• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Journal of the Korean Geotechnical Society
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• v.15 no.4
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• pp.113-132
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• 1999

Recently in Korea, application of the soil nailing is gradually extended to the sites of excavations and slopes having various ground conditions and field characteristics. Design of the soil nailing is generally carried out in two steps, The First step is to examine the minimum safety factor against a sliding of the reinforced nailed-soil mass based on the limit equilibrium approach, and the second step is to check the maximum displacement expected to occur at facing using the numerical analysis technique. However, design parameters related to the soil nailing system are so various that a reliable design method considering interrelationships between these design parameters is continuously necessary. Additionally, taking into account the anisotropic characteristics of in-situ grounds, disturbances in collecting the soil samples and errors in measurements, a systematic analysis of the field measurement data as well as a rational technique of the optimum design is required to improve with respect to economical efficiency. As a part of these purposes, in the present study, a procedure for the optimum design of a soil nailing excavation wall system is proposed. Focusing on a minimization of the expenses in construction, the optimum design procedure is formulated based on the genetic algorithm. Neural network theory is further adopted in predicting the maximum horizontal displacement at a shotcrete facing. Using the proposed procedure, various effects of relevant design parameters are also analyzed. Finally, an optimized design section is compared with the existing design section at the excavation site being constructed, in order to verify a validity of the proposed procedure.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.36-51
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• 2012

The demand for a climatological dataset with a regular spaced grid is increasing in diverse fields such as ecological and hydrological modeling as well as regional climate impact studies. PRISM(Precipitation-Elevation Regressions on Independent Slopes Model) is a useful method to estimate high-altitude precipitation. However, it is not well discussed over the optimization of PRISM parameters and DEM(Digital Elevation Model) resolution in South Korea. This study developed the PRISM and then optimized parameters of the model and DEM resolution for producing a gridded annual average precipitation data of South Korea with 1km spatial resolution during the period 2000-2005. SCE-UA (Shuffled Complex Evolution-University of Arizona) method employed for the optimization. In addition, sensitivity analysis investigates the change in the model output with respect to the parameter and the DEM spatial resolution variations. The study result shows that maximum radius within which station search will be conducted is 67km. Minimum radius within which all stations are included is 31km. Minimum number of stations required for cell precipitation and elevation regression calculation is four. Optimizing DEM resolution is $1{\times}1km$. This study also shows that the PRISM output very sensitive to DEM spatial resolution variations. This study contributes to improving the accuracy of PRISM technique as it applies to South Korea.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.21-30
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• 2024

High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements. In particular, in a multi-label environment, higher complexity is required as much as the number of labels. This paper proposes a feature selection method to improve classification performance in multi-label settings. The method considers three types of relationships: between features, between features and labels, and between labels themselves. To achieve this, a regression-based objective function is designed. This objective function calculates the linear relationships between features and labels and uses mutual information to compute relationships between features and between labels. By minimizing this objective function, the optimal weights for feature selection are found. To optimize the objective function, a gradient descent method is applied to develop a fast-converging algorithm. The experimental results on six multi-label datasets show that the proposed method outperforms existing multi-label feature selection techniques. The classification performance of the proposed method, averaged over six datasets, showed a Hamming loss of 0.1285, a ranking loss of 0.1811, and a multi-label accuracy of 0.6416. Compared to the AMI(Approximating Mutual Information) algorithm, the performance was better by 0.0148, 0.0435, and 0.0852, respectively.

• 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 the Computational Structural Engineering Institute of Korea
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• v.28 no.5
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• pp.441-449
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• 2015

In this paper we perform a linearized buckling analysis using the Kirchhoff plate theory and the von Karman nonlinear strain-displacement relation. Design sensitivity analysis(DSA) expressions for plane elasticity and buckling problems are derived with respect to Young's modulus and thickness. Using the design sensitivity, we can formulate the topology optimization method for minimizing the compliance and maximizing eigenvalues. We develop a topology optimization method applicable to plate buckling problems using the prestress for buckling analysis. Since the prestress is needed to assemble the stress matrix for buckling problem using the von Karman nonlinear strain, we introduced out-of-plane motion. The design variables are parameterized into normalized bulk material densities. The objective functions are the minimum compliance and the maximum eigenvalues and the constraint is the allowable volume. Through several numerical examples, the developed DSA method is verified to yield very accurate sensitivity results compared with the finite difference ones and the topology optimization yields physically meaningful results.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.264-264
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• 2017

국외의 상수도 원격검침 시스템 내 데이터 전송방식은 도시 규모, 계량기의 밀도, 전력공급 여부 및 통신망의 설치 여부 등을 종합적으로 고려하여 결정되었다. 대부분의 스마트워터미터 제조업체들은 계량기의 부호기가 공급하는 판독 내용(데이터)을 전송할 검침단말기와 근거리 통신망(neighborhood area network)을 연계하여 개발 및 판매하였으며, 자체 소유 통신 프로토콜을 사용하여 라디오 주파수(RF) 통신 기술을 사용하고 있다. 광역통신망(wide area network)의 경우, 노드(말단의 계량기 및 센서)들과 이에 연결된 통신망 들을 포함한 네트웍의 배열이나 구성이 스타(star), 메쉬(mesh), 버스(bus), 나무(tree) 등의 형태로 통신망이 구성되어 있으나, 스타와 메쉬형 통신망 구성형태가 가장 널리 활용되는 것으로 조사되었다. 시스템 통합운영관리 업체들인 IBM, Oracle, Itron 등은 용수 인프라 관리 또는 통합네트워크 솔루션 등의 통합 물관리 시스템(integrated water management system)을 개발하여 현장적용을 하고 있으며, 원격검침 시스템을 통해 고객들의 현재 소비량과 과거 누적 소비량, 누수 감지 서비스 및 실시간 요금 고지 등을 실시간으로 웹 포털과 앱을 통해 제공하고 있다. 또한, 일부 제조업체들은 도시 용수공급/소비 관리자가 주민의 용수사용량을 모니터링하여 일평균 용수사용량 및 사용 경향을 파악하고, 누수를 검지하여 복구 및 용수 사용 지속가능성 지수를 제시하고, 실시간으로 주민의 용수사용량 관련 데이터를 모니터링하여 용수공급의 최적화를 위한 의사결정지원 서비스를 용수공급자에게 제공하고 있다. 최근에는 인공지능을 활용해 가정용수의 용도별(세탁용수, 화장실용수, 샤워용수, 식기세척용수 등) 사용량 곡선을 패터닝하여 profiling 기법을 도입해, 스마트워터미터에서 용수사용량이 통합되어 검지될 시 용수사용량의 세부 용도별 re-profiling 기법을 도입하여 가정용수내 과소비되는 지점을 도출 후 절감을 유도하는 기술이 개발 중이다. 또한, 미래 용수 사용량 예측을 위해 다양한 시계열 자료를 분석하는 선형 종속 모형(자기회귀모형, 자기회귀이동평균모형, 자기회귀적분이동평균모형 등)과 비선형 종속 모형(Fuzzy Logic, Neural Network, Genetic Algorithm 등)을 활용한 예측기능이 구축되어 상호 비교하여 최적의 용수사용량 예측 도구를 제공되고 있다.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.23-31
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• 2010

Due to the fact that fisheye lens can provide super wide angles with the minimum number of cameras, field-of-view over 180 degrees, many vehicles are attempting to mount the camera system. Not only use the camera as a viewing system, but also as a camera sensor, camera calibration should be preceded, and geometrical correction on the radial distortion is needed to provide the images for the driver's assistance. In this thesis, we introduce a geometric correction technique to minimize the loss of the image data from a vehicle fish-eye lens having a field of view over $180^{\circ}$, and a asymmetric distortion. Geometric correction is a process in which a camera model with a distortion model is established, and then a corrected view is generated after camera parameters are calculated through a calibration process. First, the FOV model to imitate a asymmetric distortion configuration is used as the distortion model. Then, we need to unify the axis ratio because a horizontal view of the vehicle fish-eye lens is asymmetrically wide for the driver, and estimate the parameters by applying a non-linear optimization algorithm. Finally, we create a corrected view by a backward mapping, and provide a function to optimize the ratio for the horizontal and vertical axes. This minimizes image data loss and improves the visual perception when the input image is undistorted through a perspective projection.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.26-35
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• 2009

OFDM (Orthogonal Frequency Division Multiplexing) system is robust to frequency selective fading and narrowband interference in high-speed data communications. However, an OFDM signal consists of a number of independently modulated subcarriers and the superposition of these subcarriers causes a problem that can give a large PAPR(Peak-to-Average Power Ratio). Phase rotation method can reduce the PAPR without nonlinear distortion by multiplying phase weighting factors. But computational complexity of searching phase weighting factors is increased exponentially with the number of subblocks and considered phase factor. Therefore, a new method, which can reduce computational complexity and detect phase weighting factors efficiently, should be developed. In this paper, a modeling process is introduced, which apply metaheuristic algerian in phase rotation method and optimize in PTS (Particle Swarm Optimization) scheme. Proposed algorithm can solve the computational complexity and guarantee to reduce PAPR We analyzed the efficiency of the PAPR reduction through a simulation when we applied the proposed method to telecommunication systems.

• The Magazine of the IEIE
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• v.30 no.5
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• pp.95-95
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• 2003

현재의 라우팅 프로토콜은 다양한 사용자 요구를 만족시켜주기 위해서는 네트워크의 처리량을 최대화하고 동시에 사용자의 요구 시 QoS를 보장해주는 기법이 요구되고 있다. 기존의 최단경로 라우팅 프로토콜은 단일경로 라우팅으로 인해 병목현상의 단점을 지니고 있다. 즉, 원천과 목적지간 최단경로는 낮은 활용도를 나타내는 경로들이 많이 존재하지만 단일경로를 선택하므로서 폭주(congestion)의 발생확률이 높다. 최근에 들어 사용자의 QoS 요구 시, 다양한 QoS를 패킷 네트워크에서 처리할 수 있도록 IETF에서 DiffServ, RSVP, MPLS 등과 같은 패킷 QoS 기법에 대한 표준화 작업이 진행중이며, 그 중에서 Diffserv 네트워크가 대표적이다. 따라서 본 논문에서는 이 DiffServ 네트워크상에서 다양하게 유입되는 트래픽의 종류에 따라 사용자의 응용에 적절히 대응하여 트래픽을 처리하는 라우팅 기법 및 알고리즘을 연구하고 기존의 최선형 (Best effort) 트래픽을 처리하기 위한 트래픽 분산 라우팅 프로토콜 (Traffic-Balanced Rout-ing Protocol''TBRP)을 제안하였으며, 최적의 중간 노드를 선택하여 높은 순위의 상호형 데이터를 처리하기 위한 계층적 라우팅 프로토콜(또ierarchicalTra(fic-Scheduling Routing Protocol : HTSRP)을 연구하였다. 본 연구에서 제시한 프로토콜은 유, 무선망의 통합에 따른 다양한 엑세스망과 백본망에 유연한 트래픽 처리기법으로서 계층적 라우팅 알고리즘으로 적합하였다. 본 실험에서는 사용자의 QoS요청 시 제공되는 상호형 또는 스트리 밍 데이터를 위한 HTSRP_Q(Hierarchical Traffic-Scheduling Routing Pro-tocol for QoS)에 대해 성능이 우수함을 입증하였으며, 각 엑세스 단에서 요청하는 QoS 파라미터에 따라 자원을 최적화하여 QoS를 보장하고, 특히 지연에 민감한 트래픽을 처리하였으며, 제안한 프로토콜을 이용하여 사용자 요구 트래픽 종류에 따라 대화형 클래스, 스트리밍 클래스, 높은 순위의 상호형 클래스, 낮은 순위의 상호형 클래스, 그리고 background 클래스등 5개의 서비스 클래스로 분리하여 트래픽 특성에 맞게 처리할 수 있었다. QoS 관련 실험에서는 QoS 요청데이터를 균등하게 1에서 10Mbps 사이에 분포하였고 연결된 호에 대한 지속시간은 5분으로 하였다. 이러한 환경에서 프로토콜을 MaRS에 의해 실험을 하였고 기존의 거리-벡터 라우팅과 링크-상태 라우팅 프로토콜과 비교해서 처리량, 메시지 손실, 블럭킹율 등에서 비교적 우위의 성능을 확인할 수 있었으며, 특히, 차별화된 서비스의 특성에 맞게 라우팅 기법을 적용하므로서 망의 효율성과 안정성을 꾀할 수가 있었다. 연결 수 대 처리량에서는 HTSRP 프로토콜이 연결이 적을 때 DVR, LSR보다 우월하였으며 특히, 선형을 유지하였다. 연결 수 대 패킷 손실에서 HTSRP프로토콜에서 메시지 손실은 연결의 수가 낮거나 높을 때 다른 DVR과 LSR 라우팅 프로토콜과 유사한 결과를 나타내었다. Hotspo에서 TBRP, HTSRP프로토콜은 hotspot 연결의 수가 9일 때까지 DVR, LSR 보다 좋은 처리량를 나타냈고 HTSRP는 연결의 수가 6 이상일 때 가장 높은 처리량을 나타내었다. 일반 트래픽과 QoS 트래픽이 흔재할 경우는 트래픽이 증가할수록 HTSRP_Q가 가장 월등하였으며 , 로드가 증가할수록 낮은 블록킹률을 나타내었다. 본 논문에서는 점대점 전송을 기반으로 하였다. 앞으로 다양한 응용 S/W는 멀티캐스트 기반이 예상되므로 멀티캐스트 라우팅에 대한 연구가 필요하다. 본 논문의 프로토콜은 원천과 목적지간의 최단경로가 폭주상태가 아닌 해당 중간 노드를 이용한다. 최단경로의 모든 링크상의 트래픽 부하가 낮을 때 중간노드의 사용은 지연을 증가시킨다. 향후 최적의 성능을 위해 보완이 필요하다. 아울러, 2계위에서는 일반 트래픽과 QoS 트래픽이 혼재할 때 자동으로 네트워크의 효율적을 고려한 방법 선택이 필요하다.