• 한국공작기계학회논문집
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• 제14권3호
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• pp.1-7
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• 2005

The prediction for tool life is one of the most important factors for increasing reliability, stability, and productivity of manufacturing system. This paper deals with a tool life prediction method in view of reliability assessment for cutting tools. In this study, flank wear was focused among multi-factors deciding the tool wear state. First, tool life was predicted by correlation between flank wear and cutting time, based on the extended Taylor tool life equation of turning, including parameters of cutting speed, feed rate, and cutting depth. Second, each of cutting conditions of end-milling was equivalently converted to apply ball end-mill data to the extended Taylor equation. The web-based prediction program for tool life was developed as one of reliability assessment programs for machine tools.

• IEIE Transactions on Smart Processing and Computing
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• 제5권2호
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• pp.143-151
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• 2016

This paper presents new prediction methods to improve compression performance of the so-called near-lossless RGB-domain image coder, which is designed to effectively decrease the memory bandwidth of a system-on-chip (SoC) for image processing. First, variable block size (VBS)-based intra prediction is employed to eliminate spatial redundancy for the green (G) component of an input image on a pixel-line basis. Second, inter-color prediction (ICP) using spectral correlation is performed to predict the R and B components from the previously reconstructed G-component image. Experimental results show that the proposed algorithm improves coding efficiency by up to 30% compared with an existing algorithm for natural images, and improves coding efficiency with low computational cost by about 50% for computer graphics (CG) images.

• 한국지진공학회논문집
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• 제20권1호
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• pp.45-54
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• 2016

This study suggests a prediction model of ground motion spectral shape considering characteristics of earthquake records in Korea. Based on the Graizer and Kalkan's prediction procedure, a spectral shape model is defined as a continuous function of period in order to improve the complex problems of the conventional models. The approximate spectral shape function is then developed with parameters such as moment magnitude, fault distance, and average shear velocity of independent variables. This paper finally determines estimator coefficients of subfunctions which explain the corelation among the independent variables using the nonlinear optimization. As a result of generating the prediction model of ground motion spectral shape, the ground motion spectral shape well estimates the response spectrum of earthquake recordings in Korea.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 춘계학술대회
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• pp.131-133
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• 2017

과거의 누적된 데이터는 미래를 예측하는데 있어서 기본 데이터를 제공한다. 우연성이론에 근거하여 많은 분야에서의 예측 방법들이 활용되고 있지만, 로또번호의 예측은 우연성이론에 근거하지 않는다. 본 연구에서는 누적된 데이터를 통하여 발생하는 예측력의 변화를 알아보는 방법을 제시하고자 한다.

• 전기학회논문지
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• 제60권7호
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• pp.1396-1403
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• 2011

It is not easy to analyze the strong nonlinear time series and effectively design a good prediction system especially due to the difficulties in handling the potential uncertainty included in data and prediction method. To solve this problem, a new design method for fuzzy prediction system is suggested in this paper. The proposed method contains the followings as major parts ; the first-order difference detection to extract the stable information from the nonlinear characteristics of time series, the fuzzy rule generation based on the hierarchically classifying clustering technique to reduce incorrectness of the system parameter identification, and the IT2TSK fuzzy logic system to reasonably handle the potential uncertainty of the series. In addition, the design of the multiple predictors is considered to reflect sufficiently the diverse characteristics concealed in the series. Finally, computer simulations are performed to verify the performance and the effectiveness of the proposed prediction system.

• International journal of advanced smart convergence
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• 제10권4호
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• pp.104-109
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• 2021

In the aftermath of the global pandemic that started in 2019, there have been many changes in the import/export and supply/demand process of agricultural products in each country. Amid these changes, the necessity and importance of each country's food self-sufficiency rate is increasing. There are several conditions that must accompany efficient agricultural activities, but among them, temperature is by far one of the most important conditions. For this reason, the need for high-accuracy climate data for stable agricultural activities is increasing, and various studies on climate prediction are being conducted in Korea, but data that can visually confirm climate prediction data for farmers are insufficient. Therefore, in this paper, we propose an artificial intelligence-based temperature prediction algorithm that can predict future temperature information by collecting and analyzing temperature data of farms in Gyeonggi-do in Korea for the last 10 years. If this algorithm is used, it is expected that it can be used as an auxiliary data for agricultural activities.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.59-64
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• 2020

In this paper, we found the solution using data based machine learning regression method to check the pore shape, to solve the problem of the experiment quantity occurring when producing scaffold with the 3d printer. Through experiments, we learned secured each print condition and pore shape. We have produced the scaffold from scaffold pore shape defect prediction model using multiple linear regression method. We predicted scaffold pore shapes of unsecured print condition using the manufactured scaffold pore shape defect prediction model. We randomly selected 20 print conditions from various predicted print conditions. We print scaffold five times under same print condition. We measured the pore shape of scaffold. We compared printed average pore shape with predicted pore shape. We have confirmed the prediction model precision is 99 %.

• 한국지능시스템학회논문지
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• 제10권6호
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• 대기
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• 제15권1호
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• pp.35-46
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• 2005

Today, the outreach of National Meteorological Service such as PC cluster based Numerical Weather Prediction (NWP) technique is vigorous in the world wide. In this regard, WMO (World Meteorological Organization) asked KMA (Korea Meteorological Administration) to formulate a regional project, which cover most of RA II members, using similar technical system with KMA's. In that sense, Meteorological Research Institute (METRI) in KMA developed Mongolian NWP System (MNWPS) based on PC cluster and transferred the technology to Weather Service Center in Mongolia. The hybrid parallel algorithm and channel bonding technique were adopted to cut cost and showed 41% faster performance than single MPI (Message Passing Interface) approach. The cluster technique of Beowulf type was also adopted for convenient management and saving resources. The Linux based free operating system provide very cost effective solution for operating multi-nodes. Additionally, the GNU software provide many tools, utilities and applications for construction and management of a cluster. A flash flood event happened in Mongolia (2 September 2003) was selected for test run, and MNWPS successfully simulated the event with initial and boundary condition from Global Data Assimilation and Prediction System (GDAPS) of KMA. Now, the cluster based NWP System in Mongolia has been operated for local prediction around the region and provided various auxiliary charts.

• 한국정보통신학회논문지
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• 제17권1호
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• pp.183-189
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• 2013

본 논문에서는 영상의 방향성에 따른 적응적 예측 기법과 컨텍스트 기반 엔트로피 부호화 방법을 주요 구성요소로 한 무손실 영상 압축 방법을 제안한다. 적응적 예측 기법에서는 부호화 픽셀을 중심으로 각 방향에 대한 상관도를 분석하고, 이를 이용하여 적절한 예측 픽셀을 선택한다. 또한 예측 에러를 더욱 줄이기 위하여 주변 픽셀의 복잡도 및 방향성을 이용한 컨텍스트 모델 기반 예측 에러 보정 과정을 수행한다. 정보이론의 관점에서 조건부 엔트로피에 의해 부호화 효율이 더욱 향상된다는 점을 이용하여 본 논문에서는 엔트로피 부호화 방식으로 컨텍스트 기반 Golomb-Rice 부호화를 적용한다. 실험결과 제안한 무손실 영상 압축 방식은 다양한 영상에 대해서 기존의 저 복잡도 및 고효율의 JPEG-LS에 비해 평균 1.3%의 압축효율 향상을 나타내었고, 특히 방향성이 뚜렷한 영상에 대해서 성능이 좋음을 알 수 있다.