• Ocean and Polar Research
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• v.35 no.3
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• pp.249-258
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• 2013

Vertical and horizontal mixing processes in the ocean mixed layer determine sea surface temperature and temperature variability. Accordingly, simulating these processes properly is crucial in order to obtain more accurate climate simulations and more reliable future projections using an ocean general circulation model (OGCM). In this study, by using Modular Ocean Model version 4 (MOM4) developed by Geophysical Fluid Dynamics Laboratory, the upper ocean temperature and mixed layer depth were simulated with two different vertical mixing schemes that are most widely used and then compared. The resultant differences were analyzed to understand the underlying mechanism, especially in the Tropical Pacific Ocean where the differences appeared to be the greatest. One of the schemes was the so-called KPP scheme that uses K-Profile parameterization with nonlocal vertical mixing and the other was the N scheme that was rather recently developed based on a second-order turbulence closure. In the equatorial Pacific, the N scheme simulates the mixed layer at a deeper level than the KPP scheme. One of the reasons is that the total vertical diffusivity coefficient simulated with the N scheme is ten times larger, at maximum, in the surface layer compared to the KPP scheme. Another reason is that the zonal current simulated with the N scheme peaks at a deeper ocean level than the KPP scheme, which indicates that the vertical shear was simulated on a larger scale by the N scheme and it enhanced the mixed layer depth. It is notable that while the N scheme simulates a deeper mixed layer in the equatorial Pacific compared to the KPP scheme, the sea surface temperature (SST) simulated with the N scheme was cooler in the central Pacific and warmer in the eastern Pacific. We postulated that the reason for this is that in the central Pacific atmospheric forcing plays an important role in determining SST and so does a strong upwelling in the eastern Pacific. In conclusion, what determines SST is crucial in interpreting the relationship between SST and mixed layer depth.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.746-756
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• 2017

An algorithm is developed to generate measurement data of deep space network for Korea Pathfinder Lunar Orbiter (KPLO) mission. The algorithm can provide corrected measurement data for the Orbit Determination (OD) module in deep space. This study describes how to generate the computed data such as range, Doppler, azimuth angle and elevation angle. The geometric data were obtained by General Mission Analysis Tool (GMAT) simulation and the corrected data were calculated with measurement models. Therefore, the result of total delay includes effects of tropospheric delay, ionospheric delay, charged particle delay, antenna offset delay, and tropospheric refraction delay. The computed measurement data were validated by comparison with the results from Orbit Determination ToolBoX (ODTBX).

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.10
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• pp.119-126
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• 2007

Recently, the mobility of users and mobile communication technologies have developed rapidly. The users in this state also want to connect their devices and to receive services anywhere, anytime. Hierarchical Mobile IPv6 (HMIPv6) has been proposed by the Internet Engineering Task Force (IETF) to compensate for such problems as handover latency and signaling overhead when employing Mobile IPv6 (MIPv6). HMIPv6 supports micro-mobility within a domain and introduces a new entity, namely Mobility Anchor Point (MAP) as a local home agent. However, HMIPv6 has been found to cause longer handover latency when the inter-domain handover occurs. This is because a Mobile Node (MN) has to generate two addresses and register them to Home Agent (HA) a MAP, respectively. In order to solve such problems, we propose a scheme that an MN generates one address and registers it to HA for supporting fast handover during the inter-domain handover process. In the proposed scheme, the load of MAP and MAP domain is reduced because the number of MNs which are managed by MAP is decreased and the MAP does not perform proxy Neighbor Discovery Protocol (NDP) to intercept packets destined to MNs. We evaluate the performance of proposed scheme in comparison to HMIPv6 through the simulation and numerical analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• Journal of Korea Water Resources Association
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• v.43 no.11
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• pp.985-994
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• 2010

Typoon Maemi landed on the southern coast of Korean Peninsula at 21:00, September 12, 2003 with a central pressure of 950 hPa. A three dimensional (3D) inundation model was established to calculate the storm surge and flooded area due to Typoon Maemi. A field survey of storm surge traces in Masan City was carried out to evaluate the inundation water depth. Hydromet-Rankin Vortex model was used to calculate the atmospheric pressure and the surface wind fields. The inundation area, storm surge and typoon-induced current were calculated using the 3D model. The peak of computed storm surge in Masan Port using the 3D model was 238 cm, and the observed peak was 230 cm. The simulated storm surge and the inundation area showed good agreement with field survey data. The comparison of the 3D and the two dimensional (2D) models of storm surge was carried out, and the 3D model was more accurate. The computed typoon-induced currents in the surface layer of Masan Bay went into the inner bay with 30~60 cm/s, while the currents in the bottom layer flowed out with 20~40 cm/s.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.7
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• pp.349-358
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• 2005

False sharing is a result of co-location of unrelated data in the same unit of memory coherency, and is one source of unnecessary overhead being of no help to keep the memory coherency in multiprocessor systems. Moreover. the damage caused by false sharing becomes large in proportion to the granularity of memory coherency. To reduce false sharing in a page-based DSM system, it is necessary to allocate unrelated data objects that have different access patterns into the separate shared pages. In this paper we propose call-site tracing-based shared memory allocator. shortly CSTallocator. CSTallocator expects that the data objects requested from the different call-sites may have different access patterns in the future. So CSTailocator places each data object requested from the different call-sites into the separate shared pages, and consequently data objects that have the same call-site are likely to get together into the same shared pages. We use execution-driven simulation of real parallel applications to evaluate the effectiveness of our CSTallocator. Our observations show that by using CSTallocator a considerable amount of false sharing misses can be additionally reduced in comparison with the existing techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.751-764
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• 2013

In cognitive radio ad-hoc networks, common control channel overload and packet collisions are occured due to indiscriminate broadcasting of control packets. So that the path reliability is reduced and control channel is easily saturated. In this paper, we propose a new routing algorithm considering the probability of appearance of primary user and channel status of neighbor nodes. When the source node needs to transmit a data packet to the destination, it performs route discovery process by exchanging control messages using a control channel in ADOV CR Ad-hoc networks. If any intermediate node doesn't have common data channel with previous node to transmit data, it doesn't rebroadcast control packet. And if it has common data channels with previous node, each node determines channel contribution factor with the number of common channels. Based on the channel contribution factor, each node performs different back-off broadcasting. In addition, each node controls control packet flooding by applying to proposed advanced mode using such as number of available channels and channel stability. With the proposed method, the number of control packets to find the data transmission path and the probability of collision among control packets can be decreased. While the path reliability can be increased. Through simulation, we show that our proposed algorithm reduces packet collisions in comparison with the traditional algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.25-39
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• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.215-224
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• 2008

The effect of streamflow considering future land use change and vegetation index information by climate change scenario was assessed using SLURP (Semi-distributed Land-Use Runoff Process) model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for the upstream watershed ($260.4km^2$) of Gyeongan water level gauging station. By applying CA-Markov technique, the future land uses (2030, 2060, 2090) were predicted after test the comparison of 2004 Landsat land use and 2004 CA-Markov land use by 1996 and 2000 land use data. The future land use showed a tendency that the forest and paddy decreased while urban, grassland and bareground increased. The future vegetation indices (2030, 2060, 2090) were estimated by the equation of linear regression between monthly NDVI of NOAA AVHRR images and monthly mean temperature of 5 years (1998-2002). Using CCCma CGCM2 simulation result based on SRES A2 and B2 scenario (2030s, 2060s, 2090s) of IPCC and data were downscaled by Stochastic Spatio-Temporal Random Cascade Model (SST-RCM) technique, the model showed that the future runoff ratio was predicted from 13% to 34% while the runoff ratio of 1999-2002 was 59%. On the other hand, the impact on runoff ratio by land use change showed about 0.1% to 1% increase.