• 대한기계학회논문집A
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• 제37권5호
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• pp.601-607
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• 2013

본 논문은 Matlab/simulink 기반 휠로더 시뮬레이션 모델의 개발과 검증에 대한 논문이다. 휠로더 시뮬레이션 모델의 개발 및 검증은 실제 휠로더의 생산단계에 앞서 휠로더의 성능을 평가하고 개선하기 위한 목적을 두고 있다. 휠로더 시뮬레이션 모델은 전체적으로 주행부/유압부 동력전달계 모델, 주행부/작업장부 동역학 모델을 포함한 4 가지 모델로 나뉘어져 있다. 휠로더의 주행 및 작업성능을 평가하고 개선하기 위해서는 언급된 4 가지 모델의 통합 시뮬레이션이 필요하며 통합된 시뮬레이션 모델은 성능평가 외의 연료효율의 최적화, 하이브리드 시스템 및 지능형 휠로더 모델의 개발로써 작업효율 향상에 기여할 수 있을 것이다. 본 논문에 제안된 시뮬레이션 모델은 주행부와 작업부 실험 데이터와의 비교를 통해 검증 되었다.

• 한국생명과학회:학술대회논문집
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• 한국생명과학회 2005년도 국제학술심포지움 The 44th Annual Meeting of Korean Society for Life Science
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• pp.23-36
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• 2005

Bax, a mammalian pro-apoptotic member of the Bcl-2 family, induces cell death when expressed In yeast. To investigate whether .Bax expression can induce cell death in plant, we produced transgenic Arabidopsis plants that contained murine Bax cDNA under control of a glucocorticoid-inducible promoter. Transgenic plants treated with dexamethasone, a strong synthetic glucocorticoid, induced Bax accumulation and cell death, suggesting that some elements of cell death mechanism by Bax may be conserved among various orgarusms. Therefore, we developed novel yeast genetic system, and cloned several Plant Bax Inhibitors (PBIs). Here, we report the function of two PBIs In detail. PBIl is ascorbate peroxidase (sAPX). Fluorescence method of dihydrorhodamine123 oxidation revealed that expression of Bax in yeast cells generated reactive oxygen species (ROS), and which was greatly reduced by co-expression with sAPX. These results suggest that sAPX inhibits the generation of ROS by Bax, which in turn suppresses Bax-induced cell death in yeast. PBI2 encodes nucleoside diphosphate kinase (NDPK). ROS stress strongly induces the expression of the NDPK2 gene in Arabidopsis thaliana (AtNDPK2). Transgenic plants overexpressing AtNDPK2 have lower lovels of ROS than wildtype plants. Mutants lacking AtNDPK2 had higher levels of ROS than wildtype. H$_{2O2}$ treatment induced the phosphorylation of two endogenous proteins whose molecular weights suggested they are AtMPK3 and AtMPK6. In the absence of H2O2 treatment, phosphorylation of these proteins was slightly elevated in plants overexpressing AtNDPK2 but markedly decreased In the AtNDPK2 deletion mutant. Yeast two-hybrid and in vitro protein pull-down assays revealed that AtNDPK2 specifically interacts with AtMPK3 and AtMPK6. Furthermore, AtNDPK2 also enhances the MBP phosphorylation activity of AtMPK3 i'n vitro. Finally, constitutive overexpression of AtNDPK2 in Arabidopsis plants conferred an enhanced tolerance to multiple environmental stresses that elicit ROS accumulation In situ. Thus, AtNDPK2 appears to play a novel regulatory role in H2O2-mediated MAPK signaling in plants.

• The Plant Pathology Journal
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• 제19권1호
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• pp.24-29
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• 2003

SS2-2, a hypernodulating soybean mutant was isolated by EMS mutagenesis from Sinpaldalkong 2. This auto-regulation mutant showed greater number of nodules and smaller plant size than its wild type Sinpaldalkong 2. SSR markers were used to identify DNA variation at SSR loci from different soybean LG. The only SSR marker that detected a length polymorphism between SS2-2 and its wild type ancestor was Satt294 on LG C1 instead of LG H, locating a hypernodulating gene. Sequencing data of flanking Satt294 indicated that the size variation was due to extra stretch of TTA repeats of the SSR motif in SS2-2, along with $A\longrightarrow$G transversion. In spite of phenotypic differences between the wild type and its hypernodulating mutants, genomic DNA poly-morphisms at microsatellite loci could not control regulation of nodule formation. The cDNA-AFLP method was applied to compare differential display of cDNA between Sinpaldalkong 2 and SS2-2. After isolation and sequence comparison with many AELP fragments, several interesting genes were identified. Northern blot analysis, immunolocalization and/or the yeast two-hybrid system with these genes might provide information on regulation of nodule development in SS2-2.

• 지능정보연구
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• 제16권2호
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• pp.95-108
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• 2010

본 연구에서는 동영상으로부터 동적 수신호 패턴을 효과적으로 인식하기 위한 방법론으로서 복합형 신경망 모델을 제안한다. 제안된 모델은 특징추출 모듈과 패턴분류 모듈로 구성되는데, 이들 각각을 위하여 수정된 구조의 CNN 모델과, WFMM 모델을 도입한다. 또한 목표물의 움직임 정보에 기초한 시공간적 템플릿 구조의 데이터표현을 소개한다. 본 논문에서는 우선 수신호 패턴 데이터에서 특징점의 시간적 변이 및 공간적 변이에 의한 영향을 보완하기 위하여 3차원 수용영역 구조로 확장된 CNN 모델을 제시한다. 이어서 패턴분류 단계를 위하여 가중치를 갖는 구조의 FMM 신경망 모델을 소개하고, 신경망의 구조와 동작특성에 관해 기술한다. 또한 제안된 모델이 기존의 FMM 신경망에서 중첩 하이퍼박스의 축소과정에서 발생하는 학습효과의 왜곡현상을 개선할 수 있음을 보인다. 응용으로 가전제품 원격제어 문제를 전제하여 간략화된 수신호패턴 인식 문제에 적용한 실험결과로부터 제안된 이론의 타당성을 고찰한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.2964-2985
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• 2019

Global data center IP traffic is expected to reach 20.6 zettabytes (ZB) by the end of 2021. Intra-data center networks (Intra-DCN) will account for 71.5% of the data center traffic flow and will be the largest portion of the traffic. The understanding of traffic distribution in IntraDCN is still sketchy. It causes significant amount of bandwidth to go unutilized, and creates avoidable choke points. Conventional transport protocols such as Optical Packet Switching (OPS) and Optical Burst Switching (OBS) allow a one-sided view of the traffic flow in the network. This therefore causes disjointed and uncoordinated decision-making at each node. For effective resource planning, there is the need to consider joining the distributed with centralized management which anticipates the system's needs and regulates the entire network. Methods derived from Kalman filters have proved effective in planning road networks. Considering the network available bandwidth as data transport highways, we propose an intelligent enhanced SDN concept applied to OBS architecture. A management plane (MP) is added to conventional control (CP) and data planes (DP). The MP assembles the traffic spatio-temporal parameters from ingress nodes, uses Kalman filtering prediction-based algorithm to estimate traffic demand. Prior to packets arrival at edges nodes, it regularly forwards updates of resources allocation to CPs. Simulations were done on a hybrid scheme (1+1) and on the centralized OBS. The results demonstrated that the proposition decreases the packet loss ratio. It also improves network latency and throughput-up to 84 and 51%, respectively, versus the traditional scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5723-5743
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• 2018

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment-RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

• Advances in nano research
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• 제13권2호
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• pp.187-197
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• 2022

The synthesis of organic and hybrid organic-inorganic perovskites directly from solution improves the cost- and energy-efficiency of processing. To date, numerous research efforts have been devoted to investigating the influence of the various solvent parameters for the synthesis of lead halide perovskites, focused on the effects of different single solvents on the efficiency of the resulting perovskites. In this work, we investigated the effect of solvent blends for the first time on the structure and phase of perovskites produced via the Lewis base vapor diffusion method to develop a new synthetic approach for water-stable CsPbBr₃ particles with nanometer-sized dimensions. Solvent blends prepared with DMF and water-miscible solvents with different Gutmann's donor numbers (D_N) affect the Pb ions differently, resulting in a variety of lead bromide species with various colors. The use of a DMF/isopropanol solvent mixture was found to induce the formation of the Ruddlesden-Popper perovskite based on lead bromide. This perovskite undergoes a blue color shift in the solvated state owing to the separation of nanoplatelets. In contrast, the replacement of isopropanol with DMSO, which has a high DN, induces the formation of spherical CsPbBr₃ perovskite nanoparticles that exhibit green emission. Finally, the integration of acetone in the solvent system leads to the formation of lead bromide complexes with a yellow-orange color and the perovskite CsPbBr₃.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• 시스템엔지니어링학술지
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• 제18권2호
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• 한국습지학회지
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• 제15권4호
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• pp.535-541
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• 2013

도시화로 인한 불투수층의 증가는 강우시 유출량 증가, 침투량 감소, 증발산량 감소 등을 유발시키고, 다량의 비점오염물질을 유출시킨다. 이러한 도시화에 의한 환경영향을 최소화하기 위하여 환경부는 2012년 이후 그린빗물인프라(Green Stormwater Infrastructure, GSI)기법을 정책적으로 도입하여 자연적 물순환을 구축하고 비점오염 유출저감을 저감하고자 한다. 본 연구에서는 자연적 물순환 구축을 통한 비점오염저감을 위하여 다양한 강우사상에 적용 가능한 침투여과기술을 개발하고자 한다. 기술의 실제 적용성 평가에 앞서 연구실 규모의 기술평가를 실시하였으며, 8회의 유량변화를 통한 평가를 수행하였다. 연구실 실험결과, 시설의 침투수, 저류수 및 유출수의 오염물질별 평균 EMC의 저감효율은 모든 오염물질 항목에서 50~90%의 범위로 높게 나타났는데 이는 높은 침투유량(약 35%)과 저류량(39%)에 의한 유출저감에 의해 나타났다. 침투여과시설의 지속적 효율은 막힘현상의 최소화로 나타나는데 본 기술의 공극 막힘현상은 누적 TSS 양이 $8.3{\sim}9.0kg/m^2$의 범위에 도달할 때 발생하였으며, 이 값은 타 연구결과에 비해 큰 값으로 나타나 장기간 높은 효율을 유지할 수 있는 것으로 분석되었다. 초기침강지를 설치하지 않은 상태에서도 시설로부터 유출되는 시료내 평균 입경크기는 $10{\mu}m$로 나타났기에 침강지를 설치할 경우 입자제거에 효과적일 것으로 판단된다.