In the context of remodeling old buildings, enhancing insulation performance in the exterior skin necessitates an accurate assessment of a wall’s thermal performance. The conventional method for determining the thermal transmittance (U-value) of a wall is the heat flow meter (HFM) as outlined in the ISO 9869-1. However, this measurement is susceptible to errors influenced by indoor and outdoor environmental conditions and the wall’s material composition. This study evaluates the U-value of an internally insulated wall, specifically constructed for this purpose, utilizing both the average and dynamic methodologies of an HFM. Furthermore, it introduces a novel estimation method: the extended average method (EXAM). The effectiveness of this proposed method is ascertained by comparing the accuracy and convergence of the U-value estimations with those derived from existing methodologies. Additionally, the study explores the limitations of the HFM by analyzing the heat flow traversing the interior of a wall. The findings revealed that the EXAM method enhanced the precision of U-value estimation in all scenarios. Particularly, in walls with superior insulation, the HFM tended to underestimate the heat flow observed indoors, leading to negative errors. The EXAM method, incorporating considerations of both insulation and structural materials, offers an accurate in situ measurement of the U-value relative to the HFM.

Accurate and rapid in situ measurements of the thermal resistance (R-value) of building envelopes are necessary for assessing planned performance and identifying appropriate retrofitting strategies. Although there are several approaches for in situ R-value estimation, the average method of ISO 9869-1 based on the heat flow meter method is the most widely used. However, discrepancies between theoretical and in situ R-values are frequently reported in many studies that employ this method. This study aimed to investigate the cause of this discrepancy in estimating in situ R-values of highly insulated building walls using the average method of ISO 9869-1 by conducting long-term experiments. This study was made possible due to a specially constructed test wall wherein more sensors were installed than are required by the ISO 9869-1 standard. The findings showed that discrepancies between heat fluxes on the internal surface and heat passing through the wall is the main cause of the error in in situ R-value estimation. Measurement results from winter showed that deviation from the theoretical R-value was 9.12% for the average method and 0.6% for the extended average method, determined by additionally using the temperature and heat flux inside the wall.

To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS.

Many studies have sought to overcome the two main limitations of the average method of ISO 9869-1—its long test duration and low accuracy. These studies reported that the reliability of the results is dependent on the temperature differences. This consensus was based on the results of studies that measured a few cases with specific temperature differences, and the convergence criteria utilized for the representative thermal transmittance (U-value) were rarely examined. This study analyzed the influence of the average temperature difference and test duration on the convergence characteristics and accuracy of U-value estimation using the average method. Data from a north-facing exterior wall with a theoretical U-value of 0.145 W/m²·K were measured between June 2022 and May 2023. The influences of different measurement conditions were analyzed for cases divided based on four measurement periods and 11 average air temperature differences. The findings show that an accurate U-value can be obtained from 7 days’ worth of measurement data with an average temperature difference of 10 °C or higher and that the improvement in accuracy is minimal under stricter conditions. To achieve a convergence probability of over 90% for temperature differences of 10 °C or greater, the second and third criteria required measurement periods of 7 and 15 days respectively.

이 논문은 벨라 바르톡(Béla Bartok, 1881-1945)의 오페라 ≪푸른 수염 공작의 성≫(Duke Bluebeard’s Castle, 1911)을 여주인공 ‘유디트(Judith)’에 초점을 맞춰 비평한다. 위반하는 주체로서 유디트의 중심성은 원작동화 「푸른 수염 이야기」(1697)에서부터 관측되었을 뿐 아니라 오페라로 각색되는 과정에서 새롭게 강화되었다. 이에 착안해, 유디트가 실천하는 ‘위반의 행위성’을 내러티브의 핵심으로 보고 이를 토대로 작품의 서사적·음악적 흐름을 재구성하고자 한다. 프랑스의 후기구조주의 사상가 조르주 바타유(Georges Bataille, 1897-1962)가 『에로티즘』(1957)에서 포착한 위반(transgression) 개념은 여러 내러티브 속 ‘위반하는 주체’를 이해하는 데 중요한 바탕이 되어 왔다. 바타유는 경계 너머를 욕망한 위반자가 금기와 위반의 영역 사이를 유동적으로 오가며 금기와 충돌하기도 하고 금기를 확장하고 드러내기도 한다고 설명하였다. 본 논문에서는 유디트가 남편인 푸른 수염이 언명한 금기를 적극적으로 어기기를 반복한다는 점, 일곱 개의 문 뒤 풍경들에 다채롭게 대응하며 금기와의 관계를 재편해나간다는 점에서 그녀를 위반하는 주체로 상정하였다. 위반자 유디트는 오페라에서 음악적 요소를 통해 구체적으로 재현된다. 작품의 서사적 구조는 음악을 중심으로 하여 유디트의 위반 실천을 다양한 방식으로 뒷받침한다. 세부적으로, 유디트의 위반자 정체성, 금기 언명자인 푸른 수염과의 관계성, 오케스트라가 묘사하는 풍경에서의 위치성은 각 국면에 나타나는 음고류공간과 리듬, 관현악 음향 등을 통해 정체화된다. 이 논문에서는 오페라 속 유디트가 대변하는 일반론적 의미의 ‘위반자’들을 관통하는 속성을 점검하고, 이것이 음악 비평과 맺는 관계를 이해하고자 한다.

Wildfires on permafrost covered with the boreal forest can influence vegetation composition, surface soil moisture, and the active layer. Since wildfires on permafrost occur extensively in unpredictable areas, remote sensing is a useful tool for monitoring burn severity and ecosystem changes. Optical spectral indices such as the differenced normalized burn ratio (dNBR) and normalized difference vegetation index (NDVI) were traditionally used to detect burn severity and vegetation regrowth. However, since optical imagery is significantly affected by cloud cover and weather conditions, there is a limitation in acquiring temporally dense images. Synthetic Aperture Radar (SAR) can obtain images regardless of day/night or weather conditions, so it is possible to densely observe the area of interest spatiotemporally. In addition, SAR images, unlike optical images, can acquire information on the active layer of the permafrost in the winter season. This study aimed to analyze winter season time-series SAR backscattering coefficient change with burn severity in south Northwest Territories, Canada using optical and SAR data. The study area, south Northwest Territories, belongs to the discontinuous permafrost zone and consisted of the taiga. Burn severity and vegetation regrowth were estimated by dNBR and NDVI using optical imagery. To increase the temporal resolution, Landsat-8 OLI and Sentinel-2 MSI were acquired through the cloud-based Google Earth Engine (GEE) in the summer season. C-band dual-polarization Sentinel-1 and X-band single-polarization TerraSAR-X were obtained to understand the multi-frequency backscattering coefficient to fire-induced changes. The changes pattern of the SAR backscattering coefficient varies according to the burn severity, especially in the winter season, not affected by vegetation and soil moisture. It can be seen that the wildfires affected the changes in the scattering mechanism in permafrost on the boreal forests. These results represent that C-band and X-band SAR images have the potential to monitor the changes of the active layer with burn severity.