• Journal of KIBIM
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• v.2 no.1
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• pp.27-39
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• 2012

Public construction projects need complex and multi-functional management skill, since the most of public construction projects are comprised of multi-project and mega-projects. In order to effectively manage construction projects, PMIS is widely used. However the majority of the current PMIS have been developed as a single project-oriented business management system. Thus compatibility problems are encountered during the process of integrating the entire systems to manage the multi-complex projects. In addition, the form of orders applying BIM are increased recently, but the research and development of BIM based PMIS are still lacking. In this study, therefore, the functions of PMIS main objectives based on the analysis of PMIS As-Is and To-Be of PMIS, the dual management system utilizing Internet and Intranet will be proposed to integrate the individual PMIS with Integrated Program Management System. Rather than combining commercial BIM tool and PMIS directly, which is the common method of failure, the sequential process model to adopt BIM based PMIS is also explained. Step-by-step development method of BIM based PMIS is suggested to prepare for the activation of BIM technology in the nearest future.

• Microbiology and Biotechnology Letters
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• v.40 no.4
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• pp.414-418
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• 2012

The quantity of research on microbial fuel cells has been rapidly increasing. Microbial fuel cells are unique in their ability to utilize microorganisms and to generate electricity from sewage, pig excrement, and other wastewaters which include organic matter. This system can directly produce electrical energy without an inefficient energy conversion step. However, with MFCs maximum power production is limited by several factors such as activation losses, ohmic losses, and mass transfer losses in cathodes. Therefore, electron acceptors such as nitrate and ferric ion in the cathodes were utilized to improve the cathode reaction rate because the cathode reaction is very important for electricity production. When 100 mM nitrate as an electron acceptor was fed into cathodes, the current in single-cathode and dual-cathode MFCs was noted as $3.24{\pm}0.06$ mA and $4.41{\pm}0.08$ mA, respectively. These values were similar to when air-saturated water was fed into the cathodes. One hundred mM nitrate as an electron acceptor in the cathode compartments did not affect an increase in current generation. However, when ferric ion was used as an electron acceptor the current increased by $6.90{\pm}0.36$ mA and $6.67{\pm}0.33$ mA, in the single-cathode and dual-cathode microbial fuel cells, respectively. These values, in single-cathode and dual-cathode microbial fuel cells, represent an increase of 67.1% and 17.6%, respectively. Furthermore, when supplied with ferric ion without air, the current was higher than that of only air-saturated water. In this study, we attempted to reveal an inexpensive and readily available electron acceptor which can replace platinum in cathodes to improve current generation by increasing the cathode reaction rate.

• Tunnel and Underground Space
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• v.28 no.5
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• pp.400-425
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• 2018

This study presents the research results and current status of the DECOVALEX-2019 project Task B. Task B named 'Fault slip modelling' is aiming at developing a numerical method to simulate the coupled hydro-mechanical behavior of fault, including slip or reactivation, induced by water injection. The first research step of Task B is a benchmark simulation which is designed for the modelling teams to familiarize themselves with the problem and to set up their own codes to reproduce the hydro-mechanical coupling between the fault hydraulic transmissivity and the mechanically-induced displacement. We reproduced the coupled hydro-mechanical process of fault slip using TOUGH-FLAC simulator. The fluid flow along a fault was modelled with solid elements and governed by Darcy's law with the cubic law in TOUGH2, whereas the mechanical behavior of a single fault was represented by creating interface elements between two separating rock blocks in FLAC3D. A methodology to formulate the hydro-mechanical coupling relations of two different hydraulic aperture models and link the solid element of TOUGH2 and the interface element of FLAC3D was suggested. In addition, we developed a coupling module to update the changes in geometric features (mesh) and hydrological properties of fault caused by water injection at every calculation step for TOUGH-FLAC simulator. Then, the transient responses of the fault, including elastic deformation, reactivation, progressive evolutions of pathway, pressure distribution and water injection rate, to stepwise pressurization were examined during the simulations. The results of the simulations suggest that the developed model can provide a reasonable prediction of the hydro-mechanical behavior related to fault reactivation. The numerical model will be enhanced by continuing collaboration and interaction with other research teams of DECOLVAEX-2019 Task B and validated using the field data from fault activation experiments in a further study.

• Journal of Microbiology and Biotechnology
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• v.20 no.9
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• pp.1331-1338
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• 2010

This study was designed to investigate the potentially protective effects of Curcuma longa Linn. extract (CLE) on carbon tetrachloride ($CCl_4$)-induced hepatotoxicity in rats. Male Sprague-Dawley rats were pretreated with 50 or 100mg/kg of CLE or 100mg/kg of butylated hydroxytoluene(BHT) for 14 days before $CCl_4$ administration. In addition, the CLE control group was pretreated with 100mg/kg CLE for only 14 days. Three hours after the final treatment, a single dose of $CCl_4$ (20mg/kg) was administrated intraperitoneally to each group. After the completion of this phase of the experiment, food and water were removed 12 h prior to the next step. The rats were then anesthetized by urethane and their blood and liver were collected. It was observed that the aspartate aminotransferase and alanine aminotransferase activities of the serum, and the hepatic malondialdehyde levels had significantly decreased in the CLE group when compared with the $CCl_4$-treated group. The antioxidant activities, such as superoxide dismutase, catalase, and glutathione peroxidase activities, in addition to glutathione content, had increased considerably in the CLE group compared with the $CCl_4$-treated group. Phase II detoxifying enzymes, such as glutathione S-transferase, were found to have significantly increased in the CLE group as opposed to the $CCl_4$-treated group. The content of Nrf2 was determined by Western blot analysis. Pretreated CLE increased the level of nuclear translocated Nrf2, and the Nrf2 then increased the activity of the antioxidant and phase II detoxifying enzymes. These results indicate that CLE has protective effects against $CCl_4$-induced hepatotoxicity in rats, via activities of antioxidant and phase II detoxifying enzymes, and through the activation of nuclear translocated Nrf2.

• Journal of Embryo Transfer
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• v.29 no.4
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• pp.339-344
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• 2014

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein (Cas9) system can be applied to produce transgenic pigs. Therefore, we applied CRISPR/Cas9 system to generate FoxN1-targeted pig parthenogenetic embryos. Using single guided RNA targeted to pig FoxN1 genes was injected into cytoplasm of in vitro matured oocyte before electrical activation. In results, regardless of the concentrations of vector, the cleavage rate were significantly (p<0.05) decreased ($4ng/{\mu}l$, 51.24%; $8ng/{\mu}l$, 40.88%; and $16ng/{\mu}l$; 45.22%) compared to no injection group (70.44%). The blastocyst formation rates were also decreased in vector injected 3 groups ($4ng/{\mu}l$, 7.96%; $8ng/{\mu}l$, 6.4%; and $16ng/{\mu}l$; 9.04%) compared to no injection group (29.07%). In addition, the blastocyst formation rates between sham injected group (13.51%) and no injection group (29.07%) also showed significant difference (p<0.05). The mutation rates were comparable between groups ($4ng/{\mu}l$, 18.4%; $8ng/{\mu}l$, 12.5%; and $16ng/{\mu}l$; 20.0%). The sequencing analysis showed that blastocysts derived from each group were successfully mutated in FoxN1 loci regardless of the vector concentrations. However, the deletion patterns were higher than the patterns of point mutation and insertion regardless of the vector concentrations. In conclusion, we described that cytoplasmic microinjection of FoxN1-targeted CRISPR/Cas9 vector could efficiently generate transgenic pig parthenogenetic embryos in one-step.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.363-377
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• 2021

The present study investigated the effects of dynamic tubing gait training(I and II) on the postural alignment, gait, and quality of life in chronic patients with Parkinson's disease. This study is based on the case study that recruited a total of 3 patients with chronic Parkinson's disease (Hoehn and Yahr Stage of 1 to 3 each one person). Dynamic tubing gait training (I and II) applied to chronic patients with Parkinson's disease for 25 sessions, 30 minutes a day, 5 days a week, over 5 weeks period. To investigate the effects of this study, evaluating using the postural alignment test, muscle activity tests, gait analysis, and quality of life scale for patient with Parkinson's disease. After the intervention of Dynamic tubing gait training (I and II), Trunk flexion was decreased. Also, during walking from initial contact (IC) to mid stance (Mst), muscle activity of Quadriceps, Hamstring, and Tibialis Anterior (TA) was increased and muscle activity of Gastrocnemius was decreased. The muscle activation of Erector Spinae (ES T12, L3) was increased in the H&Y I and III stages and decreased in the H&Y II stage. Length of gait line, single support line, ant/post position and lateral symmetry of center of pressure (COP) parameters improved. The spatio-temporal gait parameters including of step length, stride length, and velocity was increased, and cadence decreased. Further the quality of life of patients with Parkinson's disease was improved. Based on these findings, Dynamic tubing gait training (I and II) could be applied as a new approach to improve posture, gait, quality of life in chronic patients with Parkinson's disease for more than 5 years, whose drug resistance is halved.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.