• Clean Technology
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• v.25 no.1
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• pp.81-90
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• 2019

A dielectric barrier discharge (DBD) plasma reactor packed with $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst was used for the dry ($CO_2$) reforming of propane (DRP) to improve the production of syngas (a mixture of $H_2$ and CO) and the catalyst stability. The plasma-catalytic DRP was carried out with either thermally or plasma-reduced $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst at a $C_3H_8/CO_2$ ratio of 1/3 and a total feed gas flow rate of $300mL\;min^{-1}$. The catalytic activities associated with the DRP were evaluated in the range of $500{\sim}600^{\circ}C$. Following the calcination in ambient air, the ${\gamma}-Al_2O_3$ impregnated with the precursor solution ($Ni(NO_3)_2$ and $Ce(NO_3)_2$) was subjected to reduction in an $H_2/Ar$ atmosphere to prepare $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst. The characteristics of the catalysts were examined using X-ray diffraction (XRD), transmission electron microscopy (TEM), field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectrometry (EDS), temperature programmed reduction ($H_2-TPR$), temperature programmed desorption ($H_2-TPD$, $CO_2-TPD$), temperature programmed oxidation (TPO), and Raman spectroscopy. The investigation revealed that the plasma-reduced $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst exhibited superior catalytic activity for the production of syngas, compared to the thermally reduced catalyst. Besides, the plasma-reduced $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst was found to show long-term catalytic stability with respect to coke resistance that is main concern regarding the DRP process.

• Journal of Practical Agriculture & Fisheries Research
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• v.16 no.1
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• pp.37-53
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• 2014

The purpose of this research is to find what difficulties the agricultural successors, the Korea National College of Agriculture and Fisheries (KNCAF) graduates, face with in implementing eco-friendly agriculture in Jeju, and what solutions they can be provided with. This research, a case study on the basis of open-ended survey questions, has 6 cases out of 8 graduates who have or had implemented eco-friendly citrus farming. In Jeju, 24 graduates have involved in citrus farming. According to the case study, only one case was environment-friendly farming method at the pesticide-free level, and the others at organic farming level. All the cases have tried to alter main crops or to diversify management for coping with global climate change and market-opening. On analyzing operating cost to gain product of merchantable quality, it revealed that the environment-friendly farming method needs much more managing efforts than the conventional farming does. But to the contrary, the materials cost in the environment-friendly farming method was lower than in the conventional farming method. In the total production and the price, the environment-friendly farming was 20~50% lower and 10~50% higher than the conventional farming, respectively. Difficulties which the graduates confronted with in implementing the environment-friendly agriculture are as below. Firstly, many of the difficulties have resulted from lack of the environment-friendly farming techniques, and the high cost of farm scale improvement due to high price of land and topographical features of Jeju. Secondly, the agricultural successors, the KNCAF graduates, have trouble in obtaining approval of their parents to changeover from the conventional farming to the environment-friendly farming. Lastly, there is no advisory organizations and experts for environment-friendly farming in the given area. For shift to the environment-friendly farming, followings are needed. Agricultural Technology & Extension center, with cooperation of leading farms in environment-friendly farming, should have a key role in offering education and consults on the environment-friendly farming techniques. Also, this organization should inform rapidly the research results to the farmers, and their feed-back should be involved in the next research. Therefore, it is suggested that the forum called 'Environment-friendly Organic Farming Forum in Jeju' tentatively is organized.

• 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.