• Journal of Sensor Science and Technology
• /
• v.32 no.1
• /
• pp.62-65
• /
• 2023

The wave mode calculation of a large-scale optical system in comparison to the working wavelength is practically impossible because the computational cost increases exponentially. In this paper, we propose a method that can obtain the optical mode in a large-scale optical system. The method carries out simulations by dividing the calculation area into blocks and moving along the light axis along which the light propagates. By applying this method to the calculation of resonant modes in a ring-type optical resonator, which is mainly used for ring laser optical gyro sensors, the efficiency of the proposed method was verified.

• Journal of Ocean Engineering and Technology
• /
• v.37 no.6
• /
• pp.273-281
• /
• 2023

Recently, offshore structures for eco-friendly energy, such as wind and solar power, have been developed to address the problem of insufficient land space; in the case of energy generation, they are designed on a considerable scale. Therefore, the scalability of offshore structures is crucial. The Korea Research Institute of Ships & Ocean Engineering (KRISO) developed multi-linked floating offshore structures composed of floating bodies and connection beams for floating photovoltaic systems. Large-scale floating photovoltaic systems are mainly designed in a manner that expands through the connection between modules and demonstrates a difference in structural response with connection conditions. A fluid-structure coupled analysis was performed for the multi-linked floating offshore structures. First, the wave load acting on the multi-linked offshore floating structures was calculated through wave load analysis for various wave load conditions. The response amplitude operators (RAOs) for the motions and structural response of the unit structure were calculated by performing finite element analysis. The effects of connection conditions were analyzed through comparative studies of RAOs and the response's maximum magnitude and occurrence location. Hence, comparing the cases of a hinge connection affecting heave and pitch motions and a fixed connection, the maximum bending stress of the structure decreased by approximately 2.5 times, while the mooring tension increased by approximately 20%, confirmed to be the largest change in bending stress and mooring tension compared to fixed connection. Therefore, the change in structural response according to connection condition makes it possible to design a higher structural safety of the structural member through the hinge connection in the construction of a large-scale multi-linked floating offshore structure for large-scale photovoltaic systems in which some unit structures are connected. However, considering the tension of the mooring line increases, a safety evaluation of the mooring line must be performed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.11
• /
• pp.2769-2792
• /
• 2013

In this paper, we propose an authenticated key agreement scheme, TinyIBAK, based on the identity-based cryptography and bilinear paring, for large scale sensor networks. We prove the security of our proposal in the random oracle model. According to the formal security validation using AVISPA, the proposed scheme is strongly secure against the passive and active attacks, such as replay, man-in-the middle and node compromise attacks, etc. We implemented our proposal for TinyOS-2.1, analyzed the memory occupation, and evaluated the time and energy performance on the MICAz motes using the Avrora toolkits. Moreover, we deployed our proposal within the TOSSIM simulation framework, and investigated the effect of node density on the performance of our scheme. Experimental results indicate that our proposal consumes an acceptable amount of resources, and is feasible for infrequent key distribution and rekeying in large scale sensor networks. Compared with other ID-based key agreement approaches, TinyIBAK is much more efficient or comparable in performance but provides rekeying. Compared with the traditional key pre-distribution schemes, TinyIBAK achieves significant improvements in terms of security strength, key connectivity, scalability, communication and storage overhead, and enables efficient secure rekeying.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.1
• /
• pp.136-151
• /
• 2016

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06d
• /
• pp.322-326
• /
• 2008

시스템의 규모가 대형화되어 감에 '시스템의 시스템'의 형태로써 대규모 사이즈의 프로그램 다양한 목적을 가진 사용자들, 대규모 저장 데이터양과 처리, 소프트웨어 컴포넌트간의 복잡한 연결상과 상호 의존성, 하드웨어의 다양성등을 포함하는 초대형 규모로 발전하고 있다. 또한 유, 무선 인터넷의 보편화와 소형기기들의 인터넷화 및 기존 서비스들의 개방화가 진행됨에 따라 새로운 독자적인 서비스를 만들기 보다는 SOA기반의 기존 시스템을 통합하여 새로운 서비스를 만드는 시도가 일어나고 있다. 최근 진행되고 있는 국가 및 산업의 대형 프로젝트들은 이러한 흐름에 따라 IT기술을 융합한 소프트웨어 기반의 초 대형 시스템 (Ultra Large Scale System)을 필요로 하고 있다. 이에 본 논문에서는 이러한 정보와 시스템의 대규모화에 대한 즉각적인 대처를 할 수 있는 Ultra Large Scale 시스템의 자율적응형 (Self-Adaptive) 기술 연구를 위하여 Self-Healing, Self-Integrating, Self-Orchestrating, Self-Managing, Self-Adaptring의 5가지 관점에서의 연구를 제안한다. 본 논문에서 제안하고 있는 연구의 파급 효과를 극대화 할 수 있는 영역은 e-Biz 시스템 u-city 시스템, USN 기반 물류 시스템 자동차 및 조선 사업의 IT융합 등의 대규모 시스템이 될 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.7
• /
• pp.3213-3230
• /
• 2016

Sensor node (SN) is a crucial part in any remote monitoring system. It is a device designed to monitor the particular changes taking place in its environs. Wireless sensor network (WSN) is a system formed by the set of wirelessly connected SNs placed at different geographical locations within a target region. Precise placement of SNs is appreciated, as it affects the efficiency and effectiveness of any WSN. The manual placement of SNs is only feasible for small scale regions. The task of SN placement becomes tedious, when the size of a target region is extremely large and manually unreachable. In this research article, an automated mechanism for fast and precise deployment of SNs in a large scale target region has been proposed. It uses an assembly of rotating cannons to launch the SNs from a moving carrier helicopter. The entire system is synchronized such that the launched SNs accurately land on the pre-computed desired locations (DLs). Simulation results show that the proposed model offers a simple, time efficient and effective technique to place SNs in a large scale target region.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.12
• /
• pp.5972-5989
• /
• 2019

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

• Smart Structures and Systems
• /
• v.24 no.5
• /
• pp.587-595
• /
• 2019

Deformation measurement of large scale structures, such as the ground beds of high-rise buildings, tunnels, bridge, and railways, are important for insuring service quality and safety. The pose-relay videometrics method and displacement-relay videometrics method have already presented to measure the pose of non-intervisible objects and vertical subsidence of unstable areas, respectively. Both methods combine the cameras and cooperative markers to form the camera series networks. Based on these two networks, we propose two novel videometrics methods with closed-loop camera series network for deformation measurement of large scale structures. The closed-loop camera series network offers "closed-loop constraints" for the camera series network: the deformation of the reference points observed by different measurement stations is identical. The closed-loop constraints improve the measurement accuracy using camera series network. Furthermore, multiple closed-loops and the flexible combination of camera series network are introduced to facilitate more complex deformation measurement tasks. Simulated results show that the closed-loop constraints can enhance the measurement accuracy of camera series network effectively.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.537-537
• /
• 2015

In recent decades, the independence and identical distribution (iid) assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, in the current study we propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.3
• /
• pp.551-569
• /
• 2024

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.