• Biomaterials and Biomechanics in Bioengineering
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• v.2 no.3
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• pp.143-157
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• 2015

Successful integration of cementless femoral stems using porous surfaces relies on effective periimplant bone healing to secure the bone-implant interface. The initial stages of the healing process involve protein adsorption, fibrin clot formation and cell osteoconduction onto the implant surface. Modelling this process in vitro, the current work considered the effect of fibrin deposition on the responses of human mesenchymal stromal cells cultured on ferritic fibre networks intended for magneto-mechanical actuation of in-growing bone tissue. The underlying hypothesis for the study was that fibrin deposition would support early stromal cell attachment and physiological functions within the optimal regions for strain transmission to the cells in the fibre networks. Highly porous fibre networks composed of 444 ferritic stainless steel were selected due to their ability to support human osteoblasts and mesenchymal stromal cells without inducing untoward inflammatory responses in vitro. Cell attachment, proliferation, metabolic activity, differentiation and penetration into the ferritic fibre networks were examined for one week. For all fibrin-containing samples, cells were observed on and between the metal fibres, supported by the deposited fibrin, while cells on fibrin-free fibre networks (control surface) attached only onto fibre surfaces and junctions. Initial cell attachment, measured by analysis of deoxyribonucleic acid, increased significantly with increasing fibrinogen concentration within the physiological range. Despite higher cell numbers on fibrin-containing samples, similar metabolic activities to control surfaces were observed, which significantly increased for all samples over the duration of the study. It is concluded that fibrin deposition can support the early attachment of viable mesenchymal stromal cells within the inter-fibre spaces of fibre networks intended for magneto-mechanical strain transduction to in-growing cells.

• Computers and Concrete
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• v.12 no.5
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• pp.613-625
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• 2013

In this study, Artificial Neural Networks (ANN) analysis is used to predict the compression strength of polypropylene fibre mixed concrete. Polypropylene fibre admixture increases the compression strength of concrete to a certain extent according to mix proportion. This proportion and homogenous distribution are important parameters on compression strength. Determination of compression strength of fibre mixed concrete is significant due to the veridicality of capacity calculations. Plenty of experiments shall be completed to state the compression strength of concrete which have different fibre admixture. In each case, it is known that performing the laboratory experiments is costly and time-consuming. Therefore, ANN analysis is used to predict the 7 and 28 days of compression strength values. For this purpose, 156 test specimens are produced that have 26 different types of fibre admixture. While the results of 120 specimens are used for training process, 36 of them are separated for test process in ANN analysis to determine the validity of experimental results. Finally, it is seen that ANN analysis predicts the compression strength of concrete successfully.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.187-189
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• 2009

Fibre to the home (FTTH) in KOREA has grown rapidly in recent years. The number of FTTH subscribers exceeded 6.6 millions by end of DEC 2008. It seems that FTTH has moved from the initial, the growth stage to the mature stage. Some design techniques have been developed in order to construct the access network effectively for the mature stage. This paper deal with design, construction and maintenance of optical fibre access networks immediately, effectively and economically.

• Computers and Concrete
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• v.15 no.4
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• pp.503-514
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• 2015

Fibre-added concretes are frequently used in large site applications such as slab and airports as well as in bearing system elements or prefabricated elements. It is very difficult to determine the mechanical properties of the fibre-added concretes by experimental methods in situ. The purpose of this study is to develop an artificial neural network (ANN) model in order to predict the compressive and bending strengths of hybrid fibre-added and non-added concretes. The strengths have been predicted by means of the data that has been obtained from destructive (DT) and non-destructive tests (NDT) on the samples. NDTs are ultrasonic pulse velocity (UPV) and Rebound Hammer Tests (RH). 105 pieces of cylinder samples with a dimension of $150{\times}300mm$, 105 pieces of bending samples with a dimension of $100{\times}100{\times}400mm$ have been manufactured. The first set has been manufactured without fibre addition, the second set with the addition of %0.5 polypropylene and %0.5 steel fibre in terms of volume, and the third set with the addition of %0.5 polypropylene, %1 steel fibre. The water/cement (w/c) ratio of samples parametrically varies between 0.3-0.9. The experimentally measured compressive and bending strengths have been compared with predicted results by use of ANN method.

• Smart Structures and Systems
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• v.2 no.1
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• pp.81-100
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• 2006

To calculate the shear capacity of concrete beams reinforced with fibre-reinforced polymer (FRP), current shear design provisions use slightly modified versions of existing semi-empirical shear design equations that were primarily derived from experimental data generated on concrete beams having steel reinforcement. However, FRP materials have different mechanical properties and mode of failure than steel, and extending existing shear design equations for steel reinforced beams to cover concrete beams reinforced with FRP is questionable. This paper investigates the feasibility of using artificial neural networks (ANNs) to estimate the nominal shear capacity, Vn of concrete beams reinforced with FRP bars. Experimental data on 150 FRP-reinforced beams were retrieved from published literature. The resulting database was used to evaluate the validity of several existing shear design methods for FRP reinforced beams, namely the ACI 440-03, CSA S806-02, JSCE-97, and ISIS Canada-01. The database was also used to develop an ANN model to predict the shear capacity of FRP reinforced concrete beams. Results show that current guidelines are either inadequate or very conservative in estimating the shear strength of FRP reinforced concrete beams. Based on ANN predictions, modified equations are proposed for the shear design of FRP reinforced concrete beams and proved to be more accurate than existing equations.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.22-26
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• 2007

In the fibre to the home (FTTH) era, it is expected that broadband network provisioning will require thousands of optical fibres to be accommodated in a central office for optical access networks. An optical fibre line testing system could be used to reduce maintenance costs and improve service availability depending on the PON's element manager capabilities and the maintenance procedures adopted by the network supplier. When monitoring optical fibres transmitting communication lights, a wavelength of 1625 nm is used for maintenance testing. The splitter installed optical filters allow the communication light to pass but that cut off the test light in the front of optical line terminals (OLTs) and optical network terminals (ONTs). In the economic point of view, We design new planer lightwave circuit splitter embedded filtter and study it optical property.

• Journal of applied mathematics & informatics
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• v.13 no.1_2
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• pp.67-84
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• 2003

A quadrature rule for the finite Hilbert Trasnform via trapezoid type inequalities is obtained . Some numerical experiments for different divisions of the interval [a, b] are also presented.

• Journal of the Korean Wood Science and Technology
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• v.49 no.5
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• pp.491-503
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• 2021

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

• Journal of Communications and Networks
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• v.1 no.3
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• pp.145-152
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• 1999

A new switch architecture is presented to enhance out-put queuing in photonic packet switches. Its appkication is for a packet switching enviroment based on the optical transport of fixed length packets. This architecture consists of a couple of cas-cading switching elements with output queuing, whose buffer ca-pacity is limited by photonic technology. The introduction of a suitable buffer management allows a very good and balanced ex-ploitation of the available optical memories, realized with fiber de-lay lines. In particular, packet loss performance is here evaluated showing the improvement with respect to the single switch and a way to design large optical switches is shown in order to meet broadband network requirements.

• Smart Structures and Systems
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• v.6 no.3
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• pp.239-262
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• 2010

Tube Lines has carried out a "knowledge and investigation programme" on the deep tube tunnels comprising the Jubilee, Northern and Piccadilly lines, as required by the PPP contract with London Underground. Many of the tunnels have been in use for over 100 years, so this assessment was considered essential to the future safe functioning of the system. This programme has involved a number of generic investigations which guide the assessment methodology and the analysis of some 5,000 individual structures. A significant amount of investigation has been carried out, including ultrasonic thickness measurement, detection of brickwork laminations using radar, stress measurement using magnetic techniques, determination of soil parameters using CPT, pressuremeter and laboratory testing, installation of piezometers, material and tunnel segment testing, and trialling of remote photographic techniques for inspection of large tunnels and shafts. Vibrating wire, potentiometer, electro level, optical and fibre-optic monitoring has been used, and laser measurement and laser scanning has been employed to measure tunnel circularity. It is considered that there is scope for considerable improvements in non-destructive testing technology for structural assessment in particular, and some ideas are offered as a "wish-list". Assessment reports have now been produced for all assets forming Tube Lines' deep tube tunnel network. For assets which are non-compliant with London Underground standards, the risk to the operating railway has to be maintained as low as reasonably practicable (ALARP) using enhanced inspection and monitoring, or repair where required. Monitoring techniques have developed greatly during recent years and further advances will continue to support the economic whole life asset management of infrastructure networks.