일반적으로 전기변색소자들은 전기변색물질, 전해질 그리고 상대전극물질로 구성되어 있다. 그 중에서, 상대전극물질은 소자의 장기전환 안정성 및 낮은 작동 전압 등과 같은 전기변색소자들의 성능을 향상하는데 매우 중요하다. 본 연구에서는 다이메틸 프탈레이트 기반 전기변색소자에 페로센과 카본 전극을 적용하고 그 성능을 테스트 하였다. 특히 카본 전극에서 우수한 장기전환 안정성이 구현되었다.

본 연구에서는 3차원 순환 모델인 MOHID (MOdelo HIDrodinâmico) 모델을 적용하여 한반도를 포함한 동아시아 해역의 2016년 해황 변동을 재현하였다. 재현 결과는 객관 분석장(EN4, ARMOR3D, AVISO, SIO 자료)과 현장 관측 자료(정선 해양 자료, 부이 자료)를 사용하여 검증하였다. 검증 결과 MOHID로 재현된 수온, 해면 고도 편차, 표층 유속 및 혼합 층 깊이 등의 전반적인 해황 구조가 동아시아 해역의 객관 분석 자료들과 유사하게 나타났다. 특히 연안의 부이 자료와 비교하였을 때, 표층 수온 및 해면 고도 편차의 상관 계수는 모두 0.8 이상이며, 표준화된 표준편차는 0.85-1.15의 값을 보였다. 그러나 여름철 동해의 수온 약층의 구조 및 황해와 남해의 강한 성층 현상 재현에 관한 추가적인 개선이 필요할 것으로 분석되었다.

PURPOSES : The purpose of this study was to suggest a quantitative trend of the daily and seasonal cyclic movement of transverse crackwidth based on measurements in CRCP(Continuously Reinforced Concrete Pavement) within the first year of construction. METHODS : Crack gauges were installed in eight normal cracks, two induced cracks, and two construction joints of newly constructed CRCP. Crack width movements were continuously collected for about a year to investigate the cyclic behavior after construction. The daily and seasonal crack width movements were quantitatively analyzed and compared. RESULTS: Crack width movement in hot weather was relatively less than in cold weather. As a result of frequency analysis of the daily cyclic behavior, it was revealed by measurement that the minimum crack width from 2 p.m. to 4 p.m. was caused by expansion of the concrete; and that the maximum width from 6 a.m. to 8 a.m. was caused by contraction. Average crack width movements were calculated for every month and showed seasonal cyclic behavior. Maximum crack width was measured from December to January. Average crack width was investigated from March to April. Daily crack width movement in relation to concrete temperature was calculated from -0.00017 to -0.03844 mm/℃ and showed gradual decrease in absolute value with time caused by change in the crack spacing. It was found that the relationships between the monthly average crack width and concrete temperature are from -0.004 to -0.012 mm/℃. CONCLUSIONS : Crack-width movement shows a daily and seasonal cyclic behavior. Crack-width measurement in any time or season will have variance caused by daily and seasonal cyclic movement. Variances and trends were obtained in this study based on measurements for various cracks. The long-term behavior of cracks should be surveyed and compared with these measurements to investigate trends of convergence with time, caused by convergence of crack spacing.

PURPOSES : The objective of this study was to evaluate the field applicability of chip seals using recycled aggregates by comparing performance between natural aggregates and recycled aggregates for chip seals. METHODS : In order to check the performance of chip seals using recycled and natural aggregates, Bitumen Bond Strength (BBS) test, Vialit and bleeding tests were carried out. Cationic emulsions (RS(C)-2 and latex modified RS(C)-2L) were used in the tests. Granite aggregates were used as the natural aggregate and recycled aggregate from road wastes were used as the recycled aggregate. The aggregate was used with uniform gradation between 10 mm and 4.75 mm to clearly compare the performance difference between natural and recycled aggregates. RESULTS : Test results showed that the aggregate retention was low for both natural and recycled aggregates when applying RS(C)-2 (unmodified emulsion), but there was almost no difference between them when applying RS(C)-2L and RS(C)-2L-1 (modified emulsion) in the Vialit test results. In the bleeding tests, there was no bleeding for both natural and recycled aggregates when applying RS(C)-2 and RS(C)-2L. CONCLUSIONS: It was possible to apply chip seals using recycled aggregates in the field because the chip seals with recycled aggregates and RS(C)-2L (modified emulsion) showed aggregate retention similar to that of natural aggregates, and there was no bleeding.

Recycled aggregate is a solution to reduce construction waste and to be environmentally friendly, but concrete using it has various disadvantages in terms of structure. Therefore, the interaction effect of the two materials can be expected by filling the cyclic aggregate concrete in the CFT column. Eighteen specimens were constructed to confirm the compressive behavior of RCFT (Recylced Concrete Filled Tube) columns, which can be applied to real buildings by making high strength concrete with recycled aggregate. Variable is the shape and thickness of steel pipe, concrete strength and mixing ratio, and coarse aggregate and fine aggregate are all used as recycled aggregate. A total of three recycled aggregate concrete preformulations were used to find the optimal mixing ratio and the compressive behavior was analyzed through the load - displacement curves of RCFT columns.

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of gas energy consumption in an air handling unit. To this end, we consider the volatility of the time series and demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the gas consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the gas consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of “context units” in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the gas consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

Concrete using recycled aggregate instead of natural aggregate reduces environmental waste and is a future oriented material. However, use of the structure is limited to negative recognition of recycled aggregate quality. In this study, 50 MPa concrete was developed using recycled aggregate. In order to verify the possibility of using as a column member, we aimed to confirm the compressive behavior of RCFT (Recycled Concrete Filled Tube) columns filled with concrete using recycled aggregate. Circular type steel pipe was used, and concrete strength (30, 40, 50MPa) and mixing ratio were the experimental parameters. Through 72 specimen compression tests, 50MPa strength of recycled aggregate concrete was confirmed and stable behavior of 9 RCFT columns was confirmed.

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of “context units” in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.