• Smart Structures and Systems
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• 제6권5_6호
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• pp.595-618
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• 2010

This paper describes the application of a wireless sensor network to a 31 meter-tall medieval tower located in the city of Trento, Italy. The effort is motivated by preservation of the integrity of a set of frescoes decorating the room on the second floor, representing one of most important International Gothic artworks in Europe. The specific application demanded development of customized hardware and software. The wireless module selected as the core platform allows reliable wireless communication at low cost with a long service life. Sensors include accelerometers, deformation gauges, and thermometers. A multi-hop data collection protocol was applied in the software to improve the system's flexibility and scalability. The system has been operating since September 2008, and in recent months the data loss ratio was estimated as less than 0.01%. The data acquired so far are in agreement with the prediction resulting a priori from the 3-dimensional FEM. Based on these data a Bayesian updating procedure is employed to real-time estimate the probability of abnormal condition states. This first period of operation demonstrated the stability and reliability of the system, and its ability to recognize any possible occurrence of abnormal conditions that could jeopardize the integrity of the frescos.

• 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.

• 자동차안전학회지
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• 제14권2호
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• 한국인터넷방송통신학회논문지
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• 제12권1호
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• pp.29-36
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• 2012

무선 센서 네트워크에서, 각 노드들의 정확한 위치 정보를 파악하는 것은 효율적인 네트워크 환경 구축과 수집된 정보를 효율적으로 활용하기 위해 필수적이다. 노드의 위치를 추정하는 다양한 기법들 중, 일반적으로 많이 사용되는 수신신호세기(RSS) 기법은 추가적인 하드웨어 자원 없이 쉽게 구현될 수 있으나 채널의 상태 혹은 장애물 등 외부의 간섭으로 인한 신호의 왜곡 또는 감쇄가 발생하므로 이를 이용한 위치 추정 시 오차에 의한 영향을 충분히 고려하여야 한다. 위치 추정의 정확도를 향상시키기 위해, 일반적으로 충분한 수의 수신 신호 세기 표본의 획득하지만, 표본수가 늘어날수록 전송 시 에너지 소모가 발생한다. 본 논문에서는, 에너지 효율의 문제와 위치 추정의 정확도를 향상시키기 위해 전력 손실 지수 추정을 통한 베이지안 압축 센싱(Bayesian Compressive Sensing)을 사용하는 수신신호세기 기반 위치 추정 기법을 제안한다. RSS 기반 위치 추정 시 중요한 요소인 전력 손실 지수의 추정을 통해, 실제 채널 환경에서의 적응적인 위치 추정을 가능하게 하며 또한 위치 추정의 정확도를 향상시킬 수 있다. 그리고 적은 수의 표본으로 신호를 복원하는 기술인 압축 센싱(Compressive Sensing) 기법을 무선 센서 네트워크에 적용함으로써 에너지 효율적인 위치 추정 기법을 가능하게 한다. 시뮬레이션 결과에서, 제안하는 기법은 적은 수의 측정으로 다수의 불특정 노드에 대한 정확한 위치 추정이 가능하게 하며 채널 환경에 상관없이 강인한 성능을 가짐을 확인하였다. 그리고 제안하는 방법은 압축된 수신 신호 세기를 취급하므로 네트워크 트래픽과 에너지 소모를 줄이는데 효율적임을 검증하였다.

• 지질공학
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• 제26권4호
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• pp.485-495
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• 2016

본 연구에서는 방파제 하부지반을 저치환율 재료로 보강 및 개량하기 위한 치환율과 재하중 방치기간을 확률론적 최적화 기법을 이용하여 분석하였다. 해석에 필요한 확률변수의 불확실성을 최소화하기 위하여 사전자료를 활용한 베이지안 갱신결과 최대 39.8% 포인트까지 불확실성이 감소하였고, 특히 사전함수의 표본수가 더 많은 구간의 감소폭이 컸다. 치환율 결정을 위하여 저치환율 단면 중 15~40% 범위에서 일계신뢰도법 및 몬테카를로 시뮬레이션 방법에 의해 해석한 결과 목표파괴확률을 만족하는 치환율은 심층고결처리 및 쇄석다짐말뚝 구간에서 각각 20% 및 25% 이상으로 나타났다. 치환율에 대한 최적화를 위하여 생애주기비용 분석을 실시한 결과 목표파괴확률을 만족하는 범위 내에서 최적 치환율이 산정되었으며, 두 구간에서 각각 20% 및 30%가 가장 경제적인 것으로 결정되었다. 재하중의 방치기간에 대한 확률론적 해석결과 3개월 이상인 경우 모두 목표파괴확률을 만족하는 것으로 나타났다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(1)
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• pp.189-192
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• 2000

This paper proposes a fuzzy-based random access controller with a superimposed frame structure (F$^2$RAC) fur voice/data-integrated wireless networks. F$^2$RAC adopts mini-slot technique for reducing contention cost, and these mini-slots of which number may dynamically vary from one frame to the next as a function of the traffic load are further partitioned into two regions for access requests coming from voice and data traffic with their respective QoS requirements. And F$^2$RAC is designed to properly determine the access regions and permission probabilities for enhancing the data packet delay while ensuring the voice packet dropping probability constraint. It mainly consists of the estimator with Pseudo-Bayesian algorithm and fuzzy logic controller with Sugeno-type of fuzzy rules. Simulation results prove that F$^2$RAC can guarantee QoS requirement of voice and provide the highest throughput efficiency and the smallest data packet delay amongst the different alternatives including PRMA［1］, IPRMA［2］, and SIR［3］.

• Smart Structures and Systems
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• 제26권1호
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• 한국도로학회논문집
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• 제19권5호
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• pp.1-11
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• 2017

PURPOSES : This study is aimed at development of a stochastic pavement deterioration forecasting model using National Highway Pavement Condition Index (NHPCI) to support infrastructure asset management. Using this model, the deterioration process regarding life expectancy, deterioration speed change, and reliability were estimated. METHODS : Eight years of Long-Term Pavement Performance (LTPP) data fused with traffic loads (Equivalent Single Axle Loads; ESAL) and structural capacity (Structural Number of Pavement; SNP) were used for the deterioration modeling. As an ideal stochastic model for asset management, Bayesian Markov multi-state exponential hazard model was introduced. RESULTS:The interval of NHPCI was empirically distributed from 8 to 2, and the estimation functions of individual condition indices (crack, rutting, and IRI) in conjunction with the NHPCI index were suggested. The derived deterioration curve shows that life expectancies for the preventive maintenance level was 8.34 years. The general life expectancy was 12.77 years and located in the statistical interval of 11.10-15.58 years at a 95.5% reliability level. CONCLUSIONS : This study originates and contributes to suggesting a simple way to develop a pavement deterioration model using the total condition index that considers road user satisfaction. A definition for level of service system and the corresponding life expectancies are useful for building long-term maintenance plan, especially in Life Cycle Cost Analysis (LCCA) work.

• Smart Structures and Systems
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• 제17권4호
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• pp.611-629
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• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• Structural Monitoring and Maintenance
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• 제6권3호
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• pp.219-235
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• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.