• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1380-1390
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• 2021

The video face recognition (FR) is one of the most popular researches in the field of computer vision due to a variety of applications. In particular, research using the attention mechanism is being actively conducted. In video face recognition, attention represents where to focus on by using the input value of the whole or a specific region, or which frame to focus on when there are many frames. In this paper, we propose a novel attention based deep learning method. Main novelties of our method are (1) the use of combining two loss functions, namely weighted Softmax loss function and a Triplet loss function and (2) the feasibility of end-to-end learning which includes the feature embedding network and attention weight computation. The feature embedding network has a positive effect on the attention weight computation by using combined loss function and end-to-end learning. To demonstrate the effectiveness of our proposed method, extensive and comparative experiments have been carried out to evaluate our method on IJB-A dataset with their standard evaluation protocols. Our proposed method represented better or comparable recognition rate compared to other state-of-the-art video FR methods.

• 한국유체기계학회 논문집
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• 제11권1호
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• pp.52-60
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• 2008

Staggered dimples printed on opposite walls of an internal cooling channel are formulated numerically and optimized to enhance heat transfer performance. Nusselt number and friction factor based objectives are considered and a weighted average surrogate model is used to approximate the data generated by numerical simulation. The dimpled channel shape is defined by three geometric design variables, and the design point within design space are selected using Latin hypercube sampling. A weighted-sum method for multi-objective optimization is applied to integrate multiple objectives into a single objective. By the optimization, the objective function value is improved largely and heat transfer rate is increase much higher than pressure loss increase due to shape deformation. Channel with vertically non-symmetric optimum dimples is tested and found that the best appears if dimples on opposite wall are displaced by one quarter of dimple spacing.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.617-635
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• 2020

The purpose of this study is to discuss how to improve the maneuverability of lifting and diving for underwater vehicle's vertical motion. Therefore, to solve these problems, applied the 3-D numerical simulation, Taguchi's Design of Experiment (DOE), and intelligent parameter design methods, etc. We planned four steps as follows: firstly, we applied the 2-D flow simulation with NACA series, and then through the Taguchi's dynamic method to analyze the sensitivity (β). Secondly, take the data of pitching torque and total resistance from the Taguchi orthogonal array (L9), the ignal-to-noise ratio (SNR), and analysis each factorial contribution by ANOVA. Thirdly, used Radial Basis Function Network (RBFN) method to train the non-linear meta-modeling and found out the best factorial combination by Particle Swarm Optimization (PSO) and Weighted Percentage Reduction of Quality Loss (WPRQL). Finally, the application of the above methods gives the global optimum for multi-quality characteristics and the robust design configuration, including L/D is 9.4:1, the foreplane on the hull (Bow-2), and position of the sail is 0.25 Ls from the bow. The result shows that the total quality is improved by 86.03% in comparison with the original design.

• 한국음향학회지
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• 제18권4호
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• pp.23-30
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• 1999

본 연구에서는 통과대역폭, 리플, 저지대역 감쇠도의 필터특성을 고려하여 주어진 입력사양에 부합하는 withdrawal SAW 필터의 새로운 최적화 알고리즘을 개발하고자 하였다. 필터의 성능해석을 위해 델타함수 모델을 이용하였으며, 대표적인 필터구조로서 균일한 입력 IDT에 대해 출력단을 withdrawal weighting하는 경우를 선정, 해석하였다. 이를 위해 여덟개의 설계변수를 선정하였으며 각각의 변화가 성능변수에 미치는 영향을 고려하여 세 단계를 거쳐 최적화 알고리즘을 완성하였다. 첫 단계에서는 삽입손실을 고려하여 입출력 전극수와 형상의 규격을 결정하고 다음 단계에서는 위상반전 방법을 이용하여 대역폭을 조절하며, 마지막 단계에서는 전극을 추가, 제거하는 방법을 통해 저지대역의 특성규격을 만족시켰다. 본 연구를 통해 제시하는 새로운 withdrawal weighting 필터 설계방법은 기존의 설계 방법들과는 달리 통과대역폭, 리플. 저지대역 억압도, 삽입손실 등의 필터특성을 동시에 고려하며 주어진 입력사양에 부합하는 필터를 최적화할 수 있다.

• 기술사
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• 제22권2호
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• pp.19-25
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• 1989

Utilizing a cosine transform in image compression has several recognized performance benefits, resulting in the ability to attain large compression ratio with small quality loss. Also, various models incorporating Human Visual System (HVS) to Discrete Cosine Trans-form (DCT) scheme are considered. Using the exact frequency components of DCT basis function, the optimum modulation transfer function (MTF) is obtained analytically. The errors at a block boundary which is important factor in transform coder are criteria for error measurement. The HVS weight coding results in perceptually higher quality images compared with the unweighted scheme.

• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.481-490
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• 2013

Classification is an important research area as data can be easily obtained even if the number of predictors becomes huge. The support vector machine(SVM) is widely used to classify a subject into a predetermined group because it gives sound theoretical background and better performance than other methods in many applications. The SVM can be viewed as a penalized method with the hinge loss function and penalty functions. Instead of $L_2$ penalty function Fan and Li (2001) proposed the smoothly clipped absolute deviation(SCAD) satisfying good statistical properties. Despite the ability of SVMs, they have drawbacks of non-robustness when there are outliers in the data. We develop a robust SVM method using a weight function with the SCAD penalty function based on the local quadratic approximation. We compare the performance of the proposed SVM with the SVM using the $L_1$ and $L_2$ penalty functions.

• 산업공학
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• 제24권2호
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• pp.151-155
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• 2011

In this study, we propose a signomial classification method with ₀-regularization (₀-)which seeks a sparse signomial function by solving a mixed-integer program to minimize the weighted sum of the ₀-norm of the coefficient vector of the resulting function and the $L_1$-norm of loss caused by the function. $SC_0$ gives an explicit description of the resulting function with a small number of terms in the original input space, which can be used for prediction purposes as well as interpretation purposes. We present a practical implementation of $SC_0$ based on the mixed-integer programming and the column generation procedure previously proposed for the signomial classification method with $SL_1$-regularization. Computational study shows that $SC_0$ gives competitive performance compared to other widely used learning methods for classification.

• 한국의학물리학회지:의학물리
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• 제9권4호
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• pp.217-225
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• 1998

본 연구에서는 A청감보정 소음레벨과 폭로기간에 대한 소음유발 청력손실과 소음폭로 사이의 관계를 해석하였다. 연령과 소음폭로등 다양한 변수에 대하여 청력손실과 청감민감도 변화를 조사하였다. 연구결과 전체 청력손실은 음압의 시간적분에 의한 소음폭로 레벨에 비례하였다. 만약 소음폭로가 노인성 난청보다 크면 연령과 소음에 의해 발생하는 청력손실은 주된 원인이 소음에 의한 것임을 확인하였다. 과도한 소음은 일시적 청력손실의 원인이며, 폭로가 길어지거나 강력하면 영구적 청력손실의 원인이 될 수 있다. 소음유발 청력손실을 겪는 사람의 청력도는 4kHz 영역에서 청감민감도의 급격한 손실을 보여주며, 이 영역은 여러 형태의 산업소음으로 인해 가장 손상받기 쉬운 전형적인 주파수영역임을 확인하였다.

• International Journal of Fluid Machinery and Systems
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• 제4권1호
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• pp.172-178
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• 2011

This paper presents an optimization procedure for high-efficiency design of a mixed-flow pump. Optimization techniques based on a weighted-average surrogate model are used to optimize a vane diffuser of a mixed-flow pump. Validation of the numerical results is performed through experimental data for head, power and efficiency. Three-level full factorial design is used to generate nine design points within the design space. Three-dimensional Reynoldsaveraged Navier-Stokes equations with the shear stress transport turbulence model are discretized by using finite volume approximation and solved on hexahedral grids to evaluate the efficiency as the objective function. In order to reduce pressure loss in the vane diffuser, two variables defining the straight vane length ratio and the diffusion area ratio are selected as design variables in the present optimization. As the results of the design optimization, the efficiency at the design flow coefficient is improved by 7.05% and the off-design efficiencies are also improved in comparison with the reference design.

• ETRI Journal
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• 제41권4호
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• pp.415-425
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• 2019

Generative adversarial networks (GAN) have been successfully used in many image restoration tasks, including image denoising, super-resolution, and compression artifact reduction. By fully exploiting its characteristics, state-of-the-art image restoration techniques can be used to generate images with photorealistic details. However, there are many applications that require faithful rather than visually appealing image reconstruction, such as medical imaging, surveillance, and video coding. We found that previous GAN-training methods that used a loss function in the form of a weighted sum of fidelity and adversarial loss fails to reduce fidelity loss. This results in non-negligible degradation of the objective image quality, including peak signal-to-noise ratio. Our approach is to alternate between fidelity and adversarial loss in a way that the minimization of adversarial loss does not deteriorate the fidelity. Experimental results on compression-artifact reduction and super-resolution tasks show that the proposed method can perform faithful and photorealistic image restoration.