• Smart Structures and Systems
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• 제31권5호
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.792-799
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• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.

• 적정기술학회지
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• 제6권2호
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• pp.136-145
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• 2020

개발도상국에 있어서 농업은 국가 경제의 중추임에도 불구하고, 대부분의 개도국에서는 장비와 지능형 시스템, 데이터 모니터링 등을 이용한 현상에 대한 통합적 판단 없이 인력에 의해 농업을 수행하고 있다. 농업의 중요한 요소인 관개는 작물 생산에 영향을 미치는 핵심적인 과정으로서, 연간 강우량의 변동에 대응하고자 대부분의 농장에서는 관개 시스템을 적용하고 있다. 그러나, 농장 관개 시스템의 모니터링과 제어 등에 대한 기술적 기반이 부족하여 생산성의 증대와 효율적인 농업용수 관리가 어려운 실정이다. 본 논문에서는 탄자니아 농촌 지역 관개 시스템의 스마트화를 위하여 433 MHz 무선 주파수 및 2G 기반 스마트 관개 측정 시스템과 농업용수 선불 시스템을 제안한다. 개발된 스마트 관개 시스템은 기상 데이터와 토양 수분 데이터를 하이브리드로 분석하도록 설계되었는데, 탄자니아 Arusha 지역의 Ngurudoto 마을로의 적용을 목적으로 한다. 제안된 시스템은 기상 측정 컨트롤러, 토양 수분 센서, 수류 센서, 솔레노이드 밸브 및 선불 시스템으로 구성되었는데, 센서를 통해 수집된 데이터는 433 MHz 무선 주파수 및 2G 기반 통신 아키텍처 모듈을 통해 서버로 전송된다. 본 시스템은 인터넷 운용이 제한되는 지역에 적합할 뿐만 아니라, 데이터 기반의 상태 판단과 실시간 예측이 가능하다. 개발된 시스템의 데이터 분석 알고리즘은 동적 회귀 알고리즘과 Naïve Bayes 알고리즘을 적용하여 선형 및 비선형분석 모두에 있어서 높은 정밀도를 보인다. 또한, 농장의 용수공급 시기와 용수의 양, 소요되는 전력에 대한 판단 뿐만 아니라 전체 시스템 하드웨어의 작동 및 오류에 대한 모니터링이 가능하다. 부가하여, 사용자가 농업용수를 공급받기 전에 선금을 지불하는 시스템을 적용하여 관리의 효율성을 도모하였으며, 농업의 전 과정에서 측정된 센서 데이터 및 용수 사용량은 사용자 인터페이스를 통하여 실시간으로 모니터링이 가능하도록 개발되었다. 본 연구를 통하여 개발된 RF(Radio Frequency) 및 2G 기반 스마트 관개 모니터링 시스템은 현장 적용의 편의성과 함께 사용자 중심의 모니터링 시스템을 통해 개발도상국의 경제, 사회 분야에 긍정적인 영향을 미칠 것으로 기대한다.

• Smart Structures and Systems
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• 제21권4호
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• pp.435-448
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• 2018

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

• Journal of Biosystems Engineering
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• 제33권3호
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• pp.186-195
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• 2008

In this paper, a prototype tele-operative system with a mobile base was developed in order to automate cultivation of house melon. A man-machine interactive hybrid decision-making system via tele-operative task interface was proposed to overcome limitations of computer image recognition. Identifying house melon including position data from the field image was critical to automate cultivation. And it was not simple especially when melon is covered partly by leaves and stems. The developed system was composed of 5 major modules: (a) main remote monitoring and task control module, (b) wireless remote image acquisition and data transmission module, (c) three-wheel mobile base mounted with a 4 dof articulated type robot manipulator (d) exchangeable modular type end tools, and (e) melon storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. Once task was selected from the task control and monitoring module, the analog signal of the color image of the field was captured and transmitted to the host computer using R.F. module by wireless. A sequence of algorithms to identify location and size of a melon was performed based on the local image processing. Laboratory experiment showed the developed prototype system showed the practical feasibility of automating various cultivating tasks of house melon.

• 설비공학논문집
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• 제28권10호
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• pp.387-393
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• 2016

A ground source heat pump system maintains a constant efficiency due to its stable heat source and radiant heat temperature which provide a more effective thermal performance than that of the air source heat pump system. As an eco-friendly renewable energy source, it can reduce electric power and carbon dioxide. In this study, we analyzed one year of data from a web based remote monitoring system to estimate the thermal performance of GSHP with the capacity of 3RT, which is installed in a low energy house located in Daejeon, Korea. This GSHP system is a hybrid system connected to a solar hot water system. Cold and hot water stored in a buffer tank is supplied to six ceiling cassette type fan coil units and a floor panel heating system installed in each room. The results are as follows. First, the GSHP system was operated for ten minutes intermittently in summer in order to decrease the heat load caused by super-insulation. Second, the energy consumption in winter where the system was operated throughout the entire day was 7.5 times higher than that in summer. Moreover, the annual COP of the heating and cooling system was 4.1 in summer and 4.2 in winter, showing little difference. Third, the outlet temperature of the ground heat exchanger in winter decreased from $13^{\circ}C$ in November to $9^{\circ}C$ in February, while that in summer increased from $14^{\circ}C$ to $17^{\circ}C$ showing that the temperature change in winter is greater than that in summer.

• Smart Structures and Systems
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• 제15권5호
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• pp.1373-1392
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• 2015

Placing sensors at appropriate locations is an important task in the design of an efficient structural health monitoring (SHM) system for a large-scale civil structure. In this paper, a hybrid optimization algorithm called virus monkey algorithm (VMA) based on the virus theory of evolution is proposed to seek the optimal placement of sensors. Firstly, the dual-structure coding method is adopted instead of binary coding method to code the solution. Then, the VMA is designed to incorporate two populations, a monkey population and a virus population, enabling the horizontal propagation between the monkey and virus individuals and the vertical inheritance of monkey's position information from the previous to following position. Correspondingly, the monkey population in this paper is divided into the superior and inferior monkey populations, and the virus population is divided into the serious and slight virus populations. The serious virus is used to infect the inferior monkey to make it escape from the local optima, while the slight virus is adopted to infect the superior monkey to let it find a better result in the nearby area. This kind of novel virus infection operator enables the coevolution of monkey and virus populations. Finally, the effectiveness of the proposed VMA is demonstrated by designing the sensor network of the Canton Tower, the tallest TV Tower in China. Results show that innovations in the VMA proposed in this paper can improve the convergence of algorithm compared with the original monkey algorithm (MA).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• 한국시뮬레이션학회논문지
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• 제33권1호
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• pp.1-17
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• 2024

숲을 체계적으로 관리하고 경영하기 위해서는 나무생장 변화에 대한 신뢰성 있는 예측이 필요하다. 독일의 아이펠 지역에서는 주요 목재종인 유럽너도밤나무가 식재되어 관리되어 지고 있다. 본 지역의 산림관리자의 실제 산림경영의 경험과 조언을 토대로, 다양한 산림 관리에 따른 단기 및 장기 효과를 예측하고자 지역 특수성을 지니는 시뮬레이션 모델의 접근방법을 개발하고자 하였다. 시뮬레이션 모델은 (1) 묘목 생성, (2) 나무 사멸 조절 (3) 나무 생장의 세 가지 모듈로 구성된다. 산림관리자에 의해 제공된 너도밤나무 숲의 실제 부피 변화를 근사화하기 위해 다양한 변수(나무수, 나무간 거리, 씨앗의 분포, 경쟁)를 반복적으로 수정하여 세 가지 모듈을 결합한 하이브리드 시뮬레이션 모델을 개발할 수 있었다. 본 연구를 통해 유럽너도밤나무 숲의 350년을 모의하여 생장 변화를 예측하였으며, 아이펠 지역의 세 가지 다른 관리 방법 (숲을 보호한 상태에서 목재벌채, 선택적 벌목, 보호림) 시나리오를 적용하였을 때 모의된 결과를 비교하였다. 시뮬레이션 결과를 통해 나무 생장의 변화가 현실적으로 잘 반영되었다는 것을 확인할 수 있었으며, 미래에 장기간 실제 축적된 산림 데이터를 획득하여, 검증과 보정의 과정을 반복한다면 더 높은 정확도의 지역 맞춤형 모델이 개발될 수 있을 것으로 사료된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1396-1401
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• 2004

In recent years, robot has been used widely in industrial field and has been expanded as a result of continous research and development for high-speed and miniaturization. The goal of this paper is to design the special serial manipulator through the understanding of the structure, mobility, and analysis of serial manipulator. Thereafter we control the position and orientation of end-effector with respect to time. In general, a structure of industrial robot consists of several links connected in series by various types of joints. Typically revolute and prismatic joints. The movement of these joints is determined in inverse kinematic analysis. Compared to the complicated structure of parallel and hybrid robot, open loop system retains the characteristic that each link is independent and is controlled easily by AC servomotor that is used to place the robot end-effector toward the accurate point with the desired speed and power while it is operated by position control algorithm. The robot end-effector should trace the given trajectory within the appropriate time. The trajectory of 3D end-effector model made by OpenGL can be displayed on the monitor program simultaneously