• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.4
• /
• pp.1248-1266
• /
• 2012

This paper presents a multilevel reversible data hiding method based on histogram shifting which can recover the original image losslessly after the hidden data has been extracted from the stego-image. The method of prediction is adopted in our proposed scheme and prediction errors are produced to explore the similarity of neighboring pixels. In this article, we propose two different predictors to generate the prediction errors, where the prediction is carried out using the center prediction method and the JPEG-LS median edge predictor (MED) to exploit the correlation among the neighboring pixels. Instead of the original image, these prediction errors are used to hide the secret information. Moreover, we also present an improved method to search for peak and zero pairs and also talk about the analogy of the same to improve the histogram shifting method for huge embedding capacity and high peak signal-to-noise ratio (PSNR). In the one-level hiding, our method keeps image qualities larger than 53 dB and the ratio of embedding capacity has 0.43 bpp (bit per pixel). Besides, the concept with multiple layer embedding procedure is applied for obtaining high capacity, and the performance is demonstrated in the experimental results. From our experimental results and analytical reasoning, it shows that the proposed scheme has higher PSNR and high data embedding capacity than that of other reversible data hiding methods presented in the literature.

• Journal of Convergence for Information Technology
• /
• v.11 no.11
• /
• pp.44-50
• /
• 2021

Recently, enterprise storage systems that require large-capacity storage devices to accommodate big data have used large-capacity flash memory-based storage devices with high density compared to cost and size. This paper proposes a high-efficiency life prediction method with slope descent to maximize the life of flash memory media that directly affects the reliability and usability of large enterprise storage devices. To this end, this paper proposes the structure of a matrix for storing metadata for learning the frequency of defects and proposes a cost model using metadata. It also proposes a life expectancy prediction policy in exceptional situations when defects outside the learned range occur. Lastly, it was verified through simulation that a method proposed by this paper can maximize its life compared to a life prediction method based on the fixed number of times and the life prediction method based on the remaining ratio of spare blocks, which has been used to predict the life of flash memory.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.4
• /
• pp.595-600
• /
• 2024

As the use of artificial intelligence technologies such as machine learning increases in research fields that predict learning outcomes or optimize learning pathways, the use of artificial intelligence in education is gradually making progress. This research is gradually evolving into more advanced artificial intelligence methods such as deep learning and reinforcement learning. This study aims to improve the method of predicting future learning performance based on the learner's past learning performance-history data. Therefore, to improve prediction performance, we propose conditional probability applying the Markov Chain method. This method is used to improve the prediction performance of the classifier by allowing the learner to add learning history data to the classification prediction in addition to classification prediction by machine learning. In order to confirm the effectiveness of the proposed method, a total of more than 30 experiments were conducted per algorithm and indicator using empirical data, 'Teaching aid-based early childhood education learning performance data'. As a result of the experiment, higher performance indicators were confirmed in cases using the proposed method than in cases where only the classification algorithm was used in all cases.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1987.11a
• /
• pp.61-65
• /
• 1987

An algorithm for estimating incident angles of multiple broaband signals is proposed. The method adopts semicausal model for two dimensional linear prediction filter coefficients such that the arithmatic averag of the mean squared values of the forward and reverse prediction arrors is minimized. Preliminary results demonstrating the performance of the proposed method are presented. Simulation results indicate that the performance depends on signal-to-noise ratio and prediction order in spatial demension.

• IE interfaces
• /
• v.11 no.1
• /
• pp.175-182
• /
• 1998

The maintainability prediction of an underwater military system is considered. A general and parctical prediction method for maintainability using MIL-HDBK-472 is presented. We develop a computer program to predict MTTR of an underwater military system. A case study is made to explain the proposed maintainability prediction method.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.5
• /
• pp.1841-1851
• /
• 2018

For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.10
• /
• pp.2409-2416
• /
• 2015

This paper presents an orientation-based adaptive prediction method for effective lossless image compression. For a robust prediction, the proposed method estimates the directional information and the property near the current pixel in a support region-based fashion, not a pixel-based one which is sensitive to a small variation. We improve the prediction performance effectively by selection of the prediction pixel adaptively according to the similarity between support regions of the current pixel and the neighboring pixels. Comprehensive experiments demonstrate that the proposed scheme achieves excellent prediction performance measured in entropy of the prediction error compared to a number of conventional prediction methods such as MED, GAP, and EDP. Moreover the complexity of the proposed algorithm measured by average execution time is low compared to MED which is the simplest prediction method.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.9 no.2
• /
• pp.81-86
• /
• 2014

In the scalable video coding standard, inter-layer prediction based on the coding information of the base layer was adopted to increase the coding performance. This prediction tool results in new syntax elements called motion_prediction_flag (mPF) and residul_prediction_flag(rPF), which are carried to notify the motion vector predictor (MVP) and reference block required in the motion compensation of the decoder. In this paper, an efficient coding method for mPF is proposed to enhance coding efficiency of the salable video coding standard. Through an analysis on the transmission of mPF based on the relationship between the MVPs, we discover the conditions where mPF is unnecessary at the decoder and suggest a modified rate-distortion (RD) cost function to make RD optimization more effective. Simulation results show that the proposed method offers BD rate savings of approximately 1.4%, compared with the conventional SVC standard.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2314-2333
• /
• 2023

It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2010.03a
• /
• pp.457-467
• /
• 2010

The settlement prediction is very important to preloading method for a construction site on a soft ground. At the design stage, however, it is hard to predict the settlement exactly due to limitations of the site survey. Most of the settlement prediction is performed by a regression settlement curve based on the field data during a construction. In Korea, hyperbolic method has been most commonly used to align the settlement curve with the field data, because of its simplicity and many application cases. The results from hyperbolic method, however, may be differed by data selections or data fitting methods. In this study, the analyses using hyperbolic method were performed about the field data of $\bigcirc\bigcirc$ site in Pusan. Two data fitting methods, using an axis transformation or an alternative method, were applied with the various data group. If data was used only after the ground water level being stabilized, fitting results using both methods were in good agreement with the measured data. Without the information about the ground water level, the alternative method gives better results with the field data than the method using an axis transformation.