• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.4
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• pp.1-9
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• 2002

The distribution characteristics of data retention time for DRAM was studied in connection with the probability distribution of the cell parameters. Using the cell parameters and the transient characteristics of cell node voltage, data retention time was investigated. The activation energy for dielectric layer growth on cell capacitance, the recombination trap energy for leakage current in the junction depletion region, and the sensitivity characteristics of sense amplifier were used as the random variables to perform the Monte Carlo simulation, and the probability distributions of cell parameters and distribution characteristics of cumulative failure bit on data retention time in DRAM cells were calculated. we found that the sensitivity characteristics of sense amplifier strongly affected on the tail bit distribution of data retention time.

• International Journal of Contents
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• v.15 no.3
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• pp.7-12
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• 2019

The purpose of this study was to analyze characteristics and patterns of 'better class' by using the longitudinal text mining big data analysis technique from subjective lecture evaluation comments. First, this study classified upper 30% classes to deduce certain characteristics and patterns from every five-year subjective text data for 10 years. A total of 47,177courses (100%) from spring semester 2005 to fall semester 2014 were analyzed from a university at a metropolitan city in the mid area of South Korea. This study extracted meaningful words such as good, course, professor, appreciation, lecture, interesting, useful, know, easy, improvement, progress, teaching material, passion, and concern from the order of frequency 2005-2009. The other set of words were class, appreciation, professor, good, course, interesting, understanding, useful, help, student, effort, thinking, not difficult, explanation, lecture, hard, pleasant, easy, study, examination, like, various, fun, and knowledge 2010-2014. This study suggests that the characteristics and patterns of 'better class' at college, should be analyzed according to different academic code such as liberal arts, fine arts, social science, engineering, math and science, and etc.

• Transactions on Electrical and Electronic Materials
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• v.3 no.2
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• pp.10-15
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• 2002

In this paper, the electrical characteristics of fabricated p+-n junction diode are demonstrated and interpreted with different theoretical calculations. Dopants distribution by boron ion implantation on silicon wafer were simulated with TRIM-code and ICECaEM simulator. In order to make electrical activation of implanted carriers, thermal annealing treatments are carried out by RTP method for 1min. at $1000^{circ}C$ under inert $N_2$ gas condition. In this case, profiles of dopants distribution before and after heat treatments in the substrate are observed from computer simulations. In the I-V characteristics of fabricated diodes, an analytical description method of a new triangular junction model is demonstrated and the results with calculated triangular junction are compared with measured data and theoretical calculated results of abrupt junction. Forward voltage drop with new triangular junction model is lower than the case of abrupt junction model. In the C-V characteristics of diode, the calculated data are compared with the measured data. Another I-V characteristics of diodes are measured after proton implantation in electrical isolation method instead of conventional etching method. From the measured data, the turn-on characteristics after proton implantation is more improved than before proton implantation. Also the C-V characteristics of diode are compared with the measured data before proton implantation. From the results of measured data, reasonable deviations are showed. But the C-V characteristics of diode after proton implantation are deviated greatly from the calculated data because of leakage currents in defect regions and layer shift of depletion by proton implantation.

• Steel and Composite Structures
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• v.49 no.6
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• pp.701-711
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• 2023

In order to fully reflect variation characteristics of composite concrete dam health state, the monitoring data is applied to diagnose composite concrete dam health state. Composite concrete dam lesion development to wreckage is a precursor, and its health status can be judged. The monitoring data are generally non-linear and unsteady time series, which contain chaotic information that cannot be characterized. Thus, it could generate huge influence for the construction of monitoring models and the formulation of corresponding health diagnostic indicators. This multi-scale diagnosis process is from point to whole. Chaotic characteristics are often contained in the monitoring data. If chaotic characteristics could be extracted for reflecting concrete dam health state and the corresponding diagnostic indicators will be formulated, the theory and method of diagnosing concrete dam health state can be huge improved. Therefore, the chaotic characteristics of monitoring data are considered. And, the extracting method of the chaotic components is studied from monitoring data based on fuzzy dynamic cross-correlation factor method. Finally, a method is proposed for formulating composite concrete dam health state indicators. This method can effectively distinguish chaotic systems from deterministic systems and reflect the health state of concrete dam in service.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.785-794
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• 2019

Recently Deep Learning technology, one of the fourth industrial revolution technologies, is used to identify the hidden meaning of network data that is difficult to detect in the security arena and to predict attacks. Property and quality analysis of data sources are required before selecting the deep learning algorithm to be used for intrusion detection. This is because it affects the detection method depending on the contamination of the data used for learning. Therefore, the characteristics of the data should be identified and the characteristics selected. In this paper, the characteristics of malware were analyzed using network data set and the effect of each feature on performance was analyzed when the deep learning model was applied. The traffic classification experiment was conducted on the comparison of characteristics according to network characteristics and 96.52% accuracy was classified based on the selected characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.96-96
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• 2020

In order to establish the risk criteria of inundation due to typhoons or heavy rainfall, research is underway to predict the limit rainfall using basin characteristics, limit rainfall and artificial intelligence algorithms. In order to improve the model performance in estimating the limit rainfall, the learning data are used after the pre-processing. When 50.0% of the entire data was removed as an outlier in the pre-processing process, it was confirmed that the accuracy is over 90%. However, the use rate of learning data is very low, so there is a limitation that various characteristics cannot be considered. Accordingly, in order to predict the limit rainfall reflecting various watershed characteristics by increasing the use rate of learning data, the watersheds with similar characteristics were clustered. The algorithms used for clustering are K-Means, Agglomerative, DBSCAN and Spectral Clustering. The k-Means, DBSCAN and Agglomerative clustering algorithms are clustered at the impervious area ratio, and the Spectral clustering algorithm is clustered in various forms depending on the parameters. If the results of the clustering algorithm are applied to the limit rainfall prediction algorithm, various watershed characteristics will be considered, and at the same time, the performance of predicting the limit rainfall will be improved.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.841-848
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• 2008

This paper examined dynamic characteristics of high speed trains using a time varying frequency transform. Fourier transform based methods are frequently used for the calculation of the dynamic characteristics of trains in the frequency domain, but they cannot represent the time-varying characteristics. Therefore it is necessary to examine their characteristics using a time-varying frequency transform. For the examination, the non-stationary vibration of wheelset, bogie, and carbody are measured using accelerometers and stored in a data aquisition system. They are processed with localization of the data by modulating with a window function, and Fourier transform is taken to each localized data, called the short-time Fourier transform. From the processed results, time varying auto-spectral density, cross-spectral density, frequency response, and coherence functions have been calculated. From the analysis, it is confirmed that the time varying frequency transform is a useful method for analyzing the dynamic characteristics of high speed trains.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.163-169
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• 2022

In this paper, we propose a data hiding technique that effectively hides confidential data in the LSB of an image pixel by using the characteristics of the neighboring pixels of the image and the encryption techniques. In the proposed technique, the boundary surface of the image and the flat surface with little change in pixel values are investigated. At the boundary surface of the image, 1 bit of confidential data is encrypted and hidden in the LSB of the boundary pixel to preserve the characteristics of the boundary surface. In the pixels of the plane where the change in pixel value is small, 2 bits secret data is encrypted and hidden in the lower 2 bits of the corresponding pixel. In this way, when confidential data is hidden in an image, the amount of confidential data hidden in the image is greatly increased while maintaining excellent image quality. In addition, the security of hidden confidential data is strongly maintained. When confidential data is hidden by applying the proposed technique, the amount of confidential data concealed increases by up to 92.2% compared to the existing LSB method. The proposed technique can be effectively used to hide copyright information in commercial images.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.11
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• pp.1249-1254
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• 2014

Big data based on numerous data made by the people are used in order to obtain useful information. We can obtain more useful information if it can apply machine learning techniques added deformation of human memory on the characteristics of the computer program. And big data is predicted by using these conclusions. Humans are used to remember similar data as an original data, so big data processing technology should reflect these human characteristics. In this study, this algorithm to provide the selectivity of information is proposed. This algorithm is the technology to reflect the above factors. This algorithm is selected the data with high selectivity to determine similar data based on the deformation characteristics of the data.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.142-148
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• 2023

In this paper, we identify performance issues in executing compute kernels from PolyBench, which includes compute kernels that are the core computational units of various data-intensive workloads, such as deep learning and data-intensive applications, on Processing-in-Memory (PIM) devices. Therefore, using our in-house simulator, we measured and compared the various performance metrics of workloads based on traditional out-of-order and in-order processors with Processing-in-Memory-based systems. As a result, the PIM-based system improves performance compared to other computing models due to the short-term data reuse characteristic of computational kernels from PolyBench. However, some kernels perform poorly in PIM-based systems without a multi-layer cache hierarchy due to some kernel's long-term data reuse characteristics. Hence, our evaluation and analysis results suggest that further research should consider dynamic and workload pattern adaptive approaches to overcome performance degradation from computational kernels with long-term data reuse characteristics and hidden data locality.