessment of sliding artifacts are compared. The fragility curves of the sliding artifacts in both freestanding and restrained showcases placed on different floors of a four-story reinforced concrete frame structure are developed. The seismic sliding fragility of the artifacts within a real-world museum subjected to bi-directional horizontal ground motions is also assessed using the proposed IM and engineering demand parameter. Results show that the peak floor acceleration including only values initiating sliding is an efficient IM. Moreover, the sliding fragility estimate for the artifact in the restrained showcase increases as the floor level goes higher, while it may not be true in the freestanding showcase. Furthermore, the artifact is more prone to sliding failure in the restrained showcase than the freestanding showcase. In addition, the artifact has slightly worse sliding performance subjected to bi-directional motions than major-component motions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.10
• /
• pp.3230-3255
• /
• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• Industrial Engineering and Management Systems
• /
• v.15 no.4
• /
• pp.374-384
• /
• 2016

The efficiency of a supply chain can be extremely affected by its design which includes determining the flow pattern of material from suppliers to costumers, selecting the suppliers, and defining the opened facilities in network. In this paper, a multi-objective multi-echelon multi-product supply chain design model is proposed in which several suppliers, several manufacturers, several distribution centers as different stages of supply chain cooperate with each other to satisfy various costumers' demands. The multi-objectives of this model which considered simultaneously are 1-minimize the total cost of supply chain including production cost, transportation cost, shortage cost, and costs of opening a facility, 2-minimize the transportation time from suppliers to costumers, and 3-maximize the service level of the system by minimizing the maximum level of shortages. To configure this model a graph theoretic approach is used by considering channels among each two facilities as links and each facility as the nodes in this configuration. Based on complexity of the proposed model a multi-objective Pareto-based vibration damping optimization (VDO) algorithm is applied to solve the model and finally non-dominated sorting genetic algorithm (NSGA-II) is also applied to evaluate the performance of MOVDO. The results indicated the effectiveness of the proposed MOVDO to solve the model.

• Journal of Korea Port Economic Association
• /
• v.22 no.1
• /
• pp.175-198
• /
• 2006

This paper presents a network capacity model that can be used as an analytical tool for strategic planning and resource allocation for multimodal transportation systems. In the context of freight transportation, the multimodal network capacity problem (MNCP) is formulated as a mathematical model of nonlinear bi-level optimization problem. Given network configuration and freight demand for multiple origin-destination pairs, the MNCP model is designed to determine the maximum flow that the network can accommodate. To solve the MNCP, a heuristic solution algorithm is developed on the basis of a linear approximation method. A hypothetical exercise shows that the MNCP model and solution algorithm can be successfully implemented and applied to not only estimate the capacity of multimodal network, but also to identify the capacity gaps over all individual facilities in the network, including intermodal facilities. Transportation agencies and planners would benefit from the MNCP model in identifying investment priorities and thus developing sustainable transportation systems in a manner that considers all feasible modes as well as low-cost capacity improvements.

• Journal of Korean Society of Transportation
• /
• v.22 no.2 s.73
• /
• pp.65-76
• /
• 2004

교통망설계문제란, 교통시스템을 최적상태로 만들기 위한 최적의 설계변수를 결정하는 문제이다. 대표적인 교통망설계문제로는 도로를 신설하거나 확장하는 문제가 있으며, 이외에 교통신호시간의 결정, 교통정보의 제공, 혼잡통행료 부과, 새로운 교통수단의 도입 등 여러 교통정책분야가 교통망설계문제에 포함된다고 볼 수 있다. 일반적으로 교통망설계문제는 bi-level 구조로 구축되는데, 기존 대부분의 연구들은 상위문제와 하위문제를 서로 협력없이(Noncooperative) 자신들만의 목적을 최적화시키는 Cournot-Nash게임형태로 구성하여 풀고 있으나, 실제 교통분야에서 다루는 문제들은 리더(leader)와 추종자(follower)가 존재하는 Stackelberg게임에 가깝다고 할 수 있다. 기존 bi-level 문제들이 Cournot-Nash게임형태로 구성되어 풀고 있는 이유는 Stackelberg게임으로 구성할 경우 풀기가 어렵기 때문이다. 이런 측면에서 본 연구는 리더와 추종자가 존재하는 Stackelberg게임으로 교통망설계문제를 구성하며, 설계 변수값에 따른 통행자의 행태변화도 인지오차(perceived error)를 고려한 확률적 통행배정문제로 구성하여 좀더 현실적인 결과를 도출하도록 한다. 제시된 모형을 풀기 위하여 민감도분석(Sensitivity analysis)을 이용하며, 설계문제의 해를 구하는 알고리듬도 제시한다. 또한, 이 기법을 일반 도로교통망(general transportation road network)에 적용할 수 있도록 민감도(sensitivity) 유도과정을 자세히 기술하였다. 개발된 모형을 평가하기 위하여 2개의 예제 교통망을 대상으로 모형을 적용한 결과, 합리적인 값들을 도출하고 있음을 확인할 수 있었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.10 no.5
• /
• pp.14-22
• /
• 2011

This study is to estimate delay time model of signalized intersection by using travel data of Bus Information System. BIS, which applies the advanced information technology to an existing bus system, has been developing and operating in many cities. However, even though some useful traffic informations have been collected from BIS operation, utilization of real-time data to the traffic operation has not been promoted due to the inhomogeneity of modal speeds. Accordingly, in this study, a fundamental research is performed for traffic controls in urban areas and providing a traffic information throughout a methodology for estimating delay time using the data from BIS was developed. This delay time model setting bus travel time excluding service time of a bus stop as explanatory variables was constructed as a regression model, and the coefficient of determination of a linear regression model most highly appeared as 0.826. As a result of performing T-test with field survey values and model estimation values for verifying constructed models statistically, it was analyzed to be statistically significant in a confidence level of 95%.

• Structural Engineering and Mechanics
• /
• v.38 no.4
• /
• pp.405-416
• /
• 2011

An efficient two-level domain decomposition parallel algorithm is suggested to solve large-DOF structural problems with nonlinear material models generating unsymmetric tangent matrices, such as a group of plastic-damage material models. The parallel version of the stabilized bi-conjugate gradient method is developed to solve unsymmetric coarse problems iteratively. In the present approach the coarse DOF system is solved parallelly on each processor rather than the whole system equation to minimize the data communication between processors, which is appropriate to maintain the computing performance on a non-supercomputer level cluster system. The performance test results show that the suggested algorithm provides scalability on computing performance and an efficient approach to solve large-DOF nonlinear structural problems on a cluster system.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.99-109
• /
• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.