• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.353-361
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• 2020

VVC (Versatile Video Coding) is a new video compression technique that is being standardized, and it supports HD / UHD / 8K video, and High Dynamic Range (HDR) video with a goal of approximately 2 times higher coding efficiency than the conventional HEVC. It also aims to support a variety of functionalities such as screen content coding, adaptive resolution changes, and independent sub-pictures. In this paper, we investigate the signaling process of intra prediction mode first, and develop an effective coding method of the chroma intra prediction mode. In case of the DM mode, the proposed method simplifies the prediction mode of the chorma intra prediction mode when referring to the angular mode of the luminance block. It can improve coding efficiency of the chroma intra prediction mode, and the proposed process can also consider the size of the block in order to further improve its coding efficiency.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2006.06a
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• pp.55-60
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• 2006

Draw-bead is applied to control the material flow in a stamping process and improve the product quality by controlling the draw-bead restraining force (DBRF). Actual die design depends mostly on the trial-and-error method without calculating the optimum DBRF. Die design with the predicted value of DBRF can be utilized at the tryout stage effectively reducing the cost of the product development. For the prediction of DBRF, a simulation-based prediction model of the circular draw-bead is developed using the Box-Behnken design with selected shape parameters such as the bead height, the shoulder radius and the sheet thickness. The value of DBRF obtained from each design case by analysis is approximated by a second order regression equation. This equation can be utilized to the calculation of the restraining force and the determination of the draw-bead shape as a prediction model. For the evaluation of the prediction model, the optimum design of DBRF in sheet metal forming is carried out using response surface methodology. The suitable type of the draw-bead is suggested based on the optimum values of DBRF. The prediction model of the circular draw-bead proposes the design method of the draw-bead shape. The present procedure provides a guideline in the tool design stage for sheet metal forming to reduce the cost of the product development.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.27-32
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• 2005

As a large quantity of Genome sequencing has happened to be done a very much a surprising speed in short period, an automatic genome annotation process has become prerequisite. The most difficult process among with this kind of genome annotation works is to finding out the protein-coding genes within a genome. The main 2 subjects of gene prediction are Eukaryotes and Prokaryotes ; their genes have different structures, therefore, their gene prediction methods will also obviously varies. Until now, it is found that among of the 231 genome sequenced species, 200 have been found to be prokaryotes, therefore, for study of biotechnology studies, through comparative genomics, prokaryotes, rather than eukaryotes could may be more appropriate than eukaryotes. Even more, prokaryotes does not have the gene structure called an intron, so it makes the gene prediction easier. Former prokaryotes gene predictions have been shown to be 80%～ to 90% of accuracy. A recent study is aiming at 100% of gene prediction accuracy. In this paper, especially in the case of the E. coli K-12 and S. typhi genomes, gene prediction accuracy which showed 98.5% and 98.7% was more efficient than previous GLIMMER.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.609-618
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• 2024

System reliability prediction in the development stage is increasingly crucial to reliability growth management to satisfy its target reliability, since modern system usually takes a form of complex composition and various complicated functions. In most cases of development stage, however, the information available for system reliability prediction is very limited, making it difficult to predict system reliability more precisely as in the production and operating stages. In this study, a system reliability prediction process is considered when the reliability-related information such as SIL (Safety Integrity Level) and MTTFd (Mean Time to Dangerous Failure) is available in the development stage. It is suggested that when the SIL or MTTFd of a system component is known and the field operational data of similar system is given, the reliability prediction could be performed using the scaling factor for the SIL or MTTFd value of the component based on the similar system's field operational data analysis. Predicting a system reliability is then adjusted with the conversion factor reflecting the temperature condition of the environment in which the system actually operates. Finally, the case of applying the proposed system reliability prediction process to a high performance mooring platform is dealt with.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.4
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• pp.359-366
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• 2012

This is the final one of the two companion papers dealing with accuracy of accumulated fatigue damage estimation under wide band process. It is stated that four kinds of wide band models exist: typed of equivalent stress, combined PDF, correction factor, and damage combination. For the idealized ESDs from full scale measurement data on an 8100TEU container vessel, fatigue damages are compared for a narrow band prediction model based on Rayleigh PDF and five wide band fatigue prediction models of Dirlik, Wirsching-Light, Jiao-Moan, Benasciutti and DNV. DNV model consistently overestimates fatigue damages regardless of variation of ESDs. Predictions by Jiao-Moan model, which is understood as standard method for design of offshore platforms, are also in conservative side. Best accuracy is found from the results by Dirlik and Benasciutti models, but Benasciutti model is preferred since it can easily combined with narrow band fatigue damage based on Rayleigh PDF.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.5
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• pp.587-596
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• 2008

For Wireless internet service in Metropolitan area, optimum location selection for base station and cell planning are critical process in determining service coverage by accurate prediction of Wave Propagation Characteristics. Due to different kinds of characteristics in service area such as lay of land, natural feature and material, height and width of artificially made building, it has a great impact on the transmission and distance recovery of wireless network service. Therefore, these facts may cause substantial barriers in predicting & analyzing the expected level of service quality and providing it to subscribers. In this thesis, we have simulated the process to improve quality and coverage of the service by adjusting the location of Base station and the antenna angle that influence the service after the basic location of base station is selected according to the wave prediction model. Based on this simulations test, we have demonstrated the results in which subscribers would get higher quality of wireless internet service along with bigger coverage and the improved quality in the same service coverage area through optimization process of base station.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.271-277
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• 2013

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.93-100
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• 2021

Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.134-137
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• 2001

In this paper on-line model is derived from investigating via series of finite element process simulation. Some variables that little affect on non-dimensional parameters. ie. forward slip and torque factor. is extracted from composing on-line model Especially, this research focused on deriving on-line model which exactly predict roll force and roll power in the roughing mill process under small shape factor and small reduction ratio. The prediction accuracy of the proposed model is examined through comparison with predictions from a finite element process model