• Journal of Information Processing Systems
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• 제15권5호
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• pp.1179-1191
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• 2019

We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the LMPF method can decrease the information loss effectively because of its "learning" ability. Experiments on ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those previous methods to a great extent.

• 대한전자공학회논문지SD
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• 제43권9호
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• pp.59-63
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• 2006

VLSI의 복잡도가 증가함에 따라, 보다 복잡한 고장이 나타나게 되었다. 단일 고장 진단을 위한 많은 방법들이 연구되어 왔다. 때로는 오류가 존재하는 칩에 대한 다중 결함이 실제 현상을 보다 더 정확하게 반영한다. 따라서 다중 고착 고장을 위한 효율적인 고장 진단 알고리듬을 제한하겠다. 제안하는 매칭 알고리듬은 완전일치공통부분을 고장 진단의 중요한 기준으로 사용함으로써 단일 고착 고장 시뮬레이터 환경에서도 다중 고착 고장을 진단할 수 있다. 또한 각 고장간의 식별성을 높여 다중 고착 고장을 진단함에도 불구하고, 고장 후보의 수를 획기적으로 줄일 수 있었다. 이를 위하여 출력단의 수에 따른 가중치 개념과 가산, 감산 연산을 사용하였다. 제안한 매칭 알고리듬은 ISCAS85회로와 완전 주사 스캔이 삽입된 ISCAS89회로에서 실험하여 성능을 입증하였다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권11호
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• pp.856-860
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• 2009

본 논문은 Wang-Ward의 WTHE(Weighted and Thresholded Histogram Equalization) 방법에 히스토그램 분할 개념을 적용한 새로운 영상 화질 개선 방법(DWTHE: Decomposable WTHE)을 제안한다. DWTHE는 먼저 영상의 평균 자기 값 또는 명도 균등 분할점을 기준으로 입력 히스토그램의 영역을 분할하고, 분할된 각 영역의 확률밀도 값을 가중치로 사용하여 새로운 히스토그램을 만든 후, 히스토그램 평활화 과정을 수행하게 된다. 하나의 가중치를 사용하는 WTHE 방법과 다르게, 제안 방법은 히스토그램 분할로 인한 복수외 가중치 값을 사용하게 되며, 실험 결과 제안 방법은 기존 방법에 비해 우수한 화질 개선 효과를 보여주었다.

• 수산해양교육연구
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• 제20권1호
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• pp.78-89
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• 2008

This study is intended to analyse the economic sensitivity of imported fishery products due to decrease in or elimination of tariff rates through the progress of free trade. Forty-seven species of fishes were selected for this study on the basis of the HS Code. The substitution and price effects were calculated using the price elasticities of both domestic and imported demands for fishery products under the assumption of 5% decrease in a tariff rate. Seven main economic variables were extracted from the fishery industry which can mediate the substitution and price effects. A multiple regression analysis was conducted to obtain the influence weights of these main economic variables on both effects. The order of sensitivity of the fishes was calculated using these weights. The 47 fish species were classified into four groups according to their sensitivity based on the means and the standard deviations of their total scores on seven main economic considerations. Nine fish species such as squids, hair tails, shellfishes, and crabs belonged to the hyper-sensitive group, whereas 15 fishes such as eels, sea breams, and sea weeds belonged to the sensitive group. Twelve species including common sea basses, cods, and abalones were among the less-sensitive group, and 11 species including skate rays and mud fishes comprised the non-sensitive group.

• 정보처리학회논문지C
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• 제12C권7호
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• pp.1025-1030
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• 2005

협력 통신 방법은 많은 사용자들 간의 다중접속이 있는 무선 환경에서, 물리적인 안테나 배열의 제약에 상관없이 다중 안테나 시스템의 강력한 장점을 얻을 수 있는 효과적인 방법이다. 본 논문에서는 수신단에서 송신단으로 궤환되는 채널 상태 정보(CSI)의 장점을 이용해 전송 전력이 제한된다는 가정하고, 최대우도 판정기 출력의 에러 확률을 최소화시키기 위해 상대 사용자 신호들의 전송 무게(Transmit Weight)를 최적화하는 방법을 제안한다. 제안한 시스템의 성능평가를 위해 레일리 주파수 비선택적 페이딩과 AWGN 채널이 합해진 채널에서 모의실험을 하였다.

• 한국안전학회지
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• 제32권5호
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• pp.103-114
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• 2017

The landslide disaster damage has been increased by mountain development, leading to construction of educational facilities, medical facilities, petty industrial facilities, and large housing complexes. Therefore, effective regulation is required as an effort in urban planning solutions. For suggesting specific mitigation strategies on urban landslide, this study aims to define evaluation criteria for urban planning management of debris-flow disaster. AHP (Analytic Hierarchy Process), one of the multiple criterion decision making methods, was utilized in this study. This study makes use of 16 sub-criteria under the framework of hazard, exposure, and vulnerability, and well-planned expert survey measures their weights. The weights are also applied to evaluate each grid in urban space (min $10{\times}10m$) and classify it with red, orange, yellow, or green grade so that areas at higher risk are clearly identified. This study concludes that the suggested method is useful to support a strategies for urban planning management of debris-flow disaster, particularly in a GIS base.

• Journal of applied mathematics & informatics
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• 제35권1_2호
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• pp.121-130
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• 2017

This study is concerned with the existence of positive solution for the following nonlinear elliptic system $$\{-M_1(\int_{\Omega}{\mid}x{\mid}^{-ap}{\mid}{\nabla}u{\mid}^pdx)div({\mid}x{\mid}^{-ap}{\mid}{\nabla}u{\mid}^{p-2}{\nabla}u)\\{\hfill{120}}={\mid}x{\mid}^{-(a+1)p+c_1}\({\alpha}_1A_1(x)f(v)+{\beta}_1B_1(x)h(u)\),\;x{\in}{\Omega},\\-M_2(\int_{\Omega}{\mid}x{\mid}^{-bq}{\mid}{\nabla}v{\mid}^qdx)div({\mid}x{\mid}^{-bq}{\mid}{\nabla}v{\mid}^{q-2}{\nabla}v)\\{\hfill{120}}={\mid}x{\mid}^{-(b+1)q+c_2}\({\alpha}_2A_2(x)g(u)+{\beta}_2B_2(x)k(v)\),\;x{\in}{\Omega},\\{u=v=0,\;x{\in}{\partial}{\Omega},$$ where ${\Omega}$ is a bounded smooth domain of ${\mathbb{R}}^N$ with $0{\in}{\Omega}$, 1 < p, q < N, $0{\leq}a$ < $\frac{N-p}{p}$, $0{\leq}b$ < $\frac{N-q}{q}$ and ${\alpha}_i,{\beta}_i,c_i$ are positive parameters. Here $M_i,A_i,B_i,f,g,h,k$ are continuous functions and we discuss the existence of positive solution when they satisfy certain additional conditions. Our approach is based on the sub and super solutions method.

• Industrial Engineering and Management Systems
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• 제11권1호
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• pp.54-69
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• 2012

Chan and Qi (SCM 8/3 (2003) 209) developed an innovative measurement method that aggregates performance measures in a supply chain into an overall performance index. The method is useful and makes a significant contribution to supply chain management. Nevertheless, it can be cumbersome in computation due to its highly complex algorithmic fuzzy model. In aggregating the performance information, weights used by Chan and Qi-which aim to address the imprecision of human judgments-are incompatible with weights in additive models. Furthermore, the default assumption of linearity of its scoring procedure could lead to an inaccurate assessment of the overall performance. This paper addresses these limitations by developing an alternative measurement that takes care of the above. This research integrates three different approaches to multiple criteria decision analysis (MCDA)-the multiattribute value theory (MAVT), the swing weighting method and the eigenvector procedure-to develop a comprehensive assessment of supply chain performance. One case study is presented to demonstrate the measurement of the proposed method. The performance model used in the case study relies on the Supply Chain Operations Reference (SCOR) model level 1. With this measurement method, supply chain managers can easily benchmark the performance of the whole system, and then analyze the effectiveness and efficiency of the supply chain.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.212-222
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• 2021

Multiple waves of COVID-19 highlighted one crucial aspect of this pandemic worldwide that factors affecting the spread of COVID-19 infection are evolving based on various regional and local practices and events. The introduction of vaccines since early 2021 is expected to significantly control and reduce the cases. However, virus mutations and its new variant has challenged these expectations. Several countries, which contained the COVID-19 pandemic successfully in the first wave, failed to repeat the same in the second and third waves. This work focuses on COVID-19 pandemic control and management in Saudi Arabia. This work aims to predict new cases using deep learning using various important factors. The proposed method is called Deep Learning and Dynamic Weighing-based (DLDW) COVID-19 cases prediction method. Special consideration has been given to the evolving factors that are responsible for recent surges in the pandemic. For this purpose, two weights are assigned to data instance which are based on feature importance and dynamic weight-based time. Older data is given fewer weights and vice-versa. Feature selection identifies the factors affecting the rate of new cases evolved over the period. The DLDW method produced 80.39% prediction accuracy, 6.54%, 9.15%, and 7.19% higher than the three other classifiers, Deep learning (DL), Random Forest (RF), and Gradient Boosting Machine (GBM). Further in Saudi Arabia, our study implicitly concluded that lockdowns, vaccination, and self-aware restricted mobility of residents are effective tools in controlling and managing the COVID-19 pandemic.

• 전기전자학회논문지
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• 제27권4호
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• pp.430-438
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• 2023

The Modular Multilevel Converter (MMC) has emerged as a key component in HVDC systems due to its ability to efficiently transmit large amounts of power over long distances. In such systems, accurate estimation of the MMC capacitor voltage is of utmost importance for ensuring optimal system performance, stability, and reliability. Traditional methods for voltage estimation may face limitations in accuracy and robustness, prompting the need for innovative approaches. In this paper, we propose a novel distributed neural network observer specifically designed for MMC capacitor voltage estimation. Our observer harnesses the power of a multi-layer neural network architecture, which enables the observer to learn and adapt to the complex dynamics of the MMC system. By utilizing a distributed approach, we deploy multiple observers, each with its own set of neural network layers, to collectively estimate the capacitor voltage. This distributed configuration enhances the accuracy and robustness of the voltage estimation process. A crucial aspect of our observer's performance lies in the meticulous initialization of random weights within the neural network. This initialization process ensures that the observer starts with a solid foundation for efficient learning and accurate voltage estimation. The observer iteratively updates its weights based on the observed voltage and current values, continuously improving its estimation accuracy over time. The validity of proposed algorithm is verified by the result of estimated voltage at each observer in capacitor of MMC.