• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.319-325
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• 2018

The use of 3D printing (Additive Manufacturing) technology has expanded from initial model production to the mass production of parts in the industrial field based on the continuous research and development of materials and process technology. As a representative polymer material for 3D printing, the polyamide-based material, which is one of the high-strength engineering plastics, is used mainly for manufacturing parts for automobiles because of its light weight and durability. In this study, the specimens were fabricated using Selective Laser Sintering, which has excellent mechanical properties, and the flexural characteristics were analyzed according to the temperature of the two types of polyamide 12 and glass bead reinforced PA12 materials. The test specimens were prepared in the directions of $0^{\circ}$, $45^{\circ}$, and $90^{\circ}$ based on the work platform, and then subjected to a flexural test in three test temperature environments of $-25^{\circ}C$, $25^{\circ}C$, and $60^{\circ}C$. As a result, PA12 had the maximum flexural strength in the direction of $90^{\circ}$ at $-25^{\circ}C$ and $0^{\circ}$ at $25^{\circ}C$ and $60^{\circ}C$. The glass bead-reinforced PA12 exhibited maximum flexural strength values at all test temperatures in the $0^{\circ}$ fabrication direction. The tendency of the flexural strength changes of the two materials was different due to the influence of the plane direction of the lamination layer depending on the type of stress generated in the bending test.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.555-561
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• 2020

In this study, the applicability of Hunt's analytical solution for a two-layered leaky aquifer system, which was developed to estimate stream depletion due to the groundwater pumping of the upper shallow aquifer, was evaluated. The 5-year averaged stream depletions were estimated using Hunt's analytical solution for various combinations of hydraulic characteristic values such as transmissivity, storage coefficient of the two aquifers, interlayer leakage coefficient, stream-well distance, hydraulic conductivity of the streambed, and stream width. Through comparison with the numerical solution accurately simulated with a MODFLOW groundwater flow model, the analytical solution derived by regarding the stream width as a point was evaluated. It was found that the error in the stream depletion calculated by the analytical solution can be reduced to less than 0.05 when the stream-well distance is greater than the stream width or when the stream depletion factor (SDF) is more than about 3,000 days. In addition, when the streambed hydraulic conductivity is less than 1 m/d, the hydraulic diffusion coefficient of the lower aquifer layer is less than 100 ㎡/d, the hydraulic diffusion coefficient ratio of the upper and lower aquifer layers is 5 or more, and the leakage coefficient between the layers is less than 0.0004 m/d, the overall analytical solutions were overestimated compared with the numerical solutions.

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

• Advances in aircraft and spacecraft science
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• v.11 no.1
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• pp.83-103
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• 2024

Adhesive bonding is currently widely used in many industrial fields, particularly in the aeronautics sector. Despite its advantages over mechanical joints such as riveting and welding, adhesive bonding is mostly used for secondary structures due to its low peel strength; especially if it is simultaneously exposed to temperature and humidity; and often presence of bonding defects. In fact, during joint preparation, several types of defects can be introduced into the adhesive layer such as air bubbles, cavities, or cracks, which induce stress concentrations potentially leading to premature failure. Indeed, the presence of defects in the adhesive joint has a significant effect on adhesive stresses, which emphasizes the need for a good surface treatment. The research in this field is aimed at minimizing the stresses in the adhesive joint at its free edges by geometric modifications of the ovelapping part and/or by changing the nature of the substrates. In this study, the finite element method is used to describe the mechanical behavior of bonded joints. Thus, a three-dimensional model is made to analyze the effect of defects in the adhesive joint at areas of high stress concentrations. The analysis consists of estimating the different stresses in an adhesive joint between two 2024-T3 aluminum plates. Two types of single lap joints(SLJ) were analyzed: a standard SLJ and another modified by removing 0.2 mm of material from the thickness of one plate along the overlap length, taking into account several factors such as the applied load, shape, size and position of the defect. The obtained results clearly show that the presence of a bonding defect significantly affects stresses in the adhesive joint, which become important if the joint is subjected to a higher applied load. On the other hand, the geometric modification made to the plate considerably reduces the various stresses in the adhesive joint even in the presence of a bonding defect.

• Journal of the Korean Geotechnical Society
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• v.39 no.5
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• pp.51-63
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• 2023

Numerical analysis was conducted to determine the effect of soil behavior by thawing and freezing of seasonal frozen soil on pile foundations. The analysis was performed using the finite element method (FEM) to simulate soil-pile interaction based on the atmosphere temperature change. Thermomechanical coupled modeling using FEM was applied with the temperature-dependent nonlinear properties of the frozen soil. The analysis model cases were applied to the MCR and HDP models to simulate the elastoplastic behavior of soil. The numerical analysis results were analyzed and compared with various conditions having different length and width sizes of the pile. The results of the numerical analysis showed t hat t he HDP model was relat ively passive, and t he aspect and magnit ude of t he bearing capacit y and displacement of the pile head were similar depending on the length and width of the pile conditions. The vertical displacement of the pile head by thawing and freezing of the ground showed a large variation in displacement for shorter length conditions. In the MCR model, the vertical displacement appeared in the maximum thaw settlement and frost heaving of 0.0387 and 0.0277 m, respectively. In the HDP model, the vertical displacement appeared in the maximum thaw settlement and frost heaving of 0.0367 and 0.0264 m, respectively. The results of the pile bearing capacity for the two elastoplastic models showed a larger difference in the width condition than the length condition of the pile, with a maximum of about 14.7% for the width L condition, a maximum of about 5.4% for M condition, and a maximum of about 5.3% for S condition. The significance of the effect on the displacement of the pile head and the bearing capacity depended on the pile-soil contact area, and the difference depended on the presence or absence of an active layer in the soil and its thickness.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.239-259
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• 2024

In this study, a qualitative meta-analysis was conducted on research papers on earth science education to derive knowledge of students' understanding of specific science topics-greenhouse effect, global warming, and climate change-within the context of collective Pedagogical Content Knowledge (PCK). Twenty-two research papers addressing students' alternative conceptions (misconceptions) about these topics were selected and analyzed for their respective definitions, causes (mechanisms), and impacts. Semantic network analysis and a mental model framework were applied to synthesize the findings. The meta-analysis revealed several key insights: (1) Regarding the greenhouse effect, students often used the terms "greenhouse effect" and "global warming" interchangeably, lacked knowledge about the types of greenhouse gases, and misunderstood their roles. They commonly associated the greenhouse effect with environmental pollution or changes in the ozone layer, failing to recognize its relation to the heat balance between the surface and atmosphere. (2) Concerning global warming, students confused it with sea level rise and linked it to pollution, ozone layer changes, and glacier melting. They understood global warming as a disruption of the heat balance between the surface and atmosphere but had misconceptions about its environmental impacts. (3) In terms of climate change, students used the term interchangeably with global warming, weather change, and climate anomalies. They associated climate change with atmospheric pollution and ozone layer depletion but misunderstood its environmental impacts. As result, three mental models-categorical, mechanistic, and hierarchical misconceptions-were identified as collective PCK. The implications for enhancing earth science teachers' PCK were discussed based on these findings.

• Journal of Veterinary Clinics
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• v.23 no.2
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• pp.105-110
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• 2006

A three-layer, feed-forward artificial neural network (ANN) with sixteen input neurons, three hidden neurons, and one output neuron was developed to identify the presence of infectious bronchitis (IB) infection as early as possible in laying hen flocks. Retrospective data from flocks that enrolled IB surveillance program between May 2003 and November 2005 were used to build the ANN. Data set of 86 flocks was divided randomly into two sets: 77 cases for training set and 9 cases for testing set. Input factors were 16 epidemiological findings including characteristics of the layer house, management practice, flock size, and the output was either presence or absence of IB. ANN was trained using training set with a back-propagation algorithm and test set was used to determine the network's capability to predict outcomes that it has never seen. Diagnostic performance of the trained network was evaluated by constructing receiver operating characteristic (ROC) curve with the area under the curve (AUC), which were also used to determine the best positivity criterion for the model. Several different ANNs with different structures were created. The best-fitted trained network, IBV_D1, was able to predict IB in 73 cases out of 77 (diagnostic accuracy 94.8%) in the training set. Sensitivity and specificity of the trained neural network was 95.5% (42/44, 95% CI, 84.5-99.4) and 93.9% (31/33, 95% CI, 79.8-99.3), respectively. For testing set, AVC of the ROC curve for the IBV_D1 network was 0.948 (SE=0.086, 95% CI 0.592-0.961) in recognizing IB infection status accurately. At a criterion of 0.7149, the diagnostic accuracy was the highest with a 88.9% with the highest sensitivity of 100%. With this value of sensitivity and specificity together with assumed 44% of IB prevalence, IBV_D1 network showed a PPV of 80% and an NPV of 100%. Based on these findings, the authors conclude that neural network can be successfully applied to the development of a screening model for identifying IB infection in laying hen flocks.

• Journal of the korean academy of Pediatric Dentistry
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• v.31 no.2
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• pp.228-235
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• 2004

The development and proliferation of the mandibular condyle can be altered by changes in the biomechanical environment of the temporomandibular joint. The biomechanical loads were varied by feeding diets of different consistencies. The purpose of the present study was to determine whether changes of masticatory forces by feeding a soft diet can alter the trabecular bone morphology of the growing mouse mandibular condyle, by means of micro-computed tomography. Thirty-six female, 21 days old, C57BL/6 mice were randomly divided into two groups. Mice in the hard-diet control group were fed standard hard rodent pellets for 8 weeks. The soft-diet group mice were given soft ground diets for 8 weeks and their lower incisors were shortened by cutting with a wire cutter twice a week to reduce incision. After 8 weeks all animals were killed after they were weighed. Following sacrifice, the right mandibular condyle was removed. High spatial resolution tomography was done with a Skyscan Micro-CT 1072. Cross-sections were scanned and three-dimensional images were reconstructed from 2D sections. Morphometric and nonmetric parameters such as bone volume(BV), bone surface(BS), total volume(TV), bone volume fraction(BV/TV), surface to volume ratio(BS/BV), trabecular thickness(Tb. Th.), structure model index(SMI) and degree of anisotropy(DA) were directly determined by means of the software package at the micro-CT system. From directly determined indices the trabecular number(Tb. N.) and trabecular separation(Tb. Sp.) were calculated according to parallel plate model of Parfitt et al.. After micro-tomographic imaging, the samples were decalcified, dehydrated, embedded and sectioned for histological observation. The results were as follow: 1. The bone volume fraction, trabecular thickness(Tb. Th.) and trabecular number(Tb. N.) were significantly decreased in the soft-diet group compared with that of the control group (p<0.05). 2. The trabecular separation(Tb. Sp.) was significantly increased in the soft-diet group(p<0.05). 3. There was no significant differences in the surface to volume ratio(BS/BV), structure model index(SMI) and degree of anisotropy(DA) between the soft-diet group and hard-diet control group (p>0.05). 4. Histological sections showed that the thickness of the proliferative layer and total cartilage thickness were significantly reduced in the soft-diet group.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.