• 사상체질의학회지
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• 제34권2호
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• pp.1-14
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• 2022

Objectives The aim of this study was to investigate the change in Sasang constitution prediction value in 2 years and find the factors associated with it. Methods Cohort data from Korean medicine data center was used. Using Korean Sasang Constitutional Diagnostic Questionnaire (KS-15) which consist of questions related to body shape, temperament, and symptoms, participants were categorized into Tae-Yang (TY), Tae-Eum (TE), So-Yang (SY), and So-Eum (SE). Sasang constitution was assessed on the baseline and after two years. Result Total 5,784 participants were analyzed. (TE 3, 341; SE 911; SY 1,532). Among them, 1,402 participants (24.2%) showed different prediction value in KS-15 after two years. The proportion of participants showing different prediction value in two years was the highest in SY, and the lowest in TE group. The factors associated with the change in Sasang constitution prediction value were different by constitution type. The change in feeling after sweating was significantly associated with the change in prediction value in TE and SY groups, not in SE group. Although temperament was not significantly associated with the change in prediction value from TE to SE, it was significantly associated with that in the change from TE to SY. The change in BMI and appetite were associated with the change in constitution prediction value in all three constitution types. Conclusion Although the factors associated with the change in prediction value of Sasang constitution were different by each constitution type, BMI and appetite were significant in all three types. These factors could be useful for developing Sasang constitution questionnaire and deciding re-prediction needs of Sasang constitution. Further research about the factors related to Sasang constitution diagnosis need to be conducted.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.77-86
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• 2006

This study is to investigate the error of prediction value with related variables from the recommended system and to examine the error of prediction value with related variables. To decrease the error on the collaborative recommended system on user based, this research explored the effects on the prediction related response pair between raters' demographic variables and Pearson's coefficient and sparsity. The result shows comparative analysis between existing error of prediction value and conditioned one.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(3)
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• pp.55-58
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• 2000

To achieve high performance by exploiting instruction level parallelism(ILP) aggressively in superscalar processors, value prediction is used. Value prediction is a technique that breaks data dependences by predicting the outcome of an instruction and executes speculatively it's data dependent instruction based on the predicted outcome. In this paper, the performance of a hybrid value prediction scheme with dynamic classification mechanism is measured and analyzed by using execution-driven simulator for SPECint95 benchmark set.

• Journal of the Korean Data and Information Science Society
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• 제21권1호
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• pp.185-194
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• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

• 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.

• 한국정보과학회논문지:시스템및이론
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• 제28권11호
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• pp.592-600
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• 2001

슈퍼스칼라 프로세서는 성능향상을 위해 명령어 반입폭과 이슈율을 증가시키고 있다. 데이터 종속성은 ILP(Instruction-Level Parallelism)를 향상시키는데 주요 장애요소가 되고 있으며, 최근 여러 논문에서 데이터 종속성을 제거하기 위해서 명령어의 결과값을 예상하는 메커니즘이 연구되었다. 그러나 이러한 예측기들은 예상한 명령어의 실제 결과값으로 예상 테이블을 갱신하기 전에 그 명령어를 다시 예상할 때 부적절(stale)한 데이터를 사용함으로써 예상 실패율이 증가하여 프로세서의 성능을 감소시킨다. 본 논문에서는 부적절 데이터 사용을 줄여 높은 성능을 얻을 수 있는 새로운 하이브리드 예측 메커니즘을 제안한다. 제안된 하이브리드 결과값 예측기는 예상 테이블을 모험적으로 갱신할 수 있기 때문에 부적절 데이터로 인해 잘못 예상되는 명령어의 수를 효과적으로 감소시킨다. 16-이슈폭 슈퍼스칼라 프로세서에서 SPECint95 벤치마크 프로그램에 대해 모험적 갱신을 사용함으로써 모험적 갱신을 사용하지 않은 경우의 평균 예상 정확도 59%에 비해 평균 예상 정확도가 72%에 비해 평균 예상 정확도가 72%로 크게 향상되었다.

• 한국정보처리학회논문지
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• 제7권5호
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• pp.1419-1425
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• 2000

When we predict of software reliability, we can't know the testing stopping time and how many faults be residues in software the (the maximum value of data) during these software testing process, therefore we assume the maximum value and the training result can be inaccuracy. In this paper, we present neural network approach for software reliability prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data.

• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• KIEAE Journal
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• 제4권4호
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• pp.11-18
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• 2004

In this study, the measurement system was developed for the measurement of pollutants from building materials, and specimens were made with concrete, gypsum board, mortar and wall paper. Characteristics of VOCs and TVOC concentration and Emission Factor as a function of time were assessed, and the conclusion was drawn as follows. (1) From predicting TVOC concentration decrease of specimen 7 with the wall paper attached to the concrete, the graph may become linear by converting the value of y-axis into the log function, and the prediction equation can be expressed as $y=34906{\ast}e^{-0.0093{\ast}time}$. Moreover, chi-square value was 0.83 which is relatively high value, indicating that TVOC concentration can be properly predicted if the same materials are used indoors. (2) From predicting VOCs Emission Factor decrease of specimen 7, the prediction equation can be expressed as $EF=15111{\ast}e^{-0.0093{\ast}time}$, and chi-square value was 0.83. (3) From predicting TVOC concentration decrease of specimen 7, prediction equation can be considered to be $y=254323{\ast}(1-e^{-0.1046{\ast}time})$, and chi-square was 0.994 which is significantly high value, indicating that indoor TVOC concentration can be properly predicted if the same materials are used indoors. Furthermore, the prediction of concentration decrease using cumulative value of hourly measured concentration is considered to be more accurate than that using just hourly measured value directly. (4) From predicting Emission Factor decrease with cumulative hourly data of Emission Factor, chi-square appeared to be higher than that by just using hourly data of Emission Factor directly. Therefore, the prediction of Emission Factor with cumulative hourly data can provide more reliable prediction equation than the case by using just hourly concentration directly.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제8권1호
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• pp.107-115
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• 2002

데이타 종속성을 제거하기 위해서 명령어의 결과값을 예상하는 여러 결과값 예측기의 장점을 이용하여 높은 성능을 얻을 수 있는 새로운 혼합형 예측 메커니즘을 제안한다. 제안된 혼합형 결과값 예측기는 예상 테이블을 모험적으로 갱신할 수 있기 때문에 부적절한(stale) 데이타로 인해 잘못 예상되는 명령어의 수를 효과적으로 감소시킨다. 또한 정적 분류 정보를 사용하여 명령의 반입시 적절한 예측기에 할당함으로써 예상 정확도를 더욱 향상시키며, 하드웨어 비용을 효율적으로 감소시키도록 하였다. 5개의 SPECint 95 벤치마크 프로그램에 대해 SimpleScalar/PISA 3.0 툴셋을 사용하여 실험하였다. 16-이슈 폭에서 모험적 갱신을 사용한 평균 예상 정확도는 73%의 실험 결과가 나왔으며, 정적 분류 정보를 사용하였을 경우 예상 정확도가 88%로 증가된 결과를 얻었다.