• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.32-43
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• 2023

Resistive Random Access Memory (RRAM), based on resistive switching characteristics, is emerging as a next-generation memory device capable of efficiently processing large amounts of data through its fast operation speed, simple device structure, and high-density implementation. Interface type resistive switching offer the advantage of low operation currents without the need for a forming process. Especially, for RRAM devices based on transition metal oxides, various studies are underway to enhance the memory characteristics, including precise material composition control and improving the reliability and stability of the device. In this paper, we introduce various methods, such as doping of heterogeneous elements, formation of multilayer films, chemical composition adjustment, and surface treatment to prevent degradation of interface type resistive switching properties and enhance the device characteristics. Through these approaches, we propose the feasibility of implementing high-efficient next-generation non-volatile memory devices based on improved resistive switching properties.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.312-318
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• 2006

The conventional lip-synch system has a two-step process, speech segmentation and recognition. However, the difficulty of speech segmentation procedure and the inaccuracy of training data set due to the segmentation lead to a significant Performance degradation in the system. To cope with that, the connected vowel recognition method using Head-Body-Tail (HBT) model is proposed. The HBT model which is appropriate for handling relatively small sized vocabulary tasks reflects co-articulation effect efficiently. Moreover the 7 vowels are merged into 3 classes having similar lip shape while the system is optimized by employing a class dependent SCHMM structure. Additionally in both end sides of each word which has large variations, 8 components Gaussian mixture model is directly used to improve the ability of representation. Though the proposed method reveals similar performance with respect to the CHMM based on the HBT structure. the number of parameters is reduced by 33.92%. This reduction makes it a computationally efficient method enabling real time operation.

• The Journal of Advanced Prosthodontics
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• v.16 no.3
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• pp.163-173
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• 2024

PURPOSE. This study assessed the effect of storage time on chemical structure of a single-bottle and a two-bottle experimental ceramic primer and micro-shear bond strength (µSBS) of composite to ceramic. MATERIALS AND METHODS. This study was conducted on 60 sintered zirconia and 60 feldspathic porcelain blocks. Half of the specimens (n = 30) were subjected to surface treatment with the single-bottle Clearfil ceramic primer (n = 15) and two-bottle experimental primer (n = 15) after 24 hours. The remaining half received the same surface treatments after 6 months storage in distilled water. Composite cylinders were bonded to the ceramics, and they were then subjected to µSBS test. Also, the primers underwent Fourier-transform infrared spectroscopy (FTIR) after 24 hours and 6 months to assess their chemical structure. Data were analyzed with 3-way ANOVA and adjusted Bonferroni test (alpha = 0.05). RESULTS. The µSBS of both ceramics significantly decreased at 6 months in one-bottle ceramic primer group (P = .001), but it was not significantly different from the two-bottle experimental primer group (P = .635). FTIR showed hydrolysis of single-bottle primer, cleavage of silane and 10-MDP bonds, and formation of siloxane bonds after 6 months. CONCLUSION. Six months of storage caused significant degradation of single-bottle ceramic primer, and consequently had an adverse effect on µSBS.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1282-1282
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• 2024

Given the widespread use of intelligent surveillance cameras at construction sites, recent studies have introduced vision-based deep learning approaches. These studies have focused on enhancing the performance of vision-based excavator activity recognition to automatically monitor productivity metrics such as activity time and work cycle. However, acquiring a large amount of training data, i.e., videos captured from actual construction sites, is necessary for developing a vision-based excavator activity recognition model. Yet, complexities of dynamic working environments and security concerns at construction sites pose limitations on obtaining such videos from various surveillance camera locations. Consequently, this leads to performance degradation in excavator activity recognition models, reducing the accuracy and efficiency of heavy equipment productivity analysis. To address these limitations, this study aimed to conduct sensitivity analysis of excavator activity recognition performance based on surveillance camera location, utilizing synthetic videos generated from a game-engine-based virtual environment (Unreal Engine). Various scenarios for surveillance camera placement were devised, considering horizontal distance (20m, 30m, and 50m), vertical height (3m, 6m, and 10m), and horizontal angle (0° for front view, 90° for side view, and 180° for backside view). Performance analysis employed a 3D ResNet-18 model with transfer learning, yielding approximately 90.6% accuracy. Main findings revealed that horizontal distance significantly impacted model performance. Overall accuracy decreased with increasing distance (76.8% for 20m, 60.6% for 30m, and 35.3% for 50m). Particularly, videos with a 20m horizontal distance (close distance) exhibited accuracy above 80% in most scenarios. Moreover, accuracy trends in scenarios varied with vertical height and horizontal angle. At 0° (front view), accuracy mostly decreased with increasing height, while accuracy increased at 90° (side view) with increasing height. In addition, limited feature extraction for excavator activity recognition was found at 180° (backside view) due to occlusion of the excavator's bucket and arm. Based on these results, future studies should focus on enhancing the performance of vision-based recognition models by determining optimal surveillance camera locations at construction sites, utilizing deep learning algorithms for video super resolution, and establishing large training datasets using synthetic videos generated from game-engine-based virtual environments.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.795-805
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• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

• Economic and Environmental Geology
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• v.54 no.3
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• pp.373-387
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• 2021

In this study, the impact of clustered groundwater usage facilities and the proper amount of groundwater usage in the Daejeong-Hangyeong watershed of Jeju island were evaluated based on the data-driven analysis methods. As the applied data, groundwater level data; the corresponding precipitation data; the groundwater usage amount data (Jeoji, Geumak, Seogwang, and English-education city facilities) were used. The results show that the Geumak usage facility has a large influence centering on the corresponding location; the Seogwang usage facility affects on the downstream area; the English-education usage facility has a great impact around the upstream of the location; the Jeoji usage facility shows an influence around the up- and down-streams of the location. Overall, the influence of operating the clustered groundwater usage facilities in the watershed is prolonged to approximately 5km. Additionally, the appropriate groundwater usage amount to maintain the groundwater base-level was analyzed corresponding to the precipitation. Considering the recent precipitation pattern, there is a need to limit the current amount of groundwater usage to 80%. With increasing the precipitation by 100mm, additional groundwater development of approximately 1,500m³-1,900m³ would be reasonable. All the results of the developed data-driven estimation model can be used as useful information for sustainable groundwater development in the Daejeong-Hangyeong watershed of Jeju island.

• Journal of the Korea Concrete Institute
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• v.24 no.6
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• pp.651-658
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• 2012

This paper concentrates on the evaluation of microcracks in thermal damaged concrete on the basis of the nonlinear ultrasonic modulation technique. Since concrete structure exposed to high temperature accompanies the development of microcracks due to the physical and chemical changes from temperature and exposed time, the adoption of nonlinear approach is required. Instead of using the conventional ultrasonic nondestructive methods which have the limitation in evaluating excessive microcracks, accordingly, a nonlinear ultrasonic modulation method which shows better sensitivity in quantifying microcracks is introduced. Upon the analysis for the modulation of ultrasonic wave and low frequency impact to measure the nonlinearity parameter, which can be used as an indicator of thermal damage, the verification processes for the introduced technique are followed: SEM investigation and permeable pore space test are performed to characterize thermally induced microcracks in concrete, and ultrasonic pulse velocity tests are performed to confirm the outstanding sensitivity of nonlinear ultrasonic modulation technique. In advance, compressive strength of thermal damaged concrete is measured to represent the effect of microcracks on performance degradation. Correlation studies between experimental data and measured data show that nonlinear ultrasonic modulation technique can effectively be used to quantify thermally induced microcracks, and to estimate the compressive strength of thermally damaged concrete.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.5
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• pp.738-747
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• 2018

Objective: In a previous study, analysis of Illumina sequenced metagenomic DNA data of bacteria in Vietnamese goats' rumen showed a high diversity of putative lignocellulolytic genes. In this study, taxonomy speculation of microbial community and lignocellulolytic bacteria population in the rumen was conducted to elucidate a role of bacterial structure for effective degradation of plant materials. Methods: The metagenomic data had been subjected into Basic Local Alignment Search Tool (BLASTX) algorithm and the National Center for Biotechnology Information non-redundant sequence database. Here the BLASTX hits were further processed by the Metagenome Analyzer program to statistically analyze the abundance of taxa. Results: Microbial community in the rumen is defined by dominance of Bacteroidetes compared to Firmicutes. The ratio of Firmicutes versus Bacteroidetes was 0.36:1. An abundance of Synergistetes was uniquely identified in the goat microbiome may be formed by host genotype. With regard to bacterial lignocellulose degraders, the ratio of lignocellulolytic genes affiliated with Firmicutes compared to the genes linked to Bacteroidetes was 0.11:1, in which the genes encoding putative hemicellulases, carbohydrate esterases, polysaccharide lyases originated from Bacteroidetes were 14 to 20 times higher than from Firmicutes. Firmicutes seem to possess more cellulose hydrolysis capacity showing a Firmicutes/Bacteroidetes ratio of 0.35:1. Analysis of lignocellulolytic potential degraders shows that four species belonged to Bacteroidetes phylum, while two species belonged to Firmicutes phylum harbouring at least 12 different catalytic domains for all lignocellulose pretreatment, cellulose, as well as hemicellulose saccharification. Conclusion: Based on these findings, we speculate that increasing the members of Bacteroidetes to keep a low ratio of Firmicutes versus Bacteroidetes in goat rumen has resulted most likely in an increased lignocellulose digestion.

• Journal of Life Science
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• v.25 no.9
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• pp.984-992
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• 2015

Sanguinarine is a benzophenanthridine alkaloid originally isolated from the roots of Sanguinaria canadensis. It has multiple biological activities (e.g., antioxidant and antiproliferative) and immune-enhancing potential. In this study, we explored the proapoptotic properties and modes of action of sanguinarine in human hepatocellular carcinoma HepG2 cells. Our results revealed that sanguinarine inhibited HepG2 cell growth and induced apoptosis in a dose-dependent manner. The induction of apoptosis by sanguinarine was associated with the up-regulation of Fas and Bax, the release of cytochrome c from the mitochondria to the cytosol, and the loss of the mitochondrial membrane potential. In addition, sanguinarine activated caspase-9 and -8, initiator caspases of the intrinsic and death extrinsic pathways, respectively, and caspase-3, accompanied by proteolytic degradation of poly (ADP-ribose) polymerase. Sanguinarine also triggered the generation of reactive oxygen species (ROS). The elimination of ROS by N-acetylcysteine reversed sanguinarine-induced apoptosis. Furthermore, sanguinarine induced the dephosphorylation of Akt and the phosphorylation of mitogen-activated protein kinases, including extracellular signal-regulated kinase (ERK), c-jun N-terminal kinase (JNK), and p38. The growth inhibition was enhanced by the combined treatment of sanguinarine with a phosphatidylinositol 3'-kinase (PI3K) inhibitor and an ERK inhibitor but not JNK and p38 inhibitors. Overall, our data indicate that the proapoptotic effects of sanguinarine in HepG2 cells depend on ROS production and the activation of both intrinsic and extrinsic signaling pathways, which is mediated by blocking PI3K/Akt and activating the ERK pathway. Thus, our data suggest that sanguinarine may be a natural compound with potential for use as an antitumor agent in liver cancer.