• Journal of the Korean Institute of Gas
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• v.23 no.2
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• pp.1-8
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• 2019

Because of environmental pollution problem, interests in hydrogen energy has been concentrating sharply. Especially in Korea, the market related with fuel cell vehicles and hydrogen refueling stations is increasing actively under the government-led. However, the actual contributions to environmental improvement effect of hydrogen energy is required to be evaluated with representing reality. In this sense, lots of conventional analyzing tools have some limitations to adapt in Korea's situation directly. It is caused by the differences of raw energy market between the US and Korea. That is, most of analytic tools are developed by representing energy market of the US, where can produce variety of raw feed energy sources. Therefore, in this paper, we propose mass balance based numerical analyzing method, which is suitable for the actual hydrogen production process in Korea for exact evaluation of $CO_2$ emission amount in this country. Using proposed method, we has demonstrated reformed hydrogen from natural gas, LPG and naphtha, electrolysis-based hydrogen, and COG-based hydrogen. Furthermore, with the comparison of GREET program analysis results, robustness of numerical analysis method is demonstrated.

• Convergence Security Journal
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• v.19 no.2
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• pp.91-104
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• 2019

In this paper, we present a novel local descriptor, Local Prominent Directional Pattern (LPDP), to represent the description of facial images for gender recognition purpose. To achieve a clearly discriminative representation of local shape, presented method encodes a target pixel with the prominent directional variations in local structure from an analysis of statistics encompassed in the histogram of such directional variations. Use of the statistical information comes from the observation that a local neighboring region, having an edge going through it, demonstrate similar gradient directions, and hence, the prominent accumulations, accumulated from such gradient directions provide a solid base to represent the shape of that local structure. Unlike the sole use of gradient direction of a target pixel in existing methods, our coding scheme selects prominent edge directions accumulated from more samples (e.g., surrounding neighboring pixels), which, in turn, minimizes the effect of noise by suppressing the noisy accumulations of single or fewer samples. In this way, the presented encoding strategy provides the more discriminative shape of local structures while ensuring robustness to subtle changes such as local noise. We conduct extensive experiments on gender recognition datasets containing a wide range of challenges such as illumination, expression, age, and pose variations as well as sketch images, and observe the better performance of LPDP descriptor against existing local descriptors.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.51-56
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• 2019

This paper related with the performance comparison of CCA and RMMA blind adaptive equalization in order to reduce the intersymbol interference which is occurred in channel when transmitting the 16-QAM signal, high spectrum efficiencies of nonconstant modulus characteristic. The CCA possible to improve the misadustment and initial convergence by compacting the every signal constellation of 16 by using the sliced symbol of the decision device output, namely statistical symbol, but incresing the computational cost. The RMMA possible to minimize the fast convergence speed and misadjustment and channel tracking capability without increasing the computational cost by obtain the error signal after transform to 4 constant modulus signal based on the region of signal constellation located. In this paper, these algorithm were implemented in the same channel, and the blind adaptive equalization performance were compared using the equalizer output signal constellation, residual isi, MSE, SER. As a result of simulation, the RMMA has better performance in output signal constellation, residual isi and MSE compared to the CCA, but has slow convergence speed about 1.3 times. And the SER performance presenting the robustness to the noise signal, the CCA has more beeter in less SNR, but the RMMA has better in greater than 6dB in SNR.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.193-204
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• 2019

The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.681-692
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• 2021

Vegetation processes have a significant impact on rainfall runoff processes through evapotranspiration control, but are rarely considered in the conceptual lumped hydrological model. This study evaluated the model performance of the Hapcheon Dam watershed by integrating the ecological module expressing the leaf area index data sensed remotely from the satellite into the hydrological partition module. The proposed eco-hydrological model has three main features to better represent the eco-hydrological process in humid regions. 1) The growth rate of vegetation is constrained by water shortage stress in the watershed. 2) The maximum growth of vegetation is limited by the energy of the watershed climate. 3) The interaction of vegetation and aquifers is reflected. The proposed model simultaneously simulates hydrologic components and vegetation dynamics of watershed scale. The following findings were found from the validation results using the model parameters estimated by the SCEM algorithm. 1) Estimating the parameters of the eco-hydrological model using the leaf area index and streamflow data can predict the streamflow with similar accuracy and robustness to the hydrological model without the ecological module. 2) Using the remotely sensed leaf area index without filtering as input data is not helpful in estimating streamflow. 3) The integrated eco-hydrological model can provide an excellent estimate of the seasonal variability of the leaf area index.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.269-274
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• 2021

In secret communication, steganography is the sending and receiving of secret messages without being recognized by a third party. In the spatial domain method bitwise information is inserted into the virtual bit plane of the decomposed pixel values of the image. That is, the bitwise secret message is sequentially inserted into the least significant bit(LSB) of the image, which is a cover medium. In terms of application, the LSB is simple, but has a drawback that can be easily detected by a third party. If the upper bit plane is used to increase security, the image quality may deteriorate. In this paper, I present a method for concealing Hangul secret messages in image steganography based on the lo-th bit plane and the decomposition of modified pixel intensity values. After decomposing the Hangeul message to be hidden into choseong, jungseong and jongseong, then a shuffling process is applied to increase confidentiality and robustness. PSNR was used to confirm the efficiency of the proposed method. It was confirmed that the proposed technique has a smaller effect in terms of image quality than the method applying BCD and Fibonacci when inserting a secret message in the upper bit plane. When compared with the reference value, it was confirmed that the PSNR value of the proposed method was appropriate.

• Journal of Acupuncture Research
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• v.38 no.4
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• pp.312-319
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• 2021

Background: Lumbar herniated intervertebral disc (LHIVD) is a frequently presented condition/disease in Korean medical institutions. In this study, the economics of thread embedding acupuncture (TEA) was evaluated in a randomized controlled trial comparing TEA with sham TEA (STEA). Methods: This economic evaluation was analyzed from a limited social perspective, and the per-protocol set was from a basic analysis perspective. The cost-effectiveness analysis was based on the change in visual analog scale score, and the cost-utility analysis was based on the quality-adjusted life years. The final results were expressed as the average cost-effectiveness ratio and incremental cost-effectiveness ratio, and furthermore sensitivity analysis was performed to confirm the robustness of the results observed. Results: The cost-effectiveness analysis showed that TEA was 9,908 won lower than STEA, while the decrease in 100 mm visual analog scale score was 8.5 mm greater in the TEA group compared with the STEA group (p > 0.05). The cost-utility analysis showed that TEA was 9,908 won lower than STEA, while the quality-adjusted life years of TEA was 0.0026 years higher than STEA (p > 0.05). These results were robust in the sensitivity analysis, but were not statistically significant. Conclusion: In treating LHIVD, TEA appeared to have cost-effectiveness and cost-utility compared with STEA. However, there were no significant differences between the groups in terms of cost, effectiveness, and utility indicators. Therefore, results must be interpreted prudently; this study was the 1^st to conduct an economic evaluation of TEA for LHIVD.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.1-11
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• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.189-197
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• 2021

This study aims to optimize the appropriate concentration of fertilizers for Sageretia thea by analyzing growth performances (height and root collar diameter) and physiological characteristics (photosynthesis, chlorophyll contents, and chlorophyll fluorescence reaction). As fertilizer concentration was increased to 1.5 g·L^-1, growth increased, but it decreased at 2.0 g·L^-1 treatment. Root collar diameter growth was reduced because of higher fertilizer concentrations. Photosynthesis reactions showed the highest CO₂ reaction curves, maximum photosynthesis rate, and maximum carboxylation rate in the 1.5 g·L^-1 fertilizer treatment. The chlorophyll fluorescence reaction and SPAD values revealed that fertilizer treatment improves photosynthesis efficiency and robustness compared with untreated control. Therefore, the appropriate fertilizer concentration for producing good seedling quality of Sageretia thea is 1.0~1.5 g·L^-1.