Performance Prediction Model of University Students Based on the Grey BP Neural Network

Liao Yu,Liu Zongxin 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.10

This article counted the best performance of students entrepreneurship courses from 2005 to 2014, and took the best performance prediction of 2014 entrepreneurship course as the research object. According to the best annual performance of entrepreneurship courses from 2005 to 2014, this article established the grade prediction model of series combination of GM (1, 1) grey prediction model and BP neural network prediction model, and the established model was used to predict the best annual performance of students entrepreneurship course. Through comparing the actual value of the best annual performance of 2014 entrepreneurship course and the predicted value c by the model, this article analyzed the application of grey BP neural network prediction model in the students entrepreneurship performance prediction. The research results showed that for entrepreneurship performance prediction problem, the grey BP neural network prediction model had high prediction precision , simple application, and it can be widely used, and had more advantages than single GM (1, 1) grey prediction model and BP neural network model.

A cavitation performance prediction method for pumps: Part2-sensitivity and accuracy

Long, Yun,Zhang, Yan,Chen, Jianping,Zhu, Rongsheng,Wang, Dezhong Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.11

At present, in the case of pump fast optimization, there is a problem of rapid, accurate and effective prediction of cavitation performance. In "A Cavitation Performance Prediction Method for Pumps PART1-Proposal and Feasibility" [1], a new cavitation performance prediction method is proposed, and the feasibility of this method is demonstrated in combination with experiments of a mixed flow pump. However, whether this method is applicable to vane pumps with different specific speeds and whether the prediction results of this method are accurate is still worthy of further study. Combined with the experimental results, the research evaluates the sensitivity and accuracy at different flow rates. For a certain operating condition, the method has better sensitivity to different flow rates. This is suitable for multi-parameter multi-objective optimization of pump impeller. For the test mixed flow pump, the method is more accurate when the area ratios are 13.718% and 13.826%. The cavitation vortex flow is obtained through high-speed camera, and the correlation between cavitation flow structure and cavitation performance is established to provide more scientific support for cavitation performance prediction. The method is not only suitable for cavitation performance prediction of the mixed flow pump, but also can be expanded to cavitation performance prediction of blade type hydraulic machinery, which will solve the problem of rapid prediction of hydraulic machinery cavitation performance.

A cavitation performance prediction method for pumps PART1-Proposal and feasibility

Yun, Long,Rongsheng, Zhu,Dezhong, Wang Korean Nuclear Society 2020 Nuclear Engineering and Technology Vol.52 No.11

Pumps are essential machinery in the various industries. With the development of high-speed and large-scale pumps, especially high energy density, high requirements have been imposed on the vibration and noise performance of pumps, and cavitation is an important source of vibration and noise excitation in pumps, so it is necessary to improve pumps cavitation performance. The modern pump optimization design method mainly adopts parameterization and artificial intelligence coupling optimization, which requires direct correlation between geometric parameters and pump performance. The existing cavitation performance calculation method is difficult to be integrated into multi-objective automatic coupling optimization. Therefore, a fast prediction method for pump cavitation performance is urgently needed. This paper proposes a novel cavitation prediction method based on impeller pressure isosurface at single-phase media. When the cavitation occurs, the area of pressure isosurface S_iso increases linearly with the NPSH_a decrease. This demonstrates that with the development of cavitation, the variation law of the head with the NPSH_a and the variation law of the head with the area of pressure isosurface are consistent. Therefore, the area of pressure isosurface S_iso can be used to predict cavitation performance. For a certain impeller blade, since the area ratio R_s is proportional to the area of pressure isosurface S_iso, the cavitation performance can be predicted by the R_s. In this paper, a new cavitation performance prediction method is proposed, and the feasibility of this method is demonstrated in combination with experiments, which will greatly accelerate the pump hydraulic optimization design.

Quantum Computing Impact on SCM and Hotel Performance

Adhikari, Binaya,Chang, Byeong-Yun The Institute of Internet 2021 International Journal of Internet, Broadcasting an Vol.13 No.2

For competitive hotel business, the hotel must have a sound prediction capability to balance the demand and supply of hospitality products. To have a sound prediction capability in the hotel, it should be prepared to be equipped with a new technology such as quantum computing. The quantum computing is a brand new cutting-edge technology. It will change hotel business and even the whole world too. Therefore, we study the impact of quantum computing on supply chain management (SCM) and hotel performance. Toward the goal we have developed the research model including six constructs: quantum (computing) prediction, communication, supplier relationship, service quality, non-financial performance, and financial performance. The result of the study shows a significant influence of quantum (computing) prediction on hotel performance through the mediating role of SCM in the hotel. Quantum prediction is highly significant in enhancing the SCM in the hotel. However, the direct effect between the quantum prediction and hotel performance is not significant. The finding indicates that hotels which would install the quantum computing technology and utilize the quantum prediction could hugely benefit from the performance improvement.

Performance Prediction of Interleave-Division Multiple Access Scheme based on Log-likelihood Ratio (LLR) for An Efficient 4G Mobile Radio System

정연호,Chung, Yeon-Ho The Korea Institute of Information and Commucation 2009 한국정보통신학회논문지 Vol.13 No.7

본 논문은 효율적인 4세대 이동통신 시스템을 위한 대수가능성율 기반의 인터리버분할 다중접속 시스템의 성능 예측 메카니즘을 제안한다. 기존 시스템에서는 대수가능성율을 수신기에 단순 전달을 통해 반복적으로 가능성값을 개선시켜 성능 향상을 얻는다. 본 연구에서는 대수가능성율의 단순 전달이전에 대수가능성율 값을 분석하여 비트오율을 예측한다. 이러한 예측을 통하여 수신기의 불필요한 반복 연산을 줄일 수 있으며 성능을 예측할 수 있어 효율적 시스템 설계가 가능하다. 다중 사용자 인터리버 분할 다중접속 시스템의 다양한 전송 시나리오를 구성하여 제안한 메카니즘을 분석하였는데 예측 메카니즘의 성능을 확인하였으며 향후 4세대 시스템중의 하나로 고려되고 있는 인터리버 분할 다중접속 시스템 개발에 유용하게 사용할 수 있을 것이다. This paper presents a prediction mechanism of performance for an efficient interleave-division multiple access (IDMA) scheme that is being considered as 4th generation mobile radio system. The scheme is based upon log-likelihood ratio (LLR) to predict the performance of the IDMA. The conventional IDMA system simply passes the LLR values to a coarse estimation process in the receiver over a pre-defined number of iterations for an acceptable performance. The proposed IDMA system uses the LLRs to predict its BER performance and thus the iterative operation at the receiver can significantly be reduced when the performance attains an acceptable level. Performance evaluation shows that the proposed scheme of the IDMA with the LLRs used for the prediction provides a comparable BER performance. The use of the LLRs can facilitate an efficient design of the IDMA system that is a strong candidate system for 4G mobile radio systems.

Prediction of rolling bearing performance degradation based on sae and TCNattention models

Yaping Wang,Dekang Hou,Di Xu,Sheng Zhang,Chaonan Yang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.4

A single feature cannot show the operational state of a bearing during its entire life cycle. Therefore, a rolling bearing performance deterioration prediction method based on an SAE and the TCN-attention model is proposed. The SAE method is used to fuse the timedomain indicator and the frequency-domain indicator to construct the performance degradation characteristic indicator. The evaluation indices are used to comprehensively evaluate multiple performance degradation indices, and the fused feature indices together, to filter out the features that have a good overall performance. Attention is added to the TCN model, and the output state weight of the TCN model is calculated through a scoring function to increase the important information weight and the prediction accuracy. The appropriate network structure and parameter configuration are determined, and the rolling bearing performance degradation prediction model is established. A validation is performed using publicly available datasets from the University of Cincinnati and XJTU-SY. The results show that the method is more sensitive to the critical information part of the long time series than the other models. At the same time, the average absolute error and the root mean square error are minimized, the accuracy of the rolling bearing performance degradation prediction is high, and the model has a strong robustness and generalization abilities. Additionally, the model has practical engineering value for predicting the health status of equipment.

PION TCIⓇ 펌프를 사용한 AquafolTM 목표농도조절주입의 예측수행도

김계민 대한마취통증의학회 2015 Anesthesia and pain medicine Vol.10 No.4

Background: The performance of a target controlled infusion (TCI) system needs to be confirmed in a clinical setting. AquafolTM, a microemulsion propofol, can be used for TCI with its pharmacokinetic parameters. The aim of this study is to evaluate the predictive performance of AquafolTM TCI by using a PION TCIⓇ pump incorporating the previously established pharmacokinetic parameters and ke0. Methods: Thirty adult patients were enrolled in the study. General anesthesia was maintained with TCI of AquafolTM and remifentanil using a PION TCIⓇ pump. During the maintenance of anesthesia with a constant target effect-site concentration of propofol for at least for 20 minutes, blood was drawn and the propofol plasma concentration was measured. The predictive performance of AquafolTM TCI was evaluated by determining the median performance error (MDPE), median absolute performance error (MDAPE), divergence, and wobble from the intra-individual and pooled performance errors. The acceptability of the TCI system was determined based on the pooled predictive performance. Results: A total of 153 propofol blood samples were analyzed. The estimates of pooled MDPE, MDAPE, divergence and wobble were 8.59% (1.61), 19.1% (1.12), −1.12%/h and 9.87% (1.01), respectively. The MDAPE indicating the accuracy of the TCI infusion system was within the clinically acceptable range (< 20–30%) and the bias (MDPE) was also acceptable (< 10–20%). Conclusions: The performance of AquafolTM TCI using a PION TCIⓇ pump was acceptable for the clinical use.

시계열 분석을 통한 프로야구리그 경기력 예측

오승욱,한진욱 한국스포츠산업경영학회 2023 한국스포츠산업경영학회지 Vol.28 No.5

본 연구는 국내 프로야구 경기력 변화 트렌드를 파악하고 향후 경기력을 예측하는 데 그 목적이 있다. 이러한 연구목적을 달성하기 위해 ‘R’ 프로그래밍 언어를 이용하여 ARIMA 시계열 분석을 실시하였으며, 이에 따른 결과 및 결론은 다음과 같다. 첫째, 홈런에 대한 경기력 변화예측은 지금까지 나타난 프로야구리그의 평균보다 약간 높은 수준을 보여주고 있다. 둘째, 타율에 대한 경기력 변화예측은 지금까지 나타난 국내 프로야구리그의 평균보다 매우 낮은 수준을 보여주고 있다. 셋째, 타점에 대한 경기력 변화예측은 지금까지 나타난 프로야구리그의 평균보다 높은 수준을 나타내고 있다. 본 연구 결과에 대한 인사이트 및 제언이 제시되었다. The purpose of this study was to identify trends in changes in domestic professional baseball performance and predict future performance. In order to achieve the study objective, ARIMA time series analysis was conducted using the ‘R’ programming language. The results are as follows. First, the prediction of changes in performance for home runs is slightly higher than the average in professional baseball leagues so far. Second, the prediction of changes in performance regarding batting average shows a much lower level than the average of domestic professional baseball leagues shown so far. Third, the prediction of changes in performance regarding RBIs is at a higher level than the average in the professional baseball league so far. Additionally discussion and practical implication were also suggested.

데이터마이닝 기반의 사고심각도 가중치 적용 예측변수를 활용한 교차로 사고예측모형 개발

손승오(SON, Seung-oh),박준영(PARK, Juneyoung) 대한교통학회 2022 대한교통학회지 Vol.40 No.2

본 연구에서는 데이터마이닝 기법을 통해 도출한 사고심각도 가중치를 적용한 환산 사고건수를 종속변수로 하는 도심부 교차로 사고예측모형을 개발하였다. 일반적으로 사고예측모형(Crash prediction model)은 안전성능함수(Safety performance functions)로도 불리며, 분석대상인 구간 또는 교차로에서 집계된 사고건수를 종속변수로 하는 회귀모형이다. 그러나 여기서 사고건수는 단순히 집계된 사고의 빈도이며 사고심각도 및 사고의 특성변수가 반영되지 않은 데이터이다. 본 연구는 국내 교차로에서 발생한 사고를 대상으로 사고심각도 분석을 수행하여 심각한 사고 발생에 유의한 영향을 미치는 변수를 정량화하였으며, 이를 사고건수에 반영한 가중치 적용 사고건수를 종속변수로 설정하여 모형을 개발하였다. 교차로 사고심각도 분석에는 Random forest(RF)와 Extreme gradient boosting(XGB) 방법론이 활용되었으며, 사고예측모형은 NB, Com-poisson, 그리고 XGB 회귀트리가 활용되었다. 최종적으로 RF와 XGB 가중치 결과가 반영된 지표가 예측성능이 가장 우수한 것으로 나타났다. 제안된 종속변수는 우수한 예측성능뿐만 아니라 사고심각도 분석 결과를 반영하고 있기 때문에, 심각도 요인 기반의 중요한 시사점을 제시할 수 있다. 본 연구에서 제시한 모형은 개별 교차로의 안전성 평가 및 정책 설계에 유효한 자료로 활용될 수 있다. In this study, crash prediction models for urban intersections were developed using an index as dependent variables reflecting the crash severity weight from data mining technique. In general, the crash prediction model is also called Safety performance function (SPF), and is a regression model with the number of crashes aggregated in the sections or intersections to be analyzed as dependent variables. However, the number of crashes is simply the counted frequency of crashes, and the data does not reflect the characteristics of the crash severity factors. In this study, the crash severity analysis were conducted on crashes that occurred at urban intersections. In addition, the crash prediction models were developed using the crash score index reflecting the results of the severity analysis as a dependent variable. Random forest (RF) and Extreme boosting (XGB) were used for the analysis of intersection crash severity, and NB, Com-poisson, and XGB regression tree model were developed for crash prediction models. Finally, the index reflecting the RF and XGB weight results showed the best predictive performance. Since the proposed dependent variable reflects the results of crash severity analysis as well as excellent predictive performance, important implications based on severity factors can be presented. The model presented in this study can be used for safety evaluation and policy design of individual intersections.

A Process Mining Technique for Performer Recommendation Using Decision Tree

Aekyung Kim(김애경),Jae-Yoon Jung(정재윤) 대한산업공학회 2012 대한산업공학회 추계학술대회논문집 Vol.2012 No.11

This paper demonstrates that the process model discovered from historical event log can be extended to predict business performance and recommend performers of running instances. For the performance prediction and the performer recommendation, we adopt decision tree, which is a decision support tool in management science. Decision trees are commonly used to help identify alternative most likely to reach a goal. The proposed approach is aimed at supporting managers in performer decision-making processes, based on historical data such as completion time and cost according to the performers. To provide effective performer recommendation, we use several filtering with performers to the decision tree, which allows for a suitable recommendation according to characteristics processes. The proposed technique is evaluated through an experiment using real-life event log in telecom service industry. The main contribution of this paper is to provide a real-time decision support tool by recommending the best performer for a target performance indicator during process execution based on historical data.