세계 각국은 온실가스 배출을 줄이기 위하여 활발한 노력을 하고 있다. 한국도 노후화된 석탄발전을 폐지하고 LNG 발전으로 전환하는 정책을 추진하고 있다. 그러나, 한국은 가스터빈 제작 기술이 국산화되지 않아서 해외 제작사로부터 전량 수입하여 설치하고 있다. 따라서, 본 논고에서는 가스터빈 발전산업에 대한 국내·외 환경을 살펴보고, 이를 기반으로 PEST-SWOT-AHP 방법론을 적용하여 한국형 가스터빈 기술개발 및 보급 확대 추진전략을 제안하였다. 연구결과, 한국형 가스터빈 기술개발과 보급 확대 전략은 1) 가스터빈 기술개발 및 사업화 촉진을 위한 발전공기업의 역할 강화, 2) 온실가스 감축을 위한 LNG 발전 비중 확대, 3) 정부의 적극적인 가스터빈 산업 생태계 조성 노력, 4) LNG 발전소 건설에 대한 국민 공감대 형성 등으로 요약할 수 있다.

본 연구는 대학 창의융합수업에서 Appreciative Inquiry 교수학습방법을 적용한 후 창의융합역량 학습 성과 효과를 분석하는 것을 목적으로 하였다. 이를 위하여 창의융합교육을 위한 창의융합역량 학습성과 평가도구를 개발하고 이를 타당화하였다. 이후, 경기도 소재 G 대학교 경영대학원에서 교육경영 교과목을 수강하는 대학원생 12명을 대상으로 교수학습 방법을 적용한 창의융합교육을 실시하고 창의융합역량 학 습성과 효과를 분석하였다. 연구결과, 팀기반 Appreciative Inquiry 교수학습 방법이 학습자의 창의융합역 량 학습성과 증진에 유의미한 역할을 하였다. 학습자-학습자간, 교수자-학습자간 평가로 실시한 학습성 과 평가에서 Appreciative Inquiry의 4D 활동이 혁신적 사고, 다양한 아이디어, 경험의 결합, 협력적 태도 역량 증진에 효과적인 것으로 분석하였다. 이를 통해 창의융합교육에서 학습자의 적극적인 참여와 협업을 바탕으로 긍정적인 분위기의 아이디어 발산과 공유의 팀기반 교수학습 방법이 창의융합역량 학습성과에 효과가 있다 결론을 도출하였다.

High-Manganese (Mn) austenitic steel, with over 24 wt% Mn content, offers outstanding mechanical properties in cryogenic settings, making it a potential replacement for existing cryogenic materials. This high manganese steel exhibits high strength, ductility, and wear resistance, making it promising for applications like LNG tanks, flanges, and valves. To operate in cryogenic environments, hot forging and heat treatment processes are vital, especially in flange production. The cooling rate during high-temperature cooling after hot forging plays a critical role in influencing the microstructure and mechanical properties of high manganese steel. The rate at which cooling occurs during this process influences the size of the grains and the distribution of manganese and consequently has an impact on mechanical properties. This study assessed the microstructure and mechanical properties based on different cooling rates during the hot forging of High-Mn steel flanges. Comparing air and water cooling after hot forging, followed by heat treatment, revealed notable differences in grain size. These differences directly impacted mechanical properties such as tensile strength, hardness, and Charpy impact property. Understanding these effects is crucial for optimizing the performance and reliability of High-Mn steel in cryogenic applications.

Safe operation of freight vehicles is an important issue for drivers, cargo, and other road users. In particular, the center of gravity of a freight vehicle is directly related to the stability of the vehicle, and this can fluctuate in real time depending on weight changes. Every time a freight vehicle loads or unloads cargo, its center of gravity changes, and these changes greatly affect the risk of vehicle rollover. We researched a continuous center of gravity measurement system for freight vehicles for safe driving.

This study investigates the utilization of existing CCTV networks for road traffic volume measurement, a key indicator of road infrastructure utility. Traditionally, traffic studies, which are costly and time-consuming, are divided into continuous or ad-hoc surveys. By leveraging current CCTV systems, the proposed method eliminates the need for new installations, conserving resources and increasing efficiency. Preliminary results indicate that this approach offers a time and cost-effective alternative for traffic assessments, with the potential to transform traditional survey techniques.

In contemporary society, traffic concerns and road safety are of paramount importance. Overloading and improper loading significantly jeopardize road safety. Leveraging AI Hub's CCTV traffic videos, overloading hazard data, and Yeosu Sunsin Bridge HS-WIM data from Jeollanam-do, we've crafted detection models. These models facilitate the development of a vehicle detection system calibrated for specific scenarios, making the detection of overloading and loading discrepancies more accessible. Such a system promises to streamline detection operations while substituting manual efforts with AI, thereby economizing on both time and resources.

In the case of the companies selected for noise measurement in this study, even though noise exposure in the workplace begins with noise management for large machines or high-output machines, they only manage to write reports on how much workers are exposed to noise for 8 hours a day. is focused on. When large machines are delivered, little information on noise received from the manufacturer is kept, and noise information indicated on design drawings is also difficult to find. History management of noise information of large machines is necessary. There is also a problem with the professionalism of managers who manage noise information. Like odors and fine dust, it is judged that noise needs to be managed by a professional manager at the workplace and managed through a database of noise information.

전 세계 선박 통행량의 증가에 따른 선박 충돌 사고의 증가는 큰 경제적, 환경적, 물리적 및 인간적 손해를 가져왔다. 선박 사 고의 원인은 선원의 판단 오류나 부주의, 항로의 복잡성, 기상 조건, 선박의 기술적 결함 등 다양한 요인이 겹쳐 작용하여 사고를 유발하 기 때문에 문장의 깊은 의미와 문맥 정보를 고려할 수 있는 방법론이 필요하다. 따라서, 본 연구는 부산해심 지역에서의 최근 20년 동안 의 선박 충돌사고 데이터를 포함하고 있는 해양안전심판 재결서를 SentenceBERT 모델을 활용해 분석하였다. 분석 결과 사고의 주요 원인 이 될 수 있는 키워드가 도출되었으며, 특정 키워드 출현 빈도를 바탕으로 군집 분석을 시행하고 시각화하였다. 추후 사고의 원인을 미리 파악함으로써, 이를 통해 선박 충돌 사고의 예방 및 사고 대응 전략 개발의 기초 자료로써 활용하고자 한다.

본 연구는 Q방법론을 적용하여 사회복지학에서의 융합교육의 전망에 관한 실증적 연구이다. 심층면접과 문헌연구를 통해 Q표본 38개를 선정 하고. 사회복지학 전공교수, 현장전문가, 융합연구경험이 있는 전문가 등 23명을 대상으로 Q분류를 실시하였다. QUANL 프로그램으로 자료를 분 석하여 3개 요인으이 추출되었고, 전체 변량의 49.24%를 설명한다. 3개 요인은 융합교육이 추구하는 인재상, 교육에서의 강조점, 시기성을 핵심 요인으로 하여 총 6개 유형으로 세분화하여 해석하였다. 구체적으로 인 재상(요인1)에 따라 디지로그형과 학습자주도형, 교육에서의 초점(요인2) 에 따라 핵심역량강조형과 교육방안강조형, 시기성(요인3)에 따라 융합교 육보류형과 융합교육현재실행형으로 구분할 수 있다. 각 유형의 특성을 분석하여 사회복지학에서의 ICT 융합교육에 대한 인식유형을 밝혔으며, 연구결과를 기반으로 함의를 제시하였다.

본 연구는 국립공원 재계획중 타당성조사 기준과 방법의 적절성 분석을 목적으로 하였다. 가야산국립공원, 설악산과 주왕산국립공원은 절대평가(제2차) 방법이 상대평가(제3차) 보다 ‘해제 대상’이 낮은 비율로 도출되었다. 제3차 평가방 법이 해제가능지역 10%라는 비율을 정해놓고 해제보다는 존치를 통한 공원면적 유지를 지향한 방법임에도 절대평가 방법이 결코 해제가능 지역을 더 많이 추출하지는 않았다. 연구대상지에 제2차와 제3차 생태기반평가를 적용했을 때 2011년 실제 해제지역이 나타나는지를 분석한 결과 제2차, 제3차 생태기반평가 모두 해제지역을 반영하지 못했다. 생태기반평가는 해제지역을 결정하는 중요한 의사결정 수단이 아닌 보조수단일 뿐이었다. 공원계획 타당성조사의 구역조정 중 해제는 외적 요소인 상호교환, 해제기준의 우선적용 등이 더욱 중요하게 작용함을 알 수 있었다.

In this study, Ni-Y2O3 powder was prepared by alloying recomposition oxidation sintering (AROS), solution combustion synthesis (SCS), and conventional mechanical alloying (MA). The microstructure and mechanical properties of the alloys were investigated by spark plasma sintering (SPS). Among the Ni-Y2O3 powders synthesized by the three methods, the AROS powder had approximately 5 nm of Y2O3 crystals uniformly distributed within the Ni particles, whereas the SCS powder contained a mixture of Ni and Y2O3 nanoparticles, and the MA powder formed small Y2O3 crystals on the surface of large Ni particles by milling the mixture of Ni and Y2O3. The average grain size of Y2O3 in the sintered alloys was approximately 15 nm, with the AROS sinter having the smallest, followed by the SCS sinter at 18 nm, and the MA sinter at 22 nm. The yield strength (YS) of the SCS- and MA-sintered alloys were 1511 and 1688 MPa, respectively, which are lower than the YS value of 1697 MPa for the AROS-sintered alloys. The AROS alloy exhibited improved strength compared to the alloys fabricated by SCS and conventional MA methods, primarily because of the increased strengthening from the finer Y2O3 particles and Ni grains.

Organoleptic parameters such as color, odor, and flavor influence consumer perception of drinking water quality. This study aims to evaluate the taste of the selected bottled and tap water samples using an electronic tongue (E-tongue) instead of a sensory test. Bottled and tap water's mineral components are related to the overall preference for water taste. Contrary to the sensory test, the potentiometric E-tongue method presented in this study distinguishes taste by measuring the mineral components in water, and the data obtained can be statistically analyzed. Eleven bottled water products from various brands and one tap water from I city in Korea were evaluated. The E-tongue data were statistically analyzed using multivariate statistical tools such as hierarchical clustering analysis (HCA), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results show that the E-tongue method can clearly distinguish taste discrimination in drinking water differing in water quality based on the ion-related water quality parameters. The water quality parameters that affect taste discrimination were found to be total dissolved solids (TDS), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), sulfate (SO4 2-), chloride (Cl-), potassium (K+) and pH. The distance calculation of HCA was used to quantify the differences between 12 different types of drinking water. The proposed E-tongue method is a practical tool to quantitatively evaluate the differences between samples in water quality items related to the ionic components. It can be helpful in quality control of drinking water.

This study presents a novel method for addressing the issue of high-concentration contaminants (ammonium, phosphate, antibiotics) in leachate arising from decomposing livestock carcasses. Antibiotics, developed to eliminate microorganisms, often have low biodegradability and can persist in the ecosystem. This research proposes design elements to prevent contamination spread from carcass burial sites. The adsorbents used were low-grade charcoal (an industrial by-product), Alum-based Adsorbent (ABA), and Zeolite, a natural substance. These effectively removed the main leachate contaminants: low-grade charcoal for antibiotics (initial concentration 1.05 mg/L, removal rate 73.4%), ABA for phosphate (initial concentration 2.53 mg/L, removal rate 99.9%), and zeolite for ammonium (initial concentration 38.92 mg/L, removal rate 100.0%). The optimal mix ratio for purifying leachate is 1:1:10 of low-grade charcoal, ABA, and zeolite. The average adsorbent usage per burial site was 1,800 kg, costing KRW 2,000,000 per ton. The cost for the minimum leachate volume (about 12.4 m3) per site is KRW 2,880,000, and for the maximum volume (about 19.7 m3) is KRW 4,620,000. These findings contribute to resolving issues related to livestock carcass burial sites and suggest post-management strategies by advocating for the effective use of adsorbents in leachate purification.

Currently, there is no definitive regulation for the appropriate frequency of data sampling in water distribution networks, yet it plays a crucial role in the efficient operation of these systems. This study proposes a new methodology for determining the optimal frequency of data acquisition in water distribution networks. Based on the decomposition of signals using harmonic series, this methodology has been validated using actual data from water distribution networks. By analyzing 12 types of data collected from two points, it was demonstrated that utilizing the factors and cumulative periodograms of harmonic series enables similar accuracy at lower data acquisition frequencies compared to the original signals.

Although many attempts have been made to solve the atmospheric diffusion equation, there are many limits that prevent both solving it and its application. The causes of these impediments are primarily due to both the partial differentiation term and the turbulence diffusion coefficient. In consideration of this dilemma, this study aims to discuss the methodology and cases of utilizing a passive air sampler to increase the applicability of atmospheric dispersion modeling. Passive air samplers do not require pumps or electric power, allowing us to achieve a high resolution of spatial distribution data at a low cost and with minimal effort. They are also used to validate and calibrate the results of dispersion modeling. Currently, passive air samplers are able to measure air pollutants, including SO2, NO2, O3, dust, asbestos, heavy metals, indoor HCHO, and CO2. Additionally, they can measure odorous substances such as NH3, H2S, and VOCs. In this paper, many cases for application were introduced for several purposes, such as classifying the VOCs’ emission characteristics, surveying spatial distribution, identifying sources of airborne or odorous pollutants, and so on. In conclusion, the validation and calibration cases for modeling results were discussed, which will be very beneficial for increasing the accuracy and reliability of modeling results.

최근 소셜미디어 이용이 심리적 웰빙에 미치는 영향이 부각되고 있으나 어떤 요소가 소셜미디어 상에서의 관계 의 질을 예측할 수 있는지에 대한 연구는 상대적으로 드물다. 본 연구는 머신러닝 기법을 이용하여 COVID-19로 인한 자가격리 동안 인스타그램 활동과 외로움, 우울 등의 심리 상태가 소셜미디어 상에서의 관계의 질을 예측할 수 있는지 알아보고자 하였다. 성인 95명을 대상으로 자가격리 중과 자가격리 해제 후 시점에서 외로움, 인스타그 램 활동, 소셜미디어 상에서의 관계, 우울 등에 대해 자기보고식 설문에 응답하도록 하였다. 그 후, 다차원 척도법과 표상유사성분석, 분류분석을 각 시점에 대해 수행하였다. 다차원척도법 결과, 1차원에서 인스타그램 이용 시간과 우울이 다른 변인들과 구별되었으며, 2차원에서 외로움과 수동적 이용이 다른 변인들과 구별되었다. 그 후 소셜미 디어 상에서의 관계의 질의 고,저 집단에 대해 표상유사성분석을 실시한 결과, 소셜미디어 상에서의 관계의 질이 높은 집단은 낮은 집단보다 자가격리의 영향을 더 많이 받는 것으로 나타났다. 분류분석 결과에서도 소셜미디어 상에서의 관계의 질 예측 변인이 사회적 고립의 여부에 따라 달라지는 것으로 나타났다. 따라서 본 연구의 결과는 사람들이 사회적 고립 상황에 있지 않을 때 인스타그램 이용 변인과 심리적 변인이 소셜미디어 상에서의 관계를 더 잘 예측할 수 있음을 시사한다.

PURPOSES : This study is to develop an comprehensive validation methodology for autonomous mobility-on-demand system with level 4 automated driving system. METHODS : The proposed method includes the quantitative techniques for validating both automated driving system and center system using each optimal indicators. In addition, a novel method for validating the whole system applying multi-criteria decision methodology is suggested. RESULTS : The relative weights for the vehicle system was higher than the center systems. Moreover, the relative weights of failure rate for validating the vehicle system was the highest, in addition to, a relative weight for accuracy of dynamic routing algorithm within center system was the highest. CONCLUSIONS : The proposed methodology will be applicable to validate the autonomous mobility on demand system quantitatively considering the relative weights for each systems.

Water utilities are making various efforts to reduce water losses from water networks, and an essential part of them is to recognize the moment when a pipe burst occurs during operation quickly. Several physics-based methods and data-driven analysis are applied using real-time flow and pressure data measured through a SCADA system or smart meters, and methodologies based on machining learning are currently widely studied. Water utilities should apply various approaches together to increase pipe burst detection. The most intuitive and explainable water balance method and its procedure were presented in this study, and the applicability and detection performance were evaluated by applying this approach to water supply pipelines. Based on these results, water utilities can establish a mass balance-based pipe burst detection system, give a guideline for installing new flow meters, and set the detection parameters with expected performance. The performance of the water balance analysis method is affected by the water network operation conditions, the characteristics of the installed flow meter, and event data, so there is a limit to the general use of the results in all sites. Therefore, water utilities should accumulate experience by applying the water balance method in more fields.

The levels of Indoor radon can increase in newly built apartments that mainly use construction materials such as concrete. However, radon measurement in newly built buildings has only been implemented recently, and there is a lack of sufficient data for radon management purposes. This study aimed to determine the levels of indoor radon in newly built apartments by regions, sizes of household, and floor (lower, middle, upper floor), and to explore the trend of radon reduction by time according to ventilation methods. Indoor radon was measured in 48 households in four regions (Gyeonggi-do and Gangwon-do). Measurements were performed for 48 hours and 24 hours under closed and ventilated conditions using real-time measurement devices FRD400 and RAD7, respectively. To analyze changes in radon concentration by floors, data were collected by dividing households into lower, middle, and upper floors according to their vertical height. The concentrations of indoor radon ranged from 12.1 Bq/m3 to 559.0 Bq/m3 when windows were closed and 12.0 Bq/m3~500.0 Bq/m3 when ventilated (including both mechanical and natural ventilation). On average, the concentration of closed conditions of household when sealed (arithmetic average 185.3 Bq/m3) was 1.5 times higher than the concentration during ventilation (including mechanical and natural ventilation: 123.8 Bq/m3), and around three times higher than the average for mechanical ventilation (65.7 Bq/m3). It was found that the indoor radon concentration statistically increased as the height increased and the size of the house became smaller. Typically, it took 7 to 8 hours to exceed the standard of 148 Bq/m3 after windows were closed. It decreased to less than 148 Bq/m3 within 3 to 4 hours after operating the mechanical ventilation system. However, in the case of natural ventilation, it takes more than 24 hours to reach below the standard value, and the concentrations stagnated after 24 hours. In conclusion, radon concentration in narrow spaces can be relatively high if ventilation is not sufficient due to the nature of apartments with wall-type structures. Therefore, it is necessary to prepare various radon reduction measures according to floor, size, and ventilation methods.

Particulate matter is known to have adverse effects on health, making it crucial to accurately gauge its concentration levels. While the recent advent of low-cost air sensors has enabled real-time measurement of particulate matter, discrepancies in concentrations can arise depending on the sensor used, the measuring environment, and the manufacturer. In light of this, we aimed to propose a method to calibrate measurements between low-cost air sensor devices. In our study, we introduced decision tree techniques, commonly used in machine learning for classification and regression problems, to categorize particulate matter concentration intervals. For each interval, both univariate and multivariate multiple linear regression analyses were conducted to derive calibration equations. The concentrations of PM10 and PM2.5 measured indoors and outdoors with two types of LCS equipment and the GRIMM 11-A device were compared and analyzed, confirming the necessity for distinguishing between indoor and outdoor spaces and categorizing concentration intervals. Furthermore, the decision tree calibration method showed greater accuracy than traditional methods. On the other hand, during univariate regression analysis, the proportion exceeding a PM2.5/PM10 ratio of 1 was significantly high. However, using multivariate regression analysis, the exceedance rate decreased to 79.1% for IAQ-C7 and 89.3% for PMM-130, demonstrating that calibration through multivariate regression analysis considering both PM10 and PM2.5 is more effective. The results of this study are expected to contribute to the accurate calibration of particulate matter measurements and have showcased the potential for scientifically and rationally calibrating data using machine learning.