저층 건축물의 횡-비틀림 거동은 고차모드 효과를 증폭시킬 수 있으며, 내진성능평가 시 관련 기준은 고차모드 지배 구조물에 대해 비선형정적해석과 함께 선형동적해석을 추가로 수행하도록 규정하고 있다. 선형동적절차에는 상당한 안전계수가 적용되므로, 이는 과도한 내진보강설계로 이어질 수 있다. 이를 방지하기 위해 엔지니어들은 내진보강 시 고차모드 효과를 줄이기 위해 시행착오법을 사용해 왔다. 그러나 시행착오법에는 많은 시간과 노력이 소요되며, 결정된 보강안이 최적인지 확인하기 어렵다. 본 연구는 저층 건 축물의 수학적 모델을 수립하고 응답스펙트럼해석을 통해 고차모드 효과에 비틀림이 독립적으로 미치는 영향을 파악하였다. 이를 바탕으로 효율적인 내진보강 설계를 위해 활용될 수 있는 도표와 절차를 제시하였다. 제시된 절차를 통해 최소한의 내진보강으로 횡- 비틀림 거동하는 저층 건축물의 고차모드 효과를 효율적으로 감소시킬 수 있음을 확인하였다.

본 논문에서는 딥러닝을 활용하여 복합재 적층판의 파괴 모드를 결정하는 방법을 제안하였다. 수많은 엔지니어링 응용 분야에서 적층 복합재의 사용이 증가함에 따라 무결성과 성능을 보장하는 것이 중요해졌다. 그러나 재료의 이방성으로 인해 복잡하게 나타나 는 파괴모드를 식별하는 것은 도메인 지식이 필요하고, 시간이 많이 드는 작업이다. 따라서 이러한 문제를 해결하기 위해 본 연구에서 는 인공 지능(AI) 기술을 활용하여 적층 복합재의 파괴 모드 분석을 자동화하는 것을 목표로 하였다. 이 목표를 달성하기 위해 적층된 복합재에서 파손된 인장 시험편의 주사 전자 현미경(SEM) 이미지를 얻어 다양한 파괴 모드를 확보하였다. 이러한 SEM 이미지는 섬 유 파손, 섬유 풀아웃, 혼합 모드 파괴, 매트릭스 취성 파손 및 매트릭스 연성 파손과 같은 다양한 파손 모드를 기준으로 분류하였다. 다 음으로 모든 클래스의 집합 데이터를 학습, 테스트, 검증 데이터 세트로 구분하였다. 두 가지 딥 러닝 기반 사전 훈련 모델인 DenseNet 과 GoogleNet을 이용해 각 파괴 모드에 대한 차별적 특징을 학습하도록 훈련하였다. DenseNet 및 GoogleNet 모델은 각각 (94.01% 및 75.49%) 및 (84.55% 및 54.48%)의 훈련 및 테스트 정확도를 보여주었다. 그런 다음 훈련된 딥 러닝 모델은 검증 데이터 세트를 활용해 검증하였다. 더 깊은 아키텍처로 인해 DenseNet 모델이 고품질 특징을 추출하여 84.44% 검증 정확도(GoogleNet 모델보다 36.84% 더 높음)를 얻을 수 있음을 확인하였다. 이는 DenseNet 모델이 높은 정밀도로 파괴 모드를 예측함으로써 적층 복합재의 파손 분석을 수행 하는 데 효과적이라는 것을 알 수 있다.

Many older reinforced concrete (RC) buildings were constructed and designed with only gravity loads in mind. Columns in those buildings have insufficient reinforcement details that do not satisfy the requirements specified in current seismic design standards. This study aims to develop drift-based fragility functions for lightly RC columns. For this purpose, a database of 193 lightly RC columns was constructed to determine central and dispersion values of drift ratios for individual damage states. Additionally, to develop more accurate fragility functions of the columns, the failure mode of RC columns was incorporated into fragility functions. The classification procedure for column failure mode is proposed in this study. Fragility functions for older RC columns are constructed according to four different damage states. The main variables of the fragility functions proposed in this study are column properties and failure mode.

PURPOSES : This study explores the preference of shared autonomous vehicle service in an underground dedicated environment. METHODS : A stated preference survey was conducted to examine the mode choice behaviors on autonomous vehicle service competing with existing modes. Multinomial logit was employed to estimate the parameters of explanatory variables from the surveyed data. The model was estimated with alternative specific parameters rather than generic parameters. The value of time was also estimated using the parameters of the mode choice model. RESULTS : The results showed that the travel cost had the highest sensitivity to public transportation and the lowest to private cars. We also found that the value of the in-vehicle travel time was highest for private cars, lowest for public transportation, and intermediate for SAVs, suggesting that SAVs could serve as a premium public transport option. Additionally, the out-vehicle time coefficient was higher for public transportation compared to that for SAVs, indicating that users are more willing to tolerate longer out-vehicle times for SAVs due to their high-speed service compared to that of public transportation. CONCLUSIONS : This study presents a direction for policy regarding the adoption of shared autonomous vehicle services by considering the attributes that are valued by users of each mode.

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

아날로그TV 방송 시대에는 Mono/Stereo 오디오 방송과 음 성다중방송이 있었고, 디지털HDTV 방송 시대에는 Dolby AC-3 기술에 의한 5.1채널 입체오디오 방송이 추가 서비스로 이루어졌다. 지금의 4K-UHDTV 방송 시대에는 채널기반 오디 오(Channel-Based Audio) 7.1.4채널 입체오디오 방송을 비롯하 여 객체기반 오디오(Object-Based Audio) 기본방송 및 편파방 송과 해설방송 등 유저의 선택과 맞춤형 방송이 가능하고, 장 면기반 오디오(Scene-Based Audio) 몰입형 공간수음효과 3D 입체음향 방송 등이 MPEG-H 기술에 의해 활용될 수 있다. 본 논문은 국내 UHDTV 방송 전송방식(ATSC 3.0)과 함께 Audio 규격으로 규정된 MPEG-H 기술 응용에 대한 실험을 통 해 오디오 서비스 개발의 가능성을 연구하는데 그 목적을 두고 있다. 이러한 연구를 위해 ‘2022 카타르월드컵’ 기간 중 카타르와 SBS 간 전송링크 구간의 국제신호(IS) 현장음 및 SBS 아나운 서와 해설자 음성 전송용 음향채널 오디오를 소스로 사용하였 으며, 객체기반의 기본, 해설, 현장, 집중 4가지 오디오 모드를 구성하여 SBSi 채널을 통해 모바일 스트리밍 전송을 하였다. 그리고 수신은 SBS 모바일 앱에 Decoder를 설치하여 유저의 음향모드 선택과 활용 및 향후 MPEG-H 기술에 의한 다양한 형태의 오디오 서비스 개발의 가능성을 실험하였다.

There is a growing trend towards consumption of meat alternatives. In response to rising demand, many brands add meat alternative options to their offerings (e.g., Impossible Whopper from Burger King, McPlant from McDonald’s). Consistent with this trend, a growing literature explores how to encourage consumers to reduce meat consumption and increase acceptance of meat alternatives. However, there are two major limitations in literature. First, most studies in this area do not employ real behavioral outcomes as the focal dependent variable. As a result, we cannot be certain that such findings can be extrapolated into real-world settings. Second, the majority of studies have focused on cognitive factors in investigating acceptance of meat alternatives. Hence, there is a need to attend more to affective factors when investigating how we can advertise meat alternatives. Against this backdrop, the present research investigates the interactive effect between regulatory mode (a motivational factor) and positive emotion (an affective factor) for driving social media engagement in advertising meat alternatives. Regulatory mode theory posits that consumers engage in goal pursuit by adopting locomotion (a motivation to “just do it”) and assessment orientations (a motivation to “do the right thing”). We propose that different, discrete positive emotions could interact with regulatory mode to create “fit” effects. Specifically, the fit between assessment orientation and the emotion of awe, and between locomotion orientation and the emotion of love will lead to increased social media engagement.

본 연구의 목적은 의미 있는 대상의 선호도 판단에 영향을 미치는 것으로 알려진 원리인 내적 편향 원리와 규범적 관점 간의 상호작용 양상을 연구하는 데에 있다. 개별 개체의 친숙도가 규범적 관점에 영향을 미친다는 점 또한 고려 하여 친숙도 수준에 따른 두 선호도 원리의 작용 양상을 검증하고자 하였다. 이를 위하여 이중 대안 선택 과제를 활용한 행동 반응 실험을 진행하였으며, 친숙도 수준(높음/낮음)에 따라 각 8개의 단일 개체를 실험 재료로 하여 내 적 편향 원리 준수 여부와 규범적 관점 준수 여부를 조작하였다. 실험 결과, 내적 편향 원리를 준수한 상태에서 개체 가 제시된 경우가 내적 편향 원리가 위배된 상태로 개체가 제시된 경우보다 더 선호되었다. 선호 판단에 걸리는 반응 시간 또한 더 짧은 것으로 나타났다. 그러나 규범적 관점 준수 여부는 선택 빈도 및 반응 시간에 영향을 미치지 않는 것으로 나타났다. 더불어, 개체의 친숙도는 선호도 판단과 관련된 의사 결정에 간섭 요인으로 작용하는 것으로 나타났다. 이와 같은 결과는 개체 선호도 원리 중 내적 편향의 강도가 규범적 관점에 비해 더 우세하며, 단일 개체가 제시될 때 그에 대한 선호 판단에 있어서 그 개체가 가진 속성보다 시각적 문맥과의 상호작용이 중요하게 작용한다 는 것을 시사한다.

It is important that the plasma torch used in the waste treatment field has a high output to increase throughput. In order to increase the output of the plasma torch, there is a method of increasing the current or extending the length of the plasma arc. Among these methods, high power can be easily achieved simply by increasing current, but it is difficult to ensure electrode life. Therefore, it is necessary to check the appropriate current and arc length conditions to achieve high power and stable operation. In this paper, the power performance according to the arc length, current, and operation mode was confirmed in the transfer mode plasma torch. The test conditions are the distance (arc length) between the plasma torch and the external electrode was set to 5-180 mm, and the current was set to be in the range of 90-460 A. As a result of the test, it was confirmed that the reverse polarity operation had a maximum output of 159 kW depending on the arc length and current, and the positive polarity operation had a maximum output of 138 kW. Through this result, it was confirmed that the arc length had an effect on increasing the output, and that the reverse polarity operation had a longer arc than the positive polarity operation.

Steel brace strengthening is the most popular seismic rehabilitation method for school buildings. This is because the design can be conducted by using relatively easy nonlinear pushover analysis and standard modeling in codes. An issue with steel brace strengthening is that the reinforced building should behave elastically to satisfy performance objectives. For this, the size of steel braces should be highly increased, which results in excessive strengthening cost by force concentration on existing members and foundations due to the considerable stiffness and strength of the steel braces. The main reason may be the brittle failure mode of columns, so this study investigated the relationship between the efficiency of steel brace strengthening and column failure modes. The result showed that the efficiency is highly dependent on the shear capacity ratio of columns and structural analysis methods. School buildings reinforced by steel braces do not need to behave elastically when the shear capacity ratio is low, and pushover analysis is used, which means reducing steel material is possible.

Discharge limits for nuclear power plant gaseous effluents are presented as dose constraints or on the basis of radioactivity or radioactivity concentration. Accordingly, the operator evaluates the amount of radioactive material discharged from a specific nuclear power plant to the environment and periodically reports them to regulators. Multi-step sampling and analysis and calculation are performed during the radioactivity evaluation process of radioactive effluent, and the uncertainty generated in each step causes the uncertainty of the final radioactivity. Considering that the purpose of evaluating radioactivity discharged from nuclear power plants to the environment is to verify the satisfaction of discharge limits and safety margins, it is necessary to accurately evaluate the discharged radioactivity as much as possible, understanding of the uncertainty contained in the reported value of radioactivity and efforts to reduce it. In this study, modelling of the radioactivity evaluation procedure in gaseous effluent discharged as batch mode from nuclear power plant has performed, a generalized framework was established to evaluate the uncertainty based on ISO/IEC Guide 98-3 (GUM: 1995) involved in the whole process, and the uncertainty contained in the calculated radioactivity of each radionuclide (group) was evaluated and its characteristics. In addition, through probabilistic evaluation, the actual probabilistic distribution and statistical characteristics of radioactive effluent releases reported as a single value were confirmed. As a result, the range of values expected to be included in the confidence level of approximately 95% of the distribution of values for radioactivity in a gaseous effluent discharged as a batch mode from nuclear power plant was calculated. And, the priority of each input parameter turned out to be (1) gaseous waste volume, (2) sample bottle volume, and (3) measured radioactivity of the sample. In addition, the probability distribution of the radioactivity was simulated by Monte Carlo method. As such, the mean, minimum, and maximum values in confidence level of 95% were obtained, and they were reasonably matched the calculated value within 5% deviation. It was shown that radioactivity to the environment, which has been reported as a single value, has a specific probabilistic distribution form.

The high-level nuclear waste (HLW) repository is a 500-1,000 m deep geological disposal system with a very long life expectancy for disposing of high-level waste, which is known to have a half-life of several thousand years. This repository is subject to harsh environmental conditions, such as high temperature and radiation from high-level waste, that can cause deterioration and crack. When radiation escapes through cracks, it can injure persons on the ground. Therefore, it is essential to install a sensor that can detect problems such as cracks. But, since the high-level nuclear waste (HLW) repository is sealed with bentonite and backfill, the sensor cannot be removed or replaced once it has been installed. Therefore, it is necessary to develop a highly durable monitoring sensor that can withstand harsh environmental conditions. Before attempting to improve durability, it is first required to assess durability quantitatively. And an accelerated life test is a widely used method for assessing durability. However, it is important to obtain the same failure mode when conducting a reliability test, such as an accelerated life test. If the accelerated life test is conducted using different failure modes, the dependability of the results is inevitably diminished. Therefore, in this study, a representative failure mode for the piezoelectric sensor used in the accelerated life test was derived through experiments and literature research.