There have been meaningful changes in column stirrup spacing by KDS 41 20 00 in 2022, which is to decrease one of the spacing limits from the minimum section dimension to half of the minimum section dimension. Decreased column stirrup spacing increases the seismic shear resistance of columns and the seismic performance of the entire building. Among the effects of the column stirrup spacing change, this study focused on deformation compatibility in the seismic design of building frame system buildings with ordinary shear walls for seismic design category D. The beams and columns in building frame systems shall satisfy moment and shear strength, or deformation capability induced by seismic design displacement for satisfaction of the deformation compatibility. The commentary of KDS 41 17 00 describes that the deformation compatibility check can be ignored if the members in moment frames are upgraded to intermediate section details. The study showed that the deformation compatibility of columns was satisfied without additional consideration if the building frame systems were designed by the decreased column spacing in KDS 41 20 00. However, beams adjacent to walls needed further consideration, such as the recommendation of commentary in the code.

Volatile fatty acids (VFAs) are designated as offensive odor substances, and they are known for their strong polarity and adsorptive properties, which can lead to significant losses during sample collection and analysis. This study evaluates two analytical methods currently outlined in the odor process test standards, alongside an analytical system utilizing adsorption tubes and another system that uses ion chromatography (IC). Furthermore, suitable analytical methods were proposed for analyzing concentrations below the odor threshold and emission limits. When assessing SPME-GC/FID, SPME-GC/MSD, TD-GC/MSD, and IC based on the internal quality control standards specified in the process test standards, all methods were found to have met these criteria. The absolute injection amounts (1 atm, 25oC) satisfying the emission limits ranged from 3 to 95 ng, while those that met the odor thresholds ranged from 0.2 to 6.5 ng. Based on these criteria, analytical systems suitable for the specified concentration range and odor thresholds were identified. The results are as follows. 1. The analytical systems confirmed to be suitable for quantifying limits were TD-GC/ MS and IC. 2. In terms of recovery and precision, both TD-GC/MSD and IC were found to be suitable. 3. Regarding detection limits, both previously mentioned systems were satisfactory. 4. Finally, concerning quantitation limits, both systems were adequate; however, TD-GC/MSD slightly exceeded the odor threshold analysis range for propionic acid by approximately 1.5 ng. The odor thresholds for the four VFAs were converted to absolute quantities (1 atm, 25oC), confirming that the IC system met the following criteria: (1) calibration range and curve, (2) accuracy and precision, and (3) instrumental detection and quantitation limits.

This study evaluated the field applicability of a real-time odor monitoring system combined with ozone water spraying technology to effectively control odors generated in livestock manure recycling facilities. Research was conducted at a Natural Circulation Agriculture Center located in N City, where concentrations of ammonia (NH3), hydrogen sulfide (H2S), and volatile organic compounds (VOCs) were measured in real time. Based on real-time data, ozone water was sprayed to assess the odor reduction rate, and the impact on surrounding areas was predicted through odor dispersion modeling. The results showed that the ammonia concentration measured at the upper section of the liquid aeration tank before ozone water spraying was 8.02 ppm, exceeding the emission limit of 1 ppm. VOCs were also found to have significantly contributed to odor generation. However, after spraying ozone water at a rate of 7 L/min and maintaining a concentration of 2.5 mg/L, ammonia was reduced by approximately 50%, and VOCs were reduced by about 98%, demonstrating a strong odor-reducing effect. Odor dispersion modeling using the CALPUFF modeling system simulated the range of odor dispersion before and after ozone water spraying. The results indicated that after ozone water spraying, the ammonia concentration at the facility boundary met the emission limit, effectively suppressing odor dispersion. In particular, the ozone water spraying system linked with the real-time sensor enabled automated odor control based on real-time data, demonstrating its potential for resolving odor complaints and ensuring compliance with environmental regulations.

Nitrogen fertilizers are generally known to be of great help in improving crop yields, but excessive nitrogen fertilizer usage can not only destroy the environment but also negatively affect crop growth. This study aims to develop a decision-making system for optimal nitrogen fertilizer use for efficient production of Chinese cabbage (Brassica rapa), one of the major vegetables. The proposed system has the functions of detecting farmland based on satellite images, predicting cabbage yields and greenhouse gas (e.g., nitrous oxide) emissions according to nitrogen fertilizer use, and making decisions using the prediction results. To develop the proposed system, a generalized prediction model is developed using experimental data collected from South Korea, Egypt, India, Canada, Lithuania, and China, and the effectiveness of the proposed system is validated through experiments. As a result, the proposed system will enable farmers to conduct eco-friendly agricultural activities through appropriate nitrogen fertilizer use while stably maximizing productivity of Chinese cabbages.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

본 연구는 2016년 SM엔터테인먼트가 론칭한 다국적 보이그룹 NCT(Neo Culture Technology)의 유닛 시스템이 가진 차별화된 특성이 K-Pop 산 업에 미친 영향과 확산 과정을 분석하였다. 연구 방법은 질적 내용분석을 선택하였고, 로저스의 혁신 확산 이론의 네 가지 핵심 요소(혁신, 커뮤니케 이션 채널, 시간, 사회 시스템)를 분석 프레임워크로 활용하였다. 분석 결 과, NCT 유닛 시스템은 콘텐츠 다양화, 시장 확장성, 리스크 분산, 아티스 트 개발 측면에서 상대적 이점을 가진 혁신으로, SM의 전략적 커뮤니케이 션과 팬 커뮤니티의 정보 공유가 확산에 중요한 역할을 했음을 발견하였 다. 시간적 측면에서는 2016년부터 현재까지 초기 도입기, 확산 성장기, 급속 확산기, 안정화 단계로 이어지는 S자형 확산 곡선이 관찰되었다. 또 한 NCT 유닛 시스템은 K-Pop 산업의 기존 규범에 도전하며 아이돌 그룹 의 정체성 형성, 경력 관리, 글로벌 확장 전략, 인재 개발 방식에 변화를 가져왔다. 본 연구는 NCT 유닛 시스템이 K-Pop 그룹의 지속 가능한 성장 모델, 글로벌 시장 접근 전략, 유연한 인재 관리, 다층적 팬덤 참여, 미디 어 기술 적응, 비즈니스 모델 다각화 측면에서 K-Pop 산업의 미래 발전 방향에 중요한 시사점을 제공함을 확인하였다.

This study proposes an improved method for updating finite element models (FEM) by incorporating the random field characteristics of concrete material properties in reinforced concrete structures. Traditional FEM often assumes homogeneous material properties, which can lead to significant discrepancies between predicted and actual dynamic responses, especially in structures where the Young’s modulus (E) of concrete varies due to factors like curing conditions, material composition, and construction methods. We employed a Gaussian random field model and a system identification (SI) technique to address this limitation to optimize sensor placement. We developed an FEM updating method that incorporates the spatial variability of concrete elasticity. This optimization allowed for a more accurate capture of dynamic properties across various structural locations, resulting in FEM updates that reflect concrete’s inherent heterogeneity. The proposed method was validated through numerical examples, comparing dynamic response accuracy in models before and after updating. Results demonstrated that error values, measured in terms of maximum value error and normalized root mean squared Error (NRMSE), were significantly reduced in the updated models compared to the pre-update model. This approach effectively addresses the limitations of homogeneous assumptions in FEM.

본 연구는 인공지능(AI) 기술의 발전이 음원 제작 시스템에 미치는 영향을 중심으로, 작곡, 편 곡, 믹싱, 마스터링의 핵심 제작 단계에서 인공지능 기술이 어떻게 적용되고 있는지를 체계적으로 분석하였다. 특히 뮤즈넷(MuseNet), 마젠타(Magenta), 수노(SUNO), 아이바(AIVA) 등 대표적인 인 공지능 작곡 도구의 기술 구조와 기능을 시대별로 비교함으로써, 음악 창작의 자동화 수준과 기술 적 한계를 실증적으로 조명하였다. 또한 하이브(HYBE), 에스엠(SM), 와이지(YG) 등 국내 주요 엔 터테인먼트 기업의 인공지능 기술 수용 사례를 통해, 산업 현장에서의 실제 활용 방식과 그로 인 한 제작 및 유통 시스템의 변화 양상을 분석하였다. 연구 결과, 인공지능은 음원 제작의 효율성과 확장성을 획기적으로 높이는 동시에, 콘텐츠 생산 방식과 산업 구조의 재편을 촉진하는 주요 요인 으로 작용하고 있음을 확인하였다. 본 연구는 이러한 변화를 바탕으로 향후 음악 산업이 나아가야 할 기술 통합 전략과 대응 방향에 대해 제언하고자 한다.