• 한국경영과학회지
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• 제37권1호
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• pp.29-43
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• 2012

We design a DEA-AR model using multiple regression analysis with new methods which limit weights. When there are multiple input and single output variables, our model can be used, and the weights of input variables use the regression coefficient and coefficient of determination. To verify the effectiveness of the new model, we evaluate the efficiency of the Regional Corporations in Korea. Accordance with statistical analysis, it proved that there is no difference between the efficiency value of the DEA-AR using AHP and our DEA-AR model. Our model can be applied to a lot of research by substituting DEA-AR model relying on AHP in the future.

• 대한토목학회논문집
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• 제29권3A호
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• pp.199-207
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• 2009

본 논문에서는 신뢰도기반 도로교설계기준을 위한 새로운 활하중모형을 개발하였다. 합리적 하중모형과 함께 하중의 통계적 특성의 구축은 신뢰도기반 설계기준의 개발에 매우 중요하다. 이전 논문에서는 WIM 또는 BWIM시스템을 이용하여 수집된 국내 8개 지역의 자료를 분석하여 교량수명기간동안의 예상최대중량을 구하였다. 차종별 총중량의 확률분포는 상위 20%의 자료를 이용하여 극한분포(Gumbel분포)로 가정되었으며 이 확률분포를 사용하여 교량수명기간동안의 최대중량을 예측하였다. 이 논문에서는 교량상에 두 대 이상의 차량이 동시에 재하되는 경우를 분석하였다. 여러 자료를 이용하여 동시재하의 확률을 구하였으며 이에 따른 동시재하차량의 총중량을 이전 논문과 같은 확률분포를 이용하여 구하였다. 10-200 m까지의 지간별로 예측된 하중효과를 모사할 수 있는 공칭하중모형이 제안되었다. 제안된 하중모형은 기존의 하중모형 뿐만 아니라 국외의 여러 기준들과 비교분석되었다.

• 대한수학회지
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• 제60권1호
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• pp.195-212
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• 2023

In this paper, two weight conditions are introduced and the multiple weighted strong and weak characterizations of the multilinear fractional new maximal operator 𝓜_ϕ,β are established. Meanwhile, we introduce the ${\mathcal{S}}_{({\vec{p}},q),{\beta}}({\varphi})$ and $B_{({\vec{p}},q),{\beta}}({\varphi})$ conditions and obtain the characterization of two-weighted inequalities for 𝓜_ϕ,β. Finally, the relationships of the conditions ${\mathcal{S}}_{({\vec{p}},q),{\beta}}({\varphi}),\,{\mathcal{A}}_{({\vec{p}},q),{\beta}}({\varphi})$ and $B_{({\vec{p}},q),{\beta}}({\varphi})$ and the characterization of the one-weight $A_{({\vec{p}},q),{\beta}}({\varphi})$ are given.

• Industrial Engineering and Management Systems
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• 제1권1호
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• pp.64-72
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• 2002

In the field of plant maintenance, data that are gathered by sensors on multiple machines are handled and analyzed. Online or pseudo online data handling is required on such fields. When the data occurrence speed exceeds the data handling speed, multiple data should be handled at a time (batch data handling or pseudo online data handling). If l amount of data are received at one time following N amount of data, how to estimate the new parameters effectively is a great concern. A new simplified calculation method, which calculates the N data's weights, is introduced. Numerical examples show that this new method has a fairly god estimation accuracy and the calculation time is less than 1/10 compared with the case when the whole data are re-calculated. Even under the restriction calculation ability in the apparatus is limited, this proposed method makes the failure detection of equipments possible in early stages with a few new coming data. This method would be applicable in many data handling fields.

• 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.

• 지능정보연구
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• 제6권1호
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• pp.97-112
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• 2000

The basic idea of case-based reasoning is to solve a new problem using the previous problem-solving experiences. In this research we develop a case-based reasoning system for equipment malfunction diagnosis. We first divide the case base into clusters using the case-based clustering technique. Then we develop an appropriate case-based diagnostic system for each cluster. In other words for individual cluster a different case-based diagnostic system which uses different weights for attributes is developed. As a result multiple case-based reasoning system are operating to solve a diagnostic problem. In comparison to the performance of the single case-based reasoning system our system reduces the computation time by 50% and increases the accuracy by 5% point.

• 한국전자통신학회논문지
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• 제17권3호
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• pp.515-520
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• 2022

다중 센서를 활용하는 상황인식에 있어서 각각의 센서가 감지하여 보내온 센서 데이터를 활용할 때, 센서 별로 가중치를 달리하여야 할 필요가 있다. 같은 상황에 대하여 같은 종류의 센서를 구성하였더라도 다른 부차적인 요인 때문에 가중치 부여를 달리하여야 하는 경우가 있다. 실제 세계의 이벤트에 가중치 부여를 하지 않을 수 없으며, 다중 센서를 활용하는 상황인식 시스템에서 활용할 수 있는 가중치 부여 방안은 필요하다고 할 수 있다. 본 연구에서는 시간이 경과하면서 센서들이 계속 감지 활동을 하는 가운데 호스트로 보고하는 각 센서에 대한 가중치 부여 방안을 제안한다. 대부분의 사물인터넷 환경에서 센서는 감지 활동을 지속적으로 이어나가며, 감지한 값이 사전에 정해 둔 범위 이상의 변화양상을 보일 때, 호스트로 보고하는 것을 기본으로 한다. 이러한 것을 일종의 데이터 스트림 환경이라고 할 수 있다. 데이터 스트림 환경에서 다중 센서로부터의 감지 데이터를 대상으로 하는 가중치 부여 방안에 대하여 제안하였으며, 새로운 가중치 부여 방안은 스트림 상에서 상황 변화를 주도적으로 나타내는 데이터를 선별하여 가중치를 부여하는 것으로 하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 통신소사이어티 추계학술대회논문집
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• pp.191-194
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• 2003

In IEEE 802.11b, Medium Access Control Sublayer consists of DCF (Distributed Coordination Function) and PCF (Point Coordination Function). DCF provides contention based services and PCF provides contention free services for QoS satisfaction. DCF uses CSMA/CA (Carrier Sense Multiple Access/Collision Avoidance) as an access protocol. And PCF uses Polling Scheme. In this paper, a modified New-PCF, which gives weights to channels with heavier traffic load, was suggested. NS-2 simulations were conducted to compare the service performances with original DCF, PCF and the modified New-PCF respectively. Simulation results has shown the increased overall throughput with the proposed New-PCF compared with other cases.

• 대한수학회지
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• 제55권3호
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• pp.671-694
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• 2018

In this paper, we investigate the weighted vector-valued bounds for a class of multilinear singular integral operators, and its commutators, from $L^{p_1}(l^{q_1};\;{\mathbb{R}}^n,\;w_1){\times}{\cdots}{\times}L^{p_m}(l^{q_m};\;{\mathbb{R}}^n,\;w_m)$ to $L^p(l^q;\;{\mathbb{R}}^n,\;{\nu}_{\vec{w}})$, with $p_1,{\cdots},p_m$, $q_1,{\cdots},q_m{\in}(1,\;{\infty})$, $1/p=1/p_1+{\cdots}+1/p_m$, $1/q=1/q_1+{\cdots}+1/q_m$ and ${\vec{w}}=(w_1,{\cdots},w_m)$ a multiple $A_{\vec{P}}$ weights. Our argument also leads to the weighted weak type endpoint estimates for the commutators. As applications, we obtain some new weighted estimates for the $Calder{\acute{o}}n$ commutator.

• 한국통신학회논문지
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• 제37권3A호
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• pp.172-178
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• 2012

This paper studies the application of a fuzzy-ARTMAP (FAM) neural network to multi-user detector (MUD) for direct sequence (DS)-code division multiple access (CDMA) system. This method shows new solution for solving the problems, such as complexity and long training, which is found when implementing the previously developed neural-basis MUDs. The proposed FAM based MUD is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capabilities of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of FAM based MUD is compared with other neural net based MUDs in terms of the bit error rate.