• Journal of the Korean Vacuum Society
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• v.21 no.5
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• pp.273-278
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• 2012

Over the last decade, the hafnium-based gate dielectric materials have been studied for many application fields. Because these materials had excellent behaviors for suppressing the quantum-mechanical tunneling through the thinner dielectric layer with higher dielectric constant (high-K) than $SiO_2$ gate oxides. Although high-K materials compensated the deterioration of electrical properties for decreasing the thickness of dielectric layer in MOSFET structure, their nano-mechanical properties of $HfO_2$ thin film features were hardly known. Thus, we examined nano-mechanical properties of the Hafnium oxide ($HfO_2$) thin film in order to optimize the gate dielectric layer. The $HfO_2$ thin films were deposited by rf magnetron sputter using hafnium (99.99%) target according to various oxygen gas flows. After deposition, the $HfO_2$ thin films were annealed after annealing at $400^{\circ}C$, $600^{\circ}C$ and $800^{\circ}C$ for 20 min in nitrogen ambient. From the results, the current density of $HfO_2$ thin film for 8 sccm oxygen gas flow became better performance with increasing annealing temperature. The nano-indenter and Weibull distribution were measured by a quantitative calculation of the thin film stress. The $HfO_2$ thin film after annealing at $400^{\circ}C$ had tensile stress. However, the $HfO_2$ thin film with increasing the annealing temperature up to $800^{\circ}C$ had changed compressive stress. This could be due to the nanocrystal of the $HfO_2$ thin film. In particular, the $HfO_2$ thin film after annealing at $400^{\circ}C$ had lower tensile stress, such as 5.35 GPa for the oxygen gas flow of 4 sccm and 5.54 GPa for the oxygen gas flow of 8 sccm. While the $HfO_2$ thin film after annealing at $800^{\circ}C$ had increased the stress value, such as 9.09 GPa for the oxygen gas flow of 4 sccm and 8.17 GPa for the oxygen gas flow of 8 sccm. From these results, the temperature dependence of stress state of $HfO_2$ thin films were understood.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9B
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• pp.610-618
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• 2005

As developed Internet and Computer Capability, Many Users take the many information through the network. So requirement of User that use to network was rapidly increased and become various. But it spend much time to accept user requirement on current network, so studied such as Active network for solved it. This Active node on Active network have the capability that stored and processed execution code aside from capability of forwarding packet on current network. So required execution code for executed packet arrived in active node, if execution code should not be in active node, have to take by request previous Action node and Code Server to it. But if this execution code take from previous active node and Code Server, bring to time delay by transport execution code and increased traffic of network and execution time. So, As used execution code stored in cache on active node, it need to increase execution time and decreased number of request. So, our paper suggest ANC caching technique that able to decrease number of execution code request and time of execution code by efficiently store execution code to active node. ANC caching technique may decrease the network traffic and execution time of code, to decrease request of execution code from previous active node.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.31-38
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• 2008

Ad-Hoc network is a network architecture which has no backbone network and is deployed temporarily and rapidly in emergency or war without fixed mobile infrastructures. All communications between network entities are carried in ad-hoc networks over the wireless medium. Due to the radio communications being extremely vulnerable to propagation impairments, connectivity between network nodes is not guaranteed. Therefore, many new algorithms have been studied recently. This study proposes the secondary header approach to the cluster based routing protocol (CBRP). The primary header becomes abnormal status so that the primary header can not participate in the communications between network entities, the secondary header immediately replaces the primary header without selecting process of the new primary header. This improves the routing interruption problem that occurs when a header is moving out from a cluster or in the abnormal status. The performances of proposed algorithm ACBRP(Advanced Cluster Based Routing Protocol) are compared with CBRP. The cost of the primary header reelection of ACBRP is simulated. And results are presented in order to show the effectiveness of the algorithm.

• Journal of the East Asian Society of Dietary Life
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• v.12 no.2
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• pp.91-99
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• 2002

This study was carried out to develop a diabetic-oriented convenience flood using 7 medicinal plants (Schisandra chinensis, Coix lachryma-jobi, Dioscorea batatas, Ophipogon japonicus, Lyicium chinense, Houttuynia cordata, Polygonatum sibiricum) and chicken. Portion size was 310g, total calorie was 551.6 kcal and carbohydrate, lipid and protein were consisted of 53.0%, 20.9% and 26.1%, respectively. Calcium, zinc and iron content were 268.9mg, 5.4mg and 6.1mg, respectively. Crude fiber content was 22.9g. In sensory evaluation, the scores of taste, color, texture and overall acceptability were higher than normal diabetic meal. Hypoglycemic effect of the device meal for diabetic persons was excellent compared to that of normal diabetic meal. The above results indicate that the 7 medicinal plants can be used as functional ingredients fur diabetic-oriented convenience flood industry. Also, device meal can be used as ready-prepared food for weight control.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.7
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• pp.505-514
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• 2009

Aqueous potassium salt of serine was proposed as an alternative $CO_2$ absorbent to monoethanolamine (MEA) and its $CO_2$ absorption characteristics were studied. The experiment has been conducted using screening test equipment with NDIR type gas analyzer and vapor-liquid equilibrium apparatus. $CO_2$ absorption/desorption rate and net amount of $CO_2$ absorbed in cyclic process are the criteria to assess the $CO_2$ absorption characteristics in this study. Effective $CO_2$ loading of potassium salt of serine and MEA are 0.425 and 0.230 respectively. Cyclic capacities are 0.354 and 0.298 for potassium salt of serine and MEA. The absorption rate of the potassium serinate decreased sharply at $CO_2$ loading is 0.1 and were maintained approximately at half of MEA. To enhance the absorption rate of aqueous potassium salt of serine, small quantities of rate promoters, namely piperazine and tetraethylenepentamine were blended, so that rich $CO_2$ loading were increased by 13.7% and 18.7% respectively. The rich $CO_2$ loading of potassium salt of serine was 29.2% and 35.0% higher than those of aqueous sodium and lithium salt of serine, respectively. The absorption rate of potassium salt of valine and isoleucine which have similar molecular structures to serine were lower than that of serine because of the presence of bulky side group. Precipitation phenomena during $CO_2$ absorption were discussed by the aid of literatures.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.1
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• pp.167-175
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• 2019

To effectively deal with the fire in tunnel which is mostly the vehicle fire, it's more important to suppress the fire at early stage. In urban tunnel, however, accessibility to the scene of fire by the fire fighter is very limited due to severe traffic congestion which causes the difficulty with firefighting activity in timely manner and such a problem would be further worsened in underground road (double-deck tunnel) which has been increasingly extended and deepened. In preparation for the disaster in Korea, the range of life safety facilities for installation is defined based on category of the extension and fire protection referring to risk hazard index which is determined depending on tunnel length and conditions, and particularly to directly deal with the tunnel fire, fire extinguisher, indoor hydrant and sprinkler are designated as the mandatory facilities depending on category. But such fire extinguishing installations are found inappropriate functionally and technically and thus the measure to improve the system needs to be taken. Particularly in a double-deck tunnel which accommodates the traffic in both directions within a single tunnel of which section is divided by intermediate slab, the facility or the system which functions more rapidly and effectively is more than important. This study, thus, is intended to supplement the problems with existing tunnel life safety system (fire extinguishing) and develop the remote-controlled automatic fire extinguishing system which is optimized for a double-deck tunnel. Consequently, the system considering low floor height and extended length as well as indoor hydrant for a wide range of use have been developed together with the performance verification and the process for commercialization before applying to the tunnel is underway now.

• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.1
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• pp.47-56
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• 2023

This study describes the successful synthesize strategy for the silica nanoparticles utilizing various water sources, including tap, industrial, and stream waters without using deionized water. Also, as-synthesized silica nanoparticles are employed as dispersive materials for the electro-responsive smart fluid application. Specifically, homogeneous silica nanoparticles with sizes of 500-700nm are successfully prepared in large scale at once (ca. 12.0 g) with the described experimental method and showing similar structural and chemical characteristics with silica nanoparticles synthesized using the deionized water. The size of silica nanoparticles are varied according to the ion conductivity differences of tap, industrial, stream water, and deionized water. The size of silica nanoparticles decresed with the increased ion conductivity, indicating the ion suppression of growth of silica nanoparticles. Moreover, as-synthesized silica nanoparticles from various water sources of electro-responsive characteristic are investigated by the smart fluid application. The smart fluids containing silica nanoparticles synthesized by tap, industrial, and stream water exhibited higher shear stress compared to the deionized water, owing to the more rigid fibril-like structures formed by the smaller silica nanoparticles. Conclusively, uniform silica nanoparticles from various water sources without any purification are able to successfully prepared without usage of deionized water and resulting silica nanoparticles manifested higher electro-responsive performance.

• 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.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.215-235
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• 1996

The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad ＆ fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH＋7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad ＆ fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).