• 수산해양기술연구
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• 제59권1호
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• pp.44-54
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• 2023

In this study, the second-order Nomoto's nonlinear expansion model was implemented as a Tagaki-Sugeno fuzzy model based on the heading angular velocity to design the automatic steering system of a ship considering nonlinear elements. A Tagaki-Sugeno fuzzy PID controller was designed using the applied fuzzy membership functions from the Tagaki-Sugeno fuzzy model. The linear models and fuzzy membership functions of each operating point of a given nonlinear expansion model were simultaneously tuned using a genetic algorithm. It was confirmed that the implemented Tagaki-Sugeno fuzzy model could accurately describe the given nonlinear expansion model through the Zig-Zag experiment. The optimal parameters of the sub-PID controller for each operating point of the Tagaki-Sugeno fuzzy model were searched using a genetic algorithm. The evaluation function for searching the optimal parameters considered the route extension due to course deviation and the resistance component of the ship by steering. By adding a penalty function to the evaluation function, the performance of the automatic steering system of the ship could be evaluated to track the set course without overshooting when changing the course. It was confirmed that the sub-PID controller for each operating point followed the set course to minimize the evaluation function without overshoot when changing the course. The outputs of the tuned sub-PID controllers were combined in a weighted average method using the membership functions of the Tagaki-Sugeno fuzzy model. The proposed Tagaki-Sugeno fuzzy PID controller was applied to the second-order Nomoto's nonlinear expansion model. As a result of examining the transient response characteristics for the set course change, it was confirmed that the set course tracking was satisfactorily performed.

• Smart Structures and Systems
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• 제31권5호
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• Advances in nano research
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• 제15권1호
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• pp.49-57
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• 2023

As composite materials are used in many applications, the modern world looks forward to significant progress. An overview of the application of composite fiber materials in sports equipment is provided in this article, focusing primarily on the advantages of these materials when applied to sports equipment, as well as an Analysis of the influence of sports equipment of fiber-reinforced composite material on social sports development. The present study investigated surface morphology and physical and mechanical properties of S-glass fiber epoxy composites containing Al₂O₃ nanofillers (for example, 1 wt%, 2 wt%, 3 wt%, 4 wt%). A mechanical stirrer and ultrasonication combined the Al₂O₃ nanofiller with the matrix in varying amounts. A compression molding method was used to produce sheet composites. A first physical observation is well done, which confirms that nanoparticles are deposited on the fiber, and adhesive bonds are formed. Al₂O₃ nanofiller crystalline structure was investigated by X-ray diffraction, and its surface morphology was examined by scanning electron microscope (SEM). In the experimental test, nanofiller content was added at a rate of 1, 2, and 3% by weight, which caused a gradual decrease in void fraction by 2.851, 2.533, and 1.724%, respectively, an increase from 2.7%. The atomic bonding mechanism shows molecular bonding between nanoparticles and fibers. At temperatures between 60 ℃ and 380 ℃, Thermogravimetric Analysis (TGA) analysis shows that NPs deposition improves the thermal properties of the fibers and causes negligible weight reduction (percentage). Thermal stability of the composites was therefore presented up to 380 ℃. The Fourier Transform Infrared Spectrometer (FTIR) spectrum confirms that nanoparticles have been deposited successfully on the fiber.

• Geomechanics and Engineering
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• 제34권3호
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• 한국음향학회지
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• 제29권3호
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• pp.200-208
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• 2010

본 논문에서는 배경잡음에 강인한 잡음제거 알고리즘 설계를 위해서 minimum statistics (MS) 기법을 codebook driven short-term predictor parameter estimation (CDSTP) 기법에 접목하는 방법을 제안한다. MS는 stationary 배경잡음에는 강인하지만, non-stationary 배경잡음에는 상대적으로 취약하다. CDSTP는 non-stationary 배경잡음에 강인한 특성을 보이지만, 코드북에 없는 배경잡음 환경에는 취약하다. 따라서 non-stationary 배경잡음에 강인한 CDSTP 방법과 별도의 코드북 학습 과정이 필요 없는 MS를 결합해서 다양한 배경잡음에 강인한 알고리즘을 제안한다. 제안방법은 MS나 CDSTP 방법에 비해서 전체적으로 향상된 perceptual evaluation of speech quality (PESQ) 성능을 나타냈으며, 특히 stationary 배경잡음과 non-stationary 배경잡음이 섞여 있는 mixed 배경잡음 환경에서 강인한 특성을 보였다.

• 품질경영학회지
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• 제52권1호
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• pp.43-56
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• 2024

Purpose: The injection molding process, crucial for plastic shaping, encounters difficulties in sustaining product quality when replacing injection machines. Variations in machine types and outputs between different production lines or factories increase the risk of quality deterioration. In response, the study aims to develop a system that optimally adjusts conditions during the replacement of injection machines linked to molds. Methods: Utilizing a dataset of 12 injection process variables and 52 corresponding sensor variables, a predictive model is crafted using Decision Tree, Random Forest, and XGBoost. Model evaluation is conducted using an 80% training data and a 20% test data split. The dependent variable, classified into five characteristics based on temperature and pressure, guides the prediction model. Bayesian optimization, integrated into the selected model, determines optimal values for process variables during the replacement of injection machines. The iterative convergence of sensor prediction values to the optimum range is visually confirmed, aligning them with the target range. Experimental results validate the proposed approach. Results: Post-experiment analysis indicates the superiority of the XGBoost model across all five characteristics, achieving a combined high performance of 0.81 and a Mean Absolute Error (MAE) of 0.77. The study introduces a method for optimizing initial conditions in the injection process during machine replacement, utilizing Bayesian optimization. This streamlined approach reduces both time and costs, thereby enhancing process efficiency. Conclusion: This research contributes practical insights to the optimization literature, offering valuable guidance for industries seeking streamlined and cost-effective methods for machine replacement in injection molding.

• Archives of Plastic Surgery
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• 제51권2호
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• pp.258-261
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• 2024

Career building can be challenging for young surgeons, especially when topics such as lifestyle, work-life balance and subspecialization arise. Suggestions and advice from senior colleagues is very valuable but many young surgeons do not have such opportunities or are limited to a few senior surgeons. The International Microsurgery Club (IMC), in collaboration with the World Society of Reconstructive Microsurgery, organized a combined webinar for this topic and invited world renownedmicrosurgery masters polled by the IMCmembers to join, including Prof. Peter Neligan (Emeritus from University of Washington, United States), Prof. Raja Sabapathy (Ganga Hospital, India), Dr. Gregory Buncke (The Buncke Clinic, United States), Prof. Isao Koshima (Hiroshima University Hospital, Japan), Prof. David Chwei-Chin Chuang (Chang Gung Memorial Hospital, Taiwan), and Prof. Eric Santamaria (Hospital General Dr. Manuel Gea Gonzalez, Mexico) on May 1, 2022. Prof. Joon-Pio Hong (Asan Medical Center, South Korea) and Prof. Fu-Chan Wei (Chang Gung Memorial Hospital, Taiwan) were also selected but unfortunately could not make it and were therefore invited to another event in April 2023, summarized in a recently published paper. There is ample literature reporting on different aspects of developing a microsurgical career but the goal of this session was to offer an opportunity for direct exchange with experienced mentors. Moreover, insights from experienced microsurgeons from different part of the world were more likely to offer different perspectives on aspects such as career building, failure management, and team culture. This webinar event was moderated by Dr. Jung-Ju Huang (Taiwan), Dr. Susana Heredero (Spain), and Dr. Wei F. Chen (United States).

• 지능정보연구
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• 제22권1호
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• pp.139-157
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• 2016

앙상블 분류기란 개별 분류기보다 더 좋은 성과를 내기 위해 다수의 분류기를 결합하는 것을 의미한다. 이와 같은 앙상블 분류기는 단일 분류기의 일반화 성능을 향상시키는데 매우 유용한 것으로 알려져 있다. 랜덤 서브스페이스 앙상블 기법은 각각의 기저 분류기들을 위해 원 입력 변수 집합으로부터 랜덤하게 입력 변수 집합을 선택하며 이를 통해 기저 분류기들을 다양화 시키는 기법이다. k-최근접 이웃(KNN: k nearest neighbor)을 기저 분류기로 하는 랜덤 서브스페이스 앙상블 모형의 성과는 단일 모형의 성과를 개선시키는 데 효과적인 것으로 알려져 있으며, 이와 같은 랜덤 서브스페이스 앙상블의 성과는 각 기저 분류기를 위해 랜덤하게 선택된 입력 변수 집합과 KNN의 파라미터 k의 값이 중요한 영향을 미친다. 하지만, 단일 모형을 위한 k의 최적 선택이나 단일 모형을 위한 입력 변수 집합의 최적 선택에 관한 연구는 있었지만 KNN을 기저 분류기로 하는 앙상블 모형에서 이들의 최적화와 관련된 연구는 없는 것이 현실이다. 이에 본 연구에서는 KNN을 기저 분류기로 하는 앙상블 모형의 성과 개선을 위해 각 기저 분류기들의 k 파라미터 값과 입력 변수 집합을 동시에 최적화하는 새로운 형태의 앙상블 모형을 제안하였다. 본 논문에서 제안한 방법은 앙상블을 구성하게 될 각각의 KNN 기저 분류기들에 대해 최적의 앙상블 성과가 나올 수 있도록 각각의 기저 분류기가 사용할 파라미터 k의 값과 입력 변수를 유전자 알고리즘을 이용해 탐색하였다. 제안한 모형의 검증을 위해 국내 기업의 부도 예측 관련 데이터를 가지고 다양한 실험을 하였으며, 실험 결과 제안한 모형이 기존의 앙상블 모형보다 기저 분류기의 다양화와 예측 성과 개선에 효과적임을 알 수 있었다.

• 생명과학회지
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• 제28권5호
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• pp.621-626
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• 2018

최근 의공학과 의학기술의 발달로 인간의 수명이 늘어나고 있으며, 이제는 수명에 대한 관심보다는 건강한 노후에 대한 관심에 초점을 맞추어 항노화 산업과 의과학 및 스포츠 과학이 발달하고 있다. 노화의 자연스런운 과정동안 노인들은 면역기능과 생리학적인 기능이 소실되고, 제2형 당뇨병, 고혈압, 골다공증, 골관절염, 심혈관 질환 및 인지감소 등을 경험하게 된다 하지만 규칙적인 운동을 참여할 때 건강한 노후를 맞이할 수 있다. 하지만 이제까지 노인들에게 규칙적인 운동활동의 참여가 건강체력, 정신건강, 인지기능 및 면역력의 변화를 확인하는 연구가 부족한 것으로 여겨진다. 따라서 본 연구는 노인에게서 일상생활도 관리 및 규칙적인 운동활동의 참여가 건강한 노후와 면역력유지에 미치는 효과를 분석하고자 한다. 본 연구를 수행하기 위하여 최근 20여년간 국내,외 이 분야에서 수행된 최신 연구결과들은 펍메드 데이터 베이스를 활용하여 비교 및 분석하고자 한다. 본 연구결과 레저활동을 포함하는 규칙적인 운동활동은 노인의 근육량과 골밀도를 증진시키고, 아울러 당뇨병, 고혈압, 동맥경화, 관절염 등과 같은 성인병을 예방하며, 아울러 인지기능 증가에 따른 치매의 예방과 치료 뿐만 아니라 면역력의 증진을 통한 만성질환과 암의 예방에도 필수적인 것으로 사료된다. 특히 노인에게는 요가나 필라테스를 기반으로 하는 수행하기 쉬운 운동이 좋으며, 흥미있고 자주 할 수 있는 운동이 권장된다. 체력이 전반적으로 약하기 때문에 무리한 운동은 오히려 심혈관계의 부담, 항상성의 교란, 및 면역저하를 동반할 수 있으므로, 레저스포츠 활동, 근력운동을 포함하는 저항운동, 및 일상생활도 증가를 통한 체력증진, 충분한 휴식, 최적의 영양관리가 필요하며 추후 건강한 노인에 최적화된 스포츠 장비, 영양소 섭취와 스포츠 음료 등의 계발에 관한 연구가 이루어져야 할 것이다.

• 생명과학회지
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• 제33권3호
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• pp.242-251
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• 2023

마우스 모델을 사용하여, 8주 케톤체(베타-하이드록시뷰티레이트, β-HB)가 지구성 운동 수행능력과 골격근의 단백질 합성 및 분해에 미치는 영향을 조사하였다. 48마리의 수컷 ICR 마우스(8주령)를 무작위로 4개 그룹으로 나누었다: 비운동 통제군(Sed+Con), 비운동+베타-하이드록시뷰티레이트(Sed+β-HB), 운동 통제군(Exe+Con), 운동+베타-하이드록시뷰티레이트(Exe+β-.HB). β-HB 투여를 위해 β-HB를 PBS (150 mg/mL)에 용해시켜 8주 동안 매일 피하 주사(250 mg/kg)하였다. 운동 훈련을 위해 마우스는 8주 동안 20분 트레드밀 달리기 운동 훈련을 주 5일 수행하였다. 훈련은 10° 경사도에서 10 m/min 속도에서 5 min 동안 실시하고 나서, 매 1 min 마다 1 m/min의 속도를 나머지 15 min 동안 증가시켰다. 8주간의 처치 후, 내장 지방량과 골격근량, 혈액 매개변수, 자가포식 및 단백질 합성 마커가 분석되었다. 데이터는 SPSS 21 프로그램을 사용하여 ANOVA (p<0.05)로 분석되었다. Exe+β-HB 그룹의 혈중 젖산 수치는 모든 3개 그룹(Sed+Con, Sed+β-HB 및 Exe+β-HB)보다 유의하게 낮았다(p<0.05). Sed+Con 및 Exe+Con 그룹에 비해, 8주 Exe+β-HB 처치는 최대 달리기 수행 시간(탈진 시간)을 유의하게 증가시켰다(p<0.05). 8주 β-HB 투여는 마우스의 골격근에서 자가포식 유동과 자가포식 관련 단백질을 유의하게 감소시켰다(p<0.05). 반대로, β-HB와 지구력 운동 훈련의 조합된 처치는 단백질 합성(mTOR 신호 및 번역 속도)을 증가시켰다(p<0.05). 8주간의 β-HB 처치와 지구력 운동 훈련은 마우스 골격근에서 지구력 수행능력 증가, 단백질 합성 증가, 단백질 분해 감소 효과를 보여주었다.