In this study, we investigated the dynamic characteristics of three irregular building models to analyze the effectiveness of displacement response control with Tuned Mass Damper (TMD) installation in twisted irregular buildings. The three irregular models were developed with a fixed angle of twist per story at one degree, subjected to three historical seismic loads and resonant harmonic loads. By designing TMDs with linear and dashpot attributes, we varied the total mass ratio of the installed TMDs from 0.00625% to 1.0%, encompassing a total of 10 values. Two TMDs were installed at the center of the top story of the analysis model in both X and Y directions to evaluate displacement response control performance based on TMD installation. Our findings suggest that the top displacement response control performance was most effective when a 1.0% TMD was installed at the top layer of the analysis model.

In this study, an experimental analysis of noise reduction in road traffic by applying the Micro Grooving technique to concrete highway pavements is explored. Initiated in 1984 to address the aging and damage issues observed in South Korea's concrete highways, Micro Grooving is known for creating fine grooves on the cement pavement surface to increase friction, prevent hydroplaning, and inhibit ice formation, while reducing vehicle friction noise by 3∼5dB(A). It is determined from noise measurement results that the application of the Micro Grooving method can be expected to reduce roadside noise and enhance the safety of drivers' driving experience.

This paper proposes the armored combat bulldozer, essential for amphibious tasks, requires water ingress prevention and submersion capabilities, typically addressed by a centrifugal pump. This study aims to boost the bulldozer's drainage pump efficiency by replacing the traditional aluminum 3-blade impeller with one made of ASA material using 3D printing. Analysis via ANSYS Fluent revealed that the 5-blade impeller increased discharge volume by 19.31% and efficiency by 6.07%, while the 6-blade variant saw a 27.07% increase in discharge volume and 8.81% efficiency improvement. Further scrutiny with ANSYS Static Structure ensured the new impellers' structural integrity and robustness under extreme conditions. This research confirms the potential of 3D printing in enhancing military equipment, demonstrating significant improvements in pump performance and opening paths for advanced manufacturing techniques to meet the demanding needs of combat vehicles.

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

In this study, the seismic response characteristics of the three analysis model with or without TMD were investigated to find out the effective dome shape. The three analysis models are rib type, lattice type and geodesic type dome structure composed of space frame. The maximum vertical and horizontal displacements were evaluated at 1/4 point of the span by applying the resonance harmonic load and historical earthquake loads (El Centro, Kobe, Northridge earthquakes). The study of the effective TMD installation position for the dome structure shows that seismic response control was effective when eight TMDs were installed in all types of analysis model. The investigation of the efficiency of TMD according to dome shape presents that lattice dome and geodesic dome show excellent control performance, while rib dome shows different control performance depending on the historical seismic loads. Therefore, lattice and geodesic types are desirable for seismic response reduction using TMD compared to rib type.

Raman distributed temperature sensor can be used as temperature instruments as well as monitoring abnormalities in next-generation nuclear systems. Since noise reduction and Measuring Frequency enhancement are required, integration time adjustment has been mainly used so far. In this study, a new data processing method using Moving Average Filter was analyzed to see if noise reduction and Measuring Frequency could be reduced, and improvement measures were suggested.

This paper is a study on the malfunction that occurred during the power supply logic of the Gunner Display Device during Mortar Functional Firing under low temperature conditions. As a result of the phenomenon reproduction test and its analysis, the cause of the malfunction of the Gunner Display Device was Glitch, which occurred in the process of converting the image signal, and the improved software was applied to the Gunner's Display System by ignoring some of the image signal conversion process that causes Glitch. The improved Gunner Display Device passed the validity test and applied the improvement to the mortars. As a result of this study, several suggestions for power supply and control logic were proposed. It is expected that this study will be used as a reference in the future design of similar weapons systems.

본 연구는 2020년 이후 한류에 대한 소비자의 인식을 살피고자 중국 내 한류에 대 한 인식과 한국 내 한류 인식을 파악하여 양국의 현황을 분석하는 것을 목적으로 한 다. 이를 위해 텍스톰(TEXTOM)을 활용하여 2020년∼2023년 동안의 주요 포털사이 트 및 SNS에서 ‘한류’와 관련된 빅데이터를 수집하였다. 단어빈도분석, 감성분석, 네 트워크 시각화, 구조적 등위성 분석을 수행하여 유의한 정보를 추출하였다. 연구결과 팬데믹 이후 양국의 영상콘텐츠에 대한 높아진 관심은 공통적인 현상이었으나, 한류 에 대한 긍‧부정 감성, 담론에 있어서는 새롭고 유의미한 차이점이 도출되어 이를 기반으로 학문적, 실무적 시사점을 제시하였다. 본 연구가 신(新)한류 시기에 K-콘텐 츠의 한·중 인식파악을 위한 유용한 자료로 활용될 수 있길 바란다.

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure’s safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

본 연구는 우리나라 곰솔 현실림의 생장특성을 반영한 지위지수곡선을 개발하고자 최근 산림자원조사를 통해 획득한 493 plots의 곰솔 임분 자료를 수집하여 진행되었다. 수고생장모델 개발을 위해 생장추정에 널리 쓰이고 있는 Chapman-Richards, Schumacher, Gompertz 및 Weibull 대수차분방정식을 적용한 결과, 적합도가 가장 높은 Weibull식을 수고 생장모델에 대한 최적합식으로 선정하였으며, 유도곡선법을 이용하여 지위지수 곡선을 개발하였다. 개발된 해안⋅내륙 각각의 곰솔 지위지수 분류곡선을 이용하여 임령에 따른 독립된 두 집단의 우세목 수고생장을 비교 분석한 결과, 임령 50년 이후에서 유의적 차이가 있는 것으로 나타났다(p<0.05). 따라서 본 연구는 변화된 곰솔 현실림의 생장특성을 반영한 지위지수를 통해 지역 간 생장특성 차이를 구명함으로써 지역별 맞춤형 임분관리계획 수립 시 활용가치가 더욱 기대되는 바이다.

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.

컴퓨터 성능의 발전으로 빅데이터의 효율적인 사용이 가능해지면서, 심층 학습(deep learning)은 다양한 의료 분야에 활용할 수 있는 핵심적인 인공지능(artificial intelligence, AI) 기법으로 각광받고 있다. 이에 본 종설은 뇌종양 진단과 치료에 사용되는 자기공명영상(magnetic resonance imaging, MRI)의 심층 학습 기법을 소개하고자 하였다. 먼저 국내 AI의 의료 분야 도입의 동향을 분석하고, 이를 바탕으로 MRI를 활용한 뇌종양의 진단과 치료에 적용할 수 있는 심층 학습 기법과 그 결과들을 기술하였다. 뇌종양 진단과 치료 시, 심층 학습을 이용한 최근 사례는 영상 분류, 영상 품질 개선, 영상 분할로 나타났으며, 질병의 진단과 치료에 적용할 수 있는 객관적이고 높은 성능 수치를 나타내면서 그 유용성을 확인 할 수 있었다. 종합하자면, 심층 학습은 질병의 진단과 치료에 적용할 수 있는 유용한 지표이며, AI 역량을 지닌 의료진의 지도하에 점진적인 도입이 이뤄진다면 질병의 진단과 치료에 큰 도움을 주는 훌륭한 소프트웨어로 활용될 것으로 여겨진다. 본 종설이 심층 학습을 이해할 때 많은 도움이 되길 바라며, 향후 관련 연구를 수행할 때 가이드라인으로 활용될 것을 기대 한다.

Tuned mass damper (TMD) is widely used to reduce dynamic responses of structures subjected to earthquake loads. A smart tuned mass damper (STMD) was proposed to increase control performance of a traditional passive TMD. A lot of research was conducted to investigate the control performance of a STMD based on analytical method. Experimental study of evaluation of control performance of a STMD was not widely conducted to date. Therefore, seismic response reduction capacity of a STMD was experimentally investigated in this study. For this purpose, a STMD was manufactured using an MR (magnetorheological) damper. A simple structure presenting dynamic characteristics of spacial roof structure was made as a test structure. A STMD was made to control vertical responses of the test structure. Two artificial ground motions and a resonance harmonic load were selected as experimental seismic excitations. Shaking table test was conducted to evaluate control performance of a STMD. Control algorithms are one of main factors affect control performance of a STMD. In this study, a groundhook algorithm that is a traditional semi-active control algorithm was selected. And fuzzy logic controller (FLC) was used to control a STMD. The FLC was optimized by multi-objective genetic algorithm. The experimental results presented that the TMD can effectively reduce seismic responses of the example structures subjected to various excitations. It was also experimentally shown that the STMD can more effectively reduce seismic responses of the example structures conpared to the passive TMD.

광식성 난방제 해충인 파밤나방(Spodoptera exigua)의 친환경적 방제원으로써 이용을 위해 국내에서 분리된 파밤나방 핵다각체병바이러 스(S. exigua nucleopolyhedrovirus K1: SeNPV-K1)의 형태 및 전체 유전체 서열을 분석하였다. SeNPV-K1의 다각체(polyhedra)는 0.6-1.8 um 크기의 부정형으로, 기 보고된 SeNPV와 외형적 차이는 보이지 않았다. 전체 유전체의 염기서열을 분석한 결과, 기 보고된 SeNPV와 비교할 때 145 bp 더 많은 135,756 bp로 확인되었으며, G+C 함량은 44% 였고 상동반복영역은 6개로 두 바이러스간에 차이는 없었다. ORF 분석결과, SeNPV-K1은 기 보고된 것과 비교할 때 2개 더 적은 137개를 가지며, SeNPV-K1에만 존재하는 ORF는 4개가 확인되었다. 이들 4개의 ORF는 비필수 유전자로 바이러스의 특성에는 큰 영향을 주지 않을 것으로 여겨졌다. 유전체의 vista 분석 결과, SeNPV-K1과 기 보고된 SeNPV의 전체 염기서열 유사도가 매우 높은 것으로 확인되었다. 국내에서 처음으로 분석한 SeNPV-K1의 전체 유전체는 기 보고된 SeNPV와 유사한 것으로 나타났으나 서로 다른 분리주로 국내 고유자원임을 확인하였다.

본 연구는 2013년 1차 솎아베기 및 가지치기를 시행한 35년생 종가시나무 인공림을 대상으로 2014년과 2021년에 임분조사를 실시하여 임목 생장⋅형질 반응 특성을 분석하였다. 가지치기 시업임분에 대한 솎아베기 강도별 임목생장 특성을 비교 분석한 결과, 평균수관면적은 강도구가 대조구보다 유의적으로 넓으며(p<0.05), 정기생장률도 높아 흉고직경과 유사한 경향을 보였다. 가지치기 시업임분에 대한 솎아베기 처리구별 가지발 달 특성을 분석한 결과, 약도구를 제외한 모든 처리구의 지하고는 가지치기 처리구에서 대조구보다 유의적으로 높게 분석되었다(p<0.05). 고사지 발생률의 경우 가지치기 처리구에서 29.6∼36.7%, 대조구에서 40.0∼53.3%로 각각 나타났다. 활엽수 입목형질등급평가 기준에 따른 연구대상지의 형질등급 분석 결과, 1등급 입목의 비율은 가지치기 처리구에서 21.0%, 대조구에서 11.6%로 각각 나타나 유의적인 차이를 보였으며, 이로부터 가지치기 시업에 따른 임목형질개선 효과에 대한 실증적 시험연구결과를 도출할 수 있었다.

하도급거래의 현실은 수직적 구조를 특징으로 하기 때문에 계약관계에서 우월적 지위가 전제된다. 이러한 구조적 문제점으로 인한 불공정 하도급거래행위를 억제하고 공정한 하도급거래 시장 환경을 구축하기 위한 다양한 제도적 장치가 마련되어 왔다. 하지만 경제나 기술적 환경, 노동시장의 변화 등으로 인하여 하도급계약을 둘러싸고는 여전히 많은 쟁점이 도출되고 있으며 이에 대한 제도적 접근에 대한 논의가 진행되고 있다. 최근 논의가 활발해지고 있는 납품 단가 연동제 역시 COVID-19나 우크라이나 사태와 같이 평소 예기치 못한 특별한 사정이 발생하여 원자재 가격 등이 상승함으로 발생한 위험을 하도급 계약의 당사자 간에 어떻게 분담할 것인가에 대한 논의를 다시 촉발시켰다. 원자재 가격 등이 급등하여 하도급계약에서 정한 제품이나 용역을 제공하기 위해 필요한 비용이 크게 증가하였음에도 불구하고 납품단가에 대하여 다시 검토하지 않는 것은 원사업자의 우월적 지위를 남용하여 수급사업자에게 실질적 으로 하도급계약을 통한 대가를 인하하는 것과 같은 불이익을 주게 될 수 있다.이러한 점을 고려하면 현행 조정협의제의 실효성에 한계가 노정된 경우 납품 단가 연동제를 통한 보완책을 마련하는 것은 고려할 수 있는 하나의 대안으로 생각된다. 그러나 건설 하도급거래는 전통적으로 도급계약이 가장 많이 활용되는 분야이다. 아울러 건설공사는 통상의 제조업과는 달리 정형적인 제품을 일률적으로 생산 하는 것이 아니라 발주자의 수요에 따라 다양한 형태의 생산물이 요구된다. 그리고 건설공사의 수직적 구조에 참여하는 다양한 건설주체들에 의해 만들어 지는 중간생산물이 긴밀하게 연계되어 최종 생산물을 완성하게 된다. 결국, 건설공사를 이루는 수직적 구조에서는 참여자 간에 협의 및 조정을 통한 협력이 매우 중요하다. 따라서 하도급대금의 조정에 관한 입법정책은 계약의 당사자들 간의 자발적 협의 및 조정을 강화할 수 있는 방향으로 설계되고 운용되는 것이 바람직할 것이다.

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyperparameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

When the center of stiffness and the center of mass of the structure differ under the seismic load, torsion is caused by eccentricity. In this study, an analysis model was modeled in which the positions of the core and the plane rotation axis of a 60-story torsional atypical structure with a plane rotation angle of 1 degree per floor were different. The structural behavior of the analysis model was analyzed, and the earthquake response behavior of the structure was analyzed based on the time history analysis results. As a result, as the eccentricity of the structure increased, the eccentricity response was amplified in the high-rise part, and the bending and torsional behavior responses were complex in the low-order vibration mode. As a result of the analysis, the maximum displacement and story drift ratio increased due to the torsional behavior. The maximum story shear force and the story absolute maximum acceleration showed similarities for each analysis model according to the shape of the vibration mode of the analysis model.