• Journal of the Korean Geographical Society
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• v.51 no.6
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• pp.799-817
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• 2016

This article aims to reveal the ways in which a "space of difference" is produced and interpret the space as a heterotopic space, drawing the case of Filipino elderly care-givers in Israel. The in-betweens and temporariness of the migrants'experiences in the Israeli society lead to the creation of a heterotopic space. Paying attention to their particular life rhythm as live-in care-givers, namely weekdays-workplace and weekend-flat, this article explores how the migrant care-givers build their own society through a variety of spatial practices and multiple social relations based on the flat. In making the flat a perfect form of a lifestyle for their own, the migrants inscribe their presence in the flat in unique ways for different purposes, in a way different to that which surrounds it. However, the structure of flat not only signifies the migrants' marginality but also reflects the challenging position. The flat has functions in relation to all other space that remains, even if such connection often creates effects of contrast and difference. In this light, the flat is not merely an alienated and circumscibed exotic migrant enclave but a heterotopic space which is dynamically constructed in relation to other sites in the wider societal order.

• 월간 기계설비
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• s.296
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• pp.66-72
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• 2015

LH공사가 인천광역시 서창지구에 총 약 210만$m^2$ 규모, 1만4천여 가구, 약 5만여명이 거주하는 대규모 자족도시 단지를 조성하고 국민 공동주택을 공급하고 있다. 인천 서창지구는 인천의 대표적 주말 나들이 공원인 인천대공원이 근접해 있고 장아산, 거마산, 관모산, 장수천 등 산과 들, 습지(그린)와 바다(블루)로 둘러싸여 있으며, 시립미추홀도서관, 문학경기장, 문화예술회관, 길병원, 백화점, 인천터미널, 남동구청, 인천시청 등 풍부한 생활인프라와 함께 고속도로, 지하철 및 각종 도로가 결합된 멀티 교통망을 두루 갖춘 친환경 생태 해양 문화도시(Ecological & Culture Ocean City)이다. 해성산업개발(주)(대표 이연풍)는 서창2지구 중 1블럭 아파트 기계설비공사를 지난 해 10월 완공 후 3월 6일 입주 전까지 5개월간 최종 점검을 마쳤다. 이 현장은 LH공사가 주계약자 공동도급 시범사업으로 선정한 3개 현장 중 한곳으로 기계설비 건설업계의 관심이 집중된 곳이다. 본지는 입주 전 최종점검을 마친 현장에서 해성산업개발(주)(대표 이연풍) 안선준 기계 부장을 만나 시공 및 주계약자 공동도급에 대한 얘기를 들어보았다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.394-394
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• 2022

최근 스마트 수도미터의 보급을 통해 수용가구별 물 사용 자료를 수집할 수 있다. 이런 수용가구별 물 사용 패턴은 주말, 날씨 등 다양한 요인으로 인해 비선형적 특성을 가지고 있다. 그로인해 전통적인 시계열 예측 모형인 ARIMA 모형으로 적용하기 어렵다. 따라서 본 연구에서는 딥러닝 기반의 LSTM 모형을 통해 수용가구별 물 소비량 예측 모형을 개발하였다. 이 모형은 비선형적인 물 소비 패턴을 학습하기 위해 다양한 변수를 고려하였다. 서로 다른 종류의 4개 type (A : 단독주택, B: 아파트, C: 음식점, D : 초등학교)의 수용가구에 대한 ARIMA 모형과 LSTM 모형을 개발하였고, 학습에 사용되지 않은 새로운 데이터를 적용하여 정량적으로 예측성능을 비교했다. 그 결과, 모든 수용가구에서 LSTM 모형이 ARIMA 모형보다 성능이 우수하였다 (상관계수 : 평균89% | RMSE : 평균 5.60m³). 따라서 본 연구에서 제안한 모형은 수용가구별 물 사용량을 예측하는데 높은 활용도를 보일 것으로 기대된다.

• Journal of the Korean Regional Science Association
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• v.38 no.4
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• pp.75-91
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• 2022

With the recent increase in interest in climate change issues, the use of bicycles is complementing public transportation and attracting attention as one of the eco-friendly means of transportation. Daejeon Metropolitan City has been operating Tashu, a public bicycle, since 2008. This study empirically analyzed the spatial characteristics that affect the use of public bicycles by grasping the current status and characteristics of public bicycles and applying spatial econometrics analysis, an analysis model that considers the spatial dependence of spatial data. In addition, a comparative analysis was performed by deriving the results of analyzing six models in terms of rental, return, peak time, non-peak time, weekday, and weekend based on the spatial error model identified as the optimal spatial econometrics model. The analysis model results showed that significant spatial characteristics differed according to the type of public bicycle use. In general, the use of public bicycles was high in areas with a high proportion of young people, a high number of public transportation users, good access to universities and rivers, and relatively low land use mix, and high proportion of apartments. These results indicated that public bicycles are used for commuting purposes on weekdays and leisure purposes on weekends, and if the convenience of using bicycles is improved, the use of public bicycles can be further increased.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).