신뢰성 있는 토양의 이산요소모델을 개발하기 위해서는 토양의 특성을 고려하여 매개변수를 교정해야 한다. 본 연구에서는 이산요소모델을 구성하는 각 매개변수가 토양 입자의 거동에 미치는 영향을 분석하였고, 분석된 결과를 이용하여 토양의 이산요소모델을 개발하였다. 민감도 분석의 대상이 되는 매개변수는 전단 계수, 마찰 계수, 표면 에너지 등으로 선정하였으며, 교정의 기준이 되는 토양의 특성은 가비중, 안식각, 점착력 및 내부마찰각으로 선정하였다. 또한, 토성이 서로 다른 해안가, 논 및 밭을 구성하는 토양을 대상으로 연구를 수행하여 다양한 토성에 대한 적용성을 확인하였다. 결과적으로 본 연구에서 수행한 민감도 분석 결과를 이용하여 각 토양의 거동을 모사할 수 있는 이산요소모델을 교정하였으며, 시험 결과와의 비교를 통해 교정된 이산요소모델을 검증하였다.

빠르게 발전하는 이미지 인식 기술에도 불구하고 표 형식의 문서와 수기로 작성된 문서를 완벽하게 디지털화하기에는 아직 어려움이 따른다. 본 연구는 표 형식의 수기 문서인 선박 항해일지를 작성하는 데에 사용되는 규칙을 이용하여 보정 작업을 수행함으로 써 OCR 결과물의 정확도를 향상시키고자 한다. 이를 통해 OCR 프로그램을 통하여 추출된 항해일지 데이터의 정확성과 신뢰성을 높일 것 으로 기대된다. 본 연구는 목포해양대학교 실습선 새누리호의 2023년에 항해한 57일간의 항해일지 데이터를 대상으로 OCR 프로그램 인 식 후 발생한 오류를 보정하여 그 정확도를 개선하고자 하였다. 이 모델은 항해일지 기재 시 고려되는 몇 가지 규칙을 활용하여 오류를 식별한 후, 식별된 오류를 보정하는 방식으로 구성하였다. 모델을 활용하여 오류를 보정 후, 그 효과를 평가하고자 보정 전과 후의 데이터 를 항차별로 구분한 후, 같은 항차의 같은 변수끼리 비교하였다. 본 모델을 활용하여 실제 셀 오류율은 약 11.8% 중 약 10.6%의 오류를 식 별하였고, 123개의 오류 중 56개를 개선하였다. 본 연구는 항해일지 중 항해정보를 기입하는 Dist.Run부터 Stand Course까지의 정보만을 대 상으로 수행하였다는 한계점이 있으므로, 추후 항해정보 뿐만 아니라 기상정보 등 항해일지의 더 많은 정보를 보정하기 위한 연구를 진 행할 예정이다.

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

현대 해양 산업은 기술적 발전을 통해 신속한 발전을 이루고 있다. 이러한 발전을 주도하는 주요 기술 중 하나는 데이터 처리 기술이며, 이 중 자연어 처리 기법은 사람의 언어를 기계가 이해하고 처리할 수 있도록 하는 기술이다. 본 연구는 자연어 처리 기법을 통해 해양안전심판원의 재결서를 분석하여 이미 재결이 이루어진 선박 충돌사고의 원인 제공 비율을 학습한 후, 새로운 재결서를 입력 하면 원인 제공 비율을 예측하는 모델을 개발하고자 하였다. 이 모델은 사고 당시 적용되는 항법과 원인 제공 비율에 영향을 주는 핵심 키워드의 가중치를 이용하여 사고의 원인 제공 비율을 계산하는 방식으로 구성하였다. 이 연구는 이러한 방식을 통해 제작한 모델의 정 확도를 분석하고, 모델의 실무 적용 가능성을 검토함과 동시에 충돌사고 재발 방지 및 해양사고 당사자들의 분쟁 해결에 기여할 것으로 기대한다.

생태계서비스란 자연생태계가 인간에게 주는 혜택을 통틀어 일컫는 용어이다. 생태계서비스를 정량화하기 위하여 다양한 모형이 개발되어 적용되고 있다. InVEST는 대표적인 생태계서비스 모형이며 그 중 서식지질 평가가 폭넓게 사용되고 있다. 우리나라에서는 국립공원을 대상으로 서식지질 평가가 실시되고 있다. 서식지질 평가를 위해서는 서식 지질 초기값으로 위협인자에 대한 민감도 평가가 이뤄져야 하는데, 이는 국가 및 적용분야에 따라 판이하다. 그래서 그동안 진행된 국립공원 서식지질 평가를 바탕으로 전문가 설문(AHP)을 실시하여 서식지질 초기값인 민감도의 기준을 조정하였다. AHP실시 결과 자연초지, 경지정리가 안된 밭 등 10개 항목이 상향조정되었으며, 하천, 호소 등 8개 항목이 하향조정되어 총 18개 항목이 조정되었다. 조정된 민감도 결과를 바탕으로 도시형인 북한산국립공원과 계룡산국립공 원, 사적형인 경주국립공원, 해상해안형인 한려해상국립공원 그리고 산악형인 지리산국립공원과 설악산국립공원을 대상지 서식지질 분석을 실시하였다. 분석결과 국립공원내 분포하는 시가화건조지역과 수역에 대한 부분이 서식지질 평가에 반영되었음을 알 수 있었다. 향후 이 기준을 이용하여 자연공원의 서식지질 평가가 가능할 것이다.

본 연구의 목적은 알츠하이머질환(Alzheimer’s disease: AD) 동물 모델을 대상으로 트레드밀 운 동(Treadmill exercise: TE)과 환경강화(environmental enrichment: EE) 처치가 인지기능, 근 기능, 및 밀 착연접 단백질 발현에 미치는 영향을 확인하는데 있다. AD 동물 모델을 제작하기 위해 aluminum chloride(AlCl3)를 90일간(40mg/kg/하루) 투여 하였으며 동시에 TE(10-12m/min, 40-60min/day) 혹은 EE에 노출시켰다. 그 결과 AlCl3 투여에 의한 인지기능 저하와 근 기능 감소가 TE와 EE에 의해 완화된 것 으로 나타났다. 또한, TE와 EE는 AD 질환에서 나타나는 β-amyloid(Aβ), alpha-synuclein 및 tumor necrosis factor-α(TNF-α) 단백질의 발현 증가를 감소시킨 것으로 나타났다. 게다가 TE와 EE는 AlCl3 투여에 의해 감소된 밀착연접 단백질(Occludin, Claudin-5 및 ZO-1)의 발현을 통계적으로 유의하게 증가시킨 것으로 나타났다. 마지막으로 Aβ 단백질과 밀착연접 단백질과의 상관분석을 실시한 결과 부적 상 관관계(Occludin: r=-0.853, p=0.001; Claudin-5 : r=-0.352, p=0.915; ZO-1 : r=-0.424, p=0.0390) 로 나타났다. 따라서 이를 종합해 보면 TE 혹은 EE 처치는 AD에 나타나는 병리학적 특징들을 일부 완화 시켜 인지기능과 근 기능을 일부 개선 시킬 수 있는 효과적인 운동 방법이라고 생각된다.

PURPOSES : To enhance the accuracy of predicting the compressive strength of practical concrete mixtures, this study aimed to develop a machine learning model by utilizing the most commonly employed curing age, specifically, the 28-day curing period. The training dataset consisted of concrete mixture sample data at this curing age, along with samples subjected to a total load not exceeding 2,350 kg. The objective was to train a machine learning model to create a more practical predictive model suitable for real-world applications. METHODS : Three machine learning models—random forest, gradient boosting, and AdaBoost—were selected. Subsequently, the prepared dataset was used to train the selected models. Model 1 was trained using concrete sample data from the 28th curing day, followed by a comprehensive analysis of the results. For Model 2, training was conducted using data from the 28th day of curing, focusing specifically on instances where the total load was 2,350 kg or less. The results were systematically analyzed to determine the most suitable machine learning model for predicting the compressive strength of concrete. RESULTS : The machine learning model trained on concrete sample data from the 28th day of curing with a total weight of 2,350 kg or less exhibited higher accuracy than the model trained on weight-unrestricted data from the 28th day of curing. The models were evaluated in terms of accuracy, with the gradient boosting, AdaBoost, and random forest models demonstrating high accuracy, in that order. CONCLUSIONS : Machine learning models trained using concrete mix data based on practical and real-world scenarios demonstrated a higher accuracy than models trained on impractical concrete mix data. This case illustrates the significance of not only the quantity but also the quality of the data during the machine learning training process. Excluding outliers from the data appears to result in better accuracy for machine learning models. This underscores the importance of using high-quality and practical mixed concrete data for reliable and accurate model training.

PURPOSES : In this study, an optimal model for compressive strength prediction was derived by learning and directly comparing several machine learning models based on the same data. METHODS : Approximately 478 pieces of concrete compressive strength data were obtained to compare the performance of the machine learning models. In addition, five machine learning models were trained based on the obtained data. The performance of the learned model was compared using three performance indicators. Finally, the performance of the model trained using additional data was reviewed. RESULTS : As a result of comparing the performance of machine learning models, the XGB(eXtra Gradient Boost) model showed the best performance. In addition, as a result of the verification based on additional data, highly reliable results can be obtained if the XGB model is used to predict the compressive strength of concrete. CONCLUSIONS : If a concrete strength prediction model is derived based on a machine learning model, a highly reliable model can be derived.

PURPOSES : In this study, the applicability of the water content, suction, and suction stress in a resilient modulus prediction model for a subbase was reviewed. METHODS : To compare the applicability of water content, suction, and suction stress models for resilient modulus prediction, the suction stress was determined based on the soil water characteristic curve. The model parameters for each approach were derived from the measured resilient moduli. Finally, the relationships between the degree of saturation and resilient modulus were analyzed using the calculated model parameters. RESULTS : Prediction models of the resilient modulus based on water content and suction demonstrated high correlation with measured values, but overestimated the resilient modulus at saturation levels beyond the laboratory testing range. In contrast, the model accounting for suction stress effectively reduced this overestimation, likely owing to a decrease in suction stress as the suction increased. CONCLUSIONS : Based on the above results, the resilient modulus of subbase materials could be estimated through the change in the degree of saturation and the stress-dependent resilient modulus model using the suction stress proposed in this study.

PURPOSES : In this study, an empirical approach was established to estimate the parameters of the resilient modulus based on various geotechnical properties of subgrade soils. METHODS : Multiple regression analyses were performed to analyze the relationship between resilient modulus (k1) and deformation. The most important factors are the #200 sieve passing ratio, moisture content, and dry unit weight of the soil. The applicability of this approach was verified using selected field data and the literature. RESULTS : The correlation between the results predicted using the prediction equation of the model constant (k1) and the actual k1-value was high. The applicability of the prediction equation was considered high owing to its high suitability with the existing data. The range of values obtained using the constant prediction equation of the proposed model was also judged to be reasonable. In the comparison of the CBR value of the subgrade material of the actual design section and the predicted elastic modulus (k1), almost no relationship was observed between the CBR and the model coefficient (k1). Thus, the estimation of the elastic modulus through CBR is likely to contain errors. CONCLUSIONS : Based on these results, the parameters of the universal model can be predicted using the stress-dependent modulus model proposed in this study.

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

본 연구는 선교사의 심리적 탈진 예방을 위한 새로운 접근 방식인 ‘자기성찰 디브리핑(Debriefing) 모델’의 제안에 초점을 두었다. 기존 의 디브리핑 모델의 외상 경험 이후의 정서 안정화에 초점을 둔 것과 달리 문제 상황 이전에 적응적 대처 전략을 세울 수 있는 예방 모델이다. 선교사의 과거 경험과 현재의 갈등 상황 간 상호 연관성을 강조하며 “미해결 정서”를 인지 및 감각적으로 이해하고 부적응적인 스키마와 대처 전략에 대한 교육을 통해 심리적 탈진을 예방하고 인지적인 변화를 촉진하는 것에 중점을 두고 있다. 이 모델은 기존 디브리핑 모델의 장점을 토대로 심리도식치료와 신경언어프로그래밍(NLP) 이론을 접 목하여 7개의 단계로 구성되었다. 각 단계는 안전지대 형성 단계, 현재 갈등 상황 파악 단계, 과거 미해결 정서 파악 단계, 해석과 통찰 단계, 교육과 인지전략 단계, 긍정적 자원 강화 단계, 그리고 전망 단계로 구성되며, 각각의 단계에서 활용 가능한 접근 방식을 제시한다. 본 연구는 선교사의 심리적 탈진 예방과 이 분야의 디브리핑 실천을 발전시키는 데 기여할 수 있다.

이 글은 한국어 학습을 위한 웹 기반 한자 사전 모델을 제시하기 위한 것이다. 중국 문화권 학습자를 대상으로 한 이 연구에서는 웹 기반 한자 사전의 필요성을 확인하고 기존 사전의 한자 단어 학습의 문제점 을 분석하였다. 한자 단어 학습 시 한자 원어 형태소 인식이 중요하다고 보고 이를 반영한 별도의 팝오버 (popover) 창을 개발하였다. 한자 사전의 미시구조에는 항목, 단어 유형, 발음, 한-베트남어 단어(있는 경우), 한자의 어원, 한-베 소리(voice)가 포함되며, 이밖에 베트남어 어원에 대한 설명, 한자의 원래 형태소 와 관련된 한-베 단어, 번역, 설명, 예시 등이 포함되어야 함을 밝혔다. 그런 다음, 한자어 형태소와 한자- 베트남어의 발음을 포함하는 어원 틀, 베트남어 및 한자어의 한자 형태소 의미를 포함한 웹 사전을 구성하 고 그 활용 방안을 보여주었다. 본 연구는 한자어 형태소와 관련된 베트남 단어에 대한 정보를 추가하고, 프로그래밍 기술, 데이터베이스 업데이트 과정, 300개 규모의 한자 사전 모델을 제시한 기초연구로써 이후 한국어 학습자를 위한 웹 한자 사전 구축에 보탬이 되고자 한다.

The conventional multi-scale modelling approach that predicts carbon nanotube (CNT) growth region in heterogeneous flame environment is computationally exhaustive. Thus, the present study is the first attempt to develop a zero-dimensional model based on existing multi-scale model where mixture fraction z and the stoichiometric mixture fraction zst are employed to correlate burner operating conditions and CNT growth region for diffusion flames. Baseline flame models for inverse and normal diffusion flames are first established with satisfactory validation of the flame temperature and growth region prediction at various operating conditions. Prior to developing the correlation, investigation on the effects of zst on CNT growth region is carried out for 17 flame conditions with zst of 0.05 to 0.31. The developed correlation indicates linear ( zlb=1.54zst +0.11) and quadratic ( zhb=zst(7-13zst )) models for the zlb and zhb corresponding to the low and high boundaries of mixture fraction, respectively, where both parameters dictate the range of CNT growth rate (GR) in the mixture fraction space. Based on the developed correlations, the CNT growth in mixture fraction space is optimum in the flame with medium-range zst conditions between 0.15 and 0.25. The stronger relationship between growth-region mixture-fraction (GRMF) and zst at the near field region close to the flame sheet compared to that of the far field region away from the flame sheet is due to the higher temperature gradient at the former region compared to that of the latter region. The developed models also reveal three distinct regions that are early expansion, optimum, and reduction of GRMF at varying zst.

스마트팜형 시설 딸기에 예찰 없이 작물 정식 초기에 천적을 먼저 적용하는 생태공학적 Natural Enemy in First (NEF) 기법이 총채벌레류 와 진딧물류의 밀도에 미치는 영향을 확인하였다. 대조구는 약제를 처리하여 비교하였다. NEF 처리구에서 총채벌레류와 진딧물류의 천적과 서식 처로 참멋애꽃노린재와 Portulaca sp.를 적용하여 작기 종료시점까지 해충의 밀도를 대조구와 유사하게 효과적으로 관리할 수 있었다.

This article aims to suggest a model of critical literacy in English as a Foreign Language (hereafter EFL) contexts. The paper will introduce previous critical literacy models in first or second language teaching and learning and those in EFL contexts. Then, several empirical studies based on the models are introduced suggesting important issues to consider in implementing critical literacy in EFL contexts. A model of critical literacy in EFL contexts is, consequently, suggested with the three key elements for successful critical literacy implementation in EFL contexts, language for criticality development, affects and criticality development and citizenship and criticality development. The model pursues balancing conventional literacy education, critical literacy education and citizenship education. The researchers suggest balancing conventional skill-based literacy, affective pedagogy, and citizenship education with the development of critical literacies. Teacher-initiated practice and guidance, incorporation of community-sensitive topics and materials, and students’ active participation are key elements practitioners should consider in their adaptation of critical literacy instruction in EFL contexts.

During wartime, the operation of engineering equipment plays a pivotal role in bolstering the combat prowess of military units. To fully harness this combat potential, it is imperative to provide efficient support precisely when and where it is needed most. While previous research has predominantly focused on optimizing equipment combinations to expedite individual mission performance, our model considers routing challenges encompassing multiple missions and temporal constraints. We implement a comprehensive analysis of potential wartime missions and developed a routing model for the operation of engineering equipment that takes into account multiple missions and their respective time windows of required start and completion time. Our approach focused on two primary objectives: maximizing overall capability and minimizing mission duration, all while adhering to a diverse set of constraints, including mission requirements, equipment availability, geographical locations, and time constraints.

In the (3,3) close combat model based on the Lanchester Square Law, this study proposes a plan to optimally allocate residual combat power after the battle to other battlefields. As soon as the two camps of three units can grasp each other's information and predict the battle pattern immediately after the battle began, the Time Zero Allocation of Force (TZAF) scenario was used to initially allocate combat power to readjust the combat model. It reflects travel time, which is a "field friction" in which physical distance exists from battlefields that support combat power to battlefields that are supported. By developing existing studies that try to examine the effect of travel time on the battlefield through the combat model, this study forms a (3,3) combat model, which is a large number of minimum units. In order to achieve the combat purpose, the principle of optimal combat force operation is presented by examining the aspect that support combat power is allocated to the two battlefields and the consequent battle results. Through this, various scenarios were set in consideration of the travel time and the situation of the units, and differentiated results were obtained. Although the most traditional, it can be used as the basic logic of the training or the commander's decision-making system using the actual war game model.

Maintaining sea superiority through successful mission accomplishments of warships is being proved to be an important factor of winning a war, as in the Ukraine-Russia war. in order to ensure the ability of a warship to perform its duties, the survivability of the warship must be strengthened. In particular, among the survivability factors, vulnerability is closely related to a damage assessment, and these vulnerability data are used as basic data to measure the mission capability. The warship's mission capability is usually measured using a wargame model, but only the operational effects of a macroscopic view are measured with a theater level resolution. In order to analyze the effectiveness and efficiency of a weapon system in the context of advanced weapon systems and equipments, a warship's mission capability must be measured at the engagement level resolution. To this end, not the relationship between the displacement tonnage and the weight of warheads applied in the theater level model, but an engagement level resolution vulnerability assessment method that can specify physical and functional damage at the hit position should be applied. This study proposes a method of measuring a warship’s mission capability by applying the warship vulnerability assessment method to the naval engagement level analysis model. The result can be used as basic data in developing engagement algorithms for effective and efficient operation tactics to be implemented from a single unit weapon system to multiple warships.

The primary therapeutic approach for Brucella species infections has mainly been based on antibiotic treatment. However, the development of vaccines for brucellosis control remains controversial. Furthermore, there is currently no licensed vaccine available for human brucellosis. This study aims to evaluate the effect of a combination of recombinant protein vaccines against Brucella (B.) abortus infection using a mouse model. Two B. abortus genes, namely dapB and gpm, were cloned and expressed in competent Escherichia (E.) coli DH5α using the pCold-TF vector. Successfully cloned vectors were subjected to PCR amplification using specific primer pairs. The apparent sizes of dapB and gpm were detected at 807 bp and 621 bp, respectively. Besides, the purified recombinant proteins dapB and gpm were detected using SDS-PAGE electrophoresis with correct sizes of 82.86 kDa and 87.61 kDa, respectively. These recombinant proteins were used to immunize mice as a combined subunit vaccine (CSV) to elicit host immunity against B. abortus infection. Mice immunized with CSV exhibited increased proliferation of CD4+ and/or CD8+ T cells at week 7th and 9th before sacrifice, in comparison to the control group. Notably, CSV immunization showed a significant decrease in bacterial burden in the spleen compared to the control group. Altogether, CSV using dapB and gpm induced host adaptive immune response against Brucella infection, suggesting its potential as an effective new subunit vaccine candidate.