• Architectural research
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• 제23권2호
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• pp.19-28
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• 2021

Reduction in competence makes older adults with dementia more sensitive to the influence of the physical environment. The aim of the longitudinal study was to examine whether residents with dementia in long-term facilities with variability in physical environmental characteristics in Vancouver (N= 11), Canada and Stockholm (N=13), Sweden had a difference in their quality of life (QoL). QoL was assessed using Dementia Care Mapping tool three times over one year for the reliability of data. The results of the study demonstrated that the residents with dementia living in a homelike and positive stimulating setting showed less withdrawn behaviors and a higher level of well-being compared to those in a large-scale institutional setting. This study also found that the residents living in a large-scale institutional environment spent more monotonous times than the other groups, which may be to provision of fewer structured activity programs or less social interaction with neighbors or staff members. Residents living in a large-scale institutional setting in Canada showed so far as five times more agitated/ distressed behaviors and twice more withdrawal compared to the ones living in a small-scale homelike setting in Sweden. The study supports that the large-scale institutional environment was considerably associated with levels of lower quality of life among the residents with dementia.

• 대한산업공학회지
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• 제36권1호
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• pp.13-21
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• 2010

The prediction of contract cancellation of customers is essential in insurance companies but it is a difficult problem because the customer database is large and the target or cancelled customers are a small proportion of the database. This paper proposes a new data mining approach to the binary classification by handling a large-scale unbalanced data. Over-sampling, clustering, regularized logistic regression and boosting are also incorporated in the proposed approach. The proposed approach was applied to a real data set in the area of insurance and the results were compared with some other classification techniques.

• 한국지리정보학회지
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• 제20권2호
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• pp.89-101
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• 2017

본 연구는 소상공인의 상권 활성화를 위해 경상남도 창원시 도시지역을 대상으로 GIS 기법을 활용하여 전통시장과 대형할인점의 상권 특성을 분석하였다. 전통시장과 대형할인점의 정보를 GIS로 구축한 후, Kernel 밀도 분석, Network analysis, Huff 확률모형 등 다양한 GIS 공간분석기법을 적용하였다. 전통시장의 공간특성은 대형할인점이 밀집된 지역에 위치하는 것으로 나타났다. 상권특성을 도출한 결과, 대형할인점을 이용하는 소비자가 157,071명으로 전통시장 59,953명보다 약 3배 많았다. 이러한 현상의 원인은 인구가 밀집되고 전통시장에 인접한 지역에 대형할인점이 위치하고 있기 때문으로 판단된다. 따라서 소상공인의 상권 활성화를 위해서는 대형할인점 입지선정에 대한 기준 및 규제가 마련되어야 할 것이다. 본 연구는 도시지역의 상권 특성을 공간적이고 정량적으로 도출하였다. 향후 소상공인 상권 활성화를 위한 기초적인 자료로 활용될 것이다.

• 수산경영론집
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• 제40권1호
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• pp.1-26
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• 2009

The aquaculture management considers the maintenance of households lifehood more than profit maximization. As aquaculture industry has developed enterprise farms appeared, and the small and the large scale farms coexist. The features of coexistence could be summarized as followings. First of all, the large scale farms show the higher net profit while the small scale farms show the higher profit per 1ha and the earning rate. Secondly, in the case of over 2ha, the earning rate is stable in spite of the scale expansion. Moreover, in processing method, dried seaweed occupy the biggest proportion in the small scale farms while the raw seaweed occupy the biggest proportion in the large scale farms. Lastly, the scale of farms becomes larger, the participation rate of household labor rises. This thesis analyses the efficiency of Korean seaweed farms in the way of DEA model and suggests the improvements for the efficiency management. The mean technical, pure technical and scale efficiencies were measured to be 0.88, 0.96 and 0.91, respectively. Among the 20 farms included in the analysis, 10 were technically efficient and 12 were scale efficient. In conclusion, it is shown that the aquaculture farms has been becoming the form of coexistence. This appearance results in the effort for reducing the cost in the small scale farms and in profit maximization in the large scale farms. On the other hand, middle scale farms is inefficient compared with the small or large scale farms. Therefore, in order to achieve the efficiency, it is necessary to accomplish economy of scale by extending farm size or to cut expenses by reducing farm area. In other word, the efforts for achieving the efficiency is required in a different direction in spite of the same scale.

• Smart Structures and Systems
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• 제7권3호
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• pp.229-240
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• 2011

Recent developments in Smart Structures with very large scale embedded sensors and actuators have introduced new challenges in terms of data processing and sensor fusion. These smart structures are dynamically classified as a large-scale system with thousands of sensors and actuators that form the musculoskeletal of the structure, analogous to human body. In order to develop structural health monitoring and diagnostics with data provided by thousands of sensors, new sensor informatics has to be developed. The focus of our on-going research is to develop techniques and algorithms that would utilize this musculoskeletal system effectively; thus creating the intelligence for such a large-scale autonomous structure. To achieve this level of intelligence, three major research tasks are being conducted: development of a Bio-Inspired data analysis and information extraction from thousands of sensors; development of an analytical technique for Optimal Sensory System using Structural Observability; and creation of a bio-inspired decision-making and control system. This paper is focused on the results of our effort on the first task, namely development of a Neuro-Morphic Engineering approach, using a neuro-symbolic data manipulation, inspired by the understanding of human information processing architecture, for sensor fusion and structural diagnostics.

• Asian Journal of Atmospheric Environment
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• 제5권3호
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• pp.189-195
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• 2011

Korea-specific GHG emissions should be estimated correctly in order to ensure effective measurement of climate change variables. The use of country-specific data that reflects fuel and technology characteristics is needed for accurate GHG emissions estimation. Oxidation factors are used to convert existing data into equivalent GHG emissions, and changes in these oxidation factors are directly related to changes in emissions. As such, the oxidation factor is one of the most important variables in using country-specific data to determine GHG emissions. In this study, the oxidation factor of bituminous coal in large scale boilers was estimated using 4,527 data points sampled from eight large-scale boilers that had been using bituminous coal for two years. The average oxidation factor was determined to be 0.997, which is lower than the oxidation factor of 1 that is recommended by the IPCC G/L for large scale boilers when estimating national GHG emissions. However, an oxidation factor less than 1 is assumed for fluidized bed boilers, internal combustion engines, and other small-scale boilers. Accordingly, studies on oxidation factor estimation should be continued to allow for accurate estimation of GHG emissions.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2004년도 봄 학술발표회 논문집
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• pp.389-395
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• 2004

We consider the interface between the parallel distributed memory multifrontal solver and the finite element method. We give in detail the requirement and the data structure of parallel FEM interface which includes the element data and the node array. The full procedures of solving a large scale structural problem are assumed to have pre-post processors, of which algorithm is not considered in this paper. The main advantage of implementing the parallel FEM interface is shown up in the case that we use a distributed memory system with a large number of processors to solve a very large scale problem. The memory efficiency and the performance effect are examined by analyzing some examples on the Pegasus cluster system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.551-569
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• 2024

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.

• Journal of Communications and Networks
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• 제17권4호
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• pp.384-393
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• 2015

This paper proposes an insect behavior-inspired routing algorithm for large-scale wireless mesh networks. The proposed algorithm is adapted from the behavior of an insect called Bombyx mori, a male silkmoth. Its unique behavior is its flying technique to find the source of pheromones. The algorithm consists of two steps: the shortest-path algorithm and the zigzag-path algorithm. First, the shortest-path algorithm is employed to transmit data. After half of the total hops, the zigzag-path algorithm, which is based on the movement of the male B. mori, is applied. In order to adapt the biological behavior to large-scale wireless mesh networks, we use a mesh topology for implementing the algorithm. Simulation results show that the total energy used and the decision time for routing of the proposed algorithm are improved under certain conditions.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.155-160
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.