• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.175-178
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• 2009

H.264/AVC is a video coding standard of ITU-T and ISO/IEC, and widely spreads its application due to its high compression ratio more than twice that of MPEG-2 and high image quality. In this paper, we explained Intra Prediction in H.264/AVC, which is able to achieve higher compressing efficiency from correlation removal of adjacent samples in spatial domain, and proposed efficient Intra Predictor architecture design for H.264/AVC decoder. The proposed system reduced computation cycle using processing element and precomputation processing element and also reduced the number of access to external memory using efficient register. We designed the proposed system with Verilog-HDL and verified with suitable test vector. The proposed Intra Predictor achieved about 60% cycle reduction comparing with existing Intra Predictors.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.181-181
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• 2018

설계홍수량 산정 시 지점확률강우량을 대상유역 내 면적강우량으로 환산하기 위하여 면적우량 환산계수(areal reduction factor, ARF)를 적용한다. ARF 산정방법은 크게 면적고정형 방법과 호우중심형 방법으로 나뉜다. 면적고정형 방법은 현재 국내 하천설계기준에서 설계강우량 산정 시 활용하고 있는 방법이지만, 동 시간에 발생한 강우사상을 활용하지 않고 지점강우량과 면적강우량의 독립적인 빈도해석을 통해 산정되므로 비현실적인 값이라고 볼 수 있다. 본 연구에서는 강우사상의 공간분포 특성을 효과적으로 반영할 수 있는 레이더 강우 자료를 활용하여 한강권역의 호우중심형 ARF를 활용하였다. 호우중심형 ARF는 지속기간 1, 3, 6, 12, 24시간에 대하여 산정하였으며, 재현기간은 강우 사상의 규모에 따라 총 다섯 구간(0-10, 10-20, 20-50, 50-80, 80-100년)으로 분류하였다. 지속기간 및 재현기간에 따른 호우중심형 ARF는 강우 사상마다 산정되므로 다양한 값이 산재(scattered)되어 있는데, 대푯값을 선정하기 위하여 Weibull 분포의 비초과확률 95%의 값을 추출하였다. 두 가지 방법으로 산정된 ARF는 지속기간에 대하여 로그형태로 증가하였으나, 재현기간에 따른 관계에서는 차이를 보였다. 면적고정형 ARF는 재현기간에 대한 민감도가 매우 낮았으나, 호우중심형 ARF는 재현기간에 따라 감소하였다. 또한 호우중심형 ARF는 지속시간이 길수록 재현기간에 대한 민감도가 점차 낮아졌으며 지속기간 24시간 이후로는 일정한 값을 보였다. 이러한 차이는 레이더 실 강우를 활용한 호우중심형 ARF 산정 시에 면적고정형 ARF 산정과정에서 고려되지 않는 강우의 시 공간적 특성을 반영하기 때문인 것으로 사료된다. 따라서 설계홍수량 산정 시 호우중심형 ARF를 적용한다면 보다 현실적인 값을 제시할 수 있을 것으로 판단된다.

• Journal of Microbiology and Biotechnology
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• v.31 no.6
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• pp.815-822
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• 2021

Indigenous fungus-feeding nematodes may adversely affect the growth and activity of introduced biocontrol fungi. Alginate pellets of the biocontrol fungus Trichoderma harzianum ThzID1-M3 and sclerotia of the fungal plant pathogen Sclerotinia sclerotiorum were added to nonsterile soil at a soil water potential of -50 or -1,000 kPa. The biomass of ThzID1-M3, nematode populations, and extent of colonization of sclerotia by ThzID1-M3 were monitored over time. The presence of ThzID1-M3 increased the nematode population under both moisture regimes (p < 0.05), and fungivores comprised 69-75% of the nematode population. By day 5, the biomass of ThzID1-M3b and its colonization of sclerotia increased and were strongly correlated (R² = 0.98), followed by a rapid reduction, under both regimes. At -50 kPa (the wetter of the two environments), fungal biomass and colonization by ThzID1-M3 were less, in the period from 5 to 20 days, while fungivores were more abundant. These results indicate that ThzID1-M3 stimulated the population growth of fungivorous nematodes, which in turn, reduced the biocontrol ability of the fungus to mycoparasitize sclerotia. However, colonization incidence reached 100% by day 5 and remained so for the experimental period under both regimes, although hyphal fragments disappeared by day 20. Our results suggest that indigenous fungivores are an important constraint for the biocontrol activity of introduced fungi, and sclerotia can provide spatial refuge for biocontrol fungi from the feeding activity of fungivorous nematodes.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.193-202
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• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

• Journal of the Korean GEO-environmental Society
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• v.23 no.7
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• pp.5-10
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• 2022

The industrial waste is becoming a big problem in the aspect of spatial and environmental in domestic and international. Therefore, the waste reduction and recycling policy has been being implemented as a way to solve this problem. The engineered stone sludge, which is waste, is generated duing the engineered stone production process. since engineered stone sludge is mostly treated by landfill, an increase in the amount of the sludge leads to an increase in landfill sites and treatment costs. therefore, there is a need for a method of resourcization with engineered stone sludge. So, laboratory tests (Plastic and liquid limits, compaction, unconfined compression and permeability test) were conducted to confirm the possibility of using engineered stone sludge mixed with weathered granite soil as a cover material for landfill in this study. The result shows that the mixed soil material with less that 62.5% of engineered stone sludge can be used as a cover material for landfill.

• Structural Engineering and Mechanics
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• v.82 no.1
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• pp.1-16
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• 2022

This work elaborately investigates the influences of the guideway geometry parameters and track irregularity on the dynamic performances of the suspended monorail vehicle-guideway system (SMVGS). Firstly, a spatial dynamic analysis model of the SMVGS is established by adopting ANSYS parameter design language. Then, the dynamic interaction between a vehicle with maximum design load and guideway is investigated by numerical simulation and field tests, revealing the vehicle-guideway dynamic features. Subsequently, the influences of the guideway geometry parameters and track irregularity on the dynamic performances of the SMVGS are analyzed and discussed in detail, and the reasonable ranges of several key geometry parameters of the guideway are also obtained. Results show that the vehicle-guideway dynamic responses change nonlinearly with an increase of the guideway span, and especially the guideway dynamic performances can be effectively improved by reducing the guideway span; based on a comprehensive consideration of all performance indices of the SMVGS, the deflection-span ratio of the suspended monorail guideway is finally recommended to be 1/1054~1/868. The train load could cause a large bending deformation of the pier, which would intensify the car-body lateral displacement and decrease the vehicle riding comfort; to well limit the bending deformation of the pier, its cross-section dimension is suggested to be more than 0.8 m×0.8 m. The addition of the track irregularity amplitude has small influences on the displacements and stress of the guideway; however, it would significantly increase the vehicle-guideway vibrations and rate of load reduction of the driving tyre.

• Journal of Korean Society on Water Environment
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• v.38 no.4
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• pp.177-189
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• 2022

Nature-based Solutions (NbS) are defined as practical and technical approaches to restoring functioning ecosystems and biodiversity as a means to address socio-environmental challenges and provide human-nature co-benefits. This study reviews NbS-related literature to identify its key characteristics, techniques, and challenges for its application in climate-adaptive water management. The review finds that NbS has been commonly used as an umbrella term incorporating a wide range of existing ecosystem-based approaches such as low-impact development (LID), best management practices (BMP), forest landscape restoration (FLR), and blue-green infrastructure (BGI), rather than being a uniquely-situated practice. Its technical form and operation can vary significantly depending on the spatial scale (small versus large), objective (mitigation, adaptation, naturalization), and problem (water supply, quality, flooding). Commonly cited techniques include green spaces, permeable surfaces, wetlands, infiltration ponds, and riparian buffers in urban sites, while afforestation, floodplain restoration, and reed beds appear common in non- and less-urban settings. There is a greater lack of operational clarity for large-scale NbS than for small-scale NbS in urban areas. NbS can be a powerful tool that enables an integrated and coordinated action embracing not only water management, but also microclimate moderation, ecosystem conservation, and emissions reduction. This study points out the importance of developing decision-making guidelines that can inform practitioners of the selection, operation, and evaluation of NbS for specific sites. The absence of this framework is one of the obstacles to mainstreaming NbS for water management. More case studies are needed for empirical assessment of NbS.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.422-428
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• 2023

Ultrafast ultrasound imaging has been applied to various imaging approaches, including shear wave elastography, ultrafast Doppler, and super-resolution imaging. However, these methods are still challenging in real-time implementation for three Dimension (3D) or portable applications because of their massive data rate required. In this paper, we proposed an adaptive quantization method that effectively reduces the data rate of large Radio Frequency (RF) data. In soft tissue, ultrasound backscatter signals require a high dynamic range, and thus typical quantization used in the current systems uses the quantization level of 10 bits to 14 bits. To alleviate the quantization level to expand the application of ultrafast ultrasound imaging, this study proposed a depth-sectional quantization approach that reduces the quantization errors. For quantitative evaluation, Field II simulations, phantom experiments, and in vivo imaging were conducted and CNR, spatial resolution, and SSIM values were compared with the proposed method and fixed quantization method. We demonstrated that our proposed method is capable of effectively reducing the quantization level down to 3-bit while minimizing the image quality degradation.

• Steel and Composite Structures
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• v.48 no.3
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• pp.275-291
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• 2023

This paper has focused on presenting vibration analysis of trapezoidal sandwich plates with 3D-graphene foam reinforced polymer matrix composites (GrF-PMC) core and FG wavy CNT-reinforced face sheets. The porous graphene foam possessing 3D scaffold structures has been introduced into polymers for enhancing the overall stiffness of the composite structure. Also, 3D graphene foams can distribute uniformly or non-uniformly in the plate thickness direction. The effective Young's modulus, mass density and Poisson's ratio are predicted by the rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes' effective aspect ratio as the filler content increases. The First-order shear deformation theory of plate is utilized to establish governing partial differential equations and boundary conditions for trapezoidal plate. The governing equations together with related boundary conditions are discretized using a mapping-generalized differential quadrature (GDQ) method in spatial domain. Then natural frequencies of the trapezoidal sandwich plates are obtained using GDQ method. Validity of the current study is evaluated by comparing its numerical results with those available in the literature. It is explicated that 3D-GrF skeleton type and weight fraction, carbon nanotubes (CNTs) waviness and CNT aspect ratio can significantly affect the vibrational behavior of the sandwich structure. The plate's normalized natural frequency decreased and the straight carbon nanotube (w=0) reached the highest frequency by increasing the values of the waviness index (w).

• Atmosphere
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• v.33 no.1
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• pp.61-72
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• 2023

Accurate estimation of forest carbon stocks is important in establishing greenhouse gas reduction plans. In this study, we estimate the spatial distribution of forest carbon stocks using machine learning techniques based on high-resolution remote sensing data and detailed field survey data. The high-resolution remote sensing data used in this study are Landsat indices (EVI, NDVI, NDII) for monitoring vegetation vitality and Shuttle Radar Topography Mission (SRTM) data for describing topography. We also used the forest growing stock data from the National Forest Inventory (NFI) for estimating forest biomass. Based on these data, we built a model based on machine learning methods and optimized for Korean forest types to calculate the forest carbon stocks per grid unit. With the newly developed estimation model, we created forest carbon stocks maps and estimated the forest carbon stocks in South Korea. As a result, forest carbon stock in South Korea was estimated to be 432,214,520 tC in 2020. Furthermore, we estimated the loss of forest carbon stocks due to the Donghae-Uljin forest fire in 2022 using the forest carbon stock map in this study. The surrounding forest destroyed around the fire area was estimated to be about 24,835 ha and the loss of forest carbon stocks was estimated to be 1,396,457 tC. Our model serves as a tool to estimate spatially distributed local forest carbon stocks and facilitates accounting of real-time changes in the carbon balance as well as managing the LULUCF part of greenhouse gas inventories.