• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.185-194
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• 2000

Rapid Prototyping(RP) techniques have their unique characteristics according to the working principles: stair-stepped surface of parts due to layer-by-layer stacking, low build speed caused by line-by-line solidification to build one layer, and additional post processing to improve surface roughness, so it is required very high cost to introduce and to maintain of RP apparatus. The objective of this study is to develop a new RP process, Variable Lamination Manufacturing using linear hotwire cutting technique and expandable polystyrene foam sheet as part material(VLM-S), and to investigate characteristics of part material, cutting characteristics by using linear hotwire cutting system and bonding. Experiments were carried out to investigate mechanical properties of part material such as anisotropy and directional tensile strength. In order to obtain optimal dimensional accuracy, surface roughness, and reduced cutting time, addition experiments were performed to find the relationship between cutting speed and cutting offset of hotwire, and heat generation of hotwire per unit length. So, adhesion strength tests according to ASTM test procedure showed that delamination did not occur at bonded area. Based on the data, a clover-shape was fabricated using unit shape part(USP) it is generated hotwire cutting. The results of present study have been reflected on the enhancement of the VLM-S process and apparatus.

• Journal of the Microelectronics and Packaging Society
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• v.20 no.2
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• pp.47-51
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• 2013

Chemical mechanical polishing (CMP) has become one of the key processes in wafer level stacking technology for 3D stacked IC. In this study, two-step CMP process was proposed to polish $Cu/SiO_2$ hybrid bonding surface, that is, Cu CMP was followed by $SiO_2$ CMP to minimize Cu dishing. As a result, Cu dishing was reduced down to $100{\sim}200{\AA}$ after $SiO_2$ CMP and surface roughness was also improved. The bonding interface showed no noticeable dishing or interface line, implying high bonding strength.

• Medical Lasers
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• v.9 no.2
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• pp.159-165
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• 2020

Background and Objectives The ultimate goal in current skin rejuvenation practice is to achieve a good result with minimal pain and downtime. Nonablative skin rejuvenation (NSR) is one technique. The efficacy of the long-pulsed 1064 nm Nd:YAG laser (LPNDY) has not been assessed in NSR. Materials and Methods Three target areas were selected (bilateral cheeks and glabellar region) in six volunteer subjects. A LPNDY with an integral skin temperature monitor delivered three stacked shots to each target area (1064 nm, 12 mm spot, 13 J/cm², 1 Hz) without any skin cooling or anesthesia. The skin temperature was recorded before, during, and after each set of shots using the system monitor and in real-time using a high-sensitivity (±0.001℃) near-infrared video camera. The skin reaction was observed with the naked eye, and pain and discomfort were assessed by the subjects during and after treatment. Results The subjects reported a mild feeling of heat with no discomfort during or after the test treatments. Mild erythema was observed around the treatment areas, without noticeable edema. A series of three ascending skin temperature stepwise peaks, with a decrease in skin temperature towards the baseline after the third shot, was observed consistently. The mean temperatures for shots 1, 2, and 3 for the cheeks were 39.5℃, 42.0℃, and 44.4℃, respectively, and for the glabella, 40.8℃, 43.9℃, and 46.2℃, respectively. Similar ranges were indicated on the system integral temperature monitor. Conclusion A set of three stacked pulses with the LPNDY at a low fluence achieved ideal dermal temperatures to achieve some dermal remodeling but without any downtime or adverse events. The temperature data from the integral thermal sensor matched the video camera measurements with practical accuracy for skin rejuvenation requirements. These data suggest that LPNDY would satisfy the necessary criteria to achieve effective NSR, but further studies will be needed to assess the actual results in clinical practice.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.19-24
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• 2024

Recent advances in large-scale data processing technologies such as big data, cloud computing, and artificial intelligence have increased the demand for high-performance storage devices in data centers and enterprise environments. In particular, the fast data response speed of storage devices is a key factor that determines the overall system performance. Solid state drives (SSDs) based on the Non-Volatile Memory Express (NVMe) interface are gaining traction, but new bottlenecks are emerging in the process of handling large data input and output requests from multiple hosts simultaneously. SSDs typically process host requests by sequentially stacking them in an internal queue. When long transfer length requests are processed first, shorter requests wait longer, increasing the average response time. To solve this problem, data transfer timeout and data partitioning methods have been proposed, but they do not provide a fundamental solution. In this paper, we propose a dual queue based scheduling scheme (DQBS), which manages the data transfer order based on the request order in one queue and the transfer length in the other queue. Then, the request time and transmission length are comprehensively considered to determine the efficient data transmission order. This enables the balanced processing of long and short requests, thus reducing the overall average response time. The simulation results show that the proposed method outperforms the existing sequential processing method. This study presents a scheduling technique that maximizes data transfer efficiency in a high-performance SSD environment, which is expected to contribute to the development of next-generation high-performance storage systems

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.3
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• pp.226-232
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• 2023

As a potential alternative to flash memory, HfO₂/Al₂O₃ stacks appear to be a viable option as charge capture layers in charge trapping memories. The paper undertakes a review of HfO₂/Al₂O₃ stacks as charge trapping layers, with a focus on comparing the number, thickness, and post-deposition heat treatment and γ-ray and white x-ray treatment of such stacks. Compared to a single HfO₂ layer, the memory window of the 5-layered stack increased by 152.4% after O₂ annealing at ±12 V. The memory window enlarged with the increase in number of layers in the stack and the increase in the Al/Hf content in the stack. Furthermore, our comparison of the treatment of HfO₂/Al₂O₃ stacks with varying annealing temperatures revealed that an increased annealing temperature resulted in a wider storage window. The samples treated with O₂ and subjected to various γ radiation intensities displayed superior resistance. and the memory window increased to 12.6 V at ±16 V for 100 kGy radiation intensity compared to the untreated samples. It has also been established that increasing doses of white x-rays induced a greater number of deep defects. The optimization of stacking layers along with post-deposition treatment condition can play significant role in extending the memory window.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.1
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• pp.1-13
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• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

• Korean Journal of Materials Research
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• v.11 no.2
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• pp.88-93
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• 2001

The thermal behavior and the crystallographic characteristics of an epitaxial $C49-TiSi_2$ island formed in a Si (001) substrate by $N_2$, treatment were investigated by X-ray diffraction (XRD) and high-resolution transmission electron microscopy (HRTEM). It was found from the analyzed results that the epitaxial $C49-TiSi_2$ was thermally stable even at high temperature of $1000^{\circ}C$ therefore did not transform into the C54-stable phase and did not deform morphologically. HRTEM results clearly showed that the epitaxial $TiSi_2$ phase and Si have the orientation relationship of (060)[001]$TiSi_2$//(002)[110]Si, and the lattice strain energy at the interface was mostly relaxed by the formation of misfit dislocations. Furthermore, the mechanism on the formation of the epitaxial $_C49-TiSi2$ in Si and stacking faults lying on the (020) plane of the C49 Phase were discussed through the analysis of the HRTEM image and the atomic modeling.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Bulletin of the Korean Chemical Society
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• v.31 no.6
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• pp.1519-1526
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• 2010

The behavior of peptide or protein solutes in saline aqueous solution is a fundamental topic in physical chemistry. Addition of ions can strongly alter the thermodynamic and physical properties of peptide molecules in solution. In order to study the effects of added ionic salts on protein conformation and dynamics, we have used the molecular dynamics (MD) simulations to investigate the behavior of Staphylococcus aureus Hfq protein under two different ionic concentrations: 0.1 M NaCl and 1.0 M NaCl in presence and absence of RNA (a hepta-oligoribonucleotide AU5G). Hfq, a global regulator of gene expression is highly conserved and abundant RNA-binding protein. It is already reported that in vivo the increase of ionic strength results in a drastic reduction of Hfq affinity for $Q{\beta}$ RNA and reduces the tendency of aggregation of Escherichia coli host factor hexamers. Our results revealed the crucial role of 0.1 M NaCl Hfq system on the bases with strong hydrogen bonding interactions and by stabilizing the aromatic stacking of Tyr42 residue of the adjacent subunits/monomers with the adenine and uridine nucleobases. An increase in RNA pore diameter and weakened compactness of the Hfq-RNA complex was clearly observed in 1.0 M NaCl Hfq system with bound RNA. Aggregation of monomers in Hfq and the interaction of Hfq with RNA are greatly affected due to the presence of high ionic strength. Higher the ionic concentration, weaker is the aggregation and interaction. Our results were compatible with the experimental data and this is the first theoretical report for the experimental study done in 1980 by Uhlenbeck group for the present system.

• 한국지구물리탐사학회:학술대회논문집
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• 2002.09a
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• pp.24-45
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• 2002

For the last several decades, high-resolution shallow marine seismic technique has been used for various resources, engineering and geological surveys. Even though the multichannel method is powerful to image subsurface structures, single channel analog survey has been more frequently employed in shallow water exploration, because it is more expedient and economical. To improve the quality of the high-resolution seismic data economically, we acquired digital seismic data using a small air gun, 6 channel streamer and PC-based system, performed data processing and produced high-resolution seismic sections. For many years, such test acquisitions were performed with other studies which have different purposes in the area of off Pohang, Yellow Sea and Gyeonggi-bay. Basic data processing was applied to the acquired data and the processing sequence included gain recovery, deconvolution, filtering, normal moveout, static corrections, CMP gathering and stacking. Examples of digitally processed sections were shown and compared with analog sections. Digital seismic sections have a much higher resolution after data processing. The results of acquisition and processing show that the high-resolution shallow marine seismic surveys using a small air gun, 6 channel streamer and PC-based system may be an effective way to image shallow subsurface structures precisely.