• Journal of applied mathematics & informatics
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• 제14권1_2호
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• pp.377-386
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• 2004

A technique that provides prediction intervals based on a model called an empirical linear model is discussed. The technique, high-dimensional empirical linear prediction (HELP), involves principal component analysis, factor analysis and model selection. HELP can be viewed as a technique that provides prediction (and confidence) intervals based on a factor analysis models do not typically have justifiable theory due to nonidentifiability, we show that the intervals are justifiable asymptotically.

• International Journal of Internet, Broadcasting and Communication
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• 제12권1호
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• pp.111-115
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• 2020

In this paper, I proposed an effective technique for accurately predicting pixel values using edge components. Adjacent pixel values are similar to each other. That is, generally, similarity exists between adjacent pixels in an image. In the proposed algorithm, edge components are detected using the surrounding pixels in the first step, and pixel values are estimated using the edge components in the second step. Therefore, the prediction accuracy of the pixel value is improved and the prediction error is reduced. Pixel value prediction is a necessary technique for various applications such as image magnification and confidential data concealment. Experimental results show that the proposed method has higher prediction accuracy and fewer prediction error. Therefore, the proposed technique can be effectively used for applications such as image magnification and confidential data concealment.

• 산업경영시스템학회지
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• 제27권2호
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• pp.10-16
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• 2004

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing Process. The Purpose of this Paper is to model the recognition of defect type Patterns and Prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.166-174
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• 2021

A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.

• 정보처리학회논문지A
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• 제16A권5호
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• pp.347-356
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• 2009

정교한 분기 예측기의 설계는 오늘날의 프로세서 성능 향상에 중요한 역할을 하게 되었다. 분기 예측의 정확도가 더욱 더 중요해 지면서 정확도의 향상을 위한 다수의 기법들이 제안되었지만, 기존의 연구들은 예측 지연 시간을 간과하는 경향이 있었다. 본 논문에서는 예측 지연 시간 문제를 해결하고자 조기 예측 기법 (ESP, Early Start Prediction)을 제안한다. 조기 예측 기법은 분기 예측에 있어서 활용되는 분기 명령어의 주소 대신 그것과 일대일 대응이 되는 기본 블록의 시작 주소 (BB_SA, Basic Block Start Address)를 이용한다. 즉, 분기 명령어의 주소가 사용되는 기존의 환경에서, BB_SA를 활용하여 조기 예측을 시작함으로써, 예측 지연 시간을 숨긴다. 또한 제안된 기법은 짧은 간격 숨김 기법(short interval hiding technique)을 통해 보다 더 나은 성능 향상을 기대할 수 있다. 실험 결과 본 논문에서 제안된 기법은 예측 지연 시간을 줄임으로써, 예측 지연 시간이 1 사이클인 이상적인 분기 예측기의 성능에 0.25% 이내로 근접한 IPC 결과를 얻었다. 또한 기본 블록의 시작주소와 분기 명령어 사이에 짧은 간격을 가질 경우에 대한 개선 방법을 추가적으로 적용시킬 경우, 기존의 방식과 비교하여 평균 4.2%, 최대 10.1%의 IPC 향상을 가져왔다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
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• pp.2844-2851
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• 2011

The purpose of this study is to set up an acoustic prediction technique and to perform the IL test of scale down noise reducing device for the development of the noise reducing device as the development of 400km/h class high speed train. First of all, the IL prediction of noise reducing device was performed with the 2D BEM method. And the noise test of scale down noise reducing device in anechoic chamber was performed for the verification of acoustic prediction technique and IL performance evaluation. As the results, the acoustic prediction technique for the development of noise reducing device was verified because the averaged IL difference between prediction and test is in 2dB(A). And the measured IL value of noise reducing device is less than 2dB(A), and additional IL with polyester absorption material is increased about 0.5dB(A).

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2006년도 춘계 학술발표회 논문집(II)
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• pp.621-624
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• 2006

This paper presents a novel optimum technique for optimum mix-proportion using database-based prediction model of material properties for an object function or a constraint condition. The proposed technique provides high reliability of results introducing effective region model, which assesses whether the prediction model is effective or not, in optimization process. In order to validate the proposed technique, a genetic algorithm was adopted as a optimum technique, and an artificial neural network was adopted as a prediction model for material properties and as a model for assessing effective region. The mix-proportion obtained from the proposed technique is more reasonable than that obtained from a general optimum technique.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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• pp.233-242
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• 2009

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

• 한국통신학회논문지
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• 제21권1호
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• pp.33-46
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• 1996

Inthis paper, we investigate the compatible coding technique, which receives much interest ever since the introduction of HDTV. First, attempts have been made to analyze the theoretical transform coding gains for various hierarchical decomposition techniques, namely subband, pyramid and DCT-based decomposition techniques. It is shown that the spatical domain techniques proide higher transform coding gains than the DCT-based coding technique. Secondly, we compare the performance of these spatial domain techniques, in terms of the PSNR versus various rate allocations to each layer. Based on these analyses, it is believed that the pyramid decomposition is more appropriate for the compatible coding. Also in this paper, we propose a hybrid prediction pyramid coding technique, by combining the spatio-temporal prediction in MPEG-2[3] and the adaptive MC(Motion Compensation)[1]. In the proposed coding technigue, we also employ an adaptive DCT coefficient scanning technique to exploit the direction information of the 2nd-layer signal. Through computer simulations, the proposed hybrid prediction with adaptive scanning technuque shows the PSNR improvement, by about 0.46-1.78dB at low 1st-layer rate(about 0.1bpp) over the adaptive MC[1], and by about 0.33-0.63dB at high 1st-layer rate (about 0.32-0.43bpp) over the spatio-temporal prediction[3].

• 디지털산업정보학회논문지
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• 제8권4호
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• pp.143-149
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• 2012

MANET is a network that is a very useful application to build network environment in difficult situation to build network infrastructure. But, nodes that configures MANET have difficulties in data retrieval owing to resources which aren't enough and mobility. Therefore, caching scheme is required to improve accessibility and availability for frequently accessed data. In this paper, we proposed a technique that utilize mobility prediction of nodes to retrieve quickly desired information and improve data availability. Mobility prediction of modes is performed through distance calculation using location information. We used technique which global cluster table and local member table is managed by cluster head to reduce data consistency and query latency time. We compared COCA and CacheData and experimented to confirm performance of proposed scheme in this paper and efficiency of the proposed technique through experience was confirmed.