• Journal of Advanced Research in Ocean Engineering
• /
• v.1 no.4
• /
• pp.239-251
• /
• 2015

In this study, a spectral fatigue analysis was performed for the topside structure of an offshore floating vessel. The topside structure was idealized using beam elements in the SACS program. The fatigue analysis was carried out considering the wave and wind loads separately. For the wave-induced fatigue damage calculation, motion RAOs calculated from a direct wave load analysis and regular waves with different periods and unit wave heights were utilized. Then, the member end force transfer functions were generated covering all the loading conditions. Stress response transfer functions at each joint were produced using the specified SCFs and member end force transfer functions. fatigue damages were calculated using the obtained stress ranges, S-N curve, wave spectrum, heading probability of each loading condition, and their corresponding occurrences in the wave scatter diagrams. For the wind induced fatigue damage calculation, a dynamic wind spectral fatigue analysis was performed. First, a dynamic natural frequency analysis was performed to generate the structural dynamic characteristics, including the eigenvalues (natural frequencies), eigenvectors (mode shapes), and mass matrix. To adequately represent the dynamic characteristic of the structure, the number of modes was appropriately determined in the lateral direction. Second, a wind spectral fatigue analysis was performed using the mode shapes and mass data obtained from the previous results. In this analysis, the Weibull distribution of the wind speed occurrence, occurrence probability in each direction, damping coefficient, S-N curves, and SCF of each joint were defined and used. In particular, the wind fatigue damages were calculated under the assumption that the stress ranges followed a Rayleigh distribution. The total fatigue damages were calculated from the combination with wind and wave fatigue damages according to the DNV rule.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.637-639
• /
• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

• Korean Journal of Remote Sensing
• /
• v.21 no.4
• /
• pp.341-369
• /
• 2005

Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.

• Korean Journal of Remote Sensing
• /
• v.26 no.6
• /
• pp.693-703
• /
• 2010

High spectral resolution of hyperspectral data enables analysis of complex natural phenomena that is reflected on the data nonlinearly. Although many manifold learning methods have been developed for such problems, most methods do not consider the spatial correlation between samples that is inherent and useful in remote sensing data. We propose a manifold learning method which directly combines the spatial proximity and the spectral similarity through kernel PCA framework. A gain factor caused by spatial proximity is first modelled with a heat kernel, and is added to the original similarity computed from the spectral values of a pair of samples. Parameters are tuned with intelligent grid search (IGS) method for the derived manifold coordinates to achieve optimal classification accuracies. Of particular interest is its performance with small training size, because labelled samples are usually scarce due to its high acquisition cost. The proposed spatial kernel PCA (KPCA) is compared with PCA in terms of classification accuracy with the nearest-neighbourhood classification method.

• Korean Journal of Remote Sensing
• /
• v.7 no.2
• /
• pp.149-163
• /
• 1991

This paper discusses the capabilities of airborne remotely sensed data to detect and classify forest damades. In this work the AMS (Aircraft Multiband Scanner) was used to obtain digital imagery at 300m altitude for forest damage inventory in the Black Forest of Germany. MSS(Multispectral Scanner) digital numbers were converted to spectral emittance and radiance values in 8 spectral bands from the visible to the thermal infrared and submitted to a maximum-likelihood classification for : (1) tree species ; and. (2) damage classes. As expected, the resulted, the results of MSS data with high spatial resolution 0.75m$\times$0.75m enabled the detection and identification of single trees with different damages and were nearly equivalent to the truth information of ground checked data.

• Earthquakes and Structures
• /
• v.17 no.4
• /
• pp.365-372
• /
• 2019

Post-earthquake crisis management is a key capability for a country to be able to recover after a major seismic event. Instrumental seismic data transmitted and processed in a very short time can contribute to better management of the emergency and can give insights on the earthquake's impact on a specific area. Romania is a country with a high seismic hazard, mostly due to the Vrancea intermediate-depth earthquakes. The elastic acceleration response spectrum of a seismic motion provides important information on the level of maximum acceleration the buildings were subjected to. Based on new data analysis and knowledge advancements, the acceleration elastic response spectrum for horizontal ground components recommended by the Romanian seismic codes has been evolving over the last six decades. This study aims to propose a framework for post-earthquake warning based on code spectrum exceedances. A comprehensive background analysis was undertaken using strong motion data from previous earthquakes corroborated with observational damage, to prove the method's applicability. Moreover, a case-study for two densely populated Romanian cities (Focsani and Bucharest) is presented, using data from a $5.5M_W$ earthquake (October 28, 2018) and considering the evolution of the three generations of code-based spectral levels for the two cities. Data recorded in free-field and in buildings were analyzed and has confirmed that no structural damage occurred within the two cities. For future strong seismic events, this tool can provide useful information on the effect of the earthquake on structures in the most exposed areas.

• Journal of the Optical Society of Korea
• /
• v.5 no.1
• /
• pp.9-15
• /
• 2001

The ocean Scanning Multi-spectral Imager (OSMI) is a payload on the KOrea Multi-Purpose SATellite (KOMPSAT) to perform worldwide ocean color monitoring f the study of biological oceanography. OSMI performs solar and dark calibrations for on-orbit instrument calibration. The purpose of the solar calibration is to monitor the degradation of imaging performance for each pixel of 6 spectral bands and to correct the degradation effect on OSMI image during the ground station date processing. The design, the operation concept, and the radiometric characteristics of the solar calibration are investigated. A linear model of image response and a solar calibration radiance model are proposed to study the instrument characteristics using the solar calibration data. The performance of spectral responsivity and spatial response uniformity. The first solar calibration data and the analysis results are important references for further study on the on-orbit stability of OSMI response during its lifetime.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.5 no.1
• /
• pp.136-145
• /
• 1997

To analyzed ride quality and to predict durability in vehicle dynamics, it is essential to describe a road surface profile precisely. This paper presents a technique to generate road surface profiles in a spatial domain by using a power spectral density function. A single track power spectral density function is proposed to describe a road surface profile, which is also applicable for multi-track vehicle response analysis, The derived road surfaces are compared to ISO(International Organization for Standardization) standards and classifications, proposed by the MIRA(Motor Industry Research Association). The methodology in this paper is also proposed to generate road roughness description with a limited external data. A small amount of external curve data is combined with an internal PSD function to generate road surface roughness in a spatial domain.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.732-734
• /
• 2003

In the middle -resolution remote sensing, the Ground Sampled Distance(GSD) sensed and sampled by the detector is generally larger than the size of objects(or materials) of interest, in which case several objects are embedded in a single pixel and cannot be detected spatially. This study is intended to solve this problem of a hyperspectral data with high spectral resolution. We examined the detection algorithm, Linear Spectral Mixing Model, and also made a test on the Hyperion data. To find class Endmembers, we applied two methods, Spectral Library and Geometric Model, and compared them with each other.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.1
• /
• pp.166-174
• /
• 1994

A critical evaluation is made of the spectral bias which occurs in the use of a laser doppler velocimeter(LDV). Two processing algorithms are considered for spectral estimates: the sample and hold interpolation method(SH) and the nonuniform Shannon reconstruction technique(SR). Assessment is made of these for varying data densities $(0.05{\le}d.d.{\le}5)$ and turbulence levels(t.i.=30%, 100%). As an improved version of the spectral estimator, the utility of POCS (the projection onto convex sets) has been tested in the present study. This algorithm is found useful to be in the region when $d.d.{\gep}3.$