PURPOSES : This paper proposes an artificial neural network (ANN)-based real-time traffic signal time design model using real-time field data available at intersections equipped with smart intersections. The proposed model generates suitable traffic signal timings for the next cycle, which are assumed to be near the optimal values based on a set of counted directional real-time traffic volumes. METHODS : A training dataset of optimal traffic signal timing data was prepared through the CORSIM Optimal Signal Timing program developed for this study to find the best signal timings, minimizing intersection control delays estimated with CORSIM and a heuristic searching method. The proposed traffic signal timing design model was developed using a training dataset and an ANN learning process. To determine the difference between the traditional pre-time model primarily used in practice and the proposed model, a comparison test was conducted with historical data obtained for a month at a specific intersection in Uiwang, Korea. RESULTS : The test results revealed that the proposed method could reduce control delays for most of the day compared to the existing methods, excluding the peak hour periods when control delays were similar. This is because existing methods focus only on peak times in practice. CONCLUSIONS : The results indicate that the proposed method enhances the performance of traffic signal systems because it rapidly provides alternatives for all-day cycle periods. This would also reduce the management cost (repeated field data collection) required to increase the performance to that level. A robust traffic-signal timing design model (e.g., ANN) is required to handle various combinations of directional demands.

PURPOSES : The primary objective of this study is to analyze the relationship between the factors that affect traffic incident duration in the mainline, tunnel, and ramp segments of an expressway. In addition, this study derived the most suitable statistical prediction model based on various incident duration distributions. METHODS : South Korean expressway crash data for 11 years, from 2011 to 2021, were analyzed. The incident durations on the mainline, tunnel, and ramp segments were selected using the accelerated failure time model, which is a parametric survival analysis approach. RESULTS : The mainline segment showed that the incident duration increased during accidents, including guard pipe collisions, multivehicle collisions, and snowfall. In particular, collisions in a tunnel with shoulder facilities increase the incident duration, while decreasing the time in the ramp segment. CONCLUSIONS : The incident duration model for each segment type yielded the most accurate results when applying a log-logistic distribution.

PURPOSES : The objective of this study is to develop the data driven pavement condition index by considering the traffic and climatic characteristics in Incheon city. METHODS : The Incheon pavement condition index (IPCI) was proposed using the weighted sum concept with standardization and coefficient of variation for measured pavement performance data, such as crack rate, rut depth, and International Roughness Index (IRI). A correlation study between the National Highway Pavement Condition Index (NHPCI) and Seoul Pavement Condition Index (SPI) was conducted to validate the accuracy of the IPCI. RESULTS : The equation for determining the IPCI was developed using standardization and the coefficient of variation for the crack rate, rut depth, and IRI collected in the field. It was found from the statistical analysis that the weight factors of the IPCI for the crack rate were twice as high as those for the rut depth and IRI. It was also observed that IPCI had a close correlation with the NHPCI and SPI, albeit with some degree of scattering. This correlation study between the NHPCI and SPI indicates that the existing pavement condition index does not consider the asymmetry of the original measured data. CONCLUSIONS : The proposed pavement condition provides an index value that considers the characteristics of the original raw data measured in the field. The developed pavement condition index is extensively used to determine the timing and method of pavement repair, and to establish pavement maintenance and rehabilitation strategies in Incheon.

Thermoelectric (TE) energy harvesting, which converts available thermal resources into electrical energy, is attracting significant attention, as it facilitates wireless and self-powered electronics. Recently, as demand for portable/wearable electronic devices and sensors increases, organic-inorganic TE films with polymeric matrix are being studied to realize flexible thermoelectric energy harvesters (f-TEHs). Here, we developed flexible organic-inorganic TE films with p-type Bi0.5Sb1.5Te3 powder and polymeric matrices such as poly(3,4-eethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and poly (vinylidene fluoride) (PVDF). The fabricated TE films with a PEDOT:PSS matrix and 1 wt% of multi-walled carbon nanotube (MWCNT) exhibited a power factor value of 3.96 μW ‧ m-1 ‧ K-2 which is about 2.8 times higher than that of PVDF-based TE film. We also fabricated f-TEHs using both types of TE films and investigated the TE output performance. The f-TEH made of PEDOT:PSS-based TE films harvested the maximum load voltage of 3.4 mV, with a load current of 17.4 μA, and output power of 15.7 nW at a temperature difference of 25 K, whereas the f-TEH with PVDF-based TE films generated values of 0.6 mV, 3.3 μA, and 0.54 nW. This study will broaden the fields of the research on methods to improve TE efficiency and the development of flexible organic-inorganic TE films and f-TEH.

췌장낭성병변은 최근 영상기술의 발전으로 우연히 발견되는 비율이 점차 증가하고 있으며, 유병률은 복부컴퓨터단층촬영을 시행한 사람에서 많게는 13.5%까지 보고되었다. 그러나 췌장낭성질환의 정확한 진단은 양성에서 악성 질환까지 다양한 형태로 보일 수 있어 영상학적 진단만으로는 매우 어렵다. 초음파내시경은 췌장낭성병변을 비교적 정확하게 진단하고 치료를 결정하는 데 매우 중요한 도구로 사용된다. 그러나 내시경 초음파는 악성으로 진행 가능한 점액성 췌장낭종과 다른 췌장낭종질환을 구분하는 데 정확도가 65-75%에 불과하다. 인공지능은 대장암, 폐암, 유방암과 같은 여러 종류의 암 진단의 정확도를 향상시키는 데 효과적인 도구로 사용되고 있으며, 최근 연구에서는 췌장낭성병변에서도 점액성 종양과 비점액성 종양을 구분하고 악성으로 진행 위험도를 평가하는 데 도움이 되는 것으로 보고되고 있다. 인공지능의 적용은 영상분석에도 국한되지 않고 최근에는 췌장낭종의 액체 분석, 유전자 분석, confocal laser endomicroscopy 등 다양한 분야에서 활용되고 있으며 기대되는 연구 결과를 발표하고 있다. 인공지능은 의료 분야에서 아직 시작단계에 있어, 임상에 적용하기 위해 적절한 알고리즘을 개발하는 데에는 개발자들의 큰 노력이 필요하다. 그러나 이러한 기술은 앞으로 췌장낭종 병변을 보다 정확하게 진단하고 효과적이고 효율적으로 관리하는 데에 도움을 줄 수 있는 잠재력을 가지고 있다고 하겠다.

본 연구는 자녀와의 상호작용에서 나타나는 부모의 피드백 특성을 에니어그 램 성격유형에 기반하여 살펴보고, 부모 스스로 자신의 피드백 특성을 파악하 고, 바람직한 피드백을 실행할 수 있도록 돕는 자료를 제공할 수 있는 척도를 개발하고 타당화하는 것을 목적으로 한다. 이를 위해 문헌조사 및 워크숍을 실시하여 에니어그램 성격유형별 부모의 피 드백 특성을 추출하고, 전문가 내용타당도 및 문항 분석을 통해 최종 107문항을 선정하였다. 선정된 문항의 신뢰도는 문항 내적 합치도를 통해 산출하였으며, 전체 Cronbach ɑ 계수는 .93, 각 유형별 .56∼.84로 나타났다. 또한, 개발된 척도 의 타당화는 비전에니어그램 성인용 척도(VETI-A)와의 공인타당도를 실행하 였으며, 집단별 교차타당도를 실행하여 검증하였다. 본 연구에서 개발된 에니어그램 성격유형에 기반한 부모의 피드백 척도는 부 모와 자녀의 상호작용에 있어 부모 자신의 피드백 특성을 파악하고, 심층적인 이해를 도모함으로써 긍정적이고 발전적인 피드백을 할 수 있도록 돕는 실질적 인 도구를 개발하고, 신뢰도와 타당도를 검증하였다는 점에서 의미가 있다.

본 연구는 국내 대학교 교육 현장을 중심으로 대학생 대상 중국어 관련 수업에서 활용할 수 있는 온라인 국제협력학습 기반 중국어, 상호문화 교육 프로그램 개발을 목표로 관련 교수-학습모형을 설계하고 제시하고자 한다. 온라인 국제협력학습 기반 중국어 프로그램은 우리나라 중국어 학습자가 중국어를 활용할 수 있는 실제적인 상 황을 제공할 수 있어 원어민 학생들과 유의미한 의사소통이 가능하다. 또한 중국어 학습과 함께 중국 문화를 이해하고 우리 문화를 소개 및 성찰할 수 있는 학습경험을 제공할 수 있다. 이러한 활동이 성공적으로 운영되고 유지되기 위해서는 온라인 매 체 관리, 학습 방안, 매체 활용 단계 및 방안, 학습자와 교사의 역할, 학습자 간 역할 및 상호작용 확대, 학습 전략, 교수 전략 등의 절차가 명확하게 계획되고 구성될 필 요가 있으며, 본 연구가 중국어 교육과 한중 상호문화교육을 설계하고 개발하는데 기초 자료가 되고, 국내 중국어 학습자들의 상호문화역량, 글로벌 역량 등에 긍정적 인 영향을 줄 수 있기를 기대한다.

The cutting process, which is a key processing technology in various industrial fields is achieving continuous growth, and the demand for high-quality cutting surfaces is continuously demanded. Plasma cutting continues to be studied for its excellent workability and productivity, but problems with cutting surface quality such as dross formation occur, so research to secure excellent cutting surface quality through appropriate control of process variables is essential. In this study, we propose a method for predicting surface roughness using real-time current and cutting speed data obtained while performing plasma cutting on A106 B steel pipe. Surface roughness was predicted based on the RBF algorithm applicable to prediction and control models. It was shown that the surface roughness of the plasma cutting surface can be predicted with the arc current waveform and process speed data. This study can be used as a basic study to control the surface roughness of the cut surface in real time.

우리나라는 기상 악화 속에서 감항성이 확보된 선박만이 항해할 수 있도록 선박출항을 해사안전법에 근거하여 통제하고 있으 나 통제 대상선박 지정에 대한 과학적 평가 결과 및 정량적 근거가 미비하여, 항행안전의 확보와 합리적 출항통제 운영을 위한 개선 의견 이 제기되고 있다. 본 연구는 풍랑 주의보 발효 시 주요 통제대상 선종인 예인선의 실선계측을 통해, 현행 출항통제 기준의 적정성을 평 가하고 현실성 있게 개선될 수 있도록 정량적 근거를 제시하는 것에 연구 목적이 있다. 이를 위해 예인선에 선박의 3축 운동과 선체가속 도를 측정하는 Sensor를 설치하여 유의파고 3m인 해역 내에서 운항하여 선체운동 성능을 계측하였고, 계측된 수치를 내항성능 평가요소 및 한계 값 기준을 바탕으로 비교 분석하였다. 실측 선박은 톤수에 따른 현행 통제기준에서 제외되었으나, 분석 결과 Pitch 값이 Operation 기준을 넘어 항행안전에 위험성이 존재하였다. 본 연구 결과는 선박 출항통제 대상에 대한 검토와 다양한 선종 및 제원을 대표할 수 있는 추가적 실측연구가 필요함을 시사한다.

The demand for energy is steadily rising because of rapid population growth and improvements in living standards. Consequently, extensive research is being conducted worldwide to enhance the energy supply. Transpiration power generation technology utilizes the vast availability of water, which encompasses more than 70% of the Earth's surface, offering the unique advantage of minimal temporal and spatial constraints over other forms of power generation. Various principles are involved in water-based energy harvesting. In this study, we focused on explaining the generation of energy through the streaming potential within the generator component. The generator was fabricated using sugar cubes, PDMS, carbon black, CTAB, and DI water. In addition, a straightforward and rapid manufacturing method for the generator was proposed. The PDMS generator developed in this study exhibits high performance with a voltage of 29.6 mV and a current of 8.29 μA and can generate power for over 40h. This study contributes to the future development of generators that can achieve high performance and long-term power generation.

Aluminum alloys are widely utilized in diverse industries, such as automobiles, aerospace, and architecture, owing to their high specific strength and resistance to oxidation. However, to meet the increasing demands of the industry, it is necessary to design new aluminum alloys with excellent properties. Thus, a new method is required to efficiently test additively manufactured aluminum alloys with various compositions within a short period during the alloy design process. In this study, a combinatory approach using a direct energy deposition system for metal 3D printing process with a dual feeder was employed. Two types of aluminum alloy powders, namely Al6061 and Al-12Cu, were utilized for the combinatory test conducted through 3D printing. Twelve types of Al-Si-Cu-Mg alloys were manufactured during this combinatory test, and the relationship between their microstructures and properties was investigated.

This study investigated the feasibility of adopting an automatic scoring system (ASS) in a domestic English-speaking education context. Scope, test items, assessment criteria, scoring methods, and reporting strategies of six overseas English-speaking tests utilizing ASSs were examined. Moreover, a comparative analysis was conducted to identify disparities between ASS-based and non-ASS-based speaking tests. Findings were: 1) some ASS-based tests utilized ASS technology throughout the assessment, while others adopted a hybrid scoring system involving human raters; 2) compared to non-ASS-based tests, ASS-based tests used more test items targeting low-level skills such as sound and forms but fewer test items targeting conversation and discourse level skills; 3) pronunciation, fluency, and vocabulary were widely employed as evaluation criteria with sparse use of organization, content, and task completion in most ASS-based tests; 4) differences were minimal in assessment criteria application and score calculation between ASS-based and non-ASS-based tests; and 5) some ASS-based tests provided criteria-specific results and feedback with total scores and proficiency levels.

본 연구는 가상현실 지질학습장을 개발하여 학교 현장에서 겪는 어려움으로 인해 소홀히 여겨지는 야외 답사를 대체하는 방안을 찾고자 하는 데 목적이 있다. 이를 위해 교육과정 내용 요소와 가상현실 개발 가능성을 검토하여 적 상산 일대를 중심으로 총 5곳의 지질학습장을 선정하였다. 가상현실 지질학습장은 학생들이 주도적으로 가상의 공간에 서 암석과 지질구조를 관찰하고 탐구할 수 있도록 구성하였으며, 구상화강편마암, 응회암, 역암, 사암, 층리, 절리 등 학 습 요소를 포함하였다. 개발한 학습장이 갖는 교육적 가치를 알아보고자 중학교 1학년 학생 7명에게 적용하고 반-구조 화된 면담을 실시하였으며, 학생 반응 결과를 언어 네트워크를 이용하여 단어 간 의미 있는 관계를 분석하였다. 그 결과로 학생들은 가상현실일지라도 현실감을 느낄 수 있었으며, 이는 곧 암석의 특징을 이해하고 기억하는 데 긍정적인 영향을 주고 있었다. 이는 가상현실 야외지질학습 프로그램을 통해 야외에서 이루어지는 현실감을 학생들에게 간접적으 로 경험하게 함으로써 학습에 긍정적인 영향을 줄 수 있음을 시사한다. 본 연구를 바탕으로 무주군이 지닌 지질 학습 자원이 가상현실을 통해 학교 현장에서 유의미한 학습 자료로 활용되기를 기대한다.

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

Small ponds, which exhibit unstable succession pattern of plankton community, are less well studied than large lakes. Recently, the importance of small ponds for local biodiversity conservation has highlighted the necessity of understanding the dynamics of biological community. In the present study, we collected zooplankton from three small reservoirs with monthly basis and analyzed their seasonal dynamics. To understand the complicated zooplankton community dynamics of small reservoirs, we categorized zooplankton species into four groups (LALF Group, Low Abundance Low Frequency; LAHF Group, Low Abundance High Frequency; HALF Group, High Abundance Low Frequency; HAHF Group, High Abundance High Frequency) based on their occurrence pattern (abundance and frequency). We compared the seasonal pattern of each group, and estimated community diversity based on temporal beta diversity contribution of each group. The result revealed that there is a relationship between groups with the same abundance but different occurrence frequencies, and copepod nauplii are common important component for both abundance and frequency. On the other hand, species included with LALF Group throughout the study period are key in terms of monthly succession and diversity. LALF Group includes Anuraeopsis fissa, Hexarthra mira and Lecane luna. However, groups containing species that only occur at certain times of the year and dominate the waterbody, HALF Group, hindered to temporal diversity. The results of this study suggest that the species-specific occurrence pattern is one key trait of species determining its contribution to total annual biodiversity of given community.

하천시설물은 정보의 관리주체가 국가하천과 지방하천 등에 따라 달라서 통합되어 관리되지 못하고 있는 점, 설계 및 시공 단계의 정보화 축적이 미흡하여 시설물 정보의 망실이 우려되는 점 등 하천분야로 BIM 도입을 하기 위해서는 해결해야 할 과제들이 산재해 있다. 또한, 이로 인해 유지운영단계에서의 시설물 정보 활용 수준 역시 상당히 미흡한 편이다. 따라서, 하천시설물의 효과적인 유지 운영을 위해서는 표준분류체계에 따라 시설물 정보를 정리함으로 데이터의 일관성을 확보하고 효율성을 증대시킬 필요가 있다. 본 연구에서는 이러한 하천분야의 시설물 특성을 고려하여 하천분야의 BIM 정보모델 도입과 3차원 기반의 하천시설의 효율적인 유지 운영 전환을 위한 BIM 표준분류체계를 적용하는 방안을 제시한다.

Gate valves are hydraulic components used to shut-off the water flow in water distribution systems. Gate valves may fail owing to various aspects such as leakage through seats, wearing of packing, and corrosion. Because it is considerably challenging to detect valve malfunctioning until the operator identifies a significant fault, failure of the gate valve may lead to a severe accident event associated with water distribution systems. In this study, we proposed a methodology to diagnose the faults of gate valves. To measure the pressure difference across a gate valve, two pressure transducers were installed before and after the gate valve in a pilot-scaled water distribution system. The obtained time-series pressure difference data were analyzed using a machine learning algorithm to diagnose faults. The validation of whether the flow rate of the pipeline can be predicted based on the pressure difference between the upstream and downstream sides of the valve was also performed.

This study was conducted to select target fish species as baseline research for accumulation analysis of major hazardous chemicals entering the aquatic ecosystem in Korea and to analyze the impact on fish community. The test bed was selected from a sewage treatment plant, which could directly confirm the impact of the inflow of harmful chemicals, and the Geum River estuary where harmful chemicals introduced into the water system were concentrated. A multivariable metric model was developed to select target candidate fish species for hazardous chemical analysis. Details consisted of seven metrics: (1) commercially useful metric, (2) top-carnivorous species metric, (3) pollution fish indicator metric, (4) tolerance fish metric, (5) common abundant metric, (6) sampling availability (collectability) metric, and (7) widely distributed fish metric. Based on seven metric models for candidate fish species, eight species were selected as target candidates. The co-occurring dominant fish with target candidates was tolerant (50%), indicating that the highest abundance of tolerant species could be used as a water pollution indicator. A multi-metric fish-based model analysis for aquatic ecosystem health evaluation showed that the ecosystem health was diagnosed as “bad conditions”. Physicochemical water quality variables also influenced fish feeding and tolerance guild in the testbed. Eight water quality parameters appeared high at the T1 site, indicating a large impact of discharging water from the sewage treatment plant. T2 site showed massive algal bloom, with chlorophyll concentration about 15 times higher compared to the reference site.

Purpose: This study aimed to development and test the effects of patient safety/infection control simulation program based on a brain-based learning framework for nursing students. Methods: This pilot study used a one group pre-post test design. The study was conducted in one university in Korea. Participants were recruited using a convenience sample. Fifteen nursing students participated in this study. Results: The levels of perception of importance of patient safety management (Z=3.41, p=.001), confidence on patient safety (Z=3.30, p=.001), attitude toward personal protective equipment (Z=3.10, p=.002), and efficacy of personal protective equipment (Z=3.35, p=.001) were significantly increased. Conclusion: The application of brain-based learning framework in nursing simulation could be an effective education for nursing students.

1,3-다이옥솔란은 용매, 전해질 및 시약으로서 화학, 페인트 및 제약 산업에서 광범위한 관심을 받고 있는 화합물 이다. 1,3-dioxolane은 주로 독성, 발암성, 폭발성, 자동 인화성이 없으며 다기능성을 가지고 있어, 대부분의 유기 및 수성 용 매 조건에서 우수한 용해성을 가져 고분자 전구체로서 사용된다. 최근 몇 년 동안 이 물질은 배가스 및 천연 가스 혼합물에 서 CO2를 분리하기 위한 CO2 선택적 고분자 전구체로서 점점 더 많은 관심을 받고 있다. Poly(1,3-dioxolane) (PDXL)은 폴 리에틸렌 옥사이드(PEO)보다 높은 에테르 산소 함량을 가지고 있으며, 이는 극성 에테르 산소 그룹이 CO2에 대해 강한 친화 력을 나타내기 때문에 우수한 막 CO2/N2 분리 특성을 보인다. 따라서 PDXL 기반 분리막은 비극성(N2, H2 및 CH4) 가스에 대해 탁월한 CO2 용해도 선택성을 보인다. 그러나 PEO와 마찬가지로 PDXL의 극성기는 고분자 사슬의 밀집도를 증가시키고 고분자 결정화를 유발하여 기체 투과도를 감소시켜 이에 대한 개선이 필요하다. 이 논문에서는 기체 분리 응용 분야에서 PDXL기반 고분자막의 최근 발전과 한계에 대해 알아보고자 한다. 또한 CO2 분리 공정에서 1,3-dioxolane 기반 고분자의 한 계를 극복하기 위한 몇 가지 분자 설계방안에 대해 다루어 보기로 한다.