• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.396-396
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• 2013

The resistive random access memory (ReRAM) has several advantages to apply next generation non-volatile memory device, because of fast switching time, long retentions, and large memory windows. The high mobility of monolayered graphene showed several possibilities for scale down and electrical property enhancement of memory device. In this study, the monolayered graphene grown by chemical vapor deposition was transferred to $SiO_2$ (100 nm)/Si substrate and glass by using PMMA coating method. For formation of metal-oxide nanoparticles, we used a chemical reaction between metal films and polyamic acid layer. The 50-nm thick BPDA-PDA polyamic acid layer was coated on the graphene layer. Through soft baking at $125^{\circ}C$ or 30 min, solvent in polyimide layer was removed. Then, 5-nm-thick indium layer was deposited by using thermal evaporator at room temperature. And then, the second polyimide layer was coated on the indium thin film. After remove solvent and open bottom graphene layer, the samples were annealed at $400^{\circ}C$ or 1 hr by using furnace in $N_2$ ambient. The average diameter and density of nanoparticle were depending on annealing temperature and times. During annealing process, the metal and oxygen ions combined to create $In_2O_3$ nanoparticle in the polyimide layer. The electrical properties of $In_2O_3$ nanoparticle ReRAM such as current-voltage curve, operation speed and retention discussed for applictions of transparent and flexible hybrid ReRAM device.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.3
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• pp.195-202
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• 1998

The LPCVD system of batch type for the massproduction of semiconductor fabrication has a problem of phosphorous concentration uniformity in the boat. In this paper we study an improvement of the uniformity for phosphorous concentration and sheet resistance. These property was improved by using the nitrogen process and modified long nozzle for gas injection tube in the doped polysilicon deposition system. The phosphorous concentration and its uniformity for polysilicon film are measured by XRF(X-ray Fluorescence) for the conventional process condition and nitrogen process. In conventional process condition, the phosphorous concentration, it uniformity and sheet resistance for polysilicon film are in the range of 3.8~5.4$\times$10\ulcorner atoms/㎤, 17.3% and 59~$\Omega$/ , respectively. For the case of nitrogen process the corresponding measurements exhibited between 4.3~5.3$\times$10\ulcorner atoms/㎤, 10.6% and 58~81$\Omega$/ . We find that in the nitrogen process the uniformity of phosphorous concentration improved compared with conventional process condition, however, the sheet resistance in the up zone of the boat increased about 12 $\Omega$/ . In modified long nozzle, the phosphorous concentration, its uniformity and sheet resistance for polysilicon films are in the range of 4.5~5.1$\times$10\ulcorner atoms/㎤, 5.3% and 60~65$\Omega$/ respectively. Annealing after $N_2$process gives the increment of grain size and the decrement of roughness. Modification of nozzle gives the increment of injection amount of PH$_3$. Both of these suggestion result in the stable phosphorous concentration and sheet resistance. The results obtained in this study are also applicable to process control of batch type system for memory device fabrication.

• The Korean Journal of Financial Management
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• v.25 no.3
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• pp.111-130
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• 2008

This paper compares long term equilibrium relation of KOSPI 200 which is underling stock and its futures by using general method fractional cointegration instead of existing integer cointegration. Existence of integer cointegration between two price time series gives much wider information about long term equilibrium relation. These details grasp long term equilibrium relation of two price time series as well as reverting velocity to equilibrium by observing difference coefficient of error term when it renounces from equilibrium relation. The result of this study reveals existence of long term equilibrium relation between KOSPI200 and futures which follow fractional cointegration. Difference coefficient, d, of 'two price time series error term' satisfies 0 < d < 1/2 beside bandwidth parameter, m(173). It means two price time series follow stationary long memory process. This also means impulse effects to balance price of two price time series decrease gently within hyperbolic rate decay. It indicates reverting speed of error term is very low when it bolts from equilibrium. It implies to market maker, who is willing to make excess return with arbitrage trading and hedging risk using underling stock, how invest strategy should be changed. It also insinuates that information transition between KOSPI 200 Index market and futures market does not working efficiently.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.505-511
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• 2023

In the present study, a deep learning model was established to predict the motion response of small fishing vessels. Hydrodynamic performances were evaluated for two small fishing vessels for the dataset of deep learning model. The deep learning model of the Long Short-Term Memory (LSTM) which is one of the recurrent neural network was utilized. The input data of LSTM model consisted of time series of six(6) degrees of freedom motions and wave height and the output label was selected as the time series data of six(6) degrees of freedom motions. The hyperparameter and input window length studies were performed to optimize LSTM model. The time series motion response according to different wave direction was predicted by establised LSTM. The predicted time series motion response showed good overall agreement with the analysis results. As the length of the time series increased, differences between the predicted values and analysis results were increased, which is due to the reduced influence of long-term data in the training process. The overall error of the predicted data indicated that more than 85% of the data showed an error within 10%. The established LSTM model is expected to be utilized in monitoring and alarm systems for small fishing vessels.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.37-58
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• 1997

An AI approach with tabu search is designed to solve multi-level knapsack problems. The approach performs intelligent actions with memories of historic data and learning effect. These action are developed ont only by observing the attributes of the optimal solution, the solution space, and its corresponding path to the optimal, but also by applying human intelligence, experience, and intuition with respect to the search strategies. The approach intensifies, or diversifies the search process appropriately in time and space. In order to create a good neighborhood structure, this approach uses two powerful choice rules that emphasize the impact of candidate variables on the current solution with respect to their profit contribution. "Pseudo moves", similar to "aspirations", support these choice rules during the evaluation process. For the purpose of visiting as many relevant points as possible, strategic oscillation between feasible and infeasible solutions around the boundary is applied. To avoid redundant moves, short-term (tabu-lists), intemediate-term (cycle-detection), and long-term (recording frequency and significant solutions for diversfication) memories are used. Test results show that among the 45 generated problems (these problems pose significant or insurmountable challenges to exact methods) the approach produces the optimal solutions in 39 cases.lutions in 39 cases.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.185-192
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• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.480-488
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• 2022

Images of tobacco retail licenses have complex unstructured characteristics, which is an urgent technical problem in the robot process automation of tobacco marketing. In this paper, a novel recognition approach using a double attention mechanism is presented to realize the automatic recognition and information extraction from such images. First, we utilized a DenseNet network to extract the license information from the input tobacco retail license data. Second, bi-directional long short-term memory was used for coding and decoding using a continuous decoder integrating dual attention to realize the recognition and information extraction of tobacco retail license images without segmentation. Finally, several performance experiments were conducted using a largescale dataset of tobacco retail licenses. The experimental results show that the proposed approach achieves a correction accuracy of 98.36% on the ZY-LQ dataset, outperforming most existing methods.

• Transactions of Materials Processing
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• v.33 no.4
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• pp.277-284
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• 2024

Interest in remanufacturing of part has significantly increased to reduce used material and energy together. The directed energy deposition (DED) process has widely applied to remanufacturing of the part. An excessive residual stress takes place in the vicinity of the deposited region by the DED process due to rapid heating and rapid cooling (RHRC) phenomenon. The excessive residual stress decreases the reliability of the remanufactured part. Therefore, thermo-mechanical analysis for the remanufacturing of the part is needed to investigate heat transfer and residual stress characteristics in the vicinity of the deposited region. The thermo-mechanical analysis of a large volume deposition is significantly difficult to perform due to the requirement of a long computation time and a large computer memory. The goal of this paper is to investigate thermo-mechanical characteristics for remanufacturing of the ATC part using a DED process. The methodology of the thermo-mechanical analysis for a large volume deposition is proposed. From the results of analysis, heat transfer and residual stress characteristics during deposition and cooling stages are investigated. In addition, the proper deposition strategy from the viewpoint of the residual stress is discussed.

• 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