• Journal of the Korean Society for Nondestructive Testing
• /
• v.21 no.1
• /
• pp.80-90
• /
• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Composites Research
• /
• v.34 no.1
• /
• pp.8-15
• /
• 2021

In the shipbuilding and marine industry, as a technology for reducing the weight of parts to reduce energy and improve operational efficiency of ships is required, a method of applying fibers-reinforced composites which is high-strength lightweight materials, as part materials can be considered. In this study, the possibility of applying fibers-reinforced composites to the pipe support clamps was evaluated to reduce the weight of LNGC. The fibers-reinforced composites were manufactured using carbon fibers and glass fibers as reinforcing fibers. Through the computer simulation program, the properties of the reinforcing materials and the matrix materials of the composites were inversely calculated, and the performance prediction was performed according to the change in the properties of each fiber lamination pattern. In addition, the structural analysis of the clamps according to the thickness of the composites was performed through the finite element analysis program. As a result of the study, it was confirmed that attention is needed in selecting the thickness when applying the fibers-reinforced composites of the clamp for weight reduction. It is considered that it will be easy to change the shape of the structure and change the structure for weight reduction in future supplementary design.

• Journal of the Korean Institute of Gas
• /
• v.25 no.1
• /
• pp.20-29
• /
• 2021

During multi-stage fracturing in a low permeable shale formation, stress interference occurs between the stages which is called the "stress shadow effect(SSE)". The effect may alter the fracture propagation direction and induce ununiform geometry. In this study, the stress shadow effect on the hydraulic fracture geometry and the well productivity were investigated by the commercial full-3D fracture model, GOHFER. In a homogeneous reservoir model, a multi-stage fracturing process was performed with or without the SSE. In addition, the fracturing was performed on two shale reservoirs with different geomechanical properties(Young's modulus and Poisson's ratio) to analyze the stress shadow effect. In the simulation results, the stress change caused by the fracture created in the previous stage switched the maximum/minimum horizontal stress and the lower productivity L-direction fracture was more dominating over the T-direction fracture. Since the Marcellus shale is more brittle than more dominating over the T-direction fracture. Since the Marcellus shale is more brittle than the relatively ductile Eagle Ford shale, the fracture width in the former was developed thicker, resulting in the larger fracture volume. And the Marcellus shale's Young's modulus is low, the stress effect is less significant than the Eagle Ford shale in the stage 2. The stress shadow effect strongly depends on not only the spacing between fractures but also the geomechanical properties. Therefore, the stress shadow effect needs to be taken into account for more accurate analysis of the fracture geometry and for more reliable prediction of the well productivity.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1246-1246
• /
• 2001

The quality of meat is highly variable in many properties. This variability originates from both animal production and meat processing. At the pre-slaughter stage, animal factors such as breed, sex, age contribute to this variability. Environmental factors include feeding, rearing, transport and conditions just before slaughter (Hildrum et al., 1995). Meat can be presented in a variety of forms, each offering different opportunities for adulteration and contamination. This has imposed great pressure on the food manufacturing industry to guarantee the safety of meat. Tissue and muscle speciation of flesh foods, as well as speciation of animal derived by-products fed to all classes of domestic animals, are now perhaps the most important uncertainty which the food industry must resolve to allay consumer concern. Recently, there is a demand for rapid and low cost methods of direct quality measurements in both food and food ingredients (including high performance liquid chromatography (HPLC), thin layer chromatography (TLC), enzymatic and inmunological tests (e.g. ELISA test) and physical tests) to establish their authenticity and hence guarantee the quality of products manufactured for consumers (Holland et al., 1998). The use of Near Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise and non-destructive analysis of a wide range of organic materials has been comprehensively documented (Osborne et at., 1993). Most of the established methods have involved the development of NIRS calibrations for the quantitative prediction of composition in meat (Ben-Gera and Norris, 1968; Lanza, 1983; Clark and Short, 1994). This was a rational strategy to pursue during the initial stages of its application, given the type of equipment available, the state of development of the emerging discipline of chemometrics and the overwhelming commercial interest in solving such problems (Downey, 1994). One of the advantages of NIRS technology is not only to assess chemical structures through the analysis of the molecular bonds in the near infrared spectrum, but also to build an optical model characteristic of the sample which behaves like the “finger print” of the sample. This opens the possibility of using spectra to determine complex attributes of organic structures, which are related to molecular chromophores, organoleptic scores and sensory characteristics (Hildrum et al., 1994, 1995; Park et al., 1998). In addition, the application of statistical packages like principal component or discriminant analysis provides the possibility to understand the optical properties of the sample and make a classification without the chemical information. The objectives of this present work were: (1) to examine two methods of sample presentation to the instrument (intact and minced) and (2) to explore the use of principal component analysis (PCA) and Soft Independent Modelling of class Analogy (SIMCA) to classify muscles by quality attributes. Seventy-eight (n: 78) beef muscles (m. longissimus dorsi) from Hereford breed of cattle were used. The samples were scanned in a NIRS monochromator instrument (NIR Systems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced presentation to the instrument were explored. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.

• Composites Research
• /
• v.36 no.1
• /
• pp.48-52
• /
• 2023

Fiber-reinforced composites have anisotropic material properties, so the mechanical properties of composite structures can vary depending on the stacking sequence. Therefore, it is essential to design the proper stacking sequence of composite structures according to the functional requirements. However, depending on the manufacturing condition or the shape of the structure, there are many cases where the designed stacking angle is out of range, which can affect structural performance. Accordingly, it is important to analyze the stacking angle in order to confirm that the composite structure is correctly fabricated as designed. In this study, the stacking angle was predicted from real cross-sectional images of fiber-reinforced composites using convolutional neural network (CNN)-based deep learning. Carbon fiber-reinforced composite specimens with several stacking angles were fabricated and their cross-sections were photographed on a micro-scale using an optical microscope. The training was performed for a CNN-based deep learning model using the cross-sectional image data of the composite specimens. As a result, the stacking angle can be predicted from the actual cross-sectional image of the fiber-reinforced composite with high accuracy.

• Tunnel and Underground Space
• /
• v.33 no.3
• /
• pp.169-188
• /
• 2023

Fires in tunnels are characterized by higher temperature rise and higher maximum temperatures compared to ground fires. For this reason, countries such as the Netherlands and Germany have developed separate temperature-time curves for use in tunnel fires. Fires in tunnels cause damage to the tunnel lining, such as loss of section and deterioration of the material properties. This study reviewed the design concept of fire stability of structures, section loss due to spalling, changes in physicochemical and mechanical properties of tunnel lining materials, fireproofing materials for structure safety, and fire damage prediction models. In order to secure the stability of a structure against fire, it is necessary to identify the type of structure and the possible fire load at the design stage, identify the expected section loss and damage range, and prepare for such damage through fireproofing materials. In this study, we have summarized the matters that can be referred to in performing such a series of tasks and presented our opinions on them.

• Journal of Bio-Environment Control
• /
• v.29 no.2
• /
• pp.161-170
• /
• 2020

There is systematic spatial variations in environmental properties due to sensitive reaction to external conditions at plastic greenhouse occupied 99.2% of domestic agricultural facilities. In order to construct 3 dimensional distribution of temperature, relative humidity, CO₂ and illuminance, measurement matrix as 3 by 3 by 5 in direction of width, height and length, respectively, dividing indoor space of greenhouse was designed and tested at experimental site. Linear regression analysis was conducted to evaluate optimal estimation method in terms with horizontal and vertical variations. Even though sole measurement point for temperature and relative humidity could be feasible to assess indoor condition, multiple measurement matrix is inevitably required to improve spatial precision at certain time domain such as period of sunrise and sunset. In case with CO₂, multiple measurement matrix could not successfully improve the spatial predictability during a whole experimental period. In case with illuminance, prediction performance was getting smaller after a time period of sunrise due to systematic interference such as indoor structure. Thus, multiple sensing methodology was proposed in direction of length at higher height than growing bed, which could compensate estimation error in spatial domain. Appropriate measurement matrix could be constructed considering the transition of stability in indoor environmental properties due to external variations. As a result, optimal measurement matrix should be carefully designed considering flexibility of construction relevant with the type of property, indoor structure, the purpose of crop and the period of growth. For an instance, partial cooling and heating system to save a consumption of energy supplement could be successfully accomplished by the deployment of multiple measurement matrix.

• Geotechnical Engineering
• /
• v.12 no.4
• /
• pp.157-178
• /
• 1996

Current design methods for reinforced earth structures take no account of the magnitude of the strains induced in the tensile members as these are invariably manufactured from high modulus materials, such as steel, where straits are unlikely to be significant. With fabrics, however, large strains may frequently be induced and it is important to determine these to enable the stability of the structure to be assessed. In the present paper internal design method of analysis relating to the use of fabric reinforcements in reinforced earth structures for both stress and strain considerations is presented. For the internal stability analysis against rupture and pullout of the fabric reinforcements, a strain compatibility analysis procedure that considers the effects of reinforcement stiffness, relative movement between the soil and reinforcements, and compaction-induced stresses as studied by Ehrlich 8l Mitchell is used. I Bowever, the soil-reinforcement interaction is modeled by relating nonlinear elastic soil behavior to nonlinear response of the reinforcement. The soil constitutive model used is a modified vertsion of the hyperbolic soil model and compaction stress model proposed by Duncan et at., and iterative step-loading approach is used to take nonlinear soil behavior into consideration. The effects of seepage pressures are also dealt with in the proposed method of analy For purposes of assessing the strain behavior oi the fabric reinforcements, nonlinear model of hyperbolic form describing the load-extension relation of fabrics is employed. A procedure for specifying the strength characteristics of paraweb polyester fibre multicord, needle punched non-woven geotHxtile and knitted polyester geogrid is also described which may provide a more convenient procedure for incorporating the fablic properties into the prediction of fabric deformations. An attempt to define improvement in bond-linkage at the interconnecting nodes of the fabric reinforced earth stracture due to the confining stress is further made. The proposed method of analysis has been applied to estimate the maximum tensions, deformations and strains of the fabric reinforcements. The results are then compared with those of finite element analysis and experimental tests, and show in general good agreements indicating the effectiveness of the proposed method of analysis. Analytical parametric studies are also carried out to investigate the effects of relative soil-fabric reinforcement stiffness, locked-in stresses, compaction load and seepage pressures on the magnitude and variation of the fabric deformations.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_1
• /
• pp.933-944
• /
• 2019

This study analyzes the tide deformation of land boundary regions on the east (Region A) and west (Region B) sides of the Ross Ice Shelf in Antarctica using Double-Differential Interferometric Synthetic Aperture Radar (DDInSAR). A total of seven Sentinel-1A SAR images acquired in 2015-2016 were used to estimate the accuracy of tide prediction model and Young's modulus of ice shelf. First, we compared the Ross Sea Height-based Tidal Inverse (Ross_Inv) model, which is a representative tide prediction model for the Antarctic Ross Sea, with the tide deformation of the ice shelf extracted from the DDInSAR image. The accuracy was analyzed as 3.86 cm in the east region of Ross Ice Shelf and it was confirmed that the inverse barometric pressure effect must be corrected in the tide model. However, in the east, it is confirmed that the tide model may be inaccurate because a large error occurs even after correction of the atmospheric effect. In addition, the Young's modulus of the ice was calculated on the basis of the one-dimensional elastic beam model showing the correlation between the width of the hinge zone where the tide strain occurs and the ice thickness. For this purpose, the grounding line is defined as the line where the displacement caused by the tide appears in the DDInSAR image, and the hinge line is defined as the line to have the local maximum/minimum deformation, and the hinge zone as the area between the two lines. According to the one-dimensional elastic beam model assuming a semi-infinite plane, the width of the hinge region is directly proportional to the 0.75 power of the ice thickness. The width of the hinge zone was measured in the area where the ground line and the hinge line were close to the straight line shown in DDInSAR. The linear regression analysis with the 0.75 power of BEDMAP2 ice thickness estimated the Young's modulus of 1.77±0.73 GPa in the east and west of the Ross Ice Shelf. In this way, more accurate Young's modulus can be estimated by accumulating Sentinel-1 images in the future.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.159-172
• /
• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.