• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.4
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• pp.70-76
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• 2022

Calcium-phosphate-based bioceramics are promising biomaterials for scaffolds because they can assist in bone regeneration. In this study, a laser sintering deposition system was developed, and 3D hydroxyapatite (HA) scaffolds were fabricated. The main process conditions of the HA scaffolds were laser power, table velocity, and laser focal distance. As the laser power increased, the line width, line height, and layer thickness also increased. Further, the line width, line height, and layer thickness decreased as the table velocity increased. As the laser focal distance increased, the line width increased, but the line height and layer thickness decreased. The fabricated green scaffolds were sintered at 1050 ℃ and 1150 ℃. The sintered scaffolds had a uniform and continuous interconnected shape, with pore sizes ranging from 850 to 950 ㎛ having 53% porosity. The compressive strength of the scaffolds decreased from 0.72 MPa (1050 ℃) to 0.53 MPa (1150 ℃). The biocompatibility of the scaffolds was investigated by analyzing the adhesion of osteoblast-like MG-63 cells cultured on the surfaces of the scaffolds. The results indicate that the scaffold sintered at 1050 ℃ had good mechanical and biological properties compared to that at 1150 ℃.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.220-228
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• 2022

The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising and empowering. For assessment of the outcomes four boundary, MSE, RMSE, MAPE and R2 are determined. The best R2 esteem is gotten for 1-overlap and it was 0.98.34 for train information and for test information is acquired 0.96. Additionally for train information the RMSE esteem in 1-overlap is superior to different Folds and this worth was 0.018.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1420-1427
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• 2023

Seismic risk has received increased attention since the 2011 Fukushima accident in Japan. The seismic risk of a nuclear power plant is evaluated via seismic probabilistic safety assessment (PSA), for which several methods are available. Recently, the discrete approach has become widely used. This approximates the seismic risk by discretizing the ground motion level interval into a small number of subintervals with the expectation of providing a conservative result. The present study examines the effect of the number of subintervals upon the results of seismic risk quantification. It is demonstrated that a small number of subintervals may lead to either an underestimation or overestimation of the seismic risk depending on the ground motion level. The present paper also provides a method for finding the boundaries between overestimation and underestimation regions, and illustrates the effect of the number of subintervals upon the seismic risk evaluation with an example. By providing a method for determining the effect of a small number of subintervals upon the results of seismic risk quantification, the present study will assist seismic PSA analysts to determine the appropriate number of subintervals and to better understand seismic risk quantification.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1630-1643
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• 2023

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.9
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• pp.739-745
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• 2014

Although medication is given to heart disease patients, conventional medication alone is not sufficient to treat heart disease. However, it has been reported that left ventricular assist device (LVAD) transplantation is an effective bridge to heart transplantation by assisting cardiac function. This study used long-term animal testing and emergency situations with a bovine model (Holstein) and canine model (Labrador-retriever) to evaluate the biocompatibility of LibraHeart-I (LH-1), which is a bent-tube type of LVAD that was developed in a previous study. In the long-term animal testing with the bovine model, the subjects survived for 49 days with no irregularities observed in their complete blood cell counts or the vital sign tests that were carried out during the test period. In short-term animal testing with the canine model, it was observed that blood did not remain inside the LH-I even without power support from an external drive source. In this study, the biocompatibility of the LH-I that was developed in a previous study was verified by the ejection performance during long-term animal testing and emergency situations.

• 한국문화재보존과학회:학술대회논문집
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• 2005.11a
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• pp.100-103
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• 2005

Lichens play an important role in breaking down rocks physically and chemically. Physical weathering occurs mechanically through hyphae and rhizines growing into the rocks. Lichen substances such as oxalic acid, acidic polysaccharides and depsides may assist altering rocks chemically. These actions partly contribute to soil formation but prove harmful for the substratum. Some people do not like to keep lichens on stained glassof churches or on gravestones mainly because of its unglamorous appearance. A biocide, 'Koretrel', is very effective in removing lichens and blue-green algae from the surface of concrete. After spraying 'Koretrel' in the open air, lichens (Caloplaca sp., Endocarpon petrolepideum, Physciella melanchra, Phaeophyscia hispidula, etc.) growing on buildings died within three months and peeled off fromthe substratum. One year after treatment, concrete surfaces were almost lichen free for three or four years. The biocide makesuse of a kind of lichen substance and harmless both for substances and environment. Our recent experiments show that it is also useful for water grasses growing on the surface of an aqueduct of a hydroelectric power station.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2696-2700
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• 2003

Nuclear robots should be developed for the reduction of radiation exposure, lower man hours, shorter power outage, and also improved worker safety concerns in performing hazardous and dangerous tasks. Among the components of a nuclear robot system, a robot control system equivalent to a human brain is a crucial point because a nuclear robot does not work without a control system. Therefore, in this paper, we will explain the requirements for a robot control system for a nuclear robot from a general point of view and also review the robot control systems of nuclear robots that were developed domestically, to assist a researcher beginning with the design for the control system of nuclear robots. The explained robot control system will be useful to develop the control system for industrial robots, home robots and other robots which are needed for tele-operation and are controlled through the internet.

• ICROS
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• v.21 no.1
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• pp.31-36
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• 2015

운전자에게 편의성을 제공하는 차량의 주행관련 Advanced driver assist system (ADAS)에는 차량의 종방향과 횡방향 운동에 대한 제어기가 요구된다. 횡방향 제어를 위해서는 조향 시스템의 조향각 제어가 요구되는데 최근 구조적으로 간단하고 연비향상, 차량의 중량 감소, 빠른 응답성을 가지고 있는 전기적 파워 조향 (Electric power steering, EPS) 시스템이 자동차 산업에서 널리 사용되고 있다. 차량의 주행관련 ADAS를 사용하여 자율 주행 시 EPS 시스템은 상위 제어기에서 계산된 필요한 조향각을 추종 할 수 있도록 조향 핸들의 각 제어를 해야 한다. 그러나 일반적인 EPS 시스템은 운전자가 조향 핸들에 인가된 토크를 보조해 줄 수 있는 토크를 출력해 준다. 본 논문에서는 이러한 문제를 해결하는 방법들을 설명한다. 먼저 EPS 시스템의 기본 기능에 대해서 설명을 하고, 자율 추행 차량을 위한 조항 핸들의 각 제어를 위한 proportional-integral 제어, 슬라이딩 모드 제어 (Sliding mode control), 관측기 기반 비선형 댐핑 제어(Observer based nonlinear damping control) 등과 같은 다양한 기법의 제어 알고리즘들에 대한 방법들이 고찰되었다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.50-58
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• 2002

The test method using simulator to objectively measure the steering feel from several drivers was proposed. It has also described the ideas to analyse the principal factors affecting the steering feel of the driver using the correlation analysis of the measured data and the questionnaire. Proportional Derivative(PD) controller has been used to measure the steering feel, and the control parameters have been selected to obtain the optimal steering feel. Membership frictions of Sugeno fuzzy model are constructed from the assist torque values calculated from PD controller at each steering state. Moreover to verify the performance, this fuzzy controller has been compared with the another fuzzy controller of which membership frictions are derived from the knowledge of drivers. As a result it can be concluded that the proposed fuzzy controller improves the steering feel at each steering state more than any other conventional methods.