• Smart Structures and Systems
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• 제30권4호
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

• Genomics & Informatics
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• 제21권2호
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• pp.17.1-17.12
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• 2023

Coronavirus has left severe health impacts on the human population, globally. Still a significant number of cases are reported daily as no specific medications are available for its effective treatment. The presence of the CD147 receptor (human basigin) on the host cell facilitates the severe acute respiratory disease coronavirus 2 (SARS-CoV-2) infection. Therefore, the drugs that efficiently alter the formation of CD147 and spike protein complex could be the right drug candidate to inhibit the replication of SARS-CoV-2. Hence, an e-Pharmacophore model was developed based on the receptor-ligand cavity of CD147 protein which was further mapped against pre-existing drugs of coronavirus disease treatment. A total of seven drugs were found to be suited as pharmacophores out of 11 drugs screened which was further docked with CD147 protein using CDOCKER of Biovia discovery studio. The active site sphere of the prepared protein was 101.44, 87.84, and 97.17 along with the radius being 15.33 and the root-mean-square deviation value obtained was 0.73 Å. The protein minimization energy was calculated to be -30,328.81547 kcal/mol. The docking results showed ritonavir as the best fit as it demonstrated a higher CDOCKER energy (-57.30) with correspond to CDOCKER interaction energy (-53.38). However, authors further suggest in vitro studies to understand the potential activity of the ritonavir.

• 한국구조물진단유지관리공학회 논문집
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• 제10권2호
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• pp.123-133
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• 2006

본 연구의 목적은 다단계 동적계획법 및 축차무제약 최소화기법을 이용하여 강접 및 다양한 반강접 접합부 모델을 가진 뼈대 구조물의 최적화 알고리즘을 개발하는데 있다. Bowing effect를 고려한 비선형 보-기둥이론을 사용하였으며, 보-기둥의 접합부는 반강접접합부인 양면 복부앵글을 가진 접합부, 상 하플랜지 접합부, 양면복부앵글을 가진 상 하플랜지 접합부를 고려하여 연구를 수행하였으며, 각 접합부의 해석모델은 수정된 지수모델, 다항식 모델, 파워모델을 사용하였다. 최적화문제에 있어서 목적함수는 강재의 중량을 취하였으며, 설계변수는 부재의 단면치수를 선택하였다. 설계제약조건은 축력, 전단력 및 휨모멘트의 저항성과 사용성에 대해 수식화하였다. 본 연구에서 개발된 기하학적 비선형을 고려한 2차 탄성해석법을 이용하여 강접 및 다양한 모델을 가진 반강접 강뼈대 구조물의 종합적인 연속 최적설계 프로그램을 개발하였다.

• 대한안전경영과학회지
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• 제26권3호
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• pp.51-57
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• 2024

The purpose of this study is to compare and analyze the impact range of explosion damage due to gas leaks at LPG filling stations, focusing on propane and butane, which are components of vehicle LPG. The scenarios were designed based on the explosion incident at an LPG filling station in Gangwon-do, where an actual gas leak accident occurred, resulting in Scenario I and Scenario II. The ALOHA program, developed by the U.S. National Oceanic and Atmospheric Administration (NOAA), was used as the tool to analyze the impact range of the explosion damage for both substances. The results of the study indicated that, under identical conditions, propane had a wider impact range of damage than butane. This is presumed to be due to the greater explosion energy of propane, attributable to its physicochemical properties. Therefore, when preparing for LPG leak accidents, measures for propane need to be prioritized. As safety measures for propane, two suggestions were made to minimize human casualties. First, from a preventive perspective, it is suggested to educate workers about propane. Second, from the perspective of response measures and damage minimization, it is suggested to thoroughly prepare emergency evacuation and rescue plans, evacuation routes, designated shelters, and emergency response teams. This study compares and analyzes the impact range of radiative heat damage based on LPG components. However, hazardous accidents are critically influenced by the type of leaking substance, the form of the leak, and meteorological factors affecting the diffusion pattern of the substance. Therefore, for future research, it is proposed to model various leakage scenarios for the same substance to conduct a comprehensive risk assessment.

• 한국컴퓨터그래픽스학회논문지
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• 제22권3호
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• pp.21-29
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• 2016

본 논문은 명시야 (bright-field) 현미경 영상를 위한 데이터 기반 세포 분할 알고리즘을 제시한다. 제시된 알고리즘은 일반적인 사전 학습 기법과 다르게 동시에 두 개의 사전과 관련된 희소 코드 (sparse code)를 통해 정의된 에너지 함수의 최소화를 진행하게 된다. 두 개의 사전 중 하나는 명시야 영상에 대해 학습된 사전이고 다른 하나는 사람에 의해 수작업으로 세포 분할된 영상에 대해 학습된 것이다. 학습된 두 개의 사전을 세포 분할 될 새로운 입력 영상에 대해 적용하여 이와 관련된 희소 코드를 획득한 후 픽셀 단위의 분할을 진행하게 된다. 효과적인 에너지 최소화를 위해 합성곱 희소 코드 (Convolutional Sparse Coding)와 Alternating Direction of Multiplier Method(ADMM)이 사용되었고 GPU를 사용하여 빠른 분산 연산이 가능하다. 본 연구는 이전에 사용된 가변형 모델 (deformable model)을 이용한 세포 분할 방식과는 다르게 제시된 알고리즘은 세포 분할을 위해 사전 지식이 필요없이 데이터 기반의 학습을 통해서 쉽고 효율적으로 세포 분할을 진행할 수 있다.

• 한국지반공학회논문집
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• 제31권6호
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• pp.71-82
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• 2015

최근 시민편의를 위한 도심지 사회기반시설의 확충으로 인하여 민원최소화와 시공안정성을 고려한 쉴드터널 시공이 증가하고 있다. 대부분의 쉴드터널은 지하수가 유입되지 않는 비배수조건으로 가정하여 설계되지만 현장에서는 배수시설을 설치하여 지하수를 배출하는 배수터널처럼 운영되므로 설계 시 배수조건이 고려될 필요가 있다. 또한 우리나라 전역에 넓게 분포하고 가장 많이 이용되는 화강풍화토 지반에 대한 고려도 필요하다. 본 논문은 배수 및 응력조건을 조절할 수 있고 지하수면 아래 화강풍화토 지반에 위치한 터널을 모사할 수 있는 실험장비를 이용하여 실험하였으며, 전응력, 간극수압 및 배수내관의 유입유량을 측정하였다. 실험결과 배수조건 시 간극수압 감소로 인하여 전응력이 비배수조건보다 작게 나타났고, 유입유량은 재하응력에 비례하였다. 결과적으로 쉴드터널 설계 시 배수조건이 고려된다면 라이닝 작용 응력이 감소되기 때문에 경제성 높은 설계가 가능할 것으로 예상된다.

• 한국추진공학회지
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• 제25권1호
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• pp.1-11
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• 2021

포고 현상은 액체추진 로켓에서 발생하는 축방향의 동적 불안정 진동이다. 동체의 고유진동수와 추진제 공급계의 주파수가 가까와 지면 전체 시스템이 불안정 현상을 보인다. 포고 현상을 예측하기 위해 1단의 추진제 (산화제 및 연료) 탱크는 쉘 요소로, 나머지 구성 요소인 엔진 및 상단은 mass-spring으로 모델링하여 구조해석을 수행하였다. 추진제 공급계의 압력 및 유량 섭동예측에는 transmission line model이 사용되었다. 본 논문에서는 이와 같이 수행된 구조 및 유체 모델링을 통합하여 폐루프 전달함수를 구성하였다. 포고 억제기는 수동적인 방법으로 압력 섭동을 흡수하는 분 기관 및 accumulator로 구성되며 추진제 공급계 중간에 위치한다. 발사체의 비행과정 동안 포고현상을 억제하는 설계 최적화를 위한 설계변수로는 분기관 및 accumulator의 직경 및 길이로 설정하였다. 목적함수로는 포고 억제기의 질량, 그리고 추진제 질량에 따른 폐루프 전달함수의 에너지 최소화로 설정하여 다목적함수 최적화를 수행하였다.

• 대한원격탐사학회지
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• 제39권1호
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• pp.77-86
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• 2023

Ship detection is widely used in areas such as maritime security, maritime traffic, fisheries management, illegal fishing, and border control, and ship detection is important for rapid response and damage minimization as ship accident rates increase due to recent increases in international maritime traffic. Currently, according to a number of global and national regulations, ships must be equipped with automatic identification system (AIS), which provide information such as the location and speed of the ship periodically at regular intervals. However, most small vessels (less than 300 tons) are not obligated to install the transponder and may not be transmitted intentionally or accidentally. There is even a case of misuse of the ship'slocation information. Therefore, in this study, ship detection was performed using high-resolution optical satellite images that can periodically remotely detect a wide range and detectsmallships. However, optical images can cause false-alarm due to noise on the surface of the sea, such as waves, or factors indicating ship-like brightness, such as clouds and wakes. So, it is important to remove these factors to improve the accuracy of ship detection. In this study, false alarm wasreduced, and the accuracy ofship detection wasimproved by removing wake.As a ship detection method, ship detection was performed using machine learning-based random forest (RF), and convolutional neural network (CNN) techniquesthat have been widely used in object detection fieldsrecently, and ship detection results by the model were compared and analyzed. In addition, in this study, the results of RF and CNN were combined to improve the phenomenon of ship disconnection and the phenomenon of small detection. The ship detection results of thisstudy are significant in that they improved the limitations of each model while maintaining accuracy. In addition, if satellite images with improved spatial resolution are utilized in the future, it is expected that ship and wake simultaneous detection with higher accuracy will be performed.

• Interdisciplinary Bio Central
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• 제1권1호
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• pp.3.1-3.6
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• 2009

Introduction: GTPases known as translation factor play a vital role as ribosomal subunit assembly chaperone. The bacterial Obg proteins ($Spo{\underline{0B}}$-associated ${\underline{G}}TP$-binding protein) belong to the subfamily of P-loop GTPase proteins and now it is considered as one of the new target for antibacterial drug. The majority of bacterial Obgs have been commonly found to be associated with ribosome, implying that these proteins may play a fundamental role in ribosome assembly or maturation. In addition, one of the experimental evidences suggested that Bacillus subtilis Obg (BsObg) protein binds to the L13 ribosomal protein (BsL13) which is known to be one of the early assembly proteins of the 50S ribosomal subunit in Escherichia coli. In order to investigate binding mode between the BsObg and the BsL13, protein-protein docking simulation was carried out after generating 3D structure of the BsL13 structure using homology modeling method. Materials and Methods: Homology model structure of BsL13 was generated using the EcL13 crystal structure as a template. Protein-protein docking of BsObg protein with ribosomal protein BsL13 was performed by DOT, a macro-molecular docking software, in order to predict a reasonable binding mode. The solvated energy minimization calculation of the docked conformation was carried out to refine the structure. Results and Discussion: The possible binding conformation of BsL13 along with activated Obg fold in BsObg was predicted by computational docking study. The final structure is obtained from the solvated energy minimization. From the analysis, three important H-bond interactions between the Obg fold and the L13 were detected: Obg:Tyr27-L13:Glu32, Obg:Asn76-L13:Glu139, and Obg:Ala136-L13:Glu142. The interaction between the BsObg and BsL13 structures were also analyzed by electrostatic potential calculations to examine the interface surfaces. From the results, the key residues for hydrogen bonding and hydrophobic interaction between the two proteins were predicted. Conclusion and Prospects: In this study, we have focused on the binding mode of the BsObg protein with the ribosomal BsL13 protein. The interaction between the activated Obg and target protein was investigated with protein-protein docking calculations. The binding pattern can be further used as a base for structure-based drug design to find a novel antibacterial drug.

• 대한인간공학회지
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• 제36권3호
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.