• Structural Engineering and Mechanics
• /
• 제83권3호
• /
• pp.353-361
• /
• 2022

Linear Elastic Fracture Mechanics (LEFM) has been developed by applying stress analysis to determine the stress intensity factor (SIF, K). The finite element method (FEM) is widely used as a standard tool for evaluating the SIF for various crack configurations. The prediction accuracy can be achieved by applying an adaptive Delaunay triangulation combined with a FEM. The solution can be solved using either direct or iterative solvers. This work adopts the element-by-element preconditioned conjugate gradient (EBE-PCG) iterative solver into an adaptive FEM to solve the solution to heal problem size constraints that exist when direct solution techniques are applied. It can avoid the formation of a global stiffness matrix of a finite element model. Several numerical experiments reveal that the present method is simple, fast, and efficient compared to conventional sparse direct solvers. The optimum convergence criterion for two-dimensional LEFM analysis is studied. In this paper, four sample problems of a two-edge cracked plate, a center cracked plate, a single-edge cracked plate, and a compact tension specimen is used to evaluate the accuracy of the prediction of the SIF values. Finally, the efficiency of the present iterative solver is summarized by comparing the computational time for all cases.

• 한국산업정보학회논문지
• /
• 제7권5호
• /
• pp.59-66
• /
• 2002

대규모 비대칭 행렬의 특정 고유치들이 많은 중요한 과학, 공학 문제들에서 요구된다. 그 문제를 해결할 수 있는 방법 중의 하나인 biorthogonal 란초스 알고리즘은 심각한 문제점이 있는데, 어떤 특이한 상황에서 알고리즘을 계속할 수 없는 경우가 발생할 수 있다는 것이다. 본 논문에서는 기본적인 biorhogonal 알고리즘이 만드는 축소된 삼중 대각 행렬에 대하여 동일한 고유치를 발견할 수 있는 향상된 biorhogonal 란초스 알고리즘을 소개한다. 이 새로운 알고리즘은 대규모 비대칭 행렬의 특정 고유치들을 구할 수 있으며 기본적인 biorthogonal 란초스 알고리즘에 비해서 안정적인 방법이라는 것을 Cray 컴퓨터를 이용한 실험을 통해서 보여준다.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2011년도 하계학술대회
• /
• pp.437-438
• /
• 2011

Spectral segmentation is a major trend in image segmentation. Specially, constrained spectral segmentation, inspired by the user-given inputs, remains its challenging task. Since it makes use of the spectrum of the affinity matrix of a given image, its overall quality depends mainly on how to design the graphical model. In this work, we propose a sparse, multi-layer graphical model, where the pixels and the over-segmented regions are the graph nodes. Here, the graph affinities are computed by using the must-link and cannot-link constraints as well as the likelihoods that each node has a specific label. They are then used to simultaneously cluster all pixels and regions into visually coherent groups across all layers in a single multi-layer framework of Normalized Cuts. Although we incorporate only the adjacent connections in the multi-layer graph, the foreground object can be efficiently extracted in the spectral framework. The experimental results demonstrate the relevance of our algorithm as compared to existing popular algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권12호
• /
• pp.3798-3814
• /
• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권7호
• /
• pp.3194-3216
• /
• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• 물과 미래
• /
• 제23권2호
• /
• pp.251-263
• /
• 1990

본 연구의 목적은 도시 유역에서의 표면유출에 의한 맨홀 유입량을 추정하고 관망에서의 흐름을 해석하기 위한 모형을 개발하기 위한 것이다. 각 맨홀로의 유입량은 합리식을 이용한 단순방법으로 유입 수문곡선을 모의하는 방법과 해당유역 특성을 고려한 표면유출 모의에 의한 유입 수물곡선 결정방법이 이용된다. 관망에서의 흐름은 Saint-Venant공식의 dynamic equation에 유한음차분법(four-point implicit method)응 적용하여 동시해법으로 해석하였다. 특히 압력류(surcharge flow)흐름은 관의 상단에 좁고 긴 가상관을 연결시켜 모든 흐름을 개수로 흐름으로 해석가능 하도록 전환함으로써 해석의 단순화를 기하였고, 개발된 USS-slot모형이 부정류 우수관망 흐름을 적절히 모의할 수 있는가를 판별하기 위하여 기존에 연구된 관망에 적용하여 그 결과들을 비교 분석하였다.

• Journal of Communications and Networks
• /
• 제18권3호
• /
• pp.493-501
• /
• 2016

In-network storage is an effective technique for avoiding network congestion and reducing power consumption in continuous data collection in wireless sensor networks. In recent years, network coding based storage design has been proposed as a means to achieving ubiquitous access that permits any query to be satisfied by a few random (nearby) storage nodes. To maintain data consistency in continuous data collection applications, the readings of a sensor over time must be sent to the same set of storage nodes. In this paper, we present an efficient approach to updating data at storage nodes to maintain data consistency at the storage nodes without decoding out the old data and re-encoding with new data. We studied a transmission strategy that identifies a set of storage nodes for each source sensor that minimizes the transmission cost and achieves ubiquitous access by transmitting sparsely using the sparse matrix theory. We demonstrate that the problem of minimizing the cost of transmission with coding is NP-hard. We present an approximation algorithm based on regarding every storage node with memory size B as B tiny nodes that can store only one packet. We analyzed the approximation ratio of the proposed approximation solution, and compared the performance of the proposed coding approach with other coding schemes presented in the literature. The simulation results confirm that significant performance improvement can be achieved with the proposed transmission strategy.

• Steel and Composite Structures
• /
• 제39권5호
• /
• pp.511-528
• /
• 2021

There are recently some advances in solving numerically topology optimization problems for large-scaled trusses based on ground structure approach. A disadvantage of this approach is that the final design usually includes many bars, which is difficult to be produced in practice. One of efficient tools is a so-called filter scheme for the ground structure to reduce this difficulty and determine several distinct bars. In detail, this technique is valuable for practical uses because unnecessary bars are filtered out from the ground structure to obtain a well-defined structure during the topology optimization process, while it still guarantees the global equilibrium condition. This process, however, leads to a singular system of equilibrium equations. In this case, the minimization of least squares with Tikhonov regularization is adopted. In this paper, a proposed algorithm in controlling optimal Tikhonov parameter is considered in combination with the filter scheme due to its crucial role in obtaining solution to remove numerical singularity and saving computational time by using sparse matrix, which means that the discrete optimal topology solutions depend on choosing the Tikhonov parameter efficiently. Several numerical examples are investigated to demonstrate the efficiency of the filter parameter control algorithm in terms of the large-scaled optimal topology designs.

• Journal of Microbiology and Biotechnology
• /
• 제8권6호
• /
• pp.700-704
• /
• 1998

Each of the recombinant structural genes of Helicobacter pylori urease, ureA and ureB, was cloned and overexpressed as inclusion bodies. Solubilization and renaturation of the inclusion bodies were carried out, to accelerate the pairing of sulfhydryl groups and the incorporation of nickel ions, which would lead to the native structure with high enzyme activity. Rates of urea hydrolysis were monitored as an indication of in vitro activation of renatured ureases. The activation of the apoprotein using 1 mM nickel ion, 100 mM sodium bicarbonate and a 10:1 ratio of reducing power resulted in a weak urease activity (about 11% of the native urease activity encoded by pTZ 19R/ure-l). When a sparse matrix screen method originally discovered for the crystallization of proteins was used, the activity increased higher than that obtained using glutathione. The effect of polyethylene glycol (PEG) on the activity was noticeable, giving two-fold increase in the specific activity (about 11 U/mg of protein corresponding to 22% of the native urease activity encoded by pTZ19R/ure-1).

• JSTS:Journal of Semiconductor Technology and Science
• /
• 제16권4호
• /
• pp.436-442
• /
• 2016

An electroencephalogram (EEG)-connectome processor to monitor and diagnose mental health is proposed. From 19-channel EEG signals, the proposed processor determines whether the mental state is healthy or unhealthy by extracting significant features from EEG signals and classifying them. Connectome approach is adopted for the best diagnosis accuracy, and synchronization likelihood (SL) is chosen as the connectome feature. Before computing SL, reconstruction optimizer (ReOpt) block compensates some parameters, resulting in improved accuracy. During SL calculation, a sparse matrix inscription (SMI) scheme is proposed to reduce the memory size to 1/24. From the calculated SL information, a small world feature extractor (SWFE) reduces the memory size to 1/29. Finally, using SLs or small word features, radial basis function (RBF) kernel-based support vector machine (SVM) diagnoses user's mental health condition. For RBF kernels, look-up-tables (LUTs) are used to replace the floating-point operations, decreasing the required operation by 54%. Consequently, The EEG-connectome processor improves the diagnosis accuracy from 89% to 95% in Alzheimer's disease case. The proposed processor occupies $3.8mm^2$ and consumes 1.71 mW with $0.18{\mu}m$ CMOS technology.